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Effects of parent stress on weight-related parenting practices and child obesity risk

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Effects of parent stress on weight-related parenting practices and child obesity risk

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Effects of Parent Stress on Weight-related Parenting Practices and Child Obesity Risk

A Dissertation Presented to the
Faculty of the USC Graduate School
University of Southern California

In Partial Fulfillment of the
Requirements for the Degree
Doctor of Philosophy (Preventive Medicine)

December 2015

Eleanor Barrow Tate Shonkoff

Dedication

To Gram.
Acknowledgments
I owe a tremendous debt of gratitude to my mentor and committee chair, Genevieve F.
Dunton, for always being willing to listen, providing exactly the right advice, supporting my
development as an academic, and nudging me to strive higher. This work would not have come
to fruition without your dedication and encouragement. I would also like to express my great
appreciation to my co-mentor, Mary Ann Pentz, for your continual enthusiasm, optimism, and
belief in me; your support has sustained me and made this work possible.
I would like to acknowledge and heartily thank my committee members; Ricky
Bluthenthal, for your perspective, advice, jovial way, and continued support; Chih-Ping, for
sharing your time and immense expertise; Adam Leventhal for your support and strategic
thinking; and my first advisor, Nathaniel Riggs, for openness and encouragement.
I would like to express my deep gratitude to Marny Barovich, who takes care of students
in more ways than we can imagine. Thank you for your wit and constant support. Thank you to
Wendy Wood, Mark Leary, Donna Spruijt-Metz, Marientina Gotsis, and Mary Alice Jordan-
Marsh, for support, collaboration, and mentorship; and to Clyde Pentz for hearty encouragement.
I would like to offer my special thanks to the parents willing to be interviewed and share
their stories for this research, the incredible staff at Children’s Bureau of Southern California,
and the translators and research assistants: Cynthia Ramirez, a continually happy and productive
volunteer, Stephanie Cipres, Stephanie Mercado, Janet Jaime, Yvette Estrada, Daisy Gonzalez,
Yeini Guardia, and Asheley Hernandez.
Thank you to my amazing collaborators; Yue Liao, a top-rate collaborator and teammate;
members of team Analyze This: Kim Miller, Elizabeth Barnett, and Cheng Freddy Wen; and J.
Mark Eddy; for inspiration, dedication, and fantastic ideas; my collaborators Hee-Sung Shin,
Trevor Pickering, Malia Jones, Anuja Shah, and Gillian O’Reilly, for creativity, insight, and
great work. Special thanks to my peers (and friends) Stephanie Dyal, Lauren Martinez, Myriam
Forster, Amanda Goodrich, Daniel Chu, and the students of HBRSA, who have made graduate
school so much fun.
Special thanks to my in-laws, Jack and Fredi Shonkoff, for continual warmth and
encouragement; to my Aunt Leolene Tate, for consummate cheerleading; and to Amber Lung,
for never-ending support, a wise perspective, and a great sense of humor.
Finally, I wish to express my deep gratitude for my family, Jeff, Susan, and Adam Tate,
who inspire me, believe in me, support me in pursuing my own goals, and are cheering me
toward the finish line; and for my husband, Adam Shonkoff, who makes everything worth it.
i

Table of Contents
Specific Aims ...................................................................................................................................1
Chapter 1: Background and Significance ........................................................................................4
Problem Prevalence: Obesity in the United States ......................................................................4
Family Approaches to Obesity Prevention. .............................................................................5
Targeting Parent Stress in Family-based Obesity Prevention. .................................................5
Etiology of Child Obesity. .......................................................................................................6
Etiology of Child Diet and Physical Activity Patterns. ...........................................................6
Maternal Stress and Child Obesity Risk. .................................................................................7
Theoretical Background. ..........................................................................................................9
Maternal Stress and Weight-related Parenting. .......................................................................9
Gaps in the Literature.............................................................................................................10
Conceptual Model. .................................................................................................................11
Completed Studies .........................................................................................................................11
Chapter 2: Associations of Latent Classes of Parenting Stress with Family Meals and
Child Body Mass Index in a Nationally-representative Sample of Parents and Children .............14
Introduction ............................................................................................................................... 14
Specific Aims and Hypotheses ................................................................................................. 18
Methods..................................................................................................................................... 18
Measures ................................................................................................................................19
Data Analysis .........................................................................................................................20
Results ....................................................................................................................................... 23
Preliminary Analyses .............................................................................................................23
Four distinct subgroups of maternal stress and weight-related parenting..............................24
Odds of obesity by parent latent class ....................................................................................26
Discussion ................................................................................................................................. 26
Limitations .............................................................................................................................30
Conclusion .............................................................................................................................31
Chapter 3: Direct and Indirect Effects of Parent Stress on Child Obesity Risk and Added
Sugar Intake in a Sample of Southern California Adolescents ..................................................... 42
Introduction ............................................................................................................................... 42
Specific Aims & Hypotheses .................................................................................................... 46
Methods..................................................................................................................................... 47
Measures ................................................................................................................................48
Data analysis ..........................................................................................................................51
Results ....................................................................................................................................... 52
Description of the study sample .............................................................................................52
Preliminary analyses ..............................................................................................................53
Model results for direct and indirect effects of perceived stress on parenting
practices and change in child waist circumference and added sugar intake ..........................54
Post-hoc analysis by parent stress group ...............................................................................55
Results of post-hoc boostrapping analysis for cross-sectional direct and indirect
effects of parenting stress on child waist and added sugar intake separately ........................56
Discussion ................................................................................................................................. 57
Limitations .............................................................................................................................62
Conclusion .............................................................................................................................63
ii

Chapter 4: A Positive Deviance-Based Qualitative Study of Low-Income Mothers whose
Children Meet Federal Guidelines for Fruit and Vegetable Intake to Improve Obesity
Prevention Program Design .......................................................................................................... 78
Introduction ............................................................................................................................... 78
Specific Aims ............................................................................................................................ 85
Procedures ................................................................................................................................. 86
Results ....................................................................................................................................... 90
Description of study sample...................................................................................................90
Preliminary analysis ...............................................................................................................91
Sources of stress and worry ...................................................................................................92
Stress coping ..........................................................................................................................97
Beliefs about effects of stress on health .................................................................................98
Discussion ............................................................................................................................... 100
Limitations ...........................................................................................................................103
Implications..........................................................................................................................105
Conclusions ..........................................................................................................................107
Chapter 5: General Discussion.................................................................................................... 109
Theoretical implications.......................................................................................................109
Methodological implications ...............................................................................................112
Programmatic implications ..................................................................................................113
Suggestions for future research ............................................................................................114
Conclusions ..........................................................................................................................115
References ................................................................................................................................... 117
Appendix A Screening Survey for Study 3................................................................................. 132
Appendix B Interview Guide for Study 3 ................................................................................... 134
Appendix C Demographics Questionnaire for Study 3 .............................................................. 136
Curriculum Vitae ........................................................................................................................ 137

iii

List of Tables
Table 1. Parenting Stress Variable Names and Response Options ................................................32
Table 2. Child Health Variables and Response options .................................................................33
Table 3. Demographic Variables and Response Options...............................................................34
Table 4. Bivariate correlations between study variables (n = 31,239)...........................................35
Table 5. Unadjusted Odds Ratios for obesity status by individual stressor ...................................36
Table 6. Model fit indices for latent class analysis for parenting stress and objective
stressors ...........................................................................................................................37
Table 7. Mean level and probability of scoring 1 for each stress indicator by latent classes
of mothers of 10-17 year old US adolescents .................................................................38
Table 8. Multinomial logistic regression results predicting obesity status from latent class
membership .....................................................................................................................39
Table 9. Parent Perceived Stress ....................................................................................................65
Table 10. Parent Rules about Child Eating ....................................................................................66
Table 11. Positive Family Meal Practices ......................................................................................67
Table 12. Demographics and Covariates .......................................................................................68
Table 13. Descriptive Statistics for Study Variables .....................................................................69
Table 14. Bivariate Correlations for Study 2 .................................................................................70
Table 15. Cross-sectional relationships between parent perceived stress and child waist
circumference mediated by parenting practices, separately and together, using
bootstrapping...................................................................................................................71
Table 16. Cross-sectional relationships between parent perceived stress and child added
sugar intake circumference mediated by parenting practices, separately and
together, using bootstrapping ..........................................................................................72

iv

List of Figures
Figure 1. Conceptual Framework for Mediated Effects of Parent Stress on Child Obesity Risk. 13
Figure 2. Estimated Means for Parenting Stress and Family Meals per Week by Latent Class ... 40
Figure 3. Item response probabilities for stress indicators by parent latent class status ............... 41
Figure 4. Family Stress Model: Original and Adapted ................................................................. 73
Figure 5. Path Model for Direct and Indirect Effects of Parent Perceived Stress on Change in
Child Waist Circumference and Change in Child Added Sugar Intake......................... 74
Figure 6. Cross-sectional path model with parent stress at baseline predicting parenting practices
and child waist circumference and added sugar intake .................................................. 75
Figure 7. Path model with parent stress at baseline predicting parenting practices and change in
child waist circumference and added sugar intake one year later .................................. 76
Figure 8. Path Model for Moderated Effects of Parent Perceived Stress on Child Waist
Circumference and Added Sugar Intake by Parent Stress Level ................................... 77
Figure 9. Intersection of theoretical frameworks of Social Cognitive Theory and Structural
Ecological Model to guide study design ...................................................................... 108

1

SPECIFIC AIMS
Child obesity continues to be an important public health issue, with child overweight or
obesity affecting almost 1 in 3 children in the United States. Recent research suggests that
family stressors and parenting stress are associated with increased child obesity risk. However,
the mechanisms underlying this link remain unclear. Potentially, parent stress decreases positive
weight-related parenting practices, such as holding regular family meals, thereby increasing child
obesity risk. The current dissertation examines how family stressors, parenting stress, and
perceived psychosocial stress influence child diet by testing cross-sectional associations in a
nationally-representative dataset and a longitudinal, mediational model using a regional sample.
The first study will address how parenting stress, within the context of life stressors and regular
family meals, is associated with child obesity risk; the second study will test a longitudinal
model of the downstream effects of parent stress on parenting behaviors, child diet, and the
change in waist circumference and added sugar intake; the third study will investigate the emic
perspectives of low-income, Hispanic women regarding how stress affects child health using
qualitative methodology and a positive deviance-based approach.
Study 1: Associations of Latent Classes of Parenting Stress, Stressors, and Family Meals on
Child Body Mass Index in a Nationally-representative Sample of Parents and Children
Aim 1: To explore latent classes of mothers using observed patterns of responses to
parenting stress, regular family meals, maternal mental health, single parent status, and
poverty for children aged 10 – 17 years.
Aim 2: To examine whether mothers’ latent class status is predictive of child obesity
status, defined as ≥ 95
th
BMI percentile.
2

Study 2: Direct and Indirect Effects of Parent Stress on Child Obesity Risk and Added Sugar
Intake in a Sample of Southern California Adolescents
Aim 4: To examine longitudinal effects of parent stress on change in child waist
circumference and child added sugar intake over one year.
Aim 5: To examine indirect effects of parent stress on change in child waist
circumference via changes in parent rules about child diet and positive family meal
practices.
Aim 6: To examine indirect effects of parent stress on change in child dietary added
sugar intake via changes in parent rules about child diet and positive family meal
practices.
Study 3: A Positive Deviance-Based Qualitative Study of Low-Income Mothers with Children
Who Meet Federal Guidelines for Fruit and Vegetable Intake to Improve Obesity Prevention
Program Design
Aim 6: To locate low-income, urban families who report that their children consume an
average of 4 – 5 servings of fruits and vegetables daily and whose children are healthy,
according to BMI percentile (Positive Deviance group).
Aim 7: To uncover emic perspectives about stress and coping using in-depth interviews
in these families (Positive Deviance group) and in a group of families whose children eat
less than 2 fruits and vegetables per day on average and are obese, according to BMI
percentile (Comparison group).
Results of the current studies aid child obesity prevention program design by providing
knowledge about a novel, yet potentially effective, behavior to modify for improving child diet
and physical activity behaviors – parent stress. These studies will reveal how the context of life
3

stressors co-occurs with parenting stress, potentially shaping child health behavior, and examine
how parent perceived psychosocial stress relates to subsequent weight-related parenting practices
and, in turn, changes in child added sugar intake and waist circumference over one year. Finally,
an in-depth qualitative approach will uncover emic perspectives of low-income, Hispanic
mothers regarding stress, coping and child health. Obesity prevention programs that target
parent stress may both lower child risk for obesity and provide the benefits of improved stress
management, such as boosting parent mental health or improving family functioning. Results
from these studies will shed light on how the stress process unfolds in mother’s lives within the
context of other life stressors to affect weight-related parenting practices.

4

CHAPTER 1: BACKGROUND AND SIGNIFICANCE
Problem Prevalence: Obesity in the United States
In 2011-2012, the prevalence of pediatric obesity in the United States was 16.9%, with
the prevalence of overweight even higher, 34% among 6 – 11 year olds (Ogden, Carroll, Kit, &
Flegal, 2012). Obese children face increased risk of metabolic syndrome, cardiovascular disease,
insulin resistance syndrome, Type 2 diabetes, depression, social stigma, skeletal fractures and
musculoskeletal pain, pulmonary, gastroenterological, and endocrine issues, and adult obesity
(Arslanian, 2002; Goran & Gower, 1998; Guo & Chumlea, 1999; Heuer, McClure, & Puhl,
2011; Keddie, 2011; Must & Strauss, 1999; Steinberger & Daniels, 2003; Taylor et al., 2006;
Weiss et al., 2004). Young children who have an early adiposity rebound (i.e., BMI increase
following the nadir between ages 4-6) tend to have higher BMIs in adulthood (Dietz &
Gortmaker, 2001; Ebbeling, Pawlak, & Ludwig, 2002), and obese adolescents are likely to
become obese adults (Dietz & Gortmaker, 2001). Encouragingly, obesity rates have fallen
recently in Philadelphia, New York, Mississippi, and California (Foundation, 2012), and among
low-income and young children (aged 2 – 5 years), possibly due to national public health
recommendations, interventions, or changes in regulated assistance such as the Special
Supplemental Nutrition Program for Women, Infants and Children (Ogden, Carroll, Kit, &
Flegal, 2014; Pan, Blanck, Sherry, Dalenius, & Grummer-Strawn, 2012). However, the trend
over the past few decades consistently reveals a high prevalence of child obesity that has
increased over much of that time period. According to the Centers for Disease Control, the last
30 years have seen an increase in obesity prevalence from 7% to 18% for children aged 6 – 11
years and from 5% to 19% for children aged 12 – 19 (NCHS, 2012; C. L. Ogden, Carroll, Kit, &
Flegal, 2012). The recent downturn in obesity rates indicates that prevention and treatment
5

efforts are having a beneficial impact, suggesting that additional work is timely and would
capitalize on the momentum already built.
Family Approaches to Obesity Prevention. Family-based approaches to altering
energy intake and physical activity expenditure have been recommended for child and adolescent
obesity prevention (Dietz & Gortmaker, 2001). Parents choose which foods to purchase and
prepare, model diet and physical activity behaviors, monitor children’s sedentary screen time,
and have control over the home environment, giving them important leverage over the precursors
to child obesity (Boutelle, Fulkerson, Neumark-Sztainer, Story, & French, 2007; Cusatis &
Shannon, 1996; Schmidt et al., 2012; Welk, Wood, & Morss, 2003). However, parent-based
interventions designed to prevent and treat childhood obesity have had only modest success
(Faith et al., 2012; Kitzman-Ulrich et al., 2010), and evidence for the benefits of parent
involvement in child weight loss programs is inconsistent and limited (Faith et al., 2012).
Combined, these findings suggest the need to investigate parent factors that influence child diet,
physical activity, and sedentary behavior leading to subsequent obesity risk.
Targeting Parent Stress in Family-based Obesity Prevention. Parent stress may be a
promising avenue to explore for boosting child obesity prevention program success. Emerging
evidence suggests that parent stress may compromise weight-related parenting practices,
negatively affecting child health behavior (Hurley, Black, Papas, & Canfield, 2008; Lampard,
Jurkowski, Lawson, & Davison, 2013; Lundahl, Nelson, Van Dyk, & West, 2013; Lytle et al.,
2011; McPhie et al., 2012; Mitchell, Brennan, Hayes, & Miles, 2009; Park & Walton-Moss,
2012; Parks et al., 2012). Further, parent stress and stressors have been associated with a greater
likelihood of child obesity (Gundersen, Lohman, Garasky, Stewart, & Eisenmann, 2008; Koch,
Sepa, & Ludvigsson, 2008; Lohman, Stewart, Gundersen, Garasky, & Eisenmann, 2009; Parks et
6

al., 2012; Suglia, Duarte, Chambers, & Boynton-Jarrett, 2012), although results have been
inconsistent, with not all studies finding a significant association (Guilfoyle, Zetler, & Modi,
2010; Ievers-Landis, Storfer-Isser, Rosen, Johnson, & Redline, 2008; McPhie et al., 2012; Ellen
Moens, Braet, Bosnians, & Rosseel, 2009). Given the influence that parent stress may have on
child diet and physical activity, parent stress management may be a logical target for behavior-
based child obesity prevention interventions.
Etiology of Child Obesity. A multitude of factors coalesce to influence children’s
obesity risk, some of which occur even before conception. Both over- and under-eating in
pregnant mothers may increase child risk of subsequent obesity, as evidenced by studies of
mothers during the Dutch famine and rat models demonstrating diet-induced obesity in rat
mothers (Dietz & Gortmaker, 2001; Ebbeling et al., 2002). In infants, breastfeeding plays a role
in weight gain (Dietz & Gortmaker, 2001; Ebbeling et al., 2002; Spruijt-Metz, 2011). However,
it is widely thought that two of the strongest, modifiable proximal influences on obesity risk are
child diet and physical inactivity, situated within the larger effects of the environment (Barlow &
Dietz, 1998; Ebbeling et al., 2002; French, Story, & Jeffery, 2001; Troiano & Flegal, 1998).
High caloric intake and sedentary behavior create positive energy balance, leading to continued
weight gain and eventual obesity (Al Mamun et al., 2007; Anderson & Whitaker, 2010; Must &
Tybor, 2005; Padez, Mourao, Moreira, & Rosado, 2009; Reilly et al., 2005; Silva et al., 2011;
Spruijt-Metz, 2011; Troiano & Flegal, 1998; Woods et al., 2005). Hence, obesity programs that
improve energy balance are likely to decrease obesity risk.
Etiology of Child Diet and Physical Activity Patterns. Recent decades have seen
environmental shifts in the U.S. that influence dietary intake, such as the ubiquitous availability
of sugar-sweetened beverages, increased calories available per person, increased meals eaten
7

away from home, larger portion sizes, television advertising, health promotion media campaigns,
nutrition labeling, and food pricing (French et al., 2001). In addition to a changed food
environment, the built environment has shifted markedly in ways that affect physical activity
such as lack of sidewalks, long distances to schools that require crossing busy streets, and an
increased need for car travel vs. active commuting (Anderson & Butcher, 2006; Sallis & Glanz,
2006).
Parents may play a key role in buffering children from the obesogenic effects of these
environmental realities, yet they may find it difficult to help children make healthful choices in
ways that do not stigmatize them for having larger bodies (Schwartz & Puhl, 2003). Parents can
shape young children’s food intake through child-feeding practices, modeled eating behavior,
food availability, or food socialization (Birch & Fisher, 1998; Nicklas et al., 2001; Savage,
Fisher, & Birch, 2007; Spruijt-Metz, Li, Cohen, Birch, & Goran, 2006; Spruijt-Metz, Lindquist,
Birch, Fisher, & Goran, 2002). Parent encouragement, support, and involvement in physical
activity have been shown to increase child physical activity (Brustad, 1996; Dempsey,
Dyehouse, & Schafer, 2011; Dunton et al., 2013; Heitzler et al., 2010; Heitzler, Martin, Duke, &
Huhman, 2006; Nemet et al., 2005; Trost et al., 2003; Welk et al., 2003). Thus, improving
parents’ ability to buffer children’s health behavior from the obesogenic effects of the food and
built environments is likely to tip energy balance favorably, toward a healthier child weight.
Maternal Stress and Child Obesity Risk. While previous research has focused on
parenting practices, such as food availability and support for physical activity, recent studies
have begun to indicate that children of mothers who experience more stressors, parenting stress,
or intense psychosocial stress have higher risk of obesity, possibly as much as 4 times greater
odds (Gundersen, Lohman, Garasky, Stewart, & Eisenmann, 2008; Kozyrskyj et al., 2011; Li et
8

al., 2010; Lohman, Stewart, Gundersen, Garasky, & Eisenmann, 2009; McPhie et al., 2011;
Parks et al., 2012; Stenhammar et al., 2010; Suglia, Duarte, Chambers, & Boynton-Jarrett, 2012).
Estimates from larger studies suggest that parenting stress, number of parent stressors and parent
accumulation of social risk (e.g., maternal depression, partner violence, housing insecurity) place
children at 1.26 to 4 times the risk for obesity, depending on the sample and type of stress
measured (Parks et al., 2012; Stenhammar et al., 2010; Suglia et al., 2012). The interactive
effects of parent stress and the stressor food insecurity remain unclear, with studies finding
opposite results about whether insecurity magnifies or minimizes the relationship between parent
stress and child obesity risk (Craig Gundersen, Lohman, Eisenmann, Garasky, & Stewart, 2008;
Lohman et al., 2009). Also, not all studies find significant relationships between higher parent
stressors and increased child obesity risk (Ajslev, Andersen, Ingstrup, Nohr, & Sorensen, 2010;
McPhie et al., 2012; E. Moens, Braet, Bosmans, & Rosseel, 2009; Sowan & Stember, 2000).
Inconsistent results may stem from the use of different measures of stressors. Many
studies use composite scores that capture a wide range of stressors or different features of the
stressful contexts in which stressors occur, such as poverty, while others examine specific types
of stress, such as parenting stress, separate from the context of other stressors. Yet, the stressful
context in which an experience of stress occurs could alter the downstream effects of stress.
Stress could affect the coping process (described below) and change the health behaviors that
result from interacting with a stressor. Yet most of the previous research has examined only
objective stressors, rather than perceived stress, so little is known about the psychological
processes underlying the parent stress – child obesity link. In addition, many studies have used
cross-sectional designs, limiting inferences that can be made about effects of parent stress over
time, as changes in child weight, BMI, or waist circumference would be expected to occur.
9

Theoretical Background. A significant relationship between higher maternal stress and
increased child obesity risk aligns with the framework of the Transactional Model of Stress and
Coping (TMSC) (Glanz & Schwartz, 2008; Lazarus & Cohen, 1977; Selye, 1976). According to
TMSC, a person’s appraisal of a stressor, appraisal of resources to deal with it, and coping
efforts all influence coping outcomes, which can include functional status and health behaviors
(Cohen, 1984; Glanz & Schwartz, 2008). “Stress” cannot be defined by the stressor alone but
rather by a person’s interpretation of and interaction with it (Glanz & Schwartz, 2008).
Perceived stress is the felt experience of dealing with stressors and is characterized by feelings of
being overwhelmed and lacking control (Cohen, 1984; Glanz & Schwartz, 2008; Lazarus &
Cohen, 1977). Thus, understanding the effects of stressors on human behavior requires
considering the perception of stress, the coping process, and any changes in health behavior that
may result. Parents’ stress may alter their appraisals or coping efforts, changing the health
behaviors in which they engage. Parent health behavior could affect child behavior through
shaping the child’s environment, such as which foods are served at meals or how much support
is provided for physical activity.
Maternal Stress and Weight-related Parenting. Indeed, emerging research suggests
that maternal stress might alter weight-related parenting practices, yet little is understood about
how the context of stressors and perceived stress are related to child obesity risk or how the
mediational processes connecting parent stress to child obesity risk occur across time. Some
evidence suggests that weight-related parenting practices play a mediational role. Higher parent
stress has been associated with increased child fast-food consumption, parent feeding styles,
child feeding behaviors, and the use of pressure to eat and restricting food practices, though
interactive effects of these practices with parenting styles are unclear (Greer, Gulotta, Masler, &
10

Laud, 2008; Hurley et al., 2008; Mitchell et al., 2009; Tovar et al., 2012). One study tested
mediational models of perceived psychosocial stress and family stressors (i.e., perceived time
demands, lack of family rules about mealtime, perceived difficulty enforcing rules) on child BMI
z-scores; they found that family stressors, but not perceived psychosocial stress, were significant
mediators in families with overweight parents (Lytle et al., 2011). Higher parent stress has also
been related to lower physical activity parenting, perceived lower importance of child physical
activity, and higher child sedentary behavior, but the relationship between parent stress and child
physical activity level is unclear (Lampard et al., 2013; Lundahl et al., 2013; Parks et al., 2012).
Hence, maternal stress may affect parenting practices that alter child obesity risk, but little is
known about which specific parenting practices are influenced or how these affect child energy
balance.
Gaps in the Literature. Although this evidence indicates that parent stress may increase
child obesity risk and compromise weight-related parenting practices, little is understood about
the mediating processes. Further, perceived stress often occurs within the context of objective
stressors, yet little is known about how the combination of perceived stress and objective
stressors co-occur within people and relate to parenting practices and child obesity risk.
Understanding the context and mechanism of the effects of parent stress on child obesity risk
could improve child obesity prevention program design in at least two ways. First, targeting
high-risk subgroups of parents would allow more efficient use of program resources. For
example, some subgroups of parents may experience a greater degree of perceived stress after
being exposed to the same objective stressors as those in other subgroups. These parents may
benefit most from stress management techniques. Alternatively, subgroups of parents for whom
weight-related parenting practices vary strongly with stress may benefit more from a curriculum
11

that focuses more on diet or physical activity parenting. Second, identifying which types of diet
and physical activity parenting practices mediate the relationship between parent stress and child
obesity risk will provide information about specific, modifiable behaviors on which to intervene
in obesity prevention programs. These modifiable parenting practices, as precursors to child
obesity, would be logical targets for intervention.
Conceptual Model. Based on prior theory and research, the conceptual model presented
in Figure 1 provides an organizing framework for testing hypotheses about the effects of parent
stress on child obesity risk in the current dissertation. According to this model, parent stress
decreases the ability to maintain and enforce weight-related parenting behaviors, subsequently
increasing child multiple health risk behaviors and subsequent obesity. This framework can be
used to answer several questions that remain unclear given prior research. Specifically, it
provides a framework to test (1) the relationship of parenting stress within the context of
objective stressors to parenting practices and child BMI, and (2) the downstream, longitudinal
effects of perceived parent psychosocial stress on weight-related parenting practices, child diet
and child waist circumference.
COMPLETED STUDIES
These studies investigated theoretical pathways connecting parent stress to heightened
child obesity risk. The first study used a nationally-representative sample to investigate whether
latent subgroups of parents could be grouped according to different patterns of parenting stress,
demographics, and objective stressors, such as poverty, using latent class analysis. Parent latent
class status was then be used as a predictor variable in a multinomial logistic regression
predicting BMI percentile for children aged 10 – 17 years. The analysis was conducted cross-
sectionally for data from 2011. The second study built on the first, using a path model to
12

investigate longitudinal, mediated effects of parent perceived psychosocial stress on changes in
child dietary intake of added sugars and child waist circumference through effects on parent rules
about diet and positive family meal practices. The sample contained parents and children living
near Chino, CA; one half lived in a smart growth community and one half lived in a non-smart
growth community nearby. While Studies 1 and 2 focused on understanding the causes of child
obesity risk behaviors and their relationship to parent stress, study three focused on a solution
tailored for an ethnic minority urban population. Study 3 used a Positive Deviance-based
qualitative approach to investigate locally-sustainable, cost-effective strategies used by mothers
whose children meet federal guidelines for fruit and vegetable consumption. Study 3 lays the
groundwork for future research examining strategies more systematically over a larger
geographic and demographic base.

13

Figure 1. Conceptual Framework for Mediated Effects of Parent Stress on Child Obesity Risk

Parent stress
(e.g.,
environmental or
social stressors
such as poverty,
parenting stress)
Weight-related
parenting
behavior (e.g.,
enforcing rules
about TV-
viewing, home
food availability)
Child multiple
risk behaviors
(e.g., fast food
consumption &
sedentary
behavior)
Child obesity
(e.g., child waist
circumference,
child BMI z-
score)
14

CHAPTER 2: ASSOCIATIONS OF LATENT CLASSES OF PARENTING STRESS
WITH FAMILY MEALS AND
CHILD BODY MASS INDEX IN A NATIONALLY-REPRESENTATIVE SAMPLE OF
PARENTS AND CHILDREN
Introduction
Study 1 used a nationally-representative sample to explore latent subgroups of parents
characterized by differences in parenting stress and other stressors, testing whether subgroups
were differentially associated with odds of obesity for children aged 10 – 17 years (defined by
BMI percentile ≥ 95
th
percentile). Although some studies have found no association between
parenting stress, stress from parenting itself, and child obesity (Guilfoyle et al., 2010; Ievers-
Landis et al., 2008; McPhie et al., 2012; McPhie et al., 2011; Moens et al., 2009), other evidence
indicates that parenting stress in combination with other stressors increases child obesity risk
(Koch et al., 2008). Further, parenting stress seems to affect child diet and sedentary behavior –
antecedents to child obesity – though this research has been conducted on relatively small
samples, limiting generalizability (Guilfoyle et al., 2010; S. McPhie et al., 2012; Park & Walton-
Moss, 2012; Walton, Simpson, Darlington, & Haines, 2014). These discrepancies and gaps
suggest that excluding the stressful context surrounding parenting stress leaves out an important
element of understanding how parenting stress may relate to child obesity risk. In addition,
studies have not fully assessed the antecedents of obesity risk, and generalizability is limited due
to small sample sizes. To address these gaps, Study 1 used a nationally-representative sample of
mothers to explore latent subgroups that characterize mothers according to parenting stress in
conjunction with objective stressors (poverty level, single parent, maternal mental health issues)
15

and weight-related parenting practices (regular family meals), and the association between
subgroup membership and child obesity status.
Parent stress and weight-related parenting behaviors. General stress that parents
experience could change parents’ pro-active, weight-related parenting behaviors. One set of
weight-related parenting behaviors is family routines, a lack of which has been associated with
child obesity risk (Anderson & Whitaker, 2010; Boone, Gordon-Larsen, Adair, & Popkin, 2007;
Wethington, Pan, & Sherry, 2013). One study using a nationally-representative sample
estimated that children who ate dinners with their family more than 5 times per week had a 23%
lower likelihood of obesity (Anderson & Whitaker, 2010). Another example of a weight-related
parenting practice is enforcing limits on screen time; children who have more than 2 hours per
day of sedentary screen time may have as much as 170% higher odds of child obesity (Anderson
& Whitaker, 2010; Boone et al., 2007; Wethington et al., 2013). Yet the self-regulation required
to manage stressors could diminish parents’ capability to actively engage in preventive weight-
related parenting behaviors. Self-regulatory tasks have been shown to deplete people’s ability to
perform a subsequent, unrelated task that also requires self-regulation (Muraven & Baumeister,
2000). This reduced ability for self-regulation, called ego-depletion, may specifically contribute
to unhealthier, more obesogenic eating and exercise patterns (Oaten & Cheng, 2005).
Parenting stress. A specific type of stress, parenting stress, reflects a perceived lack of
resources and ability to deal with parenting demands. It has been characterized by a
constellation of challenges including child behavior problems, difficulty managing parenting
tasks, perceived difficulty filling the maternal role, parental distress, and a dysfunctional parent-
child interaction style (Loyd & Abidin, 1985; Quittner, Jackson, & Glueckauf, 1990). Parenting
stress, especially in the context of other life stressors, may lead to ego-depletion and negatively
16

affect weight-related parenting such as regular family meals; yet, research is limited regarding
the effects of parenting stress on these types of practices.
Parenting stress, child health behavior, and child obesity risk. Indeed, some recent
research suggests that parenting stress does negatively affect child health behavior and increase
obesity risk. One recent study found that parents with higher psychological stress – a composite
that included parenting stress – have young children who are about twice as likely to be obese,
both concurrently and three years later (Koch et al., 2008). Another large study found that
children were more than 4 times as likely to be obese if their mothers reported higher parenting
stress (Stenhammar et al., 2010). Further, parenting stress predicts decreased child vegetable
consumption over time, fewer child health-related behaviors, and lower parent estimates of
children’s weight-related health quality of life, indicating that parenting stress influences
antecedents of child obesity (Guilfoyle et al., 2010; McPhie et al., 2012; Park & Walton-Moss,
2012). Parenting stress has also been associated with vegetable intake and a composite measure
of health behaviors (McPhie et al., 2012; Park & Walton-Moss, 2012). Though some research
has failed to find significant effects of parenting stress, much of it has been conducted on
relatively small samples (McPhie et al., 2011; Moens et al., 2009; Park & Walton-Moss, 2012),
with one exception (Ievers-Landis et al., 2008). In this larger, representative, cross-sectional
study, parenting stress was examined as a separate variable, entered into the model after the
effects of parent income – which were significant – had been statistically adjusted. Although
parenting stress, alone, has not always been associated with higher concurrent child obesity risk
(Koch et al., 2008; Walton et al., 2014), stress may act in concert with other stressors, as
indicated by research examining effects of composite stress indicators (Koch et al., 2008). Given
17

these previous studies, the joint effect of parenting stress within the context of other stressors has
not been examined and remains unknown.
Current study. Parenting stress may be associated with child diet, sedentary behavior
and – in combination with other stressors – possibly child obesity. Yet, the combined effects of
parenting stress within the context of other objective stressors remain unclear. To address this
gap, the current study will use latent class analysis – a technique that allows exploration of
subgroups of parents – to examine latent parent subgroups characterized by parenting stress,
objective stressors (poverty, single parent, mental health), and a weight-related parenting practice
(regular family meals). Then, parent subgroup membership will be used to predict child obesity
(BMI percentile ≥ 95
th
) to determine whether they are associated.
Latent class analysis (LCA) is a method for detecting latent subgroups based on patterns
of observed responses (Kline, 2011). The approach is person-centered in contrast to traditional
variable-centered methods, meaning that it allows examination of effects for subgroups of
people who respond similarly on a cluster of items, rather than examining the effect of the cluster
of items itself (Lanza, Rhoades, Greenberg, Cox, & Family Life Project, 2011). Latent
subgroups reflect the way stress is thought to interact and accumulate with other stressors/risks
experienced within a person, making the methodology ideal for this study. Recently, Iannotti et
al. (2013) used LCA on a nationally-representative sample to identify three classes of
adolescents who were characterized by different patterns of physical activity, sedentary behavior,
and dietary intake. However, to the author’s knowledge, no studies have used latent class
analysis to examine the effects of parenting stress in combination with objective stressors and
weight-related parenting practices for effects on child obesity risk.
18

Data analysis for the current study proceeded in two steps. First, latent class analysis was
used to determine the number of classes of parents characterized by parenting stress, objective
stressors (maternal mental health issues, single parent status, poverty) and a weight-related
parenting practice (regular family meals). Second, multinomial logistic regression was used to
compare odds of obesity across latent classes, adjusting for child age, gender, and minority
status.
Specific Aims and Hypotheses
These specific aims addressed limitations of prior research by examining parenting stress
and family meal practices in the context of other stressors within persons. Second, this study
used a large, nationally-representative sample to examine odds of obesity across parent
subgroups.
Aim 1: To explore latent classes of mothers using observed patterns of responses to parenting
stress, regular family meals, maternal mental health, single parent status, and poverty for
children aged 10 – 17 years.
Aim 2: To examine whether mothers’ latent class status is predictive of child obesity status,
defined as ≥ 95
th
BMI percentile.
H1: Latent classes characterized by the highest levels of parenting stress will be
associated with higher risk of child obesity.
Methods
Study design. Data come from a nationally-representative study called the National
Survey of Children’s Health, conducted by the Centers for Disease Control’s National Center for
Health Statistics (http://childhealthdata.org/) collected in 2011 (CAHMI, 2012; CDC, 2013).
Surveys were conducted using randomly-sampled telephone numbers and Computer Assisted
19

Telephone Interviewing on an adult member of the household who had a child between 0 and 17
years living in the home. Sections 1 – 5 of the survey were administered to all families, Section 6
was administered to families when the randomly-selected child in the household was 0 – 5 years
of age, Section 7 was for children aged 6 – 17 years. Results are weighted to represent non-
institutionalized children in between 0 and 17 years at state and national levels based on a
formula including ranking for demographics adjustment as well as likelihood of the phone
number being selected. Weighted results do not generalize to prevalence estimates for the
population of parents, such as the percent of parents in the population who engage in a certain
behavior. Mothers comprised 68.3% of all completed interview respondents, 24.1% were
fathers, and 7.1% were other relatives or guardians. Only mother’s responses were used for the
present study. The average interview length for landline and cell phone samples was about 34
minutes. Of the 187,422 families who had age-eligible children, 95,677 completed the interview
(51%); however, eligibility could not be determined for all contacted families due to some
nonresponse before eligibility status was identified. Therefore, the number of eligible children
was estimated, and the response rate was estimated to be 54% for the landline sample and 41%
for the cellphone sample. Incentives of up to $15.00 were given to participants for completing
the interview depending upon whether the participant had a landline or cellphone and an
identifiable address match.
Measures
Parenting stress. Parenting stress was measured using a four item measure, created from
the Aggravation in Parenting Scale of the Parental Stress Index (Zalaquett & Wood, 1997) and
the Parental Attitudes about Childrearing (Easterbrooks & Goldberg, 1984) (see Table 1). One
20

negatively-worded item was reverse-coded, and an overall score was calculated by summing the
four items. Preliminary analysis indicated somewhat low reliability (α = 0.67).
Regular family meals and child BMI Class. Frequency of family meals per week was
measured with one item. Child BMI was derived from children’s height and weight as reported
by parents (NSCH Methodology Report, 2003) (see Table 2). BMI percentiles were calculated
based on gender and age-adjusted CDC 2000 growth charts, only for children aged 10 – 17 years.
Obesity was defined as ≥ 95
th
percentile and dichotomized as 0 = not obese; 1 = obese.
Objective stressors and covariates. Respondents reported poverty information, family
structure, maternal mental health status, child age, gender, and minority status (see Table 3). A
poverty level variable was calculated by NCHS based on participants’ self-reported income,
household size, and the Federal Poverty Level. For the analysis, this variable was dichotomized
such that 1 = ≤ 133% of the Federal Poverty Level and 0 = above that level. Single parent status
was dichotomized: 0 = No, 1 = Yes. For mother’s mental health, scores of Excellent, Very
Good, or Good mental health were coded as 0; scores of Fair or Poor were coded as 1.
Race/ethnicity was dichotomized into minority status vs. not (Caucasian/White = 0; non-
Caucasian/non-White = 1).
Data Analysis
Data management and analysis were conducted in IBM SPSS Statistics Version 21, SAS
for Windows 9.2, and MPlus Version 6. First, data were cleaned (e.g., re-coding of “77” or “99”
to missing “.”) and distributions checked for normality and skew. Composite scores and
descriptive statistics were calculated. Models were weighted using the “WEIGHT” functions in
Mplus and SAS SURVEYLOGISTIC. To adjust for sampling by state, the multinomial logistic
21

regression used the “STRATA” function in SAS SURVEYLOGISTIC. Thus, results are
nationally representative.
To test Hypothesis 1, Latent Class Analysis (LCA) was used to identify subgroups of
mothers (Collins & Lanza, 2010; Lanza & Rhoades, 2013). LCA is a type of finite mixture
model that reduces the total number of response patterns to “key patterns” that characterize
groups of individuals across multiple dimensions on an unobserved categorical latent variable
(Collins & Lanza, 2010; Lanza & Rhoades, 2013). The technique calculates two sets of
parameters: latent class prevalences and item response probabilities (Collins & Lanza, 2010).
The optimization algorithm was an accelerated Expectation-Maximization algorithm (EMA) that
uses Quasi-Newton and Fisher Scoring optimization steps when needed (Muthen & Muthen,
2012). To account for selection bias (i.e., certain groups were not sampled at random), MPlus
obtains Psuedomaximum Likelihood (PML) estimates by maximizing the weighted log-
likelihood, with a sandwich estimator to determine the asymptotic covariance matrix and
standard errors (Asparouhov, 2005; Muthen & Muthen, 2012). All available data were used to
estimate the model, including cases with missing values on study variables (Muthen & Muthen,
2012).
Multiple fit indices were considered in evaluating a series of models with increasing
numbers of classes (i.e., 1 class vs. 2 classes vs. 3 classes). Akaike Information Criterion (AIC),
Bayesian Information Criterion (BIC), sample size-adjusted BIC (SSBI), entropy, and Lo–
Mendell–Rubin were used to determine the number of classes, in combination with
interpretability and parsimony (Collins & Lanza, 2010; Kline, 2011; Lo, Mendel & Rubin,
2001). AIC is a measure of model fit that is based on the number of free model parameters and
the regular maximized log likelihood (Kline, 2011; Yang, 2006). According to this criterion, the
22

number of classes providing a lower AIC value is preferable. However, as sample size increases,
the adjustment for parsimony (i.e., number of parameters) decreases (Yang, 2006), which is an
issue with the very large sample in this study. Thus, the BIC and BIC
adj
were also examined.
The BICs are also tests of model fit based on the regular maximized log likelihood and number
of free model parameter estimates, but they also include the sample size (BIC) or adjusted
sample size (BIC
adj
) (Nylund et al., 2007; Yang, 2006). A change in BIC of between 2 and 6
when adding one additional class indicates moderate support for retaining the class, and a change
of greater than 6 suggests strong support for retaining the additional class (Kass, 1995). Prior
studies and simulation research suggest that the BIC and adjusted BIC may be particularly useful
for selecting latent classes (Nylund et al., 2007; Yang, 2006). Entropy is a weighted average of
individuals’ posterior probabilities of class membership (Collins & Lanza, 2010). Scores range
from 0 – 1, with scores closer to 1 indicating better latent class separation. The final criterion
was a statistical test between models with K classes to those with K - 1 classes using the
distribution of 2 times the loglikelihood difference, rather than the usual likelihood ratio chi-
squared test, to correct for the distribution for comparisons between numbers of classes
(Asparouhov et al., 2012; Group USC, 2014; Nylund et al., 2007). A significant p-value
indicates significant improvement of fit by retaining the larger number of classes (e.g., 4
compared to 3) (Asparouhov et al., 2012). Although the bootstrap likelihood ratio test is
recommended for testing K versus K – 1 classes, it could not be used with the sample-weighting
function in MPlus (Muthen & Muthen, 2012; Nylund, Asparov, & Muthen, 2007), and only the
Lo–Mendell–Rubin was used. To address Hypothesis 2, latent class membership for each child’s
mother was retained from the latent class analysis and used as a predictor of obesity status in a
23

multinomial logistic regression (1 = obese; 0 = not obese), controlling for child gender, age, and
minority status.
Results
Preliminary Analyses
Parent-reported data for 45,309 children aged 10 – 17 years were provided for 2011-
2012. Of these, 31,239 (68.9%) were mothers. This sample had a mean child age of 13.65 years
(SD = 2.32), was 52% male, 70% white, and 70% lived with two biological or adoptive parents.
Table 4 shows means, standard deviations, and correlations between study variables. Most
mothers (74%) had more than high school education. Many families (67.7%) reported eating
family meals 5 or more nights per week. Approximately 14% of children were obese (≥ 95
th

percentile for BMI), which is slightly lower than another 2011-2012 national estimate of 17.7%
for 6 – 11 year olds and 20.5% for 12 – 19 year olds (Ogden, Carroll, Kit, & Flegal, 2014). The
mean level of parenting stress was 6.53 (SD = 1.58), ranging from 4 to 15, with higher scores
indicating highest parenting stress. Of the sample, 7% of mothers reported poor or fair mental
health, 9% were single parents, and 21% lived at or below 133% of the Federal Poverty Level.
In terms of covariance coverage, missing data were very low (0.10% - 1%). The parenting stress
items were correlated significantly with the parent demographics, child health behaviors and
child BMI (ps < 0.05) (see Table 4). Unadjusted Odds Ratios for obesity status by individual
stress indicators showed that parenting stress, maternal mental health, meals per week and
poverty were independently significantly associated with greater likelihood of obesity (see Table
5). In contrast to expectations, more frequent family meals was associated with higher child
obesity, rather than lower, which is explained in further detail below.
24

Four distinct subgroups of maternal stress and weight-related parenting
Four latent classes of parents emerged from the continuous (parenting stress, meals per
week) and categorical indicator variables (maternal mental health, single parent status, poverty)
(see Table 6). The main distinguishing variable between classes was the number of days per
week that families had meals together. The largest class, class 4 (42.5%), comprised parents
who had family meals almost daily (latent class M = 6.84 days). As displayed in the figures, this
Daily meals class had the highest poverty, was relatively low in parenting stress, and moderate in
mental health issues. The second largest, class 2 (31.4%), ate family meals most days per week
(latent class M = 4.55 days). This Meals most days class was relatively average on all measures:
similar to the other classes on single parent status, slightly lower in poverty, and slightly fewer
meals per week. The third largest, class 3 (17.5%), ate family meals occasionally during the
week (latent class M = 2.55 days). The Occasional meals class had less poverty than the other
classes, similar single parent status and maternal mental health issues, and slightly higher
parenting stress. The smallest class, class 1 (8.6%), ate meals infrequently (latent class M = 0.54
days). The Rare meals, stressed mothers class had higher poverty, similar probability of being a
single parent, and high maternal mental health issues and parenting stress. Figure 3 shows latent
class means and Figure 4 shows item response probabilities for each indicator by class. Table 7
shows latent class means for continuous stress indicators and latent class probabilities for
categorical indicators.
For continuous indicators (parenting stress and meals per week), ANOVAs were
conducted to compare means across latent classes. Results were that number of meals per week
differed across classes F(3, 31190) = 210669, p < 0.001. Follow-up Tukey’s HSD tests showed
that means in each class significantly differed from the others (all ps < .05, controlling for Type
25

1 experimentwise error rate). The Daily meals, poor class had the most frequent meals (M =
6.82, SD = 0.02), followed by the Meals most days class (M = 4.57, SD = 0.50), the Occasional
meals class (M = 2.59, SD = 0.49), and the Rare meals, stressed mothers class (M = 0.50, SD =
0.50). Parenting stress also differed across classes, F(3, 31098) = 102.78, p < 0.0001. Follow-up
tests showed that it differed across all classes (all ps < .05, controlling for Type 1
experimentwise error rate). Parenting stress was highest in the Rare meals class (M = 6.91, SD =
1.82), followed by the Occasional meals class (M = 6.69, SD = 1.56), the Meals most days class
(M = 6.53, SD = 1.48), and lowest in the Daily meals class (M = 6.39; SD = 1.59).
For categorical variables (maternal mental health, single parent status, poverty), chi-
square tests of equal proportions were conducted to compare proportions across classes. A
Bonferroni correction was applied to follow-up tests (α = 0.05/6 = 0.0083). Results were that
Maternal mental health differed by class, χ2(3) = 231.13, p < .001. Follow-up chi-square tests
revealed that the proportion of mental health issues in each class differed from that in every other
class except that Daily meals (6.87%) did not differ from Occasional meals (7.17%), χ2(1) =
0.55, p = 0.46, n = 18,698. Maternal mental health issues were highest in the Rare meals,
stressed mothers class (13.90%), followed by the Occasional meals class (7.17%), the Daily
meals class (6.87%), and the Meals most days (5.28%). Single parent status differed across
classes χ2(3) = 9.04, p = 0.03. Follow-up tests showed that single parent status was the same
across classes except that the Rare meals class had a higher proportion of single mothers
(10.16%) than the Meals most days class (8.35%), χ2(1) = 8.30 p = 0.004, n = 12,366 and the
Occasional meals class (8.36%), χ2(1) = 6.98, p = 0.008, n = 8155. Poverty differed
significantly across classes, χ2(3) = 338.31, p < 0.001. Follow-up tests showed that the
proportion of poverty differed significantly across all classes except that the Rare meals, stressed
26

mothers class (23.92%) did not differ from the Daily meals class (25.72%), χ2(1) = 3.29, p =
0.07, n = 14,114.
Odds of obesity by parent latent class
A multinomial logistic regression was conducted to examine whether mothers’ latent
class status predicted child obesity, adjusting for child minority status, age (in years), and gender.
Results showed that children in the Daily meals, poor class were 55% more likely to be obese
than children of mothers who were in the Rare meals, stressed mothers class, OR = 1.55, 95% CI
[1.19, 2.02]. Obesity was not more likely for the Occasional, OR = 1.10, 95% CI [0.82, 1.48] or
Meals most days classes, OR = 1.06, 95% CI [0.80, 1.41] compared to the Rare meals, stressed
mothers class. Children of minority status had 79% higher odds of obesity, OR = 1.79, 95% CI
[1.53, 2.09]. Child age was associated with a decreased risk of obesity; a 1 year increase in age
was associated with a -.09 (SE = 0.02, p < 0.0001) decrease in relative log odds of being obese,
OR = 0.92, 95% CI [0.89, 0.95]. Girls were 31% less likely to be obese than boys, OR = 0.69,
95% CI [0.59, 0.81]. Follow-up analyses indicated that obesity status did not differ among
Occasional, Rare, or Meals most days classes, and that the Daily meals class was more likely to
be obese than any other class: 46% more likely than the Most meals class and 41% more likely
than the Occasional meals class (see Table 8).
Discussion
This study filled a gap in the literature regarding how parenting stress co-occurs with
objective stressors in mothers of 10 – 17 year old adolescents in the United States. Prior work
has been limited by small samples and has failed to examine parenting stress within the context
of other stressors and weight-related parenting practices, both of which have been linked to
obesity risk. Second, this study tested whether child obesity status differed across these
27

subgroups of mothers. Results indicated that children were at higher risk of obesity in the
subgroup of mothers characterized by the highest probability of poverty despite the fact that this
group had daily family meals.
The first aim of this study was to explore subgroups of mothers along the dimensions of
parenting stress, single parent status, poverty level, overall mental/emotional health, and
frequency of regular family meals using a nationally-representative sample. This was important
because previous research on the effects of parenting stress has tended to use small samples and
has not considered the context of objective stressors. The present results show four subgroups of
mothers who differed most by how many days per week the family had meals together. The
group that had daily meals also had the highest poverty. While initially surprising, this result is
consistent with one previous study finding that families who ate dinner together 7 days per week
were more likely to be poor and to live in single-parent households with lower education levels
(Rollins, Belue, & Francis, 2010). Those authors hypothesized that cultural differences in
feeding practices (such as placing foods within easier reach for boys) or in nutritional content of
meals (such as relying on fast food or quick meals) might account for the findings.
Alternatively, families living in poverty may have fewer extracurricular activities that interrupt
meal-time but also burn calories, such as sports practice.
Another subgroup of mothers was characterized by more single mothers who had high
parenting stress, greater mental health issues, and lived in moderate poverty. This subgroup had
the least frequent family meals – less than 1 day per week. Across subgroups, parenting stress
occurred in mothers who also experienced mental health issues. Although poverty played a role
in the lives of mothers in both of these subgroups, the relationship to regular family meals was
opposite, which may possibly be due to differences in parenting stress. When mothers had
28

higher parenting stress and maternal mental health issues with moderate poverty, family meal
frequency was very low. In contrast, when mothers had average parenting stress and relatively
low-to-moderate mental health issues, a high probability of poverty occurred with family meals
almost daily. One possibility for this difference is that high parenting stress and mental health
difficulties create cognitive load that interferes with mothers’ ability to organize, plan or
schedule meals, or to prepare dinners or gather family members when living in poverty. More
typical levels of parenting stress and mental health challenges may not cause such high cognitive
load or interference with meal planning and preparation, even when saddled with financial
limitations. Overall, frequency of family meals tended to differentiate subgroups of mothers,
though parenting stress, mental health issues and poverty status also played a role.
This study also examined whether odds of child obesity for US adolescents differed
across subgroups of mothers. Results showed that children whose mothers were characterized by
higher poverty and daily family meals had children with the highest odds of obesity. These
mothers were somewhat similar to other mothers on mental health issues, single parent status,
and parenting stress. This finding replicates previous work that obesity risk is higher for children
living in poverty (Lutfiyya et al., 2008). It also contributes to our understanding of the
relationships among parenting stress, poverty, weight-related parenting, and obesity. The class
with the most frequent family meals was associated with the highest risk of obesity. While some
research suggests a linearly decreasing relationship between higher family meals and lower child
obesity, these studies define regular meals in varying ways such as “greater than 5 per week”
(Anderson & Whitaker, 2010). Yet according to the current study, mothers in subgroups
reporting meals about 5 days per week had children with lower odds of obesity than those
reporting 7 days, highlighting the important distinction between frequent and daily meals. This
29

result could mean one of at least two things. First, poverty could increase child obesity risk in
other ways, despite healthy parenting practices, such as living in unsafe neighborhoods that limit
opportunities for physical activity or limiting parents’ ability to purchase an abundance of
healthy foods (Lutfiyya et al., 2008). Second, the meals which families living in poverty eat
could be increasing obesity risk, with the effect compounded because these types of meals are
eaten so frequently. Future research is certainly needed to investigate how other weight-related
parenting practices, such as limiting sedentary screen time or engaging in physical activity with
children, interact with poverty to influence child obesity risk in subgroups of parents across
ethnicities.
Contrary to hypotheses, the subgroup of mothers with the highest parenting stress did not
have children with higher odds of obesity, despite having regular family meals less than once per
week. Some evidence suggests that having family meals even 1 to 2 times per week (compared
to never) protects against obesity risk 10 years later (Berge et al., 2015), though that study did
not provide estimates of concurrent obesity risk. Potentially, even occasional family meals
provide healthy options, or perhaps negative consequences of fewer meals are mitigated if a
stable foundation was laid by more frequent family meals when children were young (Berge et
al., 2015). This sample contained children aged 10 – 17 years, and effects of parenting stress on
weight management behaviors may have been diluted by outside influences by the time children
reach these ages, compared to younger children. As children age, they may consume more meals
outside of the home or with peers, minimizing the effect of maternal parenting stress on child
food intake but possibly increasing risk if peers have unhealthy weight management behaviors.
That possible explanation falls in line with research on adults indicating that obesity risk can
spread through peer networks (Christakis & Fowler, 2007). Another possible explanation is that
30

children are eating more meals at schools, organized youth programs or family meals at friends’
houses, which might offset any nutritional imbalances from a lack of meals at home (ex.
National School Breakfast or Lunch Programs; USDA, 2013). One additional important
influence to consider is ethnicity; frequent family meals have been associated with increased
obesity risk for Hispanic boys in low educated families, but not white or black boys (Rollins et
al., 2010). Potentially, cultural differences affect the relationship between parent subgroup
status, which includes family meal frequency, and child obesity risk. Finally, one
methodological limitation of this study was the cross-sectional study design. Thus, the question
of whether repeated weeks of rare family meals changes child body weight over time remains
unanswered. In sum, subgroups of mothers differed by frequency of family meals, poverty and
levels of parenting stress, and children of mothers with the highest poverty and daily family
meals had the highest risk of obesity.
Limitations
While the National Survey on Children’s Health contains a comprehensive list of child
data, the measures of parenting and stress had some limitations. The Parenting Stress measure
had 4-items, somewhat low reliability (α = 0.67), and is not as comprehensive as the full Parental
Stress Inventory or Parental Attitudes about Childrearing measures. Alternative measures of
parenting stress have shown promise for investigating child health variables, such as the
Pediatric Inventory for Parents and the Stress Index for Parents of Adolescents (Guilfoyle et al.,
2010; Sheras, Abidin, & Konold, 1998). The dataset also does not contain data on parent
depression, parent BMI, or parent work hours, which could affect parenting stress or child
obesity risk, though the present study was able to adjust for overall maternal mental health
(Guxens et al., 2013; Maffeis, Talamini, & Tato, 1998; Phipps, Lethbridge, & Burton, 2006).
31

Second, the data are not longitudinal panel data on the same participants but rather a cross-
sectional (if nationally-representative) sample. Thus, statistical tests could not be conducted for
changes in latent class membership over time or effects of latent class status on subsequent child
obesity risk.
Conclusion
This study identified four subgroups of mothers of US adolescents characterized by
different frequencies of family meals, poverty levels, single parent status, parenting stress, and
maternal mental health. The largest subgroup comprised mothers with a higher probability of
poverty who had daily family meals. Children of mothers in this subgroup were at higher risk of
obesity than children from any of the other subgroups. Future research is needed to understand
how the within-daily effects of parenting stress may interact with poverty to affect other weight-
related parenting practices, such as limiting sedentary screen time, and to investigate effects over
time using longitudinal panel data.

32

Table 1. Parenting Stress Variable Names and Response Options
Question Response options
In general, how well do you feel you are coping with the
day to day demands of [parenthood / raising children]?
Would you say that you are coping very well, somewhat
well, not very well, or not well at all?
1 = Very Well
2 = Somewhat Well
3 = Not Very Well
4 = Not Very Well At All
77 = Don't Know
99 = Refused
During the past month, how often have you felt [S.C.] is
much harder to care for than most children [his/her] age?
Would you say never, rarely, sometimes, usually, or
always?
1 = Never
2 = Rarely
3 = Sometimes
4 = Usually
5 = Always
77 = Don’t Know
99 = Refused
During the past month, how often have you felt [he/she]
does things that really bother you a lot?
1 = Never
2 = Rarely
3 = Sometimes
4 = Usually
5 = Always
77 = Don’t Know
99 = Refused
During the past month, how often have you felt angry with
[him/her]?
1 = Never
2 = Rarely
3 = Sometimes
4 = Usually
5 = Always
77 = Don’t Know
99 = Refused

33

Table 2. Child Health Variables and Response options
Question Response Options
Derived. BMI for age classification for
sample child
1 = "Underweight -- less than 5th percentile"
2 = "Healthy weight -- 5th to 84th percentile"
3 = "Overweight -- 85th to 94th percentile"
4 = "Obese -- 95th percentile or above"
.M = "Missing in error"
.N = "Skip: Less than 10 yrs";

34

Table 3. Demographic Variables and Response Options
Question Response Options
Selected child's age in years at interview 0 – 17
Sex of selected child 1 = male
2 = Female
.M = DK/Ref
Family structure, recoded at NCHS, revised
version based on puf 3-20-2009
1 = "two parent--biological or adopted"
2 = "Two parent--step family"
3 = "Single mother--no father present"
4 = "Other family type"
.M = "Missing in error"
Derived. Poverty level of this household based on
dhhs guidelines - imputed; single imputation
using version 3; recoded into 4 categories
1 = "< 199% FPL"
2 = "200-299% FPL"
3 = "300-399% FPL"
4 = "400% or more FPL"
Would you say that in general [[CHILD]’s
MOTHER’s mental and emotional health is
excellent, very good, good, fair, or poor?
1 = Excellent
2 = Very good
3 = Good
4 = Fair
5 = Poor
6 = DK
7 = Refused

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Table 4. Bivariate correlations between study variables (n = 31,239)
Mean SD 1 2 3 4 5 6 7 8 9
1 Parenting stress 6.53 (1.58) 1.00

2 Meals per week 4.83 (2.08) -0.10** 1.00

3 Maternal mental health issues 2.05 (0.92) 0.33** -0.08** 1.00

4 Single parent 0.09 (0.28) 0.03** -0.01 0.01* 1.00

5 Poverty 5.72 (2.56) -0.09** -0.07** -0.27** -0.02** 1.00

6 Obesity 0.14 (0.35) 0.05** 0.03** 0.09** 0.01 -0.17** 1.00

7 Minority 0.30 (0.46) 0.07** 0.00 0.12** 0.01 -0.29** 0.11** 1.00

8 Child age 13.65 (2.32) -0.01 -0.16** 0.01 0.03** 0.05** -0.10** -0.06** 1.00
9 Child gender 1.48 (0.50) -0.03** -0.01 0.01 0.01 0.00 -0.07** 0.01 -0.01 1.00
*p <.05

**p< .001

Note: Parenting stress range = low 4 – high 15; Meals per week range = 0-7; Maternal mental health range = 1 Excellent - 5 Poor;
Single parent range 0 = No, 1 = Yes; Poverty range = 1 ≤ 133% Federal Poverty Level – 8 > 133% FPL; Obesity range 0 = No, 1 =
Yes; Minority range 0 = No, 1 = Yes; Child age range = 10 – 17 years; Child gender range = 1 Male, 2 Female

36

Table 5. Unadjusted Odds Ratios for obesity status by individual stressor
OR
95%
Confidence Limits
Wald
χ2
p-value
Parenting stress 1.06 1.01 1.11 5.22 0.02
Meals per week 1.11 1.07 1.15 28.02 <.0001
Maternal mental health issues 1.63 1.28 2.09 15.48 <.0001
Single parent 0.92 0.71 1.18 0.47 0.49
Poverty 2.31 1.96 2.72 100.36 <.0001
Note: Parenting stress range = 4 - 15; Meals per week range = 0 - 7; Maternal mental health
0 = Excellent/Very good/Good, 1 = Fair/Poor; Single parent 0 = No, 1 = Yes; Poverty 0 ≥
133% Federal Poverty Line; 1 = < 133% FPL

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Table 6. Model fit indices for latent class analysis for parenting stress and objective stressors

No. of classes

2 3 4 5
Number of parameters 13 19 25 32
Log likelihood -162283.278 -159053.839 -154762.466 Not well identified
AIC 324592.556 318145.678 309574.932 --
BIC 324701.098 318304.316 309783.666 --
N-adjusted BIC 324659.784 318243.934 309704.216 --
Testing the null hypothesis 1 vs. 2 2 vs. 3 3 vs. 4 4 vs. 5

Lo–Mendell–Rubin probability 0.000 0.000 0.000 --
Entropy 0.738 0.900 0.978 --
Notes: No. = number; AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion.
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Table 7. Mean level and probability of scoring 1 for each stress indicator by latent classes of mothers of 10-17 year old US adolescents

Rare meals,
Single mothers
with parenting
stress, mental
health issues,
and poverty
(8.6%)
Meals most days,
average across
indicators
(31.4%)
Occasional
family meals
(17.5%)
Daily family
meals, poor
(42.5%)
Class 1 Class 2 Class 3 Class 4
Indicators
Mean level of indicator for each class

Parenting stress 7.10 6.60 6.81 6.49

Meals per week 0.54 4.55 2.55 6.84
Probability of scoring 1 on the indicator for each class

Maternal mental health issues 0.18 0.07 0.09 0.09

Single parent 0.13 0.10 0.09 0.11
Poverty 0.32 0.24 0.27 0.38
Note: n = 31,327; Parenting stress range = 4 - 15; Meals per week range = 0 - 7; Maternal mental health range = 0 Fair/Poor, 1
Excellent/Very Good/Good; Single parent range 0 = No, 1 = Yes; Poverty range = 0 > 133% Federal Poverty Level, 1 > 133% FPL

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Table 8. Multinomial logistic regression results predicting obesity status from latent class membership

Rare meals, Single
mothers with
parenting stress,
mental health issues,
and poverty
Meals most days,
average across
indicators
Occasional family
meals
Daily family meals,
poor
(8.6%) (31.4%) (17.5%) (42.5%)
Class 1 Class 2 Class 3 Class 4
OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Obesity OR for each class compared to Class 1 ref 1.06 0.80 1.41 1.10 0.82 1.48 1.55 1.19 2.02
Obesity OR for each class compared to Class 2 0.94 0.71 1.25 ref 1.03 0.82 1.30 1.46 1.21 1.76
Obesity OR for each class compared to Class 3 0.91 0.68 1.22 0.97 0.77 1.21 ref 1.41 1.15 1.74
Obesity OR for each class compared to Class 4 0.65 0.50 0.84 0.69 0.57 0.83 0.71 0.58 0.87 ref
Note: OR = Odds ratio; Odds ratios significantly different from the reference group are shown in bold font; covariates are: Minority
status range 0 = No, 1 = Yes; Child age range = 10 – 17 years; Child gender range = 1 Male, 2 Female

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Figure 2. Estimated Means for Parenting Stress and Family Meals per Week by Latent Class

Note: Class 1 = Rare meals, Single mothers with parenting stress, mental health issues and poverty; Class 2 = Meals most days,
average across indicators; Class 3 = Occasional family meals; Class 4 = Daily family meals, poor

Parenting Stress Meals per week
Latent Class Means
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Figure 3. Item response probabilities for stress indicators by parent latent class status

Note: Class 1 = Rare meals, Single mothers with parenting stress and mental health issues and poverty; Class 2 = Meals most days,
average across indicators; Class 3 = Occasional family meals; Class 4 = Daily family meals, poor
Maternal mental health issues Single parent Poverty
Item Response Probabilities
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CHAPTER 3: DIRECT AND INDIRECT EFFECTS OF PARENT STRESS ON CHILD
OBESITY RISK AND ADDED SUGAR INTAKE IN A SAMPLE OF SOUTHERN
CALIFORNIA ADOLESCENTS
Introduction
Study 2 used longitudinal panel data to test a mediational model of the effects of parents’
perceived stress on weight-related parenting practices and change in child added sugar intake and
waist circumference over one year. Studies have demonstrated that family stress and objective
stressors are associated with increased child obesity risk (Gundersen et al., 2008; Lohman et al.,
2009), but little research has investigated subjective, perceived parent psychosocial stress or
examined the mediating processes underlying the stress-obesity link. In addition, higher parent
perceived stress has been shown to be correlated with higher child BMI, increased child fast food
consumption, child feeding styles, and lower parent participation in physical activity
interventions (Hurley et al., 2008; Lytle et al., 2011; Parks et al., 2012; Urizar et al., 2005).
These results suggest that subjective, perceived stress may be an important influence on child
obesity risk, potentially by affecting child diet or physical activity behavior; yet mediated effects
are poorly understood. Further, BMI has often been used to define obesity in adolescents, but
waist circumference may actually be a better indicator of abdominal adiposity and risk for adult
metabolic syndrome (Li, Ford, Mokdad, & Cook, 2006; Spolidoro et al., 2013). To address these
gaps, study two used longitudinal panel data from parent-child dyads to test a mediated path
model of parent perceived psychosocial stress leading to fewer parent rules about child diet,
fewer positive family meal practices, and, in turn, increases in child dietary added sugar intake
and larger waist circumference over one year.
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Parent stressors and child obesity risk. Parents who experience a greater number of
objective stressors seem to have children at higher risk of obesity, yet research is unclear about
whether parents’ subjective experience of stress has the same effect. Food insecurity, parenting
stress, distress, and maternal stressors (such as partner violence and food insecurity) have been
shown to be associated with higher child obesity risk (Kozyrskyj et al., 2011; Lohman et al.,
2009; McPhie et al., 2011; Stenhammar et al., 2010; Suglia et al., 2012). Girls whose families
experience more cumulative social risks, such as intimate partner violence, maternal
drug/alcohol use, and housing insecurity, when girls were ages 1 and 3 years have been found to
be at higher risk of obesity at age 5 (Suglia et al., 2012). Yet few studies have assessed
subjective parent perceived psychosocial stress. Results from one cross-sectional study were that
parents’ perceived psychosocial stress was associated with 7% higher odds of obesity in children
aged 3 – 17 years, but the relationship became non-significant after adjusting for objective parent
stressors, parent anthropometrics, child demographics, and sleep quality (Parks et al., 2012).
Another cross-sectional study found that higher parent parenting stress – a specific type of
perceived stress associated with the responsibilities of parenting – was not associated with child
risk of obesity (Walton, Simpson, Darlington, & Haines, 2014). Conceptually, objective and
subjective measures of stress would be expected to show similar results. The lack of consistency
may be due to the limitations of cross-sectional study designs. Parent perceived stress could lead
to changes in parenting that accumulate over time, leading to subsequent – but not necessarily
concurrent – increased obesity risk for children. Second, prior research has used BMI as the
indicator of child obesity risk, but waist circumference may be a better indicator of abdominal
obesity (Li, Ford, Mokdad, & Cook, 2006; Spolidoro et al., 2013). The lack of longitudinal
research on the effects of parent perceived vs. objective stress or replication across measures of
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obesity does not lead to a clear conclusion about whether perceived stress has the same effect as
objective stressors or whether behavioral mediators may cause the effect.
Weight-related parenting practices. Other research suggests that parent stress is related
to weight-related parenting practices, which may link parent stress to child obesity risk. Parent
perceived stress has been shown to be associated with parent child-feeding styles, though effects
on feeding practices may not occur beyond the effects of parenting style, parent depression, and
parent anxiety (Hurley et al., 2008; Mitchell et al., 2009). Higher parenting stress has been
cross-sectionally associated with lower likelihood of limiting children’s TV viewing time and
children meeting physical activity guidelines (Walton et al., 2014). Regular family meals have
been associated with lower obesity risk as measured by BMI z-score (Hauser et al., 2014). One
study found that children tended to consume higher quantities of fatty foods when parents had
more rules about children’s eating and mealtime but fewer fatty foods when parents had more
rules for themselves, modeling more rules (Eisenberg et al., 2012). Other research on parental
restrictive rules indicates that rules are associated with lower child dietary intake of sugar and
sweet beverages, but studies are limited to cross-sectional designs (Gubbels et al., 2009; Liem,
Mars, & De Graaf, 2004; Verzeletti, Maes, Santinello, & Vereecken, 2010). Thus, conclusions
about temporality cannot be drawn.
To date, only one study has tested a statistical mediation model of stress leading to
changes in parenting practices and subsequent obesity, using a cross-sectional sample. In
separate models testing whether positive family meal practices mediated effects on BMI z-score,
they found that lack of family rules, time demands, and difficulty enforcing rules – which they
considered to be stressors – decreased positive family meal practices, leading to higher child
BMI (Lytle et al., 2011). However, the mediational model testing whether positive family meal
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practices mediated the effect of perceived parent stress on child BMI z-score did not find
evidence of significant mediation, although parent stress was correlated with higher child BMI.
This cross-sectional study did not test effects over time, and it tested only one mediator – family
meal practices. Further, effects on waist circumference may differ from those on BMI (Li et al.,
2006; Spolidoro et al., 2013). Although these results indicate that parent stress may affect child
health behavior indirectly via changes in other parenting practices such as rules, longitudinal
mediated effects remain largely unstudied. Further, mediated effects on child diet are unknown.
Parent rules seem to be associated with child fatty food consumption, yet the effects of parent
stress on other unhealthy food intake – such as sugar or sugar-sweetened beverages (Swinburn,
Caterson, Seidell, & James, 2004) – are not well understood.
Family Stress Model. An adapted version of Family Stress Model (FSM) was used to
organize previous research into a testable mediational model (see Figure 2). In FSM, economic
stressors activate the stress process, increasing parents’ emotional distress and affecting their
relationship, which disrupts parenting and subsequently impacts children’s adjustment, all within
the context of differing biological, psychological, and social resources and vulnerabilities
(Conger, Rueter, & Conger, 2000). Applying FSM to child obesity risk, McCurdy (2010)
hypothesized that poverty would lead to parent depression and change parenting behaviors such
as the use of active feeding practices or food management strategies. The current study makes
two adaptations to FSM. First, parent stress is substituted for parent depression, but the
concomitant downstream effects on parenting and child obesity risk remain the same. Second,
economic stress will not be modeled as a precursor to parent stress. Rather, a proxy
(free/reduced lunch) will be included as a covariate in the overall analysis of downstream effects
of parent stress on parenting, child diet, and obesity risk.
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Current study. While previous findings implicate parenting practices in the connection
between parent stress and child obesity risk, the mechanisms underlying this link are not well
understood. Some evidence indicates that lack of parent rules and few positive family meal
practices play a role in increasing child BMI (Lytle et al., 2011), but the finding has not been
replicated and the limits of cross-sectional tests of mediation do not allow firm conclusions about
causal relationships. Further, Lytle and colleagues’ study conceptualized lack of parent rules as
a stressor. However, parent perceived psychosocial stress may actually influence whether a
parent is able to maintain and enforce rules, instead of rules being stressors themselves. To test
these possibilities, Study 2 used longitudinal panel data from parent-child dyads to test a
mediational model of the effects of parent perceived stress on changes in child waist
circumference and added dietary sugar.
Specific Aims & Hypotheses
A path model was used to investigate mediated effects of parent stress on changes in
child waist circumference and child dietary added sugar intake over one year via parent rules
about child diet and positive family meal practices.
Aim 3: To examine longitudinal effects of parent stress on change in child waist circumference
and child added sugar intake over one year.
H1: Higher parent stress will predict greater increases in child waist circumference and
dietary added sugar intake over one year.
Aim 4: To examine indirect effects of parent stress on change in child waist circumference via
changes in parent rules about child diet and positive family meal practices.
H2: Higher parent stress will decrease parent rules about child diet and positive family
practices, leading to increased child waist circumference over one year.
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Aim 5: To examine indirect effects of parent stress on change in child dietary added sugar
intake via changes in parent rules about child diet and positive family meal practices.
H3: Higher parent stress will decrease parent rules about child diet and positive family
practices, leading to increased child dietary added sugar over one year.
Methods
Participants and recruitment. Data were from a larger 5-year, matched-control trial
called Healthy PLACES, investigating effects of the built environment on child obesity risk in a
smart growth community (Pentz, Dunton, Huh, & Thomas, 2010; Pentz, Dunton, Wolch, et al.,
2010). Families who were living in a smart-growth community called The Preserve in Chino,
California were targeted for recruitment, and a control group was recruited from communities
within a 30-minute drive of The Preserve who had similar demographic characteristics and
income levels. Recruitment procedures have been reported in detail elsewhere (Almanza, Jerrett,
Dunton, Seto, & Pentz, 2012; Dunton, Intille, Wolch, & Pentz, 2012; Dunton, Liao, et al., 2012;
Pentz, Dunton, Huh, & Thomas, 2010; Pentz, Dunton, Wolch, et al., 2010). Participant families
included one parent and one child, aged 8–14 years. To be included in the study, participants
had to: (a) have one child enrolled in grades 4 – 8, (b) live in Chino, CA or surrounding
communities with their child, (c) read English, and (d) have an annual household income <
$210,000. Data were collected either at a local community site or the participants’ homes. The
Institutional Review Board at the University of Southern California approved the study. Parents
gave written informed consent, and children gave minor assent. Participants were 610
parent/child dyads.
Study design. Baseline data were collected between March 2009 and December 2010,
during which no data collection occurred from late July through August or during January due to
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weather conditions limiting outside activity. The second wave of data collection occurred
between 6 and 12 months after baseline.
Measures
Parent perceived stress. A 4-item version of the Cohen Perceived Stress scale was used
to assess parent stress in the past 30 days (see Table 9). Two items were reverse-coded and a
sum of the 4 items was calculated to create an overall stress score. The scale has been widely
used and validated, and internal reliability was shown to be acceptable in this sample at baseline
(α = 0.73) (Cohen, Kamarck, & Mermelstein, 1983).
Parent rules about child eating. Three items from the Lack of Family Rules scale
assessed whether parents had rules about child eating, and communicated these rules and their
consequences to children (Lytle et al., 2011) (see Table 10). Items have been used successfully
in prior research (Hearst et al., 2012), and showed high internal reliability in this sample (Time 1
α = 0.84; Time 2 α = 0.85).
Positive family meal practices. Five items used in prior research were averaged to create
an overall score for frequency of positive family meal practices in the past 30 days (Gattshall,
Shoup, Marshall, Crane, & Estabrooks, 2008; Pentz, Mihalic, & Grotpeter, 1997) (see Table 11).
Higher scores indicated more frequent use of positive family meal practices. Internal reliability
was somewhat low (Time 1 α = 0.64; Time 2 α = .61).
Block kids dietary screener. The NutritionQuest Block Kids’ Dietary Screener was used
to assess child dietary intake (Hunsberger, O'Malley, Block, & Norris, 2012). The Screener has
been validated against a 24-hour recall method for use in adolescents for whole grain and
meat/fish/poultry consumption. However, it may provide lower estimates of added sugar and
saturated fats than 24-hour recall, perhaps due to asking about food consumption over different
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time periods (3 days vs. 7 days) and categorizing some items differently (e.g., all carbohydrates
in a sugary food as “added sugar” vs. only those carbohydrates from high-fructose corn syrup)
(Hunsberger et al., 2012). The Screener tends to underestimate kilocalorie intake due to the
abbreviated food list (Hunsberger et al., 2012). Data were screened and cleaned as follows.
Outliers of below 500 calories per day and above 8,000 calories could indicate error and were
flagged (Willett, 2012). Raw distributions were examined for any entries greater than or less
than 3 standard deviations from the mean. Means, standard deviations, skew and kurtosis were
calculated, and BMI was used to cross-validate in cases where children indicated consuming
greater than 3,000 calories per day. Kilocalorie distributions were examined by age, ethnicity,
BMI, and SES. Standard adjustments to dietary variables were made, which included dividing
by total daily kilocalorie intake. Thus, total added sugar was adjusted to reflect the number of
teaspoons of added sugars per 1,000 calories consumed [(total teaspoons of added sugar/total
daily kilocalories)*1000]. All added sugars were consistent with those in the USDA My Pyramid
Equivalents Database, which includes sugars from condiments like ketchup.
Child waist circumference. Child waist circumference was measured in duplicate using
a flexible tape measure. An average of the two measures was calculated. Waist circumference
has been highly correlated with Body Mass Index as an indicator of obesity and may be a better
indicator of child abdominal obesity and adult metabolic syndrome (Li et al., 2006; Spolidoro et
al., 2013).
Covariates. Child age, child gender, child ethnicity, free/reduced lunch status, parent
gender and parent anhedonia were screened as covariates; those significantly correlating (p <
.10) with study variables were included in the final model (see Table 12). Ethnicity was
collapsed into Hispanic vs. non-Hispanic due to a sizable proportion of Hispanic participants in
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the research population. Child age was calculated based on the child’s birth day, month, and
year. Older children may be less susceptible to the effects of parent stress as peers or media
begin to play a more influential role in dietary choices (Cullen, Baranowski, Rittenberry, &
Olvera, 2000). Free/reduced lunch status was used as a proxy measure of socio-economic status,
as income or education could affect parent stress or parenting behaviors, although findings are
mixed (Moens et al., 2009; Santiago, Zazpe, Cuervo, & Martinez, 2012). Evidence indicates that
child risk for obesity tends to mirror that of their same-sex parent (ex. sons mirror fathers),
suggesting the link has an environmental basis rather than genetic (Perez-Pastor et al., 2009).
Thus, parent gender was controlled for in the analysis. Anhedonia is a reduced or low ability to
experience positive affect and is a subcomponent of depression (Leventhal, 2012). Maternal
depression may increase children’s risk for obesity and could confound the relationship with
stress (Ramasubramanian, Lane, & Rahman, 2013; Topham et al., 2010). Parent emotion and
negative affectivity may affect the parent-child interaction during feeding of fruits and
vegetables (Hughes, Power, Fisher, Mueller, & Nicklas, 2005), and adult anhedonia has been
associated with quitting a weight-loss intervention program, less weight lost, binge eating,
uncontrolled eating, and emotional eating (Keranen, Rasinaho, Hakko, Savolainen, & Lindeman,
2010; Komulainen et al., 2011). Anhedonia has been shown to be inversely associated with
walking, moderate, and vigorous physical activity (Leventhal, 2012). Anhedonia was measured
using the positive affect subscale of the Center for Epidemiologic Studies Depression scale
(Radloff, 1991). Responses from the 4 items were reverse-coded and averaged for an overall
score.
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Data analysis
Study variables were examined for outliers, skew and kurtosis. Composite scores,
descriptive statistics, and Pearson product moment correlations were calculated. Using Mplus, a
path analysis was conducted using the Maximum Likelihood estimation method for deriving
parameter estimates, which relies on the assumption of multivariate normality of continuous
variables (Kline, 2011). Nonparametric bootstrapping was used (1000 iterations) to create an
empirical sampling distribution on which to base standard errors. The requirements for the N:q
rule (i.e., a ratio of 20:1 for the number of participants to the number of parameters estimated)
will be met (Kline, 2011). The ratio was approximately 40:1, with 610 participants and 15
parameters per model.
Fit statistics were used to assess the path model. Minimization of the fit function
compared the covariance matrix of the sample to that of the proposed model and were used to
examine adequacy of the model (Kline, 2011). Fit statistics assessed whether the discrepancy
between the sample covariance and the model covariance was more than expected by chance.
Multiple fit statistics were used to assess different aspects of fit. The Model test statistic (Model
Chi Square [χ
2
]) assessed model-data discrepancy. For this test, higher test statistics values and
lower, significant p values suggest that the model is worse than expected by chance. Thus, higher
p values indicate better fit. In addition to Model Chi Square, the following approximate fit
indices were used: (a) Bentler Comparative Fit Index (CFI), which assesses the model compared
to an independence model assuming zero covariances; (b) Steiger–Lind Root Mean Square Error
of Approximation (RMSEA), which adjusts for model complexity using degrees of freedom and
Model χ
2
; and (c) Standardized Root Mean Square Residual (SRMR), which is a statistic related
to correlation residuals. Criteria used to assess model fit were: Model χ
2
> 0.05; CFI ≥ 0.95;
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RMSEA ≤ 0.05 with the upper limit on the 90% Confidence Interval ≤ 0.10, and SRMR ≤ 0.08
(and individual correlation residuals ≤ |0.10|) (Kline, 2011).
The mediational model included two time points, which allowed the prior level of the
mediator to be adjusted for to reveal potential change over time (Cole & Maxwell, 2003). The
path model shown in Figure 5 was conducted in MPlus (Muthén & Muthén, 1998-2011) with
direct and indirect effects of parent stress as hypothesized and unanalyzed associations
(covariances) between parent variables at each time point, adjusting for baseline demographics
and outcomes. Hypothesis 1 was that parent stress would directly increase child waist
circumference and child added sugar intake. To test this hypothesis, parameter estimates and
significance values were examined for the direct effect of parent perceived stress on change in
child waist circumference (change score: T2 – T1; c’
1
) and change in child total teaspoons of
added sugar (change score: T2 – T1; c’
2
). Hypotheses 2 and 3 addressed mediated effects via
parenting practices (rules about child diet and positive family meal practices). To test
Hypothesis 2, the parameter estimate and significance value were examined for the indirect
effect of parent stress on change in child waist circumference through parent rules and family
meal practices at T2 (a
1
b
1
). To test Hypothesis 3, the indirect effect of parent stress on change in
child added teaspoons of sugar through parent rules and positive family meal practices at T2
(a
2
b
2
) were examined.
Results
Description of the study sample
Parents in the study sample were mostly female (81%), 39 yrs. old (SD = 6 years), 30%
had “some college” or less, and 51% were Hispanic (n = 599). Parents reported being stressed
“sometimes” within the last month (M = 9.20 ± 2.81, range = 4 – 18, α = 0.73), having positive
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family meal practices between “sometimes” and “frequently” (M = 2.34 ± 0.67), and being
somewhat above “neutral” in agreeing that they had rules about their children’s diet (M = 3.63 ±
0.84) (see Table 13). The sample of children was 53% male, 11 yrs. old (SD = 1.53), 42%
Hispanic, and 31% received free lunch. Children consumed a little more than 6 teaspoons of
added sugar per each 1,000 kilocalories consumed per day (SD = 3.67 tsp). This is less than
nationwide estimates of daily added sugar intake for this age group. The CDC estimated that
school-aged children consumed an average of 319 calories of added sugar daily [(girls 293 +
boys 345)/2 = 319 calories)], or about 19 teaspoons, in 2005-2008. The intake in the current
sample would be approximately 10.2 teaspoons for children who consumed an average daily
intake of 1,700 kilocalories. Parent stress (∆ T2 – T1 = 0.17), parent rules (∆ T2 – T1 = -0.07),
positive family meal practices (∆ T2 – T1 = 0.02) and child added sugar intake (∆ T2 – T1 = -
0.07) did not change significantly from Time 1 to Time 2 (ps > 0.05). Child waist circumference
at baseline was 77.78 inches (SD = 12.91; range 52.50 – 146.55), which is between national
2003-2004 estimates for children ages 6 – 11 (64.5 cm) and ages 12 - 17 (79.8 cm) (Li et al.,
2006). The sample had marginally higher rates of obesity at Time 2 (9.4%) compared to Time 1
(6.4%), χ2(1) = 3.32, p = 0.07, defining obesity as age and gender-adjusted waist circumferences
over the 95
th
percentile (Fryar, Gu, & Ogden, 2012).
Preliminary analyses
One hundred thirty seven children were considered lost-to-follow-up. Attrition analysis
showed that those retained vs. lost-to-follow-up did not differ significantly on demographic
characteristics, parenting practices, covariates or outcome variables (all ps > 0.05). Bivariate
correlations at baseline (Time 1) indicated that parents with higher stress tended to have fewer
rules about child eating (r = -0.11, p < 0.01), fewer positive family meal practices (r = -0.09, p <
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0.05), and children with larger waist circumferences at baseline (r = 0.09, p < 0.05) but not
greater changes in child waist circumference from Time 1 to Time 2 (r = .06, ns) (see Table 14).
At baseline, higher parent rules about diet were correlated with greater positive family meal
practices (r = 0.31, p < 0.001) and fewer added teaspoons of child dietary sugar intake (r = -0.16,
p < 0.01). However, child teaspoons of added sugar were not significantly associated with child
waist circumference at baseline (r = -0.05) or change in waist circumference over one later (Time
2) (r = 0.08).
Model results for direct and indirect effects of perceived stress on parenting practices and
change in child waist circumference and added sugar intake
Results of path models are shown in Figures 6 (cross-sectional) and 7 (longitudinal).
Both the cross-sectional and longitudinal models fit the data well, with the exception of the chi-
square statistic, which was expected to be large due to the high number of observations. The
cross-sectional model was a saturated model and, thus, had the highest possible fit χ2(0) = 0.00,
p = 0.00; CFI = 1.00; RMSEA = 0.00, 95% CI [0.00, 0.00]; SRMR = 0.00, n = 559. The
longitudinal model was not saturated due to controlling for baseline levels of the mediators and
outcomes (which are not displayed in the figure) and had a good fit, χ2(12) = 19.99, p = 0.05;
CFI = 0.98; RMSEA = 0.04, 95% CI = [0.00, 0.07]; SRMR = 0.02, n = 385.
Contrary to Hypothesis 1, parent stress at baseline did not significantly predict change in
child waist circumference (cꞌ
1
path β = -0.03, ns) or child added sugar intake (cꞌ
2
path β = 0.06,
ns) over one year, nor were cross-sectional relationships significant (waist: cꞌ
1
path β = .05, ns;
added sugar: cꞌ
2
path β = 0.06, ns). Contrary to Hypothesis 2, the indirect effects on child waist
circumference were not significant through parent rules, longitudinally (indirect effect = 0.00, ns)
or cross-sectionally (indirect effect = 0.00, ns); nor through positive family meal practices
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longitudinally (indirect effect = 0.00, ns) or cross-sectionally (indirect effect: 0.00, ns). Contrary
to Hypothesis 3, the indirect effects on child added sugar intake were not significant through
parent rules, longitudinally (indirect effect = -0.01, ns) or cross-sectionally (indirect effect =
0.01, ns); nor through positive family meal practices longitudinally (indirect effect = 0.01, ns) or
cross-sectionally (indirect effect = 0.00, ns). However, parent rules did significantly predict
lower child added sugar intake both longitudinally (b
3
path β = -0.12, p =.021) and cross-
sectionally (b
3
path β = -0.16, p < .001). The unanalyzed correlation between parent rules and
positive family meal practices was significant and positive in the cross-sectional (r = .29, p < .01)
and longitudinal models (r = .31, p < .01). For covariates within the models (not shown in
figures), older children had less change in waist circumference over the year (β = -.13, p = .02)
and higher concurrent waist circumference (β = .41, p < .001), as expected. Larger concurrent
child waist circumference was associated with living in the smartgrowth community (β = .09, p =
.03) and with receiving free lunch (β = .14, p = .001).
Post-hoc analysis by parent stress group
A follow-up multiple group analysis was conducted in EQS to examine whether parent
stress level moderated the relationship between weight-related parenting and outcomes (child
waist and added sugar) (see Figure 8). Parents were grouped into high, medium and low levels
of parent stress, defined relative to the sample. “High stress” parents were those scoring greater
than 1 Standard Deviation (SD = 2.81) above the mean (M = 9.20; high stress n = 112),
“Medium stress” parents had scores between ±1SD (n = 422), and “Low stress” parents had
scores lower than 1SD below the mean (n = 64). Preliminary analysis on these stratified
subgroups suggested that higher parent rules were associated with lower waist circumference for
parents with high stress (B = -.35, p = 0.03) but not for those with moderate (B = -.06, ns) or low
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stress (B = -.16, ns) (complete model results not reported). However, the multiple group tests of
this, and other, potential moderated pathways did not reach significance across the three levels of
parent stress. The small sample size of the high-stress group (n = 64) may have decreased power
to detect significant effects.
Results of post-hoc boostrapping analysis for cross-sectional direct and indirect effects of
parenting stress on child waist and added sugar intake separately
An additional follow-up analysis was conducted examining each outcome separately, to
examine how power may have limited the ability to detect effects. A bootstrapping resampling
procedure was used to estimate the direct and indirect effects of parent stress on each outcome.
Bootstrapping resamples from the dataset thousands of times and calculates the indirect effect
and standard error for each sampling (Preacher & Hayes, 2008). Tables 15 and 16 show results
of testing effects with and without covariates for each parenting practice separately as well as
with both parenting practices (i.e., mediators) in the same model.
Results of the multiple mediator model (Table 15, Model 3) for child waist indicated that
parent perceived stress was associated with fewer weight-related parenting practices (i.e., parent
rules about child diet a path = -.03, SE = 0.01, p = 0.01; positive family meal practices a path = -
0.02, SE = 0.01, p = 0.03) and higher child waist circumference (c’ path = 0.43, SE = 0.18, p =
0.02), but mediated effects were not significant due to non-significant b paths for the parenting
practices (i.e, parent rules b path = -0.41, SE = 0.64, ns; positive family meal practices b path =
0.22, SE = 0.81, ns). These effects became non-significant after controlling for covariates (Table
15, Model 6). Effects for the multiple mediator model for child added sugar intake indicated that
parent perceived stress was associated with fewer parent rules about child eating (a path = -0.03,
SE = 0.01, p = 0.01) and marginally with positive family meal practices (a path = -0.02, SE =
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0.01, p = 0.06) (Table 16, Model 3). The direct effect of parent perceived stress on child added
sugar intake was not significant (c’ path = 0.03, SE = 0.06, ns); however, the indirect effect
through parent rules was significant at the p = 0.05 alpha level, indirect effect = 0.02, SE = 0.01,
95% CI = [0.01, 0.05]. However, controlling for covariates rendered these relationships non-
significant, except for the relationship between parent rules and added sugar intake (b path = -
0.72, SE = 0.20, p < 0.01) (Table 16, Model 6). Overall, higher perceived stress was associated
with fewer parent rules about child diet.
Discussion
This study tested the effects of parent perceived stress on changes in child waist
circumference and added sugar intake over one year, mediated by parent rules about child diet
and positive family meal practices. Higher parent stress was cross-sectionally correlated with
fewer rules, fewer positive family meal practices, and higher child waist circumference.
However, in contrast to hypotheses, model results indicated that higher parent stress did not
indirectly affect child waist circumference or added sugar intake through parent rules or family
meal practices. Yet, parent rules about child diet did predict lower child intake of added sugars
both concurrently and over one year. Taken together, these findings suggest that parent
perceived psychosocial stress may be related to weight-related parenting practices and child
obesity risk, but future research with larger samples is needed to test multiple weight-related
parenting practices as well as potential feedback loops, such as obesity leading to parent stress or
to changes in parenting practices.
Bivariate correlations showed that higher parent stress was related to both parenting
practices, which fits with previous work indicating that parent stress is associated with physical
activity parenting, and it extends the investigation to diet-related parenting (Walton et al., 2014).
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Stress could disrupt weight-related parenting practices during the days or months when parents
are under stress. However, the relationship became non-significant in the model. One potential
explanation is low statistical power. Supporting this possibility, the direction and magnitude of
the relationships between parent perceived stress and parenting practices in the cross-sectional
model were similar to bivariate correlations. In addition, follow-up bootstrap analysis showed
that higher perceived parent stress predicted fewer parent rules about child diet. Alternative
study designs may boost power. For example, parent perceived stress may affect parenting
practices within the day, which could be detected using ecological momentary assessment
techniques. Additionally, larger sample sizes may help examine complex effects among stress,
parenting, child diet and child obesity risk.
In the present study, higher parent stress was also correlated with higher child waist
circumference, and it predicted higher child waist circumference in cross-sectional bootstrapping
analysis, before adjusting for covariates. This finding contrasts with previous work that has not
found cross-sectional associations between perceived parent stress and child BMI (Parks et al.,
2012; Walton et al., 2014). One difference is that waist circumference could be a more sensitive
indicator of abdominal adiposity than BMI (Li, Ford, Mokdad, & Cook, 2006). However,
similar to previous work, this relationship did not remain significant within the model, and
caution should be used interpreting the correlations. While statistical power could be a limiting
factor, the fact that three studies including the present one have found a non-significant
association between parent perceived stress and concurrent child obesity risk suggests that
objective stressors and perceived stress operate differently on child obesity risk. One recent
review indicated that chronic but not acute stress experienced by children was associated with
higher child obesity risk (Wilson & Sato, 2014). Although that review examined child and not
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parent stress, the distinction between acute and chronic stress may important for the present
results. Stress that parents experienced in the last month may not have affected children’s
obesity risk yet. Further, the effects of parents’ stress last month may dissipate too quickly to
affect children’s obesity risk over one year. However, parent perceived stress that accumulates
chronically over that year may indeed affect weight-related practices during that time and
ultimately heighten child obesity risk. Future research that examines effects of chronic parent
perceived stress is needed to fully understand differences between acute and chronic perceived
stress on weight-related parenting and child obesity risk.
This study also extends prior research by examining longitudinal effects of parent rules.
While previous studies indicate an association between higher parent rules and lower child sugar
consumption (Gubbels et al., 2009; Liem et al., 2004; Verzeletti et al., 2010), cross-sectional
studies cannot untangle whether parents create rules because their children consume too many
added sugars or whether parent rules cause children to consume less sugar. The current
longitudinal findings shed light on this question, suggesting that parent rules lead to lower
subsequent sugar consumption, but not abdominal obesity, one year later.
Additionally, this study contributes to the discussion on the effects of parent dietary rules,
where prior research reveals counterintuitive, conflicting results. While rules predict lower
consumption of added sugar and sugary beverages, dietary restrictions are also associated with
child weight gain and overweight (Clark, Goyder, Bissell, Blank, & Peters, 2007; Hauser et al.,
2014). This is counterintuitive because lower sugar intake would be expected to lead to lower
obesity risk, not higher. However, restrictive rules may curb present consumption of sweets but
actually increase taste preferences for sweetness (Liem et al., 2004) or hinder children’s ability to
develop self-regulatory control over eating behavior, at least in girls (Birch & Fisher, 2000). Yet
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a second conflicting finding is that at least one study has found that parent rules actually lower
the risk of overweight (Lytle et al., 2011). Results from the current study support previous
findings that parent rules can lower sugar consumption but do not indicate that higher
rules/restrictions either increase or decrease obesity risk.
Different measures of parent rules used in previous research may be one reason why
some studies find that parent rules increase obesity risk, others find decreased risk, and still
others find no relationship (such as the present study). The measure used in the current study
assessed the presence of rules that were clearly explained to children along with the
consequences of not following rules, but it did not gauge specific rules or the
motivations/intentions behind them. In contrast, the bulk of experimental and longitudinal
evidence implicating maternal restriction of palatable foods in child weight gain comes from the
Restriction subscale of the Child Feeding Questionnaire (Birch et al., 2001; Clark et al., 2007).
Items on that scale include “I intentionally keep some foods out of my child's reach”, “If I did
not guide or regulate my child's eating, she would eat too much of her favorite foods” and “I
have to be sure that my child does not eat too many high-fat foods.” The Restriction subscale
seems to capture specific cognitions relating to a perceived need to control the child’s impulses
and access to food. In contrast, the measure of rules used in the present study did not assess
specific rules or cognitions. Thus, the difference between previous research and the current null
finding is likely due to different measures of parent rules.
The study addressed two gaps in the literature. First, the effects of subjective, perceived
stress on parenting practices were examined in contrast to previous work which has focused only
on “objective” stressors, with two exceptions (Bauer, Hearst, Escoto, Berge, & Neumark-
Sztainer, 2012; Walton et al., 2014). Findings were mixed, depending on the analysis. Overall,
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parent perceived stress was correlated with parent rules and higher concurrent child obesity, but
the association with obesity became non-significant after adjusting for covariates. Other effect
sizes were small and tended to become non-significant when accounting for other strong
influences on obesity, such as free lunch status, ethnicity, and living in a smarthgrowth
community. Also, parenting practices did not mediate the effect on obesity. Previous research
has shown that circumstances such as poverty or difficult situations such as divorce may reflect
difficult family settings that undergird health disparities in obesity. Thus, it was important to
investigate whether perceived stress – which would be assumed to co-vary with stressors –
showed the same relationship. However, results from this study suggest that parents’ felt lack of
control and worry over insufficient resources to confront life’s challenges – stress – tends to be
related to having heavier children but not above and beyond the effect of objective stressors.
Overall findings indicate that parent perceived stress does not increase child obesity risk in the
way that enduring objective stressors does.
Second, the study sought to elucidate the processes by which perceived parent stress
could affect obesity risk and child added sugar intake, specifically through parent rules and
positive family meal practices. In this sample, parents experienced stress occasionally in the last
month, which was related to fewer parent rules and positive family meal practices, but the effects
were small and became non-significant when adjusting for other influences on parenting
practices, such as child age, gender, or parent anhedonia. Given the small effect sizes, statistical
power may have limited the ability to detect effects. The timing of measures may be another
explanation for the lack of significant findings. The measure of perceived stress was
retrospective over the past month, and parenting practices were static indicators of whether
parents had any rules or tended to have regular meals. However, the effects of perceived stress
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may operate on a much more proximal scale. A particularly stressful day could affect a mother’s
likelihood of purchasing convenience foods that day or playing soccer with children after school,
even if a mother’s average level of stress does not affect overall weight-related parenting. Thus,
studies are needed to assess within-daily effects of perceived and objective stressors on child
weight-related behaviors and accumulated obesity risk over time. In sum, parent perceived stress
may have a small effect on weight-related parenting practices, but findings were inconclusive,
and future research with larger sample sizes and more proximate measures is needed.
Limitations
The current study assessed rules and family meal practices, but other parenting practices
not measured here may be correlated with parent stress or have stronger effects on child obesity
risk. For example, understanding specific rules about how or what the child eats may help
uncover which rules actually lead to changes in child obesity risk, suggesting a moderating,
rather than mediating, effect of parenting practices. Positive family meal practices had low
internal reliability in this sample, suggesting that an alternate measure may better capture these
practices in similar samples. Physical activity parenting was not measured in the current study
but could be affected by parent stress and accumulate into changes in waist circumference over
time. Other facets of weight-related parenting, such as Restrictive feeding practices, may also
interact with parent perceived stress to influence child weight. A second limitation is the time
frame, which was over one year. Effects of parent stress on parenting practices and child health
behavior could occur within very short time frames – perhaps even within the day. Yet, the time
frame of the present study would mask those effects. Also, the time frame may be too short to
capture changes in waist circumference. Third, while this study contributes to our understanding
of perceived stress, objective stressors were not studied. Hence, direct comparisons between
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effects of perceived vs. objective stressors – or how they interact – could not be investigated.
Further research is needed that contextualizes perceived stress within the pattern of objective
stressors and examines potential effects on parenting practices and child dietary intake. Fourth,
the study population was not nationally representative, and findings may have been influenced
by more highly educated parents or by features of the southern California environment. Finally,
methodological limitations such as a lack of power may have contributed to the non-significant
overall model, suggesting that studies with larger samples or repeated measures on within-daily
effects may be needed to understand these relationships.
Conclusion
Overall, parent perceived psychosocial stress did not predict parenting practices one year
later or changes in child waist circumference or added sugar intake over that year. Parents who
had specific rules about what children were allowed to eat and communicated those rules clearly
tended to have children who consumed fewer total added dietary sugars. Yet these rules were
unrelated to concurrent child obesity risk. These findings contribute to our understanding of the
effects of parent rules, replicating previous work that they can lower added sugar intake, but
suggesting that merely having any rules – not specific rules about favorite or high-fat foods –
may not affect subsequent weight gain. These results add to the growing discussion about
whether parent rules increase, decrease, or have no effect on concurrent and subsequent child
obesity risk. Future longitudinal research is needed examining effects of specific rules, other
parenting practices or potential mediating influences that might accumulate as daily influences
over longer periods of time. Further investigation is required to understand whether parent
biological stress indicators, such as cortisol or alpha-amylase, explain the link to obesity; yet no
research, to date, has investigated this topic. Potentially, objective stressors such as poverty or
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inter-partner violence heighten parents’ biological stress responses or change their behavior in
obesogenic ways, but the effect does not consistently occur with parents’ subjective perception
of a stressful experience.
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Table 9. Parent Perceived Stress

Description Response options
In the last month, how often have you felt that you
were unable to control the important things in your
life?
1=Never
2=Almost Never
3=Sometimes
4=Fairly Often
5=Very Often
In the last month, how often have you felt confident
about your ability to handle your personal problems?
1=Never
2=Almost Never
3=Sometimes
4=Fairly Often
5=Very Often
In the last month, how often have you felt that things
were going your way?
1=Never
2=Almost Never
3=Sometimes
4=Fairly Often
5=Very Often
How often have you felt difficulties were piling up so
high that you could not overcome them?
1=Never
2=Almost Never
3=Sometimes
4=Fairly Often
5=Very Often
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Table 10. Parent Rules about Child Eating

Item Response options
I have clear and specific rules about the
kinds of food my child eats
1=strongly disagree
2 disagree 3=Neutral/mixed
4=agree
5=strongly agree
I have explained to my child rules
concerning what he/she eats
1=strongly disagree
2 disagree 3=Neutral/mixed
4=agree
5=strongly agree
I have explained to my child the
consequences of not following my rules
concerning what he/she eats.
1=strongly disagree
2 disagree 3=Neutral/mixed
4=agree
5=strongly agree
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Table 11. Positive Family Meal Practices

Item Response options
In the past 30 days, how often did you prepare
meals with your child?
0=Never
1=Rarely
2=Sometimes
3=Frequently
4=Always
In the past 30 days, how often did you plan
meals/menus with your child?
0=Never
1=Rarely
2=Sometimes
3=Frequently
4=Always
In the past 30 days, how often did you eat
breakfast with your child?
0=Never
1=Rarely
2=Sometimes
3=Frequently
4=Always
In the past 30 days, how often did you have
regularly scheduled meals and snacks with your
family?
0=Never
1=Rarely
2=Sometimes
3=Frequently
4=Always
In the past 30 days, how often did you sit down
for dinner?
0=Never
1=Rarely
2=Sometimes
3=Frequently
4=Always
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Table 12. Demographics and Covariates

Description Response options
Child
What is your birthday (month)? Number
What is your birthday (day)? Number
What is your birthday (year)? Number
Are you a…? 1=boy 2=girl
Are you…? 1=White
2=Black or African-American
3=Hispanic/Latino 4=Asian
5=Mixed/Bi-racial 6=Other
Do you get free lunch at school? 1=yes
0=no
2=Don't know
Parent
What is your sex? 1=female 2=male
Anhedonia
I felt that I was just as good as
other people.
1=Rarely or none of the time (0-1 days)
2=Some or a little of the time (2-3 days)
3=Occasionally or a moderate amount of the time (4-5
days)
4=Most or all of the time (6-7 days)
I felt hopeful about the future.
I was happy.
I enjoyed life.
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Table 13. Descriptive Statistics for Study Variables

Description N Mean SD Min. Max.
Parent perceived stress T1 597 9.20 2.81 4.00 18.00
Parent perceived stress T2 437 9.38 2.85 4.00 19.00
Parent rules about child diet T1 596 3.63 0.84 1.00 5.00
Parent rules about child diet T2 448 3.60 0.85 1.00 5.00
Parent rules about child diet ∆ 446 -0.07 0.81 -3.00 2.67
Positive family meal pract. T1 596 2.34 0.67 0.00 4.00
Positive family meal pract. T2 437 2.38 0.63 0.40 4.00
Positive family meal pract. ∆ 436 0.02 0.65 -2.00 3.20
Child waist circumference T1 595 74.86 12.55 51.10 140.60
Child waist circumference T2 457 77.78 12.91 52.50 146.55
Child waist circumference ∆ 455 2.97 7.49 -25.25 81.55
Child dietary added sugar tsp. T1 539 6.20 3.67 0.31 25.20
Child dietary added sugar tsp. T2 398 6.10 3.77 0.45 28.24
Child dietary added sugar tsp. ∆ 391 -0.07 4.47 -17.72 20.85
Child age (yrs) 595 11.29 1.53 7.75 15.01
Parent age (yrs) 596 39.20 6.00 23.00 62.00
Parent commuting distance (miles) 509 39.09 38.76 0.00 300.00
Parent anhedonia 594 1.55 0.64 1.00 4.00
Note: Parent perceived stress range = 4 low, 20, high; Parents have rules about child diet range =
1 strongly disagree, 5 strongly agree; Frequency of positive family meal practices range = 0
Never, 4 always; Parent anhedonia range = 1 rarely or none of the time, 4 most or all of the time

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Table 14. Bivariate Correlations for Study 2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1. Parent perceived stress T1 1

2. Parent rules about diet T1 -.111
**
1

3. Parent rules about diet T2 .037 .532
**
1

4. Positive family meal pract. T1 -.088
*
.307
**
.189
**
1

5. Positive family meal pract. T2 -.122
*
.234
**
.337
**
.493
**
1

6. Child added sugar (tsp./adj) .045 -.158
**
-.214
**
-.051 -.135
**
1

7. ∆ Child added sugar (T2-T1) .020 .000 .018 -.046 .004 -.579
**
1

8. Child waist circ. (T1 .093
*
-.035 .048 -.005 -.089 -.052 .041 1

9. ∆ Child waist circ. (T2-T1) -.056 .055 -.015 .026 .017 .076 -.041 -.222
**
1

10. Child age (years) .075 -.097
*
-.042 -.020 -.116
*
-.062 .049 .422
**
-.175
**
1

11. Child gender^ -.013 .095
*
.055 .110
**
-.002 -.008 .072 -.101
*
-.025 -.059 1

12. Child ethnicity (Hispanic) -.025 .018 .038 .004 .013 -.006 .085 .151
**
-.023 .130
**
-.014 1

13. Child free/reduced lunch .116
**
.039 .103
*
-.015 .063 .035 .066 .213
**
-.019 .142
**
-.006 .298
**
1

14. Parent anhedonia .537
**
-.112
**
.049 -.138
**
-.021 -.036 .039 .110
**
-.024 .100
*
-.046 .010 .210
**
1

15. Parent gender^ -.063 -.068 -.069 -.041 .035 .085
*
-.042 .016 .034 -.075 -.015 -.014 -.122
**
-.030 1

16. Group (Preserve vs. control) -.052 .006 .030 .035 -.041 -.055 -.066 .001 -.019 -.128
**
.008 -.169
**
-.123
**
-.051 .109
**
1
^Note: Child gender 1 = boy, 2 = girl; Parent gender 1 = female, 2 = male; T1 = time one; T2 = time two
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

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Table 15. Cross-sectional relationships between parent perceived stress and child waist circumference mediated by parenting practices, separately
and together, using bootstrapping

a b c c' indirect CI
Coeff SE p Coeff SE p Coeff SE p Coeff SE p Boot SE L U
Model 1 Parent rules about child eating -0.03 0.01 0.01* -0.35 0.61 0.56 0.44 0.18 0.02* 0.43 0.18 0.02* 0.01 0.03 -0.03 0.08
Model 2 Positive family meal practices -0.02 0.01 0.03* 0.06 0.77 0.93 0.44 0.18 0.02* 0.45 0.18 0.02* 0.00 0.02 -0.05 0.03
Model 3
Parent rules about child eating -0.03 0.01 0.01* -0.41 0.64 0.53
0.44 0.18 0.02* 0.43 0.18 0.02*
0.01 0.03 -0.03 0.10
Positive family meal practices -0.02 0.01 0.03* 0.22 0.81 0.79 0.00 0.02 -0.06 0.03
Models with covariates
Model 4 Parent rules about child eating -0.02 0.02 0.11 0.08 0.57 0.89 0.28 0.20 0.18 0.28 0.20 0.17 0.00 0.02 -0.05 0.04
Model 5 Positive family meal practices -0.01 0.01 0.28 0.63 0.73 0.39 0.28 0.20 0.18 0.28 0.20 0.16 -0.01 0.02 -0.07 0.01
Model 6
Parent rules about child eating -0.02 0.02 0.11 -0.07 0.60 0.90
0.28 0.20 0.18 0.28 0.20 0.17
0.00 0.02 -0.03 0.06
Positive family meal practices -0.01 0.01 0.28 0.65 0.76 0.39 -0.01 0.02 -0.08 0.01
Note: Models 1 -3 include only independent variables, mediators, and outcome (n = 593); Models 4 - 6 include covariates: child age, gender, ethnicity, free lunch, intervention
group, and parent anhedonia (n = 555); significant p values are indicated by bold font and asterisk (*); Coeff = coefficient; SE = standard error; p = p value; indirect = indirect
effect; Boot = Bootstrapped estimate; CI = Confidence Interval

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Table 16. Cross-sectional relationships between parent perceived stress and child added sugar intake circumference mediated by parenting practices,
separately and together, using bootstrapping

a b c c' indirect CI

Coeff SE p Coeff SE p Coeff SE p Coeff SE p Boot SE L U
Model 1 Parent rules about child eating -0.03 0.01 0.01* -0.67 0.19 0.00* 0.06 0.06 0.32 0.03 0.06 0.56 0.02 0.01 0.01* 0.05*
Model 2 Positive family meal practices -0.02 0.01 0.06 -0.26 0.24 0.27 0.06 0.06 0.32 0.05 0.06 0.37 0.00 0.01 0.00 0.02
Model 3
Parent rules about child eating -0.03 0.01 0.01* -0.67 0.20 0.00*
0.06 0.06 0.32 0.03 0.06 0.57
0.02 0.01 0.01* 0.05*
Positive family meal practices -0.02 0.01 0.06 -0.01 0.25 0.95 0.00 0.01 -0.01 0.01
Models with covariates

Model 4 Parent rules about child eating -0.03 0.02 0.07 -0.75 0.19 0.00* 0.09 0.07 0.18 0.07 0.07 0.30 0.02 0.01 0.00 0.06
Model 5 Positive family meal practices -0.01 0.01 0.35 -0.39 0.25 0.11 0.09 0.07 0.18 0.09 0.07 0.20 0.00 0.01 0.00 0.03
Model 6
Parent rules about child eating -0.03 0.02 0.07 -0.72 0.20 0.00*
0.09 0.07 0.18 0.07 0.07 0.31
0.02 0.01 0.00 0.06
Positive family meal practices -0.01 0.01 0.35 -0.13 0.25 0.60 0.00 0.01 0.00 0.02
Note: Models 1 -3 include only independent variables, mediators, and outcome (n = 538); Models 4 - 6 include covariates: child age, gender, ethnicity, free lunch, intervention
group, and parent anhedonia (n = 505); significant p values are indicated by bold font and asterisk (*); Coeff = coefficient; SE = standard error; p = p value; indirect = indirect
effect; Boot = Bootstrapped estimate; CI = Confidence Interval
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Original

Adapted

Figure 4. Family Stress Model: Original and Adapted

Economic
pressure
Parents’
emotional
distress
Marital
conflict/in
stability
Disrupted
parenting
Adolescent
maladjustment
Economic
hardship
Biological, Psychological, and Social Resources and Vulnerabilities
Economic
pressure
Parents’
emotional
distress
Marital
conflict/in
stability
Disrupted
parenting
Adolescent
maladjustment
Economic
hardship
Biological, Psychological, and Social Resources and Vulnerabilities
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Note: Control variables include baseline child waist circumference, child total tsp. added sugar, parent rules about
child diet, positive family meal practices, child gender, child age, child ethnicity, free lunch status, parent anhedonia,
and group (Preserve vs. Control).

Figure 5. Path Model for Direct and Indirect Effects of Parent Perceived Stress on Change in
Child Waist Circumference and Change in Child Added Sugar Intake

b
2

b
1

c’
2

Parent Rules
about Child
Diet T2
a
2

∆ Child total
tsp. added sugar
(T2 – T1)
Parent PS T1
∆ Child Waist
circumference
(T2 – T1)
c’
1

Positive
Family Meal
Practices T2
a
1

b
3

b
4

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χ
2
(0) = 0.00, p = .00
CFI = 1.00
RMSEA = .00, 95% CI: .00, .00
SRMR = .00
N = 559
Child total tsp.
added sugar
T1
Parent Rules
about Child
Diet T1
Parent Stress
T1
Child Waist
circumference
T1
Positive Family
Meal Practices
T1
.05
.00
.04
.06
-
-.03
-.08
-.06
-.02
.29**
Note: Model adjusts for child sex, age, ethnicity, free/reduced lunch, group (Preserve vs.
control), parent gender and parent anhedonia
*p < 0.05
** p < 0.01
*** p < 0.001
Figure 6. Cross-sectional path model with parent stress at baseline predicting parenting practices and child waist circumference and
added sugar intake
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χ
2
(12) =19.99, p = .06
CFI = .98
RMSEA = .04, 95% CI: .00, .07
SRMR = .02
N = 385
∆ Child total
tsp. added sugar
(T2 – T1)
Parent Rules
about Child
Diet T2
Parent Stress
T1
∆ Child Waist
circumference
(T2 – T1)
-.01
Positive Family
Meal Practices
T2
-.03
-.02
.06
-.12*
-.05
.05
-.10
.02
.31**
Note: Model adjusts for child sex, age, ethnicity, free/reduced lunch, group (Preserve vs.
control), parent gender, parent anhedonia, and baseline levels of outcome and mediator
variables
* p < 0.05
** p < 0.01
*** p < 0.001

Figure 7. Path model with parent stress at baseline predicting parenting practices and change in child waist circumference and added
sugar intake one year later
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Note: Control variables include baseline child waist circumference, child total tsp. added sugar, parent rules about
child diet, positive family meal practices, child gender, child age, child ethnicity, free lunch status, and parent
anhedonia.

Figure 8. Path Model for Moderated Effects of Parent Perceived Stress on Child Waist
Circumference and Added Sugar Intake by Parent Stress Level
Parent Rules
about Child
Diet T2
Child total tsp.
added sugar
T2
Child Waist
circumference
T2
Positive Family
Meal Practices
T2
Moderator:
Parent Stress
(High/Med/Lo

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CHAPTER 4: A POSITIVE DEVIANCE-BASED QUALITATIVE STUDY OF LOW-
INCOME MOTHERS WHOSE CHILDREN MEET FEDERAL GUIDELINES FOR
FRUIT AND VEGETABLE INTAKE TO IMPROVE OBESITY PREVENTION
PROGRAM DESIGN
Introduction
Parents are thought to influence children’s development of long-term obesity risk health
behaviors; yet, parent-based obesity prevention programs have had only modest success (Faith et
al., 2012; Kitzman-Ulrich et al., 2010). Stress may impair parents’ weight-related parenting
practices such as regular family meals, potentially disrupting children’s development of healthy
eating habits, such as acquiring taste preferences for healthy foods that may require repeated
exposures, like vegetables (Anzman-Frasca, Savage, Marini, Fisher, & Birch, 2012). Thus,
understanding how stress and coping affect weight-related parenting may suggest avenues for
improving obesity prevention program success. “Positive deviance” (PD) is an approach to
changing community behavior that rests on community empowerment to identify, investigate,
and promulgate successful strategies (Pascale, Sternin, & Sternin, 2010). A PD approach may be
an innovative tool for discovering how mothers of healthy weight children cope with stressors
and engage in health-promoting parenting practices.
Key to PD is locating people within a community who are using strategies to overcome
typical risk factors – who use uncommon strategies to confront common problems – to address
complex problems by discovering the local, sustainable solutions already in use by a resourceful
few (Marsh, Schroeder, Dearden, Sternin, & Sternin, 2004). PD has been used successfully to
combat malnutrition in developing countries as well as to ameliorate a host of other complex
behavioral issues (for a comprehensive review see Pascale et al., 2010). Research on PD and
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child malnutrition has shown significant improvement in child feeding behavior and child
nutritional status using locally-sustainable solutions (Zeitlin et al., 1990; Machado, Cotta, &
Silva, 2014). Emerging PD research in the US suggests that parents of children who were obese
and then lost weight use strategies such as implementing family-level changes and creativity
overcoming resistance (Shirifi et al., 2014). Yet no research has taken a PD approach to
investigating how coping with stress may play a role in promoting positive feeding practices
among high-risk families who are able to maintain health-promoting feeding practices and have
healthy weight children.
Therefore, the first purpose of Study 3 was to identify parents from a convenience sample
of high-risk families who most closely matched the profile of “positive deviant” (PD), defined as
having children of healthy weight (BMI percentile between 5
th
and 85
th
percentile) who met
federal guidelines for fruit and vegetable intake (4 – 5 servings per day) (Centers for Disease
Control, 2011; Ogden et al. 2010). The second purpose was to discover whether stress and
coping differed between mothers in PD families and mothers in families with overweight/obese
children. Thus, a comparison group of families with overweight/obese children was recruited.
Study 3 involved 24 interviews with low-income mothers, primarily Hispanic, living near the
downtown Los Angeles area.
Health disparities in obesity risk. Nationally-representative evidence suggests that low-
income, ethnic minority children living in the United States are at higher risk for obesity
(Lutfiyya, Garcia, Dankwa, Young, & Lipsky, 2008). Of Hispanic children aged 10 – 17 years
in the US, about 22% are estimated to be obese, compared to 23% of African-Americans and
12% of Whites (NSCH, 2011/12). Obesity prevalence for Hispanic children in California is
similar to that nationwide (21%). Income status seems to be directly, linearly related to higher
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obesity risk. About 27% of children aged 10 – 17 years who live at ≤ 99% of FPL are estimated
to be obese, compared to 20% at 100 – 199% FPL, 14% at 200 – 399%, and 9% at ≥400 FPL
(NSCH, 2011/12). California’s rates closely mirror those nationwide (29%, 16%, 14% and 7%;
though the estimate of 16% for 100 – 199% FPL was based on too small of a sample size to be
deemed reliable (NSCH, 2011/12). Lutfiyya et al., (2008) found that Hispanic children living ≤
150% of the Federal Poverty Line (FPL) were twice as likely to be obese as Hispanics living
above that level, OR = 2.00, 95% CI = [1.99, 2.02], while the odds for African-American (1.22
times higher) and White children (1.54 times higher) were relatively lower, though poverty
increased risk for all.
Qualitative research on low-income parents and child diet, physical activity, and
sedentary behavior. Prior qualitative research has investigated feeding practices, food
purchasing behavior, and perceptions of physical activity and sedentary behavior in low-income
families. However, much of this research has been conducted on African-American, not
Hispanic, samples. In low-income primarily African-American samples, mothers report a variety
of challenges such as children’s repeated food requests, other adults’ undermining their eating
rules, memories of their own hunger as a child that makes denying children food difficult,
inconvenient grocery store locations, higher per-calorie cost of healthy foods, children’s
disruptive accompaniment during grocery store trips, and large child appetites (Herman et al.,
2012; Wiig & Smith, 2009). Some minority women have limitations depending on their living
situations (e.g., homeless shelters do not permit fresh fruits and vegetables because of the
possibility of pests) or skip meals to avoid children going hungry (Wiig & Smith, 2009). One
study of primarily African-American mothers of obese children found that they worried about
their children’s emotional well-being and embarrassment (Hughes et al., 2010).
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Studies on physical activity and sedentary behavior in low-income minority populations
indicate that parents believe that a lack of safe areas inhibit children’s outdoor physical activity
(Jarrett et al., 2011) but felt that television offered safe, affordable substitute supervision given
the lack of alternatives (Gordon-Larsen et al., 2004). However, low-income African-American
mothers reported using a variety of strategies to increase children’s physical activity, such as
assessing specific dangers of potential play areas, delineating areas where children were allowed
to play, chaperoning, promoting activities with family members, relying on trusted adults to
monitor children, and making the most use of existing local and non-local neighborhood areas
(Jarrett et al., 2011). In a study of low-income, minority (85% African-American) adolescents,
children reported that they engaged in a variety of types of physical activity with parents but
infrequently (Wright, Wilson, Griffin, & Evans, 2010). Also, girls felt more emotional support
(e.g., understanding or compassion) and negative support (e.g., being required to play outside
with a sibling) than boys, who reported support that was more tangible such as specific activity
suggestions or transportation to sporting activities (Wright, Wilson, Griffin, & Evans, 2010).
Research on low-income Hispanic populations is limited. One qualitative study
investigated food purchasing behaviors in low-income Hispanic mothers, most of whom had
immigrated to the U.S. in the previous 9 years. They found that parents were concerned about
children’s weight but remembered learning that large portions (such as of beans and rice)
benefitted growth, and they felt pressure to be a “good” parent by pushing food on children
(Lindsay et al., 2011). Hispanic parents recruited from schools and school clinics have reported
concerns that tap water is unsafe and the belief that sugared beverages made at home are healthy
(Bogart et al., 2013). Hispanic parents from an economically diverse community sample
reported having some positive outcome expectations about children’s TV viewing – a sedentary
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behavior – such as providing entertainment and keeping children occupied, but also concerns
about the sedentary activity such as potential exposure to age-inappropriate material or children’s
upset over turning off the TV (Rodriguez, Hilmers, & O'Connor, 2013). Results from one
review also suggested that Latino or immigrant families may reject the criteria for obesity
(Lachal et al., 2013).
Qualitative research on parent stress and weight-related parenting. While some
quantitative studies have begun investigating links between parent stress, weight-related
parenting, and child obesity (e.g. Gemmill, Worotniuk, Holt, & Skouteris, 2013), very little
qualitative research delves into parents’ emic perspectives about the role of stress in weight-
related parenting practices. In one study, parents in low-wage jobs experienced work spillover
stress that led to different foods being served, such as more convenience foods; yet, serving these
foods was also a source of stress and guilt because these types of foods did not align with
parents’ views about what children should eat (Devine et al, 2006). In another study, health care
providers serving WIC mothers believed that mothers faced enormous stressors and that planning
and preparing meals became another stressful chore (Leigh et al., 2002). They also believed that
mothers used food to reward or parent children, such as giving them inexpensive sugary treats to
show affection during times of heightened maternal stress. Yet, these studies do not address
whether mothers of healthy weight versus obese children use different coping strategies. The
literature lacks an understanding of whether low-income Hispanic mothers of healthy weight
children perceive stress differently or cope using different weight-related parenting practices
than mothers of obese children.
Positive deviance. Previous PD approaches to child malnutrition have involved locating
members of a community who were successfully able to overcome challenges and to facilitate
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dissemination of their specific practices to the larger community. Much of the formative PD
work began in 1990 in Vietnam when Jerry and Monique Sternin began applying ideas of
Zeitlin’s work on under-nutrition to their work on malnutrition in Vietnam (Bullen, 2011; Marsh,
Schroeder, Dearden, Sternin, & Sternin, 2004; Pascale et al., 2010; Zeitlin et al., 1990). At that
time, 65% of the country’s children under the age of 5 were malnourished and traditional
supplemental feeding programs had not significantly improved the situation. The Sternins
discovered that families whose children were nourished used unique feeding practices such as
adding tiny shrimps, crabs, and cooked leafy green tops from sweet potatoes to the children’s
diets. Positive deviant behaviors or strategies are typically found in 1 – 10% of people in a
community population (Marsh, Schroeder, Dearden, Sternin, & Sternin, 2004). A systematic
review of PD approaches to child under-nutrition found that studies employing the strongest
research designs (i.e., randomized control trial) showed the approach to be associated with
improvements in nutritional status and child feeding behavior (Bullen, 2011). For example, one
RCT was conducted on 238 young rural Vietnamese children, half of whom were from
communes implementing Save the Children’s integrated nutrition program based on the PD
approach and half of whom were from matched comparison communes (Pachón et al., 2002).
Results were that children in the intervention group consumed more program-promoted food,
were fed more frequently, and had a higher energy intake than comparison children. Thus,
previous research indicates that the approach could be effective for improving child nutrition
problems.
Positive deviance-based approaches to childhood obesity prevention. Emerging PD-
based research on child obesity suggests that obese children successfully lose weight with help
from family and peers and that parents’ strategies and feeding practices are perceived as
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influential. One study found that obese children living in high-risk neighborhoods who had
successfully lost weight were motivated by negative peer comparisons and avoiding bullying,
and that family and peer support were seen as helpful to their success (Sharifi et al., 2015).
Parents of these children used these strategies: “family-level changes, parent modeling,
consistency, household rules/limits, and creativity in overcoming resistance” (Sharifi et al.,
2014). A third study on a separate population found that parents of obese, overweight, and
healthy weight children differed such that parents of healthy weight children tended to use fewer
emotional feeding practices, such as rewarding children with food (Foster, Farragher, Parker, &
Hale, 2015). They also tended to have more in-depth specific beliefs about avoiding unhealthy
food, such as “because it’s high in fat,” compared to parents of obese children who tried to avoid
it for more general reasons, such as “that’s what the doctor says.” Finally, healthy weight
children seemed to consume less juice and more yogurt than overweight/obese children (Foster,
Farragher, Parker, & Hale, 2015). However, no research has taken a PD approach to
understanding how parents’ perceptions of stress and coping may play a role in weight-related
parenting practices in the context of child obesity prevention, such as using fewer emotional
feeding practices, to the author’s knowledge.
Current study. The current study addressed the literature gap on low-income Hispanic
parents’ emic perspectives about parent stress, stress coping, and beliefs about the effects of
stress by locating mothers who most closely approximate positive deviant status in a
convenience sample of low-income, inner-city mothers. In terms of this target population, half
of children living at or below 130% of the federal poverty line in California are overweight or
obese (Health, 2009), and 40% of Hispanic children are overweight or obese (CAHMI, 2007). In
Los Angeles, rates of obesity among low-income youth increased between 2003 and 2011, in
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contrast to youth from New York City, where obesity rates declined (Centers for Disease
Control, 2013). The current study was a cross-sectional, grounded theory study (Corbin &
Strauss, 2008; Creswell, 2013) to investigate low-income, Latino families in the Los Angeles
area who use uncommon strategies for maintaining children’s high levels of fruit and vegetable
intake and healthy weight. Qualitative, in-depth interviews were used to assess participant
perceptions within the frameworks of Social Cognitive Theory (SCT) (Bandura, 1977, 1989,
2001) and Structural Ecological Model (SEM) (Cohen et al., 2000) (see Figure 9). A sample of
24 mothers was interviewed: 12 PD families and 12 non-PD families.
Specific Aims
Specific aims were based on gaps in the literature described above. We employed two of
the key components of the positive deviance approach – locating members of a community who
are able to overcome common barriers successfully and identifying their techniques. The study
aimed to locate families whose children ate 4 – 5 servings of fruits and vegetables most days and
uncover mothers’ beliefs about stress and coping.
Aim 7: To locate low-income, urban families who report that their children consume an
average of 4 – 5 servings of fruits and vegetables 5 or more days per week and whose
children are healthy, according to BMI percentile (PD group).
Aim 8: To uncover emic perspectives about stress, stress coping, and beliefs about the
effects of stress on health using in-depth interviews in these families (PD group) and in a
comparison group of families whose children eat less than 2 fruits and vegetables most
days and are obese, according to BMI percentile (Comparison group).
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Procedures
Study design. A qualitative methodology was used in a cross-sectional study on a sample
of low-income, primarily Hispanic mothers. Interviews assessed mothers’ perceptions of their
own and their family’s stress, stress coping, and beliefs about the effects of stress. All interviews
took place at the Children’s Bureau of Southern California. Written informed consent for
parents was obtained prior to the interview. Consent forms were provided in Spanish and
explained to participants before signing.
Recruitment and screening. Participants were recruited into the study from the
Children’s Bureau of Southern California. According to the Fiscal Year Budget 2013-2014, the
Los Angeles Metro location of the Children’s Bureau serves over 15,000 families yearly, 93% of
whom live at or below the poverty level, and 74% of whom are Latino (CBSC, 2013). Similar to
the recruitment strategy used by Herman et al. (2012), informational flyers that contained a study
description and contact information were posted at the location. Children’s Bureau staff were
also notified about the study and signed up interested families for a phone screening. Mothers
were screened over the telephone, and eligible mothers were invited to do the in-person
interview at a later time at the Children’s Bureau. Due to different eligibility criteria for the PD
and comparison groups, mothers were assigned to the group for which they were eligible when
they were invited to participate. Recruitment, screening, and interviews were conducted on a
rolling basis between February 2014 and April 2014.
Inclusion criteria. Study eligibility was assessed with a screening survey for both groups
(Appendix A). The study aimed to recruit participants into one of four groups: (a) PD / child 2 –
5 years, (b) PD / child 6 – 10 years, (c) non-PD / child 2 – 5 years, and (d) non-PD / child 6 – 10
years. The rationale for these eligibility criteria is described below in the section on Sample Size.
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Different sets of eligibility criteria were used for the two groups. For both the PD and
comparison groups, criteria were: (a) biological mother of at least one child aged 2 - 10 years, (b)
18 years of age or older, (c) child does not have a medical condition related to weight or growth,
(d) child is not on a special diet, such as for severe food allergies, (e) eligible for government
food assistance (i.e., ≤ 130% of the federal poverty level), and (f) has primary responsibility for
feeding the child. The second set of eligibility criteria for the PD group were: (a) children eat ≥
5 servings of fruits and vegetables 4 – 5 days per week and (b) all children are of healthy BMI
(between the 5
th
and 85
th
percentile) (Centers for Disease Control, 2011; Ogden et al. 2010). For
the comparison group, additional criteria were: (a) child eats ≤ 2 servings of fruits and vegetables
4 – 5 days per week and (b) child is obese (≥ 95
th
BMI percentile) (Centers for Disease Control,
2011; Ogden et al. 2010). Mothers who could not participate if they had to bring children (such
as interviewing during school hours), were asked to weigh and measure their child the night
before the interview and bring that information. Mothers who brought their children to the
interview had height and weight of all children present measured by research staff using an
electronically calibrated digital scale (Tanita WB-110A) and professional stadiometer (PE-AIM-
101) to the nearest 0.1 kg and 0.1 cm, respectively. Measurements were taken in a private room.
Qualitative Interview. Similar to Kaufman & Karpati (2007), a semi-structured
interview was used to establish rapport and inquire about stress and health. When the participant
brought up related topics, interviewers asked follow-up questions to explore the area. Interviews
were all conducted by the primary investigator and one Spanish-speaking research assistant. The
tone was informal, and interviewers emphasized that participants could interject at any time or
bring up any other topics they believed to be relevant. An interview guide was used for the
interviews (Appendix B), but interviewers also accommodated participants’ suggestions and
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asked follow-up questions to participant-initiated lines of conversation. Interviews were
conducted in English or Spanish, depending on the participant’s preferred language. A digital
recorder was used to record the content. Recorded interviews were transcribed by bilingual
research assistants. Transcribed interviews conducted in Spanish were translated into English by
bilingual research assistants. A subset of interviews was back-translated to ensure validity.
Results of back-translations indicated high fidelity of the translation with small differences in
word-choice and formal tense.
Sample size. The study aimed to recruit 24 participants (12 PD group; 12 comparison
group) based on the range of participants in prior qualitative research investigating mothers’
perceptions of child diet and physical activity (11 to 71) (n = 11: Slater, Sevenhuysen, Edginton,
& O'Neilz, 2012, 2012; n = 26: Roshita, Schubert, & Whittaker, 2012; n = 38: Lindsay, Ferarro,
Franchello, de La Barrera, Machado, Pfeiffer, & Peterson, 2012; n = 71 Tucker, Irwin, He,
Bouck, & Pollett, 2006). Based on prevalence estimates of obesity-relevant health behaviors
occurring simultaneously in the population, between 0% and 10% of the Children’s Bureau
sample were expected to meet positive deviance criteria. According to National Health and
Nutrition Examination Survey data, about 0.4% of adolescents aged 12 – 19 years are estimated
to meet goals for fruits/vegetables, sugar-sweetened beverages, physical activity, and screen
time; 41% are estimated to meet none (Foltz et al., 2011). California Department of Public
Health (CDPH) data for families living at ≤130% of the federal poverty line indicates that 19.5%
of children eat 5 or more cups of fruits and vegetables per day (Health, 2009). Fruit and
vegetable consumption had the lowest prevalence among these risk factors, suggesting that
fruit/vegetable intake was the limiting factor for determining positive deviant groups. CDPH
prevalence rates are similar to those of children participating in a school-based obesity
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prevention program in southern California (Riggs, Sakuma, & Pentz, 2007) which indicate that
about 2% of healthy weight children aged 9 who participate in the national school free lunch
program for low-income students consume fruits/vegetables 4 – 5 times per week, do vigorous
physical activity almost every day, and drink non-diet soda 2 – 3 times per week or less. In that
sample, fruit and vegetable intake had the lowest prevalence among the risk factors; 6% reported
eating fruits and vegetables 4 – 5 times per week. Based on those prevalence estimates, if 2% of
the Children’s Bureau population met criteria, 600 people would need to be screened to locate 12
positive deviants, if 6%, 200 will need to be screened, if 20%, 120 people. Based on the number
of families regularly attending programs at the Children’s Bureau and the expected low
prevalence of children meeting fruit and vegetable guidelines (4% – 20%), screening criteria for
the positive deviance group was limited to eating fruits and vegetables 4 – 5 times per week and
having a healthy BMI.
Demographic information. Demographic information was collected at the beginning of
the interview session using a subset of items from a questionnaire used in prior research (Healthy
Places; National Cancer Institute #R01-CA-123243 (Pentz, PI) and American Cancer Society
(118283-MRSGT-10-012-01-CPPB) (Dunton, PI) (Appendix C). Demographic information
included age, sex, living situation, marital status, education, occupation, income, and languages
spoken.
Qualitative data management and analysis. Interviews were transcribed and then
translated from Spanish to English using Microsoft Word. Data management and analysis was
conducted using ATLAS.ti Version 7.5.4 [Computer sofware] (1999) Berlin, Scientific Sofware
Development. Qualitative data analysis used a grounded theory approach (Corbin & Strauss,
2008; Creswell, 2013). Researchers investigated participants’ processes of perceiving stressors,
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coping with stress, and beliefs about the effects of stress on health. The aim was to develop a
theory describing how these processes unfold in the lives of low-income, Hispanic mothers of
healthy weight versus obese children.
Memoing was used throughout the data collection and analysis phases, which involved
creating detailed notes and reactions from the ideas generated (Corbin & Strauss, 2008;
Creswell, 2013). Open coding was conducted in ATLAS.ti and began with concrete level codes
to capture subject matter of the interviews (Creswell, 2013). The primary investigator and
research assistants read the transcripts and discussed emerging themes. Axial coding and
selective coding followed, applying the categories and their intersection. The data analysis
process was collaborative. The principal investigator and research assistant held regular
meetings during the coding process. New themes, concepts, and codes were incorporated on a
rolling basis and prior interviews were updated with new codes.
Results
Description of study sample
Seventy (70) families were screened. Of these, 12 (17%) failed to meet eligibility criteria
for either group, 23 (33%) could not be reached using the contact information they provided, and
35 (50%) met eligibility criteria for either the PD or comparison group. Of these, 6 parents met
criteria for a group that was full and were put on a waiting list, 5 parents either did not show up
to the interview or were no longer interested in participating, and 24 families completed
interviews. Of those 24 interviewed, 10 were not included in the final sample. Reasons for
exclusion were: (a) parent-reported height and weight of child conflicted with the description of
the child (ex. BMI was calculated as 99
th
percentile but the mother worried that the child was too
thin and said that the doctor said the child was in the healthy range) (n = 2); (b) the child had a
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heart condition which severely limited his diet (n = 1); (c) the child was on the cusp of
overweight, depending on her actual birthday, which was not recorded by date and month (only
age in years) (n = 1); (d) the father arrived to complete the interview instead of the mother (n =
1); (e) no height/weight information could be obtained about the interviewee’s other children (n
= 2); (f) the child was very underweight, based on the age provided by the mother and
researcher-measured height and weight (n = 1); (g) mother was African-American and, since all
other participants were Hispanic, the analysis was limited to this group to adjust for any cultural
differences in feeding practices (n = 1); (h) data entry error for child height, weight, and age (n =
1). The final sample contained 6 PD mothers and 8 mothers of overweight or obese (OW/OB)
children (total n = 14).
Preliminary analysis
Target children in the PD and OW/OB groups were of relatively comparable ages, 4.2
years (PD group) and 4.6 years (OW/OB) group. Because of no-shows and interviews that had
to be excluded from the final analysis for reasons listed above, children were not equally
balanced by younger and older age groups, as was the original study design. Both groups had a
higher percentage of pre-school age target children (ages 2 – 5 years) than older children (6 – 10
years); n = 4/6 (67%) PD group, n = 7/9 (78%) in OW/OB group. There were more male target
children than female children (n = 3/6, 50% PD; n = 6/9, 67% OW/OB), but this did not include
parents’ other children. In the PD group, mothers had either one (n = 3) or two (n = 3) children;
in the OW/OB group they had one child (n = 1), two (n = 4), three (n = 2) or missing data (n =
2). Mother’s average age was around 31 years (PD group M = 30.75 years, with data missing for
one; OW/OB group M = 32.63 with data missing for one). Four women were pregnant at the
time of the interview (PD group n = 2; OW/OB group = 2). All families were two-parent
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households, though one PD mother also lived in a multigenerational household. Five PD
families had friends, renters, or other family members living with them. Eight OW/OB families
had other family members or other people living with them. PD households had 5.17 people on
average, and OW/OB households had 4.63 people (data missing for one). PD mothers were
either married to the child’s father (PD group n = 2/6, 33%; OW/OB group n = 4/9, 44%) or
cohabitated with him; none were single mothers. Mothers’ education ranged from 8
th
grade or
less to college graduation (PD group average = a little beyond high school; OW/OB group
average = almost high school graduation). Most mothers were from Mexico (n = 10), with some
OW/OB group mothers from Guatemala or El Salvador. Most household incomes were between
$5,000 – $15,000 per year in the PD group and between $10,000 – $20,000 for OW/OB group.
None had full-time jobs, though a few mothers sold handmade goods or cleaned homes for a
small amount of extra income. Women’s partners had a variety of jobs such as housekeeper,
disability, restaurant, handy-man, electrician, office manager, or day worker
(moving/gardening/construction/plumbing, etc.). Almost all mothers lived in apartments except
for two PD mothers, who lived in houses (n = 2 missing). The researcher or assistant weighed
and measured all mothers’ children except for one, who reported her child’s height and weight
and talked about the problem with overweight during the interview, thereby confirming the
group for which that family qualified. Average BMI percentile in the PD group was 46
th
;
average in the OW/OB group was 96
th
.
Sources of stress and worry
Both the PD and OW/OB groups had mothers who reported not feeling any stress or
awareness of feelings of worry or uncontrollability.
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- Translator: Okay, the next question is if you think that uhm that there are times
when you feel stressed? Or if you think that your stress or your husband’s stress
affects the growth or what it is to be healthy for your children?
Participant: Really, almost no.
Translator: You don’t feel stressed?
Participant: no
Translator: And your husband..no?
Participant: no
Translator: Uhm why do you think that you don’t feel stressed? Or what do you
do to not be?
Participant: I think that I am always with them [the children] and I always am
with them and if it wasn’t like that I wouldn’t have the will to do anything or I
would be with a bad sense of humor but it’s nothing like that. (OW/OB)

Translator: . . . what do you do when you are stressed?
Participant: Mm…stressed…I don’t know what being stressed is? I have never
heard that I stress a lot…
[. . .]
Translator: Why do you think that you don’t feel stressed?
Participant: Umm…because I don’t know what it is?
[. . .]
Translator: Ok so you say that you don’t know what it is to be stressed because
you think that you are not worried about something or because you don’t
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know…umm…the emotion? Because when a person is stressed they feel
frustrated, like they don’t have control over what happens around them…are you
worried about…about a problem that you may have…some situation?
Participant: No.
Translator: No? You don’t…are there any times where you feel frustrated
because nobody can take care of your kids? Or some problem that you may have
in your house…?
Participant: No because they behave well…they don’t fuss a lot.
[. . .]
Participant: Mm maybe tired but not stressed. (PD)
However, overall both groups had mothers who did report feeling stress and described types of
stressors. For PD mothers, typical stressors were having to rush to complete many activities
(such as grocery shopping) and feeling like they had many activities to accomplish in limited
time. They also mentioned sometimes having to complete activities alone or without a car,
which was more stressful that with children or the car, and had stress over dealing with minor
child misbehavior issues.
-Oh well sometimes she [the child] stresses me out when for example she doesn’t want to
listen to me or because there are moments because she doesn’t always behave well right.
There are moments where for example, I tell you, I take her a shower and I take her out
of the tub and she is running and running on the bed. And I tell her come I’m going to
change you and she doesn’t want to and she’s like around here. Or when we are going to
go out I tell her I’m going to change you so that we can go out and those are things that
stress me out (PD)
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In contrast, a major source of stress in the OW/OB group was managing their child’s weight and
food intake. Also, they tended to report more stressors overall. They mentioned arguments or
tense moments during dinner when taking plates of food away from children or trying to
encourage them to eat vegetables, while believing that the child was not going to do it. They
expressed a sense of frustration, powerlessness, and fatigue.
-Cause sometimes I…I just give in because I know…sometimes she gets into a plan that
she’s not gonna eat…and I don’t know if you’ve seen that commercial...I compare a lot to
that commercial where there’s this little boy, her mommy is serving little eggs with food
and he’s saying no no no…so she gives him Pediasure…so she gives in you know? That’s
how it feels. Sometimes she don’t…I’d rather have her eat some fries in her tummy than
nothing but its all about learning because there’s a way…I mean…they’re the boss. I feel
like they were in charge but right now I’m taking back, like…you know…(OW/OB)
Family tension over feeding children was also a source of stress, and only mothers in the
OW/OB group reported tension between herself and child (compared to tension with another
family member, such as a spouse). Sometimes arguments with spouses occurred over these
issues because mothers felt that their efforts were undermined by husbands or other family
members living in the home who gave children chips or sodas whenever asked. When children
were upset about not being able to eat as much as they had been allowed before beginning a
weight loss regimen, mothers used tactics such as distracting the children, taking them outside to
play, or limiting mothers’ own food intake to show solidarity hoping to make the child feel
better.
- . . . when I began to take away the food and then it was a little difficult and he would
cry a lot so I took him to play instead and he forgot that he had to eat/ it was the only
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way because I couldn’t with him, he cried and cried and cried that he wanted more/
and I knew that it was my fault and then I had to resolve that/ I I took him out to play
and he forgot and he wouldn’t ask for more food (OW/OB)
Some mothers in the OW/OB group felt stress around barriers to providing children
opportunities to engage in healthy weight-related behavior. They mentioned concerns about the
safety of parks nearby, which limited places for physical activity, and the high cost of healthy
food as a limiting factor for purchase decisions.
-It’s difficult to…maintain…the willingness because sometimes one wants to do
something but because of circumstances one can’t do it. For example, I would like to give
my child different foods but I don’t have the means. Or if you can’t buy fruits the doctor
tells me that I have to look at the calories but I don’t look at that. I look at the prices.
(OW/OB)

-Stressed? Yes, that sometimes there is no space where to play and they want to run more
but I know it is not safe/ it is what causes me some stress that I don’t have where to take
them for the same thing that we go to the park and there is people there that well are
drinking or there selling drugs and it is not worth it (OW/OB)
Mothers in the OW/OB group also reported stress over worrying about not having
enough money for rent, bills and food, or worrying that the father would not have work.
-…and of course that is stressful when there is no work…we have to pay the rent every
month, the food, and other things…more than anything it’s the rent and food…and bills.
And well I think that is most stressful. (OW/OB)
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In contrast, PD group mothers did not report concerns about bills, rent, etc. as sources of
stress, though most families in the study made about $10,000—$15,000 per year. Overall, PD
mothers displayed feelings of sufficiency, with resources to meet their family’s needs, whereas
OW/OB group mothers seemed to have a sense of impending scarcity.
…we don’t really need it…the SNAP…EBT…I mean we’ve been ok with his paycheck and
you know…aside from the fact that we live with his mom, it kind of has helped us, you
know, buy groceries and stuff like that. (PD)
In sum, mothers in the PD group tended to have stressors such as feeling pressed for time
whereas those in the OW/OB group were stressed about managing their child’s eating and
weight, barriers to practicing healthy habits, and worries about lack of money.
Stress coping
Mothers in the OW/OB group reported that sometimes stress led them to yell, throw
things, get angry, or eat ice cream, but PD group mothers did not report these reactions. Mothers
in both groups mentioned taking their children to the park to deal with stress, though one
OW/OB group mother mentioned that she did not want to be there and would prefer spending
time alone or doing “grown-up” activities such as going to the movies. Along the lines of
spending time alone, OW/OB group mothers more often mentioned that they engaged in stress
coping alone – going for a walk alone, relaxing by oneself, or using prayer. In contrast, PD
group mothers tended to prefer coping with stress while with their children, such as paying
children more attention rather than less, or watching TV or listening to music with them.
(However, one PD mother reported preferring to take a shower alone to deal with stress.)
Many PD group mothers reported trying to ignore the stress or not pay it any attention –
deciding to “channel” the energy into something else, such as spending more time with their
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children at the park. PD moms reported attempting to stay calm and prevent stress, believing
that stress was not good for people and should be avoided.
-. . . I feel like if you’re stressed out you need to find a way to I guess channel that
stress…otherwise its just gonna eat at you [laughs]…umm… (PD)

-You know, focus my energy…. instead of being upset…I’m like, “why am I gonna be
upset?”. I’d rather just…being a good mother is more important. (PD)
PD mothers tried to prevent stress from occurring, such as by taking care of errands and tasks
before children were home from school and focusing enough attention on children when they
were home. Some believed that stress was preventable and that planning could help prevent it.
Translator: and how do you maintain or try to relax to alleviate stress?
Participant: um for example I try to do things when they are not there
Translator: okay
Participant: and when they are there, pay them more attention. Like it [stress] is what
results because if I do things when they are there then I don’t pay them attention (PD)
Overall, PD mothers seemed to have a strong urge to try to prevent stress or to refocus
attention away from stressors by paying more attention to children, whereas mothers in the
OW/OB group tended to prefer to cope with stress alone and sometimes felt anger in
combination with stress.
Beliefs about effects of stress on health
PD and OW/OB group mothers shared somewhat similar beliefs about the effects of
stress, which ranged widely from believing that stress had serious health consequences to
believing that it had relatively little effect on health. Both groups had women who felt that their
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partners may have gained weight due to stress or to the lack of energy that accompanies it. Both
groups had mothers who felt that stress did not affect their children’s health because the children
did not know about worries such as money since parents dealt with these stressors after children
had gone to bed. Both had women who believed that some people overeat when stressed,
although only a mother in the OW/OB group reported actually doing this herself (rather than
thinking that stress-eating affected other people). Mothers in both groups thought that children
would notice parents’ stress:
-Like if I’m in a bad mood then I feel like he can notice. You know he’s like, “what’s
wrong Mom?”…uh…so he can tell….so I try not to let it get to me. (PD)

-Umm…well…well I notice that when I’m stressed they get stressed too. They start
fighting and screaming and crying and I’m like, “ahh, stop it!” so what I
do…I…sometimes I take them and we go for a walk. We go to the park, even though I
don’t want to be at the park and since they’re more relaxed, I calm down and think about
things. Or they like to go to the beach and play in the sand. Yeah, we do a lot of
parks.(OW/OB)
These quotes suggest the possibility that children responded differently to mothers’ stress
across groups, by expressing concern (PD group) or by fighting with siblings (OW/OB).
However, that possibility was not probed fully during interviews, so overall themes did not
emerge and the potential is left an open question for future research. In sum, mothers had varied
beliefs about whether or not stress affected their children’s health but believed it could have led
their husbands to gain weight. They also felt that children would notice when mothers were
stressed.
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Discussion
This study addressed the gap in literature about how low-income, urban, Hispanic parents
who are “positive deviants” in terms of their children’s healthy weight and fruit/vegetable intake
perceive and cope with stress. Overall, results indicated that mothers from the positive deviant
group worried about more minor issues, such as child misbehavior, whereas mothers of
overweight or obese children reported stress over lack of money and managing their children’s
eating and weight.
First, the study aimed to locate mothers whose children consumed federally
recommended amounts of fruits and vegetables and who were all of healthy BMI. Based on
estimates from California and a regional study, between 4-20% of the population was expected
to meet these criteria. Of those screened 10 (14%) met these criteria, though 4 were not
interviewed because they qualified for a group that was already full. While this prevalence
might be similar to other families who seek assistance at social service agencies serving low-
income urban families in southern California, the sample size is likely too small to generalize to
other low-income, urban populations. The recruitment strategy was designed to maximize the
chance of locating positive deviants and, thus, may have found mothers who were particularly
motivated to pursue programs, education, and assistance to support their families. Yet,
implementing the positive deviance approach hinges on leveraging the insights and practices of
members of the community whose success is uncommon in achieving positive outcomes, such as
having well-nourished children in a village of malnourished youth or avoiding malaria exposure
in a population at risk. Thus, the fact that the current sample of mothers was able to be located
suggests that a positive deviance approach for obesity prevention might be feasible in this
population if successful strategies can be discovered and leveraged.
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Second, this study examined mothers’ stressors, stress coping, and beliefs about the
effects of stress in families of overweight versus healthy weight children with different fruit and
vegetable intakes. While both groups had some mothers who did not feel much stress at all, the
stressors that were reported differed across groups. Mothers in the PD group wrestled with
getting children to mind when children were feeling playful. Conversely, mothers in the
OW/OB group were stressed about not having enough money for bills, rent, or food and about
tense meals with children who were upset over having their food restricted. Both sets of families
had comparable annual incomes, so this contrast could reflect differences in money management.
But it could also indicate perceptions of a hostile environment that limits opportunities and
contributes to feelings of scarcity versus perceptions that the environment provides abundant
resources sufficient to deal with challenges. Conversely, mothers in the PD group could have
had unrealistically optimistic views of sufficiency and be living above their means, which could
have negative long-term consequences for the sustainability of expensive food choices. Another
consideration is birth order; first born children may have been exposed to parent stress or poverty
longer than younger children. Mothers in the OW/OB group did have more children than those
in the PD group, and older children may have been obese due to longer exposure to risk factors.
Birth order may also affect obesity through parent experience. Younger siblings may be raised
by more experienced parents who are better able to cope with stress or are more prepared to
buffer children from negative influences or stressors in the environment than first born children.
The current findings increase our understanding of the stress experience of low-income, Hispanic
mothers who have healthy vs. overweight/obese children, but what remains unknown is whether
parents actually faced differed stressors or just perceived them differently.
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The one known stressor that OW/OB faced that PD mothers did not was parenting an
overweight/obese child. Mothers of overweight/obese children reported tension and stress about
preparing healthy meals, limiting children’s food intake, arguing with children, and feeling
undermined by family members who fed children unhealthy treats and snacks. Thus, having an
overweight or obese child was a unique source of stress. This result supports other qualitative
research finding that low-income mothers report coping with current stressors by indulging
children’s unhealthy food requests, but they also feel stress and guilt over feeding their children
foods that do not align with personal ideals (Devine et al., 2006; Hughes et al., 2010). This
result also implies reverse causation – child obesity causes parent stress – which may play a role
in the link between maternal stress and higher child obesity.
While it remains unknown how PD mothers would have coped with the stress of an
overweight child, comparisons in coping efforts can be considered based on the stress responses
and coping approaches that were reported. Mothers in the PD group had a strong urge to prevent
stress, believing that it was preventable and not good for people. They mentioned techniques
such as planning ahead and accomplishing tasks while children were away so that mothers could
pay full attention when children were home. In contrast, OW/OB group mothers did not express
the belief that stress was controllable or preventable. They preferred to cope with stress alone,
such as by taking a walk or relaxing away from children, which was almost the opposite of the
“pay children more attention” perspective held by PD mothers.
This difference may reflect an inclination to confront difficulties rather than flee
challenging circumstances, similar to an approach versus avoidant coping style, characterized by
directing attention toward versus away from a stressor (Roth & Cohen, 1986). In general,
approach coping tends to lead to better outcomes when the stressor or threat is controllable (Roth
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& Cohen, 1986). Mothers’ different perceptions between groups could also reflect learning
experiences – PD mothers who paid more attention to child behavior problems may have seen
children calm down, or OW/OB group mothers who dealt with stress alone may have returned to
find a more settled environment. However, mothers in both groups mentioned the notion of
ignoring the stressor, though this was mentioned more frequently by PD mothers. The pervading
belief in the PD group seemed to be that allowing thoughts of a stressor to invade one’s mind
drained mental resources, preventing mothers from being present with and attentive to the child.
The main differences in stress coping between groups seemed to be a strong belief in stress
prevention and an “approach” (versus “avoidance”) mentality of PD mothers. What this study
does not answer is the question of how parents actually behave when confronted with typical
stressors, which may differ from retrospective self-reports of the general trends they notice in
their own lives. Thus, future research using techniques such as ecological momentary
assessment will increase ecological validity and shed light on how differences in stress coping
unfold and interact with weight-related parenting in real time.
Limitations
There were limitations to this exploratory, qualitative study. One is that participants were
asked about their behaviors rather than being observed in their natural environments engaging in
activities such as meal preparation or exercising with children. Therefore, results of the study
were dependent upon participants’ self-awareness of their own actions and willingness to report
accurately. Indeed, prior Positive Deviance work indicates that self-report and observational
methods may reveal different aspects of behavior (Pascale et al., 2010). However, every effort
was made to create an interview setting that was conducive to participants’ awareness of even
“little things” and to ensuring that mothers did not feel criticized when giving honest reports of
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their own behavior as they understood it. In addition, the research assistants shared a cultural
heritage with participants, which may have made participants feel more comfortable. In contrast
to the early positive deviance work in Vietnam, this relatively more similar cultural experience
may have increased the chances that verbal communication would prompt sufficient insight into
interviewee’s practices. However, the socioeconomic circumstances of the interviewer and
research assistant differed from the participants’, and results should be considered in light of
those cultural, ethnic, and social differences. A second limitation is that participants were
recruited from a local service provider. Clientele seeking out this agency may be more educated,
motivated, have access to easier transportation, or may differ in other ways from the general
population of low-income, Hispanic mothers. While this limitation may impair generalizability,
the recruitment strategy also maximized the possibility of locating positive deviant families who
were exceptionally resilient at overcoming challenges. Third, the telephone screening process
relied on self-reported data for dietary intake, which could be biased, though height and weight
were measured objectively before the interview by program staff for families whose children
attended the interview. Researchers attempted to minimize respondent bias by masking
screening criteria and accepting participants into both the PD and OW/OB groups on a rolling
basis throughout the study time period and completing the entire screening process with each
person, rather than stopping once ineligibility was determined. In addition, the conversational
nature of the interviews and requests for additional details may have minimized the
exaggerations participants made. However, the potential exists that participants did not report
accurately.
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Implications
These findings have implications for designing health promotion interventions aimed at
changing parents’ behavior. The possibility emerged that mothers of healthy weight children
actually perceive fewer, and less severe, stressors in their environment. More research is needed
to understand whether these mothers actually spend less time and effort engaging in the coping
process, thereby releasing self-regulatory resources for other tasks. In addition, it remains
unknown whether these differences reflect mothers’ objective circumstances or perceptions of
their environment. Potentially, parent perceptions of stressors could influence weight-related
parenting behavior more than the amount of objective stressors to which they are exposed.
The study offered an opportunity for innovative theory-building. Different stressors and
coping techniques emerged between groups. This finding supports the possibility that children
whose mothers feel victim to the stressors in their environment, rather than capable agents for
preventing difficulties, are at higher risk for obesity. Though intervention research would be
needed to test this possibility, the finding fits within the framework of the Transactional Model
of Stress and Coping, which holds that (a) a person’s appraisal of how threatening a stressor is
(primary appraisal), and (b) his or her assessment of its controllability and resources to cope with
it (secondary appraisal) determines subsequent behavior. In this study, PD mothers tended to
have more positive secondary appraisals than mothers in the OW/OB group, who did not believe
that stressors were as preventable or controllable.
Mothers also differed in terms of what they perceived to be stressors. According to the
Transactional Model, the coping process is activated by a stressor, followed by the primary
appraisal that a stressor is threatening. But PD mothers did not perceive their low-incomes to be
a stressor at all, even one that was low-threat. Thus, the coping process may not have been
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activated as frequently in PD mothers. Another possibility is that PD mothers had less of a need
to engage in coping because, while they perceived the same stressors, they did not experience
felt stress because their perceived resources met perceived demands (secondary appraisal).
Whether through differences in primary or secondary appraisals, PD mothers may have spent
less attention and self-regulatory effort “coping”, engaging in less coping overall rather than
necessarily more effective coping. In contrast, mothers’ experiences in the OW/OB group
tended to lead to primary appraisals of threat and secondary appraisals of a lack of resources.
In addition, mothers’ perceptions in the OW/OB group also tended to align with Scarcity
theory from behavioral economics, which holds that the perception of insufficient or limited
resources can cause people to focus – “tunnel” – on present concerns to the detriment of longer-
term goals (Mullainathan & Shafir, 2013). According to this theory, people would be expected
to focus on short-term benefits rather than long-term outcomes in conditions of scarcity, in this
case lack of money. This was indeed found during the interviews with OW/OB group mothers.
They mentioned concerns about having to choose food at the grocery store based on price rather
than nutritional value. Perceived scarcity in this group of mothers seemed to lead to a feeling of
forced focus on short term financial issues (i.e., the cost of grocery items) rather than longer-term
outcomes (i.e., child dietary intake or weight management). Overall, Scarcity theory and the
Transactional Model of Stress and Coping tended to capture the experiences of these mothers
relatively well, with the exception that mothers in the PD group may have had less need to
engage in coping. Thus, research aimed at understanding effects of stress on parenting and child
obesity risk may need to consider multiple theoretical approaches if examining populations of
both healthy weight and obese children.
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Conclusions
This exploratory study increased understanding of the experiences of stress and coping
among low-income, urban Hispanic women whose children were obese versus those who were
healthy weight. Results suggested that mothers of overweight or obese children experience more
stress over money and managing their child’s eating, whereas mothers of healthy weight children
may believe that stress could (and should) be prevented, particularly by engaging in more
activities with children. Future research is needed to examine whether stable personality traits or
coping practices may have caused these differences; and future intervention work is needed to
test the possibility that teaching stress prevention or stress management techniques to mothers of
overweight or obese children could beneficially affect the atmosphere of eating in their home and
decrease overweight in their children.
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Figure 9. Intersection of theoretical frameworks of Social Cognitive Theory and Structural
Ecological Model to guide study design

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CHAPTER 5: GENERAL DISCUSSION
This body of research examined relationships between maternal stress, weight-related
parenting practices, and child obesity risk. Both quantitative and qualitative methods were used
to gain a more full understanding of how stress unfolds in mothers’ lives and how the context of
stressful surroundings relates to parenting that influences what and when children eat. Results
were that mother’s parenting stress co-occurred with fewer family meals but not consistently
with child obesity risk. Second, maternal perceived psychosocial stress was correlated with
fewer positive family meal practices, fewer rules, and higher child waist circumference, but did
not predict subsequent mealtime parenting or child obesity one year later. Third, low-income
mothers whose children were healthy weight reported different stressors and coping techniques
than mothers of obese children. Overall, findings were mixed. Maternal stress seems to be
consistently related to poorer mealtime parenting but not increased child obesity risk,
independent of objective stressors, yet mothers of obese versus non-obese children may cope
with stress differently.
Theoretical implications
These findings contribute to our understanding of how theory plays a role in
understanding relationships between mother’s stress, coping, and weight-related parenting
practices. Broadly, the Transactional Model of Coping did seem to capture mothers’ experiences
with stress and to provide a framework for understanding links between stress and mealtime
parenting behaviors. However, Scarcity theory from behavioral economics also seemed to
describe how mothers of obese children perceived their own food shopping behavior. Obesity
treatment and prevention programs may need to consider Scarcity theory when designing
programs for low-income, Hispanic families. Further, the best theoretical framework for
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designing a parent-focused child obesity treatment program may differ from the best one for
designing a health promotion or obesity prevention program because of possible approach versus
avoidant tendencies in these populations. For example, parents of children who are already
obese may benefit from techniques that help them attend to parenting practices that create stress
during mealtimes, so that alternative techniques can be tried at these pivotal moments. Teaching
mindfulness practices may be one way to help parents confront this unpleasant self-reflective
information while still remaining proactive (Bergomi, Ströhle, Michalak, Funke, & Berking,
2013). In contrast, parents of young children who have not yet become overweight – the target
of obesity prevention programs – may be most receptive to education about creating positive
mealtime routines. Further, if these approach-avoidant coping differences also reflect promotion
versus prevention motivational tendencies, then messages would be most effective when framed
to match parents’ motivation, a concept called, regulatory fit (Higgins, 2005). For example,
parents of obese children may be more likely to prepare healthy meals when they are asked to
“avoid feeding children junk food” (prevention frame) whereas parents of young, healthy weight
children may be more likely to do this when they are reminded to “feed children healthy meals”
(promotion frame). While Higgins did not consider promotion/prevention to represent the same
motivational distinction as approach/avoidance (Summerville & Neal, 2008), other researchers
find that that prevention-framed messages tend to be more effective when people are avoidance-
focused and promotion-framed messages are when people are approach-focused (Cavallo,
Fitzsimmons & Holmes, 2010). While these possibilites are intriguing, intervention research is
needed to test the effects of teaching stress management or using approach versus avoidant
framed messages in obesity prevention campaigns.
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A guiding theoretical assumption of much research on this topic is that family meal
practices directly affect child obesity risk. The finding that maternal stress may relate to
mealtime practices but not obesity risk in nationally-representative subgroups is important
because it suggests that the effect of family meal practices on child obesity risk is more complex
than previously thought. Research has shown that children who eat most meals at home
consume diets higher in fruits and vegetables, and more frequent consumption of restaurant
meals has been linked to higher intake of fat, calories, and greater body fatness in adults (Larson,
Neumark-Sztainer, Hannan, & Story, 2007; McCrory et al., 1999). Thus, frequent family meals
are expected to be protective against obesity because they are made of healthier foods than
restaurant meals.
However, family meals may only be protective to the degree that healthy foods are served
at those meals. Highly frequent meals that are unhealthy could actually increase obesity risk.
Maternal stress might be an important moderator of the link between mealtime parenting and
child obesity, nullifying the benefits of frequent meals on decreasing obesity risk. Energy
Balance theory suggests reasons that maternal stress could affect the link between family meals
and child obesity (Hall et al., 2012). Maternal stress could boost physical activity parenting,
even if it disrupts family meal practices, counteracting any increase in caloric intake from relying
on take-out foods. Supporting this hypothesis, results from Study 3 suggested that some parents
coped with stress by taking children to the park, which would likely boost children’s energy
expenditure. Also, these low-income mothers often performed most daily activities by foot.
Walking to a restaurant for dinner could offset the potentially increased calories consumed at
those meals. Maternal stress could also influence the type, nutritional makeup, or timing of
foods children eat, leading to less optimal nutrient intake even if it does not change the overall
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amount of energy consumed – the total caloric intake. Thus, researchers may benefit from an
inclusive Energy Balance approach that measures both physical activity and mealtime parenting
when investigating obesity risk. However, the effects of maternal stress on nutritional
composition of total child diet also remain unknown and require further investigation.
Methodological implications
These studies also have implications for methodological approaches to future research.
First, the time span between measures in the current projects – either simultaneous or
longitudinal over on year – did not permit examination of within-daily effects. However,
maternal stress could lead to daily variations in parenting practices that would not necessarily be
reflected by static measures of parenting practices, and research has been called for that makes
use of the increased precision these methods afford (Masse & Watts, 2013). Thus, studies are
needed that incorporate methodologically innovative tools such as ecological momentary
assessment in real time. A methodological innovation from Study 3 is the qualitative, positive
deviance approach, which provides a framework for designing future studies that leverage
practices and insights already being practiced by community members. This could be a new
process by which to develop parent-based child obesity interventions and improve uptake. The
low-cost approach could be used to ameliorate the problem of inconsistently-effective parent-
based programs by harnessing locally-sustainable options and strategies. Emerging research
suggests that this is a promising avenue (Foster et al., 2015; Sharifi et al., 2014, 2015). The
present study was able to locate these families, indicating that future studies taking a PD
approach may consider recruiting from social service agencies that provide education and
nutrition counseling for families.
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Programmatic implications
Some clinical implications also warrant mention. Since child obesity in Study 1 was only
higher for children with mothers in the higher poverty subgroup, nutrition education and physical
activity promotion programs for low-income mothers have the potential to decrease child obesity
risk. However, intervention programs aiming to educate higher-income mothers about these
health behaviors might not be expected to affect child obesity risk, though they might provide
health-promoting information. Also, daily meals were found to be highest in this subgroup, so
education programs or policies aimed at obesity reduction may not need to focus on boosting
family mealtimes, especially since obesity risk in subgroups that consumed family meals
between 1 – 5 times per week did not have children with higher obesity. From a policy
perspective, findings reinforce the need to consider poverty as a strong contributor to child
obesity risk. Thus, programs providing diet-related benefits to low-income populations, such as
the National School Lunch Program or the Supplemental Nutrition Assistance Program, should
consider how policy changes are likely to affect obesity risk, such as by providing incentives for
consumption of healthy foods versus limiting SNAP coverage for soda (Todd & ver Ploeg,
2015).
The implication from Study 2 for obesity prevention programs is that targeting stressful
circumstances – such as poverty or intimate partner violence – might be expected to have larger
effects on decreasing child obesity risk than targeting parents’ stressful experience per se or
teaching stress coping techniques. If parent perceived stress does not causally influence child
obesity risk, then programs aimed at boosting parent coping skills would not be expected to
affect child obesity risk. However, that does not mean that programs seeking to reduce parent
subjective stress would not be beneficial for weight-related parenting behaviors or outcomes
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other than obesity. Indeed, such programs have been shown to improve the regularity of family
meals, parent mental health, and family functioning (Bauer et al., 2012; Keypour, Arman, &
Maracy, 2011; Palmer, Henderson, Sanders, Keown, & White, 2013). Additional research is
needed to test whether policies or programs aimed at decreasing parent exposure to objective
stressors could also lower child obesity risk.
Clinical implications from Study 3 are that dieticians, health care practitioners, and health
educators working with parents whose children are struggling to lose weight may need to address
heightened stress surrounding family meal times. Potentially, helping parents develop better
stress-coping skills would benefit mealtime parenting, improving child nutrient intake profiles
through higher fruit and vegetable consumption. Also, mothers who have overweight or obese
children may benefit from learning techniques that help prevent some types of controllable
stressors from occurring or reduce perceptions of stress when confronted with stressors. In
addition, nutrition education programs aimed at improving family eating habits might boost
success by including stress-management training. One clinical intervention might be pairing
Positive Deviant group mothers with mothers of obese children to share their insights about
coping with stress around mealtimes and how they perceive typical stressors. While a control
group would need to be recruited before conclusions could be drawn about the efficacy of that
approach, the framework exists for building future parent-based intervention programs.
Suggestions for future research
While the present body of research did not find consistent, significant links between
parent stress and child obesity risk independent of objective stressors, methodological design
features may have prohibited detection of effects that occur on a smaller time scale. Studies that
use ecological momentary assessment to investigate within-daily consequents of maternal stress
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may have greater power to discover how weight-related parenting practices respond to subtle
shifts in stress levels. Thus, studies using more sophisticated methods are needed before drawing
a firm conclusion that maternal perceived psychosocial stress does not affect child obesity risk.
The present research also found that children tend to consume lower added sugars when
parents have rules about what their children are allowed to eat. Yet, the mediated effects of
stress through weight-related parenting practices on other nutritional components of child diet
remain largely unknown, with the exception of limited information about fatty food intake
(Eisenberg et al., 2012). Thus, research is needed to investigate how maternal stress influences
the nutritional make-up of children’s diets. Potentially, stressed mothers make different purchase
decisions at the grocery store or choose different cooking techniques or recipes. A positive
deviance approach could be taken to uncover what techniques mothers of healthy weight
children who consume healthy diets use to encourage fruit and vegetable intake. They may use
different encouragement strategies or cooking techniques than mothers of obese children, create
less stressful mealtimes contexts, or have different child-feeding practices (Rollins, Loken,
Savage, & Birch, 2014). Further exploration is needed into coping techniques used by mothers
of healthy versus obese children. While this research suggests that different perspectives and
practices are used, additional research is needed to probe differences in emotion- versus
problem-focused coping, approach versus avoidant coping, and engaging in coping efforts alone
versus with children.
Conclusions
Mother’s stress was related to impaired family meal practices but not consistently to child
obesity risk. Mothers of obese versus healthy weight children may perceive and cope with stress
differently. Future research should consider using the Transactional Model of Stress and
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Coping, Energy Balance theory, and Scarcity theory to design future studies. More work is
needed using ecological momentary assessment techniques, on investigating effects of maternal
stress on nutritional make-up of children’s diets, and on dimensions of stress-coping used by
mothers of healthy versus obese children. Health care practitioners, health educators, and
obesity prevention program designers may consider teaching stress-management techniques to
test potential improvements to family meal practices and children’s diets.

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APPENDIX A SCREENING SURVEY FOR STUDY 3
Thanks so much for your interest in our study! We are interested in finding out about all the
things parents do to help keep kids healthy. Did you see our flyer at Magnolia Place? Great! I
have a few questions to ask – it should only take about 5 minutes. Does that sound okay?
IF YES – go ahead
IF NO – ask what time would be better
First tier
1) Are you 18 years of age or older?
2) Are you the biological parent of at least one child under age 10 years?
3) Do you have a boy or girl?
If you have more than one child, then choose the child who is the biggest or
heaviest for his or her age.
4) How old is he/she?
5) Do you have primary responsibility for feeding your child?
6) Does your child have a medical condition or is he/she taking medication related to weight
gain or growth?
7) Is your child on a special diet, such as for food allergies (ex., gluten-free)?

Now I have a question about income. We are not asking about whether or not you do receive
any government food assistance, we are just curious about whether you are eligible. To do that,
first we start with:

8) How many people are in your household?
9) And do you make less than (read annual income for that number) per year?
2013 POVERTY GUIDELINES FOR
AND THE DISTRICT OF COLUMBIA THE 48
CONTIGUOUS STATES
Persons in family/household 130% of PG
1 $14,937
2 20,163
3 25,389
4 30,615
5 35,841
6 41,067
7 46,293
8 51,519
For families/households with more than 8 persons, add
$4,020 for each additional person.
adapted from 2013 Poverty Guidelines
(http://aspe.hhs.gov/poverty/13poverty.cfm)

Second Tier
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10) About how many days a week does your child drink a sugar-sweetened beverage (ex.,
Coke, Sprite, Fruit drinks, Gatorade)?
IF 7 DAYS: About how many times a day?

11) About how many days a week does your child eat fruits and vegetables?

12) About how many servings does he/she eat on those days?
[≥ 4 – 5 days per week]

Now I’m going to ask about your child’s/children’s height and weight. And we’re
interested in all of your children. Could we go through each child, and you tell me how
much they weigh and how tall they are?

[Show them the CDC Growth charts for boys and girls and locate child’s age and weight]

IF THEY QUALIFY:
Okay, great! That’s all the questions we have today. It looks like you do qualify for our study.
If you participate, we would set up a time to do an interview at Magnolia Place. The interview
would last about 30 minutes to an hour, and we would compensate you with a $25 Visa gift card.
Would you like to go ahead and set up an interview?
Check schedule for available times, and sign them up on the calendar.

IF THEY DO NOT QUALIFY:
Okay, great! That’s all the questions we have today, and it looks like that’s all we’ll need to do.
I’m sorry, you are not eligible for the study, but we really appreciate you taking the time to talk.
Thanks so much for doing that. (Have a great day!)

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APPENDIX B INTERVIEW GUIDE FOR STUDY 3
We’re interested in finding out more about how parents and families try to stay healthy – by
doing things like eating right and exercising – but we also want to know about the things that
make it hard to stay healthy. We’ll start with some simple background about your family, just to
get to know you a bit better, and then we can talk about things like making food and doing
physical activity. Feel free to jump in at any point, if an idea comes to mind, or you think of
something that’s related. There are no right or wrong answers. This is just to help me understand
your perspective.

OBJECTIVES (DON’T READ):

• Discover unique practices of parents whose children meet FV and physical activity
guidelines
• Understand the needs of low-income mothers in promoting children’s health

1. PRESENT CIRCUMSTANCES

- Demographics checklist

2. HISTORY OF FAMILY PRACTICES

Family background
- Where born
- Prior family composition; what did it mean to be healthy in family of origin?
- Current family composition; what does it mean to be healthy in your family?

Work
- Type? Now or in the past
- Commuting?

Typical Day
- Typical weekday for you/family/children
- Typical weekend day
- Typical things done to keep selves and kids healthy

Typical Meals/drinks
- Where from? How do you decide, shop, cook? Who does it?
- How served? Portions?
- Feeding practices (if they don’t eat or eat what you think is too much)
- Special occasions/restaurants
- Drinks?

Typical physical activity for children
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- How get involved?
- Typical activities (School, sports, friends, family, commute)

Sleep
- Parent and child

Stress
- Parent and child

Accessing support/Government Assistance
- Nutrition (SNAP, WIC, free/reduced school lunch); Unemployment (welfare); Preschool
- School programs (nutrition education, etc.)
- How find out about Children’s Bureau
- Uses here

Health
- Worry about any health concerns
- Worry/concern about weight

Unmet needs
- What are your biggest challenges to keeping kids healthy?
- What programs/policies would help? (gov’t regulation, education programs)
- What would you recommend that other mothers like you to do to keep kids healthy?

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APPENDIX C DEMOGRAPHICS QUESTIONNAIRE FOR STUDY 3

Child’s height (cm)
Child’s weight (kg)
Child age (yrs)
How is child height/weight determined? 1, Parent reported
2, Researcher measured
How many children do you have and what are their ages? Number
What is your age? Number
What is your sex? 1=female 2=male 3 = prefer not to answer
Are you currently pregnant? 1=yes 0=no
Which of the following best describes your household? 1=Single parent with children < 18
2=Two parents with children < 18
3=Multigenerational household
Does anyone else live in the home with you? 1, Renters
2, Friends
3, Other family
4, None of the above
What is your total household size? Number
What is your current marital status? 1=Never married 2=married
3=separated 4=divorced
5=Widowed
6 = unmarried, cohabitate
What is the highest grade in school that you have completed? 1= 8th grade or less
2= Some high school
3=Graduated from high school
4= Vocational/business school
5=Some college
6=Graduated from college
7=Attended graduate or professional school
What is your race? 1, Hispanic or Latino
2, American Indian or Alaska Native
3, Asian
4, Black or African-American
5, Native Hawaiian or other Pacific Islander
6, White
7, Mixed/Bi-racial
8, Other
What languages do you speak at home? String
In what country were you born? String
Income range 1, <= 5k 7, 30k-35k
2, 5k-10k 8, 35k-40k
3, 10k – 15k 9, 40k-45k
4, 15k-20k 10, >=45k
5, 20k-25k 11, Do not wish to answer
6, 25k-30k
What is your job? String
What is your spouse or partner's job? String
What are the cross-streets for your home? Numbers
Is that a house or apartment?
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Date: September 7, 2015

ELEANOR TATE SHONKOFF
CURRICULUM VITAE

A. Personal Information:

Name in Full Eleanor Tate Shonkoff, M.A.
Business Address Institute for Prevention Research
University of Southern California
3
rd
Floor Soto
2001 N. Soto Street
Los Angeles, CA 90089
Phone (336) 407- 4198
Place of Birth Athens, Georgia
Citizenship U.S.A.
E-Mail Address eleanort@usc.edu
Website eleanortate.com

B. Education:

High School Clarke Central High School, Athens, GA 1997
College or University University of Georgia, B.A., 2002
Philosophy and Cognitive Science
(concentrations: Philosophy, Linguistics)
Graduate School Wake Forest University, M.A., 2006
Psychology
University of Southern California, 2010 - Present
PhD expected 2015
Health Behavior Research
Honors and Awards

Teaching Assistant Fellow
Center for Excellence in Teaching 2014-2015

Graduate Student Government
Travel Grant award ($500) Spring 2014

Graduate Student Government
Travel Grant award ($500) Fall 2013

Winner, SPR Sloboda and Bukoski Cup Competition
Team Captain, Analyze This Spring 2013

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Early Career Prevention Network Travel Award
SPR Annual Meeting in San Francisco ($300) Spring 2013

National Cancer Institute Training Fellowship
(T32CA009492-28) Summer 2012 – Spring 2016

USC Ph.D. Student Summer Institute and fellowship ($1,000)
Summer 2011

Graduate and Professional Student Senate
Conference Travel Fund award ($500) Spring 2011

Early Career Prevention Network Travel Award
19th SPR Annual Meeting, Washington, D.C. ($250) Spring 2011

Graduate Student Research Award
Southeastern Society for Social Psychology November, 2006

C. Professional Background:

Instructor, Part-time
California State University, Los Angeles
Health Communication January – March 2015

Teaching Assistant
University of Southern California
Theoretical Principles of Health Promotion 2011 – 2012
Theoretical Principles of Health Promotion 2010 – 2011

Wake Forest University
Cognitive Psychology Spring 2006

Lab Coordinator
Social Behavior Lab
Department of Psychology
University of Southern California 2009 – 2010

Research Assistant
Marketing Department
Marshall School of Business
University of Southern California 2009 – 2010

Staff Specialist
Office of Assessment
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Trinity College
Duke University 2007 – 2009

D. Service

Society Memberships (National):
American Psychological Association, Div. 38, Health Psychology 2014 -
Behavioral Science & Policy Association 2014 -
International Society for Behavioral Nutrition and Physical Activity2013 -
Society for Behavioral Medicine 2012 -
Society for Prevention Research 2010 -

President, Health Behavior Research Students’ Association
2013 – 2015
2011 – 2012

Campus Representative, APA Division 38, Health Psychology
Spring 2014 – Present

Reviewer or Assistant reviewer
Scientific Manuscripts Peer Reviewed Journals:
Journal of Obesity 2015
Advances in Nutrition and Food Technology 2014
Pediatric Obesity 2014
Appetite 2014
Journal of Medical Internet Research 2014
Journal of Health and Place 2014
Health Education & Behavior 2013
Prevention Science 2013
Journal of Psychoactive Drugs 2013
Annals of Behavioral Medicine 2013
American Journal of Lifestyle Medicine 2013
Journal of the National Cancer Institute 2012
Western Journal of Nursing Research 2012
Journal of Addiction 2012
Obesity Reviews 2012
Childhood Obesity 2010

Conference Abstracts:
Society for Prevention Research 2013

E. Consultantships:

Whole Foods® Market
Survey design, data analysis 2014 – Present
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County of Los Angeles, Department of Public Health
Division of HIV and STD Programs
Adolescent & School Health Unit 2014 – Present

Dairy Council of California
Team evaluation of nutrition education program,
Building a Healthy Me! 2013

F. Research Activities:

Research Grants

Charles Heidelberger Memorial
Pre-doctoral Scholarship Award (PI, Tate) 11/1/13 – 10/31/14 1 calendar yr. University
Southern California $4,500 (Total costs)

Positive deviance: Locating High-risk, Healthy Families and Discovering Protective
Strategies
This qualitative interview study will investigate perceptions and behaviors of two groups
of low-income mothers. One group will have school-aged children of healthy weight
who meet guidelines for physical activity and diet. The other will have school-aged
children who do not. Emergent themes, processes and behaviors that differ between
groups will shed light on strategies used by low-income mothers to improve child health
and decrease obesity risk.

Dissertation

Effects of Parent Stress on Obesity-related Parenting Practices and Child
Obesity Risk

Study 1: Associations of Latent Classes of Parenting Stress with Family Meals and Child
Body Mass Index in a Nationally-representative Sample of Parents and Children

Study 2: Direct and Indirect Effects of Parent Stress on Child Obesity Risk and Added
Sugar Intake in a Sample of Southern California Adolescents

Study 3: A Positive Deviance-Based Qualitative Study of Low-Income Mothers whose
Children Meet Federal Guidelines for Fruit and Vegetable Intake to Improve Obesity
Prevention Program Design

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Major Areas of Research Interest

Childhood obesity prevention: Parent-child interaction, reducing barriers to child
development of self-regulation of health behavior

Effects of food marketing on parent purchase behavior and child obesity risk

Mindful approaches to improving child self-regulation ability

Effects of poverty on risk factors for obesity (both family system and directly on child)

Influences of stress on parenting practices, food purchase choices, child dietary intake,
and physical activity parenting

BIBLIOGRAPHY

PEER REVIEWED JOURNAL ARTICLES

1. Liao, Y.*, Shonkoff, E.T.*, Barnett, E., Wen, C.K.F., Miller, K.A., & Eddy, J.M. (in press).
Brief Report: Examining children’s disruptive behavior in the wake of trauma: A two-piece
growth curve model before and after a school shooting. Journal of Adolescence.
*These authors contributed equally to the work and share first authorship

2. O’Reilly, G.A., Huh, J., Schembre, S.M., Tate, E.B., Pentz, M.A., Dunton, G. (2015)
Association of Dietary Intake with Ecological Momentary Measures of Affective and Physical
Feeling States in Children. (in press, Appetite)

3. Tate, E.B., Spruijt-Metz, D., Pickering, T., & Pentz, M.A. (2015). Two facets of stress and
indirect effects on child diet through emotion-driven eating. Eating Behavior. [E-pub ahead of
print] doi:10.1016/j.eatbeh.2015.04.006

4. Dunton, G. F., Liao. Y., Dzubur, E., Leventhal, A., Huh, J., Gruenewald, T., Margolin, G.,
Koprowski, C., Tate, E., Intille, S. (2015, in press). Investigating within-day and longitudinal
effects of maternal stress on children’s physical activity, dietary intake, and body composition:
Protocol for the MATCH study. Journal of Contemporary Clinical Trials.

5. Tate, E.B., Wood, W., Liao, Y., & Dunton, G.F. (2015). Do stressed mothers have heavier
children? A meta-analysis on the association between maternal stress and child body mass index.
Obesity Reviews, 16, 351-361. doi: 10.1111/obr.12262

6. Tate, E.B., Shah, A., Jones, M., Pentz, M.A., Liao, Y. & Dunton, G.F., (2014) Toward a Better
Understanding of the Link between Parent and Child Physical Activity Levels: The Moderating
Role of Parental Encouragement. Journal of Physical Activity & Health. [E-pub ahead of print]
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7. Thompson, R.J., Walther, I., Tufts, C.J., Lee, C., Paredes, L., Fellin, L., Andrews, E., Serra, M.,
Hill, J.L., Tate, E.B., & Schlosberg, L. (2014). Development and Assessment of the
Effectiveness of an Undergraduate General Education Foreign Language Requirement. Foreign
Language Annals, 47, 653-668.

8. Tate, E.B., Unger, J.B., Chou, C., Spruijt-Metz, D., Pentz,M.A., & Riggs, N.R. (2014).
Children’s Executive Function and High Calorie, Low Nutrient Food Intake: Mediating Effects
of Child-Perceived Adult Fast Food Intake. Health Education & Behavior, 42, 163-70. doi:
10.1177/1090198114547811

9. Tate, E.B., Spruijt-Metz, D., O’Reilly, G., Jordan-Marsh, M., Gotsis, M., Pentz, M.A., &
Dunton, G.F. (2013). mHealth approaches to child obesity prevention: successes, unique
challenges, and next directions. Translational Behavioral Medicine: Practice, Policy, Research,
3, 406-41. doi: 10.1007/s13142-013-0222-3

10. Little, M.A., Riggs, N.R., Shin, H-S., Tate, E.B., & Pentz, M.A. (2013). The Effects of Fidelity
of Implementation of the Pathways to Health on Student Outcomes. Evaluation & the Health
Professions. 38, 21-41. doi: 10.1177/0163278713489879.

11. Riggs, N. R., Tate, E. B., Ridenour, T. A., Reynolds, M. D. Zhai, Z. W., Vanyukov, M.M., &
Tarter, R. E. (2013). Longitudinal associations from neurobehavioral disinhibition to adolescent
risky sexual behavior in boys: direct and mediated effects through moderate alcohol
consumption. Journal of Adolescent Health, 53, 465-470.

12. Leary, M. R., Terry, M. L., Allen, A. B., & Tate, E. B. (2009). The concept of ego threat in
social and personality psychology: In ego threat a viable scientific construct? Personality and
Social Psychology Review, 13, 151 – 164.

13. Leary, M. R., & Tate, E. B. (2007). The multifaceted nature of mindfulness. Psychological
Inquiry, 18, 251 – 255.

14. Leary, M. R., Tate, E. B., Adams, C. E., Allen, A. B., & Hancock, J. (2007). Self-compassion
and reactions to unpleasant self-relevant events: The implications of treating oneself kindly.
Journal of Personality and Social Psychology, 92, 887- 904.

15. Leary, M. R., Adams, C. E., & Tate, E. B. (2006). Hypo-egoic self-regulation: Exercising self-
control by diminishing the influence of the self. Journal of Personality, 74, 1803-1831.

ARTICLES SUBMITTED FOR REVIEW

2. Y. Liao, E. Tate, and G.F. Dunton, (under review) Bi-directional acute relationships between
physical activity and affective states in daily life: A systematic review of evidence. Journal of
Sport & Exercise Psychology.

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3. Dunton, G. F., Liao. Y., Dzubur, E., Leventhal, A., Huh, J., Gruenewald,T., Margolin, G.
Koprowski, C., Tate, E., Intille, S. (under review). Within-day and longitudinal effects of
maternal stress on children’s physical activity, eating, and adiposity: Protocol for the MATCH
study. Journal of Contemporary Clinical Trials.

4. Shin, H, Shonkoff E.T., Black, D.S., Riggs, N., & Pentz, M.A. (under review). Early adolescent
positive psychology and cognitive skills: Associations of mindfulness, self-compassion, and
executive function proficiency. Journal of Applied School Psychology.

ARTICLES IN PREPARATION

1. Blackman, K.A., Tate, E.B., & Pentz, M.A. (in preparation). Differential moderating effects of
home food environment on child food intake.

2. O’Connor, S., Tate, E.B., , & Dunton, G.F. (in preparation). Associations between maternal
stress, depression and child dietary intake.

3. Dunton, G.F., Tate, E.B., & O’Connor, S. (in preparation). The impact of stress on child obesity
risk.

4. Tate, E.B, Dunton, G.F., Chou, C., Leventhal, A., Bluthenthal, R. & Pentz, M.A. (in
preparation). Direct and indirect effects of parent stress on child obesity risk and added sugar
intake in a sample of southern California adolescents.

5. Tate, E.B., Pentz, M.A., Chou, C., Bluthenthal, R., Leventhal, A., & Dunton, G.F. (in
preparation). Associations of latent classes of parenting stress with family meals and child body
mass index in a nationally-representative sample of parents and children.

6. Tate, E.B., Bluthenthal, R., Pentz, M.A., Leventhal, A., Chou, C. & Dunton, G.F. (in
preparation). A positive deviance-based qualitative study of low-income mothers whose children
meet federal guidelines for fruit and vegetable intake to improve obesity prevention program
design.

NON PEER REVIEW

1. Leary, M. R., Tipsord, J., & Tate, E. B. (2008). Allo-inclusive identity: Incorporating the natural
and social worlds into one’s sense of self. In H. Wayment & J. Bauer (Eds.), Transcending self-
interest: Psychological explorations of the quiet ego (pp. 137-148). Washington, DC: American
Psychological Association.

CHAPTERS

1. Leary, M. R., Adams, C. E., & Tate, E. B. (2010). Hypo-egoic self-regulation. In R. H. Hoyle (Ed.),
Handbook of personality and self-regulation. New York: Guilford.

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2. Leary, M. R., & Tate, E. B. (2010). The role of self-awareness in dysfunctional patterns of
thought, emotion, and behavior. In J. E. Maddux & J. P., Tangney (Eds.), Social psychological
foundations of clinical psychology. New York: Guilford.

LECTURES

1. Tate, E.B. (April 16, 2014): Child obesity: How did we get here and where do we go from here?
California State University Los Angeles, Guest lecture

2. Riggs, N.R.; delivered by Tate, E.B. (April 12, 2012): Social Marketing. University of Southern
California, Teaching Assistantship

3. Riggs, N.R.; delivered by Tate, E.B. (Feb. 23, 2012): Stress, Coping, and Health Behavior.
University of Southern California, Teaching Assistantship

4. Riggs, N.R.; delivered by Tate, E.B. (Jan. 24, 2012): Transtheoretical Model. University of
Southern California, Teaching Assistantship

5. Riggs, N.R.; delivered by Tate, E.B. (Nov. 10, 2011): Community Organizing, University of
Southern California, Teaching Assistantship

6. Riggs, N.R.; delivered by Tate, E.B. (March 31, 2011): Social Marketing. University of Southern
California, Teaching Assistantship

7. Riggs, N.R.; delivered by Tate, E.B. (Oct. 5, 2010): Stress, Coping, and Health Behavior.
University of Southern California, Teaching Assistantship

PROGRAM PAPER PRESENTATIONS

1. Shah, A., Tate, E.B., Liao, Y., Pentz, M.A., & Dunton,G.F. (March,
2013). Understanding the Link Between Parent and Child Physical Activity Levels: The
Role of Parental Influences. Panel presentation at the 34
th
Annual Meeting and Scientific
Sessions of the Society for Behavioral Medicine, San Francisco, CA. March 20- 23,
2013.

2. Leary, M. R., Adams, C. E., & Tate, E. B. (2005, August). Adaptive self-evaluations:
Self-compassion versus self-esteem. Paper presented at the meeting of the American
Psychological Association, Washington, DC.

POSTER PRESENTATIONS

1. Tate, E.B., Bluthenthal, R., Ramirez, C., Cipres, S., & Dunton, G.F. (July 2015). A positive
deviance-based qualitative study of low-income mothers and strategies for children’s health.
Poster presented at the 8th biennial Childhood Obesity Conference, San Diego, CA. June 29-July
2, 2015.
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2. Shin, H., Tate, E.B., Black, D.S., & Pentz,M.A. (April 2015). Psychological stress and
mindfulness effects on executive function difficulties and obesity risk behaviors in early
adolescence. Poster presented at the 36
th
annual meeting of the Society for Behavioral Medicine,
San Antonio, TX. April 22-15, 2015.

3. Black, D.S., Tate, E., Shin, H., Riggs, N., Pentz, M. (2014, Nov). Effects self-compassion and
dispositional mindfulness on HOT/COLD executive function proficiency in early adolescence.
Poster presented at the Stanford School of Medicine Science of Compassion Conference, San
Francisco, CA. Nov. 10-12, 2014.

4. Tate, E.B., Pentz, M.A., & Dunton, G.F. (May 2014). Do children of stressed parents eat
differently? Parental stress and child intake of sugar-sweetened beverages and sugar. Poster
presented at the annual meeting of the International Society for Behavioral Nutrition and
Physical Activity, San Diego, CA. May 21-24, 2014.

5. Tate, E.B., Spruijt-Metz, D., & Pentz, M.A. (May 2014). Negative effects of adolescents’
chronic stress on emotional eating, vegetable consumption and high-calorie, low-nutrient dietary
intake. Poster presented at the annual meeting of the International Society for Behavioral
Nutrition and Physical Activity, San Diego, CA. May 21-24, 2014.

6. Riggs, N.R., Tate, E.B., Ridenour, T.A., Reynolds, M., & Tarter, R. (May 2013). Longitudinal
Associations from Neurobehavioral Disinhibition to Adolescent Risky Sexual Behavior in Boys:
Direct and Mediated Effects through Heavy Alcohol Consumption. Poster presented at the 21
st

Annual Meeting of the Society for Prevention Research, San Francisco, CA. May 28-31, 2013.

7. Little, M.A., Riggs, N.R., Shin, H-S., Tate, E.B., & Pentz, M.A. (March, 2013). The effects of
fidelity of implementation of the Pathways to Health on Student Outcomes. Poster presented at
the 34
th
Annual Meeting and Scientific Sessions of the Society for Behavioral Medicine, San
Francisco, CA. March 20- 23, 2013.

8. Tate, E.B, Schembre, S.M., O'Reilly, G., Pentz, M.A., & Dunton, G. (March, 2013). Do Parent
Anhedonia and Stress Contribute to Weight-Related Parenting Practices, Child Dietary Fat
Intake and Child BMI Percentile? Poster presented at the 34
th
Annual Meeting and Scientific
Sessions of the Society for Behavioral Medicine, San Francisco, CA. March 20-23, 2013

9. O'Reilly,G.A, Huh, J., Schembre, S.M., Tate, E.B., Pentz, M.A., & Dunton, G.F (March,
2013). Diet Is Associated With Real-time Measures of Mood in Children. Poster presented at the
34
th
Annual Meeting and Scientific Sessions of the Society for Behavioral Medicine, San
Francisco, CA. March 20-23, 2013

10. Tate, E.B., Riggs, N.R., & Pentz, M.A. (2011, May-June). Relationship of Adult Eating and
Exercise with Children’s Executive Cognitive Functioning. Poster presented at the meeting of
the Society for Prevention Research, Washington, D.C.

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146

11. Tate, E. B., Allen, A. B., & Leary, M. R. (2007, May). Behavioral manifestations of life
satisfaction. Poster to be presented at the meeting of the Association for Psychological Science
in Washington, D.C.

12. Tate, E. B., Adams, C. E., Hancock, J., & Leary, M. R. (2005, August). Cognitive, affective, and
interpersonal features of self-compassion. Poster presented at the meeting of the American
Psychological Association, Washington, DC.

13. Tate, E.B., & Leary, M. R. (2007, January). The surprising effects of state mindfulness on
noncompliance with task instructions. Poster presented at the meeting of the Society for
Personality and Social Psychology, Memphis, TN.

14. Tate, E. B., & Leary, M. R. (2006, November). A preliminary study of egoic reactions to
inconsequential events. Poster presented at the meeting of the Southeastern Society for Social
Psychology, Knoxville, TN.

15. Tate, E. B., & Leary, M. R. (2006, August). The relationship between self-compassion and
physical and psychological well-being. Poster presented at the meeting of the American
Psychological Association, New Orleans, LA.

16. Tate, E. B., Adams, C. E., Allen, A. B., Tolbert, J., & Leary, M. R. (2006, March). Self-
compassionate reactions to negative events: Cognitive and motivational processes. Poster
presented at Graduate Research Day, Wake Forest University, Winston-Salem, NC.

17. Adams, C. E., Tate, E. B., Hancock, J., Mays, A., & Leary, M. R. (2006, January). Moderating
effects of self-compassion on reactions to interpersonal feedback. Poster presented at the meeting
of the Society for Personality and Social Psychology, Palm Springs, CA.

18. Tate, E. B., Adams, C. E., Hancock, J., Tolbert, J., Jarrell, A. & Leary, M. R. (2005, November)
The relative benefits of self-compassion vs. self-esteem. Poster presented at the meeting of the
Society of Southeastern Social Psychologists, Atlantic Beach, FL.

Abstract (if available)

Abstract Child obesity continues to be an important public health issue, with child overweight or obesity affecting almost 1 in 3 children in the United States. Recent research suggests that family stressors and parenting stress are associated with increased child obesity risk. However, the mechanisms underlying this link remain unclear. Potentially, parent stress decreases positive weight-related parenting practices, such as holding regular family meals, thereby increasing child obesity risk. The current dissertation examines how family stressors, parenting stress, and perceived psychosocial stress influence child diet by testing cross-sectional associations in a nationally-representative dataset and a longitudinal, mediational model using a regional sample. The first study will address how parenting stress, within the context of life stressors and regular family meals, is associated with child obesity risk

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Asset Metadata

Creator Shonkoff, Eleanor Barrow Tate (author)

Core Title Effects of parent stress on weight-related parenting practices and child obesity risk

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Preventive Medicine (Health Behavior)

Publication Date 09/10/2015

Defense Date 08/11/2015

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag child obesity,maternal stress,OAI-PMH Harvest,weight-related parenting practices

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Dunton, Genevieve F. (committee chair), Bluthenthal, Ricky (committee member), Chou, Chih Ping (committee member), Leventhal, Adam M. (committee member), Pentz, Mary Ann (committee member)

Creator Email ebtate79@gmail.com,eleanort@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-179158

Unique identifier UC11274325

Identifier etd-ShonkoffEl-3892.pdf (filename),usctheses-c40-179158 (legacy record id)

Legacy Identifier etd-ShonkoffEl-3892.pdf

Dmrecord 179158

Document Type Dissertation

Format application/pdf (imt)

Rights Shonkoff, Eleanor Barrow Tate

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA