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Pediatric Food Preferences and Eating Behaviors reviews scientific works that investigate why children eat the way they

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Pediatric Food Preferences and Eating Behaviors
 0128117168, 9780128117163

Table of contents :
Cover
PEDIATRIC FOOD
PREFERENCES AND
EATING BEHAVIORS
Copyright
Dedication
Contributors
Prologue
1
Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics
Introduction
Taste Phenotypes: How to Measure Taste in Children
Detection Thresholds
Taste Intensity
Taste Hedonics
Summary
Ontogeny of Sweet and Bitter Taste
Sweet Taste
Bitter Taste
Summary
Genetics of Sweet and Bitter Taste
Taste Receptor Genetics and Nomenclature
Summary
Genotype-Taste Phenotype Relationships in Children
TAS2R Genotype-Phenotype Studies
TAS1R Genotype-Phenotype Studies
Summary
Conclusions and Practical Implications
References
2
Learning to Like: Roles of Repeated Exposure and Other Types of Learning
Introduction
Repeated Exposure Effects During Infancy
Repeated Exposure Effects During Early Childhood
Repeated Exposure Effects During Middle Childhood
Conclusions, Recommendations, and Future Directions
References
3
Effects of Modeling on Children's Eating Behavior
Effects of Modeling on Children's Eating Behavior
What Is Modeling and Why Do We Do It?
Influence of Modeling on Novel Food Intake and Choice
Influence of Modeling on Portion Sizes Eaten
Influence of Modeling on Children's Food Liking and Preferences
Different Models: Who Is Most Effective?
Individual Differences in Susceptibility to Modeling
Interventions Based on Modeling
Conclusion and Implications
References
4
Children's Challenging Eating Behaviors: Picky Eating, Food Neophobia, and Food Selectivity
Introduction
Picky Eating
Measurement and Prevalence
Factors Associated With Picky Eating
Outcomes Associated With Picky Eating
Food Neophobia
Measurement and Prevalence
Factors Associated With Food Neophobia
Outcomes Associated With Neophobia
Food Selectivity
Measurement of Food Selectivity
Factors Associated With Food Selectivity
Outcomes Associated With Food Selectivity
Conclusions
Sources of Support
References
5
Satiety Responsiveness and Eating Rate in Childhood: Development, Plasticity, and the Family Footprint
Introduction
Satiety Responsiveness
Measurement
Risk and Susceptibility
Genetic Influences
Parent-Child Feeding Practices
Associations With Weight and Dietary Intakes
Potential for Modification
Eating Rate
Measurement
Risk and Susceptibility
Genetic Influences
Parent-Child Feeding Practices
Associations to Adiposity and Dietary Intake
Potential for Modification
Research Opportunities
Conclusion
References
6
Role of Reward Pathways in Appetitive Drive and Regulation
The Role of Reward in Eating
Homeostatic and Reward Perspectives on Eating
Subcomponents of Reward (Wanting, Liking, Salience)
Inhibiting Reward-Driven Eating
Next Steps in Investigating Reward-Driven Eating
Conclusion
References
7
Appetitive Traits: Genetic Contributions to Pediatric Eating Behaviors
Introduction
Appetitive Traits
Defining and Measuring Appetitive Traits
Measuring Appetitive Traits for Genetic Research
A Dimensional Approach in the General Population
Special Consideration for Genetic Research
Genetic Studies of Child Eating Behaviors
Family Studies
Twin Studies
Assumptions of Twin Models
Limitations of Twin Models
Evaluating Twin Models
Heritability Estimates for Appetitive Traits in Childhood
A Developmental Perspective
Interpreting Heritability Estimates for Child Appetitive Traits
Future Directions
Molecular Genetic Studies
Candidate Gene Studies
FTO
Limitations and Future Directions of Gene Association Studies
Conclusions
References
8
The Influence of the Food Environment on Food Intake and Weight Regulation in Children
Introduction
Food Properties and Child Appetite Regulation
Energy Density
Portion Size
Combined Effects of Energy Density and Portion Size
Variety
Obesogenic Food Environment
Home Food Environment
Schools
Neighborhoods
Practical Implications
Summary
References
9
Parenting Influences on Appetite and Weight
Introduction
Infancy and Toddlerhood
Preschool Age
Elementary School Age
Adolescence
Summary and Conclusions
References
10
Executive Function and Self-Regulatory Influences on Children's Eating
Introduction
Theoretical Frameworks Describing the Self-Regulation of Food Intake
Neurocognitive Correlates of Eating Behavior
Potential Pathways Linking EF and Eating Behavior
Inhibitory Control and Eating Behavior
Impulsivity and Eating Behavior
Delay of Gratification and Eating Behavior
Decision Making and Eating Behavior
Cognitive Flexibility, Working Memory, and Eating Behavior
Global Measures of EF and Children's Eating Behavior
Implications for Prevention and Practice
Conclusions
References
11
Neurocognitive Influences on Eating Behavior in Children
Introduction
Studying the Brain
Brain Imaging Methodologies
Characteristics of Human Eating Behavior
Challenges in Pediatric Imaging
Brain Differences Between Adults and Children
Bidirectional Associations Between Brain Structure and Pediatric Obesity
Associations Between Brain Function and Pediatric Eating Behaviors and Body Weight
Food Motivation and Drive
Self-Control and Decision Making
Impact of Environmental Cues on Pediatric Brain Response to Food
Conclusions
References
Further Reading
12
Development of Loss of Control Eating
Overview
Assessment of LOC Eating
Cross-Sectional Correlates of LOC Eating in Youth
Genetics
Physiological Correlates
Psychosocial Correlates
Social Correlates
Psychological and Behavioral Correlates
Cognitive Correlates
Outcomes of LOC Eating
Physiological Outcomes
Psychosocial Outcomes
Predictors of LOC Eating in Youth
Biological Predictors
Social and Psychological Predictors
Theories of LOC Eating Development
Interventions for LOC Eating
Proposed Future Directions
Conclusion
References
13
Intentional Self-Regulation of Eating Among Children and Adolescents
Introduction
Defining Intentional Self-regulation of Eating
Intentional Self-Regulation of Eating, Dietary Restraint, and Dieting
Intentional Self-Regulation of Eating and Dietary and Weight Outcomes Among Children
The Development of Intentional Self-Regulation of Eating
Factors That Challenge Individuals Ability to Intentionally Self-Regulate Eating
Conclusions and Future Research Needs
Implications for Practice
References
14
Food Cognition and Nutrition Knowledge
Early Food Cognition: How Do Infants and Children Categorize Foods?
Developing Food Cognition: Categorization and Rejection
Sociocognitive Learning About Food: Observing Other People
Social Learning: Testimony About Who Likes What
Social Learning: Testimony About Health
Conclusions and Open Questions
References
Epilogue
What Have We Learned?
What Are the Next Steps in the Science?
Summary
Index
A
B
C
D
E
F
G
H
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J
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M
N
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P
Q
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Back Cover

Citation preview

PEDIATRIC FOOD PREFERENCES AND EATING BEHAVIORS

PEDIATRIC FOOD PREFERENCES AND EATING BEHAVIORS

Edited by

JULIE C. LUMENG Department of Pediatrics, Medical School, and Department of Nutritional Sciences, School of Public Health, University of Michigan

JENNIFER O. FISHER Center for Obesity Research and Education, Department of Social and Behavioral Sciences, Temple University, Philadelphia, PA

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom © 2018 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-811716-3 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Andre Gerhard Wolff Acquisition Editor: Megan R. Ball Editorial Project Manager: Amy M. Clark Production Project Manager: Prem Kumar Kaliamoorthi Cover Designer: Greg Harris Typeset by SPi Global, India

DEDICATION To Carey, Avery, Payton, and Kieran for their love, support, and patience. —Julie C. Lumeng

To LLB whose scientific curiosities and work inspired this field of study; to MWO, TRO, and IRO for their unconditional love and support, and for providing a stimulating living laboratory around the table; and to my parents for raising me with a love of learning and my mom’s homemade food. —Jennifer O. Fisher

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CONTRIBUTORS Stephanie Anzman-Frasca Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States Katherine W. Bauer Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, United States Laura L. Bellows Department of Food Science and Human Nutrition, Colorado State University, Fort Collins, CO, United States Leann L. Birch Department of Foods and Nutrition, The University of Georgia, Athens, GA, United States Jacqueline Blissett Department of Psychology, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom Nuala Bobowski Monell Chemical Senses Center, Philadelphia, PA; St. Catherine University, St. Paul, MN, United States Amanda S. Bruce Department of Pediatrics, University of Kansas Medical Center, Kansas City, KS; Center for Children’s Healthy Lifestyles and Nutrition, Children’s Mercy Hospital, Kansas City, MO, United States Brenda Burgess Department of Family Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo—SUNY, Buffalo, NY, United States Meghan Byrne Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, MD, United States Sam Chuisano Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, United States

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Contributors

Jasmine M. DeJesus Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, United States Sarah Ehrenberg Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States Myles S. Faith Department of Counseling, School and Educational Psychology, Graduate School of Education, University at Buffalo—SUNY, Buffalo, NY, United States Lori A. Francis Department of Biobehavioral Health, College of Health and Human Development, The Pennsylvania State University, University Park, PA, United States Ashley N. Gearhardt Department of Psychology, University of Michigan, Ann Arbor, MI, United States Sheryl O. Hughes Department of Pediatrics, USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX, United States Susan L. Johnson Department of Pediatrics/Section of Nutrition, University of Colorado Anschutz Medical Campus, Aurora, CO, United States Kathleen L. Keller Department of Nutritional Sciences; Department of Food Science, The Pennsylvania State University, University Park, PA, United States Katherine D. Kinzler Departments of Psychology and Human Development, Cornell University, Ithaca, NY, United States Tanja V.E. Kral Department of Behavioral Health Sciences, School of Nursing; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States Julie A. Mennella Monell Chemical Senses Center, Philadelphia, PA, United States Kameron J. Moding Department of Pediatrics/Section of Nutrition, University of Colorado Anschutz Medical Campus, Aurora, CO, United States Alissa A. Nolden Monell Chemical Senses Center, Philadelphia, PA, United States

Contributors

Thomas G. Power Department of Human Development, Washington State University, Pullman, WA, United States Nathaniel R. Riggs Department of Human Development and Family Studies, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, United States Kristin Shutts Department of Psychology, University of Wisconsin - Madison, Madison, WI, United States Marian Tanofsky-Kraff Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, MD, United States Alexis C. Wood USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX, United States

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PROLOGUE

What causes one child to have a seemingly insatiable appetite and another to be a picky eater? What is the best way to help children learn to like healthy foods? What role do parents play? Scientists have only recently begun to understand the answers to these fundamental questions. It is shocking to think that the development of children’s eating behaviors was virtually unstudied at the time the US government published its first edition of the Dietary Guidelines for Americans in 1980 to foster healthy habits. Up to that time, nutritional research was primarily concerned with what children eat, focusing on nutrient requirements and preventing deficiencies. Disciplines such as developmental psychology were also interested in eating but primarily as a context for studying parent-child interactions, with little attention given to eating behavior per se. During the late 1970s and 1980s, a scientific literature began to emerge on appetitive behaviors, ranging from groundbreaking studies of taste acceptance and sweetness among infants to those on the self-regulation of short-term energy intake. But the work existed in small isolated pockets, conducted by only a handful of scientists around the world. And perhaps this would remain the case were it not for the pressing threat of the obesity epidemic that moved research on behavioral aspects of nutrition from the fringe into the spotlight. The recognition that rapid increases in obesity prevalence could not be explained by genetics alone turned the scientific community’s attention, for the first time, to the role of the environment. This shift in thinking had the somewhat unexpected consequence of legitimizing research on behavioral aspects of nutrition and provided huge momentum to understand how healthy eating habits are established. Pediatric Food Preferences and Eating Behaviors was written to highlight current areas of research in the study of children’s eating behavior. Each chapter, written by leading researchers in the field, presents basic concepts and definitions, methodological issues pertaining to measurement, and the current state of scientific knowledge as well as directions for future research. Chapters are grouped along two organizing themes of development that have been the thrust of scientific inquiry to date—children’s food preferences and the regulation of appetite. Research in these areas has evolved in parallel fashion over the past 30–40 years, moving from basic descriptive studies to understanding etiology and, more recently, how to effectively intervene. For instance, while Clara Davis is largely credited with conducting the seminal studies of self-selected intake and appetite regulation in the early 20th century, nearly a half century passed before the more controlled studies of caloric compensation began to emerge. Those studies set the stage for work in the 1990s and the following decade that identified a wider range of appetitive dimensions. During the 1990s and into the new millennium, research on children’s

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Prologue

eating behaviors grew rapidly with the recognition that individual differences in eating behavior were linked to important health outcomes like obesity. It was also during this time that work began to identify potentially modifiable influences on the developmental trajectory of appetite, such as parenting and the home food environment. In parallel, researchers began to pursue mechanistic studies to identify genetic and neurological bases of behavior, tapping into methodological advances such as GWAS and fMRI. More recently, the field has begun to intervene to nurture healthy appetites and eating behaviors. Pediatric Food Preferences and Eating Behaviors highlights these major advances and themes in understanding of the development of food preferences and appetite regulation. Some of the most significant challenges in the field are integral to the exciting promise for the future of research on children’s eating behavior. It was clear very early on that addressing scientific problems about eating behavior required perspectives outside of the traditional nutritional sciences mainstays such as nutrient metabolism, dietary assessment, and lifecycle nutrition. By its very nature, the study of children’s eating behavior not only requires knowledge of nutritional needs, but also child development, family systems, parenting, and other social and structural environments that have bearing on the development of children’s eating habits. The absence of a single disciplinary home has led to inconsistencies in language, theoretical approach, and methodology. Pediatric Food Preferences and Eating Behaviors highlights these challenges as well as the phenomenal diversity of perspectives that enrich the study of children’s eating behaviors and have set the stage for major scientific advances in understanding how to nurture healthy eating habits for optimal health. Jennifer O. Fisher Julie C. Lumeng Leann L. Birch

CHAPTER 1

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics Julie A. Mennella*, Alissa A. Nolden*, Nuala Bobowski*† * Monell Chemical Senses Center, Philadelphia, PA, United States St. Catherine University, St. Paul, MN, United States



INTRODUCTION The perception of the five basic tastes of sweet, bitter, umami, sour, and salt is mediated by taste receptors or ion channels in the periphery (primarily in the mouth)1–3 and by multiple brain areas that are phylogenetically well conserved.4 These senses function as gatekeepers, playing a critical role in determining whether to ingest a food or liquid and whether its nutrients gain access to the digestive tract. Although taste remains the top reason for purchasing decisions among adults, the importance of taste in food choice is most evident among children—they eat what they like and leave the rest. In this chapter, we summarize the research on the ontogeny of taste that focuses on the contributions of both age and genetics to the sensitivity and hedonics of the taste system and its impact on behavior. Because of the lack of pediatric research on the relationship between a person’s genes and his or her behaviors (i.e., genotype-phenotype relationships) as related to salty, sour, and umami taste perception, we limit this review to sweet and bitter taste phenotypes. We highlight the breadth of methodologies used to measure taste phenotypes in children, the limited number of taste receptor genes studied to date, and the convergence of findings from this line of scientific inquiry that reveal how children differ from adults, as well as how each child is a unique individual.

TASTE PHENOTYPES: HOW TO MEASURE TASTE IN CHILDREN Psychophysical studies on taste provide data relevant to two separate aspects of taste sensation: the sensitivity of the system (how much of a sensation is detected) and the hedonic valence of the sensation (how much that sensation is liked or disliked).5 The psychophysical method used when the participant is a child varies depending on the objective of the study, the dimension of taste of interest, and the age and thus cognitive and language abilities of the child. Pediatric Food Preferences and Eating Behaviors https://doi.org/10.1016/B978-0-12-811716-3.00001-4

© 2018 Elsevier Inc. All rights reserved.

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Pediatric Food Preferences and Eating Behaviors

While there is no shortage of psychophysical tools purported to measure taste preferences among children,6 few have been systematically tested for validity or age appropriateness.7,8 In what follows, we describe the methods (often referred to as tools) used to measure taste preference or taste sensitivity (detection) among children. This is not a complete listing of all methods or all research studies on this topic but rather represents the methods used to determine whether children differ from adults and whether they differ from one another based on their taste genes (for more complete listing, see Mennella and Forestell7). Some of these methods were adapted or modified from those used in adults, whereas others were developed specifically for use in children. When available, we provide evidence of measurement validity (i.e., degree to which the tool measures what it purports), criterion-related internal validity (i.e., degree to which the tool relates to other outcomes within the study), and retest reliability (i.e., degree of repeatability of the measurement over time). As shown in Table 1.1, in some studies the child is simply asked whether they taste something, or his or her liking for a particular taste or sensitivity to sweetness or bitterness is inferred from dietary recall records that are typically completed by parents or caregivers. In other studies, the methodological approach is more direct in measuring responses to solutions or foods that are tasted by the child (with or without swallowing) or to tastantsoaked filter paper disks that are placed on the child’s tongue. Typically, the child is given time to acclimate to the testing room or is tested in a familiar setting,20,22,24 and in some studies, testing occurs after a 1-h fast.10,26 The most studied taste phenotype, which is often regarded as one of the most studied human traits,27 is the ability to taste compounds containing a thiourea (NdC]S) moiety, such as propylthiouracil (PTU or PROP) and its chemical relative phenylthiocarbamide (PTC). The initial discovery, made in 1931 by A. L. Fox, an organic chemist working at DuPont,28 was serendipitous: some PTC crystals were spilled in the laboratory, and some became airborne; Fox and collaborators noted and later verified through experimental studies that, while many perceived bitterness from the airborne chemical, Fox did not.29 Eight decades after the initial suggestion that human variation in PTU and PTC taste perception could be genetically linked,30,31 the molecular basis was elucidated: the TAS2R38 gene32–37 that encodes for the taste receptor protein underlying personal variation in taste detection thresholds (i.e., the lowest concentration that can be detected by the person) for PTC and PTU was discovered.

Detection Thresholds The methods that have been used on children to measure detection thresholds range from those specifically geared for children (e.g., forced-choice ascending-concentration categorization procedure) to those used on adult patients in the clinic (e.g., forced-choice staircase procedure).

Table 1.1 Partial listing of methods used to measure taste detection thresholds, taste intensity, and taste preferences (hedonics) among children Taste dimension Name of method Description of method Reference

Taste detection threshold

Two-alternative, forcedchoice staircase procedure

Taste intensity

General labeled magnitude scale (gLMS)

Children taste (without swallowing), in succession, water and then three increasing concentrations of bitter tastant. If the solution tastes like “water” or “nothing,” they give the sample to Big Bird. If the sample tastes “bad,” “yucky,” or “bitter,” they give it to Oscar the Grouch. (Some studies also recorded whether the child made a facial expression of distaste during tasting.) Grouping is based on the concentration of the first sample, if any, given to Oscar the Grouch. Children taste pairs of solutions, one of which is water and the other contains a tastant, after which they point to the solution that has a taste. Tastant concentration in the next pair is increased after a single incorrect response (water is chosen) and decreased after two consecutive correct responses. Detection threshold is the mean of the log values of the last four reversals. During training, children are asked to imagine the loudest sound they have ever heard and to use that sound as the top anchor on the 100-point scale with the bottom anchor labeled ‘no sensation’ and top anchor labeled ‘strongest imaginable sensation of any kind,’ as well as labels for intermediate sensations (e.g., weak, moderate, very strong). They then rate four solutions containing increasing concentrations of sucrose on the gLMS. Children who correctly ranked the sucrose solutions in order of intensity then rate taste stimuli using the scale.

Anliker et al.9; Mennella et al.10

Joseph et al.11; Bobowski et al.12

Feeney et al.13

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics

Forced-choice, ascendingconcentration categorization procedure

Continued

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General visual analog scale (gVAS)

Rank-by-elimination task

Rank order intensity task

Taste hedonics

Hedonic face scales

During training, children are told the leftmost end of the horizontal scale represents no sensation and the rightmost end the most intense sensation, equivalent to the loudest sound or brightest light ever experienced. Children rate the intensity of a whisper and a shout on the scale. If they correctly place whisper, shout, and loudest sound in order from least to most intense, they then rate taste stimuli using the scale. Children taste individual solutions or foods in randomized order and indicate which sample or samples are most intense. That sample(s) is removed and the child repeats the task with the remaining samples. The procedure continues until rank order is established from most to least intense (e.g., sweetness, sourness) or most to least preferred. Children taste individual solutions or foods in randomized order and group stimuli into two or more broad categories (e.g., ‘least sweet’, ‘most sweet’). Within each category, they then rank stimuli, resulting in an overall ranking from least to most intense (e.g., sweetness). A visual scale that contains 3 faces (i.e., 3-point facial hedonic scale) or more displaying emotions that range from frowning to smiling faces and a neutral face in the middle position or contains only verbal descriptors. Children taste individual solutions and point to the face on the scale that best

Timpson et al.14

Liem and Mennella15,16

De Graaf and Zandstra17

Bobowski et al.18; Bobowski and Mennella19; Suomela et al.20; Negri et al.21

Pediatric Food Preferences and Eating Behaviors

Table 1.1 Partial listing of methods used to measure taste detection thresholds, taste intensity, and taste preferences (hedonics) among children—cont’d Taste dimension Name of method Description of method Reference

Rank-by-elimination task

Forced-choice, pairedcomparison, tracking procedure

Birch et al.22; De Graaf and Zandstra17; Liem and De Graaf23

Birch24; Liem and Mennella15

Mennella et al.10; Mennella and Bobowski25

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics

Hedonic face scale followed by ranking within each category

represents how it tastes. (In some cases, investigators score intensity of children’s facial reactivity during tasting on a category scale.) Children taste individual solutions or foods and place each sample in front of one face on a 3-point hedonic face scale that best represents how it tastes. Then children order solutions within each face category, from most liked to most disliked, providing an overall ranking of samples from most to least preferred. Children taste a number of individual solutions or foods in randomized order and then point to the sample most liked. That sample is then removed and the child repeats the task with the remaining samples. The procedure continues until a rank order is established from most to least liked. Children are presented with pairs of solutions. The child tastes each solution and indicates which one tastes better. Each subsequent pair contains the selected concentration and an adjacent stimulus concentration. Tasting continues until criterion is met. The task is then repeated with pairs presented in reverse order (i.e., the lower concentration within a pair is presented first in series 1 and last in series 2). The geometric mean is the estimate of most preferred level of sweetness. (In some cases, children were asked to recall their most favorite cereals or beverages to relate to sweet preference.)

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Forced-choice, ascending-concentration categorization procedure. In 1981, Anliker, Bartoshuk, and colleagues9 developed a forced-choice procedure to determine the lowest concentration of PTU that could be detected (i.e., detection threshold). The method was adapted by Mennella and colleagues26,38,39 to determine whether variation in taste genetics explains variation in PTU detection thresholds among individuals as young as 3 years. Only a small percentage of children (5%) did not understand or finish the task.26,38,39 After a 1-h fast and 15–30 min acclimation to the room and study personnel, participants taste (but do not swallow), in ascending order, water and three different concentrations of PTU solutions (56, 180, and 560 μM), rinsing their mouths with water before and after each tasting (Table 1.1). They are told that, if the solution tastes like water, to give it to a stuffed Big Bird™ toy (a likable, well-known television character puppet), but if it tastes “yucky” or bitter, they should give it to another well-known puppet, Oscar the Grouch™, so that “he can throw it in his trash can.” Participants were categorized into one of four groups based on the first sample (56, 180, or 560 μM) given to Oscar the Grouch or if all samples are given to Big Bird (none tasted bitter). In some studies, researchers also recorded whether the participant displayed any facial expression of distaste (e.g., grimace) to assess internal validity or superiority of the outcome measure.26 Participants were categorized into one of four groups based on the first sample, if any, that the child displayed a facial expression of distaste (e.g., grimace).26 Of interest, categorization based on facial expression was less reliable and overestimated the percentage of children that were bitter sensitive to the taste of PTU than categorization based on what sample was given to Oscar the Grouch.26 Several studies retested a subset of the children several months later and found that children’s detection thresholds were reliable over time.26,38 Two-alternative, forced-choice staircase procedure. Detection thresholds can also be determined by a two-alternative, forced-choice staircase procedure originally used in adult clinic populations,40 but this task can be longer and more complicated than the forced-choice categorization method described previously. To date, children as young as 7 years have understood and completed the task.11,12 Very few children did not complete or understand the task (1%–3%11,12), but whether children younger than 7 years can use this method reliably requires further investigation. This method involves children (or adults) tasting a range of solutions (i.e., 5.6  105 to 1.0 M in quarter-log steps) to determine the lowest concentration of a tastant that the person can detect relative to water. After acclimation to room and personnel and after not eating any foods for an hour, participants are presented with pairs of solutions to taste. One sample within each pair is water and the other contains the taste stimulus under study (e.g., sucrose,11 salt,12 monosodium glutamate12). For the first pair offered to the child during the sucrose detection threshold test, the concentration of the taste stimulus is 1.0  103 M (see Refs. 11, 12 for more detailed methods). After tasting both solutions in randomized order, without swallowing and rinsing between tastings, participants are

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics

instructed to point to the one that has a taste. The concentration of the taste stimulus presented in the next pair increases after a single incorrect response (i.e., participant points to water, not taste solution, as having a taste) or decreases after two consecutive correct responses. The task continues until the participant completes four reversals (i.e., an incorrect response followed by two correct responses or vice versa), provided there are no more than two dilution steps between two consecutive reversals, and the reversals do not form an ascending pattern such that positive and negative reversals are achieved at successively higher concentrations. The participant’s detection threshold is the mean of the log values of the last four reversals. To our knowledge, no retest reliability has been determined to date in children.

Taste Intensity Ranking methods. When the task is to assess a participant’s ability to discriminate liquids or foods based on the intensity of a taste, researchers can use methods in which the children rank the items based on a taste quality or on liking.15–17 For example, children as young as 5 years are presented with a series of four or five solutions or foods that differ only in the concentration of a given tastant (e.g., sucrose,16,17 citric acid15). In some studies, children are asked to taste each stimulus and then point to the sample that tastes strongest. That sample is then removed and the participant repeats the task with the remaining samples until a rank order of intensity is established from most to least intense (i.e., rank by elimination method). In other cases, after tastings, children group stimuli into one of two broad categories (e.g., “most sweet”, “least sweet”) after which they rank stimuli within each category, which in turn results in an overall ranking from least to most sweet or least to most preferred (i.e., rank order intensity method).15,17 One study which used this method conducted a check on internal reliability and validity by presenting each child in randomized order with the most (0.25 M citric acid) and least (0) sour tasting, sweetened gelatin.15 Children were given these taste stimuli in random order to retaste and to indicate whether it tasted sour or sweet. There was strong agreement between children’s ability to rank stimuli in order of increasing taste intensity and to correctly identify the taste quality during the retest, even among those as young as 5 years of age.15 Scaling methods. Scaling methods, which are cognitively demanding, are frequently used tools to quantify perception in adults. For taste, the gold standard scaling method is the general labeled magnitude scale (gLMS), which has been validated for use in adults and shown to be superior to other rating scales (e.g., 9-point scales) when comparing taste sensations among individuals.41–43 Further, retesting of adults revealed gLMS ratings for PTU and another bitter tasting medicine are a stable phenotype that is reliable over time.44,45 On the gLMS, the vertical scale ranges from 0 to 100, with adjectives at the bottom anchor (“no sensation”) and top anchor (“strongest imaginable sensation of any kind”), as well as adjectives placed along the scale logarithmically (“barely

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detectable,” “weak,” “moderate,” “strong,” “very strong”).41,43,46 The top anchor allows participants to rate the intensity of the taste within the context of all things, including nontaste experiences, which in turn allows valid across-group comparisons. While the general visual analog scale (gVAS) is based on the same principles as the gLMS, this scale contains only the anchors and no adjectives along the horizontal 10 cm scale. To our knowledge, only a few studies on taste-genotype relationships had children between the ages of 7 and 13 years rate taste stimuli on either a gLMS or a gVAS.13,14,47 In two of the studies, training was provided. Before rating on a gLMS,13 children were told to imagine the loudest sound they had ever heard, and then to imagine something even louder, explaining that the loudest noise imaginable would be the top anchor of the scale. Children then tasted four concentrations of sucrose (0.15, 0.29, 0.44, 0.58 M) in randomized order and rated taste intensity of each on the gLMS.13 Of the 525 total children enrolled, only 1% were excluded because they were unable to properly rate these stimuli in order of concentration. A similar training session was provided for the horizontal gVAS,14 but here children were told that the leftmost end of the scale represented no sensation and the rightmost end the most intense sensation, equivalent to the loudest sound or brightest light they had ever experienced. The experimenter then had the children rate a whisper and a shout, making sure the ratings were placed on the scale in order of whisper, shout, and loudest sound. While these training sessions may provide some indication of whether the child can use these scaling methods to rank order intensity, it does not provide evidence that they understood differences in the scaling and labels (other than equating a whisper to no or little sensation). However, one study retested children 1 month later and found that 10-year-old children’s gVAS ratings of PTU were reliable over time.14 More research is needed to determine the lower age limit at which children can perform these more sophisticated scaling methods and, in particular, understand the concept of the anchor(s) in the scale. Box 1.1 summarizes methods used to measure taste detection and intensity.

BOX 1.1 Section Summary

• • •

Forced-choice, ascending concentration detection threshold methods: pairs of solutions of specific concentrations of a tastant are presented to the child in ascending order to determine the lowest concentration at which a particular taste sensation can be detected. Ranking methods: a number of taste stimuli (solution, food) are presented to the child who ranks them based on the intensity of a tastant or on liking. Rating/scaling methods: a number of taste stimuli (solution, food) are presented individually to the child who uses a numerical or description scale to rate the strength of a particular taste sensation.

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics

Taste Hedonics For people of all ages, but especially children, a general rule is that it is easier to measure hedonics (palatability) than detection.48 The methods used to measure taste hedonics vary from evaluating one taste stimulus at a time to a series of paired-choice comparisons to determine the level of a taste most preferred. To clarify, liking provides a measure of hedonic response to a single stimulus and is measured using methods such as the hedonic face scale, whereas preference provides a measure of how much a person likes a given stimulus relative to another and is measured using methods such as the rank-by-elimination task and the forced-choice, paired-comparison tracking procedure described herein. Hedonic face scale. Perhaps the most popular method for use with children is the hedonic face scale, a categorical scale made up of a range of face figures (e.g., 3–9 faces6) that depict ranges in emotion from frowns to smiles, with a neutral face typically placed in the middle of the scale. (In one case, the children were presented with a scale that had only the verbal descriptors signifying the emotion, not the faces.20) Depending on the orientation of the scale, its top and bottom (or right and left) anchors represent the most extreme frown and smile. In brief, children as young as 3 years are instructed to taste a liquid or food and then to point to the face that best represents how the liquid or food tastes to them.24 From these data, their taste responses are categorized. Some investigators have assessed whether children are capable of using the range of hedonic face scales by including other taste stimuli that vary in taste quality and hedonic valences.19 For example, when evaluating children’s ratings of liquids containing different nutritive (e.g., sucrose) or nonnutritive (e.g., sucralose) sweeteners on 5-point and 3-point hedonic face scales, investigators included others solutions containing varying amounts of potassium chloride, because it does not taste sweet and adults often describe it as tasting bitter and metallic.49 While children used the smiley faces on the scale to describe their liking for sweeteners, they used the frowning faces to describe their dislike for potassium chloride, providing evidence that these children could use the entire scale and that they understood the difference between smiling and frowning faces as they relate to taste hedonics. While this categorization of the test stimuli can be the final outcome measure, some researchers utilized the hedonic face scale as part of a rank-by-elimination task to determine rank order preference.22 That is, after children selected the face that best represented their liking for a series of taste stimuli, they were then asked to order the stimuli within each face category from most to least liked, providing an overall ranking of samples from most to least preferred.17,22,23 Rank order preferences of three versions of a food (i.e., tofu) that differed in taste (i.e., plain, sweet, salty) were reliable over time, even among children as young as 3–4 years.22 Among 8- to 10-year-olds, how they rank ordered their preferences for flavored beverages containing varying levels of sucrose significantly related to how they rated the taste of these beverages on a 5-point hedonic face

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scale. That hedonic ratings were positively related to how much of the beverage the child drank provided evidence of measurement and criterion-based validity.17 Two-series, forced-choice tracking procedure. To determine the level of a particular tastant most preferred by an individual, researchers at Monell Chemical Senses Center developed a two-series, forced-choice tracking procedure which has been used in children 3 years and older.10,50,51 First developed to determine the most preferred concentration of salt among children,50 the Monell forced-choice, paired-comparison, tracking procedure was adapted and validated for the NIH Toolbox to measure the most preferred level of sucrose among adults.10,25 In brief, this psychophysical tool involves presenting participants with pairs of solutions that differ in concentration. For example, the five concentrations of sucrose used to determine most preferred level of sweetness are 0.09, 0.18, 0.35, 0.70, and 1.05 M (which are equivalent to 3%, 6%, 12%, 24%, and 36% wt/vol). The first pair presented is from the middle range of concentrations (0.18 and 0.70 M). The child tastes each solution in randomized order and then indicates which one tastes better. Each subsequent pair of solutions presented contains the concentration selected by the child in the preceding pair and an adjacent concentration stimulus, with this pattern continuing until the child either chooses the same concentration when paired with both a higher and a lower concentration in two consecutive pairs or chooses the highest or lowest concentration twice consecutively. After a 3-min break, the task is repeated but stimulus pairs are presented in reverse order (i.e., the lower concentration is presented first in series 1 and the higher concentration is presented first in series 2), to control for position bias. The geometric mean of the concentrations selected during the two series provides the estimate of most preferred level of sucrose, a measure that over the years has provided insight into individual differences, including age,10 race/ethnicity,10,52 family history of addiction and depression,53 weight status,19,54 and taste receptor genotype26 (see “Genetics of Sweet and Bitter Taste”). Because this method consists of two series that controls for position bias, it enables researchers to determine objectively whether the child understands the task or is responding at random (measurement validity).10,25 For example, if a child tended to always point to the solution on his or her right, the level most preferred in the first series would be 3–4 steps away from most preferred in the second series. Using this criteria, only 6% of the children did not understand or complete the task.10,26,53,55 However, while this method has been used on 3-year-old children to measure salt preference,50 the vast majority of studies conducted to date have been on children 5 years of age and older. For those children who understood the task, there was significant reliability in the level most preferred in series 1 with series 2, providing evidence of internal consistency. Further, the level of sweetness most preferred in water was significantly related to

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics

children’s most preferred level of sweetness in jellies,55 as well as the sugar content of their favorite cereal and beverage,26,56 providing evidence of criterion-based internal validity.

Summary While there are several methods to determine how sensitive children are to a particular taste (the lowest level they can detect) or how much they like or dislike a taste, it is important to consider the objective of the study and the age of the child when selecting a method.57 While how sensitive a person is to a particular taste (e.g., PTU) may relate to how much that person likes a food that tastes bitter (e.g., dark green vegetables; coffee),58–60 this is not always the case.61 From an early age, children can learn to be more accepting of vegetables following repeated tastings of the foods in a positive context.62,63 Taste sensitivity and preference are two separate dimensions, with preference more dependent on prior experience and learning; children learn to like what they eat.63,64 As a general rule, when designing a study on taste that compares children with adults, the same psychophysical method should be used for all participants. However, it should be emphasized that some psychophysical methods are more sensitive to age-related differences than others (see Ref. 19). When testing children, we recommend that children be acclimated to both the room and study personnel and are provided training to determine whether they understand the task. The task should be fun for the child and take less than a half hour to complete because of their limited attention span. When evaluating taste perception, no food or beverages should be eaten 1 h beforehand, since eating as well as fasting for long periods can modify taste perception.65 Most important, methods that control for position bias or replicate the tasks (e.g., two series) are advantageous because investigators can assess whether a child is responding at random or is consistent in responses between series, respectively. Likewise, those with a forced-choice aspect circumvent the element of uncertainty for children, especially when tasting solutions at low concentrations. Further, these methods do not rely on yes/no answers, which are prone to inaccuracy because young children tend to answer in the affirmative. Categorizing a taste as good or bad provides more reliable taste detection data than categorizations based on facial reactivity, a finding consistent with developmental research, which reveals that children learn to conceal or exaggerate their actual responses to particular tastes, which in turn may lead to biased or unreliable data.66 Regardless of the method chosen, researchers need to include assessments of test reliability and internal validity and report both the number and ages of children who did not complete or understand the task, so that others have an evidence base regarding at what age children can understand and perform the psychophysical method (Box 1.2).

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BOX 1.2 Section Summary

• • •

Hedonics or palatability is often easier to measure than detection threshold. Hedonic facial scales can be used to categorize whether something is liked or disliked as well as rate the degree of liking/disliking. Two-series, forced-choice, paired comparison methods can be used to determine the most preferred concentration of a tastant.

ONTOGENY OF SWEET AND BITTER TASTE Sweet Taste The ability to detect sweet taste and to preferentially bias behaviors upon tasting something sweet is inborn, as evidenced by more rigorous sucking of a sweetened pacifier by newborns and by infants born prematurely (25–36 weeks gestational age).67 Newborns can distinguish sugar-sweetened waters made with different sweeteners, as evidenced by greater intake and enhanced sucking responses.68 Further, tasting something sweet can attenuate an infant’s expressions of pain (e.g., crying) during painful procedures such as a heel prick or circumcision.69 Using psychophysical methods, some of which are described in Table 1.1, scientists directly compared children’s taste preferences with those of older individuals. The convergence of findings from this body of research has revealed that children live in different sensory worlds than adults. As a group, children most prefer higher levels of the nutritive sugars sucrose10,19,26,55,70,71 and fructose72 and the nonnutritive sweetener (NNS) sucralose19 than do adults, with preferences declining to adult levels during adolescence, which coincides with the cessation of physical growth.55,73 The difference between children and adults in the intensity of sweetness most preferred appears to be a stable phenotype that has been observed during the past few decades.71 The age-related difference in sweet preference is striking. In a study of 949 individuals whose ages range from 5 to 55, on average children most preferred a 0.60 M sucrose whereas adults most preferred a 0.34 M sucrose solution.10 To put this in perspective, a 0.60 M sucrose concentration is equivalent to about 12 teaspoons of sugar (48 g, 4 g/ teaspoon) in 230 mL of water (8 oz glass), whereas a typical cola has a sugar concentration of 0.34 M (7 teaspoons in 8 oz of water). The concentration of sucrose that children most prefer in the laboratory has been shown to be significantly related to level of sweetness they most prefer in beverages, cereals,10,26 and puddings,70 which provides evidence of criterion-related validity and suggests that children may be more vulnerable to overconsumption of sugar-sweetened foods and beverages than adults. Not only do children most prefer higher levels of the nutritive sugars sucrose10,26 and fructose,72 and the NNS sucralose19 than do adults, but they are more likely to like the

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics

taste of higher concentrations of the NNSs sucralose and aspartame, as evidenced by ratings on the 5-point hedonic face scale.19 Given the increasing prevalence of NNSs in the food supply,74 including foods (e.g., grain products75) and beverages consumed by children, continued research is needed to determine the impact of a diet rich in sweetness due to NNSs on dietary intake, weight gain, and metabolism.76 We have argued that while adding NNSs may lower the added sugar content of the food or beverage, it retains its sweet taste or in some cases tastes sweeter. Because sweet taste is reinforcing and can condition preferences in children, the long-term consequences on food habits are an important area for future research.77,78 The rewarding properties of sweet taste for children go beyond its taste. Tasting something sweet (0.70 M, equivalent to 24% w/v sucrose solution) blunts expressions of pain among children (5–10 years) undergoing the cold pressor test (CPT).54,79 The CPT is a classic experimental paradigm in which participants immerse a hand in a cold water bath and investigators measure the latency to report pain, as well as the length of time pain is tolerated. The more children liked sucrose, the better its efficacy as an analgesic. Children who most preferred a 0.70 M sucrose solution exhibited an increased latency to report pain and tolerated pain for significantly longer periods of time when sucrose was held in their mouths relative to water, compared to children who preferred lower levels of sweetness. By adulthood, at least for women, tasting sucrose was ineffective in pain modulation during the CPT.54,80 The dichotomy of liking sweet taste and how tasting sweets make children feel is most apparent in the obese child. Although obesity is often assumed to be driven in part by a heightened liking for sweet taste (and an associated increase in intake of sweetened foods and excess calories),81 direct measurements of the most preferred level of sweetness (e.g., sucrose,19,54 sucralose19) have revealed no significant differences between normal-weight and obese children. Rather, children differed in their motives for eating palatable foods, including sweets, as assessed by the Kids Palatable Eating Motives Scale: the higher the body mass index Z-score, the greater the child’s social motives (e.g., to enjoy a party or celebration) and conformity motives (e.g., to be well liked by others) for eating palatable foods.82 What is more, sucrose was ineffective in attenuating pain from the CPT in obese compared to normal-weight children.54 These findings highlight the importance of understanding individual differences in the physiologically and psychologically rewarding properties of sweeteners as potential drivers of variation in consumption of sweettasting foods and beverages. Less studied is how sweet taste sensitivity or detection thresholds change with age and among children, though recent research suggests a potentially important link between taste sensitivity and various measures of health. Among children 7 or more years of age, detection thresholds for sucrose were negatively associated with body fat (i.e., the greater the percentage of body fat, the lower the sucrose detection threshold).11 Detection thresholds for salt were positively associated with blood pressure among

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normal-weight children but not among overweight and obese children.12 This suggests that body composition, including weight status, may have as of yet unresolved consequences on taste or, alternatively, that taste sensitivity may be a marker of a metabolic effect or physiological process.11,12

Bitter Taste Compared with sweet taste, less research has focused on the ontogeny of bitter taste, which is considered the most varied taste quality. That is, compared to the other taste qualities, how sensitive a person is to a given bitter taste stimulus can vary greatly among individuals. One person may detect bitterness at a low concentration whereas another may be insensitive to its taste or require very high concentrations to detect it.83 Shortly after birth, babies reject some but not all bitter-tasting substances, as evidenced by decreased intake and pronounced facial expressions.4,84–87 Some bitter tastes may mature postnatally (as is the case with salt tastes88), as evidenced by the rejection of urea among 14- to 180-day-old infants but not newborns.89 Further evidence that children are living in different sensory worlds than adults comes from the earliest human perception studies on the taste of PTU, used historically to treat thyroid disorders.90,91 Scientists noted that it was always easier to find a “taster” among infants and children,30,92 a finding later confirmed by studies that relate taste receptor genetics (genotype) with measures of taste perception (phenotype). As discussed in the next section, research on genotype-taste phenotype relationships has revealed that childhood is a time of heightened sensitivity for some bitters, with the adult pattern emerging during adolescence,9,26,38 as it does for sweet preferences. Beginning in the 1980s, research focused on bitter taste as a deterrent for pediatric poisonings.93,94 Children between 1 and 4 years of age were offered either a dilute soapy solution or the same solution with the bitter agent denatonium benzoate (DB) added. Children offered the solution containing DB displayed more immediate and intense aversive responses, were less likely to take a second taste of the solution, and ingested less overall than did children offered the soapy solution alone.95 Similarly, among children 1 to 3 years of age offered orange juice containing DB, the vast majority consumed small amounts, if any at all, while displaying signs of distaste and vomiting.93 However, nearly a quarter of the children consumed significant amounts (more than 10 g) of DB. Data from these original publications, as well as from as well as from poisoning databases,96 reveal that children vary in their sensitivity to DB and that it is not an effective deterrent for all children.

Summary The convergence of evidence suggests that children are living in different taste worlds from adults. From an early age, children have proclivity for sweetness, preferring higher

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics

concentrations of nutritive sugars and nonnutritive sweeteners than do adults, with the adult-like pattern emerging during mid-adolescence.77 Concordant with these data collected in the laboratory are what children like to eat (e.g., candy) and what they reject (e.g., green vegetables, bad-tasting medicines97). From the age of 2 years, an American is more likely to eat a manufactured sweet than a vegetable on a given day.98 On average, children consume approximately 16% of total daily calories from added sugars,99 compared to 13% by adults,100 well above the recommended levels of 5%–10%.101,102 While there are no differences between normal-weight and obese children in the levels of sucrose most preferred, how sensitive they are to the taste of sucrose (detection thresholds) related to measures of body fat.11 This suggests that obesity may have unknown consequences on the taste system or, alternatively, that taste sensitivity may be a marker of a metabolic effect or physiological process.11,12 While we know more about sweet taste than bitter taste development, there is evidence that children perceive some bitter tastes differently than adults. Childhood may represent a time of heightened bitter sensitivity for some but not all children. This is perhaps most evident in the inability of strong, bad-tasting bitter agents like denatonium to deter all children from ingesting potential poisons. No child is average, and each lives in his or her unique taste world that is in part genetic in origin, as described next (Box 1.3).

GENETICS OF SWEET AND BITTER TASTE Sweetness and bitterness, as sensations, start on the tongue. When a compound (commonly referred to as ligand) stimulates sweet taste receptors on taste cells, the resulting signal is transduced intracellularly via G proteins103 to activate (depolarize) taste cells, which in turn send a signal that activates pleasure-generating brain circuitry,104 where sweet taste perception and hedonics arise.105

BOX 1.3 Section Summary

• • • •

Children most prefer higher levels of sweetness from nutritive and some nonnutritive sweeteners than do adults with the changeover to the adult pattern occurring during mid-adolescence. Sweetness is a powerful analgesic among children and may be more effective in attenuating pain for normal weight than for obese children. Sensitivity to bitter varies widely among individuals and with age such that children are more sensitive to the taste of some bitters than adults Shortly after birth, babies reject some but not all bitter-tasting substances suggesting postnatal maturation for some bitter tastes.

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The sweet receptor is actually composed of two receptors (i.e., two G proteincoupled receptor [GPCR] proteins), named T1R2 and T1R3 (taste receptor family 1, receptors 2 and 3); their associated genes are TAS1R2 and TAS1R3, respectively. Bitter receptors are also GPCR proteins, but there are many more bitter receptors than sweet receptors. In humans, it is estimated that there are approximately 25 different bitter taste receptors (T2Rs; taste receptor family 2).106 The large repertoire of taste receptors that detect bitter and the inborn rejection of bitter taste evident early in life84 may be evolutionarily important in protecting against the ingestion of toxins and poisons, many of which taste bitter.107 Most bitter taste receptors are activated by more than one bittertasting compound, and one bitter-tasting compound may activate more than one receptor.108 However, this research is still in its infancy since there are many chemicals that are perceived as bitter that do not activate any known receptor,108–110 suggesting there are many bitter receptors yet to be discovered. While taste receptors were originally discovered in the oral cavity (hence the term “taste receptor”), recent scientific discoveries have revealed that taste receptors are also located in extra-oral tissues, including upper airways,111,112 throat,113 lung,114 gut,115,116 and skin.117 In what follows, we focus on genetic variability of sweet and bitter taste receptors and summarize the research, albeit limited, on the association between genetic variation and taste perception or diet of children (Box 1.4).

Taste Receptor Genetics and Nomenclature For the sweet and bitter taste receptor families, standard gene names often are used: TAS for “taste receptor,” followed by 1 or 2 for the family type, and ending with an R for “receptor” and then the member number. For example, the gene “taste receptor, type 1, member 1” has the gene symbol TAS1R1 (gene symbols are always italicized, and for humans are capitalized).118 Variation in taste genetics arises from inherited or spontaneous mutations. Even though an estimated 99% of DNA is the same across individuals, and even more so in families, genetic mutations can occur, with each offspring having an

BOX 1.4 Section Summary

• • •

Humans have two sweet receptors but as many as 25 bitter receptors. Taste receptors are not only found in the oral cavity but throughout the body including the gastrointestinal tract. Variation in gene sequences of some bitter taste receptors has been associated with variation in bitter taste detection thresholds as well as acceptance of bitter tasting foods. The strong appeal of sweet tastes and rejection of bitter tastes is thought to have evolved to favor ingestion of nutrients and avoid the ingestion of potentially toxics foods, respectively.

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics

average of 60 new mutations compared to the parents.119 These genetic mutations may result in a change in the function of the receptor. Although several types of genetic mutations can occur (e.g., deletions, insertions), the taste genotyping conducted to date in children has focused on changes in single-nucleotide polymorphisms (SNPs), which are changes in the nucleotide sequence (i.e., alanine [A], cysteine [C], thymine [T], or guanine [G]). Traditionally, SNPs are referred to by accession or reference (rs) numbers (e.g., rs713598) or by the amino position and the ancestral and mutated amino acids. For example, a SNP for the TAS2R38 gene is referenced by its rs number (rs713598) or by Ala49Pro, which indicates an amino acid change from alanine to proline at position 49 in the receptor protein. However, in some SNPs the nucleotide change does not lead to an amino acid change. For example, the rs307355 SNP on TAS1R3 is denoted as 1572 T > C (see later). In this chapter, we use both types of nomenclature as appropriate. For TAS2R38, the gene that encodes T2R38 (bitter taste receptor 38),26,28,99 three SNPs have been associated with differences in the perception of PTC and PTU: Ala49Pro (rs713598), Val262Ala (rs1726866), and Ile296Val (rs10246939). Because we inherit two sets of genes, one from each parent, an individual’s genotype comprises two haplotypes for a specific SNP, one from each set of genes. Thus, there are several possible genotypes for TAS2R38; the most common are PAV/PAV (homozygous bitter sensitive), PAV/AVI (heterozygous, bitter sensitive), and AVI/AVI (bitter insensitive). In one study,14 only Ala49Pro and Val262Ala SNPs were typed, and resulting genotypes included PA/PA, PA/AV, and AV/AV. Because of the high degree of heritability (meaning what is inherited at position 49 is associated with what is inherited at positions 262 and 296), researchers commonly only genotype for the Ala49Pro allele because it explains most of the individual differences in the taste response and is a proxy for the other variants.32,39 While other SNPs within the T2R gene family have been the focus of research on bitter taste perception and dietary intake among adults, the only other T2R studied to date in children is TAS2R31, because research indicated that whether adults liked the taste of the NNS acesulfame potassium (AceK) and whether it had a bitter aftertaste was based on variation in this taste gene.120–122 In children, four SNPs, Arg35Trp, Leu162Met, Ala227Val, and Val240Ile, and their two haplotypes, WMVI and RLAV, were related to whether they liked the taste of AceK, as determined by the 3-point hedonic face scale.18 For the human sweet taste receptor dimer, only a few studies have explored associations between genetic polymorphisms and sweet taste perception among children. The sweet taste receptor SNPs examined to date are TAS1R2 (Ile191Val; rs35874116), TAS1R3 (1266 T > C; rs35744813), and TAS1R3 (1572 T > C; rs307355). For clarity, the two latter TAS1R3 SNPs are located at nucleotide positions 1266 and 1572, respectively, which are upstream from the coding sequence of the gene (signified by the  sign), with T (thymine) as the ancestral and C (cytosine) as the mutated allele.

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Summary Humans vary in the genetics of sweet and bitter taste receptors and in turn the function of the receptors (e.g., binding of the ligands). Whereas a range of sweeteners, both nutritive and nonnutritive, can bind to and trigger the sweet receptor,123 some NNSs also bind to and activate bitter receptors,120,124 resulting in some individuals perceiving a bitter aftertaste when tasting some diet beverages. Further, due to multiple bitter receptors and the ability of bitter compounds to bind to more than one receptor, the evidence is that, of all the taste qualities, bitter is the most varied among humans. This is also why describing an individual as being “bitter sensitive” is misleading, as people may be very sensitive to one bitter compound but not to another.

GENOTYPE-TASTE PHENOTYPE RELATIONSHIPS IN CHILDREN Table 1.2 lists the research conducted to date on the association between genetics of taste receptors and taste perception in pediatric populations, although no studies have been conducted in infants. Most research focused on the TAS2R38 gene, the first bitter taste gene to be discovered, in 200332–37 with over half (10 of 19) of studies conducted by our research group at Monell. This body of research follows a larger group of studies in adults that confirmed the genetic basis of personal variation in the bitter perception of PTU.37,38,131–133 In what follows, we summarize the research on the TAS2R and TAS1R gene families separately.

TAS2R Genotype-Phenotype Studies The first study that related TAS2R genotype and phenotype among children was published in 2005,26 2 years after the discovery of the TAS2R38 gene. Like adults, children who had at least one copy of the functional amino acid proline for the Ala49Pro SNP (i.e., PP, AP genotypes) were more sensitive to the taste of PTU than were AA homozygous children,26 a finding that has been replicated in several laboratories, especially when the task is appropriate for use in children (Table 1.2). In most studies, the gene effect was incompletely dominant, with heterozygotes having an intermediate phenotype, confirming earlier suggestions about the mode of inheritance.134 Studies that included both children and adults, using simple methods that allowed for comparison between age groups, showed genotype-specific declines in PTU sensitivity with age, a finding consistent with previous reports of a smaller number of insensitive children than in adults.21,30,92 Children who were either heterozygous or PP homozygous were more sensitive to the taste of PTU than were adults of the same genotype,26,38 with the changeover to the adult pattern occurring during adolescence.38 Cruciferous vegetables contain glucosinolates, a class of compounds that contain a thiourea moiety, like PTU, that activates the TAS2R38 receptor.135 There is a rich body

Table 1.2 Summary of findings on relationships between taste receptor (TAS2R, TAS1R) genotypes and sensory and diet outcomes among pediatric populationsa Reference Outcome Methodb Stimulic Study population Findings (s) Bitter TAS2R: TAS2R38 (rs713598, rs1726866, rs10246939)

Bitter taste detection threshold

Forced-choice, ascendingconcentration categorization procedure. Investigators recorded children’s facial reactivity during tasting.

PTU (0, 56, 180, 560 μM)

USA: 143 children (5–10 years) and 114 adults

Bitter taste detection threshold

Forced-choice, ascendingconcentration categorization procedure

PTU (0, 56, 180, 560 μM)

USA: 448 children (3–10 years) and 432 adults

Bitter taste detection threshold

Forced-choice, ascendingconcentration categorization procedure. Investigators recorded children’s facial reactivity during tasting.

PTU (0, 56, 180, 560 μM)

USA: 154 children (3–10 years) and 118 adults

Ala49Pro genotype effect: PP > AP > AA. PP children detected PTU at lower concentrations and were more likely to make a facial expression of distaste than AA children, with AP children intermediate. Age-genotype effect: PP and AP children detected PTU at lower concentrations than did PP and AP adults. Ala49Pro, Val262Ala, Ile296Val agegenotype effect: PAV/AVI children detected PTU at lower concentrations than did PAV/AVI adults. Ala49Pro genotype effect: PP > AP > AA. PP children detected PTU at lower concentrations and were more likely to make a facial expression of distaste than AA children, with AP children intermediate. Age-genotype effect: PP and AP children detected PTU at lower concentrations than did PP and AP adults.

Mennella, et al.26

Mennella, et al.38

Mennella et al.125

Continued

Table 1.2 Summary of findings on relationships between taste receptor (TAS2R, TAS1R) genotypes and sensory and diet outcomes among pediatric populations— cont’d Reference Outcome Method Stimuli Study population Findings (s)

Bitter taste intensity

gVAS

Bitter taste intensity

gLMS

Bitter taste hedonics

Three-point hedonic face scale

Bitter taste hedonics

Hedonic scale with verbal descriptors. Investigators recorded children’s facial reactivity during tasting.

PTU (280, 560 μM)

Bitter taste hedonics

Children indicated whether they tasted anything and, if so, reported what it tasted like. Investigators recorded children’s facial reactivity during tasting. Two-alternative, forced-choice staircase procedure

PTU (560 μM)

Sweet taste detection thresholds

Filter paper disk soaked with PTU solution (3.2 mM) Filter paper disk soaked in PTU solution (50 mM) PTU (56 μM)

Sucrose (0.23– 158.3 M)

England: 4795 children (10 years) Ireland: 525 children (7–13 years) USA: 48 children (6–14 years) and 34 adults

Italy: 98 children (no age reported) and 120 adults Netherlands: 5894 children (5–6 years)

USA: 235 children (7–14 years)

Ala49Pro, Val262Ala genotype effect: PA/PA > AV/AV. PA/PA children perceived greater bitterness than AV/ AV children. Ala49Pro, Val262Ala, Ile296Val genotype correlated with subsequent grouping based on children’s PTU bitterness ratings. Ala49Pro genotype effect: PP > AP ¼ AA. PP children and adults disliked the taste of PTU more than did AP and AA children and adults. Ala49Pro age-genotype effect: AP children disliked the taste of PTU more than did AP adults.

Timpson et al.14

Ala49Pro genotype effect: PP and AP children were more likely to dislike and/or make a face during tasting than AA children.

Bouthoorn et al.126

Ala49Pro genotype effectd: PP > AP ¼ AA. PP children were more sensitive to sucrose (lower detection thresholds) than AP and AA children. Val262Ala genotype effect: VV > AV ¼ AA. VV children were more sensitive to sucrose than AV and VV children. Ile296Val genotype effect: VV > II. VV were more sensitive to sucrose than II children.

Joseph et al.11

Feeney et al.13; O’Brien et al.47 Bobowski et al.18

Negri et al.21

Sweet taste intensity

gLMS

Sucrose (0.15 and 0.45 M)

Ireland: 525 children (7–13 years) USA: 143 children (5–10 years) and 114 adults USA: 448 children (3–10 years)

Sweet taste hedonics

Forced-choice, paired-comparison, tracking procedure

Sucrose (0.09, 0.18, 0.35, 0.70, 1.05 M)

Sweet taste hedonics

Forced-choice, paired-comparison, tracking procedure

Sucrose (0.09, 0.18, 0.35, 0.70, 1.05 M)

Diet and liking of the taste of fruit

Nine-point hedonic scale (without faces) used for children’s tasting of food items. Food frequency of fruits and vegetables completed by parents.

12 food products containing different types of berries.

Finland: 104 children (5– 10 years)

Diet

Three-day dietary recall completed by mothers with focus on vegetables, fruits, and sweet and savory snacks

NAe

Italy: 98 children (3–19 years) and 120 adults

Diet

Three-day dietary recall and food frequency questionnaire completed by children

NA

Ireland: 483 children (7– 13 years)

No Ala49Pro, Val262Ala, Ile296Val genotype effect.

Feeney et al.13

Ala49Pro genotype effect: PP ¼ AP > AA. PP and AP children preferred higher concentrations of sucrose than did AA children. Ala49Pro genotype effect: PP > AP > AA. PP children preferred higher concentrations of sucrose and preferred beverages with higher sugar content than AA children, with AP children intermediate. Ala49Pro, Val262Ala, Ile296Val genotype effect: Liking of different products containing berries depended on the type of berry. For example, more PAV/PAV and PAV/ AVI children than AVI/AVI children reported liking sweetened dried bilberries. No genotype effect on fruit or berry intake, but PAV/AVI children consumed more vegetables than AVI/AV children. Ala49Pro, Val262Ala, Ile296Val genotype effect: PAV children were less likely to consume bitter vegetables than children with other genotypes; no effects in adults. No Ala49Pro, Val262Ala, Ile296Val genotype effect.

Mennella et al.26

Lipchock et al.56

Suomela et al.20

Negri et al.21

O’Brien et al.47 Continued

Table 1.2 Summary of findings on relationships between taste receptor (TAS2R, TAS1R) genotypes and sensory and diet outcomes among pediatric populations— cont’d Reference Outcome Method Stimuli Study population Findings (s)

Five-point hedonic face scale used for remembered liking. Three-day dietary recall completed by children.

Pictures of 12 common fruits and vegetables

Ireland: 525 children (7– 13 years)

No Ala49Pro, Val262Ala, Ile296Val genotype effect for either hedonic ratings or intake.

Feeney et al.13

Seven-day food records completed by caregivers with focus on sweet sugary foods

NA

Finland: 345 children (2– 6 years)

Hoppu et al.127

Diet

Three-day dietary recall completed by parents after which dietitians focused on sweet tasting foods and beverages

NA

Medication adherence

Retrospective report by children, verified by mothers

NA

Germany, Belgium, Italy, Poland, and Spain: 691 children (1– 6 years) USA: 448 children (3– 10 years)

Ala49Pro, Val262Ala, Ile296Val genotype effect: PAV/PAV > PAV/ AVI ¼ AVI/AVI. PAV/PAV boys consumed more candy and sugar than PAV/AVI and AVI/AVI boys. No genotype effects among girls. Ala49Pro genotype effect: PP ¼ AP > AA. PP and AP children consumed more energy from sweettasting foods than AA children.

Medication adherence

Retrospective report by children, verified by mothers

NA

Diet and remembered liking of fruits and vegetables Diet

USA: 172 children (3– 10 years)

Ala49Pro genotype effect: PP ¼ AP > AA. PP and AP children with were more likely to have taken medication in solid form than AA children. Ala49Pro genotype effect: PP ¼ AP > AA. PP and AP children were more likely to report rejecting liquid medication due to taste than AA children.

Pawellek et al.128

Lipchock et al.56

Mennella et al.129

Bitter: TAS2R31 (rs10845295, rs10743938 rs10845293, rs10772423)

Taste hedonics

Three-point hedonic face scale

Ace-K (120 μM)

USA: 48 children (6–14 years) and 34 adults

Arg35Trp, Leu162Met, Ala227Val, Val240Ile genotype effect: RLAV/ RLAV ¼ RLAV/WMVI > WMVI/ WMVI. RLAV homozygous and heterozygous adults and children disliked Ace-K more than did WMVI homozygous adults and children.

Bobowski et al.19

NA

Brazil: 596 children (3– 7 years)

Ile191Val genotype effect: II ¼ VI > VV. VV children consumed less sugar and fewer sugardense foods than II or VI children.

Melo et al.130

1266 T > C genotype effect: TT > CT > CC. TT adults most preferred higher levels of sucrose than CC adults, with CT adults intermediate. No genotype effect in children. 1266 T > C genotype effect: TT ¼ CT > CC. TT and CT adults most preferred higher level of sucrose than CC adults. No genotype effect in children. No 1266 T > C genotype effect.

Mennella et al.70

Sweet: TAS1R2 (rs35874116)

Diet

Two 24-h dietary recalls completed by parents with focus on sweet foods

Sweet: TAS1R3 (rs35744813)

Sweet taste hedonics

Forced-choice, paired-comparison, tracking procedure

Sucrose (0.09, 0.18, 0.35, 0.70, 1.05 M)

USA: 84 children (5–10 years) and 67 adults

Sweet taste hedonics

Forced-choice, paired-comparison, tracking procedure

Sucrose (0.09, 0.18, 0.35, 0.70, 1.05 M)

USA: 108 children (5– 10 years) and 83 adults

Diet

Two 24-h dietary recalls completed by parents with focus on sweet foods

NA

Brazil: 312 children (3– 7 years)

a

Mennella et al.55

Melo et al.130

As indicated, in some studies adult populations were also studied to determine age-related genotype-phenotype effects. Description of psychophysical methods can be found in Table 1.1. Abbreviations: Ace-K, acesulfame potassium; gLMS, general labeled magnitude scale; gVAS, general visual analog scale; NA, not applicable; PTU, propylthiouracil. c Stimuli were converted to molarity for consistency in reporting. d Analyses adjusted for age and gender. e Not applicable because no psychophysical testing on actual liking or remembered liking of taste stimuli was performed. b

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of evidence relating children’s detection thresholds for PTU and food preferences (see Refs. 136–139). Table 1.2 summarizes studies that related TAS2R38 genotype with food preferences or diet as determined by dietary recalls completed by mothers21 or the children themselves,47 as well as children’s ratings of pictures of fruits and vegetables using the hedonic face scale.13 Only one study found a significant effect of the TAS2R38 genotype, with children with the bitter-sensitive PAV alleles being more likely to avoid bitter vegetables and juices.21 Genetic variation in the TAS2R38 bitter taste receptor gene has also been shown to relate to acceptance of bitter taste in the context of pediatric medicines.140 Because children cannot or will not swallow pills and tablets (solid formulations), which encapsulate the bitterness of many active pharmaceutical ingredients, they are often given liquid formulations. In two studies, children between 3 and 10 years of age were genotyped, and they and their mothers were asked about whether the child either had ever taken medication in solid formulation56 or had prior problems taking liquid medications and, if so, to identify why.129 Children with a PP or AP genotype at the Ala49Pro SNP were more likely than AA homozygotes to have taken solid formulations of medicine56 and to have had a prior history of rejecting liquid medications,129 with taste complaints reported as a primary reason for rejection. This suggests that children with at least one bitter-sensitive allele for this gene (i.e., PP or AP genotype) may have more difficulty consuming liquid formulations of medicines and thus have a greater incentive to try solid formulations such as pills and capsules that encapsulate the bitter taste of the drug.56,129 Genetic variability in the TAS2R38 gene can explain variation in sweet taste perception when measuring children’s responses directly.11,26,56 Children with a bitter-sensitive AP or PP genotype most preferred higher concentrations of sugars in liquid and solid foods than did children with the non-bitter-sensitive AA genotype.11,26,56,127,128 (Please note the use of the term “bitter-sensitive” throughout does not relate to all bitters but rather the ones that bind and activate the particular receptor gene.) The heightened preference was not due to differences in detection thresholds or sensitivity to sweet taste. On the contrary, children with the bitter-sensitive genotype detected sucrose at lower concentrations.11 There are several, nonmutually exclusive explanations for such associations. First, the TAS2R38 gene could be in linkage disequilibrium with nearby genes that might influence sweet taste perception and sensitivity. Second, TAS2R38 allele frequency may be a sensitive genetic marker for racial ancestry, which relates to differences in sweet preference.26,52,141 Third, differences in diet may affect sensitivity and preference by causing changes in gene expression.58,142 For dietary intake of sugars as recalled by caregivers or children themselves, the relationship with variation in the TAS2R38 gene is equivocal. In two studies, children homozygous for the bitter-sensitive allele of the TAS2R38 gene consumed more sweet-tasting foods128 and gave higher ratings of liking for some products containing sweet-tasting berries,20 whereas in another study only boys with two bitter-sensitive

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics

alleles consumed more candy and sugar than did boys with one or two copies of the bitter-insensitive allele.127 Another study exploring TAS2R38 and intake of sugary and sweet foods found no effect of genotype.47 Whether the lack of consistency among these studies was due to issues related to dietary recall could be addressed by incorporating direct testing of the children. Only one other TAS2R gene, TAS2R31, was studied in children to determine if, as observed in adults,121,143 variation contributes to differences in the taste of the NNS Ace-K. Although predominately sweet, Ace-K may elicit off-tastes, including bitterness, in some adults. Children and adults rated how much they liked the taste of a 12 mM Ace-K solution by pointing to one of three faces on the hedonic face scale that best represented how it tasted to them (i.e., frowning face, neutral face, or smiling face). Each was genotyped for the four SNPs in the TAS2R31 gene (i.e., Arg25Trp, Leu162Met, Ala227Val, Val240Ile).18 These SNPs are inherited together and result in two common haplotypes, RLAV and WMVI. There was variation among both children and adults in how they rated the taste of Ace-K, and this variation was related to their taste genes. WMVI individuals disliked the taste of Ace-K compared to RLAV individuals.18 This SNP did not relate to variation in hedonic ratings of sucrose,18 a taste stimulus that does not elicit bitterness or activate any of the 25 bitter taste receptors; the vast majority of children and adults pointed to the smiling face after tasting sucrose.

TAS1R Genotype-Phenotype Studies Only one study to date has examined the relationship between variation in the sweet receptor TAS1R2 gene (Ile191Val SNP) and dietary intake of sugar and sugary foods among children as reported by their parents.130 Children who were VV homozygotes consumed significantly fewer calories from sugar than did VI heterozygotes or II homozygotes. Whether these children perceive sweetness differently and whether their perception differs from that of adults of the same genotype remains unknown. For the TAS1R3 gene, genetic variation was associated with individual differences in the level of sweetness most preferred, as measured in the laboratory, among adults but not children. Adults with the TT genotype preferred lower concentrations of sucrose70 and had higher detection thresholds for sucrose144 than did those with the CT and CC genotype. In contrast, genetic variation in this gene was not related to sweet taste preferences among children; TAS1R3 genotype did not explain any of the variation in the concentration of sucrose most preferred55,70 or in the dietary intake of added sugar and sugardense foods.130 All children, regardless of genotype, most preferred higher levels of sucrose than did adults.55,70 Why are children so attracted to sweets? It has been argued that the strong attraction that children have for all that tastes sweet reflects our evolutionary past and the need for nutrients during periods of maximal growth. The taste system evolved to detect and

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prefer perceptions that specify crucial nutrients such as the once rare energy (carbohydrate)-rich plants that taste sweet. While this “sweet” attraction may have served children well in a feast-or-famine setting, today it makes them vulnerable to food environments abundant in processed foods rich in added sugars and lacking healthy sweet foods, such as fruits.77

Summary Since the initial discoveries of the sweet receptor gene in 2001145–148 and the first bitter taste receptor gene in 2003,32–37 only a few studies have examined genotype-taste phenotype relationships in pediatric populations. The majority of these studies focused on variation in the TAS2R38 gene and sensitivity to PTU; the findings were consistent and reliable across studies. The robustness and reproducibility of the PTU tasteTAS2R38 gene association is such that it is regarded as a benchmark for genotype-taste phenotype39 and genome-wide association studies149; we therefore suggest that future studies that relate genotype to phenotype include this as internal control.

CONCLUSIONS AND PRACTICAL IMPLICATIONS Scientific investigations that focus on understanding the biological basis of how sensitive children are and how much they like or dislike particular tastes highlight that children are living in sensory worlds different not just from adults but from each other as well.7,150 Such personal variation—a hallmark of human perception—takes into account individual differences in people’s genes and their experiences, because no child is average. Early in life, children have sensory systems that detect and prefer the once rare calorierich foods that taste sweet, especially during periods of maximal growth,55,73 and reject potentially toxic ones that taste bitter,125 with the adult pattern emerging during adolescence.77,151 This does not mean that adults do not like sweet taste—the liking of sweet taste, a reflection of our basic biology, persists throughout the lifespan. What changes is that, after mid-adolescence, most individuals most prefer lower levels of sweetness than do children. Nor does this research imply that children should only eat sweets and never eat foods that taste bitter. Rather, children need experiences with foods in a positive context to learn to like foods they initially may not like. Research on how variation in the genetic makeup of children contributes to what makes them a unique individual152 in terms of their taste likes and sensitivity and food preferences is still in its infancy, since only a few taste genes have been studied in children and none in infants. The methods used to phenotype children for taste are diverse, and more research is needed to develop and validate methods for children younger than 5 years. When testing children, direct measurements appear to be more robust in the

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics

discovery of age- and genotype-related effects than are differences in perception inferred from dietary intake. The strongest evidence is that children vary in the sensitivity to the bitter taste of PTU based on variation in the TAS2R38 gene and that children were more sensitive (could detect lower levels of PTU) than were adults of the same genotype.21,26,38,125 This suggests that age may modify the association between genotype and taste phenotype and that for some, childhood may be a time of heightened bitter sensitivity for some chemicals. On the other hand, studies that aimed to determine whether taste genes can explain what children are eating were equivocal perhaps for several reasons. First, parents may not be aware of what children eat when not in their care. Second, research that directly measures the level of sweetness most preferred by children has consistently shown that it relates to the level of sweetness or added sugar content of foods or beverages.10,26,55,70 Thus a better approach may be to focus on the level of sweetness of a particular food most preferred by the child rather than a particular category of food. Third, many of these studies focused on only one taste gene (TAS2R38), and this gene encodes for a receptor that is a specialist,106 meaning it cannot explain variation in oral sensations beyond that which contains that particular ligand. Finally, the lack of direct relationship between genes and what we like to eat provides further evidence that our taste biology is not our destiny.63 Despite hardwired genetic differences that make some children more sensitive than others to bitter tastes, and less likely to initially like the taste of vegetables or accept liquid formulations of medicines, there is plasticity in the taste senses. Children develop a sense of what a food or beverage should taste like, how eating the food or drinking the beverage makes them feel, and whether these foods are enjoyed by parents and other family members. Whether for good or for bad, repeated exposure to a food or beverage engenders familiarity through a variety of cues (e.g., taste, visual).63,64,153–155 What becomes familiar to children through taste experiences becomes appropriate, and what is appropriate is accepted and preferred.63,64,156 Whether variation in taste genes contributes to whether infants reject or accept a food initially or how many days of exposure are needed for learning are important areas for future research. The location of taste receptors in other tissues, including the gut,115 suggests that these early experiences may have implications beyond taste. While we have no conscious access to taste information arising from these sites,157 relevant cell types in these tissues sense chemical stimuli and may teach the body how to respond to foods. Childhood is an important period for understanding the biological, genetic, and social factors that lead a child to like a varied and healthy diet, since dietary patterns establish early.98 The future holds promise in understanding what makes each child a unique individual in terms of taste genetics and the types of experiences needed to promote healthy dietary behaviors, the single most important aspect of reducing risks for preventable diseases.158–161

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ACKNOWLEDGMENTS The writing of this manuscript was supported by National Institutes of Health grants R01DC01128, National Research Service Award F32DC15172, and T32DC000014 from the National Institute of Deafness and Other Communication Disorders. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding agencies had no role in the preparation, review, or approval of the manuscript.

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47. O’Brien SA, Feeney EL, Scannell AG, Markey A, Gibney ER. Bitter taste perception and dietary intake patterns in Irish children. J Nutrigenet Nutrigenomics 2013;6(1):43–58. 48. Duffy VB, Hayes JE, Sullivan BS, Faghri P. Surveying food and beverage liking: a tool for epidemiological studies to connect chemosensation with health outcomes. Ann N Y Acad Sci 2009;1170:558–68. 49. Sinopoli DA, Lawless HT. Taste properties of potassium chloride alone and in mixtures with sodium chloride using a check-all-that-apply method. J Food Sci 2012;77(9):S319–22. 50. Cowart BJ, Beauchamp GK. Early development of taste perception. In: McBride R, MacFie H, editors. Psychological basis of sensory evaluation. London, England: Elsevier; 1990. p. 1–17. 51. Coldwell SE, Mennella JA, Duffy VB, Pelchat ML, Griffith JW, Smutzer G, Cowart BJ, Breslin PA, Bartoshuk LM, Hastings L, Victorson D, Hoffman HJ. Gustation assessment using the NIH toolbox. Neurology 2013;80(11 Suppl. 3):S20–4. 52. Pepino MY, Mennella JA. Factors contributing to individual differences in sucrose preference. Chem Senses 2005;30:i319–320. 53. Mennella JA, Pepino MY, Lehmann-Castor SM, Yourshaw LM. Sweet preferences and analgesia during childhood: effects of family history of alcoholism and depression. Addiction 2010;105:666–75. 54. Pepino MY, Mennella JA. Sucrose-induced analgesia is related to sweet preferences in children but not adults. Pain 2005;119(1–3):210–8. 55. Mennella JA, Finkbeiner S, Lipchock SV, Hwang LD, Reed DR. Preferences for salty and sweet tastes are elevated and related to each other during childhood. PLoS One 2014;9(3): e92201. 56. Lipchock SV, Reed DR, Mennella JA. Relationship between bitter-taste receptor genotype and solid medication formulation usage among young children: a retrospective analysis. Clin Ther 2012;34:728–33. 57. Guinard JX. Sensory and consumer testing with children. Trends Food Sci Technol 2000;11(8):273–83. 58. Lipchock SV, Spielman AI, Mennella JA, Mansfield CJ, Hwang LD, Douglas JE, Reed DR. Caffeine bitterness is related to daily caffeine intake and bitter receptor mRNA abundance in human taste tissue. Perception 2017;46(3–4):245–56. 59. Sandell M, Hoppu U, Mikkila V, Mononen N, Kahonen M, Mannisto S, Ronnemaa T, Viikari J, Lehtimaki T, Raitakari OT. Genetic variation in the hTAS2R38 taste receptor and food consumption among Finnish adults. Genes Nutr 2014;9:433. 60. Sandell MA, Breslin PA. Variability in a taste-receptor gene determines whether we taste toxins in food. Curr Biol 2006;16:R792–4. 61. Baranowski T, Baranowski JC, Watson KB, Jago R, Islam N, Beltran A, Martin SJ, Nguyen N, Tepper BJ. 6-n-Propylthiouracil taster status not related to reported cruciferous vegetable intake among ethnically diverse children. Nutr Res 2011;31(8):594–600. 62. Mennella JA, Daniels LM, Reiter AR. Learning to like vegetables during breastfeeding: a randomized clinical trial of lactating mothers and infants. Am J Clin Nutr 2017;106:67–76. 63. Mennella JA, Reiter AR, Daniels LM. Vegetable and fruit acceptance during infancy: impact of ontogeny, genetics, and early experiences. Adv Nutr 2016;7:211S–219S. 64. Birch LL, Doub AE. Learning to eat: birth to age 2 y. Am J Clin Nutr 2014;99(3):723S–728S. 65. Cabanac M, Duclaux R. Specificity of internal signals in producing satiety for taste stimuli. Nature 1970;227(5261):966–7. 66. Soussignan R, Schaal B. Forms and social signal value of smiles associated with pleasant and unpleasant sensory experience. Ethology 1996;102:1020–41. 67. Maone TR, Mattes RD, Bernbaum JC, Beauchamp GK. A new method for delivering a taste without fluids to preterm and term infants. Dev Psychobiol 1990;13:179–91. 68. Desor JA, Maller O, Turner RE. Taste in acceptance of sugars by human infants. J Comp Physiol Psychol 1973;84:496–501. 69. Harrison D, Stevens B, Bueno M, Yamada J, Adams-Webber T, Beyene J, Ohlsson A. Efficacy of sweet solutions for analgesia in infants between 1 and 12 months of age: a systematic review. Arch Dis Child 2010;95(6):406–13. 70. Mennella JA, Finkbeiner S, Reed DR. The proof is in the pudding: children prefer lower fat but higher sugar than do mothers. Int J Obes 2012;36:1285–91.

Measuring Sweet and Bitter Taste in Children: Individual Variation due to Age and Taste Genetics

71. Mennella JA, Bobowski NK. The sweetness and bitterness of childhood: insights from basic research on taste preferences. Physiol Behav 2015;152(Pt B):502–7. 72. Mennella JA, Colquhoun TA, Bobowski NK, Olmstead JW, Bartoshuk L, Clark D. Farm to sensory lab: taste of blueberry fruit by children and adults. J Food Sci 2017;82(7):1712–9. 73. Coldwell SE, Oswald TK, Reed DR. A marker of growth differs between adolescents with high vs. low sugar preference. Physiol Behav 2009;96:574–80. 74. Mattes RD. Low calorie sweeteners: science and controversy: conference proceedings. Physiol Behav 2016;164(Pt B):429–31. 75. Yang Q. Gain weight by “going diet?” Artificial sweeteners and the neurobiology of sugar cravings: neuroscience 2010. Yale J Biol Med 2010;83(2):101–8. 76. Swithers SE. Artificial sweeteners are not the answer to childhood obesity. Appetite 2015;93:85–90. 77. Mennella JA, Bobowski NK, Reed DR. The development of sweet taste: from biology to hedonics. Rev Endocr Metab Disord 2016;17:171–8. 78. Birch LL, McPhee L, Steinberg L, Sullivan S. Conditioned flavor preferences in young children. Physiol Behav 1990;47(3):501–5. 79. Miller A, Barr RG, Young SN. The cold pressor test in children: methodological aspects and the analgesic effect of intraoral sucrose. Pain 1994;56(2):175–83. 80. Kakeda T, Ishikawa T. Gender differences in pain modulation by a sweet stimulus in adults: a randomized study. Nurs Health Sci 2011;13(1):34–40. 81. McDaniel AH, Reed DR. The human sweet tooth and its relationship to obesity. In: MoustaidMoussa N, Berdanier C, editors. Genomics and proteomics in nutrition. New York: Marcel Dekker, Inc.; 2004. p. 49–67 82. Boggiano MM, Wenger LE, Mrug S, Burgess EE, Morgan PR. The kids-palatable eating motives scale: relation to BMI and binge eating traits. Eat Behav 2015;17:69–73. 83. Reed DR, Tanaka T, McDaniel AH. Diverse tastes: genetics of sweet and bitter perception. Physiol Behav 2006;88(3):215–26. 84. Desor JA, Maller O, Andrews K. Ingestive responses of human newborns to salty, sour, and bitter stimuli. J Comp Physiol Psychol 1975;89:966–70. 85. Rosenstein D, Oster H. Differential facial responses to four basic tastes in newborns. Child Dev 1988;59:1555–68. 86. Steiner JE. Facial expressions of the neonate infant indicating the hedonics of food-related chemical stimuli. In: Weiffenbach JM, editor. Taste and development: the genesis of sweet preference. Washington, DC: U.S. Government Printing Office; 1977. p. 173–89. 87. Ganchrow JR, Steiner JE, Daher M. Neonatal facial expressions in response to different qualities and intensities of gustatory stimuli. Infant Behav Dev 1983;6:189–200. 88. Beauchamp GK, Cowart BJ, Moran M. Developmental changes in salt acceptability in human infants. Dev Psychobiol 1986;19:17–25. 89. Kajiura H, Cowart BJ, Beauchamp GK. Early developmental change in bitter taste responses in human infants. Dev Psychobiol 1992;25:375–86. 90. Rivkees SA. Controversies in the management of Graves’ disease in children. J Endocrinol Investig 2016;39(11):1247–57. 91. Ross DS, Burch HB, Cooper DS, Greenlee MC, Laurberg P, Maia AL, Rivkees SA, Samuels M, Sosa JA, Stan MN, Walter MA. 2016 American Thyroid Association guidelines for diagnosis and management of hyperthyroidism and other causes of thyrotoxicosis. Thyroid 2016;26(10):1343–421. 92. Karam EJ, Freire-Maia N. Phenythiocarbamide and mental immaturity. Lancet 1967;1:622–3. 93. Silbert JE, Frude N. Bittering agents in the prevention of pediatric poisonings: children’s reactions for denatonium benzoate (Bitrex). Arch Emerg Med 1991;8:1–7. 94. Lawless HT, Hammer LD, Corina MD. Aversions to bitterness and accidental poisonings among preschool children. J Toxicol Clin Toxicol 1982;19(9):951–64. 95. Berning CK, Griffith JF, Wild JE. Research on the effectiveness of denatonium benzoate as a deterrent to liquid detergent ingestion by children. Fundam Appl Toxicol 1982;2(1):44–8. 96. White NC, Litovitz T, Benson BE, Horowitz BZ, Marr-Lyon L, White MK. The impact of bittering agents on pediatric ingestions of antifreeze. Clin Pediatr (Phila) 2009;48(9):913–21.

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97. Venables R, Marriott J, Stirling H. FIND OUT: key problems with children’s medicines formulations … It’s a taste issue!. Int J Pharm Pract 2012;20(S2):23. 98. Saavedra JM, Deming D, Dattilo A, Reidy K. Lessons from the feeding infants and toddlers study in North America: what children eat, and implications for obesity prevention. Ann Nutr Metab 2013; 62(Suppl. 3):27–36. 99. Ervin RB, Kit BK, Carroll MD, Ogden CL. Consumption of added sugar among U.S. children and adolescents, 2005–2008. NCHS Data Brief 2012;2012(87):1–8. 100. Ervin RB, Ogden CL. Consumption of added sugars among U.S. adults, 2005–2010. NCHS Data Brief 2013;2013(122):1–8. 101. World Health Organization. Sugars intake for adults and children. http://www.who.int/nutrition/ publications/guidelines/sugars_intake/en/. Accessed 15 September 2017. 102. U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary guidelines for Americans, 2015–2020. 8th ed. Washington, DC: Government Printing Office; 2015. 103. Reed DR, Margolskee RF. Gustation genetics: sweet gustducin!. Chem Senses 2010;35:549–50. 104. Berridge KC, Kringelbach ML. Building a neuroscience of pleasure and well-being. Psychol Well Being 2011;1(1):1–3. 105. Reed DR, McDaniel AH. The human sweet tooth. BMC Oral Health 2006;6(Suppl 1):S17. 106. Brockhoff A, Behrens M, Niv MY, Meyerhof W. Structural requirements of bitter taste receptor activation. Proc Natl Acad Sci U S A 2010;107(24):11110–5. 107. Glendinning JI. Is the bitter rejection response always adaptive? Physiol Behav 1994;56(6):1217–27. 108. Meyerhof W, Batram C, Kuhn C, Brockhoff A, Chudoba E, Bufe B, Appendino G, Behrens M. The molecular receptive ranges of human TAS2R bitter taste receptors. Chem Senses 2010;35(2):157–70. 109. Behrens M, Meyerhof W. Mammalian bitter taste perception. Results Probl Cell Differ 2009;47:203–20. 110. Lossow K, Hubner S, Roudnitzky N, Slack JP, Pollastro F, Behrens M, Meyerhof W. Comprehensive analysis of mouse bitter taste receptors reveals different molecular receptive ranges for orthologous receptors in mice and humans. J Biol Chem 2016;291(29):15358–77. 111. Finger TE, Kinnamon SC. Taste isn’t just for taste buds anymore. F1000 Biol Rep 2011;3:20. 112. Lee RJ, Cohen NA. Role of the bitter taste receptor T2R38 in upper respiratory infection and chronic rhinosinusitis. Curr Opin Allergy Clin Immunol 2015;15(1):14–20. 113. Kikut-Ligaj D, Trzcielinska-Lorych J. How taste works: cells, receptors and gustatory perception. Cell Mol Biol Lett 2015;20(5):699–716. 114. An SS, Liggett SB. Taste and smell GPCRs in the lung: evidence for a previously unrecognized widespread chemosensory system. Cell Signal 2017;41:82–8. 115. Egan JM, Margolskee RF. Taste cells of the gut and gastrointestinal chemosensation. Mol Interv 2008; 8(2):78–81. 116. Rozengurt E. Taste receptors in the gastrointestinal tract. I. Bitter taste receptors and alpha-gustducin in the mammalian gut. Am J Physiol Gastrointest Liver Physiol 2006;291(2):G171–7. 117. Reszka E, Nowakowska-Swirta E, Kupczyk M, Dudek W, Swierczynska-Machura D, Wittczak T, Rykala J, Przybek M, Jablonska E, Kreisz B, Kumna P, Wasowicz W, Palczynski C. Expression of bitter taste receptors in the human skin in vitro. J Clin Res Bioeth 2015;6:218. 118. Bachmanov AA, Bosak NP, Lin C, Matsumoto I, Ohmoto M, Reed DR, Nelson TM. Genetics of taste receptors. Curr Pharm Des 2014;20(16):2669–83. 119. Conrad DF, Keebler JE, DePristo MA, Lindsay SJ, Zhang Y, Casals F, Idaghdour Y, Hartl CL, Torroja C, Garimella KV, Zilversmit M, Cartwright R, Rouleau GA, Daly M, Stone EA, Hurles ME, Awadalla P, Genomes P. Variation in genome-wide mutation rates within and between human families. Nat Genet 2011;43(7):712–4. 120. Kuhn C, Bufe B, Winnig M, Hofmann T, Frank O, Behrens M, Lewtschenko T, Slack JP, Ward CD, Meyerhof W. Bitter taste receptors for saccharin and acesulfame K. J Neurosci 2004;24(45):10260–5. 121. Allen AL, McGeary JE, Knopik VS, Hayes JE. Bitterness of the non-nutritive sweetener acesulfame potassium varies with polymorphisms in TAS2R9 and TAS2R31. Chem Senses 2013;38 (5):379–89. 122. Roudnitzky N, Bufe B, Thalmann S, Kuhn C, Gunn HC, Xing C, Crider BP, Behrens M, Meyerhof W, Wooding SP. Genomic, genetic and functional dissection of bitter taste responses to artificial sweeteners. Hum Mol Genet 2011;20(17):3437–49.

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123. Nie Y, Vigues S, Hobbs JR, Conn GL, Munger SD. Distinct contributions of T1R2 and T1R3 taste receptor subunits to the detection of sweet stimuli. Curr Biol 2005;15(21):1948–52. 124. Hellfritsch C, Brockhoff A, Stahler F, Meyerhof W, Hofmann T. Human psychometric and taste receptor responses to steviol glycosides. J Agric Food Chem 2012;60(27):6782–93. 125. Mennella JA, Reed DR, Roberts KM, Mathew PS, Mansfield CJ. Age-related differences in bitter taste and efficacy of bitter blockers. PLoS One 2014;9(7): e103107. 126. Bouthoorn SH, Wijtzes AI, Jaddoe VW, Hofman A, Raat H, van Lenthe FJ. Development of socioeconomic inequalities in obesity among Dutch pre-school and school-aged children. Obesity (Silver Spring) 2014;22(10):2230–7. 127. Hoppu U, Laitinen K, Jaakkola J, Sandell M. The hTAS2R38 genotype is associated with sugar and candy consumption in preschool boys. J Hum Nutr Diet 2015;28(Suppl. 1):45–51. 128. Pawellek I, Grote V, Rzehak P, Xhonneux A, Verduci E, Stolarczyk A, Closa-Monasterolo R, Reischl E, Koletzko B. Association of TAS2R38 variants with sweet food intake in children aged 1–6 years. Appetite 2016;107:126–34. 129. Mennella JA, Roberts KM, Mathew PS, Reed DR. Children’s perceptions about medicines: individual differences and taste. BMC Pediatr 2015;15(1):130. 130. Melo SV, Agnes G, Vitolo MR, Mattevi VS, Campagnolo PDB, Almeida S. Evaluation of the association between the TAS1R2 and TAS1R3 variants and food intake and nutritional status in children. Genet Mol Biol 2017;40(2):415–20. 131. Drayna D, Coon H, Kim UK, et al. Genetic analysis of a complex trait in the Utah Genetic Reference Project: a major locus for PTC taste ability on chromosome 7q and a secondary locus on chromosome 16p. Hum Genet 2003;112:567–72. 132. Duffy VB, Davidson AC, Kidd JR, Kidd KK, Speed WC, Pakstis AJ, Reed DR, Snyder DJ, Bartoshuk L. Bitter receptor gene (TAS2R38), 6-n-propylthiouracil (PROP) bitterness and alcohol intake. Alcohol Clin Exp Res 2004;28:1629–37. 133. Hayes JE, Wallace MR, Knopik VS, Herbstman DM, Bartoshuk LM, Duffy VB. Allelic variation in TAS2R bitter receptor genes associates with variation in sensations from and ingestive behaviors toward common bitter beverages in adults. Chem Senses 2011;36(3):311–9. 134. Das DR. Inheritance of the P.T.C. taste character in man: an analysis of 126 Rarhi Brahmin families of West Bengal. Ann Hum Genet 1958;22:200–12. 135. Behrens M, Gunn HC, Ramos PC, Meyerhof W, Wooding SP. Genetic, functional, and phenotypic diversity in TAS2R38-mediated bitter taste perception. Chem Senses 2013;38(6):475–84. 136. Bell KI, Tepper BJ. Short-term vegetable intake by young children classified by 6-n-propylthoiuracil bitter-taste phenotype. Am J Clin Nutr 2006;84(1):245–51. 137. Keller KL, Steinmann L, Nurse RJ, Tepper BJ. Genetic taste sensitivity to 6-n-propylthiouracil influences food preference and reported intake in preschool children. Appetite 2002;38:3–12. 138. Dinehart ME, Hayes JE, Bartoshuk LM, Lanier SL, Duffy VB. Bitter taste markers explain variability in vegetable sweetness, bitterness, and intake. Physiol Behav 2006;87:304–13. 139. Yackinous C, Guinard JX. Relation between PROP taster status and fat perception, touch, and olfaction. Physiol Behav 2001;72(3):427–37. 140. Mennella JA, Spector AC, Reed DR, Coldwell SE. The bad taste of medicines: overview of basic research on bitter taste. Clin Ther 2013;35:1225–46. 141. Schiffman SS, Graham BG, Sanly-Miller EA, Peterson-Dancy M. Elevated and sustained desire for sweet taste in African-Americans: a potential factor in the development of obesity. Nutrition 2000;16:886–93. 142. Lipchock SV, Mennella JA, Spielman AI, Reed DR. Human bitter perception correlates with bitter receptor messenger RNA expression in taste cells. Am J Clin Nutr 2013;98(4):1136–43. 143. Horne J, Lawless HT, Speirs W, Sposato D. Bitter taste of saccharin and acesulfame-K. Chem Senses 2002;27(1):31–8. 144. Fushan AA, Simons CT, Slack JP, Manichaikul A, Drayna D. Allelic polymorphism within the TAS1R3 promoter is associated with human taste sensitivity to sucrose. Curr Biol 2009;19 (15):1288–93. 145. Montmayeur JP, Liberles SD, Matsunami H, Buck LB. A candidate taste receptor gene near a sweet taste locus. Nat Neurosci 2001;4(5):492–8.

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146. Bachmanov A, Li X, Reed DR, Ohmen JD, Li S, Chen Z, Tordoff MG, de Jong PJ, Wu C, West DB, Chatterjee A, Ross DA, Beauchamp GK. Positional cloning of the mouse saccharin preference (Sac) locus. Chem Senses 2001;26:925–33. 147. Max M, Shanker YG, Huang L, Rong M, Liu Z, Campagne F, Weinstein H, Damak S, Margolskee RF. Tas1r3, encoding a new candidate taste receptor, is allelic to the sweet responsiveness locus Sac. Nat Genet 2001;28(1):58–63. 148. Sainz E, Korley JN, Battey JF, Sullivan SL. Identification of a novel member of the T1R family of putative taste receptors. J Neurochem 2001;77(3):896–903. 149. Genick UK, Kutalik Z, Ledda M, Destito MC, Souza MM, Cirillo CA, Godinot N, Martin N, Morya E, Sameshima K, Bergmann S, Le Coutre J. Sensitivity of genome-wide-association signals to phenotyping strategy: the PROP-TAS2R38 taste association as a benchmark. PLoS One 2011; 6(11): e27745. 150. Mennella JA, Bobowski N, Liem DJ. Taste and smell. In: Swaiman KF, Ashwall S, Ferriero DM, Schor NF, Finkel RS, Gropman AL, Pearl PL, Shevell MI, editors. Swaiman’s pediatric neurology: principles and practices. 6th ed. New York: Elsevier; 2017. p. 58–64. 151. Desor JA, Beauchamp GK. Longitudinal changes in sweet preferences in humans. Physiol Behav 1987;39(5):639–41. 152. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015;372(9):793–5. 153. Birch LL. Effects of peer models’ food choices and eating behaviors on preschoolers’ food preferences. Child Dev 1979;51:489–96. 154. Anzman-Frasca S, Savage JS, Marini ME, Fisher JO, Birch LL. Repeated exposure and associative conditioning promote preschool children’s liking of vegetables. Appetite 2012;58(2):543–53. 155. Holley CE, Haycraft E, Farrow C. ‘Why don’t you try it again?’ A comparison of parent led, home based interventions aimed at increasing children’s consumption of a disliked vegetable. Appetite 2015;87:215–22. 156. Sharafi M, Peracchio H, Scarmo S, Huedo-Medina TB, Mayne ST, Cartmel B, Duffy VB. PreschoolAdapted Liking Survey (PALS): A brief and valid method to assess dietary quality of preschoolers. Childhood Obesity 2015;11(5):530–40. 157. Foster SR, Roura E, Thomas WG. Extrasensory perception: odorant and taste receptors beyond the nose and mouth. Pharmacol Ther 2014;142(1):41–61. 158. Duffy VB, Hayes JE, Davidson AC, Kidd JR, Kidd KK, Bartoshuk LM. Vegetable intake in collegeaged adults is explained by oral sensory phenotypes and TAS2R38 genotype. Chemosens Percept 2010; 3(3–4):137–48. 159. Hooper L, Bartlett C, Davey SG, Ebrahim S. Advice to reduce dietary salt for prevention of cardiovascular disease. Cochrane Database Syst Rev 2004;1:CD003656. 160. Rayner M, Scarborough P. The burden of food related ill health in the UK. J Epidemiol Community Health 2005;59(12):1054–7. 161. Ezzati M, Riboli E. Behavioral and dietary risk factors for noncommunicable diseases. N Engl J Med 2013;369(10):954–64.

CHAPTER 2

Learning to Like: Roles of Repeated Exposure and Other Types of Learning Stephanie Anzman-Frasca, Sarah Ehrenberg

Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States

INTRODUCTION Humans readily accept sweet tastes and reject those that are sour or bitter. While these genetic taste predispositions were adaptive in earlier periods of human history, motivating humans to seek out calories and avoid potential poisons, they can set the stage for unhealthy eating habits in the modern obesogenic environment, which is characterized by easily accessible energy-dense foods and sedentary lifestyles. Correspondingly, many children are not meeting dietary recommendations: for example, in the United States, intake of nutrient-dense foods like fruits and vegetables is already below recommended amounts during infancy and toddlerhood,1 a worrisome finding in light of evidence that early life eating habits tend to track over time.2,3 Yet the good news is that children’s food preferences are malleable. According to a substantial body of research evidence, simple repeated exposure to a variety of healthy foods is one of the most robust methods for promoting healthier food preferences and intake during infancy and childhood. In this chapter, we will explore repeated exposure as a simple and promising technique for promoting healthy food preferences, as well as additional learning paradigms that have been studied alongside it. In the 1960s, Zajonc4 demonstrated that mere exposure to a stimulus can improve one’s attitude toward it, using words and symbols as the stimuli of interest. Such effects prepare the individual to like and accept stimuli that are present in his or her environment. We review recent evidence that repeated exposure to foods and flavors can increase acceptance of these during infancy and childhood. Based on the evidence supporting the roles of repeated exposure and other learning paradigms in the establishment of healthy food preferences, we conclude with recommendations of ways that caregivers and broader environments can support the development of healthier eating among children.

REPEATED EXPOSURE EFFECTS DURING INFANCY The earliest exposures. The effects of repeated exposure on flavor acceptance can be demonstrated during infancy and even earlier, with evidence of prenatal exposure to flavors Pediatric Food Preferences and Eating Behaviors https://doi.org/10.1016/B978-0-12-811716-3.00002-6

© 2018 Elsevier Inc. All rights reserved.

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via the amniotic fluid setting the stage for later acceptance of those flavors.5 Mennella and colleagues have also shown that infants readily accept solid foods with flavor profiles that are similar to the type of milk they experienced during the exclusive milk feeding of early infancy.6 Breastfed infants experience a greater variety of flavors than formula-fed infants during this period, with flavors from the maternal diet passing to the breastmilk.5,7,8 Some studies provide evidence that exposure to particular flavors during breastfeeding can increase acceptance of those flavors in solid foods later,5 while others suggest that it is the overall variety exposure during breastfeeding that leads to later food acceptance.8,9 While their flavor experiences during early milk feeding differ from those of breastfed infants, exposure effects have been demonstrated in formula-fed infants as well. For example, early and lasting exposure to protein hydrolysate formulas, with pronounced bitter, sour, and savory tastes, increases acceptance of these formulas later in infancy.10 Breastfed infants may be at an advantage for later food acceptance due to their early variety exposure, particularly if their mothers consumed healthy, varied diets, an idea supported by longitudinal findings showing enhanced vegetable acceptance at age 6 years among infants who had been breastfed.11 Combined with evidence suggesting that exposure early in this period of exclusive milk feeding may represent a sensitive period with the potential for particularly robust effects on subsequent acceptance,8,12 these findings provide an additional reason to promote breastfeeding and the consumption of healthy, varied diets among lactating mothers. Overall, early repeated exposure can prepare infants to transition to a diet of solid foods that are available and preferred in their culture. Repeated exposure during the introduction of complementary foods and beverages. While experiences in utero and during breastfeeding can provide an early start when it comes to repeated exposure effects on food acceptance, learning continues as infants are introduced to new complementary foods and beverages during the second half of infancy. Multiple studies have shown that initial fruit acceptance tends to be higher than that of vegetables among infants13,14 although repeated exposure effects have been demonstrated with both.13,15 In one study, intake of an initially disliked vegetable was similar to that of a liked vegetable after eight exposures, and 63% of the infants in the study sample still liked and ate the initially disliked vegetable nine months later.15 In addition to supporting the idea that repeated taste exposures to particular flavors can bolster acceptance of those flavors later on, this literature also supports the effects of variety exposure on food acceptance. For example, Mennella et al.16 showed that exposure to fruit or vegetable variety led to increased acceptance of fruits or vegetables during the complementary feeding period. While many of the studies on repeated and variety exposure effects during this period focus on fruits and vegetables, there is evidence supporting exposure effects on the acceptance of other flavors as well. For example, Stein et al.17 showed that infants who were exposed to starchy table foods had a higher acceptance of salt taste.

Roles of Repeated Exposure and Other Types of Learning

Many of the studies examining exposure effects during the complementary feeding period are short-term studies that do not provide information about the extent to which effects persist over time. However, there are some existing studies that suggest the potential for lasting impacts: Maier Noth et al.11 followed a sample of children who had participated in a repeated exposure experiment as infants, and in this sample, the target vegetable was still liked and eaten by 79% of the children at age 15 months, by 73% at 3 years, and by 57% at 6 years. Additionally, infants who had experienced greater vegetable variety at weaning liked vegetables more at age 6 years.11 Overall, findings support both repeated exposure to specific foods and flavors as well as to a variety of healthy foods in general as powerful ways to increase acceptance of healthy foods during infancy. Comparing associative conditioning and repeated exposure effects. The majority of research on infants’ food preference learning is on repeated exposure and variety exposure effects specifically, with less known about alternative learning paradigms like associative conditioning during this period. Associative conditioning refers to changes in the perceived valence of a “conditioned” stimulus after repeated pairings with an already-liked (or disliked) “unconditioned” stimulus. For example, if a new flavor, such as peach, is repeatedly paired with an already-liked food, such as infant cereal, an infant would be expected to develop a liking for peaches on their own following these repeated pairings. Unconditioned stimuli can be foods and flavors, as in this example, or they may be other stimuli that have an established valence, such as a positive interaction with a caregiver. Research on associative conditioning of food preferences during early life has been focused on the former. Studies that have compared effects of associative conditioning versus mere repeated exposure during infancy have yielded mixed findings, with some suggesting that simple repeated exposure is just as effective as associative conditioning.18 Forestell and Mennella14 compared eight repeated exposures to green beans to an associative conditioning paradigm that paired green beans with peaches and found that consumption of green beans increased similarly across conditions, although the latter condition did lead to fewer infant expressions of distaste. Hetherington et al.19 compared 24 exposures to various vegetables in milk and then rice to plain milk and rice exposures among infants who were on average 5 months old, with short-term increases in vegetable intake in the former condition. These findings could demonstrate effects of associative conditioning, where the valence of vegetables increased via the pairings with rice and milk. Yet there was no condition with mere exposure to vegetables for comparison, so it cannot be ascertained whether associative conditioning or repeated exposure effects explain these results. It is also possible that some apparent repeated exposure effects observed in infants are actually associative conditioning effects as new foods and flavors are paired with positive contexts (e.g., positive interactions with a parent) and/or already-familiar flavors. For example, observed “repeated exposure” effects during breastfeeding may be associative conditioning effects as new flavors are paired with mother’s milk. More research is

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BOX 2.1 Section Summary

• • •

Food acceptance during weaning is shaped by exposure to flavors of the maternal diet experienced in utero and during breastfeeding. Breastfeeding may promote food acceptance and variety when mothers consume varied diets. Repeated exposure to foods and exposure to variety increase food and flavor acceptance during weaning.

needed on the extent to which associative conditioning effects might differ from mere repeated exposure in infants. At this stage, the literature provides a robust evidence base to support recommendations of repeated exposure to varied healthy flavors in prenatal, early milk feeding, and complementary feeding periods. Incorporating repeated exposure into interventions during infancy. The evidence supporting the power of repeated exposure during infancy has been leveraged in some intervention studies. For example, in two studies aiming to prevent early rapid weight gain and childhood obesity through behavioral interventions during infancy, repeated exposure was built into the intervention content, with participating parents learning how to repeatedly expose their infants to healthy flavors during the introduction of complementary foods.20,21 In both cases, links between the behavioral interventions and children’s eating behavior were shown, including improved acceptance of new foods (green beans, peas, and squash)20 and healthier dietary patterns in intervention groups.22 A next step for food preference learning research during infancy is to continue to move out of the laboratory and find ways to leverage the effects of early repeated exposure on subsequent acceptance and intake of healthy foods in naturalistic environments. Some research suggests that infancy is a particularly promising time for such efforts as fewer exposures may be required to establish acceptance compared to other periods.23 While this is certainly a good reason to promote repeated exposure to a variety of healthy flavors via amniotic fluid, breast milk, and complementary foods, establishing new healthy food preferences is still possible after these early periods of the life course (Box 2.1).

REPEATED EXPOSURE EFFECTS DURING EARLY CHILDHOOD Overview. There is extensive evidence that repeated taste exposures are also effective at increasing food acceptance among 2- to 5-year-old children. Early childhood repeated exposure may necessitate a greater number of exposures than those required during infancy, although multiple studies suggest that in many cases, the number of exposures needed may be lower than previously thought (e.g., 3–6).24–26 Compared to infants,

Roles of Repeated Exposure and Other Types of Learning

young children have more autonomy in making their own food choices and have more complex past experiences with foods and flavors. These factors may contribute variability to the assessment of exposure effects during this period, and specific results may depend on individual attributes of the child and/or target food as discussed herein. Repeated exposure versus associative conditioning in early childhood. Many repeated exposure studies during early childhood directly compare mere repeated exposure with some form of associative conditioning. Forms of associative conditioning that are often examined include flavor-nutrient learning, in which a flavor acquires a new valence via repeated pairings with a source of calories, and flavor-flavor learning, in which the valence of a flavor changes after repeated pairings with another flavor that is already liked (or disliked). Usually the goal of these studies is to demonstrate effects of associative conditioning over and above mere repeated exposure, but the current literature illustrates that this hypothesis is often unsupported. For example, in a study of toddlers comparing repeated exposure to a target vegetable alone or with salt or nutmeg (flavor-flavor learning), intake of the target vegetable increased and was maintained at a 6-month follow-up assessment in all groups, with the highest increase in the mere repeated exposure group.27 Similarly, when 3- to 5-year-old children were repeatedly exposed to red pepper or yellow squash alone or with a liked dip, these repeated exposure and associative conditioning strategies were equally effective at increasing liking and intake, with increases in liking by the sixth taste exposure.26 Others have shown similar findings, where associative conditioning did not confer any advantages over mere repeated exposure, using a variety of different target vegetable types and textures.24,25,28,29 This research includes recent evidence suggesting that “hiding” a target vegetable (i.e., serving spinach inside of ravioli) or adding cream to it did not lead to additional increases in intake over and above simple repeated exposure to the vegetable served plain.30 While these studies support the idea that simple repeated exposure is enough to promote healthier food preferences, with no additional benefit of or need for associative conditioning, a handful of other studies support the idea that associative conditioning may confer additional benefits under some circumstances. In two studies31,32 that show effects of associative conditioning over and above repeated exposure in this age group, the target fruit31 or vegetable32 flavor was presented as a liquid, which was sweetened in the associative conditioning condition. It is possible that this specific form of associative conditioning using a sweetened liquid may have more robust effects than others tested in aforementioned studies, or more generally, that associative conditioning effects may vary depending on the specific target food or sample characteristics. The aforementioned red pepper/yellow squash study26 did not reveal effects of associative conditioning over and above repeated exposure among preschoolers but did suggest that pairing target foods with a liked accompaniment—here, a liked dip—may help encourage the initial tasting necessary for repeated exposure effects to begin. This potential benefit could be particularly useful for children with a low initial willingness to taste new foods. Given that learning paradigms like repeated exposure have been shown to be less effective among

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children high on food fussiness33 and neophobia,30 or the fear of novel flavors, further research on strategies to increase initial willingness to taste among these children could help them to reap the benefits of repeated exposure. Strategies that may promote these children’s willingness to taste include associative conditioning paradigms, such as the vegetables with dip example discussed here, as well as the use of modeling and rewards, strategies that are discussed in more detail later. In addition to the mixed results that have emerged when comparing repeated exposure and associative conditioning as reviewed herein, sometimes there are also mixed results within individual studies. These studies have compared repeated exposure and associative conditioning and have found that the answer to “Which works best?” is “it depends.” For example, De Wild et al.34 found that flavor-nutrient learning and repeated exposure conditions increased intake of a target vegetable soup similarly, but that the flavor-nutrient condition had greater effects on vegetable preferences. Fisher et al.35 found that pairing broccoli with dip conferred additional benefits over and above repeated exposure only among children with genetic bitter sensitivity, and Capaldi Phillips et al.36 found that associative conditioning had greater effects than repeated exposure on liking and intake of a bitter, but not nonbitter, vegetable. Taken together, associative conditioning has not demonstrated strong advantages over repeated exposure across the literature as a whole but may be useful as a tool to encourage initial tasting necessary for repeated exposure effects to begin, particularly when the target vegetable is particularly bitter and/or children are sensitive to bitter tastes or are neophobic. Repeated exposure versus modeling and reward strategies in early childhood. The efficacy of repeated exposure during early childhood is largely supported by the studies reviewed herein; another set of studies has compared this strategy to modeling and/or reward strategies. Modeling, or observational learning, refers to changes in the child’s behavior after witnessing the behavior of a model, such as a parent or peer. In contrast to the learning processes reviewed herein, this process is vicarious: the child is not learning new preferences through his or her own direct experiences with target foods but is instead evaluating foods and flavors based on the model’s interactions with them. This strategy could lead to more direct learning experiences, if, for example, modeling encourages the child to try new foods, thus initiating the process of repeated exposure. In contrast, reward strategies refer to those in which a contingency is applied, such that the child receives a reward as a result of tasting a target food. The reward may be tangible (e.g., a sticker) or not (e.g., praise), but in either case, the contingency typically becomes apparent to the child: e.g., if I taste this vegetable, then I get a sticker. Modeling and reward strategies are grouped together here because studies evaluating them often involve a social component (e.g., the vicarious learning inherent in modeling; using verbal praise as the reward); in addition, these strategies have been studied together within some studies. Overall, the evidence on the effectiveness of rewards versus mere repeated exposure in encouraging healthier eating is mixed, which may be due to moderation of the effectiveness of reward

Roles of Repeated Exposure and Other Types of Learning

strategies by individual characteristics and/or past experience, as discussed further later. In general, modeling has demonstrated effectiveness across studies and may be less susceptible to such moderation effects, although more studies directly addressing this question are needed. Cooke et al.37 compared tangible and social reward strategies with repeated exposure as well as a no-treatment control and found that liking of a disliked vegetable increased equally across the reward and repeated exposure conditions. For the outcome of intake, however, the effects of the reward strategies seemed to last longer than repeated exposure (3 months). In a parent-led, home-based intervention, Holley et al.38 compared combinations of strategies and found increased intake of an initially disliked vegetable in their modeling + rewards + repeated exposure condition as well as rewards + repeated exposure, versus controls. Liking was highest in these two experimental groups and was intermediate in modeling + repeated exposure and repeated exposure groups, versus controls. Overall this study demonstrates some efficacy of each approach, with potential advantages of combination strategies that involve rewards. Horne et al.39 also combined rewards and modeling in an intervention and found increases in fruit and vegetable consumption that were maintained six months later. While these studies seem to suggest that combining rewards, modeling, and repeated exposure might be a good approach to bolster the effects of repeated exposure, other studies provide reasons to carefully consider the use of rewards. Anez et al.40 found that rewards increased intake overall, but that mere repeated exposure only increased intake among children with limited experience of “instrumental feeding,” or food rewards. This raises the possibility that the regular use of rewards in the context of eating could lead to future expectations of rewards and could decrease the effectiveness of simpler, otherwise effective strategies like repeated exposure. Only one study has examined this question to date, but its results suggest that this is an avenue of inquiry warranting further attention to fully understand the impacts of food rewards, as well as the offering of nonfood rewards for eating a target food, during early childhood. In another study, reward strategies were most effective in encouraging tasting among children with high reward sensitivity, whereas modeling was effective regardless of this individual characteristic.41 These results underscore the potential utility of being selective about the use of rewards when other strategies might work in their place. Effects of modeling were also shown among children who watched videos of other children eating bell peppers; these children later ate more bell peppers than controls.42 Overall, repeated exposure and modeling strategies are consistently supported by the literature, whereas the effectiveness of associative conditioning and rewards may depend and may best be reserved for circumstances in which they are needed to motivate initial tasting, an important first step in repeated exposure. Building upon the robust evidence on repeated exposure in early childhood: Individual differences and real-world applications. There is an emerging literature suggesting that there may be individual differences in food preference learning, as illustrated in the example from

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Vandeweghe et al. earlier.41 Additionally, Caton et al.33 found evidence that children with a high enjoyment of food and low satiety responsiveness, as well as those high on food fussiness, may learn to accept foods less readily. Similarly, in their study looking at effects of repeated exposure and flavor-flavor learning on 2- to 3-year-old children’s intake of a novel vegetable, Hausner et al.25 found that repeated exposure led to the greatest increases in intake but also that 30%–40% of children were resistant to changes. Paying greater attention to these individual differences represents an important future direction for this area of research, to increase understanding of which techniques work best for whom and where additional intervention efforts are most needed. The initial research on such individual differences highlights a need for future studies to compare the effects of different learning paradigms on willingness to taste and later eating behaviors among children who may learn less readily, such as children higher on reward sensitivity, enjoyment of food, food fussiness, and/or bitterness sensitivity or lower on satiety responsiveness. In addition to continued research on individual differences, the research on early life food preference learning would benefit from continued efforts to apply efficacious learning paradigms in naturalistic contexts. While many of the studies demonstrating effects of repeated exposure in early childhood and the potential utility of alternative strategies were conducted in controlled settings, evidence from some real-world trials lends parallel support to these learning paradigms. For example, in a parent-led exposure trial in the home, liking and intake increased more in the intervention group (repeated exposure plus a small reward—a sticker) versus a control group.43 The aforementioned study testing combinations of modeling, reward, and exposure strategies was parent led as well.38 In addition to homes, studies of these learning paradigms have been conducted in childcare centers, typically with significant involvement of researchers. In one study of repeated exposure in a community preschool, effects of repeated exposure were not demonstrated, perhaps highlighting challenges in this setting. The authors acknowledged that provision of the target foods at school differs from researcher-directed paradigms in the laboratory or parent-led paradigms at home and commented that children may be less likely to try provided target foods in the school setting, perhaps requiring more taste exposures to reveal significant effects. Consistent with this idea, results from this study showed that 10 taste exposures did not translate into 10 tastings. The provision of the target foods at lunch, as opposed to on their own, may have also been a contributing factor.44 As with studies of infants, more research is needed to test applications of promising learning paradigms in real-world settings among children, including those settings serving children at risk for unhealthy diets and obesity. This future direction is an opportunity for research in the early childhood period and in the middle childhood period as well, where repeated exposure research can—and sometimes does—extend to ecologically valid settings like schools (Box 2.2).

Roles of Repeated Exposure and Other Types of Learning

BOX 2.2 Section Summary

• • • •

Familiarity is a key component of liking. Repeated exposure to foods has been shown to promote acceptance, with as few as 3–6 exposures. Children with greater food fussiness and neophobia may require additional strategies to promote liking and acceptance. Associative conditioning can enhance acceptance by pairing a particular food with tastes/ flavors that are already familiar and liked (e.g., offering vegetables with dip). Positive experiences around eating such as social modeling and small “token” rewards may also facilitate acceptance.

REPEATED EXPOSURE EFFECTS DURING MIDDLE CHILDHOOD Overview. As in the earlier periods of the life course reviewed herein, repeated exposure has been shown to increase children’s food acceptance during middle childhood, with the most commonly studied target foods being fruits and vegetables. Compared to earlier periods of the life course, however, there is less research on repeated exposure during middle childhood (ages 6 through 11 years). Although repeated exposure earlier in life has been identified as a unique opportunity to affect food preference learning when many foods and flavors are new, and neophobia is lower, the studies that examine exposure effects in middle childhood suggest that this window does not close completely. In other words, it is not too late to affect food preferences through learning during middle childhood. This plasticity in food preference learning is important to recognize and understand, particularly because children’s contexts tend to widen at this time, with new foods, flavors, and influences emerging alongside exposure to new environments such as school and after-school settings. In addition to spending time in a wider variety of settings, children in this age range have more autonomy and increasingly complex social relationships and skills, increasing both the complexity of and the opportunity for repeated exposure efforts. Examples of repeated exposure effects in middle childhood. Overall, results from studies of elementary school children in both controlled and naturalistic settings support the continued effectiveness of repeated exposure during this period of the life course. In a study involving eight days of exposures to sour orangeade, sweet orangeade, or no orangeade among 59 6- to 11-year-old children, increased preferences were found for sweet orangeade only.45 During the intervention, children in the sour group consumed less orangeade than those in the sweet group even though these flavors were equally preferred at baseline. One explanation proposed by the researchers is that it is easier to adapt children’s preferences for sweet foods compared to sour foods as sour taste preferences are more stable. The idea that acceptance of sweet foods may be promoted particularly

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readily is consistent with research conducted during infancy, as well as studies during middle childhood46–48 which demonstrate more robust effects of efforts to promote fruit acceptance versus vegetables. While multiple studies support the idea that acceptance of sweet foods may be easiest to promote, there is evidence supporting effects of repeated exposure on acceptance of bitter foods like vegetables during earlier periods as well as the middle childhood period of the life course. Lakkakula et al.49 found that repeated exposure to small tastes of a variety of fruits and vegetables in a school-based intervention increased first, third, and fifth grade children’s liking of previously disliked foods. At least two exposures were necessary to increase their liking of peaches, apricots, and pears, compared to four exposures for cantaloupe; five for bell peppers, carrots, and tomatoes; and six for peas. The lower number of exposures necessary to increase liking of fruits compared to vegetables in this study is consistent with findings showing that children’s acceptance of fruits or foods with sweet tastes is easiest to promote. This study and a similar school-based study demonstrating effectiveness of repeated exposure in cafeteria settings in this age group both involved the tasting of target foods separate from the provision and consumption of the regular lunch meal, with researchers guiding the tastings using a standard script.49,50 The moderating role of initial liking. While some middle childhood studies demonstrate repeated exposure effects that are largely consistent with studies from earlier periods of the life course, several studies have not done so, with evidence that differing effects may in part be due to variability in the initial liking of and familiarity with the target foods. For example, Lakkakula et al.50 implemented repeated exposure to four target vegetables in four low-income elementary school cafeterias for 10 weeks. They enrolled 360 fourth and fifth graders, who were each offered up to ten tastings of four vegetables: carrots, bell peppers, tomatoes, and peas. For participants who began the program disliking the target vegetables, liking scores increased for three of the four vegetables, with increases in liking among the majority of the participants by the eighth exposure. Liking scores did not change for children who already liked the selected vegetables at baseline. Similarly, Harvig et al.51 measured the effects of repeated exposure on liking of sweet and sour Nordic berry juices with 317 children between the ages of 9 and 11 years in a laboratory setting. Children exposed to sea buckthorn juice, a naturally sour juice that had sucrose added to increase its sweetness, consistently increased intake across eight exposures and two follow-up sessions, but liking did not significantly increase. For aronia juice, a sourer and less sweet juice, neither intake nor liking increased across the exposures although intake did increase by follow-up assessments. Investigators then examined how the effects differed between groups that began the experiment as initial likers, neutral likers, and initial dislikers of each juice. In the sea buckthorn group, liking increased significantly among initial dislikers only, with similar findings in the aronia group. Familiarity significantly impacted the liking of juices across all groups, with those more familiar with the juice reporting a greater liking of it.

Roles of Repeated Exposure and Other Types of Learning

When studies divide participants based on their initial opinions of the target food at baseline, it appears that initial dislikers are most likely to increase their acceptance of the food. In addition to having less room for improvement in liking, children who begin as initial likers may also already be familiar with the food, hindering further familiarization learning. These initial likers are less likely to show changes in acceptance across exposures.50–52 Relatedly, one study examined 315 9- to 11-year-old children who were split among three groups: a control group, a group exposed to an initially disliked sea buckthorn snack bar, and a group exposed to an initially liked kamut snack bar. The group exposed to the initially disliked snack bar showed a significant increase in liking of their target food by the ninth exposure, while the other two groups did not show a significant increase in liking. At posttest, intake of the snack bars in the two experimental groups was higher than at the pretest,52 suggesting that in some cases, intake of an initially liked food may continue to increase with exposure even if children are constrained in further increasing their liking. Taken together, these results show that repeated exposure can continue to promote food acceptance during the middle childhood period, and that changes in acceptance may be the most pronounced for foods and flavors that are not already well accepted by this period. These findings fit with the aforementioned idea that repeated exposure leads us to accept stimuli that are common in our environment and illustrates that this process can continue to work even if aspects of the environment (e.g., foods available) change over developmental time. Intervention programs leveraging the power of repeated exposure and other types of learning during middle childhood. Multiple intervention programs have been developed which leverage repeated exposure effects to encourage healthier eating during middle childhood. In order for repeated exposure to be effective, children must be willing to try the food presented to them. Neophobia acts as a barrier preventing children from trying foods and benefiting from repeated exposure. Mustonen et al.53 examined whether sensory education to enhance familiarization could increase the number of foods tasted between pre- and posttests and decrease food neophobia over one and a half years in 164 children who were aged 8–11 years at baseline. Ten sensory lessons on a variety of food groups were included in the first wave of research, and five more sensory lessons were given to two-thirds of the experimental group in the second wave. The sensory lessons focused on the properties of food, teaching children about and allowing them to experience different aromas, textures, sounds, tastes, and appearances of food. At the end of the educational program, food neophobia decreased in the experimental group while no change in neophobia scores was seen in the control group, and a larger number of foods were tasted by the experimental group compared to the control group. Effects were stronger among the younger children in the study. Additional researchers have studied the use of repeated exposure in conjunction with other methods, such as modeling and rewards, to increase children’s acceptance of fruits and vegetables in school settings. In a study of 560 6- to 9-year-old children, these

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strategies were used in a 16-day school-based intervention (“Food Dudes”). The intervention group watched videos of other children eating healthy foods, were encouraged to eat fruits and vegetables themselves, and received rewards for doing so (customized “Food Dudes” items such as stickers and pens). The control group had only the repeated exposure component: they were provided with the same fruits and vegetables during the study period. The program resulted in significantly lower neophobia scores for the intervention group compared to the control group. Liking for both fruits and vegetables also increased significantly in the intervention group compared to the control group, but at a 6-month follow-up assessment, this effect only remained for fruits.54 Another intervention used the same “Food Dudes” program across 16 days and enrolled 402 4- to 11-year-old children. Consumption of both fruits and vegetables significantly increased during the intervention, as did liking for fruits and vegetables. There was no control group in this study; here, children’s scores were compared to their baseline assessments.55 Overall, these studies demonstrate the potential of combining strategies—repeated exposure, modeling, and rewards—to increase fruit and vegetable acceptance, with some evidence suggesting these combination strategies may be more effective than repeated exposure alone in the context of this school-based intervention. In contrast, another rewards-based intervention that was conducted in primary schools did not demonstrate effects over and above repeated exposure: Researchers examined the use of a reward intervention that involved giving stickers to children who ate the target food, in comparison to repeated exposure and control groups. Target vegetable (red pepper) liking and intake were measured before and after eight tasting sessions. The repeated exposure group significantly increased both liking and intake of red pepper while the rewards intervention group showed intermediate effects that did not differ significantly from the repeated exposure or control groups.56 Compared to the success of the “Food Dudes” intervention, the rewards intervention discussed here did not demonstrate any benefits over and above repeated exposure alone. This result could be interpreted as further evidence of the promise of simple, repeated exposure and fits with the aforementioned idea that the effectiveness of reward-based strategies may depend on the individual child. Another possible explanation when comparing this intervention to the multicomponent programs mentioned herein relates to the use of modeling, which was absent here and may have contributed significantly to the success of the combined strategies used in the “Food Dudes” program. Alternatively, findings across these interventions may provide evidence that offering a variety of target foods can bolster repeated exposure effects during this period, as the rewards-based program discussed here involved a single target food. Future research that continues to test repeated exposure and other learning paradigms in the context of interventions in naturalistic settings can provide further insights into these differing results.

Roles of Repeated Exposure and Other Types of Learning

BOX 2.3 Section Summary

• • •

Interventions to increase food acceptance through repeated exposure have shown positive effects in home and school settings. Repeated exposure has not only been shown to promote liking of new foods but also previously disliked foods during middle childhood. Effects of repeated exposure do not require consumption of large amounts, but rather require only small tastes.

Overall, these findings demonstrate that repeated exposure continues to be an effective method to increase children’s liking and consumption of healthy foods during middle childhood, with evidence that this strategy can be successfully applied in school settings and may be particularly effective at increasing acceptance of foods that are not already well accepted by this period. While some studies suggest that more exposures may be needed during this period compared to early life, many studies support the idea that eight or fewer exposures are enough to significantly increase acceptance,45,46,49,56 particularly among children who initially dislike the target foods. Compared to early childhood, fewer studies during this period of the life course compare repeated exposure to other learning paradigms, with mixed results among those that do. Future research should aim to further pinpoint the key learning strategies responsible for intervention effects in programs that combine multiple approaches, such as repeated exposure, modeling, and rewards. Additionally, new interventions could be developed aiming to make use of repeated exposure effects in the many other settings that children in this age group occupy, such as after-school programs, restaurants, and their homes. Future research could also continue to explore the role of moderators in this age group. Besides initial liking of the target foods, potential moderators of food preference learning that could be studied in middle childhood could mirror those that have been studied in early childhood, including bitterness sensitivity and temperament (Box 2.3).

CONCLUSIONS, RECOMMENDATIONS, AND FUTURE DIRECTIONS To date, a robust body of research has supported the power of simple repeated taste exposures to promote the acceptance of healthier foods among children. While the specifics of the research questions that have been asked and the nature of the findings vary slightly across studies and periods of the life course, some overarching conclusions may be drawn. Some research supports the idea that fewer exposures are needed to promote acceptance earlier in the life course versus later, but overall, recent literature in this area suggests that the number of exposures needed to boost acceptance may be lower than initially thought.

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Multiple studies in infancy, early childhood, and middle childhood support the idea that eight or fewer exposures can increase food and flavor acceptance. Studies also demonstrate effects of exposure to a variety of foods on acceptance of new foods, with studies focused on earlier ages more likely to examine variety exposure effects. Studies focusing on later ages are more likely to consider the moderating role of initial liking, with evidence that acceptance is more likely to increase for foods and flavors that are not already liked at baseline. It is unclear whether initial liking is a more important moderator of food preference learning in middle childhood versus earlier periods, perhaps due to more foods being unfamiliar and thus less likely to already be liked earlier in the life course, or whether this moderator has simply been studied more during the middle childhood period. Overall, findings across infancy and childhood are encouraging when considering a key practical implication of this research: communicating to parents, caregivers, and practitioners the importance of “not giving up” if a child rejects a food the first few times it is offered. One can assume that offering the food five, six, or eight times will sound less overwhelming than 15 or 20 exposures. That said, the number of exposures needed, as indicated in a study’s results, are averages, and there is an emerging literature suggesting that there are individual differences in food preference learning. Factors such as the child’s genetic sensitivity to bitterness, temperament, past experience with “instrumental feeding,” and initial liking of target foods have been shown to moderate food preference learning, highlighting opportunities for future research to delve deeper to identify effective, and potentially tailored, solutions. For example, future research should directly compare the effects of different learning paradigms on initial willingness to taste and subsequent eating behaviors among those subgroups who may be less likely to readily benefit from simple strategies like repeated exposure. It may be the case that additional strategies, such as associative conditioning, modeling, or rewards, are needed to motivate an initial willingness to taste new foods among these children, and that these approaches may pave the way for repeated exposure effects in the future. Future research designed to understand which learning strategies work best and for whom will be valuable in considering future interventions that aim to leverage and effectively apply learnings from controlled research in this area. At this stage, there is a robust evidence base supporting the effectiveness of repeated exposure on average, and the gaps to be filled by future research include further information on individual differences, as well as longitudinal effectiveness research that uncovers the contexts in which repeated exposure may be used to promote sustained healthier eating behaviors, particularly among vulnerable populations and with little involvement from researchers. Thus far, many studies of repeated exposure in real-world contexts fall into the category of efficacy, rather than effectiveness, trials as they still involve substantial involvement of researchers. Home-based exposure trials are one exception as there are successful examples of parents leading these repeated exposure interventions. Increasing the diversity of families

Roles of Repeated Exposure and Other Types of Learning

represented in such trials is another important future direction, given potential barriers to repeated exposure across different sociodemographic groups.57 In considering applications of food preference learning research, it is essential to point out that repeated exposure experiments and interventions do not occur in a vacuum. They are occurring against a backdrop that arguably becomes more and more complex with developmental time, as children’s ecologies widen, and they occupy a greater number of settings and contexts. These complexities mean that, with developmental time, there are more opportunities for experiences in the environment to interfere with repeated exposures to healthy foods, particularly considering the overarching obesogenic environment with its ubiquitous palatable foods that appeal to our genetic taste predispositions. Relatedly, Beets et al. found that children were willing to eat healthy snacks in an after-school program but not when there were unhealthy snacks offered in direct competition with the healthy options.58 This phenomenon can interfere with attempts to repeatedly expose children to healthy foods. Additionally, in a qualitative study of lowincome parents, participants reported a low willingness to repeatedly provide foods that their child previously rejected, to avoid waste.57 These examples illustrate challenges that are important considerations for interventions, programs, and/or policies that aim to build on the literature on repeated exposure to promote healthy eating among children. Future research should explore ways to mitigate these barriers in applying repeated exposure and other learning paradigms in naturalistic settings. For example, some studies reviewed herein have revealed the efficacy of offering repeated, small tastes of target foods in boosting acceptance; future studies might apply these concepts in effectiveness research with low-income families, thinking creatively about ways to offer small tastes to reduce food waste and costs (e.g., using frozen foods and defrosting small amounts of target foods at each exposure). In sum, the power of repeated exposure to promote food acceptance during infancy, early childhood, and middle childhood is well supported by the literature. Recommendations for parents and other caregivers include consistently providing exposure to a variety of foods and flavors over multiple occasions. Modeling the consumption and enjoyment of healthy foods also shows promise, and in cases in which these simpler strategies are not enough to motivate initial tasting, associative conditioning or the provision of small nonfood rewards may help to do so. Future research on the role of individual differences in food preference learning, as well as effective and feasible ways to promote repeated exposure in real-world environments among vulnerable populations, can add to this evidence base and impact public health. Efforts to change the broader environment to make healthy choices easier and more normative in environments where children spend time could also bolster the effectiveness of repeated exposures to healthy foods by eliminating contradictory influences.

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REFERENCES 1. Siega-Riz AM, Deming DM, Reidy KC, Fox MK, Condon E, Briefel RR. Food consumption patterns of infants and toddlers: where are we now? J Am Diet Assoc 2010;110(12):S38–51. 2. Fletcher S, Wright C, Jones A, Parkinson K, Adamson A. Tracking of toddler fruit and vegetable preferences to intake and adiposity later in childhood. Matern Child Nutr 2017;13(2). 3. Rose CM, Birch LL, Savage JS. Dietary patterns in infancy are associated with child diet and weight outcomes at 6 years. Int J Obes 2017;41(5):783–8. 4. Zajonc RB. Attitudinal effects of mere exposure. J Pers Soc Psychol 1968;9(2p2):1. 5. Mennella JA, Jagnow CP, Beauchamp GK. Prenatal and postnatal flavor learning by human infants. Pediatrics 2001;107(6):E88. 6. Mennella JA, Forestell CA, Morgan LK, Beauchamp GK. Early milk feeding influences taste acceptance and liking during infancy. Am J Clin Nutr 2009;90(3):780S–788S. 7. Mennella JA, Beauchamp GK. Maternal diet alters the sensory qualities of human milk and the nursling’s behavior. Pediatrics 1991;88(4):737–44. 8. Mennella JA, Daniels LM, Reiter AR. Learning to like vegetables during breastfeeding: a randomized clinical trial of lactating mothers and infants. Am J Clin Nutr 2017; ajcn143982. 9. Hausner H, Nicklaus S, Issanchou S, Molgaard C, Moller P. Breastfeeding facilitates acceptance of a novel dietary flavour compound. Clin Nutr 2010;29(1):141–8. 10. Mennella JA, Griffin CE, Beauchamp GK. Flavor programming during infancy. Pediatrics 2004;113 (4):840–5. 11. Maier-Noth A, Schaal B, Leathwood P, Issanchou S. The lasting influences of early food-related variety experience: a longitudinal study of vegetable acceptance from 5 months to 6 years in two populations. Plos One 2016;11(3):e0151356. 12. Mennella JA, Castor SM. Sensitive period in flavor learning: effects of duration of exposure to formula flavors on food likes during infancy. Clin Nutr 2012;31(6):1022–5. 13. Barends C, de Vries JH, Mojet J, De Graaf C. Effects of repeated exposure to either vegetables or fruits on infant’s vegetable and fruit acceptance at the beginning of weaning. Food Qual Prefer 2013;29:157–65. 14. Forestell CA, Mennella JA. Early determinants of fruit and vegetable acceptance. Pediatrics 2007;120 (6):1247–54. 15. Maier AS, Chabanet C, Schaal B, Leathwood PD, Issanchou SN. Breastfeeding and experience with variety early in weaning increase infants’ acceptance of new foods for up to two months. Clin Nutr 2008;27(6):849–57. 16. Mennella JA, Nicklaus S, Jagolino AL, Yourshaw LM. Variety is the spice of life: strategies for promoting fruit and vegetable acceptance during infancy. Physiol Behav 2008;94(1):29–38. 17. Stein LJ, Cowart BJ, Beauchamp GK. The development of salty taste acceptance is related to dietary experience in human infants: a prospective study. Am J Clin Nutr 2012;95(1):123–9. 18. Remy E, Issanchou S, Chabanet C, Nicklaus S. Repeated exposure of infants at complementary feeding to a vegetable puree increases acceptance as effectively as flavor-flavor learning and more effectively than flavor-nutrient learning. J Nutr 2013;143(7):1194–200. 19. Hetherington MM, Schwartz C, Madrelle J, et al. A step-by-step introduction to vegetables at the beginning of complementary feeding. The effects of early and repeated exposure. Appetite 2015;84:280–90. 20. Paul IM, Savage JS, Anzman SL, et al. Preventing obesity during infancy: a pilot study. Obesity 2012;19 (2):353–61. 21. Paul IM, Williams JS, Anzman-Frasca S, et al. The intervention nurses start infants growing on healthy trajectories (INSIGHT) study. BMC Pediatr 2014;14(1):184. 22. Hohman EE, Paul IM, Birch LL, Savage JS. INSIGHT responsive parenting intervention is associated with healthier patterns of dietary exposures in infants. Obesity 2017;25(1):185–91. 23. Cooke L, Fildes A. The impact of flavour exposure in utero and during milk feeding on food acceptance at weaning and beyond. Appetite 2011;57(3):808–11. 24. Ahern SM, Caton SJ, Blundell P, Hetherington MM. The root of the problem: increasing root vegetable intake in preschool children by repeated exposure and flavour flavour learning. Appetite 2014;80:154–60.

Roles of Repeated Exposure and Other Types of Learning

25. Hausner H, Olsen A, Moller P. Mere exposure and flavour-flavour learning increase 2–3-year-old children’s acceptance of a novel vegetable. Appetite 2012;58:1152–9. 26. Anzman-Frasca S, Savage JS, Marini ME, Fisher JO, Birch LL. Repeated exposure and associative conditioning promote preschool children’s liking of vegetables. Appetite 2012;58:543–53. 27. Bouhlal S, Issanchou S, Chabanet C, Nicklaus S. ‘Just a pinch of salt’. An experimental comparison of the effect of repeated exposure and flavor-flavor learning with salt or spice on vegetable acceptance in toddlers. Appetite 2014;83:209–17. 28. Caton SJ, Ahern SM, Remy E, Nicklaus S, Blundell P, Hetherington MM. Repetition Counts: repeated exposure increases intake of a novel vegetable in UK pre-school children compared to flavour-flavour and flavour-nutrient learning. Br J Nutr 2013;109:2089–97. 29. de Wild V, de Graaf C, Jager G. Efficacy of repeated exposure and flavour-flavour learning as mechanisms to increase preschooler’s vegetable intake and acceptance. Pediatr Obes 2015;10:205–12. 30. de Wild VW, de Graaf C, Jager G. Use of different vegetable products to increase preschool-aged children’s preference for and intake of a target vegetable: a randomized controlled trial. J Acad Nutr Diet 2017;117(6):859–66. 31. Capaldi ED, Privitera GJ. Decreasing dislike for sour and bitter in children and adults. Appetite 2008;50 (1):139–45. 32. Havermans RC, Jansen A. Increasing children’s liking of vegetables through flavour-flavour learning. Appetite 2007;48(2):259–62. 33. Caton SJ, Blundell P, Ahern SM, et al. Learning to eat vegetables in early life: The role of timing, age and individual eating traits. Plos One 2014;9(9):1–10. 34. de Wild VWT, de Graaf C, Jager G. Effectiveness of flavour nutrient learning and mere exposure as mechanisms to increase toddler’s intake and preference for green vegetables. Appetite 2013;64:89–96. 35. Fisher JO, Mennella JA, Hughes SO, Liu Y, Mendoza PM, Patrick H. Offering “dip” promotes intake of a moderately-liked raw vegetable among preschoolers with genetic sensitivity to bitterness. J Acad Nutr Diet 2012;112(2):235–45. 36. Capaldi Phillips ED, Wadhera D. Associative conditioning can increase liking for and consumption of Brussel sprouts in children aged 3 to 5 years. J Acad Nutr Diet 2016;114(8):1236–41. 37. Cooke LJ, Chambers LC, An˜ez EV, et al. Eating for pleasure or profit the effect of incentives on children’s enjoyment of vegetables. Psychol Sci 2011;22(2):190–6. 38. Holley CE, Haycraft E, Farrow C. ‘Why don’t you try it again?’ A comparison of parent led, home based interventions aimed at increaseing children’s consumption of a disliked vegetable. Appetite 2014;87:215–22. 39. Horne P, Tapper K, Lowe C, Hardman C, Jackson M, Woolner J. Increasing children’s fruit and vegetable consumption: a peer-modelling and rewards-based intervention. Eur J Clin Nutr 2004;58 (12):1649–60. 40. Anez E, Remington A, Wardle J, Cooke L. The impact of instrumental feeding on children’s responses to taste exposure. J Hum Nutr Diet 2013;26:415–20. 41. Vandeweghe L, Verbeken S, Moens E, Vervoort L, Braet C. Strategies to improve the willingness to taste: the moderating role of children’s reward sensitivity. Appetite 2016;103:344–52. 42. Staiano AE, Marker AM, Frelier JM, Hsia DS, Martin CK. Influence of screen-based peer modeling on preschool children’s vegetable consumption and preferences. J Nutr Educ Behav 2016;48:331–5. 43. Fildes A, van Jaarsveld CHM, Wardle J, Cooke L. Parent-administered exposure to increase children’s vegetable acceptance: a randomized controlled trial. J Acad Nutr Diet 2014;114(6):881–8. 44. O’Connell ML, Henderson KE, Luedicke J, Schwartz MB. Repeated exposure in a natural setting: a preschool intervention to increase vegetable consumption. J Acad Nutr Diet 2012;112(2):230–4. 45. Liem DG, de Graaf C. Sweet and sour preferences in young children and adults: role of repeated exposure. Physiol Behav 2004;83(3):421–9. 46. Osborne CL, Forestell CA. Increasing children’s consumption of fruit and vegetables: does the type of exposure matter? Physiol Behav 2012;106(3):362–8. 47. Perry CL, Bishop DB, Taylor GL, et al. A randomized school trial of environmental strategies to encourage fruit and vegetable consumption among children. Health Educ Behav 2004;31(1):65–76.

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48. Schindler JM, Corbett D, Forestell CA. Assessing the effect of food exposure on children’s identification and acceptance of fruit and vegetables. Eat Behav 2013;14:53–6. 49. Lakkakula A, Geaghan JP, Wong W-P, Zanovec M, Pierce SH, Tuuri G. A cafeteria-based tasting program increased liking of fruits and vegetables by lower, middle and upper elementary school-age children. Appetite 2011;57(1):299–302. 50. Lakkakula A, Geaghan J, Zanovec M, Pierce S, Tuuri G. Repeated taste exposure increases liking for vegetables by low-income elementary school children. Appetite 2010;55(2):226–31. 51. Hartvig DL, Hausner H, Wendin K, Ritz C, Bredie WLP. Initial liking influences the development of acceptance learning across repeated exposure to fruit juices in 9–11-year-old children. Food Qual Prefer 2015;39:228–35. 52. Hausner H, Hartvig DL, Reinbach HC, Wendin K, Bredie WLP. Effects of repeated exposure on acceptance of initially disliked and liked Nordic snack bars in 9–11-year-old children. Clin Nutr 2012;31(1):137–43. 53. Mustonen S, Tuorila H. Sensory education decreases food neophobia score and encourages trying unfamiliar foods in 8–12-year-old children. Food Qual Prefer 2010;21(4):353–60. 54. Laureati M, Bergamaschi V, Pagliarini E. School-based intervention with children. Peer-modeling, reward and repeated exposure reduce food neophobia and increase liking of fruits and vegetables. Appetite 2014;83:26–32. 55. Lowe CF, Horne PJ, Tapper K, Bowdery M, Egerton C. Effects of a peer modelling and rewards-based intervention to increase fruit and vegetable consumption in children. Eur J Clin Nutr 2004;58(3):510–22. 56. Wardle J, Herrera ML, Cooke L, Gibson EL. Modifying children’s food preferences: the effects of exposure and reward on acceptance of an unfamiliar vegetable. Eur J Clin Nutr 2003;57(2):341–8. 57. Goodell LS, Johnson SL, Antono AC, Power TG, Hughes SO. Strategies low-income parents use to overcome their children’s food refusal. Matern Child Health J 2016;1–9. 58. Beets MW, Tilley F, Kyryliuk R, Weaver RG, Moore JB, Turner-McGrievy G. Children select unhealthy choices when given a choice among snack offerings. J Acad Nutr Diet 2014;114(9):1440–6.

CHAPTER 3

Effects of Modeling on Children’s Eating Behavior Jacqueline Blissett

Department of Psychology, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom

EFFECTS OF MODELING ON CHILDREN’S EATING BEHAVIOR Humans are social animals. From the earliest days of postnatal life, infants pay attention to the behavior of those around them to learn about almost all aspects of their world. Like many animals, from observing others we learn about the safety of our environment, how to behave in a vast range of circumstances, and how to interact with others in social situations. Food selection is a social phenomenon1 so it should be no surprise that our early learning about eating behavior is significantly influenced by observing what and how other people eat and their reactions to the foodstuffs they have ingested. This chapter examines what we know about the effects of modeling on children’s eating behavior. We discuss what modeling is, why it is of such importance to human development, and the role of modeling in novel food intake, portion sizes, and food preferences. We briefly discuss the effectiveness of different kinds of models and the individual differences that may make children more or less likely to learn from observing others. Finally, we explore the use of modeling in interventions to improve children’s diets.

WHAT IS MODELING AND WHY DO WE DO IT? Albert Bandura2 first described the mechanism by which an individual can learn “vicariously” by watching the behavior of another individual and the outcome of that behavior. The person exhibiting the behavior is known as the model, and the individual learning from that behavior is the observer. The observer may copy the model’s behavior, thus exhibiting “imitation” or “observational learning.” In Bandura’s social learning theory, observers are more likely to imitate a model’s behavior when they like or admire the model, when they see the model rewarded for their behavior, when the observer is rewarded for imitating the model’s behavior, and when they see others imitating the model.2 This is a primary mechanism for children to acquire a socially acceptable repertoire of behaviors and serves the important function of facilitating learning without the need for the child to directly experience situations that may be dangerous or unpleasant. Pediatric Food Preferences and Eating Behaviors https://doi.org/10.1016/B978-0-12-811716-3.00003-8

© 2018 Elsevier Inc. All rights reserved.

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In adulthood, social context influences our eating behavior through a number of potential mechanisms, but the basic principle can be summarized that following the behavior of others is usually adaptive (for a review of social influences on adult eating behavior, see Robinson et al.3). As infants and children, we can learn about the safety, the potential taste, and whether we might like potential foodstuffs by observing others. We watch the ingestive behavior of other people who are important to us, as well as the consequences of that ingestion. For example, a baby who observes a parent consuming a food pays attention to both what the parent has chosen to put in their mouth and the facial expressions and other social information (e.g., vocalizations) exhibited by the parent during consumption. Parents who exhibit facial expressions indicating that they do not like a food may cause reduced intake of that food in the infant because the infant learns that the food may have a bad taste. This makes evolutionary sense. Similarly, in a cafe recently, I watched a toddler attempting to eat a wax crayon, despite the sandwich on offer in front of her. Her mother made a dramatic “disgust” facial expression in reaction to the child’s exploration of the taste and texture of the crayon. The mother’s nose wrinkled upward, the corners of her mouth turned downward, and her upper lip drew back. The child looked immediately at her mother’s facial expression and rapidly removed the crayon from her mouth. While the evidence that young children can accurately identify the specific emotion of disgust from others’ facial expressions is mixed, their ability to identify this characteristic facial expression as indicative of negative emotion is well established.4 By watching and learning from her mother’s disgust response, the toddler in the cafe should have a reduced probability of eating crayons again in the future. The child in the cafe, sampling wax crayons, gives us a great insight into the problems faced by all omnivorous animals. Humans are omnivores; our diets are potentially very diverse. This brings great nutritional opportunities, but also potentially great threat as a result of ingesting dangerous substances. Therefore we have to learn both what is safe and what is best to eat. We have a number of mechanisms by which this complex learning problem is solved. These include an innate preference for sweet tastes5 alongside innate rejection of bitter and sour foods,6 avoidance of foods that are followed by nausea,7 and a preference for familiar foods to which we have been repeatedly exposed.8 However, all of these mechanisms act at the stage of or after ingestion rather than at the stage of deciding what should and should not be placed in the mouth in the first place. Deciding whether a substance is a food or nonfood is a complex decision-making process that takes time to develop. By middle childhood, foods are classified by color, texture, and smell, and decisions about whether or not to ingest something can be made on these characteristics alone.9 However, even 6- to 18-month-old infants can identify plants as food sources and may be particularly attuned to working out the relative edibility of plants in comparison to other potential foods.10 This has clear evolutionary advantage given the potential for plants to be both a good source of nutrition and a potential source of toxicity and is also consistent with infants’ reticence to spontaneously touch the leaves

Effects of Modeling on Children’s Eating Behavior

of plants.11 But, despite this, children under 2 years old often readily ingest inedible or even dangerous substances.12 It is only with the development of neophobia in late infancy, which occurs with increasing autonomy and independence from the caregiver, that children become more selective in their willingness to try novel foods.13–15 Therefore young children need to make use of the knowledge and experience of others to determine safety of potential foodstuffs in their environment. Other omnivores face the same challenge of learning what and what not to eat, and also use the behavior of others in their group to guide behavior. Social referencing (taking cues from others about appropriate behaviors and emotions in specific situations) is also a useful tool for nonhuman primates to learn about novel foods. For example, infant chimpanzees look at their mothers when preparing to interact with novel but not familiar foods.16 Capuchin monkeys also eat more novel foods when their group members are eating at the same time.17 However, as a species, humans appear to be particularly good at using the behavior of others to guide their eating of specific foods. For example, 2- to 5-year-old children are more likely to accept a new food when they see someone else eating it, but only if that model is eating the same food as they are.13 In contrast, capuchin monkeys show social facilitation of eating but it is not dependent on their group eating the same food as the individual.17 Humans also pay attention to the emotion displayed by other eaters, not just the action of food consumption or food avoidance. For example, when a child observes a peer modeling food intake with positive emotional expression they are more likely to imitate that behavior than when the model has a neutral expression.18 Modeling is a core mechanism for determining food acceptance, with potential to affect our ingestion of novel foods, the amount of food that we consume, and even our food preferences (Box 3.1). We review the studies that have investigated all of these aspects, next.

INFLUENCE OF MODELING ON NOVEL FOOD INTAKE AND CHOICE Getting young children to try new foods is a commonly reported problem for parents. In one of our studies19 we examined the kinds of feeding practices parents spontaneously

BOX 3.1 Section Summary

• • •

“Modeling” is the mechanism by which an individual can learn by watching the behavior of another individual. As omnivores, young children need to learn about what to eat, and what not to eat, by observing others. Nonhuman animals, such as monkeys, show similar social learning, though humans are more sophisticated in their learning from models.

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use when they are trying to get children to eat something new. Children between 2 and 4 years old and their parents were observed eating a standardized meal which contained a portion of a fruit that the child had never consumed before. Parents were asked to do whatever they would normally do at home, to get their child to try the new fruit. Modeling was a commonly used strategy in this study: 76% of parents used modeling on average around six times in the mealtime. This included statements about their own consumption of the novel fruit (“Look, Mummy’s eating it!”) as well as positive facial expressions and noises made while eating it (e.g., “Mmmm that’s yummy!”). This suggests that parents perceive modeling to be a potentially useful method to encourage new food ingestion. This aligns with findings from one of the first experiments examining the effects of modeling on children’s food intake, which showed that children are more likely to eat a novel food if they see an adult eat it first rather than simply being offered the food.20 Another key study in this field was carried out by Addessi and colleagues.13 They demonstrated that 2- to 5-year-old children were more likely to accept, quicker to ingest, and ate more of a novel food if a familiar adult was eating the same colored novel food in comparison to conditions where the adult was eating a differently colored food or was not eating at all. This study demonstrated that, in humans, modeling effects are specific to individual foods; in other words, it is not just social facilitation of eating that is occurring here. Social facilitation of eating occurs when people (and other nonhuman animals) eat more overall, irrespective of the specific foods they choose, when they eat together rather than alone. Addessi’s study showed that children are not just eating more when others eat, but that they are imitating ingestion of specific foods. Similarly, it is not just the observation of an adult eating that matters, but also their reaction to the food. For example, when an adult model is enthusiastic about the novel food, children’s intake is encouraged. Hendy and Raudenbush21 carried out a series of studies evaluating the effectiveness of teacher modeling on preschool children’s food acceptance. Enthusiastic teacher modeling (consumption of a novel fruit, along with verbal statements such as “These are delicious!”) significantly improved children’s novel food acceptance in comparison to simple exposure to the novel fruit. However, when teacher modeling was compared with peer modeling (with the peers similarly demonstrating consumption of a different food, with an enthusiastic statement, e.g., “Mmmm, I love kiwis”) peer modeling was more powerful than teacher modeling. Subsequently, Hendy22 demonstrated that the preschool peer modeling effect, while strong, was relatively short lived and not sustained in tests of preference and consumption 1 month later. Nevertheless, we can conclude from these experiments that enthusiastic modeling, whether by a teacher or a peer, can at least increase the likelihood of preschoolers trying a new food. Just watching someone else eat food is not effective; the food must “match” between model and observer and a positive facial expression, or a statement concerning the tastiness of a food can make all the difference to the effectiveness of modeling.

Effects of Modeling on Children’s Eating Behavior

Hendy and Raudenbush’s study showed that the effect of a peer’s recommendation is a particularly powerful determinant of whether or not a child is likely to try a new food, and further studies have since supported this idea. Importantly, this powerful effect works for both promoting and discouraging the eating of a new food. One study showed that preschoolers who heard peers giving a positive endorsement to a novel food ate more of it than if they heard a negative message or no social information at all.23 In the first study, 5- to 7-year-olds were seated with similar age “confederates” who made positive comments about and then ate, or made negative comments and did not eat, target novel blue foods presented as part of a snack session. Positive peer modeling facilitated children’s consumption of the new food, as expected. Importantly, this effect was shown both when the peer was present and in a subsequent session when the child was offered snacks alone. In contrast, when children heard negative peer modeling they did not typically consume any of the new food either when that peer was present or when they were offered the food alone at the following session. Therefore negative peer modeling appears to have a very powerful deterring effect on ingesting new foods, which of course has a clear evolutionary benefit. In a similar subsequent study, 3- to 4-year-olds were seated with trained confederates who were slightly older (6–9 years). Results were very similar to the study of 5- to 7-year-olds except that in this age group, it was not possible to reverse the effects of previous negative modeling with a session of positive modeling.23 This implies that negative modeling experiences may be particularly detrimental to food acceptance in younger children and that these first experiences with new foods are very important in determining the child’s likelihood of future willingness to eat it. Most studies of modeling examine its effects on either new or familiar food consumption. However, one study has explicitly compared the effects of peer modeling on children’s choices of familiar vs. unfamiliar foods.24 Participants were 7-year-olds who were asked to choose between pictures of foods (familiar vs. unfamiliar fruits and vegetables; familiar vs. unfamiliar high-energy-dense foods; familiar fruit and vegetables vs. unfamiliar high-energy-dense foods, and unfamiliar fruit and vegetables vs. familiar high-energydense foods). They were also shown the choices of a fictitious peer, who was the same age and gender as themselves and who the experimenters pretended was making the selections on the same task in another school. The children were influenced by the fictitious peer’s choices even without seeing them eat. Children chose more unfamiliar foods when they were told that the peer had also chosen them. However, they were less influenced by the peer when they had to decide between low and high energy density foods, and low energy density unfamiliar foods were particularly unpopular. Naturally, it is typically the low energy novel foods that parents struggle to get their children to accept (curly kale, anyone?). This suggests that even the power of peer modeling will not always be sufficient to overcome children’s unwillingness to try new foods that are not likely to be sweet, fatty, or energy dense. Nonetheless, given that this study shows that the peer does not

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BOX 3.2 Section Summary

• • • • •

Modeling is an effective mechanism for promoting novel food tasting and choice. It is not enough to observe someone else eating: the food must match the target novel food, and the model should be enthusiastic and positive. Negative modeling discourages novel food tasting and may have particularly powerful negative effects on preschool children’s willingness to eat a new food. Peers may be more effective than adults in encouraging children to try new foods. Low energy new foods are more challenging to model effectively.

have to be directly observed eating the food for modeling to be effective, “remote” peer influence may be one mechanism we can exploit to help children overcome unwillingness to try some new foods (Box 3.2).

INFLUENCE OF MODELING ON PORTION SIZES EATEN Modeling is effective with regard to children’s decisions about what they eat, but does it also have influence over how much they eat? In adults, the presence of other people, particularly familiar others, tends to increase palatable food intake but individuals also rely on social cues to determine what portion sizes are acceptable.25 Infants26 and children27 tend to eat more when foods are presented in positive social contexts and when they eat with their siblings.28 Children are more likely to match their intake to each other when they are unfamiliar with each other28 and overweight children tend to consume less when eating with a healthy weight peer.29 While this suggests potential effects of peer modeling on children’s portion size selection and consumption, none of these studies are good experimental evaluations of modeling of portion size because they do not have a specified model and observer nor do they manipulate the model’s behavior. Therefore we cannot be sure who is imitating who, or whether both children in the dyad are affecting each other’s eating. However, there are a small number of studies which have experimentally examined the effects of models on the amount of food children consume. One of these studies examined whether a child’s weight influenced the effectiveness of modeling on 8- to 12-year-old girls’ food intake.30 A video model, who was at the 75th percentile for BMI, selected and ate either a large or small portion of cookies. Girls ate more cookies if they were exposed to the large serving size model and overweight girls ate more than healthy weight girls, but the interaction between these two factors was not significant. The authors concluded that peer modeling effects on portion size were equally important in both overweight and healthy weight children. However, other evidence suggests there may be differences between overweight and healthy weight children in their tendency to model portion sizes. In a study of 7- to 10-year-olds31 overweight children were more

Effects of Modeling on Children’s Eating Behavior

likely to overeat when the peer model also overate. In contrast to the first study, children who were not overweight were less affected by the amount consumed by the peer model. Importantly, what was eaten by the peer in the first session also influenced the participants’ food intake when they ate the same palatable snacks alone 2 days later, demonstrating the potential for a longer term influence of models on social norms for children’s portion size consumption.31 In a similar study32 overweight children were twice as likely as healthy weight children to imitate a confederate’s food picking movements within 5 s of the confederate’s fingers touching a palatable snack. There are many potential explanations of this phenomenon, all warranting further research. It may be because observing another person reaching for food triggers children to perform the same action and overweight children are more sensitive to this trigger. It may be because overweight children are more food cue responsive, impulsive, or show greater food cue attentional bias. Finally, it may be that in imitating their peer, they were attempting to affiliate themselves with the confederate and overweight children may feel greater pressure to affiliate with the healthy weight peer (Box 3.3).

INFLUENCE OF MODELING ON CHILDREN’S FOOD LIKING AND PREFERENCES Modeling influences willingness to choose and try new foods and the amount of food that children eat, but does it actually have any effect on whether children actually like the food they are eating? As early as 1938, Duncker33 demonstrated that children would change their liking rankings of familiar foods to be in line with a group of peers who had different preferences; and the effect was stronger for younger children observing older children. Does this translate to real changes in food preference? One of the earliest studies to examine whether modeling might actually have the power to change children’s food preferences was conducted by Leann Birch in 1980.34 In this classic study, children were seated at tables with peers whose preferences for vegetables differed from the target child. All other children at the table liked the target child’s nonpreferred vegetable. For 4 days, both vegetables were offered to the children

BOX 3.3 Section Summary

• • •

Child models who demonstrate large or small serving sizes influence child observers to consume similar sized portions. This influence might be particularly apparent for overweight children. The effect of peer modeling of portion sizes does not just occur when the model and observer are eating together: the effects on the observer’s eating behavior can be seen when they eat the same food alone 2 days later.

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with the target child choosing first on day one and the other children each choosing first on the remaining days. Vegetable choices, as well as intake and preferences, were measured. Target children were significantly more likely to choose the nonpreferred vegetable after 4 days and there was a significant increase in their preference ratings for the nonpreferred vegetable. In other words, children learned to like the vegetable that their peers showed liking for. Younger children appeared to be more susceptible to the effects of peer modeling on increasing preference for the initially nonpreferred food.34 We cannot be certain, of course, that children’s reported ratings of preference truly reflect their enjoyment of the vegetable. Nonetheless, these findings led Birch to suggest that children could be routinely exposed to other children with different preferences to improve their repertoire of accepted foods. One primary factor that may influence whether or not modeling is effective in changing preferences is the taste of the target foods. Previously, we discussed that modeling was least effective in promoting new food choice when the target food was low in energy and unfamiliar.24 Indeed, a 16-day intervention program for 6- to 9-year-olds (combining peer modeling, rewards, and repeated exposure) to improve fruit and vegetable acceptance35 showed a significant improvement in liking for fruits after the intervention, which was also maintained 6 months after the intervention was completed. In contrast, there were only short-term effects on vegetable liking, which were not maintained 6 months later.35 The specific challenge remains of increasing liking for vegetables— the generally less palatable taste and low energy density make them more challenging to learn to love! We need to carry out further research to establish how persistent modeling effects on liking are and whether there may be important variations in the effectiveness of modeling on liking of food groups that have more challenging taste profiles or less rewarding postingestive consequences (Box 3.4).

DIFFERENT MODELS: WHO IS MOST EFFECTIVE? Children don’t imitate the behavior of all models, nor all behaviors, so what makes the difference? Numerous studies have examined the characteristics that make a model more or less effective. We have already seen that familiarity with the model is a possible factor;

BOX 3.4 Section Summary

• •

Peer models have the power to change children’s reported food preferences in the short term. The taste profiles of the target foods are likely to affect how well modeling works to change preferences in the long term. Vegetables, which are generally low in fat, low in sweetness, and often have a bitter taste profile, may be particularly difficult targets for maintenance of changes in liking.

Effects of Modeling on Children’s Eating Behavior

toddlers are more likely to accept foods modeled by their mother than a stranger20 and children eat more when with siblings than with strangers.28 On the other hand, many studies don’t find an effect of familiarity on the effectiveness of modeling36 and models certainly don’t have to be familiar to be effective. While unfamiliar peers may well have a different effect on children’s eating behavior than that of friends or siblings37 most experimental work in this field uses unfamiliar peers as models and most nonexperimental work examines modeling effects of friends and familiar others. This limits our ability to make firm conclusions about the relative influence of unfamiliar vs. familiar models on children’s eating. Frazier and colleagues18 examined the impact of various model characteristics (including gender, age, race, and emotional facial expression) on preschool children’s food choices in a series of experiments using photographs of models consuming foods. Unsurprisingly, and consistent with much other work in this area, preschoolers were more likely to consume foods that they thought were being eaten by models showing positive rather than negative facial expressions. There were also some developmental changes in this: younger children were less likely to be influenced by the facial expression of the model than were older children. This may have been due to younger children failing to understand instructions or directions in the study or may suggest that sensitivity to facial expressions, at least those presented in photograph form, may still be developing in the preschool years. The actual food the child was eating in each photograph was not visible and, perhaps, younger children had greater challenge to extrapolate from this “hypothetical” food to the foods available for them to try. Nonetheless, in this study, children were also more likely to model children of the same gender as themselves. This suggests that there was a “matching” process occurring between children and the models, which influenced their effectiveness. Copying the behavior of people who are similar to us makes perfect sense if the primary function of vicarious learning is to learn about safe and socially acceptable behavior. Some intriguing work examining the effects of modeling on novel food acceptance in 12-month-old infants illustrates that, even in infancy, we are attuned to whether people are similar or dissimilar to us. Surprisingly, infants pay particular attention to cues that indicate someone is from the same culture as them, for example, the language spoken by the model, to determine whether or not to imitate their behavior. In a study by Shutts et al.38 12-month-old infants were offered novel food by French or native English speakers. In a subsequent forced choice scenario, they preferentially reached for the food presented to them in their native language. This suggests that infants may extrapolate from the language of a model to assume cultural similarity and therefore similarity in food preference or consumption patterns.39 Interestingly, work with older children has not shown effects of race of the model, or similarity of race between the child and the model, in children’s willingness to imitate eating behavior.37 This suggests that language is a more important cue for children than race, in determining likely similarity in food preference.

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A series of interesting studies examining 12- to 14-month-old infants’ reasoning about foods conducted by Liberman et al.40 gives us further insight into the cognitive mechanisms underpinning the effectiveness of modeling of food intake. Liberman et al. demonstrated that infants generalize food preferences, but not object preferences, across individuals, suggesting that infants develop specific conceptual reasoning regarding food compared to other objects or artifacts. However, infants don’t generalize food preferences between individuals when the person they are observing is socially disengaged. This gives further support to the idea that infants’ inferences about other people’s food preferences are affected by their observations of social relationships and social identity. In other words, I pay attention to your food preferences when I think you are like me and part of my social group. In older children, this may also help to explain the effectiveness of celebrity or cartoon models who are presented as part of the desirable ingroup in marketing campaigns.41 By the time infants are mobile and potentially gaining more independence in their food choice, they can reason about edibility of foods, who is likely to eat which foods and expect those with shared social identity to share food preferences.40 This in turn constrains their food preferences to those of people who belong to the same social group as they do. This social-cognitive system may also help to explain the development of neophobia in the second year of life, where unfamiliar foods are likely to be rejected and significantly greater exposure is required to facilitate acceptance of new foods during this period than earlier in infancy.42,43 However, infants can generalize information about disgust from all people irrespective of their social identities or similarity to the infant, suggesting that infants distinguish between learning about people’s food preferences (which are often tied to culture and experience within specific social groups) and learning about potentially dangerous foodstuffs (which are likely to be universally disgusting).40 This leads us to conclude that the process of modeling eating behavior is far more complex, and potentially cognitively demanding, than it first appears. Another index of similarity that may be an active determinant of a model’s effectiveness is the age of the model. There certainly seems to be firm evidence that, for children, peers seem to be more effective models of eating than adults.18,21 Within this, there may also be some gender effects (e.g., same gender peers being more powerful models, girls in general being more powerful models; peer modeling being more effective for increasing new food acceptance in girls18,21,39) but these are not consistently found.34 There is also the issue of what really constitutes a peer; many studies assessing peer modeling use models who are slightly older than the target children, yet some studies have shown a greater likelihood of children modeling other’s behavior when they are of similar age.18 However, children are also more likely to model those who they perceive to be likely to have appropriate expertise, which may sometimes apply to older children. The idea that children make judgments about who should and should not be copied based on the model’s experience and expertise was confirmed

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in a study which demonstrated that young children judge adults to be knowledgeable about nutrition but not about toys and behave accordingly.44 Children may not think of adults as reliable sources of information about whether or not they will like a food, because adults are often encouraging children to eat foods not readily accepted or preferred by children (like green vegetables) rather than more palatable foods. Adult food choices and preferences are often rather different from those of children due to a combination of changes in taste perception, exposure, and experience, among other influences.45,46 Therefore children may quickly learn that adult recommendations (“Eat your broccoli- it’s delicious!”) are less likely to match their food preferences than the recommendations of other children (“YEUCH! Kale is the worst vegetable ever! But this donut is yummy!”) (Box 3.5).

INDIVIDUAL DIFFERENCES IN SUSCEPTIBILITY TO MODELING The previous section confirmed that there are a number of factors that affect whether or not a model is likely to be effective, but are there also differences in children’s ability or willingness to imitate other people’s behavior? The first, and most consistent, effect on ability to learn from a model is those of the age of the individual and their stage of development. At birth, human infants show very basic imitation of motor gestures, such as sticking out of the tongue, which are likely reflexive and temporary.47 This rapidly develops in the first year of life into the ability to observe, remember, and then reproduce observed behavior 24 h later.48 Indeed by 14–18 months, infants can imitate a model’s behavior 1 week later.49 Therefore by the middle of the second year of life, infants are able to encode their observations of other people’s eating behavior into memory and draw on these when they are next needed for making decisions about food acceptance or rejection. There has been little work examining the role of genetics in children’s susceptibility to modeling. Nonetheless, there are a number of individual differences in children that may affect children’s ability to pay attention to other people’s eating behavior, their ability or

BOX 3.5 Section Summary

• • •

The evidence for different effectiveness of familiarity with the model, age, race, or gender of the model, on child eating behavior, is not consistent. However, there seems to be strong evidence that even in infancy, humans are more likely to model others who are similar to themselves, and the language spoken may be an important indicator of this. Peers may be more effective models than adults in changing children’s eating behavior, perhaps because children don’t expect adults to be reliable experts when it comes to judging whether or not something will taste nice.

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willingness to imitate others, or the consequences of imitating other people’s ingestive behavior. For example, children with higher sensory sensitivity (lower thresholds for sensory registration and/or greater negative behavioral reaction to sensory stimuli) may be less likely to model parental fruit and vegetable intake. This may be because of unpleasant sensory effects of ingesting fruit or vegetables (such as bitter taste experience or dislike of texture).50 Neophobic adults are less likely to imitate a neophilic model’s eating behavior and the same is likely to apply to children,51 though to date this has not been tested. Children on the autistic spectrum may also be less likely to be influenced by peer or parent models. This may be because of their atypical social and communication skills, lesser attention to facial expressions, difficulty interpreting facial expression or other social cues, or differences in sensory processing.52–54 The potential effect of atypical social functioning on modeling of eating behavior is not limited to children on the autistic spectrum. In one intervention study, typically developing children who were less sociable were less likely to show improvements in interventions that included parental modeling of food intake.55 Whether this effect is caused by a reduced desire to please or affiliate with others, lower levels of attention to the behaviors of others, or other factors, is as yet unexplored. However, this leads to the suggestion that future research examining the effectiveness of interventions may need to take into account some of these individual differences, to enable appropriate tailoring or personalization of programs to individual differences to maximize effectiveness. In one of our studies, we examined whether how “food responsive” a child is made a difference in how effective parental modeling was at encouraging them to taste a new fruit.56 Food responsiveness is defined as a child’s tendency to eat more when tasty foods are available. In this study of 120 2- to 4-year-olds, we examined the relative effectiveness of three conditions on children’s willingness to taste a novel fruit as part of a typical meal. The conditions were (a) parental modeling of eating the new fruit, (b) parental physical prompting to eat (e.g., moving the food toward the child, holding the food up for the child to see, replacing the food onto the child’s plate when they remove it), and (c) a combination of prompting and modeling. The condition without parental modeling was least effective in facilitating children’s new fruit intake. Children who were high in food responsiveness showed greatest acceptance of the new fruit when parents were modeling and physically prompting them to eat. However, children with low food responsiveness had the greatest acceptance of new fruit when parents were modeling intake but not prompting children to eat. This finding is important because it illustrates that while modeling is often naturally combined with other feeding practices by parents when they are trying to introduce new foods,19 different combinations of modeling with other strategies may be needed to facilitate healthy food intake depending on the individual characteristics of the children concerned.

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There is also some suggestion that children who have baseline preferences or behavior that are most deviant from the main group will also show greater modeling of those around them. For example, a study of 238 Canadian preschoolers57 demonstrated that preschoolers whose eating behavior and physical activity was most different from their peers showed greatest change in those behaviors across a 9-month period, whereas children who showed little difference from their peers showed little change across this period. The changes that they underwent made them more similar to their peers across time. Of course, not all change is therefore positive; change will occur in the direction of the group norm and not necessarily in the direction of health. For example, in this study, children who had relatively high physical activity at the start of the year decreased their physical activity over time to be more in line with their peers and children consuming fewer calories than their peers at baseline consumed more over time. This is consistent with other studies that show the potential for negative modeling: for example, children with less healthy diets have parents who show more modeling of snacking.58 The implication for intervention and health promotion is that social norms may be particularly powerful tools for behavior change but the key is to ensure that the social norm, and therefore what is modeled, is healthy. There are also some other psychological differences that might make children more or less susceptible to the influence of others’ modeling of eating behavior. Bevelander and colleagues59 showed that children who have lower body esteem were more likely to conform to the amount of food a peer was consuming, modeled via video. Ten-year-old children were paired with trained, healthy weight, teenage confederates and were told that they would play a computer game with each other remotely. The confederates either ate nothing, a small amount (four pieces), or a large amount (fifteen pieces) of candy while playing the game and the children had access to the same candy. In fact, the confederates were actually video recorded, so that the behavior of the child participants didn’t influence the behavior of the model. When participants had low body esteem, they were more likely to consume high amounts of candy when the confederate also ate a large amount. When the confederate ate no candy, those children with low body esteem ate significantly less candy than other participants. This raises the importance of furthering our understanding of the power of modeling via television and social media and the potentially negative impact on the eating behavior of more psychologically vulnerable children. These findings are in accordance with the idea that people with lower self-esteem are more likely to match their food intake to those around them due to a need for social acceptance, affiliation, or affirmation of social bonds59,60. Indeed, a recent systematic review of experimental evidence on the influence of social modeling on food intake and choice using either a remote or live confederate suggested that the strongest experimental evidence was present for the effectiveness of models on people’s behavior when they want to affiliate with the model or think they are similar to the model36 (Box 3.6).

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BOX 3.6 Section Summary

• • • •

The age of the child, as well as individual differences in children’s sensory processing, sociability, or autistic spectrum disorder, affect the likelihood of children imitating a model. Children’s eating behavior traits, such as food responsiveness, may affect what other strategies might need to be combined with modeling for imitation to occur. Children whose behavior is most deviant from the model may be more likely to change their behavior. Children with lower body esteem may be particularly vulnerable to the effects of models on their eating behavior.

INTERVENTIONS BASED ON MODELING Given the potential power of modeling for influencing children’s eating, it is not surprising that there have been a number of studies that have examined the potential for modeling as an intervention. Galloway et al.61 recommended that parents should place less focus on their children’s picky eating and more on their own modeling of fruit and vegetable consumption and Wardle and Cooke62 suggested that food dislikes can be reduced or reversed by a combination of taste exposure and modeling. Indeed, a number of studies have generated support for these ideas. However, few studies have isolated modeling alone as an intervention separate from effects of exposure. Nonetheless, models appear to be powerful as one component of interventions to attempt to improve the quality of children’s diets. One program that includes modeling with exposure and reward is that of the Food Dudes: an intervention designed to improve children’s consumption of fruit and vegetables, based on Social Learning Theory.63–65 Bandura proposed that modeling is particularly effective if the target behavior is exhibited by someone who is liked or admired, if several others are also observed performing the behavior and that when the behavior is copied, it is also rewarded. The Food Dudes program uses animated videos of heroic peers who get their powers from fruit and vegetables. Children watch these videos in group settings, over a period of several days, and are given small rewards (e.g., stickers) when they taste some of, or consume a whole portion of, the target fruit and vegetables. Early evaluations of this program showed substantial and sustained impact on 4- to 11-year-olds’ intake of fruits and vegetables at lunchtime and in snacks63,65 but no significant effect on fruit and vegetable consumption outside of the school setting.63 Because the effects on consumption at school were almost immediate, in comparison to baseline measures the authors propose that this effect is driven by the modeling and reward components of the intervention rather

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than the effect of prolonged exposure65 (although exposure alone has been shown to have positive effects on many children’s food acceptance in other studies66). However, with this design it is not possible to evaluate the potentially separate effects of modeling and reward components of the intervention to determine their relative efficacy. A recent review of the literature evaluating the Food Dudes program in primary school children concluded that the program is effective in the short term and within the school setting.67 However, evidence regarding long-term impact (more than 12 months after intervention), generalization to the home setting, and the ability of the program to displace unhealthy snacks from children’s diets requires further research.67 Laureati et al.35 showed positive effects of the Food Dudes program in Italian 6- to 9-year-olds on short-term fruit and vegetable acceptance as well as significant reductions in neophobia, which persisted across time, suggesting that the effects of this combined intervention may not just be specific to the target foods included but may improve children’s general willingness to try new foods in the longer term. Similar programs have also been targeted at preschoolers and show promising effects: for example, Horne et al.64 demonstrated a threefold increase in fruit and vegetable consumption in a small group of 2- to 4-year-olds who were exposed to the video peer modeling plus reward program, which was sustained 6 months after the rewards were withdrawn. Nonetheless, longer term evaluation of the program is still needed to fully evaluate the effectiveness of the program on clinically significant behavior change and studies examining the critical active ingredients of the program are warranted. A further intervention study, this time examining the effectiveness of parental modeling rather than peer modeling, on 2- to 4-year-old children’s consumption of a disliked vegetable was conducted.55 Holley et al. examined the effectiveness of four different home-based interventions: two included a modeling component: modeling with repeated exposure, and modeling with rewards and repeated exposure. In these conditions, parents were asked to eat a small piece of the target vegetable and say something positive about it. Comparison interventions were repeated exposure alone, or rewards and repeated exposure. Unfortunately, there was no modeling alone condition. Nonetheless, after 14 days of daily offering of a small piece of disliked vegetable, outside of a mealtime, significant increases in consumption and, importantly, in vegetable liking, were seen in two conditions: the modeling, rewards and exposure, as well as rewards and repeated exposure alone. This suggests that parental modeling of food consumption may need to be paired with small nonfood rewards for child eating behavior to maximize its potential to improve intake which mirrors the effective interventions delivered by peer modeling programs such as Food Dudes (Box 3.7).

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BOX 3.7 Section Summary

• • •

There are promising peer- and parent-based modeling interventions designed to improve the quality of children’s diets. Programs that combine modeling with repeated exposure and small nonfood rewards for trying new fruits and vegetables have positive effects on children’s eating behavior. We need more research to examine the influence of modeling alone as a long-term intervention, as well as how long-lasting and generalizable the effects of the existing modeling interventions are.

CONCLUSION AND IMPLICATIONS In this chapter, we have seen that modeling is an important mechanism by which children learn about what, and what not to eat, by observing the behavior of others. We have seen that humans as a species are particularly adapted for this process and are sophisticated in their use of social information to determine what and who to model. Even as very young children, we pay close attention to what is being eaten, the emotional reactions of the model, the characteristics of the model and look for social clues that the model is “like us,” to help to decide whether to copy their eating behavior choices. Peers may be more effective than adults in modeling eating behavior for children, in part because adults might not be considered as trustworthy experts concerning how pleasant foods will taste. Modeling is effective in promoting novel food choice and tasting, in the amount of food consumed as well as in changing preferences for foods. There may be some foods that are always going to be particularly challenging as targets for modeling interventions, such as low energy dense vegetables. There are also developmental changes in our willingness and ability to copy other’s behavior and there are individual differences in weight status, food responsiveness, body esteem, sensory processing, sociability, and also autistic spectrum disorder that are likely to affect how readily we imitate the behavior of others and will affect how successful modeling is likely to be. So, how we can best use our understanding of the power of modeling to develop both prevention and intervention programs that can improve the nutritional status of infants and children? We have seen that programs using peers or parents as models, which combine modeling with repeated exposure and small nonfood rewards for trying new foods, have positive effects on children’s eating behavior. Given that we have seen that the model does not need to be physically present to be effective, we must think of creative ways to use this power at a public health level, perhaps using gamification, social media, or formal marketing of healthy foods incorporating modeling. We must also think about the extent to which children are exposed to unhealthy eating models and also to models of food refusal given the detrimental effect of negative modeling.

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What is the “take home” message for parents and families? We already know that parents can make good models (even if peers can do better). There is a large literature that demonstrates the importance of parental modeling for the quality of children’s diets. For example, there are strong relationships between parent and child fruit and vegetable consumption.68 Many studies using parent report of their modeling of healthy food intake have shown a relationship with children’s better fruit and vegetable consumption69–71 but also lower fat and lower sugar food preferences.72 A recent systematic review and metaanalysis of the influence of parental practices on child eating behaviors concluded that parental modeling and food availability were the two primary correlates of children’s healthy (fruit and vegetable) and unhealthy (sugar sweetened beverage) consumption.73 The authors highlight that parental modeling can also convey attitudes, social norms, and self-efficacy beliefs to children which may underpin their consumption behavior. However, the majority of studies of the influence of parental modeling are not experiments and are therefore unable to unpack the potential multifarious influences on child eating that may feature in the home, broader social environment, and interpersonal interactions between family members where parents regularly model healthy eating. Much of this literature is actually also documenting maternal, rather than paternal, influence: even if studies are reporting on parenting, they are typically focused on the mother as primary caregiver and further work is needed to attempt to disentangle the potentially different roles of mothers, fathers, and other carers in the modeling of eating behavior. Nonetheless, family mealtimes in which parents demonstrate enthusiastic modeling of healthy food intake are a key component in improving children’s nutritional status. In conclusion, there is much research that we have yet to do to develop our understanding of how we can employ modeling to our advantage. For example, we need to identify how modeling is best used for targeted or personalized intervention depending on individual differences, identify the active ingredients of modeling interventions, and to identify the long-term, generalizable effects of such interventions. Nonetheless, social learning about what and how much to eat is a powerful influence on our eating behavior, which we can harness to improve children’s diets, their health, and their well-being.

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34. Birch LL. Effects of peer Models’ food choices and eating behaviors on preschoolers’ food preferences. Child Dev 1980;51:489–96. 35. Laureati M, Bergamaschi V, Pagliarini E. School-based intervention with children. Peer-modeling, reward and repeated exposure reduce food neophobia and increase liking of fruits and vegetables. Appetite 2014;83:26–32. 36. Cruwys T, Bevelander KE, Hermans RCJ. Social modeling of eating: a review of when and why social influence affects food intake and choice. Appetite 2015;86:3–18. 37. Houldcroft L, Farrow C, Haycraft E. Perceptions of parental pressure to eat and eating behaviours in preadolescents: the mediating role of anxiety. Appetite 2014;80:61–9. 38. Shutts K, Kinzler KD, McKee CB, Spelke ES. Social information guides infants’ selection of foods. J Cogn Dev 2009;10(1–2):1–17. 39. Shutts K, Banaji MR, Spelke ES. Social categories guide young children’s preferences for novel objects. Dev Sci 2010;13(4):599–610. 40. Liberman Z, Woodward AL, Sullivan KR, Kinzler KD. Early emerging system for reasoning about the social nature of food. Proc Natl Acad Sci U S A 2016;113(34):9480–5. 41. Kraak VI, Story M. Influence of food companies’ brand mascots and entertainment companies’ cartoon media characters on children’s diet and health: a systematic review and research needs. Obes Rev 2015;16 (2):107–26. 42. Birch LL, Marlin DW. I don’t like it; I never tried it: effects of exposure on two-year-old children’s food preferences. Appetite 1982;3(4):353–60. 43. Dovey TM, Staples PA, Gibson EL, Halford JCG. Food neophobia and ’picky/fussy’ eating in children: a review. Appetite 2008;50(2–3):181–93. 44. Vanderborght M, Jaswal VK. Who knows best? Preschoolers sometimes prefer child informants over adult informants. Infant Child Dev 2009;18(1):61–71. 45. Cowart BJ. Development of taste perception in humans: sensitivity and preference throughout the life span. Psychol Bull 1981;90(1):43–73. 46. Nicklaus S. The role of food experiences during early childhood in food pleasure learning. Appetite 2016;104:3–9. 47. Meltzoff AN, Moore MK. Imitation in newborn infants: exploring the range of gestures imitated and the underlying mechanisms. Dev Psychol 1989;25(6):954–62. 48. Meltzoff AN. Infant imitation and memory: nine-month-olds in immediate and deferred tests. Child Dev 1988;59(1):217–25. 49. Meltzoff AN. Infant imitation after a 1-week delay: long-term memory for novel acts and multiple stimuli. Dev Psychol 1988;24(4):470–6. 50. Coulthard H, Blissett J. Fruit and vegetable consumption in children and their mothers. Moderating effects of child sensory sensitivity. Appetite 2009;52(2):410–5. 51. Hobden K, Pliner P. Effects of a model on food neophobia in humans. Appetite 1995;25:101–14. 52. Lane AE, Young RL, Baker AEZ, Angley MT. Sensory processing subtypes in autism: association with adaptive behavior. J Autism Dev Disord 2010;40(1):112–22. 53. Leekam SR, Nieto C, Libby SJ, Wing L, Gould J. Describing the sensory abnormalities of children and adults with autism. J Autism Dev Disord 2007;37(5):894–910. 54. Lindner JL, Rosen LA. Decoding of emotion through facial expression, prosody and verbal content in children and adolescents with asperger’s syndrome. J Autism Dev Disord 2006;36(6):769–77. 55. Holley CE, Farrow C, Haycraft E. Investigating the role of parent and child characteristics in healthy eating intervention outcomes. Appetite 2016;105:291–7. 56. Blissett J, Bennett C, Fogel A, Harris G, Higgs S. Parental modelling and prompting effects on acceptance of a novel fruit in 2–4-year-old children are dependent on children’s food responsiveness. Br J Nutr 2016;115(3):554–64. 57. Ward S, Belanger M, Donovan D, et al. “Monkey see, monkey do”: peers’ behaviors predict preschoolers’ physical activity and dietary intake in childcare centers. Prev Med 2017;97:33–9. 58. Hendy HM, Williams KE, Camise TS, Eckman N, Hedemann A. The parent mealtime action scale (PMAS). Development and association with children’s diet and weight. Appetite 2009;52(2):328–39. 59. Bevelander KE, Anschutz DJ, Creemers DHM, Kleinjan M, Engels RCME. The role of explicit and implicit self-esteem in peer modeling of palatable food intake: a study on social media interaction among youngsters. PLoS One 2013;8(8):e72481.

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60. Robinson E, Tobias T, Shaw L, Freeman E, Higgs S. Social matching of food intake and the need for social acceptance. Appetite 2011;56(3):747–52. 61. Galloway AT, Fiorito L, Lee Y, Birch LL. Parental pressure, dietary patterns, and weight status among girls who are "picky eaters". J Am Diet Assoc 2005;105(4):541–8. 62. Wardle J, Cooke L. Genetic and environmental determinants of children’s food preferences. Br J Nutr 2008;99:S15–21. 63. Horne PJ, Tapper K, Lowe CF, Hardman CA, Jackson MC, Woolner J. Increasing children’s fruit and vegetable consumption: a peer-modelling and rewards-based intervention. Eur J Clin Nutr 2004;58 (12):1649–60. 64. Horne PJ, Greenhalgh J, Erjavec M, Lowe CF, Viktor S, Whitaker CJ. Increasing pre-school children’s consumption of fruit and vegetables. A modelling and rewards intervention. Appetite 2011;56 (2):375–85. 65. Lowe CF, Horne PJ, Tapper K, Bowdery M, Egerton C. Effects of a peer modelling and rewards-based intervention to increase fruit and vegetable consumption in children. Eur J Clin Nutr 2004;58(3):510–22. 66. Wardle J, Herrera M, Cooke L, Gibson EL. Modifying children’s food preferences: the effects of exposure and reward on acceptance of an unfamiliar vegetable. Eur J Clin Nutr 2003;57(2):341–8. 67. Taylor C, Upton P, Upton D. Increasing primary school children’s fruit and vegetable consumption a review of the food dudes programme. Health Educ 2015;115(2):178–96. 68. Wyse R, Campbell E, Nathan N, Wolfenden L. Associations between characteristics of the home food environment and fruit and vegetable intake in preschool children: a cross-sectional study. BMC Public Health 2011;11:938. 69. Granner ML, Evans AE. Variables associated with fruit and vegetable intake in adolescents. Am J Health Behav 2011;35(5):591–602. 70. Pearson N, Biddle SJH, Gorely T. Family correlates of fruit and vegetable consumption in children and adolescents: a systematic review. Public Health Nutr 2009;12(2):267–83. 71. Wind M, de Bourdeaudhuij I, te Velde SJ, et al. Correlates of fruit and vegetable consumption among 11-year-old belgian-flemish and dutch schoolchildren. J Nutr Educ Behav 2006;38(4):211–21. 72. Vollmer RL, Baietto J. Practices and preferences: exploring the relationships between food-related parenting practices and child food preferences for high fat and/or sugar foods, fruits, and vegetables. Appetite 2017;113:134–40. 73. Yee AZH, Lwin MO, Ho SS. The influence of parental practices on child promotive and preventive food consumption behaviors: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 2017;14 (1):47.

CHAPTER 4

Children’s Challenging Eating Behaviors: Picky Eating, Food Neophobia, and Food Selectivity Susan L. Johnson*, Kameron J. Moding*, Laura L. Bellows† *

Department of Pediatrics/Section of Nutrition, University of Colorado Anschutz Medical Campus, Aurora, CO, United States Department of Food Science and Human Nutrition, Colorado State University, Fort Collins, CO, United States



INTRODUCTION Eating, for most individuals, is associated with satisfaction, pleasure, and reward and, at the least, sufficient reinforcement to maintain the behavior to support life. Nicklaus (2016) asserts that pleasure is central to eating, particularly so for young children as cognitions related to food and eating are less developed.1 However, the extent to which people experience eating as reinforcing varies considerably among individuals and across stages of human development. In particular, the early childhood period is perceived to be vulnerable to the precipitous onset of challenging eating behaviors, including picky eating, food neophobia, and food selectivity. To some extent, these behaviors are present for most typically developing children; and yet, for some, they manifest to a far greater degree and can be associated with significant negative health and growth outcomes.2 Therefore it is important to consider these behaviors both from the standpoint of individual variation in “traits” of eating behaviors (e.g., the eating-related temperaments associated with children’s reactions to novel foods) but also to consider the point at which “typical” eating behaviors (e.g., the pickiness of early childhood) differentiate from detrimental, perhaps pathological, eating behaviors like extremes in food selectivity that have implications for growth and health. In this chapter, we will consider three major concepts related to children’s challenging eating behaviors: (1) picky/fussy eating; (2) food neophobia; and (3) food selectivity, with a focus on sensory sensitivity and its relation to eating behavior. For each category, definitions and prevalence estimates will be provided in an attempt to distinguish each from the others; however, it is acknowledged that these distinctions have not been consistently made in the literature and that considerable overlap exists in how these terms have been used previously. We provide an overview of measures that have been used either clinically or in research studies to diagnose or determine the degree to which individuals exhibit these traits. Factors associated with these behaviors are reviewed with the intent

Pediatric Food Preferences and Eating Behaviors https://doi.org/10.1016/B978-0-12-811716-3.00004-X

© 2018 Elsevier Inc. All rights reserved.

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of providing insights into mediating influences on their expression. Last, we discuss outcomes linked to each behavior to provide a relative evaluation of their importance and impacts on children’s health.

PICKY EATING Picky eating is recognized as a normal developmental phase which begins in early childhood.3 However, there is not a clear and consistent definition of picky or fussy eating. Rather, a range of definitions exist that include the following key characteristics: consuming a limited type and amount of foods, unwillingness to try new foods (food neophobia), and rejecting foods based on certain sensory characteristics or textures.4–6 Pickiness has been shown to be associated with a lower number and lower variety of foods consumed, raising questions regarding the nutritional adequacy of picky eaters’ diets.7 While food neophobia is a part of picky eating, it does not fully account for picky eating behaviors. Picky eating extends into children’s rejection of foods based on the appearance, aroma, feel, or flavor of foods.4,6 Boquin et al. (2014) conclude that because picky eating does not have a clear definition it may be more of an overarching term for a variety of characteristics or behaviors perceived by parents.4

MEASUREMENT AND PREVALENCE Children are typically classified as picky eaters based solely on parents’ reported perceptions or from analyses of food records. In addition to displaying food neophobia, consuming a limited variety of foods, and exhibiting sensory aversions, parents have identified additional characteristics of picky eaters to include: rigid behaviors regarding foods, disinterest or lack of focus during mealtimes, slow rate of eating, less enjoyment of foods, requests for specific food preparation methods, expression of strong food dislikes, and active avoidance of mealtime or eating altogether.4,7,8 Conversely, parents describe nonpicky eaters as children who enjoy eating, have little hesitation about eating, and are nonconfrontational and cooperative about the whole mealtime process.4 Prevalence rates of picky eating vary across studies and range between 14 and 60% in preschool-aged children and 7%–27% in later childhood.5,9,10 Variation in measurement and age at assessment are likely contributors to differences in prevalence rates.11 Further, as children get older and are increasingly able to verbalize their dislikes, parents’ perception of their child’s pickiness may increase.6 The onset and remittance, or disappearance, of picky eating behaviors also varies across children. The highest prevalence is thought to occur around age 2 years and is suggested to begin declining by 4 or 5 years of age, but may persist into adulthood.9,11 Picky eating remittance occurs with the majority of children, suggesting that picky eating is a transient behavior and part of typical

Children's Challenging Eating Behaviors: Picky Eating, Food Neophobia, and Food Selectivity

development.10 However, nearly 20% of children between 8 and 12 years are considered to be picky eaters, when defined by low variety in dietary intake.6,12 While picky eating is most often associated with early childhood, a study by Kauer (2015) reported that one-third of adults self-identified as picky eaters.13 Adults were reported to display behaviors similar to children, including consuming a limited variety of foods; a dislike for certain food groups; strong, physical reactions to food (i.e., gagging or disgust sensitivity); and enhanced taste, texture, and odor sensitivity.13 Some adults describe their picky eating as part of their identity and report difficulties and anxiety over social eating occasions.9,13 Adult picky eaters report that their eating difficulties have existed since childhood.9 Consistent with not having a clear definition, measurement of picky eating is also inconsistent. General questionnaires on children’s food habits often include a scale measuring picky eating. The most frequently used validated parent survey is the food fussiness scale of the Child Eating Behavior Questionnaire.14 In six items, parents rate the occurrence of their child’s specific eating behaviors on a Likert scale from 1 (Never) to 5 (Always). Sample items include “My child refuses new foods at first” and “My child enjoys a wide variety of foods” (reverse coded) with higher scores indicating more food fussiness. The Child Feeding Questionnaire includes three items from a Pickiness subscale; the questions focus on lack of variety, food neophobia, and pickiness.15,16 The Children’s Eating Behavior Inventory (CEBI) is a caregiver report for evaluating mealtime behaviors and eating behaviors.17 Lastly, the Child Behavior Checklist asks mothers to indicate whether their child “does not eat well” and “refuses to eat” on a 3-point Likert scale (1—Not all applicable, 2—Sometimes, and 3—Often applicable).18 Beyond these surveys, other studies have asked a single question of parents: “Is your child a picky eater?” with 5 response options (1—Never; 2—Rarely, 3—Sometimes, 4—Often, and 5—Always).8,19,20 These distinct measures reflect the complexity of picky eating and lack of consistency in defining picky eating. This overlap and redundancy in measures suggests a need for an agreed upon and validated picky eating instrument to distinguish picky eating from food neophobia and food selectivity and to further this area of research.

FACTORS ASSOCIATED WITH PICKY EATING A number of child and family characteristics are associated with picky eating. One study found that infants who are exclusively breastfed and introduced to complementary foods after 6 months of age had lower odds of picky eating in their preschool years;21 though at least two other studies have suggested that earlier introduction to complementary foods and food variety may also improve food acceptance.22–24 Birth order and having siblings have also been reported to be protective against picky eating, with older siblings proposed to serve as positive role models to younger siblings.10,25 Both child and maternal

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temperament, particularly high levels of child emotionality and maternal negative affectivity, are associated with increased risk of picky eating.25 Further, family characteristics such as younger maternal age, lower socioeconomic status, and children with nonWestern backgrounds also have been associated with picky eating.10,25 Maternal feeding practices and mothers’ own eating behaviors can either hinder or promote the development of healthful eating behaviors. Parental use of controlling practices, such as restriction of unhealthy foods and pressure to eat healthy foods, exemplifies how parents can shape children’s eating behaviors.20 The use of parental pressure to eat has been shown to reduce children’s liking and intake of a particular food.26 Parents may use pressure due to their belief that being a successful parent is being able to see their child eat ‘enough.’27 Parental role modeling can have both positive and negative consequences. Positively, maternal modeling of fruit and vegetable consumption has been shown to be associated with children’s fruit and vegetable intake.16 Conversely, if mothers have not tasted a food or they exhibit their own picky eating behaviors, children are likely to be less willing to try the food.28 Mothers who exhibit high levels of external eating (i.e., eating in the absence of hunger) engage in more attempts to control their child’s diet, perhaps leading to their child’s picky eating.29 Lastly, the availability of foods in the home environment has a positive relationship with children’s dietary intake, especially for fruits and vegetables.16,30,31 Picky eating is associated with parental stress and anxiety and has consistently been associated with interference in family functioning.8,12 Picky eating can interfere with daily routines and can be problematic to the parent, child, or parent-child relationship.5,32,33 Often parents worry about the nutritional adequacy of their child’s diet and are largely concerned about their child’s rejection of healthy foods like fruits and vegetables. Parents of picky eaters report more struggles, more concessions related to food and eating, and negative affect.7,25 Further, because of concern over the amount of food consumed by the child, or lack thereof, parents may use less than desirable strategies to get their child to eat the ‘right’ amount or type of food.34 These include hiding or disguising the food; negotiating with the picky eater; using pressure to eat, rewards, or coercion; and preparing separate meals.9 Parental feeding practices and behaviors are potentially modifiable correlates with child feeding behaviors and should be considered as targets of interventions aimed at promoting healthy eating behaviors in children.

OUTCOMES ASSOCIATED WITH PICKY EATING While it is evident that some degree of picky eating is part of normal child development, the impact of picky eating on diet, growth, and health outcomes is less clear. Consistently, studies have found that picky eaters consume less dietary variety (i.e., fewer number of accepted food items) than nonpicky eaters.5 Current research indicates that some picky eaters have lower energy intakes than nonpicky eaters.9,11,35 However, both picky and

Children's Challenging Eating Behaviors: Picky Eating, Food Neophobia, and Food Selectivity

nonpicky eaters do not meet dietary recommendations for nutrient-rich foods like fruits, vegetables, whole grains, and meats.26 Comparatively, picky eaters do have lower vegetable intake than nonpicky eaters,20,26,35 and some studies have reported lower intakes of fish and meat.11,35,36 It has been posited that picky eaters compensate with higher intakes of sweets and snacks, but study findings are inconsistent.11,20,26 The impact of picky eating on growth and weight status is unclear. In children ages 2–5 years, Dubois et al. (2007) found that picky eaters were twice as likely to be underweight at age 4.5 years compared to nonpicky eaters.36 Xue et al. (2015), in a sample of Chinese 7- to 12-year-olds, found that picky eating was associated with lower height, weight, and BMI and concluded that the effects are more pronounced with longer periods of picky eating.35 Conversely, in a longitudinal study following children from age 2 to 11 years, Mascola et al. (2010) reported no significant associations of picky eating on growth.8 Similarly, results from the KOALA birth cohort study in the Netherlands (ages 5–9 years) found no increased risk of becoming underweight at age 9 years for picky eaters. However, picky eaters were less likely to become overweight.37 These inconsistencies in study findings may be due, again, to the age of the children studied. Further research on the effect of picky eating on growth trajectories during early childhood through adolescence is needed. The relation between picky eating and other childhood health outcomes is not well known at this time. One study has reported increased prevalence of constipation associated with low fruit and vegetable intake.11 Though the relation between diet and gut health is frequently discussed, no relevant studies were found that reported impacts of picky eating on other gastrointestinal disturbances, immunity, or the microbiome.38 Overall, there is some evidence that children who are picky eaters are likely to consume fewer calories, weigh less, display behavioral problems in later childhood, and, according to some reports, picky eating has been associated with the development of anorexia nervosa and dieting in adolescence.8,39 Regardless, picky eating is a significant concern for parents and they frequently struggle to achieve what they believe is healthful eating for their children (Box 4.1).

BOX 4.1

• • •

Picky eating lacks consistent definition but has been characterized by a consumption of a limited variety of foods, food neophobia, and rejection of new foods. Picky eating is thought to peak around 2 years and starts to decline by 4 or 5 years of age, but may persist into adulthood, reflecting both normative shifts in development and individual differences in genetic and environmental influences. Picky eaters have been found to consume a reduced variety of foods, fewer calories, weigh less, and display greater behavioral problems in later childhood.

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FOOD NEOPHOBIA Food neophobia is the tendency to avoid or refuse novel or unknown foods.6 Since food neophobia focuses exclusively on foods that are new, it falls under the broader category of picky eating, which encompasses rejection of a wider variety of foods, both familiar and novel. The concept of food neophobia originated from research with nonhuman omnivores, such as rats and monkeys. It is thought that these animals do not readily accept new foods to prevent the ingestion of potentially toxic substances.40 This survival mechanism is also present in humans and potentially serves the same adaptive function.3,6,40,41 Following this line of reasoning, many define food neophobia as refusal or rejection behaviors occurring before a novel food enters the mouth. At the point where foods enters the mouth, wariness to taste the food has been surmounted and the survival mechanism has served its purpose. Rejection of tastes is believed to be outside the scope of food neophobia.6

MEASUREMENT AND PREVALENCE Food neophobia can be assessed in children using both questionnaires and observational measures, although questionnaires are more frequently used. A recent review identified seven instruments designed to measure food neophobia or its converse, willingness to try new foods, in children.42 Of the measures identified, the validated parent-reported Child Food Neophobia Scale (CFNS),43 adapted from the Food Neophobia Scale for adults,44 is the most commonly used instrument. A handful of other assessments can also be used to capture children’s self-reports of their own neophobia, but a limitation of these measures is they can only be used with older children (ages >5 years). Additionally, food neophobia can be assessed via observational paradigms.43,45–47 In these tasks, an experimenter presents a child with a variety of foods, both familiar and novel, and asks the child to select which foods they would be willing to taste. A willingness ratio is subsequently calculated as the relative ratio of reported willingness to taste novel versus familiar food items.45 However, it is important to note that a limited number of studies include a follow-up behavioral component during which children’s decisions are validated by their behaviors when they are prompted to taste the foods.46,47 Food neophobia is present in an estimated 40%–44% of young children.48,49 Furthermore, individual differences in food neophobia exist and the construct has been conceptualized as a personality trait that falls along a continuum. Neophobic individuals, who tend to dislike or avoid new foods, fall on one side of the spectrum, whereas neophilic individuals, who tend to like and approach new foods, fall on the other.45 Typically, children across the continuum can learn to overcome wariness to try a new food through multiple, positive experiences with that food over time; however, this may require as

Children's Challenging Eating Behaviors: Picky Eating, Food Neophobia, and Food Selectivity

many as 8–12 exposures to achieve increased acceptance.50–53 Additionally, positive social modeling, by parents and peers, can typically increase children’s willingness to try new foods.54–56 Therefore experts suggest that if a child’s wariness to try a new food subsides after repeated exposures or the presence of positive social influences, it should be considered a normal part of child development.55 Conversely, substantial deviations from these normative patterns may warrant professional intervention.55 Food neophobia or “fear” of novel foods emerges during the latter part of the first year of life.6,57 The advent of food neophobia coincides with the emergence of general fear,58 as well as increases in self-locomotion and independence.3 Before this time, it is considered adaptive to have minimal neophobic tendencies in order to facilitate the introduction of solid foods and because caregivers can closely monitor their infants’ food consumption.59 Subsequently, food neophobic tendencies increase as infants become more mobile and independent, characteristics which, taken together, increase the likelihood of ingesting harmful substances while away from the supervision of the caregiver.57 After infancy, neophobia is said to increase sharply, reach a peak during early childhood (between 2 and 6 years of age), and then decline during later childhood and adolescence.6,54,60 Although this developmental pattern of neophobia is widely reported in the literature, it is important to note that there is a lack of longitudinal studies following the trajectory of food neophobia from infancy to toddlerhood and beyond making assertions about the developmental path of neophobia somewhat speculative.

FACTORS ASSOCIATED WITH FOOD NEOPHOBIA Individual differences in child food neophobia have been linked to both intrinsic and extrinsic factors.41 Food neophobia is highly heritable, with estimates of heritability ranging from 72% to 78% in children.48,61 Emerging evidence also suggests that food neophobia is related to child temperament or personality. Aspects of temperament, such as negative emotionality and shyness,45 as well as low levels of approach to novelty,60 have been associated with greater levels of food neophobia in children. Results from these studies suggest that when presented with novel foods, children’s behaviors are quite consistent with their responses to other unfamiliar stimuli, such as new people, new toys, or new situations.45,47 Additionally, recent evidence has revealed links between food neophobia and children’s sensitivity to tastes and smells, as well as a lower enjoyment of tactile play.49,62,63 Taken together, these findings suggest that children with sensory sensitivities may have greater levels of food neophobia and that resistance to trying new foods may be, in part, biologically determined. The primary external factors associated with child food neophobia include parent variables, such as parent food neophobia and feeding practices. For example, highly neophobic parents also tend to have highly neophobic children.16,45,64 This association could be

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due to the genetic factors described previously48,61 or the variety of foods purchased, made available, and offered in the home. Caregivers’ food preferences, including avoidance of new foods, have been related to the variety of vegetables and other healthy foods they offer, which subsequently is associated with children’s levels of food neophobia.16,48,64 In addition to food availability, child feeding practices, such as pressuring children to eat and modeling consumption of novel foods, have been related to child food neophobia. Specifically, a greater use of pressuring feeding practices, such as trying to get a child to eat when he/she is not hungry, has been associated with greater levels of food neophobia,65 whereas modeling consumption of novel foods has been linked to children’s greater willingness to try new foods.54,56 Additional evidence suggests that subtle physical prompts may increase the likelihood that children will try a new food in the moment.66

OUTCOMES ASSOCIATED WITH NEOPHOBIA Children’s food neophobia is a concern because it has been associated with the development of their food preferences and dietary patterns. In particular, food neophobia has been linked to poor dietary outcomes in young children, such as a reduced preference for all food groups, less dietary variety, and lower consumption and liking of vegetables.6,16,67–69 In toddlers, greater levels of neophobia have been associated with lower consumption of a variety of fruits and vegetables, as well as a greater proportion of energy coming from discretionary foods.59 Although research on neophobia in adolescence is limited, one study in Germany revealed a link between greater levels of food neophobia and reduced protein intakes; however, the overall level of food neophobia in the sample was low, which is expected in adolescents and could have contributed to the lack of significant associations observed between food neophobia and other dietary patterns.70 Despite consistent evidence for associations between food neophobia and dietary patterns across childhood, the majority of studies have failed to find a link between child food neophobia and weight status. In a recent systematic review, six out of seven studies examining possible links between food neophobia and weight status found no significant associations.65 However, one study revealed a positive association between food neophobia and weight status in young children (ages 2–6 years), such that children with overweight or obesity were more likely to be neophobic.71 Notably, all studies identified in this systematic review used the parent-reported CFNS to assess children’s willingness to try new foods.65 Additional work is needed to confirm the lack of an association between food neophobia and weight status when other measures of neophobia are used, such as children’s self-reports or observational measures. Further, longitudinal designs with adequate sample sizes and analyses to identify potential associations with both under- and overweight should be undertaken (Box 4.2).

Children's Challenging Eating Behaviors: Picky Eating, Food Neophobia, and Food Selectivity

BOX 4.2

• • •

Food neophobia is the tendency to avoid or refuse novel or unknown foods that emerges in early childhood and peaks during the preschool years. Neophobia varies across individuals and is viewed as a personality trait. Food neophobia is highly heritable and is related to other aspects of child temperament including shyness, negative emotionality, and sensory sensitivity. Food neophobia has been associated with reduced dietary variety, low fruit and vegetable intake, and greater intake of discretionary foods, but does not show consistent associations with weight.

FOOD SELECTIVITY Normal feeding and eating patterns require an integration of many functions (e.g., the occurrence and integration of sensory behaviors, motor skills, and cognitions) as well as social–emotional aspects related to eating and feeding (e.g., various parenting practices, the tone of the feeding and mealtime environment, and sibling/peer influences on children’s eating behaviors).72 Perturbations in any of these areas can result in feeding and eating challenges and a range of dysfunctionality can be observed in children’s eating. In the scope of children’s food acceptance patterns, food selectivity represents a behavior that tends toward the extreme and is generally cast as a maladaptive behavior which can result in deviations in food and nutrient intakes and, therefore, has the potential to impact growth and health.73,74 Food selectivity, though often included under the umbrella term of “picky eating,” manifests more extremely. It includes more severe limitations in food acceptance (i.e., restricted food variety) and, according to some definitions, also includes limitations in energy intake.75 As noted previously, prevalence of feeding and eating difficulties in infants and children varies widely (15%–90%, depending upon the source).5,76 Eating a restricted and unpredictable number of foods is common during toddlerhood and has been reported to persist up to 8 years of age in about one-third of the general population.74 As with picky eating and food neophobia, the definition and usage of the term “food selectivity” have not been used consistently in the literature, though Bandini and colleagues (2010) operationalized it to include: (1) food refusal, (2) limited food repertoire, and (3) high-frequency single food intake (HFSFI) when they examined children’s dietary data to determine the prevalence of food selectivity and its relation to dietary adequacy and quality in their study.77 Other definitions incorporate behaviors like selective intake of certain food categories (e.g., high intake carbohydrate foods and avoidance of vegetables); food refusals; aversions to certain tastes, textures, and smells; slow rate of eating; and having 10 foods accepted over the course of at least 2 years.39,78–80

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Perhaps most compelling is the stipulation that food selectivity has negative impacts on social and emotional development, as evidenced by parent reports of frequent struggles about food and eating and a disruption in the pleasantness of family meals for both the child and the rest of the family.81 When extreme, food selectivity may hamper the child’s ability to function in a variety of social settings, including eating at school or with peers. Food selectivity should be considered more serious than picky eating, and should precipitate further evaluation of dietary adequacy and overall health. An important characteristic related to food selectivity, and often comingled in its definition, is sensory sensitivity. Sensory sensitivity focuses on individual differences in perceptions, reactivity to, and integration of sensory information.82 As pertaining to food selectivity, individuals differ in how they perceive taste (e.g., bitterness), touch/tactile sensations (e.g., sliminess, texture of foods) odors, and perhaps visual stimulation.78 Because eating requires the integration of each of these features, sensory sensitivity and the related sensory integration disorders are intuitively related to food selectivity. Both food selectivity and sensory sensitivity are associated with developmental disabilities (e.g., ASD) but the presence of disability is not a requisite feature of food selectivity. The developmental trajectory of food selectivity has been investigated in one crosssectional study and has been reported to improve with age.83 Because selective eating often has not been well distinguished from pickiness or neophobia, and because it has only recently begun to be separately defined, few data exist that follow it over the course of childhood and into adulthood. Clearly, some degree of food selectivity is common among toddlers, and it can persist into middle childhood and adolescence in some children.74 In rare cases, food selectivity is associated with Avoidant Restrictive Food Intake Disorder and though medical history may or may not identify comorbidities associated with this disorder, these cases call for substantially more detailed queries regarding presence of a medical condition, mood or anxiety disorder, ASD or other developmental disability, food allergies, gastrointestinal disturbances, difficulty swallowing, or sensory issues.84 Food selectivity, as defined by Bandini and colleagues (2010) likely persists into childhood and adolescence, unless specifically treated with behavioral therapies.77

MEASUREMENT OF FOOD SELECTIVITY Food selectivity is assessed in children using questionnaires for research studies, interviews in clinical encounters, and in some studies, by analyses of dietary intake. Cermak and colleagues reviewed studies reporting measurements of food selectivity, sensory sensitivity, and dietary intake. Measures included informal parent interviews, medical record reviews, and parent questionnaires to determine information about children’s eating behaviors.85 Diet records, recalls, and food frequencies were examined to quantify impacts of food selectivity on dietary intake and nutritional status. The most frequently used validated parent survey is the food fussiness scale of the Child Eating Behavior

Children's Challenging Eating Behaviors: Picky Eating, Food Neophobia, and Food Selectivity

Questionnaire, which has been discussed earlier.14 The Children’s Eating Behavior Inventory (CEBI) is a caregiver report for evaluating mealtime behaviors and eating behaviors. The CEBI measures the frequency of 19 different eating behaviors, utilizing a 5-point rating scale (Never, Seldom, Sometimes, Often, Always) and asks the extent to which these behaviors present a problem for the family.17 Reliability (test-retest) for the total eating problem score (r ¼ 0.87), internal consistency (generally α > 0.70 except for single parents with >2 children; α ¼ 0.58), and construct validity have been determined to be acceptable. Last, The Preschool Age Psychiatric Assessment (PAPA)86 is a semistructured psychiatric interview used to assess symptoms of psychiatric disorders in preschool children, including some items related to picky eating. Parents are interviewed about their child’s food preferences and appetite, foods consumed by their child, and whether food selectivity impairs functioning. Dietary data can be used to determine the nutritional implications of food selective behaviors. Bandini and colleagues included three domains in defining food selectivity: food refusal, limited food variety, and the occurrence of HFSFI.77 Food refusal is defined as the number or percentage of foods that the child will not eat, as determined by parent interview. Limited food variety is determined by quantifying the number of unique foods that are consumed over a 3-day period (food diary or food recall). HFSFI is defined as the foods that are eaten 4 times within 1 day. In a more recent study, these investigators included only food refusals and limited food variety, as HFSFI frequency was very low in previous studies.87 An alternative measure to determine limitations in food variety is the food preference inventory (FPI).88 The FPI includes a listing of foods from five food groups (fruits, vegetables, dairy, proteins, and starches). Caregivers indicate whether the child eats a particular food and whether the food is typically offered at meals and eaten by the family. Summed scores from the accepted foods within each food group indicate what children and parents typically eat. Sensory sensitivity is often included in measurement of food selectivity as, according to Dunn and colleagues, understanding children’s ability to tolerate sensory stimuli helps provide a framework for understanding their reactions to sensory experiences.82 The Sensory Profile is a reliable and well-validated standardized questionnaire comprised of 125 items utilized by researchers and clinicians to assess the extent to which children have sensory processing challenges.89 Factors most often assessed in congruence with food selectivity include taste/smell sensitivity, tactile sensitivity, and visual sensitivity. The Sensory Profile is completed by the caregiver using scales of Always, Often, Sometimes, Rarely, Never for each item. “Typical” responses are within 1 SD of the mean, “probable differences” lie between the 1–2 SD below the mean, and a “definite difference” will lie below the 2SD mark (higher scores indicate more typical responses). The prevalence of food selectivity in the general population is unknown. In individuals with intellectual disabilities, such as autism spectrum disorders (ASD), food

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selectivity is commonly reported as the most pervasive feeding issue associated with ASD.83,90 However, this only has, to date, been estimated in a number of small, independent samples and generalization to the population of children with autism would be premature.75,91 The prevalence of food selectivity in the general population, as defined by Bandini and colleagues (2010), has been estimated to be as high as 26% in preschool-aged children but is higher yet in preschoolers with developmental disabilities (31% for preschoolers with developmental disabilities, not including autism) and highest among preschoolers with ASD (46%; Withrow, unpublished dissertation).77,92 In a recent report from the Duke Preschool Anxiety Study cohort (n ¼ 917 children aged 24–71 months), selective eating behaviors were reported in 20% of the sample, with 3% being reported as exhibiting severely restricted eating.93

FACTORS ASSOCIATED WITH FOOD SELECTIVITY Factors associated with food selectivity are thought to be both intrinsic and extrinsic in nature. Studies have suggested that food selectivity and taste sensitivity have an intergenerational component; however, definitive studies of heritability have not been conducted in a cohort or at a population level.94 Sensory sensitivity, believed to be an intrinsic characteristic, is associated with food selectivity.82 Because eating involves the integration of a number of senses, it follows that differences in the detection of and reaction to taste, touch, vision, and smell would be associated with food preferences and acceptance patterns. Differences have been reported in individuals’ sensitivity to taste and smell and these differences are associated with a number of genetic variants.95 Tactile sensitivity or defensiveness has been related to difficulties in the transition to self-feeding without assistance and eating with utensils; difficulties which can hamper food acceptance and the approach to new foods.73,78,96 Other intrinsic factors associated with food selectivity include child traits such as anxiety and internalizing and externalizing psychopathology.97 In adults, sensory sensitivity has been associated with anxiety.98 Of note, most of what is known about food selectivity and sensory sensitivity has been documented in special populations (e.g., ASD) and therefore how these findings apply to the general population of children is, for the most part, unknown. As with picky eating and neophobia, aspects of parent feeding practices and the family environment are associated with food selectivity, including exposure to breast milk, timing and variety of introduction of complementary foods.22–24 It also should be noted that parents perceive that children’s food preferences and reactions to taste and smell are unique to the child and these parent perceptions can impact decisions regarding which foods to make available and offer to their children.68 Further, the foods that parents buy, serve, and model for their children relate to children’s food intake habits: if parents are selective, there may be both genetic and environmental aspects that influence their child’s

Children's Challenging Eating Behaviors: Picky Eating, Food Neophobia, and Food Selectivity

food selectivity.31,99 As with picky eating and neophobia, some parenting practices interact with selective eating behaviors to reinforce their persistence: (1) use of pressure to eat can rebound and discourage children from being willing to try and eat some foods; and (2) restriction of highly preferred foods may serve to reinforce their appeal and it has been suggested that some individuals who are sensory sensitive may also be more vulnerable to the presence of highly palatable foods.84

OUTCOMES ASSOCIATED WITH FOOD SELECTIVITY Food selectivity, by term and definition, implies limited food acceptance and variety and, therefore, suggests relations with poor dietary patterns and risk for nutritional inadequacy. Consistent findings have been reported for the relation between sensory sensitivity and poor fruit and vegetable intakes and, to a lesser extent, poor acceptance of some food textures.62,73 Children clinically diagnosed with tactile defensiveness (children who display an overreaction to tactile stimulations) rejected more foods, ate fewer vegetables, and refused more often to eat new foods, compared to healthy children.73 Schreck and colleagues (2004) reported that children with ASD and food selectivity exhibited a preference for energy-dense foods and beverages; however, nutrient intakes and weight status were not reported.88 A recent, small study investigating the change in food selectivity during childhood and its relation to dietary intake suggested that food selectivity resulted in limited food repertoire and that these effects persisted from early childhood into adolescence.87 However, the broader implications for nutritional status and growth remain undetermined. In clinical case reports, anxiety and obsessive-compulsive behaviors have also been found to be comorbidities of food selectivity.74,100,101 The coexistence of anxiety during eating may result in physiological reactions such as vomiting, gagging, and loss of appetite, which can cause individuals to focus on, and then avoid, aversive experiences with these foods.102 Further, the implications of children’s feeding problems for their parents and other family members should not be underestimated. Selective eating can create significant stress for parents and other caregivers (Box 4.3).103

BOX 4.3

• • •

Food selectivity is a relatively extreme variant of “picky eating” which involves severe limitations in food acceptance, variety, and in some cases energy intake. Food selectivity has been linked to ASD and sensory sensitivities to taste/smell, visual cues, and tactile stimulation. Children with food selectivity experience negative impacts on social and emotional development related to eating, as evidenced by frequent struggles about food and decreases in the pleasantness of family meals and peer interactions.

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CONCLUSIONS Challenging child eating behaviors are “part and parcel” of child development. Eating difficulties occur most often during periods when spikes in cognitive, motor, and social-emotional development coincide or when innate deficits in these systems become evident. These developmental conditions create frequent dissonance in children’s eating behaviors and nutrition needs for optimal growth and health. For the child who is: (1) succeeding at conquering eating with a spoon, (2) learning the names of a variety of new foods, and (3) becoming autonomous in feeding, eating represents a grand opportunity to develop mastery. In contrast, the child who has less dexterity and for whom the sensory experiences of eating are overwhelming may struggle significantly to eat, and find the eating experience to be far less rewarding and, in some cases, not worth the effort. These diverging ends of the eating experience continuum call for clarifications in our conceptualization and definitions of the terms pickiness, neophobia, and selectivity. We propose that picky eating is an overarching term that refers to the eating challenges of the typically developing child; challenges that generally respond over time to repeated exposure to foods and to positive modeling, either by caregivers or peers. Picky eating can be considered as developmental in nature, that is, its expression differs over the life course and is at its height during early childhood. Neophobia can be viewed as a more stable trait of personality or temperament and is manifested by behaviors that are similar to picky eating. However, neophobia, as a trait, may generalize to other areas beyond eating behaviors, such as an individual’s approach to or avoidance of a variety of interactions across experiential domains (e.g., responses to new people, environments, foods, toys, etc.). Food selectivity is more extreme in its manifestation and results in very limited food acceptance. It is more often associated with disabilities, medical conditions, and disorders and may have the most significant implications for nutritional status and growth, as well as implications for social transactions for individuals and their families. Inconsistencies in the definitions and elements considered across these terms currently include a lack of distinction and consistency regarding which attributes of food and eating define these terms. For example, each term typically includes some assessment of an individual’s response to the taste or flavor profile of foods. However, food liking is a more complex sensory experience than taste alone and each term likely should include response to other food attributes (e.g., texture, olfactory characteristics, and visual appeal).104 Currently, select measures of sensory sensitivity include some assessment of these other characteristics but assessments of picky eating and neophobia are not consistent in measuring reactions beyond food taste. The importance of understanding conceptual differences among these terms lies in the ability to aid in distinguishing among these conditions and in understanding the source of an individual’s reluctance to consume certain foods. Once clarity and agreement regarding distinctions among these terms is achieved, both researchers and clinicians would benefit from a focused evaluation and retooling

Children's Challenging Eating Behaviors: Picky Eating, Food Neophobia, and Food Selectivity

of the measures used to distinguish them. This effort also would help to identify the point at which typical challenging behaviors of childhood cross over into cause for clinical concern. Such a focus could provide the basis to design and conduct longitudinal studies that follow the developmental trajectory of these behaviors, provide insights into strategies to promote healthy behaviors, and identify critical windows for prevention and treatment. Additional research designed with the specific aim of understanding the development of food preference among individuals, particularly in the disability community, is critically needed. An emphasis on determining how genetic variation in susceptibility to food selectivity (i.e., variation in response to taste, olfactory and textural properties of foods) would contribute to the much-needed evidence base to assist clinicians to develop protocols to address these concerns in children with disabilities. Currently, the clinical treatment of picky eating and neophobia focus on repeated exposure to foods and this approach is consistent with how other challenging behaviors of early childhood are addressed: consistency, repetition, and a predictable routine.105 While caregivers are discouraged from being negative in their approach to child feeding (e.g., pressuring or bribing a child to eat), less attention has been focused upon the creation of positive experiences with food and eating. Mere exposure theory, as posited by Zajonc, hypothesizes that repeated exposures are necessary to enable children to learn to acquire new preferences.106,107 However, an often neglected component of this concept is that the emotional valence of the experience is important for reinforcing children’s learning. That is, if foods are offered in a negative manner, these negative experiences make rejection more likely. Alternatively, when the atmosphere in which new foods are offered is supportive, fun, and one of choice, these conditions should positively reinforce children’s experiences with novelty. Zajonc suggests that mere repetition of exposure (neutral offerings of foods without pressure or expectations to consume them), particularly when linked with good tasting flavors, is sufficient to produce positive affect and to engender increased liking for novelty. Therefore the tone of food experiences, linked with repeated exposure, should be emphasized with caregivers to facilitate children’s acceptance of novel foods. These same principles of positive repeated exposure hold true for addressing foods selectivity but may not be sufficient, as stand-alone methods, to produce change in food acceptance patterns. For the individual with food selectivity, a multidisciplinary approach (e.g., including teams of behavioral psychologists, occupational therapists) may be necessary to develop therapeutic protocols (e.g., food chaining, extinction, escape reduction, reinforcement) to produce behavior change.108 In addition, training for parents and other caregivers, so that these methods are consistently conducted across the environments in which children eat may prove more effective. Conditions which help to facilitate change include circumstances when the child is motivated to change their behavior (e.g., the child who wants to participate in team or social events where eating occurs and therefore is motivated to try to learn to eat differently in order to more fully participate in these events).

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When any child presents with eating challenges, whether in the realm of picky eating that seems to be part of typical development or the extremes of food selectivity, the enlistment of a registered dietician to assist in determining the extent to which limitations in dietary variety might have nutrition implications is essential. In many (if not most) cases, parents’ fears can be allayed by reassuring them that, while frustrating and worrisome, their children’s current eating behaviors do not place them at risk for nutritional deficiencies. In situations where children’s eating is extremely selective, a thorough dietary assessment can help to identify opportunities for focusing protocols for behavioral change and for addressing nutritional and growth-related risks.

SOURCES OF SUPPORT No external funding was received for the writing of this manuscript. All authors receive salary support from their respective institutions. Each receives salary support from the United States Department of Agriculture NIFA AFRI Program. SLJ and KJM receive salary from a grant from The Sugar Association. SLJ additionally receives salary support from research grants from the National Institutes of Health (NIDDK), the Health Resources and Services Administration (MCHB).

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CHAPTER 5

Satiety Responsiveness and Eating Rate in Childhood: Development, Plasticity, and the Family Footprint Brenda Burgess*, Myles S. Faith† *

Department of Family Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo—SUNY, Buffalo, NY, United States † Department of Counseling, School and Educational Psychology, Graduate School of Education, University at Buffalo—SUNY, Buffalo, NY, United States

INTRODUCTION Over the past 30 years, the prevalence of pediatric overweight and obesity within the United States has tripled, with one in three children classified as overweight and one in six as obese.1 Explanations for this rise are frequently attributed to changes in the environment that encourage sedentary behaviors or prompt overeating.2 Yet, not all children are overweight or obese, suggesting that individual variations in factors regulating body weight contribute to these disparities. To this point, a child’s predisposition to excess adiposity is connected to his/her ability to self-regulate eating. As Hughes and Frazier-Wood defined the term, “Children’s eating self-regulation refers to the ability, both inborn and socialized, to eat and stop eating in response to internal cues of hunger and fullness.”3 They further recognized two processes involved in eating regulation were satiation and satiety, noting that, “Satiation refers to the signals and processes that occur during the course of a meal that bring the meal to an end whereas satiety refers to the signals and processes that, following the end of a meal, inhibit eating before hunger returns.”3 Two appetitive traits that are directly relevant to children’s eating self-regulation and that are the focus of this chapter are satiety responsiveness (SR) and eating rate (ER). We define SR as a child’s ability to recognize and adjust eating in response to internal feelings of fullness.4 We define ER as speed of eating within a given time period. As discussed in this chapter, better SR and a slower ER may be indicative of better eating self-regulation. The purpose of this chapter is to examine evidence for familial influences on the development of SR and ER in children, how these traits relate to obesity onset, and their potential as intervention targets. For each trait, we first introduce common measurement tools (questionnaires and direct behavioral assessments). Second, we address risk and susceptibility by reviewing genetic influences, parent-child feeding practices, and Pediatric Food Preferences and Eating Behaviors https://doi.org/10.1016/B978-0-12-811716-3.00005-1

© 2018 Elsevier Inc. All rights reserved.

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associations to adiposity and dietary intake. Third, potential for modifications through interventions are discussed. Lastly, we identify research gaps and suggest topics for future investigations. We focus on SR and ER in children from birth to roughly 8 years of age. This chapter is organized around the heuristic or metaphor of a “family footprint.” Footprint can be defined as “a marked impression, effect, or impact”5; henceforth, a family footprint reflects the impression, effect, or impact of parents and/or other caregivers on the development of a child’s SR and ER. This is an overarching construct that organizes evidence for the family influence on the development of these traits, encompassing risk and susceptibility as well as potential for modification. A footprint in the sand is not “fixed” or unchangeable, but can modified. This appears to be true for SR and ER as well.

SATIETY RESPONSIVENESS Measurement SR can be measured indirectly via the Child Eating Behavior Questionnaire6 (CEBQ) or Baby Eating Behavior Questionnaire7 (BEBQ), or directly from laboratory-based feeding studies. Historically, the use of questionnaires has been favored by virtue of ease of execution and implementation within a variety of settings.8–11 Laboratory-based feeding studies, however, are often considered the “gold standard” because they are not susceptible to parent-reporting biases even though they require intensive preparations and cannot be applied easily to large sample sizes. The CEBQ,6 validated for children ages 3–13 years,12 is a 35-item psychometric survey that can be used to evaluate parents’ perceptions of child SR. Investigations have administered it to monitor the traits over time,13 evaluate their relationship to body weight,8,14 and even to compare appetites between children at low- and high-risk of obesity.15,16 Examples of questions that assess SR include: “My child leaves food on his/her plate” and “My child gets full before his/her meal is finished.” The BEBQ7 is an 18-item questionnaire adapted and refined for infants from the CEBQ. The scale was initially developed for a population-based cohort of twins in the UK, but has since been adapted by investigators in other countries.17,18 Questions assessing SR include: “My baby found it difficult to complete a feed” and “My baby got full before taking all of the milk I though he/she should have.” Turning to laboratory-based feeding methods, SR is classically assessed using a preloading paradigm. This paradigm involves children being fed a lower- versus higher-energy preload snack (typically yogurt or fruit juice) approximately 20 min before receiving access to a meal which they eat ad libitium. The notion of ‘compensation’ is that children will consume proportionally less food during the meal following the highercompared to the lower-energy preload. This degree of compensation (COMPX%) is calculated by the formula19:

Satiety Responsiveness and Eating Rate in Childhood: Development, Plasticity, and the Family Footprint

COMPX ð%Þ ¼

Meal low  Meal high ð100Þ Preload high  Preload low

Meallow ¼ energy intake from the ad libitum meal following the low-energy preload, Mealhigh ¼ energy intake from the ad libitum meal following the high-energy preload, Preloadhigh ¼ energy consumed from the high-energy preload, Preloadlow ¼ energy consumed from the low-energy preload. Lower COMPX% scores reflect the tendency to overeat following the high-energy preload relative to the low-energy preload, whereas higher scores reflect the tendency to undereat following the high-energy preload relative to the low-energy preload. The COMPX% has been used in several prior studies with children.20–22 A different laboratory protocol that is pertinent to SR—and may even be an index of the construct—is the “eating in the absence of hunger” (EAH) paradigm.23 Many investigators have used this approach, wherein children first consume a meal ad libitum until they indicate they are full. Approximately 15 min afterward, participants are given the opportunity to eat a variety of palatable snack foods during a “free access procedure.” The amount of snack foods consumed in the absence of hunger is EAH. EAH has been related to obesity predisposition24 and obesity status25 in a number of studies. Since snacking occurs after satiety has been induced, greater EAH arguably reflects poorer SR. On the other hand, in a recent and comprehensive literature review of EAH in children, Lansigan et al.26 noted that EAH “serves as a valuable behavioral index of children’s dietary disinhibition that may contribute to excess weight gain.” Thus EAH might reflect “disinhibited eating” more so than poorer SR per se in children. Because this is unclear and to help focus our discussion, EAH is not discussed in this chapter. In sum, SR has been assessed most commonly using the CEBQ, BEBQ, or preloading paradigm in the literature. Families confer risk for poorer (or better) SR through genetic and environmental pathways, the evidence for which is reviewed next (Box 5.1).

Risk and Susceptibility Genetic Influences The first study to document a genetic influence on SR in infants was reported in 2010 when Llewellyn et al.27 estimated heritability, as assessed by the BEBQ, to be 72% in

BOX 5.1

• •

Satiety responsiveness can be assessed using questionnaire-based methods such as the Children’s Eating Behavior Questionnaire. Satiety responsiveness can be assessed using laboratory-based protocols, most commonly a preloading paradigm which generates a compensation index.

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2402 infant twin pairs. A follow-up study found evidence for common genes influencing both SR and body mass index (BMI) in this sample, with a genetic correlation (95% confidence interval) of rA ¼ 0.29 (0.19, 0.40).28 Hence, some infants enter the world with greater susceptibility for poorer SR. Llewellyn et al.29 proceeded to genotype DNA from 2258 unrelated children in the Twins Early Development Study, a population-based cohort of twins in Britain. They generated a polygenic risk score (PRS) for 28 DNA variants in genes associated with obesity and assigned a score to each individual based upon their number of alleles carrying the DNA variants of interest. A higher PRS score reflects a greater genetic predisposition to obesity. Children with lower SR exhibited a high PRS, and this in turn mediated the connection between BMI and waist circumference. This study advanced evidence for a molecular genetic basis that certain genes foster poorer SR and high weight status. In an investigation of potential epigenetic influences on SR, Gardner et al.30 ran analyses on saliva samples in 64 African American children (half lean, half with obesity). They examined single nucleotide polymorphisms in FTO, MAOA, SH2B1, LEPR, DNMT3B, BDNF, and CCKAR genes and evaluated their degree of methylation, that is, they measured the amount of methyl (–CH3) groups attached to the regions of DNA encoding these genes. Methylation has the ability to stimulate or suppress expression of a certain gene, and alterations in methylation can modify a gene’s activity. The study found that no single genetic variant moderated SR levels; however, girls with poorer SR were found to have less methylation on BDNF than matched counterparts. This indicates that regulation of SR may be more complex among females, with high levels of BDNF methylation shown to promote more favorable SR. Despite these findings, a laboratory-based preloading paradigm failed to find any evidence of heritability on %COMPX (i.e., caloric compensation ability, as described previously). Faith et al.31 studied a sample of 4- to 7-year-old same-sex twins (n ¼ 69) recruited from the New York metropolitan area. In this study, heritability was estimated to be 0%, with all the familial resemble apparently due to environmental factors. These inconsistent results may be due to differences in methods (e.g., parent-report questionnaires vs. genotyping vs. laboratory observations), age, and/or sample size. This underscores the importance of measurement decisions in genetics studies of child SR. Taken together, the balance of evidence indicates that genetic variations contribute to individual differences in SR among infants and children. That is, some youth enter the world more susceptible to poorer SR for reasons that are genetic in nature, independent of subsequent environmental experiences. The family footprint has, in part, a genetic basis Parent-Child Feeding Practices Beyond genetic origins, some data suggest that heightened SR during infancy may be promoted by breastfeeding. In a UK sample of 298 infants and toddlers, Brown et al.32 reported

Satiety Responsiveness and Eating Rate in Childhood: Development, Plasticity, and the Family Footprint

that mothers who breastfed the longest reported the highest SR scores among their infants, although these mothers also express the greatest concern for their infant’s risk of underweight.33 Hence, maternal weight concerns may have been a confounding factor. For participants (n ¼ 3624) in the Amsterdam Born Children and their Development study, M€ oller et al.34 found that those who were breastfed exclusively during the first 4 months of life scored higher on the SR subscale at age 5 than those introduced to solid foods during that period. Interestingly, two investigations that assessed whether breastfeeding conferred an advantage towards obesity prevention yielded inconsistent results for SR. Disantis et al.35 found that among 3- to 6-year-olds (n ¼ 109), longer breastfeeding exposure was connected to higher current SR scores, but not related to change in BMI z-scores. Hathcock et al.36 observed that breastfeeding intensity related to a child’s obesity risk but not SR score. A longitudinal analysis37 of the aforementioned UK cohort revealed that healthy weight toddlers with better SR scores had mothers who were more likely to initiate breastfeeding at birth and utilized a baby-led weaning approach to introduce solid foods consistent with SR promotion. Further research is warranted to resolve these inconsistent outcomes for SR vs. child weight status in prospective studies. In contrast to studies of SR during infancy, there is a greater body of evidence regarding SR and parent-child feeding practices during childhood. In a pioneering study with preschoolers (n ¼ 77), Johnson et al.19 found that greater parental control over a child’s food intake was associated with poorer %COMPX in the laboratory and greater adiposity. In another study of preschoolers, Frankel et al.38 found that children whose parents scored higher in indulgent feeding practices had low SR, as evaluated by the CEBQ, and greater BMI. Curiously, a parental practice linked to poorer SR scores in infants39 but favorable scores in children40,41 is pressure to eat. Though this latter finding seems counterintuitive, Jansen et al.42 speculated “pressure to eat might also be a parent’s response to children quickly feeling ‘full’.” Longitudinal studies are needed to determine the directionality of the relationship. In summary, the family footprint for SR may be established partially through parental feeding practices, such as decisions to breast- vs. bottle-feed with infants, indulgent feeding practices, and pressuring children to eat. They may independent of, or interact with, genetic vulnerabilities to poorer SR, although research has not yet resolved this. Associations With Weight and Dietary Intakes Poor SR in early childhood is associated with elevated BMI and risk of obesity. Seminal research using the CEBQ with a London cohort of 3- to 5-year-old twins (n ¼ 572) and 6- to 7-year-olds (n ¼ 135) from the Netherlands was the first to report that lean individuals scored higher on the SR subscale than those with overweight or obesity.8,43 Comparable investigations of preschool-aged children in the Netherlands,42 Canada,44

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and Lebanon45 confirmed that SR score varied by weight status. Among 6- to 8-year-old children (n ¼ 510) participating in the PANIC Study, Eloranta et al.46 found that the negative relationship between SR and BMI also applied to body fat percentage, waist circumference, and hip circumference. Attempts to identify the temporal relationship between SR and body weight have yielded mixed results. Longitudinal analyses examining SR during infancy suggest that SR is a precursor to future BMI up through 2 years of age, but not necessarily beyond. Within the GEMINI cohort, path analyses revealed that greater weight gain from 6 to 15 months of age followed poor SR,47 especially for nonidentical twins.48 Among toddlers, one study detected a negative association between SR and current weight status,49 although this association did not replicate in a different study examining prospective associations.18 Interestingly, data from older children indicate that early appetite ratings tend to evolve over time and do not always correspond to weight status. Both Steinsbekk et al.50 and Parkinson et al.51 reported that BMI measured between 4 and 6 years of age correlated with SR measured two to 4 years later; however, Parkinson et al.51 noted that SR levels were not strongly associated with appetite measures or BMI during infancy. It is unclear how a child’s SR relates to their dietary intake or eating behaviors. It is conceivable that higher preferences for obesogenic foods might mediate the relationship between SR and weight status. In a subset of participants in the GEMINI cohort study (n ¼ 1044 infants) and NOURISH randomized controlled trial (RCT) (n ¼ 167 preschoolers), Fildes et al.52 found, paradoxically, that high liking for fruits and vegetables was tied to low SR. In laboratory-based feeding studies, children displaying low SR have been observed to consume more energy from meals and snacks than those with a high SR. Caton et al.53 offered 332 toddlers and preschoolers residing in the UK, France, and Denmark an artichoke puree cooked with no seasoning, with added sweetener, or added fat and then examined intake as a function of child SR. Results showed that participants characterized by low SR ratings consumed >75% of the food on their plates. In a study comparing energy intake from five standardized lunches following a variety of preloads (e.g., water, orange-flavored beverages, or milkshakes) in 70 preschoolers, higher SR was associated with lower food intake across all meals.54 Mallan et al.55 noted that healthy weight 4-year-olds (n ¼ 37) scored higher on the SR subscale, which in turn corresponded to less energy intake in an observational study than their overweight counterparts. Alternatively, in an investigation where 5- to 6-year-old African American children (n ¼ 100) received four dinners varying in amounts of food, children with low SR simultaneously increased their energy intake as portion size increased compared to those with high SR, and this was independent of BMI.56

Satiety Responsiveness and Eating Rate in Childhood: Development, Plasticity, and the Family Footprint

BOX 5.2

• • •

Genetic variations contribute to individual differences in SR among infants and children. SR may be established partially through parental feeding practices, such as decisions to breastvs. bottle-feed with infants, indulgent feeding practices, and pressuring children to eat. Children with low SR may have poorer satiation as reflected in greater intakes at laboratory meals, and also may be more susceptible to obesity promoting aspects of the environment such as greater portion sizes.

Collectively, the studies reviewed earlier support a relationship between SR, weight status, and dietary intake. Children with low SR may have poorer satiation as reflected in greater intakes at laboratory meals, and also may be more susceptible to obesitypromoting aspects of the environment such as greater portion sizes (Box 5.2).

Potential for Modification There has been limited intervention research targeting SR in children. These are discussed as follows. Using a pre-post design, Johnson57 tested a 6-week intervention designed to foster awareness of internal hunger and satiety cues. Participants were 31 3- to 4-year-old children enrolled in two Denver daycare facilities, 25 of whom completed the program. Intervention components included discussion of hunger and fullness sensations through play skits and videos (e.g., “Winnie the Pooh and the Honey Jar”), training children to assess their own fullness levels throughout the day by putting their hand on their belly and communicating their satiety (i.e., hungry vs. a little full vs. very full), and pointing to special constructed dolls to communicate how full they felt. The three dolls had clear glass jars as their bellies, with varying amounts of salt inside. Specifically, they contained no salt (i.e., empty stomach), a little salt (i.e., a little full), or a lot of salt (i.e., very full). During mid-morning snacks, children were prompted at least twice to see if they were still hungry and wanted more food. Additionally, they were prompted to report their fullness levels with the dolls after their snack. The primary outcome was the laboratory-based COMPX% index, as described previously in this chapter. Results indicated that the intervention successfully improved children’s SR. When looking to the breakdown of responders, 68% of children showed improvements in COMPX% scores, 16% stayed the same from baseline, and 16% showed deteriorations. Daniels et al.9,58 recruited 698 new mothers from the NOURISH trial to determine if this parenting-based feeding intervention would foster greater SR in their infants during toddlerhood. For the active phase of the intervention, mothers attended six group meetings where they were educated on three main topics1: repeated exposure to novel foods

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coupled with limited exposure to unhealthy foods for newborns2; responsive feeding that directs caregiver attention to infant hunger and fullness cues; and3 “positive parenting” that encourages caregiver autonomy, warmth, and self-efficacy. At 2 years of age, data from a 24-h recall and CEBQ showed that toddlers of mothers in the intervention group received greater exposure to vegetables, liked more fruits, and displayed higher levels of SR compared to toddlers in the control group. Three and a half years later, results from a follow-up assessment59 showed that the intervention group maintained a higher liking for fruit and SR. Most currently, Skouteris et al.60 conducted a study in 200 mother-child dyads from MEND 2–4, a program designed to promote healthier lifestyles in preschool-aged children. Over the course of two and a half months, participants attended 10 weekly meetings focused on eating wisely, increasing activity, and improving parenting behaviors. At each meeting, parents were given lessons on the aforementioned topics, while children played with the intervention leaders and received exposure to fruits and vegetables. At the conclusion of the study, children in the intervention group scored higher on SR and increased intake of vegetables compared to those in the control group. Unfortunately, these differences were no longer significant after 1 year. In summary, the studies reviewed earlier suggest that SR is a modifiable trait and not “fixed” or unchangeable due to genetic factors. That said, the number of studies investigating this is limited and more research is needed. In line with our family footprint metaphor, family-based research is needed to reveal specific strategies by which caregivers can better cultivate SR in their children. These may depend on factors such as a child’s age and developmental status (Box 5.3).

EATING RATE Measurement ER is customarily measured indirectly via the “Slowness in Eating” subscale of Child Eating Behavior Questionnaire6 (CEBQ) or Baby Eating Behavior Questionnaire7 (BEBQ), or directly from laboratory-based feeding studies. Examples of questions evaluating ER on the CEBQ include: “My child finishes his/her meal quickly” and “My child eats slowly.” Corresponding questions on the BEBQ are “My baby finished feeding quickly” and “My baby fed slowly.”

BOX 5.3

• •

SR is modifiable with behavioral interventions. More research is needed to identify effective interventions.

Satiety Responsiveness and Eating Rate in Childhood: Development, Plasticity, and the Family Footprint

BOX 5.4

• •

ER can be measured by questionnaire or by laboratory-based observation of eating that is coded for a range of indices. One study has shown stability in ER in adults.

Concerning laboratory-based feeding methods, ER is measured during a meal within a specific time frame. Investigators usually video-record the speed of food intake by children and/or their parents within a fasted state and then calculate ER from dividing food consumption by total eating time. The specific units of measurement have differed across studies and are represented as g/min, g/bite, chews/g, chews/bite, mouthfuls/min, kcal/ min, and/or total meal duration.61–64 Fogel et al.64 examined the microstructure of rapid eating—defined as g/min—among 386 children in the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) study. Children’s food intake at 4.5 years of age was video-recorded in the laboratory and then coded. Investigators found that faster eating rate was associated with a larger average bite size, fewer chews per gram of food, shorter oral exposure time per bite, and higher overall energy intake. Faster ER was also associated with greater child overweight. Finally, McCrickerd et al.65 examined the stability of ER in four samples of adults, among whom ER was assessed across four laboratory test meals separated by at least 3 days. The intra-class correlation coefficient combined across all participants was 0.87, indicating strong stability across visits (Box 5.4).

Risk and Susceptibility Genetic Influences No specific genes regulating ER in children have been identified, although data from 2404 pairs of 3-month-old twins in GEMINI cohort reported ER heritability to be 84%.27 Thus some infants more so than others enter the world with a greater genetic susceptibility to rapid eating. This susceptibility does not appear to be trivial in magnitude. Parent-Child Feeding Practices Breastfeeding may promote a slower ER in infancy. Rogers et al.66 administered the CEBQ to 81 British mothers and observed them feeding their children at 6 and 12 months. Regression analyses revealed that infants exposed to the greatest breastfeeding duration maintained lowest ERs and showed healthier weight trajectories through 6 and 12 months, respectively. Therefore either the act of breastfeeding and/or components within a mother’s milk help foster better appetite control at a young age. A few notable studies have examined this association in preschool-aged children. Jani et al.41 found that children of mothers who exerted a high pressure to eat, per a parent-report questionnaire, had lower ER scores than their matched counterparts.

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Drucker et al.67 found that delivering eating prompts to children during a videotaped lunch increased their eating pace. A caveat that remains, nevertheless, is the extent to which parental practices influence or are responsive to child ER. Associations to Adiposity and Dietary Intake An accelerated ER has been associated with higher body fat percentage in infants and children across the globe. In a sample of preschoolers (n ¼ 1322) from eight cities in central China, He et al.68 found that high ER was a hallmark of increased child obesity risk. Among 142 German children aged 3–6 years, Jahnke et al.69 reported that those classified as overweight or obese displayed a greater ER. Among children aged 6–8 years (n ¼ 510) in the Finnish PANIC Study, Eloranta et al.46 discovered that participants who scored low on the subscale for “slowness in eating” were more likely to carry excess weight around their waist and hips. Finally, in an investigation examining whether this relationship could also be detected in infancy, Mallan et al.17 applied Confirmatory Factor Analysis to the four factors of the BEBQ on data from 467 4-month-olds in the NOURISH trial and found that leaner infants had lower ER scores. With respect to prospective studies, all agree that high ER is a precursor to weight gain. Shepard et al.70 measured the height, weight, and ER of 31 infants from 2 weeks to 5 months of age. No change in ER was observed from 2 weeks to 3 months of age, although ER increased beginning at 3 months. Importantly, increases in ER coincided with a rapid growth rate during months 3–5, presumably due to a heightened consumption of energy fueled by a high ER. Similar observations of toddlers at 24 months of age49 and 42 months of age71 have been reported in the literature. In the largest investigation to date, Sugimori et al.72 found in the Toyama Birth Cohort Study (n ¼ 8170) that lean children with a high ER score at age 3 became obese at age 6. Additionally, the children with a high ER who were obese at age 3 experienced no change in weight status or eating behavior by age 6, further suggesting that ER is a stable phenotype established early in life. For laboratory-based feeding studies, Drabman et al.61 first observed in 1979 that overweight preschoolers displayed a faster ER than their lean counterparts, as measured by chews per bite. In an investigation of 37 preschoolers in the NOURISH cohort, Mallan et al.55 administered the BEBQ to assess if the children’s ER correlated with their food intake from a standardized lunch and snack. Indeed, children who were slow eaters consumed less energy during the lunch meal, though not from the snack. When Fogel et al.64 exposed 386 children in Singapore to an ad libitum lunch, they found that a high ER was associated with greater bite size and energy intake, and that this eating behavior was most prevalent among children with high BMI. Berkowitz et al.62 found that higher ER at a single laboratory meal at age 4 predicted greater weight gain over 2 years. For every 100 kcal increase in energy intake, the odds of obesity later in life was estimated to increase by 1.445.63

Satiety Responsiveness and Eating Rate in Childhood: Development, Plasticity, and the Family Footprint

BOX 5.5

• • •

ER heritability is high but specific genes have not been identified. Breastfeeding and less pressure to eat are associated with slower ER. Higher ER is robustly associated with greater weight gain.

Data are lacking on the connection between ER and taste preferences. In one study52 that investigated this relationship, researchers found that infants and toddlers with high ER scores tended to like fruit less than those with low ER scores. Thus ingestion of foods high in sugar and/or fat may override the ability to pace one’s eating speed, though prospective investigations are warranted (Box 5.5).

Potential for Modification We are not aware of any published interventions that have attempted to reduce ER among infants and young children as the primary outcome. However, a few investiga® tions targeting adolescents using a 30-s hourglass or Mandometer (similar to a universal eating monitor) have been conducted. Despite the older ages of these samples, we discuss these studies because they are directly relevant to our chapter focus and could be studied in younger children. Over a yearlong timespan, Vazquez et al.73 monitored 54 adolescents as they learned to pace the time interval between bites during meals using a miniature, 30-s hourglass. The intervention was called “Good Manners For A Healthy Future” and emphasized the process of eating more slowly. At the outset of the intervention, participants were advised to drink water before each eating occasion, avoid talking with food in their mouth, forego second helpings and snacking, and eat slowly while observing the hourglass. Each week, participants submitted journals detailing how frequently they used the hourglass in order to quantify the level of compliance; those who used it 4 days per week were classified as the ‘adhering group’ and those using it 50%; Table 7.2). There is not a clear suggestion that some appetitive traits are more heritable than others. As with most behavioral traits analyzed across middle childhoods, appetitive traits reflect a notable influence from common environmental factors with most estimates for this variance component clustering around 20%.8,37,41,42 No studies suggested that heritability estimates were largely different by gender, with only one finding a small but significant difference of up to 8% between girls and boys in the heritability of appetitive traits.37 One twin study was able to partition the variance in a trait into genetic and environmental influences, and an additional underlying influence which is a form of measurement error known as contrast effects. These contrast effects suggested that for enjoyment of food, one twin’s eating behavior influenced the other twin’s to create greater-than-expected differences between members of a twin pair.37 It is not possible to determine from these analyses whether this is a true contrast effect, where one twin observes the other’s enjoyment of food and modifies their own, or (as is more

Appetitive Traits: Genetic Contributions to Pediatric Eating Behaviors

commonly found in twin studies on child behavior), whether this reflects the tendency for parents to contrast their twins and report them as more dissimilar on a trait than is the case. The difficulties with understanding the origin of contrast effects for enjoyment of food highlight the need for mixed methods approaches, which include the direct observation of child appetitive traits.

A Developmental Perspective

Research into other noneating-related childhood traits suggests that the relative strengths of genetic and environmental components may change across childhood. Childhood behaviors typically show a “U-shaped” heritability across childhood. For example, the heritability of general cognitive ability (or intelligence quotient; IQ) is high in very young children, reaches a nadir in middle childhood, and then increases in adolescence to rise to original levels.55 Understanding this pattern of change is important because it can tell us about which influences may be relatively more or less important to focus on when designing interventions to alter child behaviors. Changes in the relative importance of the A, C, and E variance components underlying a trait may also help direct efforts to identify those influences, for example, choosing to look for environmental factors which may affect eating behaviors and differ between twins (implicated by “E” components) such as peer group,56 vs. those which are similar between twins (and implicated by “C” components) such as SES.57 The changes in heritability in IQ, for example, are often attributed to the role the family environment plays in shaping behaviors in middle childhood, which then exerts less influence as children age, and their behaviors are more subject to influences outside the family such as peers. BMI mirrors this pattern,58 with the decrease in heritability seen in middle childhood attributed to environmental factors shared by cotwins in midchildhood, and the increase in heritability after 15 years attributable to a reduction in the influence of these factors, although maternal prenatal BMI has emerged as a risk factor for pediatric obesity independently of the child’s age, which indicates that familial influence on BMI operates across all of childhood.35 However, longitudinal twin studies on eating behaviors are missing from the current literature, and the lack of a clear pattern of heritability associations for eating behaviors both within and across age groups (Table 7.2) highlights the need to address this. In psychopathology, the heritability of disordered eating as a trait (reflecting body dissatisfaction, compensatory behaviors, and preoccupation with weight) does show the expected U-shaped curve after middle childhood, with the heritability estimated at 3% at the end of middle childhood (11 years of age), and 57% at 17 years of age.43 This difference, however, was not attributed to a reduced influence of the familial environment per se, but the advent of puberty which may “switch” on genes related to disordered eating (binge eating in particular).44 Whether this pattern of findings is similar

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for child eating behaviors in the general population, which are not considered pathological, has yet to be addressed. Interpreting Heritability Estimates for Child Appetitive Traits

Taken as a whole, data from twin studies supports the notion that there is a heritable component to child appetitive traits. Since twin studies tell us as much about the influence of environment as genetics, twin studies also support the role of common environment in shaping child appetitive traits. However, as with the genetic component, the particular environmental factors which account for this influence are not identified. It is important not to take a “fatalistic view” of heritability estimates and infer that the genetic component reduces the importance of environmental influences such as parent feeding style. Heritability estimates represent a “snapshot” of a population and should not be interpreted beyond this. Moderate-to-high heritability does not mean that changes in the environment will not have a dramatic effect on a trait. Food neophobia is heritable, but may be somewhat ameliorated with repeated exposure to novel foods.59,60 Similarly, the availability and accessibility of fruits and vegetables may alter their consumption,61 as does portion size may alter the self-regulation of eating behaviors.62,63 Future Directions

The recognition that genetics influences child eating behaviors is important as the first step in recognizing that genetics must be integrated with the known role of the environment, when understanding the etiology of child appetitive traits. There is an emerging recognition that the gene-environment interplay underlying child behaviors is complex. The didactic parent-child feeding-eating relationship is underpinned by the genetics shared between the parent and child (50%), and parents respond differently to children, based in part on the child’s genetics. In turn, children’s responses to their environmental influences, including their parents, reflect their own genetic predispositions. These geneenvironment correlations (passive and active) and interactions are well examined within a twin design where the genetic and environmental control in the cotwin makes complex inferences possible. Such studies would be enhanced by longitudinal studies which examine how the relative roles of genetics and environments change across childhood, and which can examine whether the same genes underlie eating behaviors as children age, or whether new genetic influences emerge. Similarly, multivariate twin models can give an understanding of the extent to which different appetitive traits share the same underlying genetic liability, or represent the manifestation of unique genetic influences. Finally, twin studies may have a unique role in the future in helping identify candidate genes which underlie eating behaviors. Although candidate gene studies are less popular, those based on well-validated and replicated GWAS results do remain in favor, often through the use of a genetic risk score. The difficulty is in identifying which genetic loci should be examined a priori; and genetic correlations (rG) with other traits may help guide

Appetitive Traits: Genetic Contributions to Pediatric Eating Behaviors

such decisions. For example, in children ages 5–18 the amount of calories eaten at dinner appears to share significant genetic variance with the genes underlying insulin levels (rG ¼ 0.61; indicating approximately two-thirds of genes are shared between the traits), and ghrelin (rG ¼ 0.3; indicating approximately one-third of the genes are shared, although the genes act in the opposite direction on the two traits). EAH did not overlap genetic with these elements of the satiety cascade, but we can see that twin studies may be useful in designing candidate gene studies in the future (Box 7.3).6 Molecular Genetic Studies Candidate Gene Studies

Initial attempting to identify how genetic variation influences child eating behaviors relied on candidate gene or candidate locus studies. In general, for candidate gene studies, the selection of which locus or loci to examine for associations with a trait of interest has been based off the results of mouse work or off suspected gene function. The association of child eating behaviors with weight status, under the assumptions of the dimensional/ QTL approach outlined previously, has led investigators to examine whether genes known to associate with either BMI, or having obesity can also be seen to associate with child eating behaviors. FTO

The Fat Mass and Obesity Associated (FTO) gene has shown the most consistent associations with BMI in the literature and appears to account for the greatest amount of variance in weight status.64,65 FTO, initially identified as preventing fused toes in mice, was originally known as the fatso gene, but its association with human weight status is so strong and consistent that the HUGO Gene Nomenclature Committee officially renamed fatso to FTO to avoid potentially offensive terminology. The initial reports of an association between FTO and weight status pinpointed single nucleotide polymorphisms (SNPs) called rs993960966 and rs993050667 as the key contributors to weight

BOX 7.3

• • • •

Twin studies decompose the variance in traits into underlying genetic and environmental influences, but have limitations to interpretation. Most heritability estimates for appetitive traits in children are in the moderate range (about 50%). There is a lack of understanding about whether the heritability of eating behaviors changes across different behaviors and different ages, but such information might give clues that help design obesity interventions. Moderate-to-high heritability does not mean that changes in the environment cannot have a dramatic effect on a trait.

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status. These loci are only 9938 kb apart, and the variation at these two loci is highly correlated within the European-ancestral population (r2 ¼ 0.84) which makes the putative, or causal, SNP hard to identify. In the largest meta-analysis to date, rs1558902 was the most strongly associated BMI variant within FTO, accounting for over 0.3% of the trait variance in BMI,65 more than three times the amount accounted for by any other locus, and accounting for 6% of the identified genetic variance.64 However, several neighboring loci showed strong associations with BMI, including rs9939609, for which rs1558902 is also in strong linkage disequilibrium (r2 ¼ 0.90). It is this latter locus, rs9939609 which has been the focus of research into associations between FTO and child eating behaviors. Mechanistically, FTO shows potential for underlying eating behaviors, particularly those appetitive traits which may reflect, in part, the response of the child to food as a rewarding stimulus. FTO variation is highly conserved and largely expressed in the brain.64 Brain expression of FTO across the lifespan has been seen in prenatal brain tissues as well as those of adult humans. In particular, FTO is expressed in the hypothalamus68 known for its role in behaviors that affect energy homeostasis, as well as in the central and peripheral nervous tissues.69 The expression of FTO in the hypothalamus is increased after 48 h of food deprivation or 12 days of food restriction in mice, at rates of 41% and 27% increases, respectively.69 While food restriction increases the expression of FTO in the hypothalamus in rats, it decreases the expression of FTO in mice, so is difficult to generalize and form conclusions regarding human FTO expression in response to food deprivation—only that there is evidence to suggest it may be “altered” in response to food intake.70 FTO is also known to be expressed in the cortex, hippocampus, and cerebellum,69 which are brain areas thought to drive reward- and energy-driven food intake in rats.69 The wide areas known to show FTO expression suggest that FTO may exert pleiotropic effects on a range of behaviors related to both appetite control, and nonappetite-related behaviors. Candidate gene studies appear to confirm that variation in FTO is associated with child eating behaviors, although the pattern of associations across studies is somewhat inconsistent. One of the earliest studies reported that, in 3641 UK children ages 10–11 participating in the Avon Longitudinal Study of Parents and Children (ALSPAC), the A allele of rs9939609 was associated with increased energy intake from 3-day weighted food records, largely attributable to increased energy intake from saturated fat, monounsaturated fat, polyunsaturated fat, and trans fatty acids.71 This finding has been interpreted as suggesting that the A allele is associated with lower self-regulation of eating behaviors, but this conclusion is difficult to draw since self-reported energy intake should not be used as an outcome in nutritional epidemiology. Rather, it should be used as an adjustment to correct for the known underreporting of self-reported food intake. It is not clear the extent to which energy intake per se reflects selfregulation.72–74

Appetitive Traits: Genetic Contributions to Pediatric Eating Behaviors

This initial finding has been substantiated by more direct measurements of child eating behaviors. Observational studies were, by necessity, somewhat small in sample size, but have yielded the expected findings. The A allele of rs9939609 was associated with greater food intake during a test meal, and less of a reduction in food intake following a preload during a compensation trial, in 76 children.75 The same allele (A at rs9939609) was also associated with increases in highly palatable food intake (grams consumed) during the EAH protocol in 131 preschool children ages 4–5 years.76 Children (ages 6–19) are also more likely to report feeling a loss of control over eating at a buffet meal when then have at least one A allele at rs9939609 but do not differ from those without the A allele in terms of calories consumed at the buffet.77 Drawing conclusions from small-scale candidate gene studies is difficult, although the similarity in the direction of findings (the A allele associated with less self-regulation of eating behaviors) is encouraging. Larger scale studies using questionnaire assessment of eating behaviors appear to substantiate the observational data and add some insights into the specificity of the associations. In a study of 1718 children in the UK, the A allele from rs9939609 was associated with food responsiveness, but not enjoyment of food, emotional overeating nor satiety responsiveness, all as measured by the CEBQ.9,78 This may be a power issue, however, since a larger study encompassing 3337 children did find that satiety responsiveness as measured by the CEBQ9 was lower in children who had two A alleles (homozygotes) at rs9939609. This study suggested that the A allele was also associated with higher enjoyment of food scores on the CEBQ, although this latter finding did not reach significance.79 The association of the A allele at rs9939609 with obesityassociated eating behaviors, or those indicative of poorer eating self-regulation, is starting to emerge as robustly as the association between FTO and BMI overall. In terms of the causal change associated with this locus, one study suggested that FTO acts on the eating behavior directly (in this case satiety responsiveness), and through this association is seen to predict BMI. This single study needs replication, and it seems unlikely that FTO only affects eating behaviors and not other BMI-associated traits given the large effect size of the FTO-BMI association.79 Limitations and Future Directions of Gene Association Studies

Child appetitive traits are moderately heritable (Table 7.1), yet genetic association studies have only been successful in linking rs9939609 in FTO to child appetitive traits. The small effect sizes for the association of FTO with child eating behaviors indicate that only a tiny proportion of the estimated heritable variance in child appetitive traits is accounted for by variation at this locus. This problem is known as the problem of the “missing heritability”80 and is not unusual for genetic investigations of a trait—even for adult BMI, subject to some of the largest genetic investigations to date, 13,000) showed that later overweight/obesity rates were actually higher in the intervention group.9 It therefore is not clear if the associations between early breastfeeding and obesity rates are due to other confounding factors (e.g., maternal education, maternal sensitivity, later feeding, and consumption patterns) or if the effects of breastfeeding on the long-term regulation of children’s eating may vary significantly across populations. Also, it is very possible that early patterns of self-regulation in breastfed infants could be overridden by later parental feeding practices. Further experimental research in this area is sorely needed. The next major change in children’s eating usually occurs sometime in the middle of the first year of life—the introduction of complementary foods. Although the World Health Organization recommends that parents begin introducing complementary foods at 6 months,10 many parents introduce these foods much earlier. Gibson-Moore,11 for example, summarizes the results of a survey conducted in the United Kingdom showing that three-quarters of mothers introduced solid foods by 5 months and one quarter between ages 3 and 4 months. Researchers have argued that the early introduction of solid foods might lead to overeating and subsequent childhood obesity, although the data on this issue are somewhat inconsistent. One systematic review12 found no consistent associations between the timing of introducing solid food and later childhood obesity, whereas two others13,14 found that introducing solid foods before 4 months increased later childhood obesity risk. In a recent meta-analysis of 13 prospective studies, Wang and associates15 found that introducing solid foods before 4 months was positively associated with later childhood obesity. Timing of the introduction of solid foods also appears to be related to later food acceptance. A large longitudinal study in England showed that introduction of lumpy solid foods after 9 months was associated with increased fussy eating and feeding problems at 15 months16 and at 7 years.17 Similarly, a large longitudinal study in Amsterdam showed that infants who were introduced to solid foods after 6 months showed less

Parenting Influences on Appetite and Weight

enjoyment of food at age 5.18 These results are consistent with a retrospective study by Cashdan19 who found that children who were introduced to solid foods at 7 months or later ate a narrower range of foods in the elementary school years than children introduced to solid foods before 7 months. The timing of the introduction of vegetables appears to be particularly important. In a French sample, the earlier mothers offered their infants vegetables, the more likely the infants were to accept them.20 Additionally, in a study in the Netherlands, infants who were introduced to vegetables between 4 and 5 months showed the lowest levels of fussy eating at age 4.21 Variety of early exposure is important as well. In a longitudinal study in Australia, toddlers who had tried a greater number of vegetables at 14 months showed less food fussiness at 3½ years.22 The effects of variety, however, may be moderated by breastfeeding. Maier-Noth et al.23 found that children who had been breastfed, or who had been fed a high variety of vegetables at the start of weaning, were more likely, in an experimental task at 6 years, to taste and to eat more new vegetables they were offered and to report liking them more. This occurred despite the finding that breastfeeding versus bottle feeding mothers (and mothers who did or did not offer a variety of foods at weaning) did not differ in the variety of foods they offered their children at ages 3 or 6 years. These findings are consistent with the argument that breastfed babies are exposed to a greater variety of tastes in the early months than bottle-fed babies and that repeated exposure to different tastes increases children’s acceptance of a variety of foods.2 Besides the timing and variety of new foods offered, a highly debated issue concerns how complementary foods are first introduced to infants. Traditionally at weaning, infants are offered pureed foods through spoon-feeding and gradually introduced to finger foods.24 In baby-led weaning, an approach popularized by the publication of a book by Rapley and Murkett,25 mothers are encouraged to breastfeed their babies and then around 6 months, as part of family meals, to offer their children small bites of the food the family is eating, and to let the child decide if, and how much, he or she eats. Certain foods not suitable for infants are to be avoided. Although infants exposed to this approach may eat very little solid foods at first, this is not considered to be a concern because the mother continues breastfeeding for nutrition purposes. One purported advantage of this approach is that young children control what and how much they eat which is believed to facilitate the development of eating self-regulation.26 Preliminary data on this approach27 shows that parents using baby-led weaning (compared to parents using more traditional approaches) delay the introduction of solid foods, are more likely to offer whole foods as first foods, breastfeed longer, and engage in less control during feeding. A small number of follow-up studies (all employing maternal self-report data) show less picky eating, better appetite control, and lower weight in children who had experienced baby-led weaning. Although promising, as noted by Brown and associates in their review,27 the correlational designs, the small number of studies, the use of maternal reports, and the issues of self-

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selection make it difficult to draw causal influences from these findings. A recently published randomized control trial showed no significant differences in BMIz scores or energy intake between infants who had experienced a baby-led approach to weaning versus those who experienced traditional spoon-feeding.28 However, mothers of infants in the baby-led group reported that their children showed less fussy eating or picky eating at 12 months and greater enjoyment of food at both 12 and 24 months. Not surprisingly, the nutritional quality of foods offered to infants and toddlers (at weaning and beyond) is associated with child overweight and obesity. Specifically, several studies show that infants and toddlers who show low levels of fruit and vegetable consumption, and who show high levels of consumption of juice, sugar-sweetened drinks, high sugar or high fat snacks, and fast foods are at the highest obesity risk (see Wasser et al.,29 for a review). Finally, studies of parental feeding practices during the infant and toddler years suggest that responsive feeding practices (responsiveness to the children’s fullness cues during feeding) may reduce the risk of later childhood obesity.30–33 In contrast, highly controlling feeding practices (including restriction), as well as using food to soothe a fussy infant or toddler, may override young children’s responsiveness to their internal satiety cues and increase obesity risk.32–37 Please see Box 9.1 for a summary of this section.

PRESCHOOL AGE Research evidence supports the continued influence of a wide range of parental feeding behaviors on the socialization of child eating behaviors and weight status after toddlerhood.38 Feeding practices refer to specific goal-oriented directives that are used to control what and how much the child eats.39 These practices range from supportive and involved to intrusive and demanding. In their seminal article, Vaughn and colleagues40 suggested a content map to be used to guide research in this area. These authors categorized parental practices into three higher order feeding constructs—coercive control, structure, and autonomy support—with multiple specific practices within each construct.

BOX 9.1 Section Summary

• • •

Eating behaviors undergo dramatic changes during the first 2 years of life. Infants begin life consuming a single food and completely dependent on the caregiver and enter toddlerhood with the ability to self-feed and consume a varied diet. The timing of introduction to solid foods and the variety of foods offered shapes food acceptance. How children are fed may be as important as what they are fed. Baby-led approaches to weaning that afford children greater control over eating may reduce picky eating.

Parenting Influences on Appetite and Weight

Coercive control is defined as parental directives that reflect domination, pressure, and/ or attempts by the parent to impose their will on the child.40 To date, a number of coercive control type feeding practices have been examined in research studies including restrictive feeding, pressuring the child to eat, and use of threats and bribes.40 Restrictive feeding practices (commonly referred to as “restriction”) have been highly predictive of problematic child eating behaviors in preschoolers across multiple studies.39 In crosssectional studies, restrictive feeding practices along with child reports of parental restriction have been associated with higher child intake in the absence of hunger (when young children are given access to palatable snack foods) and higher child disinhibition (see Ventura and Birch39 for a review). Higher levels of restrictive feeding have also been associated with mothers’ perception of their child having a large appetite41 as well as higher consumption of junk food, sweets, and unhealthy snacks.42,43 In other cross-sectional studies, young children whose parents exhibited higher levels of restriction for foods high in sugar showed more preference for those foods.44 To summarize, a collection of crosssectional studies show supporting evidence for an association between parental restriction and problematic child eating behaviors in preschoolers.39,45 Similarly, higher levels of parental restriction during preschool predict higher levels of eating in the absence of hunger during later childhood.46–48 Evidence linking restrictive feeding during preschool to later child weight status is mixed. Parental restriction predicted child weight gain over time in some studies49,50 and not in others.51,52 One study found restriction to be associated with less child weight gain over a 3-year period.53 However, a critical concern in the literature is the directionality of the causal pathway. It may be that restriction leads to problematic child eating but it also could be that mothers restrict because children are overweight. Some studies provide evidence for a causal relationship where parental restriction preceded child weight gain (see Clark and colleagues54 for a review). In contrast, a recent longitudinal study examining the bidirectional association of parental restriction with children’s weight status showed that continued used of restrictive feeding over time appeared to be in response to the unhealthy weight of the child rather than the cause of child overweight.55 Researchers have also made distinctions between overt and covert control such that keeping certain low-nutrient high-density foods out of the house may lead to healthier eating among children.56 Covertly limiting opportunities for consumption of unhealthy food is considered a more structured parental practice as opposed to a coercive one by some experts.40 Pressuring the child to eat (commonly referred to as “pressure to eat”) has been associated with higher levels of unhealthy food intake and higher ratings of pickiness in crosssectional studies of preschoolers.38,39 More specifically, evidence supports an association between parental pressure to eat and lower dietary quality of food intake57–59 and higher levels of restraint and emotional disinhibition in preschool children.60 Experimental work has shown that pressure to eat leads to more negative comments by the child

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and reduced willingness to consume the food pressured to eat.61 Additional work has differentiated between autonomy supportive and coercive-controlling prompts to eat and has shown more observed parent coercive-controlling prompts were associated with more food refusals by children.62 One longitudinal study showed evidence that pressuring the child to eat during the preschool years was associated with poorer dietary quality and higher levels of pickiness in later childhood.63 For the most part, most studies showed pressure to eat to be associated with a lower child weight status.45 The use of threats and bribes during feeding has been shown to have a detrimental effect on young children’s eating behaviors and food preferences.3,38 Parents sometimes use sweet and savory foods as a bribe for eating new or healthy food. Using sweets as a bribe has been shown to increase preferences for the sweets and also make it more difficult for young children to form preferences for the new or healthy foods.64,65 Parents may also use playtime activities or material objects as bribes for healthy eating. This strategy may lead to increased intake of the targeted food in the short term; however, the effects become nonsignificant over time.66 The use of threats and bribes makes it less likely that a child will develop a preference for the targeted food in the long term.38 Structure is defined as parents’ organization of the child’s environment to facilitate competence including helping young children maintain certain dietary boundaries and organization of their food environment.40 The most commonly studied feeding practice within the higher order construct of structure is monitoring. Monitoring refers to the extent to which parents keep track of child intake of various foods such as sweets, snacks, and other high fat foods. Research studies showing the association between parental monitoring and child eating behaviors in young children are somewhat mixed with some studies showing an association with healthier child dietary intake51,67,68 and others showing no association (see Vaughn et al.40 for a review). Regarding child weight status, a recent systematic review found no associations between parental monitoring and child weight status.45 However, one longitudinal study found a negative relationship between monitoring practices and child weight over an 18-month period.50 Modeling, defined as both parental intake of healthy food and the frequency with which parents eat healthy food in general and demonstrate the benefits in front of young children, has been shown to have a positive relationship with healthy eating among young children across multiple studies (see Chapter 4, “Effects of Modeling on Children’s Eating Behavior”69 in this book and Yee et al.38 for a review). This suggests that parental modeling may be an important feeding strategy for helping young children to eat healthy foods. Food availability and accessibility is considered to be another important aspect of the higher order construct of structure.40 Food availability is defined as the presence or absence of foods in the home.40 Recent studies have shown that availability and accessibility are related to food intake in preschool children. For example, two recent studies have shown that fruit and vegetable intake is related to availability and accessibility of these foods in the homes of preschoolers.70,71 In a separate study, less availability of

Parenting Influences on Appetite and Weight

sugar-sweetened beverages in the home was related to less intake of that item among preschoolers.72 Collectively, food availability and accessibility practices in the home are important because they determine the home food environment of young children. Finally, the concept of control versus structure has been refined in the developmental psychology literature to differentiate between control based on intrusion and domination (coercive control) versus control based on guidance (structure).73 In the feeding literature, many of the constructs within structure have been intermingled with more coercive practices.40 Based on the differentiation by Vaughn and colleagues,40 in this chapter, we differentiate between coercive-type practices (restriction, pressure to eat, and threats and bribes) and practices based on guidance (monitoring, modeling, and food availability and accessibility). As emphasized here, some of these structured practices have shown beneficial child outcomes. Autonomy support is the third higher order construct identified by Vaughn and colleagues40 as important when examining parental feeding practices for young children. Autonomy support involves providing sufficient support to nurture young children’s capacity to self-regulate when the parent is not present.40 Autonomy supportive feeding practices include the use of praise, encouragement, and reasoning.40 The use of praise— positive reinforcement through verbal feedback—has been fairly consistently associated with healthy eating in young children.67,74,75 Similarly, the use of encouragement—ways that parents positively, gently, and supportively inspire young children to adopt healthy eating habits—has also been associated with healthy eating among preschoolers.68,76,77 The use of reasoning—parental use of logic as a way of persuading young children to change their eating habits—was associated with greater fruit and vegetable intake in one study, but not examined extensively in younger children.75 In addition to specific feeding practices, general and food specific parental styles have been examined in relationship to child eating behaviors and weight status among preschoolers. Styles of parental behavior describe an attitude and general approach parents use to raise their children.1 Practices are considered to be embedded in the larger style in which they are expressed.78 Feeding styles use a general parenting framework to examine how parents interact with children around eating. The construct of feeding styles includes the emotional climate a parent creates with their child during eating episodes, which in turn influences the child’s eating behaviors.79 Feeding styles are measured along two dimensions of demandingness (how demanding parents are during eating episodes) and responsiveness (how sensitive parents are to the child’s individual needs).80 Differences across the dimensions result in four feeding styles: authoritative (high demand/high responsiveness) defined as parents with reasonable nutritional demands and sensitivity toward the child’s needs, authoritarian (high demand/low responsiveness) defined as parents who are highly controlling with little sensitivity to the child’s individual needs during feeding, indulgent (low demand/ high responsiveness) defined as parents with high responsivity in conjunction with low

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structure around feeding, and uninvolved (low demand/low responsiveness) defined as parents with a lack of involvement in feeding.81 Of the four feeding styles, the indulgent style has been consistently associated with higher child intake of unhealthy foods and a higher weight status across multiple studies (see El-Behadli et al.81 for a review). For example, in three separate studies of preschoolers, children with indulgent parents self-selected and ate larger portion sizes,82 consumed lower amounts of fruit, vegetables, and dairy83 and whole grains.84 In another study, young children of indulgent parents consumed higher amounts of low-nutrient, energy-dense snack foods.85 Importantly, in a recent longitudinal study of young children over an 18-month period, the indulgent feeding style predicted increases in child weight from preschool to elementary school.50 One possible mechanism through which indulgent feeding may lead to childhood overweight is through a lessened ability to selfregulate eating. The indulgent feeding style was associated with lessened satiety responsiveness and higher enjoyment of food in preschool children—two known components of self-regulation around eating in children.86 In summary, there is consistent evidence that the indulgent feeding style (high parental responsiveness to the child in conjunction with low structure and boundaries around feeding) places young children at the most risk for developing childhood obesity. Less is known about associations between the other feeding styles and child outcomes. However, a handful of studies have shown that the authoritative feeding style is optimal. Two studies showed that, in contrast to the other feeding styles, young children of authoritative parents consumed lower amounts of low-nutrient, energy-dense snack foods85 and reported higher availability of fruit and vegetables in the home.87 A third study showed that children of authoritative parents consumed a dinner with significantly higher Healthy Eating Index scores (a USDA measure of diet quality) compared to the other feeding styles.88 In contrast, a study of preschoolers showed that children of uninvolved parents consumed less fruit, vegetables, and dairy.83 In a separate study of preschoolers, uninvolved parents reported higher rates of depressive symptoms and stress which may explain their lack of involvement with their preschoolers during the feeding process.89 More studies need to be conducted to clarify patterns of behavior between the other feeding styles and child outcomes. General parenting styles—attitudes and behaviors regarding how to raise children that are not necessarily focused on child eating behaviors—have also been linked to weight among preschoolers. When followed from preschool through first grade, Rhee and colleagues90 found overweight to be more prevalent in children of authoritarian parents (based on a general parenting style). Authoritarian parents are those who place strict controls and demands on their child using power-assertive behaviors. This association found between authoritarian general parenting and higher child weight status is consistent with earlier work on restrictive feeding showing that higher parental restriction during the preschool years predicted higher rates of eating in the absence of hunger during

Parenting Influences on Appetite and Weight

BOX 9.2 Section Summary

• • •

Feeding practices are goal-directed behaviors that parents use to socialize children’s eating. Feeding styles reflect the global emotional climate and approach to feeding in which feeding practices are embedded. Feeding approaches that provide structure and set limits while supporting children’s autonomy are thought to yield the best outcomes for children’s eating behaviors and weight.

elementary school.46 Most of the studies linking general parenting styles to child weight status have been conducted with older children.45 More information can be found in the following sections on older children. Please see Box 9.2 for a summary of this section.

ELEMENTARY SCHOOL AGE During the elementary school years, children encounter a much wider range of food environments. Some parents naturally modify their feeding behaviors as children grow older to allow greater autonomy to the child. These parents tend to influence children’s eating behavior inside and outside the home. Other parents continue to use the same practices they used with younger children. Among elementary school-aged children, the use of coercive control continues to be seen in the feeding literature. The coercive control practices used with older children were associated with healthy and unhealthy child food intake in some studies. In a recent systematic review of older children, about 50% of the studies reviewed showed an association between parental restriction and child intake.38 For example, restriction was associated with lower intakes of sugar-sweetened beverages, snacks, and sweets in some studies,91–93 whereas in other studies it was not.94–96 Similarly, the systematic reviewed showed an association between pressure to eat and child intake in only 14% of the studies reviewed.38 For example, pressure to eat was not associated with fruit, vegetable, snack, and/or high calorie beverage intake in a number of studies conducted with older children.92,94,95,97 Using food as a reward (considered a type of threat and bribe) was positively associated with snack intake in one study with 9-year-olds91 but not associated with vegetable, sugar-sweetened beverages, or juice intake in other studies with 10- to 12-year-olds.98,99 The pattern of results between coercive-type feeding practices and child intake in older children produced mixed results supporting the need for continued investigations to clarify these relationships. Structured feeding practices such as modeling, monitoring, and availability of healthy foods in the home have also been seen in older children. In a recent systematic review of parental feeding practices with older children, parental modeling of healthy food intake was associated with child intake of healthy food in 87% of the reviewed studies.38

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For example, parental modeling was positively associated with child intake of fruit and/or vegetables in children ages 8–12 across multiple cross-sectional studies.100–104 Parental monitoring and its association with child intake of healthy foods have been mostly seen in studies with younger children (see Vaughn et al.40 for a review). In older children, studies have failed to show significant associations between parental monitoring and child intake.105,106 In contrast, research studies have consistently shown that availability of healthy foods in the home was associated with child intake in older elementary schoolaged children.107,108 Autonomy support practices have been reported less often in older elementary schoolaged children. The use of encouragement has not been widely examined with older children, perhaps because the use of encouragement tends to decrease as children get older.109 The use of praise has been examined in older children; however, studies have shown no significant association between praise and child intake of healthy foods (see Yee et al.38 for a review). The use of reasoning with older children has shown some evidence of associations with increased fruit and vegetable intake.95,110 In summation, the use of autonomy supportive practices with older children has not been studied extensively. Future studies should examine these practices as possible strategies for getting older children to eating healthier foods.

ADOLESCENCE As children enter adolescence, they spend even greater amounts of time eating in their parents’ absence.111 Compared to younger children, adolescents make many more food choices on their own—eating fast food with friends, buying snacks at the local convenience store, drinking coffee at the local coffee shop, or eating at a friend’s house. Moreover, as described later, even when they are eating with their parents at home, parents spend less time engaged in specific feeding practices to encourage or discourage eating, and spend more time trying to indirectly influence the child’s eating behavior. Most studies of parental influences on adolescent food consumption are school-based studies where a large number of middle-school or high-school students complete questionnaires on both their own eating behavior and their mother’s and/or father’s feeding practices (see Pinard et al.112; Vollmer and Mobley113; Yee et al.38 for reviews). These studies consistently show that availability of foods in the home and modeling of food consumption (both indirect strategies) are usually the strongest correlates of both healthy and unhealthy food consumption by adolescents (most studies have focused on fruit and vegetable consumption and on the consumption of high fat/high sugar foods). In a recent review by Yee and associates,38 parental feeding studies were broken down into those of preschool children, elementary school-aged children, and adolescents. Although strategies such as active guidance, pressure to eat, reward, and praise were

Parenting Influences on Appetite and Weight

examined in studies of preschool and school-age children, these practices were not examined in the adolescent studies. Instead, only modeling, availability, and restrictive guidance were examined. Modeling and availability were positively associated with the intake of both healthy and unhealthy food; restriction was only correlated with unhealthy food consumption (in a negative direction). In an umbrella review, Cislak and colleagues114 found that parental monitoring was positively associated with adolescent’s healthy nutrition as well. Although the researchers who studied adolescents do not explain why they focused on such a narrow range of parental strategies in their research, it is very possible that strategies such as pressure to eat, reward, and praise were not examined because such direct strategies are uncommon during this age period. This is consistent with numerous studies of parenting during adolescence where parents shift from highly directive to more indirect strategies in response to their adolescent’s developing autonomy.115 The other area examined in studies of adolescents is the relationship between the parents’ general parenting style and adolescent food intake and/or weight status (see reviews by Sleddens et al.116; Vollmer and Mobley113). As found in studies of elementary-aged children, parents of adolescents exhibiting an authoritative general parenting style have adolescents who consume healthier food and have lower weight status than parents showing authoritarian, indulgent, or uninvolved styles.117–121 Please see Box 9.3 for a summary of this section.

SUMMARY AND CONCLUSIONS In summary, over the last two decades a large literature has emerged uncovering relationships between parental feeding and children’s eating behaviors and weight status. These studies show that the parental behaviors associated with children’s eating behavior, not only involve what parents feed their children, but how they feed them. As would be expected, researchers focusing on children of different ages have focused on different feeding behaviors—breast feeding and the introduction of solid foods in infancy, coercive and autonomy promoting practices in early and middle childhood, and structuring behaviors in adolescence. The most common child eating behaviors researchers have examined include the self-regulation of eating; fruit and vegetable intake; and the consumption of high fat snacks, high sugar snacks, and sweetened beverages. It is clear from Box 9.3 Section Summary

• • •

During elementary school and adolescence children exert greater autonomy over food choices and shift to eating more frequently outside the home. The availability of healthy foods in the home has an important influence on children’s eating behaviors during this time. Parental modeling of eating behaviors may play a more important role in shaping children’s eating behaviors than other feeding practices during this time.

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this literature review that parental feeding in infancy and early childhood focuses primarily on helping shape early eating habits, whereas later practices focus on providing an environment that helps reinforce earlier consumption patterns (particularly fruit, vegetable, and snack consumption). The results of these studies have numerous implications for parents and a variety of professionals. Based upon the available evidence to date, a number of feeding practices contribute to the healthy development of infants, children, and adolescents. Mothers should be encouraged to breastfeed their infants, to introduce new foods (especially vegetables) around 5–6 months, and to offer their children a wide variety of foods at weaning. For a number of reasons discussed previously, these practices can lead to the development of healthy child eating behaviors and habits into early and middle childhood. As children begin to eat adult foods, parents should model the consumption of healthy foods and make healthy foods available and accessible in the home. When feeding their children, parents should be sensitive and responsive to their children’s signals of hunger and fullness and avoid overtly restricting consumption or pressuring children to eat. Such practices appear to facilitate children’s self-regulation of eating. Parents also should help educate their children about healthy eating patterns. Finally, as children spend more time outside the home, it is important for parents to monitor their child’s food consumption, but in a nonintrusive, and autonomy supportive way. The conclusions offered here, however, need to be considered within the limitations of this research literature. Most studies are cross-sectional, examining associations between maternal feeding practices and child eating behaviors (studies of fathers and other caregivers are rare). Although a number of longitudinal studies have been conducted, researchers have rarely employed multiple methods, often relying solely on one reporter to assess both feeding patterns and child eating behavior. Studies of young children, for example, have primarily relied on maternal reports, whereas studies of adolescents have primarily relied on child reports. Not only does the reliance on a single reporter inflate the size of the relationships, self-reports have numerous limitations. For example, questions can be confusing to the responder, participants may respond to what the researchers want to hear; participants may try to make a positive impression; and participants may not accurately remember how often they engaged in specific behaviors, may not accurately average across multiple occurrences of a behavior, or may not be consciously aware of the behavior being assessed.122 This reliance on cross-sectional, single reporter studies is problematic because it is difficult to draw inferences about the direction of causality (does maternal feeding affect child eating or vice versa?) or about the specific interactional and learning processes that contribute to the development of child eating patterns. Future longitudinal studies with multiple time points utilizing multiple methods (e.g., interviews, questionnaires, and observations) and reporters along with statistical models that allow for stronger causal inferences would go a long way in helping us understand these complex processes.

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72. van Grieken A, Renders CM, van de Gaar VM, Hirasing RA, Raat H. Associations between the home environment and children’s sweet beverage consumption at 2-year follow-up: the ‘Be active, eat right’ study. Pediatr Obes 2015;10(2):126–33. 73. Grolnick WS, Pomerantz EM. Issues and challenges in studying parental control: toward a new conceptualization. Child Dev Perspect 2009;3(3):165–70. 74. Cooke LJ, Wardle J, Gibson EL, Sapochnik M, Sheiham A, Lawson M. Demographic, familial and trait predictors of fruit and vegetable consumption by pre-school children. Public Health Nutr 2004; 7(2):295–302. 75. Vereecken CA, Keukelier E, Maes L. Influence of mother’s educational level on food parenting practices and food habits of young children. Appetite 2004;43(1):93–103. 76. Lo K, Cheung C, Lee A, Tam WW, Keung V. Associations between parental feeding styles and childhood eating habits: a survey of Hong Kong pre-school children. PLoS One 2015;10(4).e0124753. 77. Shim JE, Kim J, Lee Y. Fruit and vegetable intakes of preschool children are associated with feeding practices facilitating internalization of extrinsic motivation. J Nutr Educ Behav 2016;48(5):311–7. e311. 78. Darling N, Steinberg L. Parenting style as context: an integrative model. Psychol Bull 1993;113(3):487. 79. Hughes SO, Power TG, Papaioannou MA, et al. Emotional climate, feeding practices, and feeding styles: an observational analysis of the dinner meal in Head Start families. Int J Behav Nutr Phys Act 2011;8:60. 80. Hughes SO, Power TG, Fisher JO, Mueller S, Nicklas TA. Revisiting a neglected construct: parenting styles in a child-feeding context. Appetite 2005;44:83–92. 81. El-Behadli AF, Sharp C, Hughes SO, Obasi EM, Nicklas TA. Maternal depression, stress and feeding styles: towards a framework for theory and research in child obesity. Br J Nutr 2015;113:S55–71. 82. Fisher JO, Birch LL, Zhang J, Grusak MA, Hughes SO. External influences on children’s self-served portions at meals. Int J Obes (Lond) 2013;37(7):954–60. 83. Hoerr SL, Hughes SO, Fisher JO, Nicklas TA, Liu Y, Shewchuk RM. Associations among parental feeding styles and children’s food intake in families with limited incomes. Int J Behav Nutr Phys Act 2009;6:55. 84. Tovar A, Choumenkovitch SF, Hennessy E, et al. Low demanding parental feeding style is associated with low consumption of whole grains among children of recent immigrants. Appetite 2015;95:211–8. 85. Hennessy E, Hughes SO, Goldberg JP, Hyatt RR, Economos CD. Permissive parental feeding behavior is associated with an increase in intake of low-nutrient-dense foods among American children living in rural communities. J Acad Nutr Diet 2012;112(1):142–8. 86. Frankel LA, O’Connor TM, Chen TA, Nicklas T, Power TG, Hughes SO. Parents’ perceptions of preschool children’s ability to regulate eating. Feeding style differences. Appetite 2014;76:166–74. 87. Patrick H, Nicklas TA, Hughes SO, Morales M. The benefits of authoritative feeding style: caregiver feeding styles and children’s food consumption patterns. Appetite 2005;44(2):243–9. 88. Arlinghaus KR, Vollrath K, Dholakia R, et al. Authoritative parent feeding style is associated with better child diet quality at dinner among low-income minority families. Under review. 89. Hughes SO, Power TG, Liu Y, Sharp C, Nicklas TA. Parent emotional distress and feeding styles in low-income families. The role of parent depression and parenting stress. Appetite 2015;92:337–42. 90. Rhee KE, Lumeng JC, Appugliese DP, Kaciroti N, Bradley RH. Parenting styles and overweight status in first grade. Pediatrics 2006;117(6):2047–54. 91. Rodenburg G, Kremers SP, Oenema A, van de Mheen D. Associations of parental feeding styles with child snacking behaviour and weight in the context of general parenting. Public Health Nutr 2014;17(5):960–9. 92. Vereecken C, Haerens L, De Bourdeaudhuij I, Maes L. The relationship between children’s home food environment and dietary patterns in childhood and adolescence. Public Health Nutr 2010;13(10A):1729–35. 93. Vereecken C, Legiest E, De Bourdeaudhuij I, Maes L. Associations between general parenting styles and specific food-related parenting practices and children’s food consumption. Am J Health Promot 2009;23(4):233–40. 94. Couch SC, Glanz K, Zhou C, Sallis JF, Saelens BE. Home food environment in relation to children’s diet quality and weight status. J Acad Nutr Diet 2014;114(10):1569–79. e1561.

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CHAPTER 10

Executive Function and Self-Regulatory Influences on Children’s Eating Lori A. Francis*, Nathaniel R. Riggs† *

Department of Biobehavioral Health, College of Health and Human Development, The Pennsylvania State University, University Park, PA, United States † Department of Human Development and Family Studies, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, United States

INTRODUCTION Scientists in the field of ingestive behavior have long been interested in understanding factors involved in the control of food intake. Obesogenic stimuli and messages (e.g., large portion sizes, availability of foods high in fat and sugar) that are at odds with exercising self-regulation are ubiquitous in the current society. Barring disorders that affect the regulation of food intake, such as Prader-Willi Syndrome, infants and young children demonstrate a capacity to self-regulate short-term food and energy intake. Variation in this capacity has been shown to be attributable to trait-based characteristics such as infant sucking rate or temperamental traits such as soothability (the ease with which infants are able to be soothed), as well as external factors that may disrupt regulation, such as bottle-feeding or controlling feeding practices. Reviews on the development of eating behaviors in children1,2 describe various ways in which children’s eating behaviors are influenced by nature or nurture-based processes. Nature-based processes include biological predispositions that impact the etiology of eating styles and food preferences in ways that promote or inhibit children’s ability to make healthy food choices and self-regulate their food intake. Nurture-based processes include learned behaviors (such as learning that green vegetables are likely bitter) or environmental forces that exert control over children’s eating, such as a parent who excessively controls his/her child’s food choices or models unhealthy eating behaviors. Yet, even in the face of environmental factors that disrupt energy regulation, such as controlling parental feeding practices and obesogenic eating behaviors, large portion sizes, and food availability, some young children exhibit an exquisite ability to respond to satiety cues and self-regulate their eating. However, the ability to self-regulate intake declines with age, despite age-related increases in selfregulatory capacity in general behavioral domains of development.3–5 Self-regulation, the primary focus of this chapter, is an umbrella term encompassing various cognitive, emotional, and behavioral processes by which people pursue and attain goals. These processes—mediated by the prefrontal cortex, which is responsible for higher order cognitive thought and processing—can be consciously and deliberately initiated, or Pediatric Food Preferences and Eating Behaviors https://doi.org/10.1016/B978-0-12-811716-3.00010-5

© 2018 Elsevier Inc. All rights reserved.

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generated automatically, operating outside of conscious control.6 Self-regulation is critical across several developmental domains in which problem solving is required, such as socialemotional functioning and interpersonal relationships, academic achievement, and various health behaviors including substance use, physical activity, and eating behavior. Specific to eating behaviors, self-regulation refers to the emotional, cognitive, and behavioral processes used to manage food intake and food choices. In adults, behavioral manifestations of self-regulation of eating behaviors include a myriad of often overlapping behaviors, including planning healthy meals, controlling dysregulated eating behavior (e.g., binge eating or eating in the absence of hunger), counting calories, and dieting. Self-regulation failure occurs when an individual fails to override an undesirable behavior or impulse in favor of a desired choice.7 Self-regulation failure has been implicated in the development of obesity, and several studies suggest that this pathway operates through effects on dysregulated, overeating behaviors. In the eating domain, making decisions about healthy food choices over convenient food choices requires self-regulation. Planning meals for the week requires self-regulation. Making a decision to stop eating once you feel satisfied requires self-regulation. An individual who eats in the absence of hunger, in the presence of large amounts of palatable foods, exhibits dysregulated eating. As noted previously, self-regulation is largely influenced by the development of skills subsumed by the prefrontal cortex collectively referred to as executive function (EF). Although a universally agreed-upon EF framework does not exist, three commonly referenced EF processes are inhibitory control—the capacity to inhibit a prepotent or automatic thought in favor of a more healthy response, working memory—the ability to keep multiple streams of thought “online” for potential mental manipulation, and cognitive flexibility— the ability to shift cognitive set or attention fluidly from one object to another. Other EF processes, sometimes considered to be higher order EF, include planning, organization, sequencing, and task completion. This chapter will focus on EF and its components that are related to the self-regulation of food intake. Primary attention will be given to dysregulated eating behaviors known to function as behavioral phenotypes of obesity, namely, loss of control (LOC) eating, eating in the absence of hunger (EAH), and binge eating behaviors. We will also describe relations between EF and eating-related appetitive traits. This chapter concludes with a discussion on the implications for prevention and treatment and highlights promising findings from intervention programs designed to improve children’s self-regulatory skills. First, let us begin with a discussion on theories that have often been used to understand self-regulation of eating. These theories may provide a useful framework for linking executive function to processes related to eating behavior and regulation.

THEORETICAL FRAMEWORKS DESCRIBING THE SELF-REGULATION OF FOOD INTAKE Multiple theories in the field of human ecology have been used to understand regulatory processes involved in eating. In this section, we focus on theories most closely tied to

Executive Function and Self-Regulator Influences on Children’s Eating

processes involved in self-regulation failure, beginning with the oldest theory and ending with the most recent theory. We do not hold any one theory in higher regard than another. Rather, the utility of each theory should be determined based on the empirical question at hand. The Theory of Externality, proposed by Stanley Schachter,8 suggests that compared to those without obesity, individuals with obesity are more sensitive to external cues that signal eating, such as the presence and taste of food, and the time of the day. Stanley Schachter was one of the first scientists to suggest that eating in the absence of hunger was a behavioral phenotype of obesity. He found that when exposed to food, adults with obesity ate just as much on an empty stomach as they did on a full stomach; in other words, internal cues that signaled satiety were either ignored or perhaps not as easily or clearly recognized adults with obesity. This was in stark contrast to subjects without obesity, who ate significantly less food on a full stomach. In another experiment described by Schachter, and presented in the same paper, subjects with obesity ate more in the presence of fear (or what may be referred to as a stressor in more recent studies) than in the absence of fear/stress, suggesting that affect may play a role in food intake regulation. These findings also serve as an example of self-regulation depletion theory, which will be discussed later in this section. Schachter’s early studies—although mainly tested in adult populations—gave rise to a focus on self-regulation and its role in eating behavior. In an experiment designed to examine the externality of eating in children with and without obesity (ages 7–12 years old), Costanzo and Woody9 measured children’s consumption of nuts, with or without the shell, when viewing a 10-min film (interesting or boring). Contrary to what the authors hypothesized, watching the film while in the presence of food (i.e., nuts) did not disrupt satiety cues or result in disinhibited eating. Instead, children with obesity appeared to be more responsive to the shell on the nut, such that they ate significantly more nuts that were not in a shell, than nuts that were in a shell. There were no differences in nut intake (with or without a shell) for children without obesity. This study suggests that intake in children with obesity was influenced by the ease at which the food was accessible, which is an indicator of externally motivated eating. In a more recent study using ecological momentary assessment (EMA) to examine eating behavior in undergraduate women without overweight, Thomas et al.10 had women complete 7–10 days of EMA data collection to provide information on eating behavior, appetitive traits, and food intake and availability. The authors found that women with a higher body mass index (BMI) reported overeating only in the presence of highly palatable foods; intake in women with a lower BMI was not influenced by the presence of food. Decades of research on eating behavior in youth with overweight and obesity provide some support for the theory of externality. Dysregulated eating behavior in children with overweight and obesity appears to be influenced by a number of external factors, including the presence of food, restricted access to foods, and the presence of others. Although there is a great deal of support for external eating—eating in the absence of hunger, binge eating,

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and loss of control eating—as a behavioral phenotype of obesity, the effects sizes are moderate, indicating variation in this relation. Recent studies have turned their attention to individual, appetitive traits that may characterize dysregulated eaters. One such trait is reward sensitivity. Gray11 proposed Reinforcement Sensitivity Theory, suggesting that impulsive individuals may have an underlying personality trait that makes them (1) highly sensitive to reward (e.g., deriving pleasure from eating), which is posited to lead to a motivation to act on a behavior, and/or (2) less sensitive to punishment (e.g., concern for becoming obese) which should lead to avoidance of a behavior. Gray considered these temperamental traits to be independent of one another, and individuals vary in their sensitivity in each trait (high/low reward sensitivity, high/low punishment sensitivity). Reward and punishment sensitivity are not to be confused with impulsivity or inhibition; rather, they are traits that may explain why individuals may be high or low in inhibitory control and impulsivity. Reward sensitivity is controlled by reward processing regions of the brain that initiate approach behaviors, which are behaviors used to obtain a reward.12,13 Punishment sensitivity is controlled by the behavioral inhibition system, which is mediated by inhibitory centers in the brain, whose end goal is to avoid aversion. In the context of eating regulation, it would be theorized that individuals who are most successful at regulating their eating would be low in reward sensitivity (not highly motivated by food rewards, mainly palatable foods) and high in punishment sensitivity (highly motivated by the avoidance of a negative outcome, such as obesity). Most studies addressing reinforcement sensitivity have focused on reward sensitivity. Sensitivity to reward may impact individuals’ food preferences, loss of control eating, eating in response to external cues, and emotional eating.14 Findings from multiple studies lend support to Gray’s theory. In a study of 6- to 13-year-old children in the Netherlands, van den Berg et al.15 found that children who were rated high in impulsivity were also rated high in sensitivity to reward, and both characteristics were related to overeating. In multiple Dutch samples, reward sensitivity was positively associated with fast-food consumption in youth ages 7–16 years.16,17 De Decker and colleagues18 found that the high, positive association between reward sensitivity and frequency of fast-food intake was mediated by external eating or eating in response to external cues such as the sight and smell of food. Further, this mediation effect was only shown for youth living in households with a high availability of unhealthy foods. Considering the suppositions made in the Theory of Externality, this suggests that reward sensitivity may explain WHY individuals with obesity are more sensitive to external cues than those without obesity. In other words, reward sensitivity may explain a higher order process that impacts other regulatory systems that influence eating behaviors. Additional studies have established a link between reward sensitivity and unhealthy eating behaviors among children. In a study with more than 11,000 adolescents ages 10–17 years from four different European countries, Stok et al.19 found that higher reward sensitivity to the food environment—measured using an adapted version of

Executive Function and Self-Regulator Influences on Children’s Eating

the Power of Food Scale20—was associated with greater reports of unhealthy snacking. This association was strongest in adolescents who reported lower levels of competence in intake self-regulation. In a younger sample from Belgium, Vandeweghe21 found that preschoolers who were high in reward sensitivity were rated as high in food responsiveness, an appetitive trait characterized by a high desire to eat, regardless of hunger/fullness; food responsiveness was measured using the Child Eating Behavior Questionnaire (CEBQ).22 Vandeweghe et al.21 operationalized two constructs that were hypothesized to have a clear association with reward sensitivity and punishment sensitivity—food approach and food avoidance. Food approach was defined as behaviors and thoughts that involve movement toward food, such as overeating behaviors, constant requests for food, or food enjoyment. Food avoidance includes behaviors and thoughts that involve movement away from food, such as picky eating, slow eating rate, and emotional undereating. As hypothesized, Vandeweghe and colleagues21 showed that reward sensitivity was positively associated with food approach behaviors, and punishment sensitivity was positively associated with food avoidance behaviors. An unexpected finding was that greater punishment sensitivity was also associated with greater food approach. The authors conclude that punishment sensitivity may relate most with emotional eating-related food approach behaviors, suggesting that negative affect (e.g., guilt associated with overeating) may play a role in explaining this relation. In a small sample of U.S. preschool children, Rollins and colleagues23 measured relations among preschool children’s snack food intake, reward sensitivity, and the reinforcing value of food (i.e., willingness to work for a food reward). The findings showed that children higher in reward sensitivity worked harder for the food reward and had a higher body mass index (BMI). In addition, children who worked harder for the food reward ate more of that food during a separate snack session in which they had unlimited, ad libitum access to the food. Taken together, these findings suggest that children who find food and eating to be highly rewarding may be prone to dysregulated, external eating behavior, and may be highly sensitive to external cues that signal a desire to eat. Most of the published studies on links between eating behavior and reinforcement sensitivity have focused on reward sensitivity. Given that punishment sensitivity has been shown to relate to food avoidance behaviors that may confer some protection against dysregulated eating and obesity, there is a need for more studies on punishment sensitivity. The results are unclear, however, given that punishment sensitivity was also associated with food approach behaviors in the Vandeweghe study.21 These relations may not be well understood in young children. Restraint theory. The theory that dieting or restraint is counterproductive, in that it leads to eating dysregulation and overeating behavior, is known as Restraint Theory.26 Herman and Polivy24 proposed that while individuals with obesity may be more susceptible to external cues that signal hunger or eating than typical weight individuals, dieting may be the mechanism by which dysregulated eating emerges. For example, in an early study of dieters, Herman and Polivy25 found that restrained eaters—those who deploy cognitive control over their eating, such as counting calories—showed dysregulated

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overeating in response to an obligatory milkshake preload, compared to nonrestrained eaters, who reduced intake after the preload. In other words, when forced to consume a high-calorie milkshake, restrained eaters’ ability to self-regulate their food intake was disrupted, and resulted in greater intake. A review by Johnson, Pratt and Wardle,27 however, suggests dietary restraint is far more complex than originally proposed by Herman and Polivy. Furthermore, problems with measurement (e.g., the use of different instruments, cross-sectional study design, ecological validity of laboratory-based eating, to name a few) often make it difficult to accurately characterize restrained eating. In addition, dietary restraint is often comorbid with disinhibited eating and obesity, thus, it is difficult to disentangle the effects of restraint or obesity (or some other unmeasured variable) on disinhibited or dysregulated eating. Lastly, there is no evidence from longitudinal studies to suggest that dietary restraint causes binge eating behavior or other problematic disordered eating behaviors. We are not suggesting that Restraint Theory should be discarded, but perhaps, updated to acknowledge the complexity of eating behavior, and the idea that loss of control eating may be explained by a number of upstream cognitive processes. Yes, dysregulated eating is most certainly driven by more than what Herman and Polivy26 referred to as the “what-the-hell-effect.” In fact, a more recent theory that views self-regulation as a limited resource, posits that cognitive load depletes self-regulatory capacity, which may be a useful framework for understanding individual differences in the ability to resist temptation and self-regulate food intake. The Limited Resource Theory of Self-Regulation suggests that self-regulation failure can result when constant demands deplete self-regulatory resources or stores.7 This theory posits that individuals begin (each day or hour or situation) with a limited coffer of self-regulatory stores, and that stressors, emotions, or other forces cause one to dip into his or her stores with every self-regulatory attempt. This theory has been widely used in the field of human development and child development to explain why some individuals, in the presence of adversity, show lower levels of behavioral self-regulation. In the eating domain, this theory may infer that deficits in the capacity to self-regulate energy intake can occur when one continually encounters cues (e.g., free access to palatable foods or food advertisements) that shift the focus from internal cues to external cues that signal hunger and fullness. For example, ignoring the temptation to eat from a large bowl of chocolate candies placed in front of you while in a waiting room taps into regulatory stores.28 Based on findings from the Herman & Polivy study25 described earlier, restrained eating may deplete self-regulatory capacity in ways that increase one’s risk for dysregulated eating. If we return to the experiment by Schachter8 described earlier, individuals with obesity ate more in the presence of fear, suggesting that fear or stress may have acted as a dysregulator. The limited resource theory would suggest that continually ignoring the impulse to eat certain types and amounts of food would drain self-regulatory resources and lead to self-regulation failure. Several studies in adults and children provide evidence in support of this theory, showing that individuals display increased intake in the

Executive Function and Self-Regulator Influences on Children’s Eating

BOX 10.1 Summary

• • • •

Decades of research on eating behavior in youth with overweight and obesity provide some support for the Theory of Externality. Reinforcement Sensitivity Theory suggests that impulsive individuals may have an underlying personality trait that makes them highly sensitive to reward and/or less sensitive to punishment. The theory that dieting or restraint leads to eating dysregulation and overeating behavior is known as Restraint Theory. The Limited Resource Theory of Self-Regulation suggests that self-regulation failure can result when constant cognitive demands deplete self-regulatory stores.

absence of hunger under stress.29–34 This theory can be used as a framework for understanding various factors that influence self-regulation failure. Determining the factors that explain how, when, and why children’s self-regulation of eating is disrupted will tell us more about the mechanisms involved in the development of disordered eating behaviors and obesity. Scientists have turned their attention to understanding higher order cognitive processes that drive regulatory behavior across domains of development. Executive function is one such factor implicated in the development of problems with eating dysregulation and obesity. The remainder of this chapter will focus on executive function and its links with eating behaviors in children and adolescents (Box 10.1).

NEUROCOGNITIVE CORRELATES OF EATING BEHAVIOR Although EF is clearly apparent early in life, the PFC, and associated EF processes, continues to develop well into the 20s, distinguishing this region of the brain as the last to achieve structural and functional maturity. This, in combination with heightened sensitivity to reward and sensation seeking during adolescence,35 is thought to facilitate young people’s transition to adulthood.36 However, it is also hypothesized to place young people at heightened vulnerability for dysregulated health risk behaviors, including unhealthy food intake.37 That is, heightened reward sensitivity and risk taking in combination with protracted PFC development generates a relatively prolonged developmental window during which “bottom up” reward- and emotion-related processing is particularly salient, but “top down” processes required to regulate strong impulses and emotions have yet to fully mature.35 In addition to developmental differences in EF, individual differences exist in that some young people demonstrate greater EF proficiency than others, protecting them from participation in dysregulated behaviors. Research consistently demonstrates that children and adolescents with EF problems show elevated rates of substance use, conduct disorder, and antisocial behavior.38–41 Fewer studies have investigated the role of EF in health behaviors related to overweight

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BOX 10.2 Summary





The period of heightened reward sensitivity and risk taking in combination with protracted PFC development generates a relatively prolonged developmental window during which “bottom up” reward- and emotion-related processing is particularly salient, but “top down” processes required to regulate strong impulses and emotions have yet to fully mature. Associations between cognitive function and eating may be somewhat specific to certain aspects of cognitive function, including EF, rather than general cognitive function.

and obesity in children, although some research in adult samples has revealed associations between EF and dietary behaviors as mediators to weight gain (see Nederkoorn, Houben, Hofmann, Roefs, and Jansen42). Liang and colleagues’43 review of associations between obesity and obesity-related behaviors and neurocognition in children demonstrated strong support for a link between health behaviors and select neurocognitive processes, including EF (e.g., inhibitory control, impulsivity, cognitive flexibility, planning, delay of gratification), but not others including general cognitive function, language, and memory. This pattern of results suggests that associations between cognitive function and eating may be somewhat specific to certain aspects of cognitive function, including EF, rather than general cognitive function. The remainder of this chapter will focus on inhibitory control, impulsivity, delay of gratification, and decision making, with a brief mention of studies linking cognitive flexibility and working memory to eating behavior in youth (Box 10.2).

POTENTIAL PATHWAYS LINKING EF AND EATING BEHAVIOR Fig. 10.1 provides a conceptual framework for understanding potential pathways between eating behavior and cognitive constructs with strong inhibitory components, including inhibitory control, impulsivity, delay of gratification, and decision making. Pathways suggest links with eating behaviors that are probable behavioral phenotypes of obesity, although some of these behaviors may not be evident until adolescence or adulthood (e.g., meal planning and self-monitoring). All but one pathway (cognitive flexibility) has been confirmed in research studies with children and youth; a majority of studies confirming these pathways are discussed in this chapter. This framework should be considered preliminary. For example, the hierarchical and integrated structure of executive function and other self-regulatory constructs is not accounted for. Future research is needed to more clearly articulate how each of these constructs operate to influence food intake. Inhibitory control and impulsivity are inversely related and may be related to children’s eating dysregulation through their influence on satiation, which is the amount eaten at an eating occasion, and satiety, which is the length of time between ending an eating occasion

Executive Function and Self-Regulator Influences on Children’s Eating

Executive function

Inhibitory control

Impulsivity

Satiation and Satiety • Overeating during an eating episode • Eating in the absence of hunger

Delay of gratification

Food Choices • Selection of energydense, palatable, “comfort” foods over healthy foods

Decision making

Working memory

Appetitive Traits

Cognitive flexibility

Meal/Snack Planning

• Ruminating thoughts about food

• Setting eating or dietary goals

• Food cravings • Eating rate

• Selfmonitoring • Maintaining and updating eating plans

• Emotional eating

Fig. 10.1 Conceptual framework for understanding potential pathways between eating behavior and cognitive constructs with strong inhibitory components.

and starting another. Children who show a compromised ability to regulate in these EF processes may ignore or override satiation and satiety cues, leading to overeating and eating in the absence of hunger, particularly in situations in which access to palatable foods is unrestricted. Inhibitory control and impulsivity may also impact children’s food choices, leading to choices for more preferred, energy-dense foods that disrupt satiation processes. EF processes are related to appetitive thoughts and behaviors, including constant, ruminating thoughts about or requests for food, food enjoyment, food cravings, eating rate, and eating in response to external, rather than internal cues such as emotions. Delay of gratification refers to a decision to choose an immediate, smaller reward over a larger, future reward. It is posited that choices for a larger, future reward (e.g., weight loss or a healthy dietary profile) over immediate gratification (e.g., eating palatable foods) would result in healthier eating, dietary, and weight status outcomes. The inability to delay gratification may relate to food intake patterns that disrupt satiation and satiety, and impact food choices in ways that promote greater intake of energy-dense foods.

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Delay of gratification likely relates to appetitive thoughts and behaviors associated with increased attention to food, and external eating, as well as those thoughts and behaviors that motivate individuals to consume more food (e.g., cravings and enjoyment). Less is known about relations between eating behavior, decision making, working memory, and cognitive flexibility. Decision making is somewhat similar to delay of gratification, as it requires an individual to choose a long-term reward over immediate gratification. We hypothesize that decision making, like delay of gratification, may influence satiation and satiety directly, but also indirectly through its effects on food choices. Decisions about food and eating—which most often involve weighing the risks associated with a reward vs. a punishment—are likely influenced by appetitive traits, and an individual’s propensity for risky decision making may influence appetitive thoughts and behavior; thus we hypothesize a bidirectional relationship between the two. Decision making, when beneficial, should increase one’s capacity for effective planning. Working memory should operate in similar ways, calling on memory stores that help individuals make better food choices, control maladaptive appetitive thoughts and behaviors, and increase planning efficacy. There are no known studies on cognitive flexibility and eating behavior in children, although it is hypothesized to impact one’s ability to shift thoughts and action, which may relate to choosing a healthy food in the presence of palatable foods, shift attention from food (such as managing cravings), and help with planning, and updating plans (e.g., relaxing dieting rules based on a specific social context). Next, we will review some of the research findings linking EF and eating behavior in children.

INHIBITORY CONTROL AND EATING BEHAVIOR Inhibitory control, a specific EF process referring to the capacity to inhibit a dominant response and perform a subdominant one, has been the most widely studied EF process in food intake or eating behavior studies. As stated in the previous section, inhibitory control and impulsivity are highly and inversely related.44–46 That is, a child who shows high inhibitory control will likely exhibit low levels of impulsive behaviors. In a study with nearly 200 adolescents ages 14–17 years, inhibitory control was measured using a general47 and food-specific48 Go/No-Go task. In the general task, adolescents were instructed to respond by pressing a computer key when they saw a green rectangle (Go) and to ignore the blue rectangle (No-Go). In the food-specific task, they were instructed to respond to low-fat/low-sugar foods (Go) and not to respond to highfat/high-sugar foods (No-Go). Greater false alarms on both tasks—responding to the No-Go cues—were associated with greater reports of binge eating behavior in females.49 Greater false alarms on the general Go/No-Go task were associated with reports of greater intake of sweet snacks and sweetened beverages in males. It does not appear as though the food-related task was a more potent correlate of eating behavior, which suggests that impulsivity may not be domain specific. Kittel and colleagues50 measured inhibitory control using the Stroop Color-Word Interference Test (adapted from

Executive Function and Self-Regulator Influences on Children’s Eating

Golden51) in a small sample of 66 adolescents (mean age 14 years). This tasks requires individuals to either say the word shown or say the printed color of the word shown; the task increases in difficulty, such that word becomes incongruent with the printed color of the word. For example, individuals are first required to say BLACK when the word is printed in black ink, but then the rules change, requiring them to say red when the word BLACK is printed in red ink. The authors found that adolescents with binge eating disorder (BED) had lower inhibitory control than typical weight adolescents without BED; this difference remained significant after controlling for the effects of education. Shifting attention to younger samples, Groppe & Elsner52 measured inhibitory control using a Fruit Stroop Task53 in a German sample of over 1600 children ages 6–11 years, followed over a one-year period. This task is very similar to the Stroop Color-Word Interference Task; however, children were asked to either say the color presented or the correct fruit color as quickly as possible (e.g., say yellow when a green banana is presented). The authors combined this measure of inhibitory control with a measure of attention shifting and updating to create a latent construct of “cool” EF, which refers to EF measures related to problem solving. Findings showed that lower levels of cool EF were related to a greater increase in food responsiveness and enjoyment of food, another appetitive trait under the food approach umbrella. Given the earlier discussion on reward sensitivity, these findings suggest that the behavioral inhibition system is tied to reward motivation, and individuals with lower levels of inhibitory control, cognitive flexibility, and problem-solving skills may also be more sensitive to reward. Tan and Holub54 measured inhibitory control using the Children’s Behavior Questionnaire (CBQ55). Their findings showed that higher levels of parent-reported inhibitory control in children ages 3–9 years were associated with greater reports of children’s ability to selfregulate their intake, measured using a parent-report questionnaire that assessed parents’ perception of children’s ability to respond to hunger and fullness cues. Examples of items on the inhibitory control subscale of the CBQ include, “My child can easily stop an activity when s/he is told no” and “My child is usually able to resist temptation when told s/he is not supposed to do something.” In a longitudinal study of 5-year-old girls followed over 2 years, Rollins et al.23 also measured inhibitory control (at age 7) using the CBQ and measured children’s eating in the absence of hunger (EAH) in the laboratory. Results revealed that girls with low levels of inhibitory control, and with parents who reported very little or too much control over their daughters’ eating, showed the greatest increases in EAH over 2 years. Revisiting the limited resource theory of self-regulation, controlling child-feeding practices may deplete regulatory resources, and this depletion may be exacerbated in children who begin with lower regulatory stores (e.g., lower inhibitory control). These findings have implications for prevention and intervention, which will be discussed later in this chapter. Findings from studies with preschool children have shown mixed results. In a Canadian study with 193 four-year-old children, Levitan and colleagues56 measured inhibitory control using a stop signal task57 in which children were asked to press a computer key

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when they saw an arrow pointing to the left, unless they hear a tone (stop signal) after the arrow appears. Children also participated in a 30-min, laboratory-based snack test, in which they had ad libitum access to a variety of foods. Children’s higher stop signal test scores (lower inhibitory control) were associated to greater carbohydrate and sugar intake in the snack test but were not associated with greater caloric intake overall. Hughes and colleagues,58 in a sample of low-income preschoolers, used a peg tapping59 and gift delay task60 to measure inhibitory control. In the peg tapping task, children were required to tap a wooden peg twice when the experimenter tapped once, and tap once when the experimenter tapped twice. In the gift delay task, the child was told not to peek while the experimenter noisily wrapped a gift for 2 min; the number of peeks is used as an indicator of inhibitory control. Findings from Hughes et al.58 did not show any association between inhibitory control and a laboratory-based measure of eating in the absence of hunger in children. However, inhibitory control was associated with satiety responsiveness—an appetitive trait that characterizes children’s responsiveness to cues that signal fullness—and food responsiveness, discussed earlier. Related to reinforcement sensitivity theory, these findings show that the behavioral approach system and behavioral inhibition system are related; lower levels of inhibitory control were related to food approach. Inhibitory control studies were often limited by small sample sizes, and it is also problematic that the measures used to assess inhibitory control varied across studies. Perhaps certain aspects of self-regulation are associated with self-report vs. laboratory-based measures of eating behavior; reward-driven or satiety self-report measures (CEBQ) may not fully capture the constructs measured using weighed food intake in laboratory-based measures. There also may be unmeasured variables that moderate the relationship between self-regulation and food intake, such as parental control or dietary restraint (Box 10.3).

IMPULSIVITY AND EATING BEHAVIOR A study comparing adolescents with and without obesity found that adolescents with obesity who were binge eaters exhibited greater levels of impulsivity and lower levels

BOX 10.3 Summary

• •

The behavioral inhibition system is tied to reward motivation, and individuals with lower levels of inhibitory control, cognitive flexibility, and problem-solving skills may also be more sensitive to reward. The behavioral approach system and behavioral inhibition system are related, such that lower levels of inhibitory control are related to greater food approach.

Executive Function and Self-Regulator Influences on Children’s Eating

of inhibitory control in two laboratory-based behavioral tasks than adolescents with obesity who were not binge eaters.61 Nederkoorn and colleagues62 sought to replicate their findings on links between adults’ impulsivity and dysregulated eating in a sample of children ages 7–9 years. The authors found that children who were high in impulsivity were more sensitive to the presence of palatable snack foods and ate more high energydense foods than children low in impulsivity. Findings from a longitudinal study with girls assessed at 4 time points between ages 10 and 16, Goldschmidt et al.63 showed that impulsivity (measured at age 10 using a subscale from the Child Symptom Inventory, 4th edition) was positively associated with changes in BMI from age 10 to 16, and this relation was mediated by girls’ reported binge eating behaviors. This suggests that dysregulated eating behavior may be the mechanism by which impulsivity in linked with obesity. Hartmann and colleagues64 examined impulsivity using a stop signal task65 in three groups of youth ages 10–15 years with LOC eating, attention-deficit hyperactivity disorder, or neither issue. The stop signal task was administered before and after induction of negative mood using an ostracization task. There was a greater increase in impulsivity and negative mood in youth with LOC eating, compared to the other groups. The authors suggest that the presence of negative stimuli may increase impulsivity in some youth, which may explain eating dysregulation. In a study of more than 1300 youth ages 8–12 years, Scholten et al.66 found that higher levels of impulsivity—measured using the Temperament in Middle Childhood Questionnaire67—were associated with higher levels of unhealthy snack food intake. Children’s impulsivity was also positively associated with BMI. Together, these studies suggest that impulsive children may be more sensitive to the presence of palatable foods (perhaps indicative of a higher reward sensitivity to food), consume more of these foods, and show greater gains in adiposity over time, relative to less impulsive children. In addition, the findings presented suggest that impulsive children, particularly girls, may be at risk for the development of BED eating behaviors in adolescence; however, more work is needed in this area.

DELAY OF GRATIFICATION AND EATING BEHAVIOR Very few studies have examined the relationship between delay of gratification and eating behaviors in children. Delay of gratification requires individuals to choose between an immediate reward, and a larger, delayed reward. In a longitudinal study on the associations between EF and changes in appetitive traits and eating behavior in children ages 6–11 years, a compromised ability to delay gratification was associated with greater increases in emotional overeating over a one-year period.52 The authors measured delay of gratification by offering children the choice between receiving one small reward (candy or a small toy) immediately, or receiving more of that same reward 1 week later. This delay time is longer than what has been used by many studies assessing delay of gratification in children. Hughes and colleagues,58 in the study with low-income

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preschoolers described previously, found an unexpected, positive association between delay gratification and eating in the absence of hunger. That is, preschool children with a greater ability to delay gratification ate less in the absence of hunger. In this study, delay of gratification was measured using a classic protocol that asked children to choose from a small, immediate, preferred food reward, or wait 7 min for a larger reward that would be received if they waited for the researcher to return. The authors suggested that children who were willing to wait for the larger reward may have been highly motivated by their interest in the food. Pieper and Laugero68 measured delay of gratification using a task that gave children a choice from a small immediate reward (one sticker, candy or penny), or a larger amount of the same reward that the child would receive at the end of the task if they chose to wait.69 Eating in the absence of hunger was measured in the laboratory. They demonstrated that emotional arousal—measured via skin conductance—was associated with eating in the absence of hunger, but only for a subgroup of preschool children unable to delay gratification. These studies suggest that delay of gratification is related to dysregulated eating, although the findings are mixed. For some children, an inability to delay gratification may be linked to an underlying inability to respond to cues that signal satiety. Other children who eat in the absence of hunger may be highly motivated for earning a larger food reward. Perhaps reward sensitivity is a potential mediator of the relation between delay of gratification and dysregulated eating. It should also be noted that all three studies included a food reward; it would be interesting to examine these relations using a protocol that includes only a non-food reward for all participants.

DECISION MAKING AND EATING BEHAVIOR There are fewer studies that have tested associations with EF processes (e.g., decision making, cognitive flexibility, working memory) other than those directly associated with inhibitory control, appetitive behaviors, and weight. Decision making is often measured by asking an individual to make a decision about making a risky choice that may lead to a punishment or reward and has been shown to relate to LOC eating behavior in adults,70,71 and more recently, in adolescents. Macchi and colleagues72 measured decision making in 14- to 18-year-old adolescents using the Balloon Analog Risk Task, which measures risky decision making. In this task, participants receive points for inflating a balloon and need to make decisions about when to stop inflating before the balloon bursts. The authors reported that risky decision making was related to adolescents’ poor food choices (reported consumption of more energy-dense foods); however, risky decision making was also related to lower reports of overeating. This suggests that decision making may be an indicator of behavior (food choices), but perhaps it has less to do with processes related to inhibition (overeating). Groppe & Elsner73 measured decision making in 6- to 11-year-olds using the Hungry Donkey Task. In this task (adapted as an age-appropriate version of the Iowa Gambling Task),74 children worked toward helping a donkey win

Executive Function and Self-Regulator Influences on Children’s Eating

BOX 10.4 Summary

• • •

Impulsive children may be more sensitive to the presence of palatable foods, consume more of these foods, and show greater gains in adiposity, relative to less impulsive children. Delay of gratification and emotional eating may be associated, and emotional arousal may lead to dysregulated eating in the presence of the inability to delay gratification. Findings of studies examining decision making as a predictor of children’s eating behaviors are mixed.

apples by opening different doors, but there was a chance that doors may result in a loss of apples. Results showed that poorer decision making was associated with increases in restrained eating over a year, satiety responsiveness, and slowness in eating in girls. These behaviors are typically thought to confer a degree of protection against obesity, thus, this is contrary to expectations that poorer decision making would be related to more problematic eating behaviors. Among boys, the authors found that poorer decision making was related to increased food responsiveness over the 1-year period. Perhaps the cognitive processes involved in decision making (unmeasured in the Groppe & Elsner73 study) have little to do with those involved in inhibitory control problems. Kittel et al.50 examined decision making and a number of EF indicators in youth with or without binge eating disorder (BED) and/or obesity. Decision making was measured using the Iowa Gambling Task, which requires participants to choose from decks of cards, and make decisions based on the potential to win or lose money; different decks have varying levels of risk (gains or losses). BED youth exhibited poorer decision-making skills than nonBED or normal weight youth. Overall, the findings from studies of decision making and eating self-regulation are mixed; it is unclear whether this process is very meaningful in young children, who are more present- than future-oriented. Further research is needed to develop a clearer understanding of the ways in which decision is related to children’s eating behaviors (Box 10.4).

COGNITIVE FLEXIBILITY, WORKING MEMORY, AND EATING BEHAVIOR At least two studies have demonstrated significant associations between cognitive flexibility and obesity.75,76 Li and colleagues77 demonstrated that children who were overweight performed significantly worse on an attention and working memory task than did those who were not overweight. Working memory has been hypothesized to facilitate health-related goals by protecting against distraction (e.g., unhealthy food cues) or craving.78 Riggs and colleagues demonstrated that working memory predicted membership into two latent classes reporting high fat, high sugar food intake79 and Houben and colleagues78 demonstrated that a working memory intervention for individuals who were overweight

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BOX 10.5 Summary

• •

Links between cognitive flexibility or working memory and eating behavior or weight status are mixed. EF and healthy behaviors are positively associated, whereas negative associations have been found between EF and unhealthy behaviors.

increased self-regulation of food intake. However, other studies have produced either mixed80 or null findings75,76 between working memory and weight status (Box 10.5).

GLOBAL MEASURES OF EF AND CHILDREN’S EATING BEHAVIOR Global measures of executive function have often been assessed using paper-and-pencil questionnaires. Using the Behavioral Rating Inventory of Executive Function (BRIEF),81 Tate and colleagues82 used a combined measure of EF (emotional control, inhibitory control, working memory, and organization) to examine associations with 9-year-old children’s food intake. Results showed that deficits in EF were associated with greater intake of energy-dense, nutrient-sparse foods. Riggs, Pentz and colleagues,83 in a study with 4th grade youth participating in the school-based Pathways to Health (Pathways) curriculum, explored relationships between EF and patterns of food intake, physical activity, and sedentary behavior. The authors consistently found EF proficiency to be associated with lower unhealthy food intake and sedentary behavior, but greater physical activity. Specific findings from their work shed light on the ways in which EF relates to various patterns and constellations of obesity risk behaviors.79,84 For example, in a sample of predominantly low-income and Latino 4th-grade students in California, greater EF (measured using the BRIEF) was associated with lower levels of snack food intake, measured using a food frequency questionnaire.84 Taken together, these findings show that global measures of EF have established relations between EF and an array of obesity-related behaviors such that EF and healthy behaviors are positively associated, whereas negative associations have been found between EF and unhealthy behaviors.

IMPLICATIONS FOR PREVENTION AND PRACTICE Considerable attention has been focused on understanding dysregulated eating behavior as a mechanism for the development of obesity and other cardiometabolic disorders in children and adults. Nederkoorn and colleagues85,86 demonstrated that when compared to children without obesity, children with obesity demonstrated significantly less inhibitory control. In a small laboratory-based study designed to examine relations between stress, EAH, and BMI in 43 children ages 5–9 years, Francis et al.31 found that greater

Executive Function and Self-Regulator Influences on Children’s Eating

impulsivity (measured using the CBQ) was related to higher BMI in children, but not to EAH. The authors hypothesized that there would be shared biological and behavioral underpinnings linking impulsivity to EAH, but concluded that the small sample size made it difficult to further explore this relation. More work may be needed to understand the different aspects of self-regulation that are relevant to food intake. Thamotharan and colleagues’87 2014 meta-analysis of impulsivity’s role in pediatric obesity demonstrated that among the dimensions of impulsivity, disinhibition and decision-making demonstrated the strongest associations with obesity and weight status. The findings reviewed in this chapter provide evidence that interventions to enhance food intake regulation and reduce or prevent obesity in children will benefit from efforts to increase executive function and improve self-regulation in general domains of development. Promising intervention findings show that programs designed to improve selfregulation skills in general domains of behavior can produce significant improvements in weight status, appetitive traits, and eating behavior. Riggs and colleagues88 described results from an intervention pilot study designed to improve 5th-grade children’s emotion regulation, impulse control, and problem solving. This intervention was modeled off of a well-known early childhood emotion-regulation training program, Promoting Alternative Thinking Strategies (PATHS).89 The investigators demonstrated that the program improved children’s attitudes toward perceived self-regulation (e.g., acknowledging that it is good to stop eating when full), as well as food choices and sedentary behaviors (television viewing). Suppressing appetitive thoughts related to fixation on food should decrease children’s responsiveness to food, particularly highly desirable, palatable foods.84 Greater EF may confer protection against eating dysregulation and may increase more mindful eating.90 In a younger sample of 42 Chinese children ages 6–7 years, Jiang et al.91 measured inhibitory control using a computer-based Go/No-Go task92,93 which required children to press a computer button when they see a black square, and do nothing when they see a black circle. The authors trained one group of children to improve their performance on the Go/No-Go task; children in both groups participated in a “taste test” before and after training, in which they were asked to taste food (a bogus test) and consume as much as they wanted. Compared to the controls, children in the inhibition training group ate significantly less than controls in the taste test, providing some evidence for the effect of the training on intake. Daniel et al.94 measured delay discounting in 42 children ages 9–12 years using a delay task95 that required children to choose between a smaller, immediate financial reward, or a larger, delayed financial reward (up to $50) that could be obtained over the course of days, weeks, or months. Children were given ad libitum access to a variety of energy-dense snack foods after participating in a taste test, which served as a disinhibitor. The study findings revealed that children who were told to think about the future displayed lower levels of delay discounting and lower levels of intake during the snacking session. While the Daniel et al.94 study was not designed as an

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intervention or experimental study, the results suggest that training children to focus on future goals may shift attention from immediate rewards and may have implications for more mindful eating behaviors. Finally, in a study of 26 children who were overweight ages 8–12 years participating in a weight loss program, children high in impulsivity had lower levels of weight loss success compared to children low in impulsivity.96 In a study that examined treatment outcomes of adult women with disordered eating, Manasse et al.97 showed that women with higher levels of food-specific impulsivity had more severe eating pathology after treatment than those with lower food-specific impulsivity. These findings provide some evidence for a moderated effect of EF on treatment success, suggesting that treatment or intervention programs may benefit from inclusion of components designed to improve self-regulation. Several interventions designed to improve children’s self-regulation in general domains of development show promise.98–100 Graziano and colleagues98 exposed different groups of prekindergarten children to 8 weeks of daily activities; groups varied in their exposure to curriculum designed to (1) reduce behavioral problems, (2) improve literacy and math skills, (3) improve socioemotional outcomes, and (4) improve self-regulation. The authors found that children who received self-regulation training, in addition to other components, showed the greatest improvement in a number of school-readiness skills. Lumeng and colleagues99 conducted an obesity prevention study with low-income preschoolers and their families, which included classroom lessons for children, nutrition educator-led lessons for parents, and a home visiting component. Results showed that the intervention improved teacher reports of child self-regulation, including reports of internalizing (e.g., anxiety related) and externalizing (e.g., disruptive, oppositional) behaviors. No effects were evident on BMI; however, with follow-up at less than a year from the start of the intervention, it would have been difficult to see any such changes. Results from a laboratory-based study by Power and colleagues100 showed that self-regulation strategies during an executive function task (delay of gratification) were related to lower weight status in a sample of low-income preschoolers. Strategies included behaviors that distracted children from the desired reward and included behaviors like shutting out the stimulus (covering eyes), restraining their hands so they would not touch the reward, and distracting themselves. Children who displayed the highest levels of self-regulation on the task (longer wait times) also displayed more shut out, movement restraining, and distraction behaviors. Children who restrained movement had lower BMI scores, and children who were more focused on the reward (e.g., touching the reward) had higher BMI z-scores. Thus programs that include training on self-regulation strategies (such as distraction) may result in improvements in children’s inhibitory control and delay of gratification. Tominey and McClelland101 used a series of games and tasks to teach preschool children to self-regulate their behavior over an 8-week period in the childcare setting. Results showed that the intervention produced improvements in inhibitory control in

Executive Function and Self-Regulator Influences on Children’s Eating

children with the lowest level of inhibitory control before the study. Children can also be taught to regulate their intake and focus attention on cues that signal hunger and fullness. Relevant to the eating domain, results from a study by Johnson102 showed that preschoolers improved their ability to self-regulate their intake following exposure to 6 weeks of training on recognizing and responding to satiety cues. Lastly, positive parenting may also be an important target for intervention; the Triple P (Positive Parenting Program)103 has been shown to be efficacious in improving general parenting behavior, such as warmth and sensitivity, particularly in parents who have children with behavioral problems. Children’s ability to self-regulate is evident across multiple domains of development; regulation occurs in behavioral, emotional, eating, and biological domains. What is unclear is whether interventions that target self-regulation in one domain of development would produce improvements in other domains as well. For example, might an intervention focused on improving executive function also lead to improvements in eating regulation? The hopeful answer is, yes, if after reading this chapter we have convinced you that regulation is NOT domain specific. Relevant to the eating domain, Rollins and colleagues23 found that increases in EAH were most pronounced in young girls who were low on inhibitory control AND had a parent who was highly controlling of their eating. The most positive outcomes were seen for girls who exhibited high inhibitory control and had parents who set appropriate limits, without exerting high levels of control. Connell and Francis104 showed that children who were high on ability to delay gratification, AND had a warm, sensitive parent, had the slowest increase in BMI from age 4 to age 15; the most problematic BMI trajectories were evident for children with selfregulation failure and with a harsh or neglectful parent. Taken together, these findings provide some hope for intervention efforts focused on improving parenting or regulatory capacity early in life. If successful, it may be possible to stem the development of more problematic eating behaviors and weight outcomes in the future.

CONCLUSIONS This chapter describes components of EF related to eating regulation in children and highlights the promise for programs that include components designed to improve selfregulatory capacities in youth. Further work is needed to better understand which aspects of self-regulation are related to eating regulation in children. The results from many studies reviewed in this chapter were mixed, due to inadequate samples sizes, differences in the measurement of EF and eating outcomes, and the absence of additional variables that may moderate or mediate associations. Furthermore, the conceptualization and measurement of EF and its components is still inconsistent across disciplines. Some scientists refer to EF as a singular construct (global EF), while others more systematically measure its separate components. Some believe EF is malleable, while others believe it is more trait than

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state. A more unified approach is needed in order to better understand the ways in which EF is associated with eating regulation and its maladaptive outcomes. If EF is malleable (we believe it is), is there a sensitive or critical period in which interventions may be most successful? Does the point of intervention (e.g., age, developmental period) depend on the EF component being targeted? The study of EF and its relation to eating regulation and obesity is an emerging field, and these questions will help to push the field forward.

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CHAPTER 11

Neurocognitive Influences on Eating Behavior in Children Kathleen L. Keller*,†, Amanda S. Bruce‡,§ *

Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States Department of Food Science, The Pennsylvania State University, University Park, PA, United States ‡ Department of Pediatrics, University of Kansas Medical Center, Kansas City, KS, United States § Center for Children’s Healthy Lifestyles and Nutrition, Children’s Mercy Hospital, Kansas City, MO, United States †

INTRODUCTION Each day, we make hundreds of decisions about food. Some of these decisions are deliberate, others are automatic, but all of them are controlled by the brain. Some of the first decisions we make in the morning are likely related to what we will eat for breakfast, how we will take our morning coffee, and perhaps what we will pack for lunch later in the day. But the food-related decisions that allowed us to get to this point likely began much earlier, with food planning, shopping, selection, and preparation. Each of these choices is controlled by a network of brain signals communicating in concert to drive one of the most fundamental and complex of human behaviors—eating. Additionally, with each bite, we are continually modifying brain circuitry as we learn more about our eating environment, including what foods are tasty, and what foods elicit disgust. Over the past 20 years, cognitive neuroscientists have begun to better understand the brain’s role in eating behaviors. However, much less is known about the neurophysiology underlying food decision making and eating behavior in children. Investigations to date have demonstrated that the neurocircuitry of appetitive behaviors includes not only traditional reward-processing regions, like striatum and ventral tegmental area, but also regions implicated in evaluating the overall salience or importance of food relative to other reinforcers, like orbitofrontal (OFC) and ventromedial prefrontal cortices (vmPFC) (Table 11.1, Fig. 11.1). Likewise, we have gathered insight about the role of the prefrontal cortex (PFC) in decision making and self-control, both of which are critical for facilitating healthy eating behaviors and moderating consumption. Because children’s brains are undergoing developmental changes, studying the neurobiological underpinnings of eating behavior in youth can be challenging. However, the better we understand early brain differences, the more likely we are to successfully prevent unhealthy eating patterns from being established. Studying pediatric populations is critical to clarifying the etiology of obesity. Moreover, there is plasticity in brain

Pediatric Food Preferences and Eating Behaviors https://doi.org/10.1016/B978-0-12-811716-3.00011-7

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Table 11.1 Brain regions implicated in pediatric eating behaviors Brain region Location Description of role

Anterior cingulate Dorsolateral PFC and Inferior frontal gyrus Hippocampus Insula

Cortex Cortex

Middle temporal gyrus Sensory and visual processing Striatum Ventromedial/orbitofrontal Thalamus

Cortex Cortex Basal ganglia Cortex Superior to midbrain

Temporal lobe Cortex

Cognitive control Self-control, deliberate decision making Memory and reward processing Primary taste cortex and interoception Self-control Taste, texture, appearance Reward processing and motivation Value assessment (valuation) Sensory processing

development, and as children learn more about their eating environments, both at home and away from home, neural pathways are modified to reflect this information. As a result, prevention efforts aimed at youth have the potential to impact brain development for years to come, and ideally, change one’s susceptibility to chronic conditions like obesity and its associated morbidity. In this chapter, we review the role of the brain in children’s food choices and appetitive behaviors. Throughout, we use the terms “food decision/choice” and “eating” to refer more generally to ingestive behaviors that also include beverage consumption, which is an important contributor to excess energy intake in children (i.e., sugarsweetened beverages—SSB). There are two themes that serve as our framework. First, much of what is known about the neurobiological controls of eating behavior has been inferred by studying the brain’s response to food cues that vary by palatability and/or energy content or by comparing the neural mechanisms of processing food cues under different physiological states (e.g., fed versus fasted, obese versus normal weight participants). However, little is known about how these brain processes relate to objective measures of eating behavior. Therefore we cannot yet use neurological phenotypes to predict which children are likely to become obese. As such, we make the case that using a “brainas-predictor” approach to understand the neural contributors to food intake decisions and consumption patterns, before obesity develops, is a critically important question for future research. The second theme guiding this work is that studying children presents certain challenges, not only for the fMRI environment, but also for understanding eating behaviors. Experimental designs that take into context the dynamic nature of not only the brain, but also pediatric ingestive behaviors are necessary to advance the field. To date, these issues have been underaddressed in the literature, but should be carefully considered in the future.

Neurocognitive Influences on Eating Behavior in Children

Sensory cortex

Visual cortex

dlPFC

vmPFC Middle temporal gyrus

OFC

(A)

Striatum

rtex

ngulate co

Anterior ci Insula Thalamus

s

Hippocampu

(B) Fig. 11.1 Visual display of the brain regions implicated in pediatric eating behaviors. (A) dlPFC— dorsolateral prefrontal cortex and the middle temporal gyrus; OFC and vmPFC—orbitofrontal cortex and ventromedial prefrontal cortex; sensory and visual cortices. (B) Hippocampus; thalamus; insula; anterior cingulate cortex.

STUDYING THE BRAIN Brain Imaging Methodologies Over the past two decades, we have gained tremendous knowledge of the human brain owing in part to noninvasive imaging technologies like magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography (MEG), and positron

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emission tomography. In this chapter, we will focus on MRI because it is the most common methodology for studying brain function and structure in youth. The benefits of MRI are recognized by cognitive neuroscientists interested in understanding the role of the brain in eating behavior because it is noninvasive, safe, and relatively flexible in the types of designs that can be employed. As the name implies, MRI uses strong magnetic fields to produce images of biological tissues.1 While MRI has relatively poor temporal resolution (lag time of  3 s), it has excellent spatial resolution, which makes it ideal for identifying structural and functional brain differences related to complex behaviors like eating. MRI can be used to investigate differences in brain structure (i.e., volume and size of anatomical regions, gray matter, white matter). For example, structural MRI differentiates the brain’s gray matter (i.e., nerve cell bodies) from white matter (i.e., axons and myelin sheaths). Information processing primarily occurs in the gray matter, while white matter tracts can be thought of as the “highways” that relay information from one region to another. Using methods such as voxel-based morphometry, scientists can study changes in gray matter volume that occur with weight gain,2 or they can evaluate the volume of individual brain regions between groups of participants (e.g., healthy weight versus overweight; successful versus nonsuccessful dieters). These methods can provide insight about structural brain differences underlying overeating and the development obesity. In addition to evaluating the volume of relevant brain regions, MRI can also elucidate how brain structures are physically connected. For example, methods like diffusiontensor MRI measure the perfusion of water through tissues, allowing the study of white matter connections between brain regions. Studies have begun to identify abnormalities in brain microstructure that are associated with adiposity,3,4 but the causal pathway linking these structural differences to obesity is not known. While structural MRI has demonstrated alterations in brain anatomy and white matter connectivity that may be associated with both food cue responsivity and obesity, functional MRI (fMRI) has been the method of choice to understand how the brain functions. fMRI most commonly uses the blood oxygen-dependent level or BOLD signal. The BOLD signal can be measured when a person is engaged in a task. An active brain region will require an increase in blood flow which results in a greater concentration of oxygenated to deoxygenated blood, otherwise known as the hemodynamic response. This increase in oxygenated blood causes a small change in the MR signal which can be related to neural activity. Through carefully planned paradigms, research has identified brain regions implicated in food liking and wanting,5,6 value,7,8 energy content,9–14 portion size,9,15 and food branding.16,17 We will discuss these in more detail in upcoming sections of this chapter. Most fMRI studies infer brain function by interpreting the contrast between two distinct experimental conditions. In the case of food motivation, much of what we have

Neurocognitive Influences on Eating Behavior in Children

learned about the brain has come from experiments that compare the difference in MR signal when a participant is evaluating food cues that vary by some property(ies) (e.g., fat content, appeal, energy density, or portion size), or by comparing differences in brain activity between participants that vary by appetitive state or weight status.18–20 In these experiments, greater brain activation when a participant is viewing highly salient stimuli (e.g., chocolate cake, pizza) compared to when they are viewing images of lower biological salience (e.g., fruit or vegetables or a nonfood object) has been interpreted as evidence of functional involvement of a brain region in the motivational and appetitive responses to food. In some experiments, researchers have separated the anticipatory responses to small tastes of milkshake compared to water or a saliva like control from the brain responses that occur with milkshake receipt.21,22 These studies illuminate aspects of reward processing that, over time, may predispose children and adolescents to overeating and obesity. If used in conjunction with carefully designed paradigms, fMRI can effectively help identify where specific functions are located in the brain, but it is less well suited to elucidate the causal mechanisms underlying these functions.23 One cannot infer causality from fMRI.24 Greater activation in OFC when a child is viewing appetizing relative to unappetizing food cannot be interpreted as demonstrating definitively that OFC is involved in evaluations of food palatability. For one, appetizing and unappetizing foods may differ by numerous aspects, including visual characteristics, portion size, energy content, sweetness, etc. In addition, OFC reportedly has numerous functions, including cueassociated learning,25,26 uncoding taste and texture cues,27 and decision making.28–30 Therefore one must avoid reverse inference when interpreting the results of fMRI studies. With appropriate cautions, fMRI is a useful and versatile technique to help scientists investigate the neurocircuitry implicated in processing the salient aspects of food and characterize how this neurocircuitry varies with physiological state. In addition to limitations in interpreting causal mechanisms underlying eating behaviors, BOLD fMRI provides an incomplete picture of the networks between brain regions. A more sophisticated approach is to look at functional or effective connectivity. Functional connectivity is the association between anatomical regions while the participant is either at rest or engaged in a task. Effective connectivity, on the other hand, models the influence of one anatomical region over another. Directionality in brain network communications can be inferred from effective connectivity but not from functional connectivity.31 Both methods provide insight on the overall pattern and network of connections underlying complex processes like eating behavior. Both MRI and fMRI can provide insight about the brain’s role in driving childhood overconsumption and the development of obesity. MRI can be used to identify structural brain differences between children with and without obesity. On the other hand, fMRI can contribute useful insight on the patterns of brain activation that occur when children are presented with food cues that differ by salient attributes. By combining these measures

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BOX 11.1

• • •

MRI can provide insight on brain structure, volume, and connections between brain regions. Correlating these metrics with objectively assessed overconsumption may elucidate brain differences that are related to the development of pediatric obesity. Functional MRI (or fMRI) allows us to isolate regions that are implicated in a task or cognitive process. In the case of child eating behavior, fMRI can shed light on the brain regions involved in processing food cues.

with careful phenotyping of eating behavior, one can begin to characterize the neurobiological characteristics associated with pediatric obesity (Box 11.1).

CHARACTERISTICS OF HUMAN EATING BEHAVIOR Human eating behavior is complex and multifaceted. We are only beginning to understand the role of the brain in food intake decisions. To date, only a few studies have examined the relationship between the brain’s response to food cues and objectively measured food intake.32 In order to fully characterize the underlying neurophysiology of the appetitive network, eating behavior must be characterized at the microstructural level. The term meal “microstructure” was first used by ingestive behavior scholar John D. Davis to describe the behavioral reflexes that generate short-term intake in animals, including licks, chews, bites, and swallows.33 Kissileff and colleagues applied this terminology to their studies of cumulative intake curves to understand mechanisms of individual differences in eating behaviors exhibited by those with obesity34 and disordered eating.35 Determining the brain network that predicts individual variability in meal microstructure is particularly important as studies identify links between obesity and specific ingestive behaviors, like eating rate,36,37 eating in the absence of hunger,38 and loss of control eating.39,40 By doing this, we can identify pediatric phenotypes to describe who is at greatest risk for overconsumption in the obesogenic food environment, and ideally, develop tailored health interventions to target these phenotypes. Ingestive behavior can be divided into five phases: initiation, procurement, consumption, termination, and postconsumatory.41 In the initiation phase, the incentive value for food increases which allows an organism to turn their attention away from other competing interests and toward food. An overlapping construct that has been the focus of several neuroimaging studies of reward processing in adults19,42,43 and children20,44–46 is food anticipation or motivation. By separating the neurobiological drives of anticipation from consumption, studies have provided critical insight on the relationship between reward processing and obesity by demonstrating that increased brain responses to the anticipation of food rewards may be a risk factor for excess adiposity.47

Neurocognitive Influences on Eating Behavior in Children

Once the drive to seek food, which is highly influenced by physiological hunger, becomes sufficiently high, an organism enters the procurement phase. In animals, this can be thought of as foraging, but in humans, this may involve aspects such as food and beverage selection, shopping, meal planning, and preparation. Food “likes” and “dislikes” may be thought of as related constructs as they can increase or decrease the drive to engage in these foraging behaviors. Neuroimaging studies in humans have attempted to clarify the neural drivers of some of these characteristics, most notably “liking,” the affective response to a food, and “wanting,” the incentive salience of a food.48 These studies have helped to shed light on the neural controls of food reward. In addition, studies by Hare and colleagues7,8 have begun to elucidate the role of brain regions like vmPFC and dorsolateral PFC in assigning value and salience to foods. Food valuation, liking, and wanting are essential components of food procurement as they help determine the types and amounts of food that will be sought, as well as how hard one will work to obtain them. The food consumption phase involves all the processes that occur once the food is attained and eating begins.41 Because humans typically eat in distinct bouts or meals, the consumption phase includes the behaviors that make up the meal microstructure, including eating frequency, bite size, and frequency of chews per weight of food.49 To date, the brain regions associated with these microstructural components have been largely unexplored. However, several studies in children have assessed trait-based measures of appetite (e.g., satiety responsiveness, emotional eating, enjoyment of food) using the Children’s Eating Behavior Questionnaire50 to characterize differences in brain function that might be associated with these characteristics.9,51 Because behaviors such as eating rate have been associated with weight gain and obesity36,52,53 it will be critical to investigate the relationship between individual differences in brain neurocircuitry and components of the meal microstructure. Meal termination refers to the processes and signals that occur to bring an ingestive behavior bout to a close.41 Related concepts that are relevant to childhood obesity include trait-based measures like satiety responsiveness54 and loss of control eating.55 In addition, satiation and total meal intake can also be assessed to provide insight about the meal termination phase. Finally, once the meal has come to a close, the postconsummatory phase begins and refers to the time between the end of the meal and beginning of the next bout of ingestion.41 Concepts that are related to the postconsummatory phase in the human literature include satiety and meal frequency (Table 11.1). Studies are needed that employ a “brain-as-predictor”56 approach to characterize the neurological underpinnings of the range of eating behaviors exhibited by children. We have begun carrying out such fMRI studies in our lab to characterize differences in children’s susceptibility to overeating from larger portions, also known as the portion size effect. We measured the portion size effect by assessing 47, 7- to 10-year-old children’s intake across 4 multi-item meals of high- and low-energy-dense food options where the

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Table 11.2 Phases of ingestive behavior Phase

Definition

Initiation

Incentive value for food increases relative to competing interests

Procurement

Foraging for food begins

Consumption

All processes that occur to facilitate ingestion (i.e., the meal) Processes that bring the meal to a close Time from meal termination to the beginning of a next bout of ingestion

Termination Postconsummatory

Related concepts in human ingestive behavior

Anticipation of food Motivation for food Food responsiveness Food shopping, meal planning, meal preparation, liking, wanting Bite frequency, bite size, slowness in eating, enjoyment of food Satiety responsiveness, loss of control, satiation, meal intake Satiety, meal frequency

portions served of all items were simultaneously increased. We found that brain activation (i.e. BOLD responses) to images of large compared to small portions of food in regions implicated in reward processing and valuation (i.e., vmPFC and OFC) was positively associated with children’s intake from increasing portions of food in the lab. On the other hand, children who had higher activation in brain regions implicated in cognitive control (e.g., dlPFC) were less susceptible to overeating when served large portions in the lab. These relationships were independent of how much children weighed.57,58 Future studies such as these will be critical for understanding the brain determinants of overeating and obesity (Table 11.2). Future neuroimaging studies should include validated indicators of overconsumption. These include, but are not limited to, appetitive traits, eating styles, and components of the meal microstructure that are predictive of the development of obesity. Ideally, these behavioral characteristics could be studied prior to the onset of obesity, but would provide relevant neuromarkers that could be used to predict the likelihood of weight gain in the future.

Challenges in Pediatric Imaging The majority of neuroimaging studies have been done in adults and older adolescents, yet investigating the neural processes associated with overeating in youth is critically important to shed light on the etiology of obesity, particularly because such studies may identify early brain predictors of weight gain. There are, however, some challenges with conducting fMRI in pediatric populations. For one, children are smaller than adults which can result in difficulties with correct placement in the scanner and an underestimation of brain activity.59 Younger children may be intimidated by the MRI environment, therefore, using age-appropriate non-intimidating terminology (e.g., “brain camera”) may help

Neurocognitive Influences on Eating Behavior in Children

BOX 11.2

• • • •

Ingestive behavior can be divided into five phases: initiation, procurement, consumption, termination, and postconsummatory. The microstructure of consumption (i.e., bite rate) has been largely unexplored. Identifying the neurocircuitry of behaviors that predict future obesity is an essential next step in research. More neuroimaging research is needed in children, but has several practical challenges, such as children’s comfort and attention span in the MRI environment.

to ease anxiety experienced by both children and parents. Another consideration is scan length, as children have shorter attention spans than adults and may need incremental motivators (e.g., virtual stickers) to successfully complete a paradigm. Children are also more susceptible to increased motion during the scan, and this can result in blurring of brain anatomy and a loss of data.60 Use of a mock or simulation scanner to train children prior to entering the MRI environment is essential for achieving high quality data. Based on suggestions outlined in a paper,61 we developed an intensive training protocol for use with 7- to 10-year-olds that involves first introducing children to a mock scanner by allowing them to ask questions on the first visit. On the second visit, we introduce children to more specific aspects of what they will experience during the actual scan by playing the sounds they will hear and showing pictures and/or introducing tasks similar to what will be done in the magnet.58 Using this protocol, we have achieved over 90% scanning success rates (Box 11.2).

BRAIN DIFFERENCES BETWEEN ADULTS AND CHILDREN In addition to the practical challenges that accompany imaging in pediatric samples, there are developmental changes in brain structure and connectivity across adolescence that have implications for data analysis and interpretation. The brain restructuring that occurs during this time is likely driven by sex hormones, testosterone and estradiol.62–64 Despite achieving 90%–95% of adult brain volume by age six,62 continued development of both gray and white matter after this age can impact the efficiency of brain function. Gray matter, which reflects neuronal cell bodies, increases following an inverted U-shaped trajectory, with volume peaking between ages 9 and 11 years old and declining into adulthood65 in correspondence with the synaptic pruning that occurs across development.66 White matter, which reflects the degree of myelination, increases by approximately 1%–2% per year overall across childhood and adolescence.67 These developmental changes influence brain processing speed and the timing of neuronal firing patterns.68 Of particular relevance to eating behavior, the prefrontal regions important for decision making are the last to mature, while at the same time subcortical brain regions

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that drive motivated behaviors mature earlier in development. In addition, the connections between the areas of the brain responsible for self-control and drive are weaker in youth.69 This mismatch between the maturation of brain regions implicated in cognitive control and motivational responding, known as the dual systems model of brain development69,70 has been hypothesized to drive increased risk taking and sensation seeking that occur from childhood to adolescence.71 The protracted maturation of cognitive control circuitry in youth may have implications for eating behaviors by driving poor dietary choices during this critical period of development. Because children’s brains are undergoing dynamic changes in both structure and function, it is theorized that their response to food cues will be different from that of adults, particularly in regions implicated in reward processing and self-control. However, the literature has been mixed in this area. Holsen and colleagues45 compared brain response to visual food cues among 10- to 17-year-olds tested during the fasted and fed states and found that similar brain regions (e.g., OFC, amygdala, insula) make up part of the appetitive network in both children and adults. However, Killgore and Yurgelun Todd11 found that age correlated positively with brain activation to food cues in OFC, but negatively to activation in anterior cingulate gyrus in 9- to 15-year-olds, implying that neural processing of food cues changes during this adolescent period. In a meta-analysis of studies of children and adults, the most common regions that responded to visual food cues in both adults and children included amygdala, insula, inferior frontal gyrus/lateral OFC, and the hippocampus.72 Despite some overlap between children and adults, authors also reported some key differences as well. For one, children’s activation in lateral OFC was more consistent than in the adult literature, while the opposite was true of activation in fusiform gyrus. In addition, children did not activate the prefrontal cognitive control regions to the same extent as adults. In sum, the authors argued that adults process food-related cues using a broader and more complex system of neurocircuitry than children.72 To date, there has been only one study that has compared brain responses to food cues in both children (10–12 years) and their parents using the same experimental paradigm.73 During an fMRI scan, children and adults viewed images of healthy and unhealthy foods (categorized according to the Nutrient Rich Foods Index).74 Overall, children showed greater activation to unhealthy relative to healthy foods in several regions implicated in cognitive control (e.g., inferior frontal gyrus, middle frontal gyrus), and reward processing and memory (e.g., hippocampus). In contrast, adults showed greater activation to unhealthy relative to healthy foods only in visual processing regions (e.g., middle occipital gyrus and right calcarine sulcus). When comparing the two groups, children activated left precentral gyrus when viewing unhealthy foods to a greater extent than adults. The precentral gyrus is implicated in motor coordination and planning, and heightened activation in this region is thought to be reflective of motor planning and motivation for ingestion,75 although the implications of these neurocognitive differences across brain development for eating behavior are not known.

Neurocognitive Influences on Eating Behavior in Children

BOX 11.3

• •

Developmental changes in brain structure and connectivity during childhood and adolescence have implications for data analysis and interpretation. Adults process food-related cues using a broader and more complex system of neurocircuitry than children.

Children’s brains differ from those of adults, particularly in regions that are critical for the processing of food cues and controlling food intake behaviors. Children’s brains also change with growth and development, which may have implications for measurement and interpretation data acquired using fMRI. There is a critical need to conduct longitudinal studies to characterize the relationship between changes in brain function and anatomy as they relate to childhood eating behaviors and the development of obesity (Box 11.3).

BIDIRECTIONAL ASSOCIATIONS BETWEEN BRAIN STRUCTURE AND PEDIATRIC OBESITY Cognitive neuroscience has advanced our mechanistic understanding of the appetitive drivers of eating behavior. Brain regions responsible for pleasure, reward, taste, homeostasis, deliberate decision making, and self-regulation are integral for processing of food cues and for making food decisions. However, while the brain drives food intake decisions on a day-to-day basis, new research is highlighting the bidirectional nature of this relationship by demonstrating that diet76,77 and obesity can impact brain structure,78–82 as well as function.4,83 Widespread alterations in brain structure that have been related to adiposity and peripheral metabolism may be present early in development. Pediatric obesity has been associated with alterations in brain volume as well as gray and white matter integrity. Studies have consistently reported reduced gray matter among obese children in regions such as middle temporal gyrus, thalamus, pre- and postcentral gyrus, cerebellum,84 caudate,85 as well as frontal and limbic lobes.80 Additionally, decreases in gray matter volume in putamen predicted gains in body fat among adolescents across 2–3 years.2 The reported relationship between white matter and adiposity is less consistent, with some showing positive associations between white matter volumes and both current BMI and future weight gain3,4 while others have found deficits in white matter among obese youth with type 2 diabetes.85 In adolescents without insulin resistance, Yau and colleagues82 reported reductions in the thickness of both orbitofrontal and anterior cingulate cortices and reduced integrity of white matter tracts. Of concern, these structural abnormalities were associated with cognitive deficits. Yau and colleagues79 also found reduced hippocampal volumes, increased cerebrospinal fluid, and reduced white

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BOX 11.4

• • •

Structural alterations in gray matter, white matter, and brain volume have been associated with pediatric obesity. Hormonal and metabolic changes that occur with weight gain and diets high in energydense, palatable foods may mediate the relationship between structural brain alterations and adiposity. The implications of these changes in brain structure require additional investigation.

matter integrity in adolescents with metabolic syndrome. Other studies have also found reduced hippocampal and prefrontal lobe volumes in adolescents with type 2 diabetes that were inversely related to measures of glycemic control.86 In contrast, children who are more physically fit been shown to have increased hippocampal volumes, which may suggest an intervention strategy to counteract the effects of adiposity on brain structure.87 Carrying too much adiposity can negatively impact cognitive function.88 One potential mechanism is through peripheral insulin resistance and inflammation which have been shown to cause damage to key brain structures involved in cognitive processing.89–91 In addition, there is increasing evidence that excess consumption of palatable foods can reduce dopamine receptor availability.92,93 It is concerning that the effects of adiposity on the brain can be observed in youth, prior to the onset of insulin resistance.82 Because these structural changes are associated with the decline of cognitive function, they may further disrupt decision-making skills that are needed for selection of optimal nutrition as children grow into adults (Box 11.4).

ASSOCIATIONS BETWEEN BRAIN FUNCTION AND PEDIATRIC EATING BEHAVIORS AND BODY WEIGHT Functional MRI has expanded our knowledge of the brain’s appetitive network by identifying regions implicated in the processing of food-related cues, appetitive traits, and obesity. Food cues activate a broad range of brain areas involved in visual processing and attention, gustatory and sensory processing, motor response, homeostatic regulation, self-control, valuation, and decision making. One underlying theory that has served as the foundation for many fMRI studies posits that hypersensitivity to food cues in brain regions implicated in motivation and reward42,94 combined with a hyposensitivity of prefrontal cortical regions implicated in decision making and self-control95 facilitates overeating in the obesogenic environment. In combination with the protracted development of the PFC, the brain networks that facilitate motivation and drive, including vmPFC, amygdala, and striatum develop at an early age. Therefore children are fundamentally vulnerable to overeating because they have high drive and motivation to consume

Neurocognitive Influences on Eating Behavior in Children

palatable foods with little top-down control to help them curb excess consumption. Add to that social pressures from peers, parents, and the media, and one can appreciate how critical this period is for the development of lifelong healthy eating behaviors. Yet the brain is plastic and it can be shaped by our experiences, so while children may be vulnerable to risky behaviors, they may also be influenced by their environment, making this an important target for prevention. The following section will summarize studies in children and adolescents that have helped to identify regions of the brain implicated in the cognitive processes that underlie eating behaviors.

Food Motivation and Drive Functional neuroimaging research using food stimuli has uncovered consistent brain regions that are key in the evaluation of, desire for, and response to edible stimuli.72 Research has emerged to highlight a pattern of limbic and paralimbic brain regions which are implicated in both the incentive salience and reward processing of palatable foods. One way in which researchers have isolated regions of the brain involved in the anticipatory response to food cues is by comparing brain activation when children are viewing appetizing foods compared to unappetizing images (e.g., less palatable foods or nonfood stimuli). Other studies have compared brain responses to food images during the fasted (high motivation for food) and fed (low motivation for food) states.20,44,45 These studies have characterized an appetitive network of regions showing heightened responsiveness to biologically salient food cues. For example, Killgore and Yurgelun-Todd11 reported greater activation in anterior cingulate, implicated in emotional processing, when adolescents viewed images of high- relative to low-calorie foods. Additionally, we identified several regions including fusiform gyrus (object recognition and attention), insula (interoception and primary taste cortex), and anterior cingulate gyrus and caudate (reward and emotional processing) that were more responsive when 7- to 10-year-old children viewed images of high- relative to low-energy-dense foods.9,15 In a related paper from this cohort, children’s brain response to high- relative to low-energy-dense foods in substantia nigra, a region involved in dopamine signaling, positively predicted fat free mass which is a known determinant of meal size and energy intake.96 The results of these studies parallel similar investigations in adults,10,97,98 suggesting that tasty, energy-rich foods activate brain regions implicated in emotional processing, reward, and motivation. Importantly, food-cue related hyperactivation in reward regions like striatum has also been implicated in future body weight gains among adolescents.99 In other study designs that have helped to clarify the neurocircuitry implicated in appetitive behaviors, investigators compare brain responses under conditions where individuals are highly motivated to eat (i.e., fasted) to conditions where they should be less motivated to eat (postmeal). In healthy weight children tested under fasted conditions, brain regions implicated in reward processing and emotion (e.g., nucleus accumbens,

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putamen, posterior cingulate, amygdala, insular cortex, and OFC) all show heightened activation compared to when children are sated.20,44,45 Interestingly, obese children tend to show greater responses in a number of these regions even after a meal, including OFC, hippocampus, medial PFC, and insula.20,44 Additionally, resting state neurofunctional connectivity also differs as a function of pediatric adiposity level, such that obese relative to healthy weight children demonstrate increased connectivity between regions associated with reward and valuation.100 Moreover, Carnell and colleagues101 reported that adolescents at higher familial risk for obesity responded more strongly to food-related words relative to non-food-related words in insular cortex, although in other rewardand emotion-related regions like anterior cingulate cortex, adolescents at low familial risk for obesity actually showed greater brain activation to high- relative to low-energy-dense food-related words. Although they are challenging protocols for children to complete, several studies have delivered small amounts of palatable liquids for children to taste in the scanner.51,102 Boutelle and colleagues102 tested 8- to 12-year-olds and found that obese children responded more to taste cues (both sugar and water) in amygdala and insula, regions that commonly show activation in response to palatable taste cues. In obese children, brain activation to taste in bilateral striatum and insula was positively associated with consumption of palatable treat foods in the absence of hunger; however, this relationship was not apparent in healthy weight children. In a follow-up study, obese relative to lean children had reduced hippocampal volume, which was positively related to how much they consumed in the laboratory when they were not hungry.103 A small feasibility study done in younger children (ages 6–8 years)51 showed complementary results during milkshake consumption as obese children had greater activation in sensory and reward-processing regions including insula, precentral gyrus, operculum, and posterior cingulate than healthy weight children. In addition, children who had greater brain activation to the milkshake in these regions also reportedly had higher enjoyment of food as an appetitive trait.51 In adolescents girls, several studies have reported that obese youth show heightened activation in gustatory and somatosensory brain regions to both the anticipation of and receipt of a palatable milkshake compared to healthy weight individuals.21 To disentangle whether these effects were present prior to the onset of excess weight, youth at risk for obesity showed greater brain responses to milkshake receipt, but no differences were found in anticipation compared to adolescents at low risk for obesity.22 Although more work is needed, these studies provide a critical foundation for demonstrating that children can be given small tastes in the MRI environment, and that heightened brain activation to these stimuli in reward and somatosensory processing regions is predictive of future obesity. The aforementioned studies highlight variations in food-cue specific rewardprocessing that are associated with obesity. Yet, is obesity associated with a more generalized alteration in reward processing? Stice and colleagues22 found that adolescents at high risk for obesity showed greater brain activation to winning money in reward

Neurocognitive Influences on Eating Behavior in Children

processing regions when compared to low-risk adolescents. However, responses to food and money were tested in different paradigms, making it difficult to compare across incentives. We conducted the first study to compare response to food and money in the same paradigm in 7- to 11-year-olds. We used a modified card guessing task to demonstrate that children’s brains responded differently to anticipating and winning food and money.104,105 Food anticipation produced a broader activation pattern in classic reward regions (i.e., caudate, nucleus accumbens, OFC, amygdala) than money anticipation.104 However, for reward receipt children showed greater activation in OFC, striatum, and medial PFC to winning money compared to food.105 Neither brain response to anticipation or receipt of food nor money was dependent on child weight status, although we did find that children who showed greater activation to both anticipation and winning of food over money consumed more palatable foods in the laboratory.46,106 These findings suggest that primary and secondary reinforcers may activate different neurocircuitry, and that heightened responsivity to food over other incentives may increase risk for excess consumption. Later in development, this may result in excess weight accumulation.

Self-Control and Decision Making The brain drives decisions related to what and how much to eat. Understanding the food decision-making process is critical for making advancements on how and where to target empirically supported pediatric obesity prevention and intervention programs. Because the PFC, which is responsible for controlling behaviors, is the last to mature, children represent a highly vulnerable population for consuming excess calories from tasty, energy-dense foods.69 Several studies have begun to elucidate the neurocircuitry underlying self-control and decision making in children and adolescents, although we have much additional work to do in this area. Several lines of evidence support that neurocognitive deficits in inhibitory control may drive excess consumption among obese youth. Consistent with this theory, experimental studies have shown that obese children show deficits in response inhibition on go/no-go and stop-signal tasks.107,108 In addition, self-reported assessment of inhibitory control has been associated with future weight gain in children.109 Neuroimaging studies examining food decision-making processes in obese versus lean youth also show the differential involvement of self-regulation and decision-making brain regions (e.g., dorsolateral prefrontal cortex-dlPFC, inferior frontal gyrus).72 While some studies have found that obese children show greater activation in dlPFC than lean children when processing food-related cues,110 other studies in adolescents have shown the opposite relationship.73,95,111 In a recent study that included lean adolescents at high and low familial risk for obesity as well as presently overweight adolescents, activation in self-control-related networks to food-related words was highest in the lean low risk and lowest in those who were already obese.101 In younger children with obesity, it is possible that greater PFC

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activation in response to food cues may reflect increased effort to control temptations to overconsume, particularly when brain processing efficiency is undergoing development. However, as the brain becomes more efficient in its processing across adolescence, the pattern of brain response and its relationship to self-regulatory capacity likely changes. Prospective, longitudinal studies that assess the neural response to food cues are needed to address these inconsistencies. In addition to self-control, there is growing evidence that obesity may also be associated with deficits in interoceptive abilities. Interoception is one’s awareness of one’s internal state, and it is critical for the ability to perceive internal sensations such as pain, hunger, and fullness.112 Adolescents carrying excess weight show blunted brain activations in the insula, an important brain region for interoception, compared to their lean peers.113 Mata and colleagues114 demonstrated decreased interoceptive sensitivity and decreased posterior insular activation in obese adolescents compared to healthy weight adolescents during a risk-taking decision task. A meta-analysis of fMRI studies in adults by Brooks and colleagues115 also found that obese individuals have reduced activation to food images in both dlPFC and insula. These intriguing findings suggest a reduced awareness to internal homeostatic cues of hunger and fullness among individuals prone to obesity. Targeting these brain regions with real time neurofeedback approaches may offer an intervention strategy to improve homeostatic regulation.116

Impact of Environmental Cues on Pediatric Brain Response to Food Children’s decisions about what and how much to eat are heavily influenced by the food environment. Food cues like energy density and portion size117–122 and food marketing123 have powerful influences over children’s eating behaviors. In addition, parents are also potent determinants of children’s food intake decisions.124,125 Neuroimaging studies are beginning to uncover mechanisms of how environmental influences impact food decision making in youth. We investigated the brain mechanisms underlying the portion size effect in healthy 7- to 10-year-old children. The portion size effect is a well-known phenomenon whereby intake increases as individuals are presented with larger portions, particularly of energy-dense foods.119,126–128 Prior to this research, neuroimaging studies had not separated the portion size of food images from other salient characteristics, like energy content, liking, and perceived healthiness. We created a series of food images that varied by portion size (large versus small) and energy density (high versus low)58 and found that brain regions implicated in emotion, appetite, and reward were primarily responsive to food energy density (e.g., caudate, anterior cingulate, and anterior insula) while portion size activated regions implicated in cognitive control (e.g., dlPFC).9,15 Ongoing investigations are relating brain responses to portion size and energy density cues to children’s laboratory intake from larger portions and their obesity risk.

Neurocognitive Influences on Eating Behavior in Children

In a series of classic studies, Stanley Schachter demonstrated that obese adults were more likely to overconsume from foods that were palatable than bland and were less likely to attenuate consumption following a high calorie snack than were lean individuals.129 These observations led to the externality theory, whereby obese individuals are more susceptible to overconsumption due to environmental cues and less likely than lean individuals to regulate intake based on internal satiety cues. Evidence supports this hypothesis in children. Obese children show heightened attentional biases to food cues than healthy weight children, as demonstrated using Stroop tasks.130 Obese children also demonstrate increased impulsivity, decreased executive control, and increased difficulty delaying gratification relative to their lean peers.131,132 Additionally, obese children also show greater ability to recall food- relative to toy-related advertisements, and they consume more following food advert exposure123 and in the presence of food-related brands.133 Obese children may be more susceptible to external cues like food advertising in part due to developmental differences in brain neurocircuitry. Brain activation differences have been found in response to static food advertising cues (i.e., pictures of fast food brand logos), in which obese children demonstrate reduced neurofunctional reactivity in the PFC, a cortical region associated with self-control.16 In a similarly designed study in younger children (ages 7–10 years), we also found that children’s brain activation to food relative to toy brands in dlPFC was positively associated with their laboratory intake when presented with meals where food brands were present.134 Similarly, brain activity in left OFC and right insula during food commercial viewing has been associated with adiposity in adolescents.135 Taken together, these findings suggest that obese children may have altered neurofunctional responses to advertising cues which might make them vulnerable to overconsumption during their presence. The social context of food decisions may change behavioral and neural decisionmaking processes in children. Children are frequently exposed to food product advertisements that may influence whether they make healthy food decisions. We investigated how food commercials influence children’s food choices in 23 children between the ages of 8 and 14 years. After providing tastiness and healthiness attribute ratings for 60 food items, children were scanned using fMRI while making food choices (i.e., “eat” or “not eat”) after watching food and nonfood television commercials.136 These results show that watching food commercials changes the way children consider the importance of taste when making food choices. Not surprisingly, children did not use health values for their food choices, indicating children’s decisions were largely driven by hedonic, immediate rewards (i.e., “tastiness”). However, children placed significantly more importance on taste after watching food commercials compared to nonfood commercials, as evidenced by increased activation in vmPFC. This change was accompanied by faster decision times (i.e., impulsive food decisions) during food commercial trials compared to nonfood commercial trials. Overall, these results suggest that watching food

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commercials prior to making food choices may bias children’s decisions based solely upon taste, and that food marketing may systematically alter the psychological and neurobiological mechanisms of children’s food decisions. Behavioral results demonstrated that after watching food commercials, children placed more emphasis on taste during food choices. During food commercials, children exhibited greater brain activity in vmPFC, an area associated with valuation. Findings were predominantly in healthy weight children, though, and BMI differences were not assessed. To examine how parental influences interact with the neurocomputational mechanisms of food decision making in children, Lim and colleagues28 tested 25, 8- to 14-year-olds who completed food decision-making tasks during functional MRI scans. Outside the scanner, children rated 60 food items on taste and health attributes as well as overall preference. While undergoing fMRI scans, children made food decisions about how much they wanted to eat the food, and then how much they believed their mother wanted them to eat the food. We hypothesized that children would estimate their mother’s choices for them, and use this information as well as their own preferences when they made food decisions. Behavioral results showed that when children made their own food choices they used only taste values, not health values. But, they used taste ratings and health ratings to select projected mother’s choices. Activity in ventromedial PFC (vmPFC) represents the child’s own preferences and left dorsolateral PFC (dlPFC) encodes the projected mother’s choices for them at the time of the choice. Moreover, the dlPFC region shows inhibitory functional connectivity with the vmPFC at the time of children’s own choices, implying that children’s internalized maternal choices serve as an inhibitory regulator that prevents taste-oriented dietary choices (Fig. 11.1). Taken together, these results suggest one important external factor in children’s food decision making is maternal preference. Children have internalized their mother’s influence, and it is possible they internalize food advertisements and slogans as well (e.g., McDonalds: “I’m loving it”). While internalized maternal preferences may help children make healthy food choices, exposure to advertisements may have the opposite effect.

• • • • •

The PFC, which supports executive function and inhibitory control, has a protracted course of development, while the brain networks that facilitate motivation and drive, including vmPFC, amygdala, and striatum, develop at an early age. In children, heightened brain responses to palatable food in reward and somatosensory processing regions are predictive of future obesity. Associations between activation of brain regions associated with inhibition and self-control and obesity are inconsistent. Brain imaging suggests deficits in interoception among obese children. Obese children may have altered neurofunctional responses to advertising cues which might make them vulnerable to overconsumption during their presence

Neurocognitive Influences on Eating Behavior in Children

CONCLUSIONS Studies using MRI to understand individual differences in pediatric eating behaviors can clarify the brain’s role in driving food intake decisions and consumption patterns. Alterations in both brain structure and function have been implicated in the development of pediatric obesity; however, we are only beginning to understand how brain neurocircuitry drives food decision making. Critical for future advancement of this research area are studies designed to determine whether brain response to food cues predicts relevant and objectively assessed markers of overeating and food choice before the onset of excess adiposity and the development of obesity. Describing the neurobiological responses to food that predict overeating will help to clarify the etiology of obesity and identify brain regions and pathways that can be targeted by interventions to improve the long-term health of children. While fMRI has delivered numerous advancements to our understanding of human eating behavior, functional neuroimaging methodologies are imperfect. Each modality has strengths and weaknesses. While it is outside the scope of the current chapter to detail these benefits and drawbacks, it is important to understand that one technique alone (i.e., functional MRI) cannot provide a comprehensive understanding of the complete neurophysiology of eating behaviors. To best understand the brain’s role in complex food decisions, future studies should consider including additional imaging techniques (e.g., EEG or functional Near-infrared Spectroscopy-fNIRS) that may provide superior temporal resolution to fMRI and can also be worn while children are eating a meal or snack. Additionally, because children’s brain development is dynamic, longitudinal designs that include assessment over time are essential for characterizing the impact of neurocognitive processes on food choice and obesity. Within these designs, it is critical to include measures of the child’s environment that might impact food consumption, including parents, media exposure, food security, and accessibility of both healthy and unhealthy foods, as these factors may mediate the relationship between brain function and obesity. A better fundamental understanding of the neurophysiology of eating decisions and behaviors in children will allow us to design more specialized, individualized prevention and interventions for healthy lifestyles. As such, conducting neuroimaging studies that are specially designed to accommodate the needs of young children across a range of developmental stages is a research priority for the future.

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81. Schwartz DH, Dickie E, Pangelinan MM, et al. Adiposity is associated with structural properties of the adolescent brain. NeuroImage 2014;103:192–201. 82. Yau PL, Kang EH, Javier DC, Convit A. Preliminary evidence of cognitive and brain abnormalities in uncomplicated adolescent obesity. Obesity 2014;22(8):1865–71. 83. Doornweerd S, van Duinkerken E, de Geus EJ, Arbab-Zadeh P, Veltman DJ, Ijzerman RG. Overweight is associated with lower resting state functional connectivity in females after eliminating genetic effects: a twin study. Hum Brain Mapp 2017;38(10):5069–81. 84. Ou X, Andres A, Pivik RT, Cleves MA, Badger TM. Brain gray and white matter differences in healthy normal weight and obese children. J Magn Reson Imaging 2015;42(5):1205–13. 85. Rofey DL, Arslanian SA, El Nokali NE, et al. Brain volume and white matter in youth with type 2 diabetes compared to obese and normal weight, non-diabetic peers: a pilot study. Int J Dev Neurosci 2015;46:88–91. 86. Bruehl H, Sweat V, Tirsi A, Shah B, Convit A. Obese adolescents with type 2 diabetes mellitus have hippocampal and frontal lobe volume reductions. Neurosci Med 2011;2(1):34–42. 87. Chaddock L, Erickson KI, Prakash RS, et al. A neuroimaging investigation of the association between aerobic fitness, hippocampal volume, and memory performance in preadolescent children. Brain Res 2010;1358:172–83. 88. Liang J, Matheson BE, Kaye WH, Boutelle KN. Neurocognitive correlates of obesity and obesityrelated behaviors in children and adolescents. Int J Obes (2005) 2014;38(4):494–506. 89. Van Vugt DA, Krzemien A, Alsaadi H, Palerme S, Reid RL. Effect of insulin sensitivity on corticolimbic responses to food picture in women with polycystic ovary syndrome. Obesity 2013;21(6):1215–22. 90. Hoscheidt SM, Kellawan JM, Berman SE, et al. Insulin resistance is associated with lower arterial blood flow and reduced cortical perfusion in cognitively asymptomatic middle-aged adults. J Cereb Blood Flow Metab 2016;37(6):2249–61. 91. Weinstein G, Maillard P, Himali JJ, et al. Glucose indices are associated with cognitive and structural brain measures in young adults. Neurology 2015;84(23):2329–37. 92. Bello NT, Lucas LR, Hajnal A. Repeated sucrose access influences dopamine D2 receptor density in the striatum. NeuroReport 2002;13(12):1575–8. 93. Johnson PM, Kenny PJ. Addiction-like reward dysfunction and compulsive eating in obese rats: role for dopamine D2 receptors. Nat Neurosci 2010;13(5):635–41. 94. Davis C, Strachan S, Berkson M. Sensitivity to reward. Implications for overeating and overweight. Appetite 2004;42:131–8. 95. Batterink L, Yokum S, Stice E. Body mass correlates inversely with inhibitory control in response to food among adolescent girls: an fMRI study. NeuroImage 2010;52(4):1696–703. 96. Fearnbach SN, English LK, Lasschuijt M, et al. Brain response to images of food varying in energy density is associated with body composition in 7- to 10-year-old children: results of an exploratory study. Physiol Behav 2016;162:3–9. 97. Beaver JD, Lawrence AD, van Ditzhuijzen J, Davis MH, Woods A, Calder AJ. Individual differences in reward drive predict neural responses to images of food. J Neurosci 2006;26(19):5160–6. 98. Siep N, Roefs A, Roebroeck A, Havermans R. Hunger is the best spice: an fMRI study of the effects of attention, hunger and calorie content on food reward processing in the amygdala and orbitofrontal cortex. Behav Brain Res 2009;198(1):149–58. 99. Yokum S, Gearhardt AN, Harris JL, Brownell KD, Stice E. Individual differences in striatum activity to food commercials predict weight gain in adolescents. Obesity 2014;22(12):2544–51. 100. Black WR, Lepping RJ, Bruce AS, et al. Tonic hyper-connectivity of reward neurocircuitry in obese childre. Obesity (Silver Spring, Md) 2014;22(7):1590–3. 101. Carnell S, Benson L, Chang K-Y, et al. Neural correlates of familial obesity risk and overweight in adolescence. NeuroImage 2017;159:236–47. 102. Boutelle K, Wierenga CE, Bischoff-Grethe A, et al. Increased brain response to appetitive tastes in the insula and amygdala in obese compared to healthy weight children when sated. Int J Obes (2005) 2015;39(4):620–8. 103. Mestre ZL, Bischoff-Grethe A, Eichen DM, Wierenga CE, Strong D, Boutelle KN. Hippocampal atrophy and altered brain responses to pleasant tastes among obese compared with healthy weight children. Int J Obes 2017.

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104. Adise SA, Reigh NA, Belko C, et al. Food and money elicit different patterns of brain response in children, regardless of weight status. Montreal, Quebec: Society for the Study of Ingestive Behavior; 2017. 105. Adise SA, Geier C, Roberts NJ, et al. Children’s brains respond more to winning money than food, regardless of weight status. Washington, DC: Society for Neuroscience; 2017. 106. Adise SA, Caprio AM, Roberts NJ, White CN, Geier C, Keller KL. Children’s laboratory food intake is predicted by brain response to anticipation of food and money rewards. New Orleans, LA: Obesity Week; 2016. 107. Nederkoorn C, Braet C, Van Eijs Y, Tanghe A, Jansen A. Why obese children cannot resist food: the role of impulsivity. Eat Behav 2006;7(4):315–22. 108. Caleza C, Yan˜ez-Vico RM, Mendoza A, Iglesias-Linares A. Childhood obesity and delayed gratification behavior: a systematic review of experimental studies. J Pediatr 2016;169: 201–207.e201. 109. Anzman SL, Birch LL. Low inhibitory control and restrictive feeding practices predict weight outcomes. J Pediatr 2009;155(5):651–6. 110. Davids S. Increased dorsolateral prefrontal cortex activation in obese children during observation of food stimuli. Int J Obes (2005) 2010;34(1):94–104. 111. He Q, Xiao L, Xue G, et al. Altered dynamics between neural systems sub-serving decisions for unhealthy food. Front Neurosci 2014;8:350. 112. Opinion CAD. How do you feel? Interoception: the sense of the physiological condition of the body. Nat Rev Neurosci 2002;3(8):655–66. 113. Delgado-Rico E, Soriano-Mas C, Verdejo-Roma´n J, Rı´o-Valle J, Verdejo-Garcı´a A. Decreased insular and increased midbrain activations during decision-making under risk in adolescents with excess weight. Obesity 2013;21(8):1662–8. 114. Mata F, Verdejo-Roman J, Soriano-Mas C, Verdejo-Garcia A. Insula tuning towards external eating versus interoceptive input in adolescents with overweight and obesity. Appetite 2015;93:24–30. 115. Brooks SJ, Cedernaes J, Schi€ oth HB. Increased prefrontal and parahippocampal activation with reduced dorsolateral prefrontal and insular cortex activation to food images in obesity: a meta-analysis of fMRI studies. PLoS ONE 2013;8(4):1–9. 116. Frank S, Kullmann S, Veit R. Food related processes in the insular cortex. Front Hum Neurosci 2013;7(499). 117. Kling SMR, Roe LS, Keller KL, Rolls BJ. Double trouble: portion size and energy density combine to increase preschool children’s lunch intake. Physiol Behav 2016;162:18–26. 118. Kling SMR, Roe LS, Sanchez CE, Rolls BJ. Does milk matter: is children’s intake affected by the type or amount of milk served at a meal? Appetite 2016;105:509–18. 119. Mooreville M, Davey A, Orloski A, et al. Individual differences in susceptibility to large portion sizes among obese and normal-weight children. Obesity 2015;23(4):808–14. 120. Spill MK, Birch LL, Roe LS, Rolls BJ. Hiding vegetables to reduce energy density: an effective strategy to increase children’s vegetable intake and reduce energy intake. Am J Clin Nutr 2011;94(3):735–41. 121. Leahy KE, Birch LL, Fisher JO, Rolls BJ. Reductions in entree energy density increase children’s vegetable intake and reduce energy intake. Obesity 2008;16(7):1559–65. 122. Leahy KE, Birch LL, Rolls BJ. Reducing the energy density of an entree decreases children’s energy intake at lunch. J Am Diet Assoc 2008;108(1):41–8. 123. Halford JC, Gillespie J, Brown V, Pontin EE, Dovey TM. Effect of television advertisements for foods on food consumption in children. Appetite 2004;42:221–5. 124. Anzman SL, Rollins BY, Birch LL. Parental influence on children’s early eating environments and obesity risk: implications for prevention. Int J Obes 2010;34(7):1116–24. 125. Faith MS. Evaluating parents and adult caregivers as “agents of change” for treating obese children: evidence for parent behavior change strategies and research gaps: a scientific statement from the American Heart Association. Circulation (New York, NY) 2012;125(9):1186–207. 126. Mathias KC, Rolls BJ, Birch LL, et al. Serving larger portions of fruits and vegetables together at dinner promotes intake of both foods among young children. J Acad Nutr Diet 2012;112(2):266–70. 127. Fisher JO, Arreola A, Birch LL, Rolls BJ. Portion size effects on daily energy intake in low-income Hispanic and African American children and their mothers. Am J Clin Nutr 2007;86(6):1709–16. 128. Barabara JR, Roe LS, Meengs JS. Larger portion sizes lead to a sustained increase in energy intake over 2 days. J Am Diet Assoc 2006;106(4):543.

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129. Schachter S. Obesity and eating. Science 1968;161:751–6. 130. Braet C, Crombez G. Cognitive interference due to food cues in childhood obesity. J Clin Child Adolesc Psychol 2003;32(1):32. 131. Bruce AS, Black WR, Bruce JM, Daldalian M, Martin LE, Davis AM. Ability to delay gratification and BMI in preadolescence. Obesity 2011;19(5):1101–2. 132. Bruce AS, Martin LE, Savage CR. Neural correlates of pediatric obesity. Prev Med 2011;52:S29–35. 133. Forman J, Halford JCG, Summe H, MacDougall M, Keller KL. Food branding influences ad libitum intake differently in children depending on weight status: results of a pilot study. Appetite 2009;53:76–83. 134. Masterson TD, Stein WM, Beidler E, Bermudez M, English LK, Keller KL. Brain response to food brands correlates with increased intake from branded meals in children: an fMRI study. Chicago, IL: Experimental Biology; 2017. 135. Rapuano KM, Huckins JF, Sargent JD, Heatherton TF, Kelley WM. Individual differences in reward and somatosensory-motor brain regions correlate with adiposity in adolescents. Cereb Cortex (New York, NY) 2016;26(6):2602–11. 136. Gearhardt AN, Yokum S, Stice E, Harris JL, Brownell KD. Relation of obesity to neural activation in response to food commercials. Soc Cogn Affect Neurosci 2014;9(7):932–8.

FURTHER READING 1. Gibbons C, Finlayson G, Dalton M, Caudwell P, Blundell JE. Metabolic phenotyping guidelines: studying eating behaviour in humans. J Endocrinol 2014;222(2):G1–G12. 2. French SA, Epstein LH, Jeffery RW, Blundell JE, Wardle J. Eating behavior dimensions. Associations with energy intake and body weight. A review. Appetite 2012;59(2):541–9.

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CHAPTER 12

Development of Loss of Control Eating Meghan Byrne, Marian Tanofsky-Kraff

Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, MD, United States

OVERVIEW Binge eating, defined as the consumption of a large amount of food accompanied by a perceived inability to stop eating, is the hallmark feature of binge eating disorder (BED). Although BED does not typically manifest until adolescence or adulthood, with a peak age of onset occurring between 16 and 20 years old,1 youth report experiences of out of control eating as early as middle childhood. Loss of control (LOC) eating is the subjective experience of a lack of control over eating, regardless of the amount of food consumed, and is commonly reported among youth. While binge eating involves consuming an objectively large amount of food, it can be difficult to determine what constitutes a large amount in growing boys and girls in different developmental stages. Further, younger children may not have as much control over food purchasing decisions compared to older adolescents or adults, and as such may experience a lack of control over eating without necessarily consuming an objectively large amount of food. An important precursor to BED,2,3 LOC eating is often reported by children or adolescents with overweight, with prevalence rates typically twice as high in adolescent girls compared to boys.4 As defined, LOC eating encompasses binge eating episodes involving objectively large amounts of food as well as smaller, ambiguously sized eating episodes. Given most data show no difference between objectively and subjectively large amounts of food consumed for physical (e.g., weight, adiposity, metabolic syndrome components, appetitive hormones) and psychological (e.g., symptoms of depression and anxiety, disordered eating attitudes) correlates,5,6 we will use the term “LOC eating” throughout this chapter. Operationally defined, the term “LOC eating” will refer to the subjective experience of out of control eating, regardless of the amount of food consumed. As roughly 23% of youth report LOC eating in the past month, with almost 10% reporting recurrent episodes,7 there is a growing literature examining the behavior. Consistently, pediatric LOC eating has been associated with adverse physical8,9 and psychosocial10,11 correlates and outcomes, such as overweight and obesity,12,13 higher body fat,11 components of the metabolic syndrome,9 eating-disordered cognitions,3 and symptoms of depression and anxiety.2,3 However, there are considerably fewer studies examining the development of LOC eating. Therefore the current chapter will focus on the available Pediatric Food Preferences and Eating Behaviors https://doi.org/10.1016/B978-0-12-811716-3.00012-9

© 2018 Elsevier Inc. All rights reserved.

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Predictors Biological Family obesity Genetic factors Hormonal factors

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Psychological Temperament Negative affect Disordered eating

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Environmental Parental problems Weight-based teasing Dieting

Genes

Social issues

Obesity

Depression/ anxiety

LOC eating

MetS dysfunction

Disordered eating attitudes

Correlates

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Correlates

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Emotion dysregulation

Attention bias to food

Outcomes

Obesity-related health problems

Severe psychopathology -BED -BN -Depression

Fig. 12.1 Conceptual model of LOC eating. Dashed lines indicate correlation. Solid lines indicate causal relationships.

data on LOC eating, as the most salient feature of binge eating behavior, among children and adolescents.a We will discuss the assessment of LOC eating, followed by a description of its correlates and prospectively measured outcomes. Subsequently, existing literature on predictors of pediatric LOC eating will be discussed, followed by an outline of the prevailing LOC eating theories in the literature. Fig. 12.1 illustrates a conceptual model of the psychological and physical correlates, predictive factors, and outcomes in relation to LOC eating. We will offer recommendations for research to provide a more complete understanding of the factors that prospectively contribute to the development and maintenance of LOC eating in youth. Finally, interventions and potential future directions will be discussed.

ASSESSMENT OF LOC EATING Assessing LOC eating behaviors in youth can be challenging. In addition to varying nutritional needs at different developmental stages for boys and girls,14 the early signs a

Throughout the chapter, we will refer to children and adolescents by using the encompassing term “youth.”

Development of Loss of Control Eating

of LOC eating may be difficult to detect, as children may not be able to adequately describe their eating patterns.13 Nevertheless, several methods are commonly used to assess LOC eating, some of which may overcome these challenges better than others. Most studies have utilized single-item surveys, self-report questionnaires, parent reports of their child’s eating behaviors, or clinical interview methodology. While questionnaire methods have the advantage of convenience and ease of administration, responses typically vary based on the method used and the respondent. For example, parent reports of LOC eating behaviors, which are typically identical to child version except the questions are referenced to the child, have poor concordance with information gathered from child interviews in identifying presence of LOC episodes or objective overeating.15 Similarly, child self-reports of LOC eating are inconsistent with parent reports16 and even child interview methods.17–19 In one study, child reports of eating behaviors were shown to be unrelated to important outcome measures associated with obesity, while parent reports did show a relationship with adiposity, eating-disordered cognitions, and body dissatisfaction.16 The inconsistencies in assessment methods likely contribute to the differences in prevalence rates, correlates, and outcomes of LOC eating among youth. While parent and self-report questionnaires may be useful as screening tools, a clinical interview is likely necessary to identify LOC and eating disorders in children.17 Clinical interview methods are typically considered optimal, as opposed to child selfreport or parent report. The interactive nature of interview-based assessment methods allows for questions to be explained so they are understood by each individual. Agerelated developmental differences can be addressed on an individual level as well. In the case that a concept such as LOC eating is not well understood by a child, specific examples can be provided until understanding is achieved.11 Of the available interviews, the Eating Disorder Examination (EDE)20 has been used most frequently and has been adapted specifically for youth prone to obesity and who may report LOC.21,22 This interview method captures the frequency of LOC eating episodes in the past 28 days. However, the convenience of the EDE is limited in that it requires training and is more time consuming to administer than self-report questionnaires. As LOC eating episodes are typically characterized by the consumption of snacks and desserts high in sugar and fat,23,24 an alternate method used to assess pediatric LOC eating is through direct examination of energy intake at a laboratory test meal. This method typically consists of a calorically standardized buffet-style meal, which includes a broad variety of foods that range in macronutrient composition and are commonly consumed by youth. Participants are given instructions prior to the meal such as “let yourself go and eat and much as you want.”24 Several studies have used this assessment method effectively. Laboratory studies using a test meal have effectively demonstrated differences between youth with LOC eating and those without in terms of amount,25,26 macronutrient composition,24,25 and type of energy consumed.24 In these studies, LOC eating was typically either categorically predetermined by self-report questionnaires,26

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semistructured interview,24 or self-rated immediately after the experimental test meal.25 This method of assessing pediatric LOC eating allows for more specific behavioral observations and is not reliant on errors in memory recall, difficulty describing, or lack of awareness of eating patterns. However, the laboratory setting is not ecologically valid. Test meals cannot account for external factors that may stimulate or inhibit intake, such as complex behavioral interactions with peers or family members. Additionally, test meal administration requires a significant amount of time and cost to administer. To assess LOC eating in the natural environment and on a momentary level, ecological momentary assessment (EMA) is an ideal methodology. Participants are given an electronic device, such as a smartphone, on which they complete recordings or brief self-assessments, reporting on level of control over eating, throughout the day for a given time period, such as over a two-week protocol. The device may include randomly distributed signals to prompt recording or assessment completion or allow for self-initiated assessments to ensure a comprehensive sampling of the LOC eating behavior. Only a few studies have made use of EMA methodology to assess pediatric LOC eating. Therefore more data are required to determine the suitability of EMA for effective capturing LOC eating behaviors, correlates, and outcomes. Studies that have used this method with youth have had compliance rates around 70%,27–29 which is approximately 20% lower than EMA studies of adults with binge eating30 and may reflect a limitation of this method with youth given their lack of control over schedules. For example, school rules may prohibit youth from having the EMA device with them during the day. Indeed, youth showed better compliance with event-contingent recordings during the summer, holidays, and weekends.27 Although EMA is an ideal method for momentary assessment of eating in the natural environment, the various limitations to compliance among youth may pose barriers to effective use of this method with adolescents. Given the various strengths and limitations of methods, when possible, multiple assessment methods and respondents, including child and parent report, interview, test meals, and EMA should be used to evaluate the presence or absence of LOC eating in youth (Box 12.1).

BOX 12.1 Section Summary

• • •

Loss of control (LOC) eating is the subjective experience of a lack of control over eating, regardless of the amount of food consumed. LOC is commonly reported among youth and has been associated with adverse physical and psychosocial correlates and outcomes, including obesity, anxiety, and depression. While parent and self-report questionnaires can screen for LOC, clinical interviews are considered optimal. Direct observation of energy intake at a test meal and ecological momentary assessment are also used to assess LOC behaviors.

Development of Loss of Control Eating

CROSS-SECTIONAL CORRELATES OF LOC EATING IN YOUTH Genetics LOC eating appears to aggregate in families, independent of weight and obesity status,31 which reflects both genetic and environmental influences. Additive genetic factors have been shown to account for 29%–43% of the variance in LOC eating, compared to common environmental factors which account for only 8%–14% of the variance.32 Similarly, twin studies show that heritability estimates for LOC eating are around 39%–57%.33–35 Mothers’ disinhibited eating styles, including LOC eating, are similar to their children’s. After adjusting for BMI, one study found that mothers’ LOC eating was associated with their children’s LOC eating and was indirectly linked to children’s adiposity.36 LOC eating and obesity are moderately to highly heritable, with a substantial correlation between the genetic risk factors for each condition.33,37 Candidate genes appear to play a role in development of LOC eating in youth. For example, the influence of the FTO high-risk A allele, a polymorphism (i.e., variant in genetic sequence) that places youth at greater risk for obesity than those without the FTO high-risk A allele,38–41 has shown links to development of LOC eating.42 Youth with rs9939609 FTO variant high-risk A alleles are more likely to report LOC eating.43 When individuals with the FTO high-risk A allele (which is associated with higher BMI) experience societal pressure to be thin, they may be more likely to diet or adopt compensatory behaviors because of their increased BMI, which in turn could increase the risk of developing LOC eating behaviors.42,43 Given FTO is highly expressed in the hypothalamic regions of the brain, which are important for appetite regulation,44 the impact of the FTO high-risk A allele on the hypothalamus may promote a type of eating (e.g., LOC) that leads to overweight. The genetic differences found between obese individuals with and without BED may also be characterized by increased reward sensitivity. Adults with obesity and BED are more likely to be homozygous for the A2 allele of the Taq1A polymorphism and for the T genotype of the C957T marker, both of which indicate increased dopaminergic transmission.45 Dopaminergic pathways are involved in many brain functions including reward, motivation, and learning. Neurotransmission of dopamine is involved in appetitive and rewarding behaviors, including regulation of motivation to eat, which can be involved in drives to eat an abundant amount of highly palatable foods beyond caloric needs.46 Not surprisingly, research supports the link between greater density of dopamine receptor polymorphisms and LOC eating, again supporting the influence of genetic factors.45 Emerging data suggest that genes and environment influence each other bidirectionally in their impact on risk for LOC eating. A study of the 5-HTTLPR allele and childhood trauma illustrates this notion.47 For example, environmental factors such as childhood physical and sexual abuse increase risk for LOC eating in adulthood.48,49 The 5-HTTLPR allele, a polymorphism on a gene involved in serotonin transportation, is more commonly found in individuals with LOC eating.50 Taken together, the effect of early adverse life

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events on LOC eating was moderated by the 5-HTTLPR allele, such that adolescent girls who are carriers of the 5-HTTLPR allele and were exposed to repeated childhood maltreatment had a significantly elevated risk of greater LOC eating severity.47 Such data support the diathesis-stress theory which posits that severe environmental threat or stress may trigger the expression of latent genetic risk factors for LOC eating.42,51

Physiological Correlates Obesity and LOC eating are frequently comorbid. LOC eating is prevalent in almost one-third of children and adolescents with overweight and obesity, with meta-analysis estimates of 31%.52 The relationship between children who report LOC eating and higher BMI and body fat mass is well defined.11,53 Youth with LOC eating report shorter postmeal satiety duration compared to youth without LOC eating,26 showing a potential mechanism for how LOC eating affects food intake and excess weight gain. Given the link between LOC eating and excess body weight, data on the physiological correlates of LOC have typically focused on appetitive hormones and metabolic functioning. Some research has shown no differences in metabolic characteristics between youth with obesity with and without LOC eating.54 Yet, other data suggest that pediatric LOC eating is associated with higher fasting serum leptin, an adipose-tissue derived hormone which promotes hunger, food intake, and body weight regulation,8 above and beyond the contribution of adiposity. It remains unclear whether higher fasting serum leptin increases risk for LOC eating or whether LOC eating affects levels of serum leptin. Further, youth reporting LOC eating episodes show greater dysfunction in components of MetS as well, such as higher systolic blood pressure and high low-density lipoprotein cholesterol (LDL-C), even after adjusting for adiposity.55 Dietary intake and macronutrient consumption may be partially responsible for the association between LOC eating and metabolic dysfunction. Youth with LOC eating typically consume a greater amount of carbohydrates, including snack and dessert-type foods, which could potentially contribute to worsened Mets-related measures.24,55 It is possible that higher inflammation may contribute to the relationship between LOC eating and higher blood pressure and LDL-C. A primary marker of inflammation, high-sensitivity C-reactive protein (hsCRP) has shown an association with LOC eating.56 This suggests there may be unique endophenotypes, such as youth with LOC eating, at higher risk for chronic inflammation and the development of obesity. Relationships between LOC eating and markers of obesity-related health issues indicate youth with LOC may be at particular risk for adverse physiological outcomes.

Psychosocial Correlates Social Correlates Many social factors have been associated with LOC eating in childhood. Social anxiety, social insecurity, isolation, and conflict within friendships are more commonly reported

Development of Loss of Control Eating

by adolescent girls with LOC eating compared to their peers without LOC eating.57 Similar to peer influence, family influence is critical in the development of eating disorder symptoms in youth. For example, insecure attachment style has been associated with child reports of LOC eating.58,59 Further, the relationship between low self-esteem and LOC eating in youth may be better explained by insecure attachment to the mother than self-esteem alone.58 Psychological and Behavioral Correlates Depression, anxiety symptoms,53,60 and internalizing and externalizing problems11,21,61 have been associated with LOC eating. Other psychological and behavioral correlates, specific to eating disorders and of LOC eating, include dietary restraint, body image disturbance, and disinhibited eating.62 The relationship between these disordered eating attitudes and behaviors and LOC eating in youth is well defined.63–65 Disinhibited eating is an umbrella term for behaviors that involve a lack of healthy restraint over eating. In addition to LOC eating, examples of disinhibited eating behaviors are eating in the absence of hunger and emotional eating. “Eating in the absence” of hunger is a behavioral measure designed to assess the frequency of precipitants to eating when one is not hungry or when sated.66 Eating in the absence of hunger is measured by a laboratory paradigm in which youth’s ad libitum energy intake is measured after consuming a standardized meal and reporting fullness,67,68 or by questionnaire.69 Youth who self-report eating in the absence of hunger also tend to endorse LOC eating,69 suggesting this may be a useful clinical indicator to identify those at risk for LOC eating. However, some studies have suggested eating in the absence of hunger may be better characterized as uncomplicated overeating and may not be associated with adverse psychological correlates such as negative mood.6,70 Eating in the absence of hunger is also associated with weight in youth,71 and as such may be a potential risk factor for various obesity-related health outcomes. Emotional eating, another disinhibited eating behavior, involves the tendency to use food to regulate mood, but does not necessarily involve a lack of control. Emotional eating is associated with72 and predictive of LOC eating in youth across the weight spectrum.62 In a one-year follow-up study, emotional eating was associated with increased disordered eating attitudes and adiposity, but only among youth with LOC eating.65 These findings suggest that additional factors may play a specific role in the relationship between LOC eating adverse correlates and outcomes. The conceptualization of the overlap between eating in the absence of hunger, emotional eating, and LOC eating is depicted in Fig. 12.2. It is likely that LOC eating involves eating and the absence of hunger and emotional eating, but the two latter behaviors do not necessarily involve LOC eating. Consistent with the relationship between emotional eating and LOC eating, psychological traits, such as emotion dysregulation, also appear to interact with LOC to produce adverse outcomes. Youth with LOC eating tend to have maladaptive emotion regulation

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Loss of control (LOC) eating

Emotional eating (EE)

Eating in the absence of hunger (EAH)

Fig. 12.2 Conceptualization of overlaps among disinhibited eating behaviors. (Adapted from Shomaker LB, Tanofsky-Kraff M, Yanovski JA. Disinhibited eating and body weight in youth. Handbook of behavior, food and nutrition. Springer; 2011. p. 2183–200.)

strategies compared to their counterparts without LOC eating by child and parent report.73,74 Further, emotion dysregulation served as a moderator between LOC eating and weight-related variables.74 Only among youth who reported LOC, fat mass and BMIz were positively associated with emotion dysregulation. Cognitive Correlates Cognitive factors may play a role in development of LOC eating. In particular, attentional bias to food cues, or a biased cognitive processing of food-related stimuli, may be linked to LOC eating in youth with overweight. Shank et al.75 found that LOC eating and body weight interact such that only among youth with LOC, attentional bias toward highly palatable foods was positively associated with BMIz. Another cognitive process associated with LOC eating is executive functioning, such as poorer global processing and set shifting.76 Poorer executive functioning has also been shown to predict weight gain in adolescents, and LOC eating behaviors may mediate this relationship.77 Among youth with overweight or obesity, executive function impairments may play a role in development of LOC eating. In summary, LOC eating is multifaceted in its associations with numerous genetic, physiological, psychosocial, and cognitive factors. There are a number of cross-sectional studies that identify factors associated with LOC eating. Additive genetic factors reveal a high heritability of LOC eating in families, much like obesity, which is strongly associated with LOC eating as well as obesity-related adverse health outcomes. Psychosocial correlates of LOC eating include negative affect such as depressive symptoms and anxiety, social issues, emotion dysregulation, and disordered eating attitudes, as well as behavioral

Development of Loss of Control Eating

BOX 12.2 Section Summary

• • •

LOC appears to be under significant genetic influence with heritability of 39%–57%. Genetic influences are not fully understood but may operate through the FTO obesity gene as well as mood and reward sensitivity pathways. Psychosocial correlates of LOC eating include negative affect such as depressive symptoms and anxiety, social issues, emotion dysregulation, and disordered eating attitudes. Attentional biases to food cues and aspects of executive functioning reflect cognitive correlates of LOC.

correlates such as eating in the absence of hunger and emotional eating. Cognitively, studies have found association between attentional biases to food cues and LOC eating (Box 12.2).

OUTCOMES OF LOC EATING Physiological Outcomes LOC eating in childhood is associated with adverse physiological outcomes in prospective studies. Over and above the initial body weight, children and adolescents with LOC eating are at a higher risk for excess weight gain, adiposity,10,78–80 and the development of MetS components,9 including physiological measures such as worsening triglycerides and increased visceral adipose tissue. Thus pediatric LOC eating not only puts youth at higher risk for development of obesity, but increases risk for comorbid obesity-related health problems as well.

Psychosocial Outcomes LOC eating prevalence rates have been shown to increase over the course of development, with prevalence rates in girls increasing 14%–25% throughout adolescence.81–83 Not surprisingly, LOC eating is a precursor to partial- or full-syndrome BED over time.2,3 Moreover, long-term psychological consequences of LOC eating in childhood include the onset of higher anxiety3 and depressive symptoms over time3 and into adulthood.10 LOC eating onset in childhood, as opposed to onset in adulthood, also increases risk for more severe psychopathology later in life. Early onset is subsequently associated with more severe binge eating, use of multiple purging behaviors, and a full-threshold bulimia nervosa diagnosis later in life.84 Over time, youth with LOC eating are at greater risk for excess weight gain and obesity-related adverse health outcomes, development of full-syndrome BED, depressive symptoms, and full-syndrome bulimia nervosa. As such, the prognosis for youth with

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LOC eating is poor given the adverse outcomes an individual with LOC eating is likely to develop throughout their developmental trajectory.

PREDICTORS OF LOC EATING IN YOUTH In addition to the cross-sectional correlates of LOC eating, which involved studies examining variables occurring contemporaneously, there are a number of studies that examined predictors of LOC eating, which either involved retrospective examination of behaviors that occurred before onset of LOC eating, or followed individuals longitudinally to gather prospective data on LOC eating. Given the adverse physical and psychosocial correlates outcomes for youth with LOC eating, it is critical to identify the precursors to this behavior over time. However, there are limited data on longitudinal predictors of LOC eating onset.

Biological Predictors Personal and familial history of obesity is a risk factor for binge-type eating disorders.42,85 Genetic and hormonal factors also appear to contribute to the development of LOC eating in children and adolescents. For example, genetic effects on development of LOC eating emerge with higher levels of estradiol, a form of the hormone estrogen, which increases after the onset of puberty in females.86,87 Estradiol is involved in the expression of genes linked to negative valence system functioning and has been shown to mediate the relationship between genetic risk and LOC eating behaviors.88,89 Additionally, hormonal changes during adolescence place girls at increased risk for the development of eating disorder symptoms such as LOC eating. Similarly, early menarche is a risk factor for discordant socioemotional and biological development,90,91 putting adolescent girls at risk for the development of disordered eating behaviors, including LOC eating.92 Reproductive hormones may also be potential risk factors for the development of LOC eating. Females with an opposite-sex twin had decreased risk for disordered eating in late puberty, while females with a same-sex twin did not have a lower risk.93 This suggests prenatal exposure to testosterone may decrease sensitivity to ovarian hormones, therefore minimizing risk for development of LOC eating during puberty.

Social and Psychological Predictors A number of psychosocial factors have been associated with the development of LOC eating. Longitudinal studies have shown dieting,78,94 body image disturbance,64,92 and emotional eating95 as significant risk factors for the onset of LOC eating in adolescents. Females who eat in the absence of hunger at age seven subsequently report LOC eating behaviors during adolescence, after adjusting for BMI.96 Further, girls who eat in the absence of hunger with elevated BMI, negative affect, and disordered eating cognitions

Development of Loss of Control Eating

were at even greater risk for LOC eating behaviors in adolescence. Another study showed that low self-esteem and depressive symptoms in adolescence also appear to be prospective risk factors for a subsequent increase in disordered eating symptoms, including LOC eating, by adulthood.97 In addition, temperament during development, such as impulsivity and reward sensitivity, may put youth at increased risk for subsequent LOC eating as well.48,98 In a sample of overweight children between the ages of 6 and 13 years old, 25.7% reported having both LOC eating and dieting behaviors in the past. Of those, twothirds reported LOC eating preceded their dieting behaviors.21 Similarly among adolescent girls, Stice, Marti, and Durant99 found dieting behaviors to be a prospective risk factor for eating disorder onset among girls with low body dissatisfaction. However, there are a significant percentage of children who report LOC eating behaviors but have never dieted,100 and there is some evidence that supports eating disturbance begins with LOC eating rather than dieting behaviors.48 As such, dieting may not be useful as a specific risk marker for the development of LOC eating or later eating disorder onset. Maladaptive family environments can impact the onset and maintenance of LOC eating behaviors in youth.101 Parental problems, such as under involvement and critical comments on weight and shape, as well as critical life events such as change in school, are significant predictors of LOC eating in youth.102 Weight-based teasing by family members and peers has been shown to be a prospective risk factor for the development of LOC eating in both male and female adolescents103–105; however, this relationship may be mediated by negative emotions or restriction behaviors.104 These findings suggest that parents may play a role in their children’s LOC eating, particularly during critical changes in life events. Along the same lines, youth’s emotional coping skills and emotion regulation moderate the relationship between parental mental health and disordered eating attitudes and behaviors.106 Unrealistic standards of beauty, unhealthy weight control behaviors, and peer and family pressures likely all impact youth’s risk for developing LOC eating and unhealthy relationships with eating. In summary, there are various longitudinal studies that have prospectively examined risk factors for development of LOC eating over time. There are also studies that examined, retrospectively, factors that occurred before the development of LOC eating. Biologically, family history of obesity and genetic and hormonal factors increase youths’ risk for development of LOC eating over time. Psychosocial factors such as low self-esteem, impulsivity, negative affect, and disordered eating behaviors and cognitions have been shown in longitudinal studies to be precursors to the development of LOC eating in youth. Environmental factors such as dieting, maladaptive family environments, and weight-based teasing have been shown to impact LOC eating. It is likely that a combination of these risk factors contributes to LOC eating in youth over time (Box 12.3).

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BOX 12.3 Section Summary

• •

Psychosocial factors such as low self-esteem, impulsivity, negative affect, and disordered eating behaviors and cognitions have been shown in longitudinal studies to be precursors to the development of LOC eating in youth. Over time, youth with LOC eating are at greater risk for excess weight gain and obesityrelated adverse health outcomes, development of full-syndrome BED, depressive symptoms, and full-syndrome bulimia nervosa.

THEORIES OF LOC EATING DEVELOPMENT Several psychosocial theories on the development of LOC eating have been proposed. Within the literature, three theories that have been tested in pediatric samples are affect theory,107 interpersonal theory,108 and dual pathway theory.109 Affect theory proposes that LOC eating may develop as a result of maladaptive coping with negative emotions.60,110–112 Negative affect has been consistently associated with LOC eating in a range of age groups.113,114 Research supports this theory in both cross-sectional studies6,115 and as a risk factor for development of LOC in youth.116 Increases in negative affect serve as a momentary trigger to LOC eating episodes in some studies,114 supporting the theory that this behavior is a maladaptive method of alleviating negative affect.27 In a laboratory study, premeal state negative affect was related to greater consumption of carbohydrates, dessert, and snack-type foods in youth at high risk for obesity and with reported LOC eating.112 Despite a significant literature supporting affect theory, some studies have not found a relationship between negative affect and LOC eating. For example, in a naturalistic study using momentary assessments of state affect and LOC eating, negative affect alone did not significantly predict momentary LOC eating episodes.27 Similarly, another study using an EMA protocol did not find negative mood to be an antecedent to LOC eating episodes, although cognitions about body image did precede and follow LOC episodes.28 It may be that the inconsistencies in negative affect’s role as a specific precursor to LOC eating are due to an overreporting bias in retrospective recall of mood. Alternatively, it may be that youth experience a “numbing” surrounding and during an LOC eating episode, rendering it difficult to identify negative affect as a trigger.22 Interpersonal theory extends on affect theory and posits that LOC eating occurs in response to negative emotions that are promoted by interpersonal difficulties.108,110,117 A conceptual model of the interpersonal theory of LOC eating is depicted in Fig. 12.3. Youth with LOC eating commonly report social problems.110,118 LOC eating is conceptualized as a response to negative affect specifically associated with psychosocial stress, which can occur as a result of poor communication in relationships or social

Development of Loss of Control Eating

Interpersonal stressors

Negative affect

LOC eating

Fig. 12.3 Interpersonal model of LOC eating.

isolation. In a study examining self-reported negative affect and parent-reported social problems in youth with LOC eating, negative affect was found to mediate the relationship between social problems and LOC eating,110 thus providing support for the interpersonal model of LOC eating in youth. The interpersonal model has also been supported in the laboratory in a sample of adolescent girls with LOC eating, such that state mood (and specifically state anxiety) mediated the relationship between recent social stress and energy intake during a laboratory test meal.119 Further, the interpersonal model has been partially supported using a naturalistic design. An EMA study found that interpersonal stressors predicted increases in negative affect as well as momentary LOC episodes.27 Although this lends partial support to the interpersonal model, there was not a significant effect of negative affect on LOC eating. Neuroimaging data appear to support the interpersonal model. During a simulated social peer rejection task, adolescent girls with overweight and LOC experienced a blunting in the ventromedial prefrontal cortex, an area of executive function implicated in interpreting social intentions120,121 and affect regulation,122 relative to their counterparts without LOC, who experienced increased activation.123 Moreover, only among girls with reported LOC eating, greater activity in the fusiform face area was positively associated with energy intake at a laboratory test meal immediately following the imaging session.123 These findings suggest that LOC eating may be a response to negative affect, which stems from heightened sensitivity to interpersonal cues or a bias in processing negative feedback from peers. These findings lend support to the interpersonal model, suggesting interpersonal issues lead to negative affect, which in turn contributes to LOC eating. Dual pathway model of eating pathology synthesizes affect theory with sociocultural and dietary accounts of disordered eating, integrating the robust effects of social pressures on youth with negative affect and dieting behaviors.109 This model suggests perceived pressure to be thin and the internalization of the thin ideal produce body dissatisfaction, which in turn promotes negative affect and subsequent LOC eating. The onset and maintenance of LOC eating are promoted through pervasive sociocultural messages, such as pressure to be thin, in that the social pressure may lead to negative emotions, which in turn triggers LOC eating as a maladaptive attempt to reduce negative feelings of emotional distress, given eating is often believed to provide comfort or distraction from negative emotions.95,109,124 These sociocultural messages, which promote the “thin-ideal” of feminine beauty, contribute to the onset of LOC eating by exposing youth to

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unrealistic standards of beauty and unhealthy strategies for weight loss.116,125 Pressure to be thin can subsequently lead to dieting through idealization of thin beauty standards and body dissatisfaction. Low self-esteem and overvaluation of weight and shape contribute to strict dietary restraint, which in turn predicts and maintains LOC eating.126,127 For example, an individual with low self-esteem who has a lapse in strict dietary rules and an “all-or-nothing” mentality may be triggered to engage in LOC eating. Consequently, a cycle of further dieting efforts to compensate for the LOC episodes may serve to trigger future episodes and maintain the behavior over time.62 Although not experimentally tested, as previously presented, perceived pressure to be thin, either from the media, family members, or peers, has been shown to play a role in the onset of LOC eating in youth.95 In addition to the overt messages youth are exposed to from the media, interpersonal sources can influence youth’s LOC eating. Specifically, parents’ and peers’ modeling of body dissatisfaction and disordered eating behavior, such as displaying preoccupation with weight, extreme weight-control behaviors, and LOC eating, can contribute to the onset of episodes in adolescent girls through social pressure to conform to the thin ideal.95 Components of the dual pathway model are clearly supported. Experimental data, either in the laboratory or the natural environment, are required to objectively examine aspects of the dual pathway model of disordered eating.

INTERVENTIONS FOR LOC EATING There are a limited number of trials testing interventions to reduce LOC eating in youth. A pilot study of an adolescent adaptation of cognitive behavioral therapy for recurrent LOC eating showed preliminary promising results.128 Cognitive behavioral therapy adapted for adolescents with LOC eating aims to reduce disordered eating behaviors through addressing and challenging maladaptive eating-related cognitions. Adolescent girls who received cognitive behavioral therapy had significantly fewer LOC eating episodes at the end of treatment than the treatment as usual/delayed treatment control group. Further, at a six-month follow-up time point, 100% of the girls in the cognitive behavior therapy group were abstinent from LOC eating. Although promising, further data of this adapted intervention are required. An adapted group interpersonal psychotherapy for adolescent girls at risk for obesity due to being overweight and having LOC eating has shown some promise reducing LOC eating episodes.129 Interpersonal psychotherapy, based on the interpersonal model of LOC eating,129 focuses on reducing LOC eating by improving interpersonal difficulties that contribute to negative affect in youth. Although a randomized, controlled trial suggested that interpersonal psychotherapy is equally as effective as health education in reducing LOC eating and excess weight gain, the intervention was more effective at reducing LOC eating for girls of ethnic-racial minorities and for decreasing

Development of Loss of Control Eating

BOX 12.4 Section Summary

• •

Theories of LOC eating suggest that it may develop as a result of maladaptive coping with negative emotions that arise from interpersonal conflict and/or body dissatisfaction in response to perceived pressure to be thin. Approaches to treat LOC have addressed maladaptive eating-related cognitions as well as interpersonal difficulties that contribute to negative affect in youth.

objectively large LOC (binge) episodes in all girls at one-year follow-up.130 Interestingly, at a 3-year follow-up assessment, compared to a control treatment, interpersonal psychotherapy was associated with greater declines in BMIz and stabilization of adiposity gain for girls who reported high baseline social-adjustment problems or anxiety.131 Further data are needed to determine whether interpersonal psychotherapy may be particularly helpful for girls with overweight who report social problems or anxiety, with or without LOC eating. Another trial tested the efficacy of a recently developed dialectical-behavior therapy intervention, LIBER8 (“Linking Individuals Being Emotionally Real”).132 Dialecticalbehavior therapy targets behaviors associated with LOC eating through emotional and cognitive regulation techniques.133,134 This intervention was shown to effectively reduce disordered eating behaviors and eating in response to negative emotions in a sample of ethnically diverse adolescent girls,132 with high satisfaction and feasibility ratings. However, LIBER8 was not found to be significantly more effective than a weight management control group at reducing LOC eating behaviors. Additional research on the mechanisms by which LOC eating can be improved would help to further inform randomized control trials of effective interventions (Box 12.4).

PROPOSED FUTURE DIRECTIONS There are several areas of LOC eating in children and adolescents that require further exploration. Much of the research on pediatric LOC eating has focused on adolescents due to the age range when disordered eating symptomatology typically manifests. However, more data are needed to identify precursors of LOC eating. The full developmental spectrum should be examined, from prenatal to middle childhood. For example, some research has shown that mothers with BED had higher birth weight babies and higher risk for large-for-gestational age babies than mothers without BED.135 Other potential perinatal risk factors for LOC eating should be explored. Given not all youth with LOC subsequently develop full-syndrome BED in adulthood,2,3,136 more prospective studies are needed to determine trajectories of LOC eating progression or cessation from childhood into adulthood. The interactions

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among genetic, neural, psychological, and environmental aspects throughout childhood should be examined longitudinally in order to elucidate the manifestation of LOC eating. A better understanding of the risk factors for development of LOC eating in childhood through adulthood is crucial to the development of effective prevention efforts. Researchers should continue to study the construct of LOC eating in relation to other disordered eating behaviors (e.g., emotional eating) and traits (e.g., impulsivity) using comprehensive methodology to better identify child-specific phenotypes of the behavior. A better understanding of these overlapping constructs involved in various endophenotypes for LOC eating will help to determine which youth are at greatest risk. Practitioners who encounter obese youth should routinely screen for LOC eating given its high comorbidity with obesity and associations with adverse outcomes such as continuous excess weight gain. While clinical interview methods are typically considered optimal, when possible, practitioners should also use a multitude of assessment methods and respondents, including child and parent report, interview, test meals, and EMA to screen for LOC eating in obese youth. Additionally, analyzing various behavioral, physical, and emotional variables surrounding eating episodes can help clarify aberrant eating patterns in overweight youth.137 Once identified, there are a number of effective treatment strategies for LOC eating that target interpersonal difficulties and maladaptive eating-related cognitions. From a broader vantage point, educating families and youth on the consequences of weight-based teasing could help to ameliorate some of the impact of family and societal pressures on youth that contribute to LOC eating.105 Implementation of no-teasing policies in schools and community-based organizations that serve youth is needed, as well as prevention efforts to reduce verbal harassment and educate youth on conflict resolution and development of communication skills.105

CONCLUSION Given the persistence of LOC eating from adolescence into adulthood, prevention efforts will be critical to reducing the onset of disordered eating at a young age. With a greater understanding of factors contributing to the development of LOC eating in youth, those at risk for the progression of full-syndrome BED may be identified. Ideally, this will lead to interventions to prevent LOC eating and subsequent adverse outcomes.

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28. Hilbert A, Rief W, Tuschen-Caffier B, de Zwaan M, Czaja J. Loss of control eating and psychological maintenance in children: an ecological momentary assessment study. Behav Res Ther 2009;47(1):26–33. 29. Grenard JL, Stacy AW, Shiffman S, et al. Sweetened drink and snacking cues in adolescents. A study using ecological momentary assessment. Appetite 2013;67:61–73. 30. Stein RI, Kenardy J, Wiseman CV, Dounchis JZ, Arnow BA, Wilfley DE. What’s driving the binge in binge eating disorder?: a prospective examination of precursors and consequences. Int J Eat Disord 2007;40(3):195–203. 31. Hudson JI, Lalonde JK, Berry JM, et al. Binge-eating disorder as a distinct familial phenotype in obese individuals. Arch Gen Psychiatry 2006;63(3):313–9. 32. Mitchell K, Neale M, Bulik C, Aggen S, Kendler K, Mazzeo S. Binge eating disorder: a symptom-level investigation of genetic and environmental influences on liability. Psychol Med 2010;40(11):1899–906. 33. Javaras KN, Laird NM, Reichborn-Kjennerud T, Bulik CM, Pope HG, Hudson JI. Familiality and heritability of binge eating disorder: results of a case-control family study and a twin study. Int J Eat Disord 2008;41(2):174–9. 34. Reichborn-Kjennerud T, Bulik CM, Kendler KS, et al. Gender differences in binge-eating: a population-based twin study. Acta Psychiatr Scand 2003;108(3):196–202. 35. Thornton LM, Mazzeo SE, Bulik CM. The heritability of eating disorders: methods and current findings. Curr Top Behav Neurosci 2011;6:141–56. 36. Zocca JM, Shomaker LB, Tanofsky-Kraff M, et al. Links between mothers’ and children’s disinhibited eating and children’s adiposity. Appetite 2011;56(2):324–31. 37. Bulik CM, Sullivan PF, Kendler KS. Genetic and environmental contributions to obesity and binge eating. Int J Eat Disord 2003;33(3):293–8. 38. Frayling TM, Timpson NJ, Weedon MN, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 2007;316(5826):889–94. 39. Dina C, Meyre D, Gallina S, et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nat Genet 2007;39(6):724–6. 40. Scuteri A, Sanna S, Chen W-M, et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet 2007;3(7). e115. 41. Hunt SC, Stone S, Xin Y, et al. Association of the FTO gene with BMI. Obesity 2008;16(4):902–4. 42. Campbell IC, Mill J, Uher R, Schmidt U. Eating disorders, gene-environment interactions and epigenetics. Neurosci Biobehav Rev 2011;35(3):784–93. 43. Tanofsky-Kraff M, Han JC, Anandalingam K, et al. The FTO gene rs9939609 obesity-risk allele and loss of control over eating. Am J Clin Nutr 2009;90(6):1483–8. 44. Woods SC, D’Alessio DA. Central control of body weight and appetite. J Clin Endocrinol Metabol 2008;93(11_supplement_1):s37–50. 45. Davis C, Levitan RD, Yilmaz Z, Kaplan AS, Carter JC, Kennedy JL. Binge eating disorder and the dopamine D2 receptor: genotypes and sub-phenotypes. Prog Neuro-Psychopharmacol Biol Psychiatry 2012;38(2):328–35. 46. Corwin RL, Avena NM, Boggiano MM. Feeding and reward: perspectives from three rat models of binge eating. Physiol Behav 2011;104(1):87–97. 47. Akkermann K, Kaasik K, Kiive E, Nordquist N, Oreland L, Harro J. The impact of adverse life events and the serotonin transporter gene promoter polymorphism on the development of eating disorder symptoms. J Psychiatr Res 2012;46(1):38–43. 48. Hilbert A, Pike KM, Goldschmidt AB, et al. Risk factors across the eating disorders. Psychiatry Res 2014;220(1):500–6. 49. Striegel-Moore RH, Fairburn CG, Wilfley DE, Pike KM, Dohm F-A, Kraemer HC. Toward an understanding of risk factors for binge-eating disorder in black and white women: a community-based case-control study. Psychol Med 2005;35(06):907–17. 50. Monteleone P, Tortorella A, Castaldo E, Maj M. Association of a functional serotonin transporter gene polymorphism with binge eating disorder. Am J Med Genet B Neuropsychiatr Genet 2006;141(1):7–9. 51. Trace SE, Baker JH, Pen˜as-Lledo´ E, Bulik CM. The genetics of eating disorders. Annu Rev Clin Psychol 2013;9:589–620.

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52. He J, Cai Z, Fan X. Prevalence of binge and loss of control eating among children and adolescents with overweight and obesity: an exploratory meta-analysis. Int J Eat Disord 2016;50(2):91–103. 53. Morgan CM, Yanovski SZ, Nguyen TT, et al. Loss of control over eating, adiposity, and psychopathology in overweight children. Int J Eat Disord 2002;31(4):430–41. 54. Lourenc¸o BH, Arthur T, Rodrigues MD, et al. Binge eating symptoms, diet composition and metabolic characteristics of obese children and adolescents. Appetite 2008;50(2):223–30. 55. Radin RM, Tanofsky-Kraff M, Shomaker LB, et al. Metabolic characteristics of youth with loss of control eating. Eat Behav 2015;19:86–9. 56. Shank LM, Tanofsky-Kraff M, Kelly NR, et al. Pediatric loss of control eating and high-sensitivity C-reactive protein concentrations. Childhood Obes 2017;13(1):1–8. 57. Schutz HK, Paxton SJ. Friendship quality, body dissatisfaction, dieting and disordered eating in adolescent girls. Br J Clin Psychol 2007;46(1):67–83. 58. Goossens L, Braet C, Bosmans G, Decaluwe V. Loss of control over eating in pre-adolescent youth: the role of attachment and self-esteem. Eat Behav 2011;12(4):289–95. 59. Tetzlaff A, Hilbert A. The role of the family in childhood and adolescent binge eating. A systematic review. Appetite 2014;76:208. 60. Goossens L, Braet C, Van Vlierberghe L, Mels S. Loss of control over eating in overweight youngsters: the role of anxiety, depression and emotional eating. Eur Eat Disord Rev 2009;17(1):68–78. 61. Goldschmidt AB, Tanofsky-Kraff M, Wilfley DE. A laboratory-based study of mood and binge eating behavior in overweight children. Eat Behav 2011;12(1):37–43. 62. Allen KL, Byrne SM, La Puma M, McLean N, Davis EA. The onset and course of binge eating in 8- to 13-year-old healthy weight, overweight and obese children. Eat Behav 2008;9(4):438–46. 63. Decaluwe V, Braet C. The cognitive behavioural model for eating disorders: a direct evaluation in children and adolescents with obesity. Eat Behav 2005;6(3):211–20. 64. Neumark-Sztainer D, Paxton SJ, Hannan PJ, Haines J, Story M. Does body satisfaction matter? Fiveyear longitudinal associations between body satisfaction and health behaviors in adolescent females and males. J Adolesc Health 2006;39(2):244–51. 65. Stojek MM, Tanofsky-Kraff M, Shomaker LB, et al. Associations of adolescent emotional and loss of control eating with 1-year changes in disordered eating, weight, and adiposity. Int J Eat Disord 2016;. 66. Birch LL, Fisher JO, Davison KK. Learning to overeat: maternal use of restrictive feeding practices promotes girls’ eating in the absence of hunger. Am J Clin Nutr 2003;78(2):215–20. 67. Fisher JO, Birch LL. Eating in the absence of hunger and overweight in girls from 5 to 7 y of age. Am J Clin Nutr 2002;76(1):226–31. 68. Moens E, Braet C. Predictors of disinhibited eating in children with and without overweight. Behav Res Ther 2007;45(6):1357–68. 69. Tanofsky-Kraff M, Ranzenhofer LM, Yanovski SZ, et al. Psychometric properties of a new questionnaire to assess eating in the absence of hunger in children and adolescents. Appetite 2008;51(1):148–55. 70. Vannucci A, Tanofsky-Kraff M, Crosby RD, et al. Latent profile analysis to determine the typology of disinhibited eating behaviors in children and adolescents. J Consult Clin Psychol 2013;81(3):494. 71. Shunk JA, Birch LL. Girls at risk for overweight at age 5 are at risk for dietary restraint, disinhibited overeating, weight concerns, and greater weight gain from 5 to 9 years. J Am Diet Assoc 2004;104(7):1120–6. 72. Goossens L, Braet C, Decaluwe V. Loss of control over eating in obese youngsters. Behav Res Ther 2007;45(1):1–9. 73. Czaja J, Rief W, Hilbert A. Emotion regulation and binge eating in children. Int J Eat Disord 2009;42(4):356–62. 74. Kelly NR, Tanofsky-Kraff M, Vannucci A, et al. Emotion dysregulation and loss-of-control eating in children and adolescents. Health Psychol 2016;35(10):1110–9. 75. Shank LM, Tanofsky-Kraff M, Nelson EE, et al. Attentional bias to food cues in youth with loss of control eating. Appetite 2015;87:68–75. 76. Allen KL, Byrne SM, Hii H, van Eekelen A, Mattes E, Foster JK. Neurocognitive functioning in adolescents with eating disorders: a population-based study. Cogn Neuropsychiatry 2013;18(5):355–75.

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77. Goldschmidt AB, Hipwell AE, Stepp SD, McTigue KM, Keenan K. Weight gain, executive functioning, and eating behaviors among girls. Pediatrics 2015;136(4). e856-e863. 78. Field AE, Austin S, Taylor C, et al. Relation between dieting and weight change among preadolescents and adolescents. Pediatrics 2003;112(4):900–6. 79. Tanofsky-Kraff M, Yanovski SZ, Schvey NA, Olsen CH, Gustafson J, Yanovski JA. A prospective study of loss of control eating for body weight gain in children at high risk for adult obesity. Int J Eat Disord 2009;42(1):26–30. 80. Tanofsky-Kraff M, Cohen ML, Yanovski SZ, et al. A prospective study of psychological predictors of body fat gain among children at high risk for adult obesity. Pediatrics 2006;117(4):1203–9. 81. Neumark-Sztainer D, Wall M, Story M, Standish AR. Dieting and unhealthy weight control behaviors during adolescence: associations with 10-year changes in body mass index. J Adolesc Health 2012; 50(1):80–6. 82. Goldschmidt AB, Wall MM, Zhang J, Loth KA, Neumark-Sztainer D. Overeating and binge eating in emerging adulthood: 10-year stability and risk factors. Dev Psychol 2016;52(3):475. 83. Croll J, Neumark-Sztainer D, Story M, Ireland M. Prevalence and risk and protective factors related to disordered eating behaviors among adolescents: relationship to gender and ethnicity. J Adolesc Health 2002;31(2):166–75. 84. Brewerton TD, Rance SJ, Dansky BS, O’Neil PM, Kilpatrick DG. A comparison of women with child-adolescent versus adult onset binge eating: results from the National Women’s Study. Int J Eat Disord 2014;47(7):836–43. 85. Kessler RC, Berglund PA, Chiu WT, et al. The prevalence and correlates of binge eating disorder in the World Health Organization World Mental Health Surveys. Biol Psychiatry 2013;73(9):904–14. 86. Klump KL, Culbert KM, Slane JD, Burt SA, Sisk CL, Nigg J. The effects of puberty on genetic risk for disordered eating: evidence for a sex difference. Psychol Med 2012;42(03):627–37. 87. Klump KL, Keel PK, Sisk C, Burt SA. Preliminary evidence that estradiol moderates genetic influences on disordered eating attitudes and behaviors during puberty. Psychol Med 2010;40(10):1745–53. 88. Klump KL. Puberty as a critical risk period for eating disorders: a review of human and animal studies. Horm Behav 2013;64(2):399–410. 89. Vannucci A, Nelson EE, Bongiorno DM, Pine DS, Yanovski JA, Tanofsky-Kraff M. Behavioral and neurodevelopmental precursors to binge-type eating disorders: support for the role of negative valence systems. Psychol Med 2015;45(14):2921–36. 90. Day J, Schmidt U, Collier D, et al. Risk factors, correlates, and markers in early-onset bulimia nervosa and EDNOS. Int J Eat Disord 2011;44(4):287–94. 91. Gluckman PD, Hanson MA. Evolution, development and timing of puberty. Trends Endocrinol Metab 2006;17(1):7–12. 92. Stice E, Shaw HE. Role of body dissatisfaction in the onset and maintenance of eating pathology: a synthesis of research findings. J Psychosom Res 2002;53(5):985–93. 93. Culbert KM, Breedlove SM, Sisk CL, Burt SA, Klump KL. The emergence of sex differences in risk for disordered eating attitudes during puberty: a role for prenatal testosterone exposure. J Abnorm Psychol 2013;122(2):420. 94. Neumark-Sztainer D, Wall M, Haines J, Story M, Eisenberg ME. Why does dieting predict weight gain in adolescents? Findings from project EAT-II: a 5-year longitudinal study. J Am Diet Assoc 2007;107(3):448–55. 95. Stice E, Presnell K, Spangler D. Risk factors for binge eating onset in adolescent girls: a 2-year prospective investigation. Health Psychol 2002;21(2):131. 96. Balantekin KN, Birch LL, Savage JS. Eating in the absence of hunger during childhood predicts self-reported binge eating in adolescence. Eat Behav 2017;24:7–10. 97. Pearson CM, Miller J, Ackard DM, et al. Stability and change in patterns of eating disorder symptoms from adolescence to young adulthood. Int J Eat Disord 2017;50(7):748–57. 98. Cassin SE, von Ranson KM. Personality and eating disorders: a decade in review. Clin Psychol Rev 2005;25(7):895–916. 99. Stice E, Marti CN, Durant S. Risk factors for onset of eating disorders: evidence of multiple risk pathways from an 8-year prospective study. Behav Res Ther 2011;49(10):622–7.

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100. Claus L, Braet C, Decaluwe V. Dieting history in obese youngsters with and without disordered eating. Int J Eat Disord 2006;39(8):721–8. 101. Le Grange D, Lock J, Loeb K, Nicholls D. Academy for eating disorders position paper: the role of the family in eating disorders. Int J Eat Disord 2010;43(1):1–5. 102. Hartmann AS, Czaja J, Rief W, Hilbert A. Psychosocial risk factors of loss of control eating in primary school children: a retrospective case-control study. Int J Eat Disord 2012;45(6):751–8. 103. Keel PK, Forney KJ. Psychosocial risk factors for eating disorders. Int J Eat Disord 2013;46(5):433–9. 104. Haines J, Neumark-Sztainer D. Prevention of obesity and eating disorders: a consideration of shared risk factors. Health Educ Res 2006;21(6):770–82. 105. Haines J, Neumark-Sztainer D, Eisenberg ME, Hannan PJ. Weight teasing and disordered eating behaviors in adolescents: longitudinal findings from project EAT (eating among teens). Pediatrics 2006;117(2). e209-e215. 106. Martinson LE, Esposito-Smythers C, Blalock DV. The effects of parental mental health and socialemotional coping on adolescent eating disorder attitudes and behaviors. J Adolesc 2016;52:154–61. 107. Heatherton TF, Baumeister RF. Binge eating as escape from self-awareness. Psychol Bull 1991;110(1):86. 108. Wilfley DE, Pike K, Striegel-Moore R. Toward an integrated model of risk for binge eating disorder. J Gend Cult Health 1997;2:1–32. 109. Stice E. A prospective test of the dual-pathway model of bulimic pathology: mediating effects of dieting and negative affect. J Abnorm Psychol 2001;110(1):124. 110. Elliott CA, Tanofsky-Kraff M, Shomaker LB, et al. An examination of the interpersonal model of loss of control eating in children and adolescents. Behav Res Ther 2010;48(5):424–8. 111. Kenardy J, Arnow B, Agras WS. The aversiveness of specific emotional states associated with bingeeating in obese subjects. Aust N Z J Psychiatry 1996;30(6):839–44. 112. Ranzenhofer LM, Hannallah L, Field SE, et al. Pre-meal affective state and laboratory test meal intake in adolescent girls with loss of control eating. Appetite 2013;68:30–7. 113. Goldschmidt AB, Aspen VP, Sinton MM, Tanofsky-Kraff M, Wilfley DE. Disordered eating attitudes and behaviors in overweight youth. Obesity 2008;16(2):257–64. 114. Haedt-Matt AA, Keel PK. Revisiting the affect regulation model of binge eating: a meta-analysis of studies using ecological momentary assessment. Psychol Bull 2011;137(4):660. 115. Goldschmidt AB, Jones M, Manwaring JL, et al. The clinical significance of loss of control over eating in overweight adolescents. Int J Eat Disord 2008;41(2):153–8. 116. Stice E. Risk and maintenance factors for eating pathology: a meta-analytic review. Psychol Bull 2002;128(5):825. 117. Wilfley DE, MacKenzie KR, Welch RR, Ayres VE. Interpersonal psychotherapy for group. New York, NY: Basic Books; 2000. 118. Czaja J, Hartmann AS, Rief W, Hilbert A. Mealtime family interactions in home environments of children with loss of control eating. Appetite 2011;56(3):587–93. 119. Shank LM, Crosby RD, Grammer AC, et al. Examination of the interpersonal model of loss of control eating in the laboratory. Compr Psychiatry 2017;76:36–44. 120. Amodio DM, Frith CD. Meeting of minds: the medial frontal cortex and social cognition. Nat Rev Neurosci 2006;7(4):268–77. 121. Frith CD, Frith U. Mechanisms of social cognition. Annu Rev Psychol 2012;63:287–313. 122. Etkin A, Egner T, Kalisch R. Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn Sci 2011;15(2):85–93. 123. Jarcho JM, Tanofsky-Kraff M, Nelson EE, et al. Neural activation during anticipated peer evaluation and laboratory meal intake in overweight girls with and without loss of control eating. NeuroImage 2015;108:343–53. 124. Sonneville KR, Calzo JP, Horton NJ, Haines J, Austin SB, Field AE. Body satisfaction, weight gain and binge eating among overweight adolescent girls. Int J Obes 2012;36(7):944–9. 125. Stice E. Interactive and mediational etiologic models of eating disorder onset: evidence from prospective studies. Annu Rev Clin Psychol 2016;12:359–81.

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126. Byrne SM, McLean NJ. The cognitive-behavioral model of bulimia nervosa: a direct evaluation. Int J Eat Disord 2002;31(1):17–31. 127. Fairburn CG, Cooper Z, Shafran R. Cognitive behaviour therapy for eating disorders: a “transdiagnostic” theory and treatment. Behav Res Ther 2003;41(5):509–28. 128. DeBar LL, Wilson GT, Yarborough BJ, et al. Cognitive behavioral treatment for recurrent binge eating in adolescent girls: a pilot trial. Cogn Behav Pract 2013;20(2):147–61. 129. Tanofsky-Kraff M, Wilfley DE, Young JF, et al. Targeting binge eating for the prevention of excessive weight gain: interpersonal psychotherapy for adolescents at high-risk for adult obesity. Obesity (Silver Spring, Md) 2007;15(6):1345. 130. Tanofsky-Kraff M, Shomaker LB, Wilfley DE, et al. Targeted prevention of excess weight gain and eating disorders in high-risk adolescent girls: a randomized controlled trial. Am J Clin Nutr 2014;100(4):1010–8. 131. Tanofsky-Kraff M, Shomaker LB, Wilfley DE, et al. Excess weight gain prevention in adolescents: three-year outcome following a randomized controlled trial. J Consult Clin Psychol 2017;85(3):218–27. 132. Mazzeo SE, Lydecker J, Harney M, et al. Development and preliminary effectiveness of an innovative treatment for binge eating in racially diverse adolescent girls. Eat Behav 2016;22:199–205. 133. Wiser S, Telch CF. Dialectical behavior therapy for binge-eating disorder. J Clin Psychol 1999;55(6):755–68. 134. Klein AS, Skinner JB, Hawley KM. Targeting binge eating through components of dialectical behavior therapy: preliminary outcomes for individually supported diary card self-monitoring versus groupbased DBT. Psychotherapy 2013;50(4):543. 135. Bulik CM, Von Holle A, Siega-Riz AM, et al. Birth outcomes in women with eating disorders in the Norwegian mother and child cohort study (MoBa). Int J Eat Disord 2009;42(1):9–18. 136. Goldschmidt AB, Wall MM, Loth KA, Bucchianeri MM, Neumark-Sztainer D. The course of binge eating from adolescence to young adulthood. Health Psychol 2014;33(5):457. 137. Tanofsky-Kraff M, Yanovski SZ. Eating disorder or disordered eating? Non-normative eating patterns in obese individuals. Obesity 2004;12(9):1361–6.

CHAPTER 13

Intentional Self-Regulation of Eating Among Children and Adolescents Katherine W. Bauer, Sam Chuisano

Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, United States

INTRODUCTION The current obesogenic food environment promotes frequent intake of large portions of energy-dense foods. While hunger and satiety help to regulate individuals’ eating, for many, these physiologic signals are often not sufficient to ensure healthy eating and weight in our food-abundant environment. Among young children, parents often serve as the gatekeepers of children’s eating by controlling the types and quantities of foods available. However, as children age into adolescence and gain increasing ability to plan and monitor complex actions, they develop an increased capacity to make purposeful decisions about what and how much to eat. This process of managing eating through thoughts, feelings, and actions is known as intentional self-regulation of eating. Intentional self-regulation of eating is well known to be a fundamental component of effective weight management among adults, while the development and impacts of intentional self-regulation of eating among youth is gaining increased attention. In this chapter we will define intentional self-regulation of eating, present evidence for the importance of intentional self-regulation of eating for healthy eating and obesity prevention, and examine the development of intentional self-regulation of eating throughout childhood. We will then examine factors that promote or impede youths’ ability to intentionally selfregulate eating and identify current knowledge gaps and future priorities in the area of intentional self-regulation of eating.

DEFINING INTENTIONAL SELF-REGULATION OF EATING Intentional self-regulation refers to the volitional process used to manage thoughts, feelings, and actions to accomplish a goal-directed behavior.1,2 Intentional self-regulation occurs in many behavioral domains beyond eating, including academic, social, or athletic domains. The cycle of intentional self-regulation of eating involves setting a goal, selfmonitoring cognitions and behaviors, appraising progress, and either adjusting cognitions and behaviors as needed or abandoning the goal.2,3 Intentional self-regulation of eating is Pediatric Food Preferences and Eating Behaviors https://doi.org/10.1016/B978-0-12-811716-3.00013-0

© 2018 Elsevier Inc. All rights reserved.

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therefore the volitional cognitive and behavioral processes involved in setting, pursuing, and attaining eating goals.4 The process of intentional self-regulation is often initiated when individuals identify a discrepancy between their current state and a desired state.5 Engagement in intentional self-regulation of eating may arise from any number of desired states. Given the high prevalence of overweight and obesity among youth,6 as well as stigma against individuals of higher weight,7 a common desired state that prompts intentional self-regulation of eating among children and adolescents may be achieving or maintaining a healthy or lower weight. However, youth have numerous other desired states with regard to nutrition—for example, eating a specific way to enhance sports performance, to adhere to a prescribed diet for a health condition or ethical concerns, or to enhance physical and/or mental well-being. Specific goals to achieve these desired states may be approach goals, or goals to achieve a specific outcome. An example of an approach goal is eating five fruits and vegetables a day. Alternatively, individuals may set avoidance goals to achieve their desired state, for example, setting a goal to not drink any sugar-sweetened beverages. Once a goal is set, individuals must strive to achieve it through avoiding or disrupting factors that interfere with goal attainment. Some of this avoidance or disruption relies on willpower, for example, choosing to only eat half of a dessert that is served.4 However, continued reliance on willpower in the face of tempting distractions depletes individuals’ ability to intentionally self-regulate and thus is highly prone to failure.8 Planning or prospective strategies that do not rely on willpower, but instead involve changing routines and altering surroundings, have been more consistently associated with successful, sustained intentional self-regulation of eating.4 Our understanding of the process of intentional self-regulation of eating is derived primarily from the field of behavioral weight loss interventions targeting adults. Enhancing intentional self-regulation of eating is a defining feature of successful adult weight loss and weight loss maintenance interventions. In these settings, participants are taught to set rules or goals for eating, self-monitor their eating, control food stimuli in their environment, plan food purchasing and eating, and reward ones’ self for achieving eating goals.9–11 One example of the process of intentional self-regulation of eating in the weight loss setting is: (1) setting a goal to meet an identified calorie target each day; (2) employing strategies to help meet that target such as keeping tempting, high calorie foods out of the house, or inviting friends to go for a walk instead of meeting at a restaurant; (3) at the end of the day, evaluating whether the calorie goal was met, what factors interfered with achieving the calorie goal, and examining whether the calorie goal is realistic and sustainable; and (4) renewing efforts to achieve the calorie goal or deciding to modify the goal. While adults vary in their ability to self-regulate eating, those with greater intentional self-regulation gain less weight over time,12,13 are more successful with weight loss,12,14–16 and maintain weight loss longer.10,11

Intentional Self-Regulation of Eating Among Children and Adolescents

Considerably less is known about intentional self-regulation of eating among children and adolescents as compared to adults, but the information that does exist suggests that many older children and adolescents intentionally self-regulate their eating using strategies that closely mirror those fundamental to adult weight control. Further, adolescentfocused behavioral weight loss programs that promote many of the same intentional self-regulation of eating strategies as adult programs have demonstrated success in helping adolescents with obesity manage their weight.17–19 The TEMPEST study is the most comprehensive to date in identifying intentional self-regulation of eating strategies used by children and adolescents not engaged in a structured weight management program. In this study, youth between the ages of 10 and 17 years old were asked to generate and then rate the things they do to ensure healthy eating. Table 13.1 demonstrates the specific intentional self-regulation of eating strategies that participants commonly identified. Similar to adults, these youth reported using strategies including planning and preparation, setting eating rules and goals, reminding themselves of the consequences of unhealthy eating, substituting healthy options for less healthy options, and reducing food stimuli in their environment.20 It is important to note that intentional self-regulation of eating differs from the concept of self-regulation of appetite, which is commonly assessed in young children, although applies to all ages.21,22 Self-regulation of appetite is often understood in the context of inhibition—the ability of individuals to inhibit responses to external cues to eating Table 13.1 Intentional self-regulation of eating approaches and strategies used by adolescents Approach Strategy Example of strategy

Address the temptations directly

Address the psychological meaning of the temptation Address the goal directly

Avoid temptations Stimulus control— limit environmental cues to eat Planning and preparation Distraction Suppression Goal and rule setting Goal deliberation

Do not go down the candy aisle when shopping Serve individual portion of a snack and place large container away in cabinet

Always pack healthy lunch to bring to school Identify an alternative activity when craving a sweet Ignore the table of food at a party Set daily limit for number of sweets to eat Remind yourself of goal and positive aspects of goal when confronted with challenging situation

Adapted from: De Vet E, De Ridder D, Stok M, Brunso K, Baban A, Gaspar T. Assessing self-regulation strategies: development and validation of the tempest self-regulation questionnaire for eating (TESQ-E) in adolescents. Int J Behav Nutr Phys Act. 2014;11:106. PMCID:PMC4161904.

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BOX 13.1

• • •

Intentional self-regulation of eating differs from appetite self-regulation and refers to the volitional cognitive and behavioral processes involved in setting, pursuing, and attaining eating goals. Strategies that do not rely on willpower, but instead involve changing routines and altering surroundings, have been most consistently associated with successful, sustained intentional self-regulation of eating. Youth use many of the same intentional self-regulation strategies as adults.

and regulate their energy intake through attending carefully to internal cues of hunger and satiety. In contrast, intentional self-regulation of eating is a conscious process of setting and striving for specific eating goals.4 Therefore, while these concepts are related— for example, individuals may exhibit self-regulation of appetite, or the ability to inhibit responses to external cues to eat, as part of the process of intentional self-regulation of eating—current convention approaches these two constructs from separate perspectives (Box 13.1).

INTENTIONAL SELF-REGULATION OF EATING, DIETARY RESTRAINT, AND DIETING Intentional self-regulation of eating, dietary restraint, and dieting are related and commonly interchanged constructs but have important differences in conceptualization and implications for children’s and adolescents’ eating. Dietary restraint is the cognitive effort or intention to restrain caloric intake,23 and therefore may be a component of ones’ process of intentionally self-regulating their eating if the goal is limiting intake or weight loss.8 Early theoretical and empirical evidence suggested that dietary restraint (often conceptualized interchangeably with dieting and assessed using measures of dieting intention and behavior24) was not feasible long term. Studies observed that restrained eaters inevitably faltered, leading to disinhibited eating including overeating and binge eating.24–26 For this reason, there is a prominent school of thought that dietary restriction should be avoided, or very carefully monitored, particularly among older children and adolescents, who are at heightened risk for eating disorders.27–30 Recent work, however, suggests that associations of dietary restraint with physical and mental health outcomes depend greatly on characteristics of the individual, the circumstances under which restraint is employed, and the rigidity or flexibility in restrained eating. For example, individuals who demonstrate more flexible restraint, or are able to relax their intentional self-regulation of eating goals and strategies on occasion, tend to have better weight control and less disordered eating than those with rigid restraint that favors “all or none” attitudes about limiting

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eating.23 Given this emerging knowledge, many have called for a shift in research away from the concept of dietary restraint toward an enhanced understanding of intentional self-regulation of eating, including identifying how individuals who are prone to overeating can implement effective, sustainable, and flexible intentional self-regulation of eating strategies.8,23,25 Prior to the TEMPEST study, nearly all population-based research to understand intentional self-regulation of eating among youth focused on dieting and dietary restraint. Studies predominantly assessed dieting implemented by preadolescents and adolescents (generally ages 11–18) for the goal of weight loss, or examined a limited number of extreme restrictive eating strategies such as skipping meals and fasting. Similar to the broader literature on dietary restraint, these studies of adolescent dieting and extreme restrictive behaviors suggested that restriction leads to weight gain over time.24,31–36 It is hypothesized that dieting leads to weight gain because of its unsustainable nature; youth begin dieting, often using highly restrictive means, but quickly give up and eat more than they would have if they had never dieted.37 This evidence of a relation between dieting and weight gain among youth reinforces the belief that restrictive cognitions and behaviors to achieve an eating-related goal should be discouraged.27 However, as suggested by our emerging understanding of intentional self-regulation of eating, there are a wide range of cognitive and behavioral strategies that can be employed to set and achieve healthy eating goals. By utilizing a wider, more encompassing perspective of intentional self-regulation of eating, and identifying the comprehensive range of strategies that children and adolescents use to volitionally manage their eating, we can distinguish strategies that place youth at risk for disordered eating and weight gain versus those that are sustainable and health promoting, and therefore should be supported.20

INTENTIONAL SELF-REGULATION OF EATING AND DIETARY AND WEIGHT OUTCOMES AMONG CHILDREN The TEMPEST cohort, comprised of over 2700 European children aged 10–17 years, provides the strongest evidence to date regarding associations of adherence to intentional self-regulation of eating strategies with dietary quality among youth. To assess intentional self-regulation of eating, the TEMPEST study developed the Tempest Self-regulation Questionnaire for Eating (TESQ-E), which assesses the frequency of use of intentional eating self-regulation strategies identified during early qualitative work by the study team. As identified in Table 13.1, these strategies include limiting environmental cues to eat, planning ahead, setting eating goals, and distracting ones’ self when there are opportunities to eat less healthy food. Using the TESQ-E, negative associations were observed between frequency of use of intentional self-regulation of eating strategies and intake of snacks and sugar-sweetened beverages. That is, youth who reported more frequent use of intentional self-regulation of eating strategies consumed these unhealthy foods/beverages

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significantly less often. Further, interactions were observed between reports of intentional self-regulation and accessibility of unhealthy food in the environment. These results suggest that use of intentional self-regulation of eating strategies may be particularly important to prevent intake of unhealthy foods in environments where such foods are easily accessible.38 Intentional self-regulation of eating may also be particularly important to prevent overeating among youth with personal characteristics that promote overeating. For example, within the TEMPEST cohort, among youth with strong snacking habits, assessed using a 6-item scale that assessed the frequency and automaticity of snacking, those who reported frequent use of intentional self-regulation of eating strategies had lower daily snack intake. Meanwhile, youth who reported strong snacking habits but infrequent use of intentional self-regulation of eating strategies reported frequent snack intake.39 Obesity treatment interventions targeting youth provide further evidence for the role of intentional self-regulation of eating in changing or controlling dietary intake. For children 12 and younger, teaching parents strategies to limit access to less healthy foods and monitor children’s eating is one of the most effective and efficient approaches to promote children’s healthy eating and weight.40,41 However, as children enter adolescence, their increasing capacity for setting and striving for goals, as well as their growing independence from their families, suggests that supporting intentional self-regulation of eating among adolescents may be an important approach for effective weight management. Further, as studies of dieting intention and behavior among preadolescents and adolescents suggest,42,43 many youth desire to control their weight and may engage in unsustainable, highly restrictive approaches to do so. Promoting the use of appropriate, flexible, and sustainable intentional self-regulation of eating strategies in place of highly restrictive dieting strategies may achieve a two-pronged goal of supporting healthy eating and weight loss as well as preventing disordered and disinhibited eating behaviors among children and adolescents. Healthy Habits is one example of an adolescent-focused weight management intervention that, like behavioral weight loss programs for adults, promotes adolescents’ use of intentional self-regulation of eating strategies.17 Healthy Habits was designed to be initiated in the primary care setting among adolescents with obesity. The intervention focused on setting goals for intakes of energy and key food types, as well as physical activity. To achieve these goals, adolescents (mean age ¼ 14.2 years old) were taught intentional self-regulation of eating strategies including stimulus control, preplanning, problem solving, self-monitoring, and self-reward. After 4 months of intervention participation, compared to adolescents assigned to a usual care condition, adolescents participating in Healthy Habits experienced significant reductions in body mass index (BMI). Further, significant correlations were observed between greater use of intentional self-regulation of eating strategies and declines in BMI. No increases in disordered eating were found, suggesting that adolescents’ intensive engagement in goal setting and strategies to achieve those goals can improve dietary intake without a high risk of adverse

Intentional Self-Regulation of Eating Among Children and Adolescents

BOX 13.2

• •

Individuals who demonstrate more flexible restraint, or are able to relax their intentional self-regulation of eating goals and strategies on occasion, tend to have better weight control and less disordered eating than those with rigid restraint. It is important to distinguish strategies that place youth at risk for disordered eating and weight gain versus those that are sustainable and health promoting.

consequences. Jensen et al.’s44 examination of adolescents (ages 14–20 years old) who successfully completed obesity treatment further supports the importance of promoting intentional self-regulation of eating among adolescents to improve diet and manage weight. In this mixed-methods study, many adolescents endorsed intentional selfregulation as the key facilitator of weight loss and reported relying on several intentional self-regulation of eating strategies including planning meals ahead, substituting healthier versions of the foods they liked, and avoiding places that had tempting food (Box 13.2).

THE DEVELOPMENT OF INTENTIONAL SELF-REGULATION OF EATING The ability to self-regulate emotions and behaviors begins early in life. For example, infants are able to limit excessive stimulation by turning away from the source of simulation and toddlers demonstrate a rudimentary ability to plan and then perform a series of actions.45 Throughout early and middle childhood, self-regulation is increasingly used to control emotions, attention, and behavior, with a growing ability to self-monitor behavior and inhibit undesirable responses.46 For example, by age 5 most children are able to inhibit impulses to “peek” at a present when asked not to, a task that few toddlers are able to accomplish.47 These increases in self-regulatory capacity are attributable to ongoing maturation in the prefrontal cortex, the area involved in the control of novel responses. By pre- and early-adolescence, most individuals have the cognitive capacity to engage in formal operational thought—holding abstract, mental representations necessary for selecting goals, considering hypothetical problems and multiple potential solutions, and monitoring progress toward goals—the foundations of intentional self-regulation.46,48 Early childhood socialization and exposure to specific parenting styles practices also set the stage for the development of the ability to self-regulate behavior.49 Strong intentional self-regulation skills have been linked to warm, supportive parenting with consistent routines and high levels of monitoring.50,51 Genetic factors may also impact individuals’ ability to intentionally self-regulate eating. For example, variations in dopamine and serotonin neurotransmitter genes, and their promoters and inhibitors, are

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associated with measurable differences in affect, emotional control, and attentiveness, ultimately impacting one’s ability to self-regulate. These differences in neurotransmitter genes typically present as variations in adaptive versus rigid self-regulation abilities and can also influence an overall positive or negative temperament as well.52 While maturation of the prefrontal cortex provides adolescents the capacity for intentional self-regulation, there are numerous other developmental changes that occur during preadolescence and adolescence that support, and challenge, ones’ ability to intentionally self-regulate. During this developmental period, youth are frequently presented with new opportunities in which intentional self-regulation may be advantageous by fostering independence in academic, extracurricular, and social settings. The growing importance of peers increases both opportunities and challenges for intentional self-regulation. For example, an adolescent may set a goal of performing well in a class and plan to study every night to do so, but peer pressure to go out to the movies or participate in other evening activities tests the adolescents’ determination and flexibility to incorporate external norms and expectations into their strategies for achieving their goal. These challenges—in conjunction with increases in planning skills, the ability to delay gratification, self-efficacy, and accurate self-evaluation during adolescence—result in sustained ability to implement complex strategies to intentionally self-regulate behavior and gain control of the external environment.46 While no studies have comprehensively tracked the development of intentional selfregulation of eating through childhood and adolescence, studies examining the emergence of dietary restraint suggest that intentional self-regulation strategies may begin to emerge as early as middle childhood. As measured in studies of dietary restraint, children as young as age 5 report purposeful, goal-directed efforts to modify their eating to control their weight53 and these efforts become normative by age 11.54 Engagement in dietary restriction for the goal of weight control at a young age is particularly prominent among girls and children with overweight or obesity55 and is likely influenced by the strong social norms regarding the importance of weight control and thinness with strategies to limit eating and lose weight prominent in the media. While, as described earlier, highly restrictive dieting is not to be promoted among children and adolescents, especially outside of structured weight control settings, these studies demonstrate the potential for preadolescent children to identify eating goals and use cognitive and behavioral strategies to achieve those goals. While children’s capacity to intentionally self-regulate eating increases into and through adolescence, dietary quality tends to decline during this period.56 For example, intake of sugar-sweetened beverages and fast food is common during adolescence,57,58 while fruit and vegetable intake is low.59 This suggests that as adolescents age, they may increasingly choose not to self-regulate their eating or select strategies to intentionally self-regulate eating that are more prone to failure (e.g., more rigid, extreme restrictive strategies rather than flexible, moderate strategies). It is likely also the case that external

Intentional Self-Regulation of Eating Among Children and Adolescents

BOX 13.3

• •

Maturation of the prefrontal cortex provides adolescents the capacity for intentional selfregulation of eating. Environmental or social pressures during adolescence may interfere with intentional selfregulation of eating in adolescence.

pressures, such as peer and media influences or eating out of the home, increasingly disrupt intentional self-regulation of eating during adolescence. To understand developmental differences in intentional self-regulation of eating during adolescence, Taut et al.56 examined differences in frequency of use of intentional self-regulation of eating strategies across stages of preadolescence and adolescence among participants in the TEMPEST cohort. Among these youth, reports of intentional self-regulation of eating were highest among 10- to 12-year-olds, while 14- to 16-year-olds reported the least frequent use of intentional self-regulation of eating strategies. The oldest participants in the cohort, the 17-year-olds, reported slightly more frequent intentional selfregulation of eating than the 14- to 16-year-olds, but not as frequent as the 10- to 12-year-olds. Therefore, despite a greater capacity to engage in complex and flexible goal setting and striving, middle adolescents may have a relatively low likelihood of engaging in intentional self-regulation of eating. Understanding the factors that contribute to this decline during middle adolescence may help improve dietary behaviors during this critical developmental period (Box 13.3).

FACTORS THAT CHALLENGE INDIVIDUALS’ ABILITY TO INTENTIONALLY SELF-REGULATE EATING While there are strategies to reduce the cognitive burden of intentional self-regulation and increase individuals’ likelihood of successfully reaching their eating goals, there are still many factors that interfere with intentional self-regulation of eating. At the individual level, deficits in executive function, whether attributable to ongoing normative development or between individual variation, can make intentional self-regulation of eating difficult.60 Executive functions are the top-down cognitive processes that allow individuals to engage in goal-directed action. There are three related but distinct aspects of executive function: inhibitory control, which enables individuals to control attention, behaviors, and emotions; working memory, or the ability to hold and manipulate information after it is no longer perceptually present; and cognitive flexibility or task switching, which enables individuals to adapt to alternative perspectives.61 Executive functions may have a direct impact on eating behavior, but also are increasingly recognized to

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moderate the relationship between individuals’ eating goals and their likelihood of achieving these goals.60 For example, among college-aged populations, cognitive flexibility and the ability to switch easily between tasks is predictive of stronger associations between individuals’ dietary intentions (e.g., goals to consume fewer snacks) and their objective dietary behavior.62 Similarly, in laboratory-based experiments individuals with goals to forego sweets were more likely to successfully consume less sweets when offered if they also scored high on working memory tasks. Among individuals with low working memory, there was no association between a goal to forego sweets and actual intake.63 Thus working memory may enable people to translate planned behaviors into action.60 It can be hypothesized that cognitive flexibility is particularly important for ensuring flexible self-regulation of eating versus rigid dietary restraint. Strong cognitive flexibility likely enables individuals who self-monitor their cognitions and behavior to effectively modify their eating goals or strategies as needed, rather than continue to engage in ineffective thoughts or behaviors, or thoughts or behaviors that do not take into account the changing needs of the individual or the environment. Associations between cognitive flexibility and flexibility in use of strategies to achieve behavioral goals have been observed in the domain of physical activity, lending support to this hypothesis. Among college students, those with higher objectively assessed cognitive flexibility were more likely to effectively substitute an alternative exercise to meet their physical activity goals when barriers arose to participate in their intended exercise.64 Intentional self-regulation can also be strongly impacted by factors external to the individual. It is commonly understood that the ability to self-regulate behavior is a resource that can be fatigued or depleted by persistent demands that are counter to individuals’ desired goals.1 Therefore environmental cues to overeat or eat unhealthy food likely not only promote excessive consumption in general, but specifically challenge individuals’ intentions to self-regulate eating. One specific environmental cue to eat that may deplete intentional self-regulation of eating is large portion sizes. Large portion sizes of food cause excessive energy intake among children,65 particularly children who demonstrate obesogenic appetitive traits such as high food responsiveness.66 Several studies among adults examining the impact of portion size on energy intake observed no differences in intake among individuals who report goals to restrict calories,67,68 suggesting that portion size cues may override strategies to intentionally self-regulate eating. However, it has alternatively been suggested that large portion sizes may trigger the initiation of intentional self-regulation of eating strategies and thus lead to less consumption of unhealthy foods as compared to when smaller portion sizes are offered. This may occur because when large portion sizes are presented to individuals (e.g., a full-size bag of potato chips) the large amount of food available to eat comes in conflict with intentional self-regulation of eating goals. Meanwhile, foods served in smaller portion sizes

Intentional Self-Regulation of Eating Among Children and Adolescents

BOX 13.4

• • •

Executive functions may moderate the relationship between individuals’ eating goals and their likelihood of achieving these goals. Working memory and cognitive flexibility may be important contributors to intentional selfregulation of eating. Portion sizes and other factors may differentially elicit or deplete efforts to intentionally selfregulate eating.

(e.g., “snack-size” bags of potato chips) are determined to be “acceptable” and therefore do not elicit intentional self-regulation, leading to greater consumption than when the food is offered in larger portion sizes.69 This conflicting evidence regarding the role of environmental cues to eat, such as large portion sizes, in supporting, or interfering with, intentional self-regulation of eating suggests that further research is needed to understand how external forces may differentially elicit, or deplete, efforts to intentionally selfregulate eating (Box 13.4).

CONCLUSIONS AND FUTURE RESEARCH NEEDS In summary, as older children and adolescents develop an increasing capacity for intentional self-regulation, the promotion of healthy and flexible eating goals and strategies to achieve these goals likely plays an important role in healthy eating and weight maintenance. However, as noted throughout this chapter, our understanding of intentional selfregulation of eating among children is in its infancy, with few studies beyond TEMPEST seeking to understand how youth set eating-related goals and implement strategies to achieve those goals. There are lessons to be learned about intentional self-regulation of eating from behavioral weight loss trials from adults and adolescents, but the translation of promotion of intentional self-regulation of eating strategies from such highly controlled environments to community settings is contentious given the potential risk of promoting highly restrictive dietary behaviors and eating disorders. However, given the centrality of intentional self-regulation of eating to successful weight maintenance, and our relative lack of successful nutrition promotion and obesity prevention interventions among older children and adolescents, identifying how to enhance intentional selfregulation of eating among these age groups is a priority. One of the key needs in developing a deeper understanding of intentional selfregulation of eating among children is developing valid and generalizable measures of intentional self-regulation of eating. Comprehensive measures of intentional selfregulation of eating strategies are rare, even for use with adults, as the literature has

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primarily depended on measures of dietary restraint that confound measurement of intentional self-regulation of eating and disinhibited eating.8 The TESQ-E, while rigorously developed with an adolescent cohort, has not yet been tested for applicability to nonEuropean cohorts. Further, some items on the TESQ-E may be less applicable to specific subpopulations of youth. For example, items that ask about the frequency with which individuals choose not to pass fast-food restaurants or avoid aisles with sweets and chocolates in the grocery store may not be applicable to younger children. Additionally, youth with limited home food availability or who come from food insecure families may report that they do not bring fruit to school or select fruit as a snack, two strategies measured by the TESQE, not because they do not engage in intentional self-regulation of eating but because those strategies are not financially viable for them. A second key need is understanding the potential positive influences of intentional self-regulation of eating on children’s dietary intake, as well as the potential unintended consequences of intentional self-regulation of eating, such as heightened risk of disordered eating and poor mental health outcomes. As described previously, experts are moving from viewing dietary restriction as a homogeneous, consistently harmful, set of cognitions and behaviors to promoting use of the perspective of intentional selfregulation of eating, where flexible engagement in moderate strategies to limit eating may be most healthful in our current food environment. Thus future longitudinal observational research with children and adolescents would benefit from similarly expanding this perspective and assessing how, and among whom, intentional self-regulation of eating is successful in helping achieve a healthy diet versus what forms of intentional self-regulation of eating are harmful for mental and physical health. Ultimately, promotion of flexibility in intentional self-regulation of eating may be the main distinguishing factor between successful goal setting and striving, instead of elevated risk of highly restrictive disordered eating behaviors. It is therefore essential to identify how to help adolescents set realistic eating goals, engage in intentional self-regulation strategies that minimize cognitive investment in eating decisions, and effectively “bounce back” from inevitable deviations from eating goals in order to promote intentional self-regulation of eating on a population level. Given that many adolescents desire to eat healthier and control their weight, and intentional self-regulation of eating strategies such as selfmonitoring with technology are increasingly promoted, identifying how to help adolescents engage in such behavior healthfully is essential. Finally, little is known about early life factors that contribute to the emergence of effective intentional self-regulation of eating. Many youth are the target of messages to monitor eating, limit eating, or select healthy foods from parents, teachers, and clinicians, yet it is unclear what nutrition education or child feeding strategies, in particular, promote intentional self-regulation of eating. Similar to longitudinal studies that have examined childhood predictors of the emergence of dieting and dietary restraint during preadolescence and adolescence, research is needed to understand individual and social

Intentional Self-Regulation of Eating Among Children and Adolescents

factors that contribute to the development of realistic, healthy, eating goals and flexible engagement in intentional self-regulation of eating strategies.

IMPLICATIONS FOR PRACTICE While there is a priority for continued research to elucidate intentional self-regulation of eating among youth, knowledge from existing research can be informative for nutrition counseling and the development of youth-oriented healthy eating promotion interventions. Given the success of behavioral weight loss programs that promote intentional selfregulation of eating strategies, encouraging moderate, flexible intentional self-regulation of eating prior to or at the first signs of rapid weight gain may help modify individuals’ weight trajectories. This promotion of intentional self-regulation of eating may be particularly essential for those with a high propensity for overeating, who are the most in need of effective cognitive strategies to limit eating. Further, regardless of weight status, given that most children and adolescents are not engaging in recommended dietary behaviors for chronic disease prevention,70 promoting the selection of healthy eating goals, teaching youth evidence-based strategies that support behavior change, and encouraging moderate self-monitoring of eating may serve to improve the nutritional status of this population. While such scaffolding of healthy intentional self-regulation of eating among youth will need to be highly attentive to the potential for unintended consequences, including rigid dietary restraint and disordered eating behaviors, if implemented carefully, these efforts may make an important impact on child and adolescent health.

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32. Field AE, Colditz GA. Frequent dieting and the development of obesity among children and adolescents. Nutrition 2001;17(4):355–6. 33. Lowe MR, Annunziato RA, Markowitz JT, Didie E, Bellace DL, Riddell L, Maille C, Mckinney S, Stice E. Multiple types of dieting prospectively predict weight gain during the freshman year of college. Appetite 2006;47(1):83–90. 34. Neumark-Sztainer D, Wall M, Story M, Standish AR. Dieting and unhealthy weight control behaviors during adolescence: associations with 10-year changes in body mass index. J Adolesc Health 2012;50(1):80–6. 35. Pietilainen KH, Saarni SE, Kaprio J, Rissanen A. Does dieting make you fat? A twin study. Int J Obes 2012;36(3):456–64. 36. Senf JH, Shisslak CM, Crago MA. Does dieting lead to weight gain? A four-year longitudinal study of middle school girls. Obesity 2006;14(12):2236–41. 37. Neumark-Sztainer D, Wall M, Haines J, Story M, Eisenberg ME. Why does dieting predict weight gain in adolescents? Findings from project eat-ii: a 5-year longitudinal study. J Am Diet Assoc 2007;107(3):448–55. 38. De Vet E, De Wit JB, Luszczynska A, Stok FM, Gaspar T, Pratt M, Wardle J, De Ridder DT. Access to excess: how do adolescents deal with unhealthy foods in their environment? Eur J Pub Health 2013;23(5):752–6. 39. De Vet E, Stok FM, De Wit JB, De Ridder DT. The habitual nature of unhealthy snacking: how powerful are habits in adolescence? Appetite 2015;95:182–7. 40. Golan M, Kaufman V, Shahar DR. Childhood obesity treatment: targeting parents exclusively V. Parents and children. Br J Nutr 2006;95(5):1008–15. 41. Boutelle KN, Cafri G, Crow SJ. Parent-only treatment for childhood obesity: a randomized controlled trial. Obesity (Silver Spring, Md) 2011;19(3):574–80. Pmcid:Pmc4008332. 42. Neumark-Sztainer D, Wall M, Larson NI, Eisenberg ME, Loth K. Dieting and disordered eating behaviors from adolescence to young adulthood: findings from a 10-year longitudinal study. J Am Diet Assoc 2011;111(7):1004–11. 43. Lenhart CM, Bauer KW, Patterson F. Weight status and weight-management behaviors among Philadelphia high school students, 2007–2011. Prev Chronic Dis 2013;10:E164. Pmcid:Pmc3786623. 44. Jensen CD, Duraccio KM, Hunsaker SL, Rancourt D, Kuhl ES, Jelalian E, Wing RR. A qualitative study of successful adolescent and young adult weight losers: implications for weight control intervention. Childhood Obes (Print) 2014;10(6):482–90. 45. Berger A, Kofman O, Livneh U, Henik A. Multidisciplinary perspectives on attention and the development of self-regulation. Prog Neurobiol 2007;82(5):256–86. 46. Gestsdottir S, Lerner RM. Positive development in adolescence: the development and role of intentional self-regulation. Hum Dev 2008;51(3):202–24. 47. Carlson SM. Developmentally sensitive measures of executive function in preschool children. Dev Neuropsychol 2005;28(2):595–616. 48. Jurado MB, Rosselli M. The elusive nature of executive functions: a review of our current understanding. Neuropsychol Rev 2007;17(3):213–33. 49. Purdie N, Carroll A, Roche L. Parenting and adolescent self-regulation. J Adolesc 2004;27(6):663–76. 50. Brody GH, Ge X. Linking parenting processes and self-regulation to psychological functioning and alcohol use during early adolescence. J Fam Psychol 2001;15(1):82. 51. Bowers EP, Gestsdottir S, Geldhof GJ, Nikitin J, Von Eye A, Lerner RM. Developmental trajectories of intentional self regulation in adolescence: the role of parenting and implications for positive and problematic outcomes among diverse youth. J Adolesc 2011;34(6):1193–206. 52. Bell MA, Deater-Deckard K. Biological systems and the development of self-regulation: integrating behavior, genetics, and psychophysiology. J Dev Behav Pediatr 2007;28(5):409–20. 53. Rodgers RF, Wertheim EH, Damiano SR, Gregg KJ, Paxton SJ. “Stop eating lollies and do lots of sports”: a prospective qualitative study of the development of children’s awareness of dietary restraint and exercise to lose weight. Int J Behav Nutr Phys Act 2015;12:155. Pmcid:Pmc4681047. 54. Rhee KE, Appugliese DP, Prisco A, Kaciroti NA, Corwyn RF, Bradley RH, Lumeng JC. Controlling maternal feeding practices associated with decreased dieting behavior in sixth-grade children. J Am Diet Assoc 2010;110(4):619–23. Pmcid:Pmc3086849.

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55. Shunk JA, Birch LL. Girls at risk for overweight at age 5 are at risk for dietary restraint, disinhibited overeating, weight concerns, and greater weight gain from 5 to 9 years. J Am Diet Assoc 2004;104(7):1120–6. Pmcid:2562311. 56. Taut D, Baban A, Giese H, De Matos MG, Schupp H, Renner B. Developmental trends in eating self-regulation and dietary intake in adolescents. Appl Psychol Health Well Being 2015;7(1):4–21. 57. Bauer KW, Larson NI, Nelson MC, Story M, Neumark-Sztainer D. Fast food intake among adolescents: secular and longitudinal trends from 1999 to 2004. Prev Med 2009;48(3):284–7. 58. Nelson MC, Neumark-Sztainer D, Hannan PJ, Story M. Five-year longitudinal and secular shifts in adolescent beverage intake: findings from project eat (eating among teens)-ii. J Am Diet Assoc 2009;109(2):308–12. 59. Larson NI, Neumark-Sztainer D, Hannan PJ, Story M. Trends in adolescent fruit and vegetable consumption, 1999–2004: project eat. Am J Prev Med 2007;32(2):147–50. 60. Dohle S, Diel K, Hofmann W. Executive functions and the self-regulation of eating behavior: a review. Appetite 2018;124:4–9. 61. Diamond A. Executive functions. Annu Rev Psychol 2013;64:135–68. Pmcid:Pmc4084861. 62. Allan JL, Johnston M, Campbell N. Missed by an inch or a mile? Predicting the size of intentionbehaviour gap from measures of executive control. Psychol Health 2011;26(6):635–50. 63. Hofmann W, Gschwendner T, Friese M, Wiers RW, Schmitt M. Working memory capacity and self-regulatory behavior: toward an individual differences perspective on behavior determination by automatic versus controlled processes. J Pers Soc Psychol 2008;95(4):962–77. 64. Kelly SM, Updegraff JA. Substituting activities mediates the effect of cognitive flexibility on physical activity: a daily diary study. J Behav Med 2017;40(4):669–74. 65. Fisher JO, Kral TV. Super-size me: portion size effects on young Children’s eating. Physiol Behav 2008;94(1):39–47. 66. Birch LL, Savage JS, Fisher JO. Right sizing prevention. Food portion size effects on Children’s eating and weight. Appetite 2015;88:11–6. 67. Rolls BJ, Morris EL, Roe LS. Portion size of food affects energy intake in normal-weight and overweight men and women. Am J Clin Nutr 2002;76(6):1207–13. 68. Kral TV, Roe LS, Rolls BJ. Combined effects of energy density and portion size on energy intake in women. Am J Clin Nutr 2004;79(6):962–8. 69. Coelho Do Vale R, Pieters R, Zeelenberg M. Flying under the radar: perverse package size effects on consumption self-regulation. J Consum Res 2008;35(3):380–90. 70. U.S. Department of Health and Human Services, U.S. Department of Agriculture. 2015–2020 dietary guidelines for Americans; December 2015

CHAPTER 14

Food Cognition and Nutrition Knowledge Jasmine M. DeJesus*, Katherine D. Kinzler†, Kristin Shutts‡ *

Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, United States Departments of Psychology and Human Development, Cornell University, Ithaca, NY, United States ‡ Department of Psychology, University of Wisconsin - Madison, Madison, WI, United States †

Understanding food and nutrition is a critical problem of development. A few common taste preferences—specifically proclivities for sweet, salty, and familiar flavors—are evident early in development, and likely emerged to promote growth and guide humans toward substances that are safe to eat (see also Chapter 2).1–6 However, these common taste preferences do not fully account for the complexity of the human diet. As generalist animals, humans eat many different kinds of foods, including ones that are initially unfamiliar.1,7,8 Additionally, in the modern food environment, which is replete with calorie-dense foods and misleading nonnutritive sweeteners,9,10 favoring sweet and salty foods does not lead to a healthy diet. Thus the youngest members of our species face a difficult task as they learn to eat a diet that meets (and does not exceed) their nutritional needs. Young children face an additional challenge when selecting foods: Food choices vary widely across cultures.8,11 Although young infants across the globe may share some early taste predilections, flavor preferences and cultural mores surrounding food selection become highly diversified across cultural contexts. Thus as a social species, children must learn about more than just the palatability of different substances. They must also learn who eats what foods when, where, and in what contexts, absorbing cultural traditions surrounding food selection and preparation that put them in step with the practices of their community. For example, some religious traditions prohibit practitioners from eating pork, but permit eating beef, whereas others prohibit beef, but permit pork; the same foods are judged very differently depending on one’s cultural background. The development of food cognition therefore depends not only on children’s own trial-and-error experiences with different tastes and foods but also on watching other people eat and learning from what other people tell children about food. Evidence of these social and testimonial influences on early food selection provides the focus of the current chapter. In this chapter, we provide an overview of children’s early food cognition (i.e., their ability to categorize foods, learn information about foods, and reason about the properties of foods) and behavior in the food domain. Specifically, we focus on the important role Pediatric Food Preferences and Eating Behaviors https://doi.org/10.1016/B978-0-12-811716-3.00014-2

© 2018 Elsevier Inc. All rights reserved.

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that other people play in guiding children’s concepts, preferences, and choices. An additional goal of the chapter is to highlight the role that carefully controlled laboratory studies investigating social influences on food selection can play in the quest to explain—and ultimately improve—children’s eating. We begin by reviewing literature on children’s early thinking about food, including the protracted development of disgust and rejection in the food domain. We then highlight evidence demonstrating that young children solve the challenge of food selection, in part, by watching what other people eat and listening to what other people say (referred to as “testimony” in the field of cognitive development research). This testimony can range from simple statements of preferences (e.g., “I like this food” or “all the kids think this is a cool food to eat”) to detailed lessons explaining the underlying mechanisms of digestion and nutrition. To preview, studies suggest that, in addition to providing children with information that can influence children’s choices, learning about other children’s preferences can actually influence children’s perceptual experience: Foods that other people like taste better to them.12 We conclude by suggesting opportunities for developmental psychologists and public health professionals to collaborate in efforts to understand children’s thinking in the food domain and develop initiatives to improve children’s health.

EARLY FOOD COGNITION: HOW DO INFANTS AND CHILDREN CATEGORIZE FOODS? Food categorization can take several forms: Deciding whether a substance is food or not; judging whether a particular food is a fruit or a vegetable; determining whether a substance is healthy or unhealthy, sweet or not sweet, tasty or not tasty, socially acceptable to eat or taboo, disgusting or not disgusting, and so on. The ability to categorize substances allows individuals to build rich and generalizable conceptual knowledge in the food domain (and beyond).13,14 For instance, understanding that apples, in general, are edible allows children to extend that knowledge to new apples they encounter (even if those apples vary slightly in size or shape), rather than learning the properties of each new apple they encounter. The myriad ways in which foods can be categorized highlight the scope of the learning problem that faces young humans as they come to understand the food rules of their community. Studies of infants and toddlers reveal striking limitations in very young children’s ability to appropriately categorize and select foods for themselves. For example, adults, including both human adults15 and adult rhesus monkeys,16,17 know that color and texture are the most relevant properties for identifying foods, but young infants do not.16 In one study, 8-month-old infants treated a change in a food’s color and texture (e.g., green juice changing to orange sugar) as equivalent to a change in the appearance of a container that held the food (e.g., green juice in a bowl changing to green juice in glass).16 In contrast, adults recognize that a change in color and texture usually signifies a change in food type, whereas a change in container shape does not.15 In the absence of knowing which

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properties are relevant for classifying foods, it would be difficult to select foods for oneself. Supporting this suggestion, infants and toddlers are willing to put things in their mouths that adults consider to be dangerous or disgusting. For example, when presented with fake dog feces (made from limburger cheese and peanut butter), over half of 16- to 29-month-olds in one study18 were willing to eat the substance. Toddlers’ difficulty in determining what they should and should not eat is underscored by evidence that children under the age of 2 are more likely than any other age group to need treatment for accidentally poisoning themselves.19 Following infancy and toddlerhood, children’s food categorization abilities begin to improve and continue improving across the preschool years.20–23 Unlike their younger counterparts, preschool-age children recognize that color and texture are relevant for classifying foods15 and very few are willing to put disgusting items, such as the fake dog feces described previously, in their mouth.18 Additional research reveals that preschool-age children are able to determine whether a wide range of entities are edible vs. nonedible20 and can even classify different kinds of foods.21 In one study,20 3- and 4-year-old children were presented with images of foods (e.g., lemon, apple, cauliflower) and nonfoods (e.g., light bulb, dog toy) and were asked whether each item was edible or not. Children correctly indicated that edible foods were in fact edible. However, they did have a high rate of false alarms for nonfoods, classifying 50% of nonfoods as edible. Age was negatively correlated with children’s false alarm rate, suggesting that the ability to discriminate edible from inedible substances improves over this two-year age range. In another study, 2- to 6-year-old children were shown pictures of fruits and vegetables and were simply asked to sort the pictures into a fruit box and a vegetable box. Even the youngest children in the study could accurately categorize the pictures as fruits or vegetables, though children’s performance improved with age.21 Taken together, these findings highlight substantial improvement in children’s food categorization abilities into and across the preschool years (Box 14.1).

DEVELOPING FOOD COGNITION: CATEGORIZATION AND REJECTION Are children’s categorization abilities related to their food acceptance and rejection behavior? On the one hand, one could reason that children who are better at categorizing foods should demonstrate high rates of rejection and pickiness. If children can accurately identify what different substances are, they can selectively reject some and accept others. From this perspective, accepting familiar foods and rejecting unfamiliar foods could be

BOX 14.1

• •

Infants and toddlers initially show limited categorization abilities in the food domain. Children’s ability to categorize foods improves during early childhood.

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considered an achievement of development. Indeed, some have hypothesized that neophobia could be seen as a “smart” behavior, as only eating foods that one has eaten before without incident reduces the likelihood of poisoning oneself.19,24 On the other hand, one could reason that children who are worse at categorizing foods should demonstrate high rates of rejection and pickiness. First, limited categorization abilities could lead to a generalization problem: If children are not sure what something is (e.g., food or nonfood, dessert or vegetable), children may reject foods more than is necessary. Second, if children tend to reject foods, then children’s pickiness may discourage parents from exposing children to those foods, and consequently those children may have fewer opportunities to learn about food categories and properties. The limited literature on the association between food rejection and categorization is mixed. Some evidence suggests an association between rejection and food categorization abilities,21 whereas other data do not.20 These mixed findings suggest an area where more research is needed. Studies in which researchers teach new classification skills and then measure children’s consumption would be particularly illuminating, as such research would allow for causal inferences. Children’s rejection of foods based on their perception of disgust changes over the course of childhood. As noted previously, one study showed that children younger than 30 months of age were willing to put dangerous or disgusting substances in their mouths, whereas children at 30 months and older largely rejected these substances.18 Not only does this evidence reveal early limits in young children’s food categorization abilities, but it also provides initial evidence that learning to reject disgusting items has a protracted developmental timeline. Disgust is often thought to have evolved because of the importance of pathogen avoidance—the canonical disgust face uses similar movements to spitting out food (to expel toxic items that one has accidentally ingested) and disgust is often felt in response to rotten foods or bodily products (to prevent ingestion or contact in the first place).25–28 Nonetheless, studies of disgust reveal limitations and substantial development in this domain. In a recent study examining children’s disgust reactions across a wide range of ages,29 2- to 10-year-old children were shown examples of several different types of disgust elicitors, including “core” elicitors, which are thought to be especially threatening to one’s health (e.g., odors that resemble human excrement, such as organic fertilizer and fermented shrimp paste), animal elicitors (e.g., maggots, touching a glass eye), and sociomoral elicitors (e.g., stealing from a person with a disability, the marriage between a man and a much older woman). Children’s reactions to each item (e.g., whether they were willing to touch the item or endorse the behavior) and their facial expressions (i.e., did they produce a disgust face or not) were measured. Children began to demonstrate some avoidance of the core disgust elicitors (i.e., those related to bodily fluids and rotten foods) around 2.5 years of age, followed by animal products at around 4.5 years of age, and then sociomoral elicitors at around 7 years of age. In addition, parents of younger children in this study were more likely than parents of older children

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BOX 14.2

• •

Research is needed to understand how children’s abilities to categorize foods influence their food rejection behaviors. Children’s rejection of foods based on their perception of disgust develops slowly and appears to begin during the third year of life.

to demonstrate disgust to these items (as evidenced by their facial expressions), suggesting that part of the disgust learning process may involve observing the reactions of social partners. Taken together, these results indicate disgust is slow to develop, despite its relevance for human health, and highlight the potential for social learning to influence this development (Box 14.2).

SOCIOCOGNITIVE LEARNING ABOUT FOOD: OBSERVING OTHER PEOPLE Food cognition does not happen in social isolation; rather, it is informed by social contexts. Although infants and toddlers do not possess all the skills they need to select an appropriate diet on their own, they also do not need to do so. Until they are weaned, children receive food exclusively from their caregivers. Even after weaning, most of the food that is available to children is provided by caregivers—and most caregivers know which substances are poisonous, which foods comprise a culturally appropriate diet, and which foods taste good. Further, a long tradition of research shows that, beginning in infancy, children are highly attuned to the behaviors and emotions of other people,30–33 allowing children to capitalize on other people’s knowledge and behavior in the food domain.34–36 Recent research has revealed insights into how infants’ food cognition and selection is affected by the choices of people around them. First, despite infants’ ostensibly limited cognitive abilities in the food domain, infants appear to be much more sophisticated thinkers when information about food is embedded in a social context. Recent research shows that infants as young as 9 months of age see common food choice as indicative of a social relationship. In one study,34 when 9-month-old infants saw two people eat and enjoy the same food (rather than disagreeing about the food’s taste), infants expected those people to subsequently have a positive social interaction (e.g., smiling and waving at each other) rather than a negative one (e.g., crossing their arms and looking angrily away from one another). It is notable that infants’ preliminary expectations accord with those of adults: Adults infer that people who share foods have more intimate social relationships and behave more cooperatively with other people after sharing foods.37,38

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Infants also interpret food choice as being embedded within social or cultural groups. In another study,35 14-month-old infants generalized one person’s food preference to another. For instance, if infants saw one person liking food A over B, they expected a second person to have the same preference. Yet, when the two featured people had a negative social interaction, or when they spoke in two different languages (suggesting they are members of different cultural groups), infants stopped generalizing one person’s preference to another: They were no longer surprised when the two people had different preferences. From these results, it seems as though infants readily learn about the sociality of food selection early in life. In the studies described previously, infants were merely watching other people eat, and their visual responses to those events (i.e., the amount of time infants spent looking at each video) were measured. Infants and children also successfully attend to the actions of others to learn about what to eat themselves. For instance, in one study,36 18-month-old infants watched videos of a person who picked fruit from a plant and put that fruit in their mouth. When asked, “Which one can you eat?,” infants were more likely to select the fruit they saw the person put in their mouth, compared to a different fruit. This effect was not observed when the adult put the food behind his ear, suggesting that infants are specifically monitoring other people’s eating actions. In a second experiment, infants watched an actor pick a fruit from a plant or from a plastic grid and put the fruit in his mouth. Infants were more likely to select the fruit when they saw it picked from the plant compared to the plastic grid, suggesting further specificity in infants’ social attention to other people’s food choices. In another study,39 12-month-old infants saw videos of different people eating different foods and speaking in different languages (English vs. French). Infants were more likely to choose the food they saw being eaten by the English speaker (the language spoken in infants’ homes) than the food eaten by the French speaker, even though both speakers demonstrably enjoyed eating their food. In addition to learning about what they might want to eat themselves, young children can also select foods for other people. In a now-classic study,40 14- and 18-month-old children watched an adult eat foods and react with either disgust or happiness. Some children in each age group saw the adult react positively toward goldfish crackers and negatively toward broccoli (matching most toddlers’ preferences) and other children in each age group saw the adult react positively toward broccoli and negatively toward goldfish crackers (the opposite of most toddlers’ preferences). Then, the adult asked the child to hand her one of the two foods. Among children who did give the adult one of the foods, 14-month-old children tended to hand the adult a goldfish cracker, matching their own preferences. In contrast, 18-month-olds were more likely to give the adult the food she has previously reacted to with happiness (rather than disgust), even if the adult’s preference disagreed with their own. Taken together, these studies show that watching other people’s food choices influences very young children’s behavior, both when choosing foods for themselves and when selecting foods for other people (Box 14.3).

Food Cognition and Nutrition Knowledge

BOX 14.3

• • •

Infants and toddlers can learn about what is safe or desirable to eat by watching other people. Infants and toddlers can track other people’s food choices and use this information to inform their expectations about social relationships and interactions with other people. Infants’ and toddlers’ own behaviors are influenced by watching other people eat.

SOCIAL LEARNING: TESTIMONY ABOUT WHO LIKES WHAT Powerful effects of social modeling are also evident beyond infancy and toddlerhood. Across the lifespan, people tend to choose food that they observe their peers eating41–43 and eat more when receiving positive social attention44 or when eating with other people.45–47 In addition to learning from what they observe other people doing directly (as is the focus of many studies that examine the impact of social modeling on children’s food intake; see Chapter 4), children learn from what other people tell them (i.e., from their testimony). Learning from others’ testimony is a critical knowledgebuilding tool48 because much of what children need to know about the world—ranging from history to biological principles to religious tenets—cannot be observed directly.49 Learning from testimony is also efficient. If one person reports that a food does not taste good or is spoiled, then other people can avoid that food without having to experience it themselves. As is true of other content areas (e.g., learning the names of objects50), young children trust other people’s testimony in the domain of food. For example, in one such study,51 3- to 6-year-old children watched an adult report her preferences and dispreferences for different flavors of jelly beans: she rated some jelly beans as “really yummy” and others as “really yucky.” Children were then given a choice between two boxes of jelly beans to take home and were more likely to choose the box of jelly beans the adult said was “really yummy.” This study provides evidence that children consider other people’s opinions when selecting foods themselves. Further research on children’s learning from testimony has shown that children do not treat all informants equally. Instead, children tend to trust the information provided by familiar over unfamiliar people,52 reliable over unreliable people,53–55 experts over nonexperts,56,57 nice over mean people,58 members of social in-groups over members of out-groups,59,60 and people who are able bodied or of average weight over people who are physically disabled or obese.61 This conclusion is based on studies in which children are presented with two informants who differ from one another in some way (e.g., one is an expert and one is nonexpert; one nice and one is mean) and provide conflicting information about a target (e.g., the expert says a novel object is called a “dax” and the nonexpert says the same object is called a “blicket”).

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Then, as a measure of which piece of testimony children accept, participants are asked what the object is called. If a consensus is observed across children (e.g., if children say the object is a “dax”), then researchers conclude that children consider the factor manipulated in the study when deciding whose information they should accept. Although the majority of research on children’s “selective trust” (i.e., examining the contexts that influence what testimony children tend to accept, and from whom they tend to accept it) has been conducted outside the domain of food, a handful of studies have featured foods as stimuli. As in other domains, children tend to discount information about a food’s taste or healthfulness that is provided by someone who has previously proven to be unreliable,62 and also favor testimony provided by people who match their social identities.63,64 For example, one group of 4-year-old children in one study were introduced to an adult who incorrectly reported the contents of a bag (e.g., the adult said “there’s a crayon inside of the bag” when children knew that the bag contained a ball).62 Then, the adult showed children an opaque white box and told children that a food inside the box was either healthy, unhealthy, yummy, or yucky. Another group of children were introduced to an adult who accurately reported the contents of the bag, and then provided the same information about an unseen food. Children were less likely to trust the adult’s testimony when that person had been inaccurate in the past (compared to children who heard information about food from an adult who had been accurate). In other studies, 3- and 4-year-old children liked novel foods that had been endorsed by someone who matched their gender and age more than foods that had been endorsed by people who differed on these dimensions.63,64 Methods employed to study children’s learning from testimony may provide a practical solution to implementing some suggestions from the social modeling literature. Specifically, several studies have shown that children eat more of foods that they see other children eat and enjoy,41,42 but it would be impossible for parents to assemble a panel of children every time they are trying to encourage their own child to eat a food. However in one recent series of studies,12 we found that simply learning about other children’s food preferences, without actually seeing other children eat, can guide children’s eating behavior. In one experiment, 5- and 6-year-old children heard an adult teacher describe two foods: One food was described as popular among other children, whereas the other food was described as unpopular; besides these descriptions, the foods were identical. Although participants did not see any other children during the study, their eating behavior was influenced by what they were told other children thought about the foods: Participants ate more of the food that was described as popular with other children and they evaluated the food’s flavor more positively. In a second experiment that used a similar procedure, children heard that one food was popular with children and the other food was popular with adults and were offered the foods to eat if they wished. Children ate more of the food that was popular among children than the food that was popular among adults, suggesting that they are particularly sensitive to social information about peers’

Food Cognition and Nutrition Knowledge

BOX 14.4

• • •

In addition to learning from observation, children also learn a great deal about the world by listening to what people tell them. Children are selective about whose information they trust. For example, when learning about new foods, they rely on info provided by people who are familiar, accurate, and similar to themselves. Just learning that other children like a food leads children to prefer that food and perceive it as sweeter.

food preferences, even when those peers were not present. In a third experiment, children were presented with a series of foods that were modified by adding different amounts of lemon juice (1, 2, 3, or 4 mL). Foods were either described as “popular” or “unpopular” and children were asked to classify each food as “sweet” or “sour.” When the food’s flavor was ambiguous (i.e., 2 or 3 mL of lemon juice had been added), children were more likely to classify the “popular” foods as “sweet” and the “unpopular” foods as “sour.” Taken together, these studies suggest that just hearing other children’s opinions about foods can have a powerful influence on children’s eating behavior, even if children have not directly observed their peers’ behavior (Box 14.4).12

SOCIAL LEARNING: TESTIMONY ABOUT HEALTH Of course, liking and taste are not the only important pieces of information to consider when deciding what to eat. Given the importance of promoting healthy eating in childhood, understanding how best to communicate information about health to children is an important enterprise. For example, in one study, 3- and 4-year-old children were asked to select whom they would like to ask about a food’s health status (healthy or unhealthy).65 The experimenter said, for example, “If you wanted to know if a food is healthy, who would you ask? A cartoon? A chair? A child? A clown? A mom? A rock? A stranger? A teacher?” Children were especially likely to respond that they would ask a mother or a teacher about the food’s health status, compared to the other alternatives. This result and related findings66 indicate that children think adults know a great deal about nutrition and health. Consequently, children should be especially likely to learn about food from trusted adults. In addition to showing that children rely on trusted adults for information about the healthfulness of food, studies suggest that detailed messages about food and health, particularly those that refer to causal mechanisms and build on children’s existing knowledge, can also effectively promote children’s health knowledge and healthy behaviors

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in the food domain.67–69 For instance, 8- and 9-year-old children who received instruction about the causal mechanisms of disease transmission were more likely to wash their hands before preparing a snack for other people than children who did not learn causal information.68 A recent study used a similar strategy of building on children’s existing knowledge and theories to improve preschool-age children’s nutrition knowledge and healthy food choices.69 In this study, a novel lesson-based intervention was developed based on important conceptual prerequisites for understanding the link between food and nutrition. For instance, in order to understand how digesting foods transmits the nutrients contained in those foods throughout the body, children must appreciate that the foods we eat are made up of tiny particles (such as vitamins, proteins, and fats) that we cannot see directly when we look at those foods. Overall, these lessons focused on one key nutrition-related concept: Eating a variety of healthy foods is important to take in diverse nutrients. The intervention broke down this concept into multiple lessons that highlighted different aspects of variety and used analogies that built on children’s initial knowledge and theories, including that just eating one type of food is not a healthy diet (e.g., one would not build a bicycle only out of handlebars, just as one would not build a healthy diet by eating only cookies or only broccoli); that the digestive system breaks down food, extracts nutrients, and sends nutrients throughout the body; that foods can contain similar nutrients even if they look different on the outside (e.g., eggs, meat, and beans are all protein-rich foods); that nutrients exist even though they cannot be seen by the naked eye; and that different nutrients support different biological functions. One group of children received this intervention at their preschool, whereas different groups of children at the same preschool were assigned to a no-intervention control group or an intervention based on existing nutrition education materials. The existing materials modeled healthy eating and emphasized the enjoyment of healthy eating and exercise but did not build on children’s biological theories. After the 12-week intervention, children’s nutrition knowledge was measured. Children in the theory-based intervention demonstrated more accurate and thorough nutrition knowledge than children in the no-intervention control group or children in the existing nutrition education group. Children’s food choices at snack time were also observed; children in the theory-based intervention also ate more vegetables at snack time than did children in the other groups.69 This study highlights that understanding and expanding on children’s early biological theories is important to build their nutrition knowledge. Although children may be capable of learning detailed information about food health, this ability does not always translate into healthy behaviors. In one study of preschool-age children (3- to 5.5-year-olds),70 an adult introduced a character (e.g., “Tara”) who ate a food that was described in different ways to different children in the study. Some children heard the foods described with an instrumental goal (e.g., “Tara felt strong and healthy” or “Tara knows that eating baby carrots will help her know how to read”). Other children heard that Tara just thought the food tasted good (“Tara knows that eating the baby

Food Cognition and Nutrition Knowledge

carrots will be yummy and fun”). Children rated the food as less tasty and ate less food if they heard the food described with an instrumental goal rather than with information about its palatability. In another study with 9- and 10-year-old children,71 children liked a novel drink labeled as “a new health drink” less than the very same drink labeled just as “a new drink” and reported that they would be less likely to ask their parents to buy the “new health drink.” Similar effects may persist into adulthood, too. One recent study showed that providing more indulgent taste-based descriptions of healthy foods (e.g., “dynamite chili and tangy lime-seasoned beets”) encourages greater consumption than providing either a healthy but restrictive message (e.g., “lighter-choice beets with no added sugar”), a positive health message (“high-antioxidant beets”), or a very basic description (e.g., “beets”).72 Taken together, these studies suggest that hearing that foods are healthy may not actually persuade people across ages to eat them. On face, these findings may appear to be discrepant—children can learn complex information about health and nutrition in intervention studies, but do not always use information about health to select what to eat. Although describing foods as “healthy” may not be an effective way to increase the amount of healthy foods that children eat, new research suggests that describing foods as “unhealthy” may help children avoid those foods. In a series of controlled laboratory studies,73 an adult described as a teacher presented 5- and 6-year-old children with information about two foods that were in fact identical (e.g., two servings of applesauce). In three initial studies, the teacher described one food as healthy but not popular with other children and described the other food as popular with other children but not healthy. Children were told these messages by an adult teacher (either in the lab or at their elementary school) or by another child. Across these three contexts, children ate more of the food described as healthy but not popular compared to the food described as popular but not healthy; they also rated the healthy/unpopular food’s flavor more positively. This might be considered surprising given that children: (a) prefer foods that are popular rather than unpopular with peers,12,41,42 and (b) tend to avoid foods described as healthy.70,71 However, a series of follow-up studies revealed that children were avoiding unhealthy foods, rather than actually preferring healthy foods. First, children ate more of a neutral food (described as “right here”) than food described as “unhealthy,” but ate similar amounts of a neutral food and a “healthy” food. Second, children ate more of a food described as “unpopular” than a food described as “unhealthy,” even though both foods were described negatively. Taken together, these results suggest that children are less willing to eat an unhealthy food compared to other alternatives (including healthy foods and unpopular foods), but do not necessarily seek out healthy foods. These findings provide further evidence of the challenges of encouraging people to eat more healthy foods, but also reveals a potentially useful alternative strategy: Capitalizing on an early understanding to avoid unhealthy foods. Moreover, these nuanced findings demonstrate the power of laboratory studies to dig deeper into the mechanisms underlying children’s eating behavior (Box 14.5).

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BOX 14.5

• • •

Encouraging people to eat more healthy foods is challenging across the lifespan. Intervention approaches that expand on children’s early biological theories may be most likely to improve children’s nutrition knowledge and increase children’s vegetable intake. Children may not necessarily be interested in eating healthy foods, but they are interested in avoiding unhealthy foods; they also prefer foods with descriptions that appeal to taste.

CONCLUSIONS AND OPEN QUESTIONS The experiments described in this chapter highlight the important role of social learning in infants’ and children’s understanding of the food domain.74 Children’s initial food knowledge is incomplete, but even infants have some understanding of the social nature of eating and use other people’s food choices to guide their own food selection and cognition.34–36,39 As children develop, children’s understanding of food improves—in large part, we argue, because they have had additional opportunities to learn from the members of their culture and engage in social interactions surrounding food (in addition to general improvements in children’s reasoning skills). Children’s developing reasoning about food is important to consider both as it pertains to understanding children’s food choices and identifying novel intervention points to promote healthy eating, but also as a window into understanding how children think about the world and learn new information more generally by considering behaviors they engage in on a daily basis. Of course, children’s interest and ability to learn from others could be either helpful or detrimental for establishing a healthy diet. Social learning is helpful if children are surrounded by people who consistently make healthy choices and present children with accurate information about health, but potentially problematic if children are surrounded by people who make unhealthy choices or present inaccurate or inconsistent information. Similarly, children’s tendency to learn from other people makes them particularly susceptible to marketing strategies that reference social symbols, such as popular brands and characters.75,76 Again, to the extent that marketers advertise healthy foods, children’s susceptibility could be viewed in a positive light. However, despite recent efforts to limit children’s exposure to advertisements for junk food,77 the vast majority of ads to which children are exposed feature processed foods with high levels of fat, sodium, and sugar.78–81 Such advertisements are effective: Children who view food advertisements eat more of the foods featured in the advertisements, even after controlling for other factors such as socioeconomic status and overall television viewing time.82,83 These issues becomes even more challenging in the context of social media, as children are also exposed to advertisements (sometimes disguised as computer games) through online platforms,84 further highlighting that the content of social messages can either positively or negatively guide children’s health behaviors.

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Many important open questions remain regarding the nature and consequences of children’s social learning about food. First, we highlighted several studies of infants’ social learning about food. However, little is known about the potential relation between the early emerging capacity to understand the social nature of food and infants’ actual eating behavior or trajectories. Infants’ own diets undergo a rapid period of expansion (i.e., from milk to solid foods) during the same period in which many of the studies we referenced were conducted, but the effect of social modeling on infants’ acceptance of novel foods has not received much attention in the literature. Understanding individual differences in infants’ responsivity to modeling could inform the tailoring of intervention strategies, an important question given that faster rates of weight gain in infancy have been linked with future obesity risk.85,86 Second, the studies we highlighted on children’s learning from observing other people, and in particularly their learning from testimony, suggest that children readily accept information offered by people they trust, such as their parents. These studies suggest that simply pointing out which foods are unhealthy and highlighting the negative consequences of unhealthy eating may be a successful strategy for improving children’s food choices, especially in light of evidence that children are capable of learning complex information about food. However, it is unclear how frequently caregivers actually talk about health in their day-to-day interactions with their children. It is possible that many parents may underestimate their children’s capability to think about food beyond just their likes and dislikes. In addition, it is possible that this information may only be effective if it matches with the behaviors that children observe adults performing. Many school-based interventions that focus on verbally teaching children about foods have been implemented with, at best, modest effects on children’s health outcomes.87,88 However, it is possible that these messages conflict with what they actually observe other children and adults eating, particularly in light of initial evidence that the amount of food children observe other people eating predicts how much food children eat themselves.89 Discovering the kinds of messages about health that effectively guide children’s behavior is an important starting place, but it may be equally important to back up those statements with action or deliver those messages in multiple contexts (i.e., both at school and at home). Future research on questions such as these is important not only to better understand development in the food domain but also to design interventions that improve children’s health. Third, we highlighted research examining children’s food categorization, food rejections, and experience of disgust, yet we are currently lacking an integrated framework to understand how these domains causally influence each other and the contribution of other processes, such as social transmission of disgust29 and genetic differences in taste perception.90 These topics have been explored in isolation and a few studies have examined associations between some of these constructs (e.g., rejection and categorization21), but there are many open questions concerning the causal mechanisms that contribute to the way these processes unfold in the preschool years, a time when children are

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undergoing change in all three of these domains. At young ages, children often demonstrate distaste toward foods they do not like, but disgust based on one’s perceptual experience is traditionally considered to be an immature form of disgust, compared to disgust based on one’s knowledge about a food (i.e., disgust based on contamination, even if the food appears safe).25,91 The development of food preferences and picky eating are important to consider in this context; however, disgust is rarely studied directly in these investigations. For example, the Child Eating Behavior Questionnaire [CEBQ]92 includes a few items that are relevant for thinking about disgust because they include items about rejecting food on ideational grounds (such as the Food Fussiness subscale). Yet, searching for “CEBQ” and “disgust” on Google Scholar returns surprisingly few results (20 as of August 2017). In one study returned by this search, overweight children were less accurate than normal weight children when asked to identify and label the emotions they observed in a series of face photographs, but disgust faces were not included in this stimuli set.93 The dearth of research at the intersection of disgust, food selection, and health suggests important opportunities for interdisciplinary collaboration. Social reasoning may play an important role in future research on this topic, given that disgust has important cultural links.28 For instance, although insects are eaten regularly in many parts of the world, eating insects is largely considered to be disgusting in Western cultures, providing a major barrier to wider adoption of insects as a sustainable protein source.94,95 More research in this area, in concert with studies that examine children’s categorization abilities as discussed previously, would shed light on these questions. These questions are important to consider both to avoid nutritional deficiencies that can stem from extreme cases of food rejection96,97 and to help mitigate the role of children’s food rejections as a major source of stress in parent–child interactions.98 Taken together, this chapter highlights how basic developmental science research can enrich our understanding of children’s development in the food domain. Large-scale correlational studies can shed light on important health outcomes for children and are critical for developing a complete understanding of how to promote children’s health and well-being. However, carefully controlled laboratory studies can be a useful tool to test mechanisms underlying children’s thinking and eating. Collaborations between basic developmental psychologists and public health professionals, using methods that are familiar to each, may be particularly fruitful ways to advance our understanding of children’s food selection and improve children’s eating.

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32. Meltzoff AN, Moore MK. Imitation of facial and manual gestures by human neonates. Science 1977; 198(4312):75. 33. Mumme DL, Fernald A, Herrera C. Infants’ responses to facial and vocal emotional signals in a social referencing paradigm. Child Dev 1996;67(6):3219–37. 34. Liberman Z, Kinzler KD, Woodward AL. Friends or foes: infants use shared evaluations to infer others’ social relationships. J Exp Psychol Gen 2014;143(3):966–71. 35. Liberman Z, Woodward AL, Sullivan KR, Kinzler KD. Early emerging system for reasoning about the social nature of food. Proc Natl Acad Sci 2016;. 36. Wertz AE, Wynn K. Selective social learning of plant edibility in 6- and 18-month-old infants. Psychol Sci 2014;25(4):874–82. 37. Miller L, Rozin P, Fiske AP. Food sharing and feeding another person suggest intimacy; two studies of American college students. Eur J Soc Psychol 1998;28(3):423–36. 38. Woolley K, Fishbach A. A recipe for friendship: similar food consumption promotes trust and cooperation. J Consum Psychol 2016;27:1–10. 39. Shutts K, Kinzler KD, McKee CB, Spelke ES. Social information guides infants’ selection of foods. J Cogn Dev 2009;10(1–2):1–17. 40. Repacholi BM, Gopnik A. Early reasoning about desires: evidence from 14-and 18-month-olds. Dev Psychol 1997;33(1):12–21. 41. Birch LL. Effects of peer models’ food choices and eating behaviors on preschoolers’ food preferences. Child Dev 1980;489–96. 42. Hendy HM, Raudenbush B. Effectiveness of teacher modeling to encourage food acceptance in preschool children. Appetite 2000;34(1):61–76. 43. Cruwys T, Bevelander KE, Hermans RCJ. Social modeling of eating: a review of when and why social influence affects food intake and choice. Appetite 2015;86:3–18. 44. Lumeng JC, Patil N, Blass EM. Social influences on formula intake via suckling in 7 to 14-week-oldinfants. Dev Psychobiol 2007;49(4):351–61. 45. Lumeng JC, Hillman KH. Eating in larger groups increases food consumption. Arch Dis Child 2007; 92(5):384–7. 46. Salvy S-J, Vartanian LR, Coelho JS, Jarrin D, Pliner PP. The role of familiarity on modeling of eating and food consumption in children. Appetite 2008;50(2–3):514–8. 47. Salvy S-J, Jarrin D, Paluch R, Irfan N, Pliner P. Effects of social influence on eating in couples, friends and strangers. Appetite 2007;49(1):92–9. 48. Ganea PA, Shutts K, Spelke ES, DeLoache JS. Thinking of things unseen. Psychol Sci 2007; 18(8):734–9. 49. Harris PL, Koenig MA. Trust in testimony: how children learn about science and religion. Child Dev 2006;77(3):505–24. 50. Koenig MA, Harris PL. The role of social cognition in early trust. Trends Cogn Sci 2005; 9(10):457–9. 51. Lumeng JC, Cardinal TM, Jankowski M, Kaciroti N, Gelman SA. Children’s use of adult testimony to guide food selection. Appetite 2008;51(2):302–10. 52. Corriveau KH, Harris PL, Meins E, et al. Young children’s trust in their mother’s claims: longitudinal links with attachment security in infancy. Child Dev 2009;80(3):750–61. 53. Pasquini ES, Corriveau KH, Koenig M, Harris PL. Preschoolers monitor the relative accuracy of informants. Dev Psychol 2007;43(5):1216–26. 54. Corriveau KH, Harris PL. Preschoolers continue to trust a more accurate informant 1 week after exposure to accuracy information. Dev Sci 2009;12(1):188–93. 55. Koenig MA, Clement F, Harris PL. Trust in testimony: children’s use of true and false statements. Psychol Sci 2004;15(10):694–8. 56. Jaswal VK, Neely LA. Adults don’t always know best: preschoolers use past reliability over age when learning new words. Psychol Sci 2006;17(9):757–8. 57. Koenig MA, Jaswal VK. Characterizing children’s expectations about expertise and incompetence: halo or pitchfork effects? Child Dev 2011;82(5):1634–47. 58. Landrum AR, Pflaum AD, Mills CM. Inducing knowledgeability from niceness: children use social features for making epistemic inferences. J Cogn Dev 2016;17(5):699–717.

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59. Gaither SE, Chen EE, Corriveau KH, Harris PL, Ambady N, Sommers SR. Monoracial and biracial children: effects of racial identity saliency on social learning and social preferences. Child Dev 2014;85(6):2299–316. 60. Kinzler KD, Corriveau KH, Harris PL. Children’s selective trust in native-accented speakers. Dev Sci 2011;14(1):106–11. 61. Jaffer S, Ma L. Preschoolers show less trust in physically disabled or obese informants. Front Psychol 2015;5:1524. 62. Nguyen SP, Gordon CL, Chevalier T, Girgis H. Trust and doubt: an examination of children’s decision to believe what they are told about food. J Exp Child Psychol 2016;144:66–83. 63. Frazier BN, Gelman SA, Kaciroti N, Russell JW, Lumeng JC. I’ll have what she’s having: the impact of model characteristics on children’s food choices. Dev Sci 2012;15(1):87–98. 64. Shutts K, Banaji MR, Spelke ES. Social categories guide young children’s preferences for novel objects. Dev Sci 2010;13(4):599–610. 65. Nguyen SP. The role of external sources of information in children’s evaluative food categories. Infant Child Dev 2012;21(2):216–35. 66. VanderBorght M, Jaswal VK. Who knows best? Preschoolers sometimes prefer child informants over adult informants. Infant Child Dev 2009;18(1):61–71. 67. Weisman K, Markman EM. Theory-based explanation as intervention. Psychon Bull Rev 2017;1–8. 68. TK-f A, Chan CKK, T-k C, Cheung MWL, Ho JYS, Ip GWM. Folkbiology meets microbiology: a study of conceptual and behavioral change. Cogn Psychol 2008;57(1):1–19. 69. Gripshover SJ, Markman EM. Teaching young children a theory of nutrition conceptual change and the potential for increased vegetable consumption. Psychol Sci 2013;24(8):1541–53. 70. Maimaran M, Fishbach A. If it’s useful and you know it, do you eat? Preschoolers refrain from instrumental food. J Consum Res 2014;41(3):642–55. 71. Wardle J, Huon G. An experimental investigation of the influence of health information on children’s taste preferences. Health Educ Res 2000;15(1):39–44. 72. Turnwald BP, Boles DZ, Crum AJ. Association between indulgent descriptions and vegetable consumption: twisted carrots and dynamite beets. JAMA Intern Med 2017;. 73. DeJesus JM, Crain KM, Shutts K, Kinzler KD. The early power of food messages: children eat the alternative to an unhealthy food. In: Poster presented at the Association for Psychological Science Annual Convention, Chicago, IL; 2016. 74. Shutts K, Kinzler KD, DeJesus JM. Understanding infants’ and children’s social learning about foods: previous research and new prospects. Dev Psychol 2013;49(3):419–25. 75. Roberto CA, Baik J, Harris JL, Brownell KD. Influence of licensed characters on children’s taste and snack preferences. Pediatrics 2010;126(1):88–93. 76. Robinson TN, Borzekowski DL, Matheson DM, Kraemer HC. Effects of fast food branding on young children’s taste preferences. Arch Pediatr Adolesc Med 2007;161(8):792–7. 77. Abbasi J. Junk food ads reach children despite food industry self-regulation. JAMA 2017; 317(23):2359–61. 78. Batada A, Wootan MG. Nickelodeon markets nutrition-poor foods to children. Am J Prev Med 2007; 33(1):48–50. 79. Chapman K, Nicholas P, Banovic D, Supramaniam R. The extent and nature of food promotion directed to children in Australian supermarkets. Health Promot Int 2006;21(4):331–9. 80. Moore ES, Rideout VJ. The online marketing of food to children: is it just fun and games? J Public Policy Market 2007;26(2):202–20. 81. Cairns G, Angus K, Hastings G. The extent, nature and effects of food promotion to children: A review of the evidence to December 2008. World Health Organization, WHO Press; 2009. 82. Longacre MR, Drake KM, Titus LJ, et al. Child-targeted TV advertising and preschoolers’ consumption of high-sugar breakfast cereals. Appetite 2017;108:295–302. 83. Dalton MA, Longacre MR, Drake KM, et al. Child-targeted fast-food television advertising exposure is linked with fast-food intake among pre-school children. Public Health Nutr 2017; 20(9):1548–56. 84. Lee M, Choi Y, Quilliam ET, Cole RT. Playing with food: content analysis of food advergames. J Consum Aff 2009;43(1):129–54.

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EPILOGUE Julie C. Lumeng, Jennifer O. Fisher The ultimate goal of all work related to children’s eating behaviors is to improve children’s health and well-being. This volume presents the highlights of approximately the last half century of research on children’s food preferences and appetite. What have we learned and what are the next steps in the science?

WHAT HAVE WE LEARNED? The research to date has shown us a number of effective strategies to intervene upon the development of children’s food preferences and appetite. Perhaps most importantly, many of these strategies can be applied across childhood (and likely into adulthood). The food environments where children routinely eat are an important place to start. The types of foods made available to children can be shaped to capitalize on behavioral phenomena that can be used to promote health. Behavioral economic strategies—i.e., making it easier to make healthy food choices—are essential elements to creating a health-promoting environment for children. For example, limiting the availability of energy dense foods, increasing the availability of nutrient-dense foods, and providing age-appropriate portion sizes appear to collectively support appetite regulation. Similarly, limiting the availability of highly sweet foods in children’s food environment reduces stimulation by tastes that are intensely rewarding and for which children have strong innate preferences. Policies related to national food programs, food and beverage pricing strategies, and food marketing to youth are all important to support. Children’s food preferences and eating behaviors are also strongly influenced by social norms. Children learn about eating by observing the likes and eating behaviors of others, particularly other children. This route of learning is so strong that it can occur even when the peer is not physically present but the peer’s preferences are only reported remotely (i.e., on television or social media). Parents too are important as models. Positive parental modeling can increase the likelihood that a child will try a new food, but importantly can also reduce the likelihood that a child will like a new food if the parent models distaste or disgust. Parents can also capitalize on the fact that peers are more effective models than adults by attending to the influence of siblings and friends on their children’s food preferences and eating behaviors. Social influences can shape children’s food preferences and eating behaviors in positive or negative ways, depending on how they are capitalized upon. For example, children’s tendency to learn from other people makes them especially vulnerable to food marketing that uses popular brands and characters, so it is important 289

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that this advertising targets healthier foods. Likewise, since children are likely to align with the social norm, it is essential that the social norms promoted in schools and communities are health promoting. Finally, research has shown that children learn best about healthy food preferences and eating behaviors when the messaging builds on their existing knowledge and helps them to understand mechanisms of effects. Nutrition curricula for children could be made more effective by incorporating careful attention to children’s cognitive development in the food domain. Research to date has also provided a great deal of evidence to guide approaches to food parenting. A wide variety of evidence-based strategies to promote healthy eating can be used that go well beyond ensuring healthy food is available and accessible in the home. Repeated exposure to varied healthy flavors, beginning prenatally in the mother’s diet, has a proven effect on promoting healthy dietary intake, though the effect is stronger for fruits than vegetables. The first step to repeatedly exposing a child to a food’s taste, however, requires that the child overcome the reluctance to try the first bite of a new food. Pairing new foods with foods children already like (e.g., ketchup) is an effective strategy for achieving that first bite, particularly for children who may be especially reluctant or for foods that are bitter. Parents can also use developmentally appropriate plate sizes and portion sizes and allow children to serve themselves, which generally will prevent overconsumption. Providing a variety of foods at meals, particularly low-energy density foods such as fruits and vegetables, will also moderate consumption. A combination of these types of strategies is likely to be most effective. In general, parenting that balances structure and limit setting with sensitivity to and support of children’s needs and cues is thought to promote optimal outcomes for children’s eating and weight. More general parenting approaches that improve children’s executive function, self-regulation, and ability to delay gratification also appear to have promise for promoting healthful eating behaviors. Overall, parents play an essential role in shaping the family eating environment, providing a model of eating behavior, and directly socializing children’s eating through food parenting styles and practices, including educating children about healthy eating and promoting the selection of healthy eating goals.

WHAT ARE THE NEXT STEPS IN THE SCIENCE? We have much to learn, however. There are wide and varied gaps in knowledge regarding children’s food preferences and appetite. We review here the many questions that comprise the next steps in the science. A key area for future work involves clarifying and refining the conceptualization of a range of eating behaviors that characterize food preferences and appetite. For example, understanding the extent to which pickiness, neophobia, and selectivity represent distinct constructs is an important area of study. We need to determine whether and when some eating behaviors become pathological and of clinical concern, and if these behaviors

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represent only an extreme on a continuum, or a distinct clinical phenotype. Much of this work involves measurement. We need to refine and confirm the validity of different eating behavior measures for different ages and replicate methods across studies. We need to develop valid and reliable methods for phenotyping eating behaviors at younger ages. In addition, obtaining converging evidence using a multimethod approach (e.g., parentand self-report, laboratory-based protocols, etc.) to best conceptualize these behaviors across development is needed. Progress in this area will not only require empirical studies but careful consideration of theoretical perspectives on development. Another area for future work involves the stability of behaviors over time and variability in observed phenotypes. We need to understand the extent to which various eating behaviors represent states or traits, and the extent to which they are modifiable with intervention. It will be important to examine how different eating behaviors interact to have multiplicative and not just additive effects on intake. Further, it will be important to understand how individual differences in eating behaviors (e.g., reward sensitivity, food responsiveness) interact with different environmental factors (e.g., food advertising, portion size) to shape dietary intake. Understanding how individual differences in genetics, physiology, and psychological responses to food manifest as different profiles of eating behavior and dietary intake will be important. The science would also be substantially advanced by invoking a wider range of study designs. The literature is currently comprised primarily of observational and cross-sectional studies. More longitudinal studies, experiments, and trials would provide more definitive answers to pressing questions. For example, these types of designs can inform the understanding of causation or at least temporality. Does weight or body composition affect taste, brain functioning, or eating behavior, or is the direction of association in the opposite direction? Do certain diets or dietary compositions impact eating behaviors, do eating behaviors impact diet, or is the relationship bidirectional? To what extent does parenting affect children’s eating, as opposed to eating affecting parenting? In all likelihood, the child’s behavior and the parent’s parenting are transactional. Designs that take into account this complexity will be critical to advancing scientific understanding of children’s eating behaviors. Longitudinal studies can inform our understanding of the developmental trajectory and natural history of eating behaviors, as well as early life factors that predict these behaviors. These types of studies can inform our understanding of the relative roles of the genetics and environment in shaping eating behaviors over time. Longitudinal work can also help us understand the long-term (or lack of long-term) effects of environmental exposures (e.g., to sweet food), or interventions (e.g., repeated exposure or exposure to dietary variety or modeling in early life) on children’s eating behaviors. These designs will inform whether some early intervention effects are overridden by later influences. In addition, it will be essential to understand if and how interventions have long-term positive (healthy weight) or negative (self-esteem or body image) effects.

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In experiments and clinical trials, several approaches would advance the science. First, many interventions have been multicomponent, and identifying the active ingredient in these interventions using innovative trial designs will be important. Further, understanding the mechanisms of different behavioral interventions will enable further refinement and tailoring of these behavioral approaches. Examining moderators of intervention effects will also be important. How might intervention effectiveness differ based on characteristics of the child such as genetics, prior parenting, temperament, prior eating behaviors, age, or stage of development? How might intervention effectiveness be moderated by different foods (i.e., fruits vs. vegetables), identity of the interventionist (e.g., fathers vs. mothers), or approach to the intervention (e.g., verbal information vs. nonverbal modeling alone). Further, the interactive effects of various interventions (e.g., modeling and repeated exposure) remain untested. Finally, there is a need to move some interventions out of the laboratory and into naturalistic environments, or to examine their generalizability across environments such as home and school. Studies are needed to determine if interventions remain effective when delivered with less intensity, as well as whether intervention effectiveness generalizes across domains (e.g., across domains of self-regulation). Importantly, measuring outcomes that extend beyond dietary intake and weight to consider dimensions of eating behavior will be critical.

SUMMARY In summary, though much has been learned in the last 50 years about children’s eating behavior, we have so much more to learn. Much of this work may be best accomplished through interdisciplinary collaborations. Therefore communicating clearly across disciplines and engaging a range of scientists spanning stages of translation in this work will be important. With collaboration across disciplines and fields, significant advances can be made to improve children’s health and well-being in the domain of children’s food preferences and appetite.

INDEX Note: Page numbers followed by f indicate figures, t indicate tables, and b indicate boxes.

A Adiposity, 210, 212, 217–218, 223, 225 Adolescents’ eating behaviors, 174–175, 175b Affect theory, 244 Appetite, 98, 101, 104, 106 Appetitive traits measurement, 127–129, 128–129t, 130b advantages and disadvantages, 129 for genetic research, 132b dimensional approach in general population, 130–131 special consideration, 131 self-regulation of eating behaviors, 129–130 Associative conditioning, 39–40 Assortative mating, 136 Attentional biases, 116–117 Authoritarian feeding styles, 171–173 Authoritative feeding styles, 171–172 Autonomy support feeding practices, 171, 174 Avoidant Restrictive Food Intake Disorder, 82 Avon Longitudinal Study of Parents and Children (ALSPAC), 140

B Baby Eating Behavior Questionnaire (BEBQ), 94–96, 100, 102 Baby-led weaning, 167–168 Balloon Analog Risk Task, 196–197 Bandura’s social learning theory, 53 Barring disorders, 183 Behavioral interventions, 38 Behavioral Rating Inventory of Executive Function (BRIEF), 198 Binge eating definition, 233 EMA methodology, 236 inhibitory control, 192–193 Binge eating disorder (BED), 196–197, 233, 237, 241–242, 247–248 Bitter taste measurement. See Sweet and bitter taste measurement

Body mass index (BMI), 127, 128–129t, 130–133, 136–142, 148–149, 154, 158, 185–186, 200–201, 237, 260–261 BOLD fMRI, 210–211 “Brain as-predictor” approach, 208, 213–214 Brain’s response to food adults vs. children, 215–217, 217b brain regions, 207–208, 209f, 214t children’s food choices, 208, 223–224 environmental cues food advertising, 223–224 parental influences, 224 portion size effect, 222 food motivation, 219–221 meal microstructure, 212 neuroimaging studies, 209–215, 212b obesity, 217–224, 218b palatable foods, 218–221 self-control and decision making, 221–222 Breastfeeding, 101, 166

C Challenges, children’s eating behavior food neophobia, 81b definition, 78 factors, 79–80 measurement and prevalence, 78–79 outcomes, 80 food selectivity, 85b definition, 81–82 factors, 84–85 measurement, 82–84 outcomes, 85 sensory sensitivity, 82 picky/fussy eating, 77b definitions, 74 factors, 75–76 measurement and prevalence, 74–75 outcomes, 76–77 Child Eating Behavior Questionnaire (CEBQ), 75, 94–95, 97–101, 128–129t, 141, 186–187, 283–284

293

294

Index

Child Food Neophobia Scale (CFNS), 78, 80 Children’s Behavior Questionnaire (CBQ), 193 Children’s Eating Behavior Inventory (CEBI), 75, 82–83 Children’s Eating Behavior Questionnaire (CEBQ), 117–118, 213 Children’s eating self-regulation, 93, 106 Clinical interview methods, for LOC eating, 235, 248 Coercive control, of parental feeding practices, 169, 173 Cognitive behavioral therapy, 246 Cognitive flexibility, 192, 197–198, 263–264 Cognitive neuroscience. See Brain’s response to food “Competitive” foods, 157 Computer-based Go/No-Go task, 199–200 Confirmatory factor analysis, 102

D Decision making brain’s response to food, 221–222 EF, 192, 196–197 Degree of compensation (COMPX%), 94–97, 99 Delay of gratification, 191–192, 195–196 Demandingness, 171–172 Dialectical behavior therapy, 247 Didactic parent-child feeding-eating relationship, 138–139 Dietary fat, 148 Dietary restraint, 187–188, 258–259 Disinhibited eating behaviors, 95, 187–188, 237, 239, 240f, 258–259 Dopaminergic pathways, 237 Dual pathway model, 245–246 Dutch eating behavior Questionnaire (DEBQ), 128–129t Dysregulated eating behavior, 184–186, 198–199

E EAH. See Eating in the absence of hunger (EAH) Early childhood eating behavior food cognition, 272–273, 273b food neophobia, 79, 85 picky eating, 75, 77 repeated exposure effects associative conditioning, 39–40 individual differences and real-world applications, 41–42, 43b

modeling and reward strategies, 40–41 self-regulate eating, 261–262 Eating behavior challenges. See Challenges, children’s eating behavior Eating behavior modeling. See Modeling, children’s eating behavior Eating Disorder Examination (EDE), 235 Eating in the absence of hunger (EAH), 95, 118–119, 128–129t, 193, 195–196, 198–199, 201, 239 Eating rate (ER) measurement, 100–101, 101b potential for modification, 103–104, 104b research opportunities, 104–106, 105t risk and susceptibility associations to adiposity and dietary intake, 102–103, 103b genetic influences, 101 parent-child feeding practices, 101–102 Ecological momentary assessment (EMA), 185–186, 236, 244–245 Elementary school-aged children, 175b meal energy intake vs. dishware size, 156 parenting influences on appetite and weight autonomy support, 174 coercive control, 173 structured feeding practices, 173–174 Emotional climate, feeding styles, 171–172 Emotional eating, 239 Energy density (ED), 148–149 Equal environments assumption (EEA), 134–136 Estradiol, 242 Executive control, 119–120, 120b Executive function (EF), 263–264 cognitive flexibility, 192, 197–198, 198b decision making, 192, 196–197, 197b delay of gratification, 191–192, 195–196 global measures, 198 health behaviors, 189–190, 190b implications for prevention and practice, 198–201 impulsivity, 190, 194–195, 200 inhibitory control, 190, 192–194, 194b, 200–201 PFC development, 189 potential pathways, conceptual framework for, 190–192, 191f processes, 184 working memory, 192, 197–198

Index

F Family studies molecular genetic studies, 142b candidate gene studies, 139–142 fat mass and obesity associated (FTO) gene, 139–141 limitations and future directions, 141–142 twin studies, 139b assumptions, 133–134 developmental perspective, 137–138 evaluation, 134–136 future directions, 138–139 heritability estimates for appetitive traits, 135t, 136–137 interpreting heritability estimates, 138 limitations, 134 Fat Mass and Obesity Associated (FTO) gene, 139–141, 237 Fear of novel foods. See Food neophobia Feeding behaviors, 76, 79–81, 84–85 Feeding practices, 168–171, 173–174, 173b, 176 Feeding styles, 171–172 Food accessibility, 170–171 Food advertising, 223–224 Food availability, 170–171 Food categorization and rejection, 273–274, 283–284 accepting familiar foods and rejecting unfamiliar foods, 273–274 forms, 272 perception of disgust, 274–275, 275b Food cognition and nutrition knowledge categorization and rejection, 273–275, 275b, 283–284 early food cognition, 272–273, 273b food choices, 271 social learning, 282 testimony about health, 279–281, 282b testimony about who likes what, 277–279, 279b sociocognitive learning about food, 275–276, 277b Food Dudes program, 45–46, 66–67 Food environment, obesogenic. See Obesogenic food environment Food intake brain’s role, 217, 222, 225 modeling, 55–58 self-regulation, 184–189

Food neophobia, 81b definition, 78 factors, 79–80 measurement and prevalence, 78–79 outcomes, 80 Food preference inventory (FPI), 83 Food preferences learning. See Repeated exposure effects Food properties and child appetite regulation, 155b combined effects of energy density and portion size, 152–154, 153f energy density (ED), 148–149 portion sizes, 150–152, 151f practical implications, 159–160 ready-to-eat foods and beverages, 148 variety, 154–155 Food refusal, 81–83 Food responsiveness, 64, 66b Food reward strategy, 40–41 Food selectivity, 85b definition, 81–82 factors, 84–85 measurement, 82–84 outcomes, 85 sensory sensitivity, 82 Food-specific Go/No-Go task, 192–193 Forced-choice procedure ascending-concentration categorization, 3–5t, 6, 8b two-alternative staircase, 3–5t, 6 two-series, tracking procedure, 3–5t, 10–11, 12b Fruit Stroop Task, 193 Functional MRI (fMRI), 210–211, 213–215, 222–225

G GEMINI cohort study, 98, 101 General Go/No-Go task, 192–193 General labeled magnitude scale (gLMS), 3–5t, 7–8, 19–23t General visual analog scale (gVAS), 3–5t, 7–8, 19–23t Genetic studies, child eating behaviors molecular genetic studies, 142b twin studies, 132–139, 135t, 139b Genome-wide association studies (GWAS), 131, 138–139

295

296

Index

Genotype-taste phenotype relationships TAS1R genotype-phenotype studies, 16b, 19–23t, 25–26 TAS2R genotype-phenotype studies, 18–25, 19–23t Gift delay task, 193–194 “Good Manners For A Healthy Future” intervention, 103 Growing Up in Singapore Towards Healthy Outcomes (GUSTO) study, 101

H Healthy food preferences learning. See Repeated exposure effects Healthy Habits, 260–261 Hedonic face scale, 3–5t, 8b, 9–10 Heritability estimates for appetitive traits, 135t, 136–139 High-frequency single food intake (HFSFI), 81–83 High-sensitivity C-reactive protein (hsCRP), 238 Home food environment, 155–156 Homeostatic system, 111–113, 113b 5-HTTLPR allele, 237–238 Hungry Donkey Task, 196–197

I Impulsivity, 190, 194–195, 200 Indulgent feeding styles, 171–172 Infancy and toddlerhood, 168b food categorization and rejection, 272–273, 273b, 283 parenting influences on appetite and weight baby-led weaning, 167–168 breastfeed/bottle feed, 166 introduction of complementary foods, 166–168 nutritional quality of foods, 168 timing of introduction of solid foods, 166–167 timing of introduction of vegetables, 167 transition from “tube feeding” to milk consumption, 165–166 variety of early exposure, 167 repeated exposure effects comparing associative conditioning and repeated exposure effects, 37–38 earliest exposures, 35–36 interventions, 38

introduction of complementary foods and beverages, 36–37 Ingestive behavior, 212b consumption phase, 213 initiation phase, 212 postconsummatory phase, 213 procurement phase, 213 termination phase, 213 Inhibitory control, 120, 120b, 190, 192–194, 194b, 200–201, 263–264 Instrumental feeding, 41, 48 Intake-promoting effects, 150–152, 154, 159–160 Intentional self-regulation of eating children and adolescents, 256–260, 262, 265–267 cycle, 255–256 definition, 255–258, 258b developmental changes, 261–263, 263b dietary and weight outcomes, 259–261, 261b dietary restraint and dieting, 258–259 future research needs, 265–267 implications for practice, 267 individuals’ ability challenges, 263–265, 265b longitudinal observational research, 266–267 vs. self-regulation of appetite, 257–258 strategies, 257, 257t weight loss interventions, 256 Interoception, 222 Interpersonal psychotherapy, 246–247 Interpersonal theory, 244–245, 245f Iowa Gambling Task, 196–197

J Junk foods, 112, 120–122

L Laboratory-based snack test, 193–194 Laboratory test meal method, 235–236 Learning, healthy food preferences. See Repeated exposure effects Liking of food cues, 111, 115–116 Limited food variety/repertoire, 81–83, 85 Limited Resource Theory of Self-Regulation, 188–189 Linking Individuals Being Emotionally Real (LIBER8), 247 Loss of control (LOC) eating assessment, 234–236, 236b biological predictors, 242

Index

child self-reports, 234–235 clinical interview methods, 235 conceptual model, 233–234, 234f cross-sectional correlates, 241b cognitive correlates, 240–241 genetics, 237–238 physiological correlates, 238 psychological and behavioral correlates, 239–240 social correlates, 238–239 definition, 233 interventions, 246–247 laboratory test meal method, 235–236 parent reports, 234–235 physiological outcomes, 241 practitioners, 248 psychosocial outcomes, 241–242 social and psychological predictors, 242–243, 244b theories, 244–246, 247b

M Magnetic resonance imaging (MRI), 210–212 Maladaptive emotion regulation, 239–240 Mandometer, 103–104 Maternal feeding practices, 76, 176, 224 Middle childhood eating behavior attentional biases for food, 117 family environment, 137–138 repeated exposure effects intervention programs, 45–47, 47b moderating role of initial liking, 44–45 studies during, 43–44 Modeling, children’s eating behavior effective models age of the model, 62–63 facial expression, 61 familiarity, 60–61, 63b language, 61 “matching” process, 61 social-cognitive system, 62 individual differences in susceptibility to modeling, 66b age and stage of development, 63 behavior change, 65 food responsiveness, 64 psychological differences, 65 role of genetics, 63–64 influence

on children’s food liking and preferences, 59–60, 60b on novel food intake and choice, 55–58, 58b on portion sizes eaten, 58–59, 59b interventions based on modeling, 66–67, 68b model and observer, 53 necessity, 53–55, 55b reward strategies, 40–41 Moderate-to-high heritability, 138 Molecular genetic studies, 142b candidate gene studies, 139–142 fat mass and obesity associated (FTO) gene, 139–141 limitations and future directions, 141–142 Monell forced-choice, paired-comparison, tracking procedure, 10 Monitoring, child intake, 170 Mothers’ disinhibited eating styles, 237 Mothers’ own eating behaviors, 76

N Nature-based processes, 183 Negative peer modeling, 57 Neighborhood food environments, 158–159 Neurocognitive influences, of eating behavior. See Brain’s response to food Noneating-related childhood traits, 137–138 NOURISH trial, 98–100, 102 Nurture-based processes, 183

O Obesity, pediatric brain’s response to food, 217–224 dimensional approach in general population, 130–131 food-cue specific reward processing, 220–221 LOC eating, 238 prevention (see Eating rate (ER); Satiety responsiveness (SR)) special consideration, 131 treatment interventions, 260–261 working memory, 197–198 Obesogenic food environment, 160b definition, 147 home food environment, 155–156 neighborhoods, 158–159 practical implications, 159–160 schools, 156–158 Observational learning, 53

297

298

Index

P Paper-and-pencil questionnaires, 198 Parental behavior styles, 171–173 Parental feeding, 165, 168, 171, 173–176 Parental modeling, 63–64, 67–69, 68b, 76 Parent-child feeding practices eating rate (ER), 101–102 satiety responsiveness (SR), 96–97 Parenting influences on appetite and weight adolescence, 174–175, 175b elementary school age, 175b autonomy support, 174 coercive control, 173 structured feeding practices, 173–174 infancy and toddlerhood, 168b baby-led weaning, 167–168 breastfeed/bottle feed, 166 introduction of complementary foods, 166–168 nutritional quality of foods, 168 timing of introduction of solid foods, 166–167 timing of introduction of vegetables, 167 transition from “tube feeding” to milk consumption, 165–166 variety of early exposure, 167 LOC eating, 234–235 preschool age, 173b autonomy support, 171 coercive control, 169 control versus structure, 171 feeding practices, 168 feeding styles, 171–172 modeling, 170–171 parental styles, 171–173 pressuring child to eat, 169–170 restrictive feeding, 169 structure, parents’ organization of child’s environment, 170 use of threats and bribes during feeding, 170 Peer modeling, 55–60, 59–60b, 62–63, 67 Peg tapping task, 193–194 Picky/fussy eating, 77b definitions, 74 factors, 75–76 measurement and prevalence, 74–75 outcomes, 76–77 Polygenic risk score (PRS), 96 Portion size effect, 222 Positive Parenting Program (Triple P), 200–201

Positive peer modeling, 57 Power of Food Scale, 186–187 Preschool-aged children, 173b food categorization and rejection, 273 food selectivity, 84 parenting influences on appetite and weight autonomy support, 171 coercive control, 169 control versus structure, 171 feeding practices, 168 feeding styles, 171–172 modeling, 170–171 parental styles, 171–173 pressuring child to eat, 169–170 restrictive feeding, 169 structure, parents’ organization of child’s environment, 170 use of threats and bribes during feeding, 170 picky/fussy eating, 74–76 Promoting Alternative Thinking Strategies (PATHS), 199

Q Quantitative trait loci (QTL), 131

R Randomized controlled trial (RCT), 98 Rank-by-elimination method, 3–5t, 7, 9–10 Ranking methods, 7, 8b Rank order intensity task, 3–5t, 7–8 Rating/scaling methods, 8b Reinforcement Sensitivity Theory, 186 Relative reinforcing value (RRV) task, 114–115 Repeated exposure effects during early childhood associative conditioning, 39–40 individual differences and real-world applications, 41–42, 43b modeling and reward strategies, 40–41 familiarity, 43b future directions, 47–49 during infancy comparing associative conditioning and repeated exposure effects, 37–38 earliest exposures, 35–36 interventions, 38 introduction of complementary foods and beverages, 36–37 during middle childhood

Index

examples, 43–44 intervention programs, 45–47, 47b moderating role of initial liking, 44–45 neophobia, 45 Responsiveness, 171–172 Restraint Theory, 187–188 Restrictive feeding practices, 169, 172–173 Reward-driven eating, appetitive drive and regulation executive control, 119–120, 120b homeostatic system, 111–113, 113b inhibitory control, 120, 120b investigation, 120–122 relative reinforcing value (RRV) task, 114–115 repeated exposure conditions, 40–41 subcomponents, 111, 112f, 119b attentional biases, 116–117 Children’s Eating Behavior Questionnaire (CEBQ), 117–118 eating in the absence of hunger (EAH), 118–119 liking, 111, 115–116 salience, 111, 116–117 sweet tastes, 115–116 wanting, 111, 114–115 rs9930506, 139–140 rs9939609, 139–143

S Salience of food cues, 111, 116–117 Satiation, 93, 190, 213 Satiety responsiveness (SR) measurement, 94–95, 95b potential for modification, 99–100, 100b research opportunities, 104–106, 105t risk and susceptibility, 99b associations with weight and dietary intakes, 97–99 genetic influences, 95–96 parent-child feeding practices, 96–97 School food environment, 156–158 Self-regulation of appetite, 257–258 Self-regulation of eating, 129–130, 183–184 EF process (see Executive function (EF)) failure, 184 theoretical frameworks, 184–189, 189b Sensory sensitivity, 82–86 Single nucleotide polymorphisms (SNPs), 139–140

Social-cognitive system, 62 Social learning, 282 testimony about health, 279–281, 282b testimony about who likes what, 277–279, 279b Social learning theory, 53, 66–67 Social referencing, 55 Sociocognitive learning about food, 275–276, 277b, 283 Stop signal task, 193–195 Stroop Color-Word Interference Test, 192–193, 223 Structural MRI, 210 Structured feeding practices, 173–174 Structure, of parental feeding practices, 170 Sweet and bitter taste measurement detection thresholds forced-choice, ascending-concentration categorization procedure, 3–5t, 6 methods, 2–7, 3–5t two-alternative, forced-choice staircase procedure, 3–5t, 6 genetics taste receptor genetics and nomenclature, 16–17 T1R2 and T1R3, 16 genotype-taste phenotype relationships TAS1R genotype-phenotype studies, 16b, 19–23t, 25–26 TAS2R genotype-phenotype studies, 18–25, 19–23t methods, 3–5t ontogeny bitter taste, 14, 15b sweet taste, 12–14 taste hedonics hedonic face scale, 3–5t, 8b, 9–10 two-series, forced-choice tracking procedure, 3–5t, 10–11, 12b taste intensity ranking methods, 3–5t, 7 scaling methods, 3–5t, 7–8 taste phenotypes, 3–5t, 11

T TAS2R38 gene, genetic variability, 24 TAS1R genotype-phenotype studies, 16b, 19–23t, 25–26

299

300

Index

TAS2R genotype-phenotype studies, 18–25, 19–23t Taste measurement. See Sweet and bitter taste measurement Temperament in Middle Childhood Questionnaire, 195 Tempest Self-regulation Questionnaire for Eating (TESQ-E), 259–260, 265–266 TEMPEST study, 257, 259–260, 262–263, 265 Theory of Externality, 185–186 Toyoma Birth Cohort Study, 102 Twin model, 139b assumptions, 133–134 developmental perspective, 137–138 evaluation, 134–136 future directions, 138–139 heritability estimates for appetitive traits, 135t, 136–137 interpreting heritability estimates, 138

limitations, 134 Two-alternative, forced-choice staircase procedure, 3–5t, 6 Two-series, forced-choice, paired comparison methods, 12b, 19–23t Two-series, forced-choice tracking procedure, 3–5t, 10–11, 12b

U Uninvolved feeding styles, 171–172 “U-shaped” heritability, 137–138

V Value size pricing, 158

W Wanting of food cues, 111, 114–115 Working memory, 192, 197–198, 263–264