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AN INVESTIGATION OF VARIABLES THAT PREDICT PARENTAL PERCEPTIONS OF CHILDREN’S WEIGHT STATUS: THE ROLE OF DEMOGRAPHICS, HEALTH-

RELATED QUALITY OF LIFE, AND WEIGHT-RELATED INFORMATION RECEIVED FROM HEALTH PROFESSIONALS

By

ROBERT JOSHUA WINGFIELD

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2013

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© 2013 Robert Joshua Wingfield

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To my parents, grandparents, aunts, uncles, nephews, cousins, and close friends They are my silver lining

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ACKNOWLEDGMENTS

First, I thank the members of my committee for their time, energy, and support.

Specifically, I thank my advisor and chair, Dr. Nancy Waldron, who guided me with

poise and precision from inception to completion of this dissertation. She always

provided clarity and direction when I needed it the most. I also express my gratitude to

Dr. Thomas Oakland. He is by far the best mentor one could ever hope for. He is a

rare and remarkable professor and person. I also thank Drs. David Janicke and James

Algina for enhancing the quality of my research by graciously offering their expert

advice.

I would like to thank my father, Robert A. Wingfield, for instilling confidence in me

at an early age. I believe in myself because he has always believed in me. He is my

hero. I also express gratitude to my mother, Yolandé L. Wingfield. She has

consistently fasted and prayed for my success and for that, I am eternally grateful. Her

love for me is palpable even when I am far from home. I also would like to thank my

siblings: Rachel, James, Jonathan, and Jessica. They are a consummate reminder of

who I am and where I have come from. I cherish being their brother. Words cannot

accurately describe what my grandparents mean to me. I am blessed to have three

grandparents in my life: Robert Wingfield, Jr., James A. Hinkle, and Marlyn Hinkle. I

thank them for providing me with vital emotional and spiritual support from kindergarten

through graduate school. Finally, I thank God. I’ve dreamt of this moment numerous

times for many years. Without God’s grace and mercy, the fruition of this dream would

not have occurred.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 7

ABSTRACT ..................................................................................................................... 9

CHAPTER

1 INTRODUCTION .................................................................................................... 11

National and School Context for the Issue of Obesity ............................................. 11 Medical Impact ................................................................................................. 11

Social-Emotional Impact ................................................................................... 12

Academic Impact .............................................................................................. 13 Healthcare Costs .............................................................................................. 14

Reasons Childhood Obesity is Increasing .............................................................. 14

Ecological Systems Theory .............................................................................. 15 Physiological Risk Factors ................................................................................ 16

Environmental Risk Factors .............................................................................. 17 School Environment ......................................................................................... 18 Disparities ......................................................................................................... 18

2 REVIEW OF LITERATURE .................................................................................... 23

Factors Influencing Weight Perception Accuracy .................................................... 23

Transtheoretical Model ..................................................................................... 23 Optimistic Bias .................................................................................................. 26

Parental Perceptual Bias .................................................................................. 28 Trends in Misperception of Children’s Weight .................................................. 29 Demographic Variables .................................................................................... 30

Non-Demographic Variables ............................................................................ 38 Defining Health-Related Quality of Life ............................................................ 38 Health-Related Quality of Life and Childhood Obesity ..................................... 39 Relevant Studies .............................................................................................. 42 Information from Health Professionals ............................................................. 47

Summary ................................................................................................................ 51

3 METHODS AND PROCEDURES ........................................................................... 56

Participants and Setting .......................................................................................... 56 Procedures ............................................................................................................. 56

Questionnaire Data .......................................................................................... 56 Height and Weight Measurements ................................................................... 58

Sociodemographic Data ................................................................................... 58

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Measures ................................................................................................................ 59

Parent Demographics and Perceptions Questionnaire ..................................... 59 Pediatric Quality of Life Inventory ..................................................................... 60

Detecto Physician Scale ................................................................................... 63

4 RESULTS ............................................................................................................... 65

Data Analysis .......................................................................................................... 66 Preliminary Analyses ........................................................................................ 67 Demographics .................................................................................................. 67

Statistical Analyses ................................................................................................. 69 Primary Research Question ............................................................................. 69 Second-Level Questions .................................................................................. 70

Summary ................................................................................................................ 75

5 DISCUSSION ......................................................................................................... 87

Implications for Practice .......................................................................................... 93

The Role of Parents ......................................................................................... 93 The Role of Schools ......................................................................................... 94

Helping Parents ................................................................................................ 95 Limitations ............................................................................................................... 96 Conclusions ............................................................................................................ 97

LIST OF REFERENCES ............................................................................................. 102

BIOGRAPHICAL SKETCH .......................................................................................... 117

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LIST OF TABLES

Table page 3-1 Demographic characteristics: frequencies and percentages .............................. 64

4-1 Weight perceptions: frequencies and percentages according to type of estimates ........................................................................................................... 77

4-2 Demographic characteristics: frequencies and percentages according to race and weight ................................................................................................. 77

4-3 Summary of tests of multiple degree of freedom categorical independent variables in logistic regression for parental perceptions of children’s weight status .................................................................................................................. 78

4-4 Summary of logistic regression analysis for parental perceptions of children’s weight status ...................................................................................... 78

4-5 F statistics for multiple degree of freedom categorical independent variables for body mass index percentile ........................................................................... 79

4-6 Summary of linear regression analysis for body mass index percentile ............. 79

4-7 F statistics for multiple degree of freedom categorical independent variables for physical functioning ....................................................................................... 80

4-8 Summary of linear regression analysis for physical functioning ......................... 80

4-9 F statistics for multiple degree of freedom categorical independent variables for emotional functioning .................................................................................... 81

4-10 Results of linear regression analysis for emotional functioning .......................... 81

4-11 F statistics for multiple degree of freedom categorical independent variables for social functioning ........................................................................................... 82

4-12 Results of linear regression analysis for social functioning ................................. 82

4-13 F statistics for multiple degree of freedom categorical independent variables for school functioning .......................................................................................... 83

4-14 Results of linear regression analysis for school functioning ............................... 83

4-15 Summary of tests of multiple degree of freedom categorical independent variables in logistic regression for doctor’s visit in the last six months ................ 84

4-16 Summary of logistic regression analysis for doctor’s visit in the last six months ................................................................................................................ 84

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4-17 Summary of tests of multiple degree of freedom categorical independent variables in logistic regression for disclosure of child’s weight status ................. 85

4-18 Summary of logistic regression analysis for physician disclosure of child’s weight ................................................................................................................. 85

4-19 Summary of tests of multiple degree of freedom categorical independent variables in logistic regression for physician giving overweight diagnosis .......... 86

4-20 Summary of logistic regression analysis for physician giving overweight diagnosis ............................................................................................................ 86

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

AN INVESTIGATION OF VARIABLES THAT PREDICT PARENTAL PERCEPTIONS OF CHILDREN’S WEIGHT STATUS: THE ROLE OF DEMOGRAPHICS, HEALTH-

RELATED QUALITY OF LIFE, AND WEIGHT-RELATED INFORMATION RECEIVED FROM HEALTH PROFESSIONALS

By

Robert Joshua Wingfield

August 2013

Chair: Nancy L. Waldron Major: School Psychology

Children who are overweight or obese rely heavily on support from adults,

particularly parents, in order to improve their health. Viewed within the transtheoretical

model of behavior change for weight control, parents of overweight children who do not

see their child as above healthy weight are at the precontemplation stage, which means

they seemingly have no desire to help their child become a healthier weight. Thus,

parental-perception-accuracy of children’s weight status is vital. Several demographic

variables may be expected to affect parental perceptions of their children’s weight

status, including race, family SES, child’s sex, child’s weight, and child’s age. Non-

demographic variables also may affect parents’ perceptions of their child’s weight

status, including the child’s health-related quality of life, and weight-related information

received from health professionals. Although, the potential impact of these variables on

parental weight-perception-accuracy seems plausible, a comprehensive model that

includes each variable has not been tested. Thus, a primary goal of this study was to

investigate the extent to which these identified variables predicted weight-perception-

accuracy in parents of children ages 5-14. A primary research question for this

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proposed study was the following: How well do demographics, health-related quality of

life, and weight-related information from health professionals predict parental

perceptions of their child’s weight status? Measures included the Parental

Demographics and Perceptions Questionnaire (PDPQ), the Pediatric Quality of Life

Inventory (PedsQL), and children’s body mass index (BMI).

Logistic regression analysis was completed to examine the relationship of

parental perceptions of children’s weight status and both demographic and non-

demographic variables. Results revealed that parents of children who were overweight

or obese were more likely to display misperceptions about their child’s weight status.

Specifically, many parents of overweight children erroneously believed that their child

was normal weight. Similarly, parents of obese children erroneously believed that their

child was overweight or normal weight. These are considered to be errors of

underestimation. Furthermore, an association was found between mental health and

weight misperception. Specifically, if a child who was overweight or obese had a mental

health issue parents were more likely to underestimate their child’s weight status. An

association also was found between being informed of the child’s weight status during a

physician visit and parental perceptions. Specifically, if parents reported being informed

of their child’s weight status by a health professional, parental-perception-accuracy

increased.

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CHAPTER 1 INTRODUCTION

National and School Context for the Issue of Obesity

Obesity among children constitutes a serious health concern in America. Many

people are aware of its deleterious effects. For example, during the past three years,

adults have rated obesity as the leading health problem facing children in the United

States--ahead of drug abuse, tobacco use, internet safety, stress, bullying, teen

pregnancy, child abuse, child neglect, and alcohol abuse (Mott, 2010). However,

general public awareness of a problem does not necessarily translate into concerns that

impact individuals (Campbell, Williams, Hampton, & Wake, 2006). Approximately

10.4% of children age 2-5 are obese. This percentage is nearly double for children age

6-11 (19.6%) and adolescents age 12-19 (18.1%; National Center for Health Statistics,

2011). When the category identified as overweight is included, data from the

International Obesity Task Force indicate that 35% of American children are overweight

or obese (Lobstein & Jackson-Leach, 2007). Thus, approximately one in three children

in the U.S. are above-healthy weight (Bethell, Simpson, Stumbo, Carle, & Gombojav,

2010). Alarmingly, between 2007 and 2009 there was an increase of 1.1% across all

age groups, which corresponds to an additional 2.4 million individuals being obese

(Yanovski & Yanosvski, 2011). This has led to the projection that by 2050, almost the

entire U.S. population will be overweight (BMI > 25 kg/m2) or obese (BMI > 30 kg/m2;

2011). Thus, obesity has reached epidemic proportions, and shows no sign of abating.

Medical Impact

The obesity epidemic has led to a number of government and corporate- driven

health campaigns such as Let’s Move, Play60, Campaign to End Obesity, and Small

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Step. These campaigns have focused on reducing both the prevalence of and the

negative health consequences associated with childhood obesity, including non-insulin

dependent diabetes, hypertension, and sleep disturbances. Children who are obese

also are placed at greater risk for experiencing stroke, osteoarthritis, gallbladder

disease, cancer, heart disease (caused by high cholesterol or high blood pressure), and

premature death (Blom-Hoffman, 2004). Approximately 60% of children and

adolescents who are overweight have at least one additional risk factor for

cardiovascular disease (e.g. elevated blood pressure, hyperlipidemia, or

hyperinsulinemia) and more than 25% have two or more of these risk factors (Dietz,

2004). Obese children also commonly report experiencing pulmonary problems. For

example, among enrollees in a hospital-based weight control program, nearly 30% of

children who were obese were asthmatic (Must & Strauss, 1999). These health

complications are likely to persist into adulthood (American Academy of Pediatrics,

2003).

Social-Emotional Impact

Childhood obesity also is associated with a number of negative psychological

outcomes including depression, disturbed body image, and low self-concept (Davison &

Birch, 2001). Adolescents who are obese with decreasing self-esteem are likely to

report increased levels of loneliness, sadness, and nervousness, and are more likely to

smoke and abuse alcohol (Strauss, 2000). Discrimination against children who are

obese tends to begin early in childhood and becomes progressively institutionalized

(Dietz, 1998). Acts of cruelty (e.g. teasing, discrimination and victimization) of children

who are obese have become commonplace, especially at school. School-age children

who are obese are more likely than their normal-weight peers to be the perpetrators and

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the victims of bullying behaviors (Janseen, Boyce, Craig, & Pickett, 2004). It seems that

obesity predicts bullying involvement for both girls and boys as a result of their physique

deviating from appearance ideals (Griffiths, Wolke, Page, & Horwood, 2006).

Furthermore, peer victimization is linked to lower self-reported quality of life in children

who are overweight (Janicke, Marceil, Ingerski, Novoa, et al., 2007). Children who are

overweight and depressed, may be more likely to engage in unhealthy eating patterns

or more sedentary behaviors due to reduced stamina or lack of interest in social

interactions and physical activity (2007). Landmark studies conducted in the 1960s

reveal that children who are obese are uniformly ranked by other children as the least

desired friends, which further limits their social interactions (Must & Strauss, 1999).

These experiences may hinder the short- and long-term social and psychological

development of youth who are obese (Janseen et al., 2004).

Academic Impact

Obesity is associated with lower academic achievement, a 32% higher likelihood

to repeat a grade (Bethell, Simpson, Stumbo, Carle, & Gombojav, 2010), lower

attendance, underrepresentation in gifted and talented programs, decreased college

enrollment (particularly for females), increased likelihood of being identified as an

individual with a disability requiring special education services (Rimm, 2004),

depression, and negative self-image, and low self-efficacy. Educators also may display

negative biases toward overweight students and are more likely to describe them as

untidy, less likely to succeed, more emotional, and displaying psychological problems

(Neumark-Sztainer, Story, & Harris, 1999; Puhl & Brownell, 2003).

Whether obesity causes these educational outcomes or instead has an

association by means of other factors is difficult to ascertain. For example, exercise has

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been found to decrease symptoms of depression, anxiety, attention deficit hyperactivity

disorder (ADHD), and stress while improving learning ability and executive functioning.

Given that obesity is related to lower levels of exercise, perhaps the reduced activity

level associated with obesity, not obesity itself, is responsible for the impact on learning

and academic achievement. Regardless, students who are overweight are at increased

risk for various negative educational outcomes and represent an identifiable target

group that warrants intervention.

Healthcare Costs

The increased prevalence of overweight and obesity has led to increased

healthcare costs. On average, individuals who are obese pay 42% more in healthcare

expenses than their normal weight peers. Compared to normal weight clients, Medicare

pays $1,723 more; Medicaid pays $1,021 more; and private insurers pay $1,140 more

for obese beneficiaries (WIN, 2010). A growing number of individuals who are obese

have sought bariatric surgery as a treatment option. This procedure costs an estimated

$25,000-$35,000 per person, which does not include the cost of follow-up visits and

various types of pre-and post-operative counseling (Gastric Bypass Facts, 2009).

Justifiably, the U.S. Surgeon General referred to obesity as “a public health issue that is

among the most burdensome faced by the nation” (Blom-Hoffman, 2004; U.S.

Department of Health and Human Services, 2001, p.1).

Reasons Childhood Obesity is Increasing

The following section discusses factors implicated in the steady rise of childhood

obesity rates. Ecological Systems Theory (EST) is described as it provides a

framework to introduce and discuss factors believed to play important roles in the

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childhood obesity epidemic. These factors range from physiological to environmental

risks.

Ecological Systems Theory

Ecological Systems Theory, first introduced by Urie Bronfenbrenner in the 1970s

(Bronfenbrenner, 1974, 1976, 1977, 1979), highlights the importance of considering the

context or ecological niche in which a person is located in order to understand the

emergence of a particular characteristic (e.g. obesity). Two propositions specify the

central properties of Bronfenbrenner’s EST model. The first proposition states that

human development takes place through processes of progressively more complex

reciprocal interaction between an active, evolving biopsychological human organism

and the persons, objects, and symbols in its immediate environment. The presence of a

meaningful impact requires regular interactions. These interactions are referred to as

proximal processes. The second proposition states that the form, power, content, and

direction of the proximal processes effecting development vary systematically as a joint

function of the characteristics of the developing person (Bronfenbrenner, 1994).

According to Bronfenbrenner, the ecological system is composed of five socially

organized subsystems that help support and guide human growth. Level one, the

microsystem, is a pattern of activities, social roles, and interpersonal relations

experienced by the developing person. Level two, the mesosystem, comprises the

linkages and processes taking place between two or more settings containing the

developing person (e.g. the relations between home and school). Level three, the

exosystem, comprises the linkages and processes taking place between two or more

settings, at least one of which does not contain the developing person yet indirectly

influences processes within the immediate setting in which the developing person lives.

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Level four, the macrosystem, consists of the overarching pattern of micro-, meso-, and

exosystem characteristics of a given culture or subculture, specifically the material

resources, belief systems, bodies of knowledge, lifestyles, opportunity structures,

hazards, and life course options that are embedded in each of the broader systems.

Level five, the chronosystem, encompasses change or consistency over time for the

developing person and the environment in which that person lives (e.g. changes in

employment, family structure, residence; Bronfenbrenner, 1994).

Ecological Systems Theory can be useful for researchers engaged in the study of

obesity as it allows one to consider how a person’s weight is influenced by

characteristics ranging from the individual to society. In the case of a child, the

ecological niche includes the family and the school, which are in turn embedded in

larger social contexts including the community and society at large (Davison & Birch,

2001). Ethnicity, socioeconomic status (SES), work demands, school lunch programs,

school physical education , neighborhood safety, accessibility to recreational facilities,

and access to convenience foods and restaurants constitute some factors known to

influence a child’s weight (DeMattia and Denney, 2008). These factors interact in

various ways. These proximal processes play a crucial role in whether a child maintains

a healthy weight. Both physiological and environmental risk factors are discussed

below.

Physiological Risk Factors

Some physiological risk factors of childhood obesity include parental fatness,

birth weight, and timing or rate of maturation (Danielzik, Czerwinski-Mast, Dilba,

Langanse, & Muller, 2004). Furthermore, the heritability of body weight is high, and

genetic variation plays a significant role in determining the interindividual differences in

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susceptibility or resistance to the obesogenic environment (Ramachandrappa, 2011).

For example, if a child has two overweight parents (hence genetic predisposition), a

small increase in dietary consumption may produce a larger increase in weight gain

compared to a child with no familial obesity with identical food intake (Francis, Ventura,

Marini, & Birch, 2007). However, a single gene does not determine one’s weight. While

quantitative genetic studies indicate how much of the variation of weight is due to

genetic differences between persons, the genes are not identified. Weight variation is

likely due to many genes, each exerting small effects, because no major genes for

common obesity have been identified (Bell, Walley, & Froguel, 2005; Wardle, Carnell,

Haworth,& Plomin, 2008). Furthermore, part of the genetic effect may be due to

variations in appetite and satiety (e.g. feeling full), not just to the biology of fat storage

(O’Rahilly, Farooqi, Yeo, & Challis, 2003).

Environmental Risk Factors

Biological factors notwithstanding, the approximate 400% increase in childhood

obesity over the last four decades suggests that environmental factors play an important

role (Hill & Melanson, 1999; Rey-Lopez, Vincente-Rodriguez, Biosca, & Moreno, 2008).

In fact, twin and adoption studies consistently demonstrate that 20% to 50% of the

variation in body fatness is unrelated to genetic factors (Strauss, 1999). Food

commercialism, technology, and urban sprawl are contributing to the formation of what

is termed ‘obesogenic environments’ that are fostering over-eating and inactivity

(Maziak, Ward, & Stockton, 2008). Factors contributing to sedentary lifestyles in

children (e.g. television time, computer usage, unavailability of playgrounds,

neighborhood structure and safety, and school curricula) have been associated with the

obesity epidemic (Reilly, 2006; Maziak et al., 2008). Furthermore, radical change

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occurred in how people obtain and consume their food. Popular foods among children

have become more energy dense including cereals, soft drinks, and fast food (Mattes,

2006). Such food can reduce satiety and increase appetite, leading to overeating in

children and adolescents (Ludwig, Majzoub, Al-Zahrani, Dallal, Blanco, & Roberts,

1999; Rolls, 2000).

School Environment

A review of conditions in the school environment that may contribute to obesity is

worthy of special consideration because children spend a substantial amount of time

there. Over the past few decades, schools have changed in ways that may

inadvertently demote health in children. According to the National Center for Chronic

Disease Prevention and Health Promotion, only 1 in 3 students participate in daily

physical education (PE; NCCDPHP, 2010). In addition, few schools provide daily PE or

its equivalent for students for the entire school year. For example, only 3.8% of

elementary schools (excluding kindergarten), 7.9% of middle schools, and 2.1% of high

schools provide daily PE for the entire student body throughout the school year (Kann,

Brener, & Wechsler, 2007). Physical activity and health contribute to learning,

executive functioning, and academic achievement. However, too often, PE programs

are disregarded or are the target of budget cuts in favor of programs that are perceived

to be more directly linked to academic goals (Martin & Chalmers, 2007).

Disparities

The following section discusses factors associated with disparities in prevalence

rates of childhood obesity. These factors include race, eating patterns and

environment, socioeconomic status, and weight perceptions.

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Race. Racial disparities in obesity prevalence are already apparent by

preschool, with the highest prevalence found among American Indian/Native Alaskan

children, an intermediate prevalence among Hispanic and Black children, and the

lowest prevalence among White children (Anderson & Whitaker, 2009). Racial

disparities in childhood obesity may be determined even earlier than preschool by

factors that operate in pregnancy, infancy, and early childhood. For example, Black and

Hispanic children are at increased risk of rapid weight gain, shorter sleep duration

during infancy, more televisions in bedrooms, higher sugar-sweetened beverage intake,

and higher intake of fast food [compared with White children] (Taveras et al., 2010).

Differences in eating patterns and environment may help explain these racial

disparities (Kumanyika, 2008; Yancey & Kumanyika, 2007). For example,

predominantly Black or low-socioeconomic status (SES) communities tend to have a

higher density of fast food restaurants and fewer grocery stores nearby compared to

predominantly White or higher income neighborhoods (Gordon-Larsen, Nelson, Page, &

Popkin, 2006; Story, Kaphingst, Robinson-O’Brien, & Glanz, 2008). In addition, low-

SES communities tend to have fewer physical activity facilities, such as playgrounds,

parks, or YMCAs available (Gordon-Larsen et al., 2006; Sallis & Glanz, 2006), and

accessing them may be less safe. Cultural or regional patterns of eating also may

influence obesity. For example, Black children consume more calories and higher

levels of fat, cholesterol, and carbohydrates compared to White children; Hispanics

have the lowest vegetable consumption; American-Indians have the lowest fruit

consumption; and Asian-Americans consume the least amounts of fat and dairy (Patrick

& Nicklas, 2005). Further, a significantly higher prevalence of diabetes and obesity

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occurs in a recently identified cluster of states in the southeastern portion of the country,

referred to as the “diabetes belt,” compared to other parts of the country (Barker,

Kirtland, Gregg, Geiss, & Thompson, 2011). This suggests an impact of regional

lifestyle differences on food consumption and thus on weight.

Socioeconomic status. Racial disparities in obesity may be largely attributable

to differences in SES. However, given the strong association between race and SES,

attempts to tease apart the individual effects of each of these two factors are extremely

difficult (e.g., Kumanyika, 2008). In general, those living in poverty and with low

education levels (including children living in those households) are more likely to be

obese (Baum & Ruhm, 2009). In fact, between 2003 and 2007 obesity prevalence

increased by 23% to 33% for children in low-SES households whereas no significant

increases in obesity were observed for children in other socioeconomic groups (Singh,

Siahpush, & Kogan, 2010). Being born and reared in higher SES households may lead

to greater lifetime access to health information that encourages weight control and a

healthier lifestyle. These findings highlight the importance of understanding one’s value

system, which often develops based on SES, before making judgments or developing

interventions for families with overweight children.

The high cost of nutritionally sound foods has been identified as a barrier to

eating healthfully. A study that compared the cost of a standard market basket (based

on the U.S. Department of Agriculture's Thrifty Food Plan) with a more nutritionally

sound market basket, found that the cost of the Thrifty Food Plan was $194, while the

cost of the healthier market basket was $230 (Jetter & Cassady, 2006). The latter was

more expensive due to higher costs of whole grains, lean ground beef, and skinless

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poultry. The healthier basket is equal to 35 to 40% of low-income consumers' yearly

food budget of $2,410. The diet of lower SES groups provide cheap energy from such

foods as meat products, full cream milk, fats, sugars, preserves, potatoes, and cereals,

and includes few vegetables, fruits, and whole-wheat breads (James, Nelson, Ralph, &

Leather, 1997). Compared to the diet of higher SES groups, this diet is lower in

essential nutrients (e.g. calcium, iron, magnesium, folate, and vitamin C).

Weight perceptions. Weight-perception-accuracy is defined as the correct

identification of the weight category (i.e. underweight, normal weight, overweight,

obese) to which a person falls within based on objective criteria endorsed by the

Centers for Disease Control and Prevention (CDC). Conversely, weight misperception

occurs when a person incorrectly estimates the weight category to which a person falls

within. Compared to White Americans, weight misperception appears to be significantly

higher for Mexican Americans, Black Americans, Hispanic Americans, and Native

Americans (Dorsey, Eberhardt, & Ogden, 2009; Neff Sargent, McKeown, Jackson, &

Valois, 1997; Paeratakul, White, Williamson, Ryan, & Bray, 2002). It appears that

gender also influences weight-perception-accuracy. For example, mothers are more

likely to identify daughters as overweight even when sons are categorically overweight.

This suggests that social standards for males and females often differ. Furthermore,

SES may be a relevant factor in weight-perception-accuracy (Campbell, Williams,

Hampton, & Wake, 2006; Maynard, Galuska, Blanck, & Serdula, 2003; Jeffery, Voss,

Metcalf, Alba, & Wilkin, 2005). For example, low-income mothers were found to believe

that having a larger child indicated that the child was healthy and demonstrated that

they were good parents (Baughcum et al., 2000).

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Although there is no direct evidence that increasing parents’ awareness of

children’s excessive weight would prevent obesity, there is some evidence suggesting

that parents’ awareness and monitoring can help reduce risky behavior among youth

(Stanton, Cole, Galbraith, Li X, Pendleton, Cottrel et al., 2004). In the case of weight,

parents who do not recognize excessive weight in their children are less likely to take

steps to change their children’s unhealthy lifestyles (He & Evan, 2007). An under-

standing of why certain groups perceive weight differently may assist professionals in

developing interventions that minimize misperceptions.

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CHAPTER 2 REVIEW OF LITERATURE

Factors Influencing Weight Perception Accuracy

The following review discusses the importance of weight perception and factors

that influence weight-perception-accuracy. Research on parental perceptions of their

children’s weight indicates that parents often use unreliable methods to estimate the

weight of their children, which results in erroneous perceptions. For example, parents

have been shown to misunderstand or distrust clinical measures and instead base their

judgments on comparisons with other children (Jones, Parkinson, Drewett, Hyland,

Pearce, & Adams, 2011). However, such approaches frequently rely heavily on

extreme cases as a reference point (e.g. comparing one’s own son or daughter to

morbidly obese children).

In addition, this review discusses the transtheoretical model of behavior change

(DiClemente & Prochaska, 1982; Prochaska & DiClemente, 1983) to address reasons

why weight-perception-accuracy is important from an intervention perspective.

Optimistic bias (Weinstein, 1980) also is discussed to help explain why parental weight

misperception may occur from a psychological perspective (e.g. coping mechanism).

Finally, several demographic and non-demographic variables associated with weight

perception are discussed to underscore common characteristics of parents who

misperceive their children’s weight status.

Transtheoretical Model

Recent research on weight management has begun to focus on the

transtheoretical model (TTM) of behavior change (and stages of change) to better

incorporate the role of parental perception into the management of childhood weight

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problems (Rhee, De Lago, Arscott-Mills, Mehta, & Davis, 2005). This model identifies

five stages of behavior change that categorize the transition from having no interest in

changing behavior to maintaining such changes after they are made. The stages

include (1) precontemplation, (2) contemplation, (3) preparation, (4) action, and (5)

maintenance. The model has been validated for various health behaviors in adults

(Greene & Rossi, 1998; Ounpuu, Woolcott, & Greene, 2000) including smoking

cessation (Prochaska&DiClemente, 1983; Prochaska, DiClemente, Velicer, & Rossi,

1993), alcohol use (Heather, Rollnick, & Bell, 1993), and preventive health behaviors

(Prochaska, Velicer, Rossi et al., 1994). Additionally, the model has been validated in

gauging parents’ readiness to work toward changing behaviors in their children (Rhee et

al., 2005). Parents are more likely to be ready to make changes if they believe the

child’s weight is a problem and if the child is older than seven years of age (Rhee and

colleagues, 2005). The five stages of the TTM are described below.

During the precontemplationstage, people do not intend to take action any time

soon (i.e. within the next six months). Individuals may be in this stage because they are

uninformed or under-informed about the consequences of their behavior. For example,

a person who is overweight but considers his or herself to be a healthy weight may fall

within this stage. During this stage, individuals often are characterized as unmotivated.

During the contemplation stage, people do intend to change their behaviors during the

next six months. These individuals are more aware than ‘precontemplators’ of the

advantages of changing and also are acutely aware of the drawbacks of change. Too

much focus on the drawbacks of change can lead to profound ambivalence and often

traps people in contemplation for long periods of time. During the preparation stage,

25

people intend to take action soon (i.e. within the next month). Individuals in the

preparation stage typically have taken some important step toward the behavior in the

past year. In addition, they have a plan of action (e.g. joining a gym, talking to their

physician, or buying a self-help book). During the action stage, people have made

specific, overt modifications in their lifestyles (e.g. exercising or eating healthier foods).

Finally, during the maintenance stage people are working to prevent a relapse.

Individuals in the maintenance stage are less tempted to relapse and more confident

that they can continue their changes (Glanz, Rimer,& Viswanath, 2008).

Within the Stages of Change Model for weight control, an assessment is made

regarding the parent’s awareness of their child’s weight status. Parents of overweight

children who do not recognize that their child is above-healthy weight, are more likely to

have no interest in changing existing behaviors (i.e. precontemplation stage) and

unlikely to acknowledge that their child’s weight is a problem (Towns & D’Auria, 2009).

Conversely, parents who perceive their child’s weight is a problem are more likely to

employ changes (Rhee et al., 2005). Parents play a vital role in helping children

maintain a healthy weight by establishing a healthy environment and demonstrating

normative behaviors that support healthy eating and physical activity (Davison & Birch,

2001). Compared to their normal weight peers, children who are overweight often

require additional support in modifying their eating and physical activity patterns to

prevent additional weight gain (Hearst, Sherwood, Klein, Pasch, & Lytle, 2011). Parents

who perceive their child to be overweight are more likely to model healthy habits (May,

Donohue, Scanlon et al., 2007; Parry, Netuveli, Parry, et al., 2008). In view of this,

many health professionals consider weight status recognition a necessary prerequisite

26

before instructing parents about exercise and dietary plans. Parents are unlikely to

intervene if they do not first realize that a problem exists and understand why they need

to be concerned (Etelson, Brand, Patrick, & Shirah, 2004).

Optimistic Bias

An optimistic bias occurs when a person unjustifiably minimizes his or her

personal health risk or the health risk of a loved one (e.g. child). Optimistic bias is a

potent phenomenon that may distort one’s perception of weight. For example, a person

may view other people as being at risk yet does not see himself as having a similar

degree of vulnerability (Young-Hyman et al., 2000). Optimistic bias occurs frequently

and found to influence behaviors in various contexts (Chapin, 2001).

On the other end of the spectrum lies pessimistic bias. Pessimistic bias is an

effect in which people exaggerate the likelihood that negative things will happen to

them. Unlike optimistic bias, pessimistic bias almost never occurs (Gordon, Dooley,

Camfield, Camfield, & MacSween, 2002; Weinstein, 1989). Optimistic bias is

associated with a variety of potential hazards and negative life events (Miles & Scaife,

2003). These include being the victim of a mugging, being injured in a fire, being in a

car accident, becoming overweight, being injured while bungee jumping, committing

suicide, having an unwanted pregnancy, suffering smoking-related diseases, suffering

skin damage from the sun, getting AIDS or becoming infected with HIV, suffering food

poisoning, getting cancer, suffering a heart attack, becoming an alcoholic, and getting

tooth decay (Boney McCoy et al., 1992; Burger & Burns, 1988; DeJoy, 1989; Eiser &

Arnold, 1999; Fontaine & Smith, 1995;Frewer et al., 1994; Helweg-Larsen, 1999;Lek&

Bishop, 1995; Middleton et al., 1996; Raats et al., 1999, vander Velde et al., 1992;

Weinstein, 1980, 1982, 1984, 1987; Whalen et al., 1994). This body of research

27

indicates that optimistic bias is not found for all hazards (Sparks & Shepherd, 1994;

Weinstein, 1980, 1982, 1987). For example people have reported being extremely

optimistic that they will not develop a drinking problem while reporting far less optimism

about not being injured in an automobile accident. People even have different thoughts

about their likelihood of being a victim of similar types of crimes. For example, people

reported their chances of being the victim of a mugging were much lower than others,

while their chances of being the victim of a burglary was about the same as others

(Weinstein, 1980).

Although the tendency of humans to feel invulnerable is common, this feeling

may lead to irrational notions that one is immune to misfortune or adverse

consequences. Seminal studies on optimistic bias indicate that people have a tendency

to be unrealistically optimistic about the future (Weinstein, 1980). That is, people tend

to believe that they are less likely to experience negative events and more likely to

experience positive events than their peers (Miles & Scaife, 2003). Classic surveys

related to motor vehicle accidents (Roberts, 1977), crime (Weinstein, 1980), and

disease (Harris & Guten, 1979; Kirscht et al., 1966) find many people report their risk is

less than average while few report their risk is greater than average (Weinstein, 1980).

These findings have withstood the test of time. For example parents of children with

and without epilepsy were unrealistically optimistic about their children’s future health

(Gordon et al., 2002). Unrealistically high levels of optimism intersect with self-

enhancement bias, a form of bias that involves a person’s inclination to see their self in

a positive light, even when the evidence suggests otherwise (Fiske, 2003). Self-

enhancement bias is slightly different than optimistic bias because the former refers to

28

one’s self-concept or self-identity while the latter refers to one’s self-perceived

vulnerability to risks. Self-enhancement bias may be so potent that unwarranted claims

of objectivity persists even when people are informed about the prevalence of the bias

and invited to acknowledge its influence (Pronin, 2007).

Parental Perceptual Bias

Parental perceptual bias occurs when parents hold excessively favorable views

of their children. For example, it’s common for parents to believe that their toddler is the

“smartest kid in the universe” without evidence from assessment instruments that

purportedly provide an objective measure of intellectual functioning or academic

achievement to support their belief. Parental perceptual bias is not limited to

perceptions of intellect. Indeed, it may be exhibited when assessing any type of child

behavior or trait. For example, the accuracy of parental report of child temperament has

been questioned by researchers because parents’ perceptions of their children’s

behavior may be distorted by attitudes and attributions not based on a child’s actual

characteristics (Luby & Steiner, 1993). A number of theories have been offered to

explicate people’s partiality. First, persons who accept the reality that warnings of

susceptibility emerge early must acknowledge the likelihood of future risks (Weistein,

1989). For example, persons will express increased concern about acquiring a life-

altering condition such as Type II diabetes if they make the connection between

childhood obesity, adult obesity, and weight-related chronic diseases. Second,

optimistic bias functions as a coping mechanism to promote human resilience. For

example, optimism is associated with less depression. Hence, parents who foresee

chronic disease as a highly probable part of their child’s future may find this information

to be too distressing to accept, especially if they lack control and knowledge on how to

29

modify the risks. Thus, optimistic bias may represent a barrier to effective risk

messages because persons with this bias believe that the messages need to be

directed toward a more vulnerable group and fail to take personal precautions against

foreseeable hazards. Therefore, optimistic biases in risk perceptions are important to

examine as they may significantly hinder acknowledgment of risks and efforts to

promote risk-reducing behaviors, such as exercise and healthful eating (Weistein,

1989).

Trends in Misperception of Children’s Weight

The proposed study defines misperception as a belief held by a person who is

underweight, overweight, or obese that he or she is a normal weight. The term

misperception is extended to include misperceiving the weight status of another person

(e.g. one’s child). Normal weight for children is defined as bodyweight that falls within

the 6th to 84th percentile on growth charts endorsed by the Center for Disease Control

and Prevention (CDC), as well as the American Association of Pediatricians (AAP). An

individual’s perception of weight may depend on a number of factors (sex, age, and

race) as well as social values that dictate what constitutes an acceptable weight. For

example, as the prevalence of obesity rises, people may consider higher weights to be

more acceptable, leading to greater weight misperception (Salcedo, Gutierrez-Fisac,

Guallar-Castillon, & Rodriguez-Artalejo, 2010). Some experts suggest that the

increasing prevalence of childhood obesity may be desensitizing parents to its

seriousness or normalizing the condition, thus contributing to the inability of caregivers

to recognize when their own children are overweight. Some believe that stereotypes of

overweight children portrayed in the media tend to be at the extreme end of the

30

continuum and may distort non-expert’s perceptions of overweight because, in reality,

many overweight children blend in with the crowd (Campbell et al., 2006).

Demographic Variables

The following section discusses several variables that research indicates may

influence parental perceptions of their children’s weight status. The demographic

variables include race, socioeconomic status, child’s weight, child’s sex, and child’s age.

The non-demographic variables include a child’s health-related quality of life and

information received from health professionals.

Race. Racial differences exist in weight misperception. For example, among an

adult sample, weight misperception was more common in Blacks than in Whites, and

more common in men than women (Paeratakul, White, Williamson, Ryan & Bray, 2002).

Similarly, weight misperception was more prevalent among Mexican Americans and

Blacks than Whites (Dorsey et al., 2009). A cross sectional study of females found that

White adolescent females were 1.57 times more likely than their Black counterparts to

perceive themselves as overweight. However, BMI was not objectively measured.

Thus, the extent to which their perceived weights were discrepant from their actual

weight is unknown (Neff et al., 1997). The motivation for thinness among White females

seemingly is a reflection of the norm used in western society (Furnham & Alibhai, 1983).

Consequently, White females may be more sensitive to increases in weight, more

aware of whether they meet the “thin ideal”, and more aware of whether they are

overweight. A recent study found that both White and Black women desire a

curvaceous figure while more White women preferred to be slender with medium

breasts whereas more Black women preferred to be curvier with medium breasts and a

large buttocks (Overstreet, Quinn, Bede, Agocha, & 2010). Furthermore, Black females

31

are more likely to have mothers and role models who are overweight. These older

women may highly influence Black females’ views on bodyweight.

With regard to motivation for thinness, research suggests that Black females may

be less persuaded by majority-cultural standards because they have a different set of

influences and body-image criteria (Furnham & Alibhai, 1983, Gillum, 1987, Rucker et

al., 1992). After controlling for age, BMI, and education, Black females are more

satisfied with their appearance than Whites, thus suggesting that Blacks are content

with a larger body size (Smith et al., 1999).

Consistent with these points, Black women who identify themselves as

overweight may be aware of their category but have little to no social pressure to lose

weight (Kumanyika et al., 1993). Since overweight Black women are more likely than

their overweight White peers to view themselves as physically attractive (Kumanyika,

1993), they may self-report that they are not overweight because overweight commonly

is equated with being unattractive. Thus, Black women’s perception of weight may have

more cultural connotations than medical connotations (Moore, et al., 2008). Studies

that examine opposite sex body preferences reveal that Black males find a larger

female figure to be more attractive than do White males (Jackson &McGill, 1996; Jones,

Fries, Danish, & 2007; Thompson, Sargent, & Kemper, 1996). This view likely adds to

Black women’s personal acceptance of larger body sizes. Furthermore, Black women’s

personal acceptance of larger body sizes is likely to extend to an acceptance of larger

body sizes in their children.

An examination of caregiver perception of children’s weight-related health risks in

Black families found that, despite almost 70% of their study’s sample being obese, only

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44% of caregivers perceived their child’s weight to be a health problem (Young-Hyman

et al., 2004). While these are eye-opening findings, the study did not report whether

parents perceived their child to be overweight and instead focused on whether parents

felt that their child’s weight was a problem. It is possible that parents recognized that

their child was overweight but failed to see the health risks associated with their

overweight status. Thus, accuracy of parental perceptions of children’s weight status

could not be determined based on this investigation. An examination of relationships

between weight misperception and race in adults found that both Blacks and Mexican

Americans had a higher prevalence of weight misperception compared to Whites.

Specifically, compared to Whites, Black men were 3 times more likely to misperceive

their weight; Black women were 3.4 times more likely to misperceive their weight,

Mexican American men were 1.3 times more likely to misperceive their weight, and

Mexican American women were 1.8 times more likely to misperceive their weight.

Despite this relatively thorough analysis, how these tendencies influence different racial

groups’ ability to accurately identify their children’s weight status was not uncovered.

Not all studies have found differences across races’ weight perceptions. For

example, Baughcum et al., (2000) did not detect a significant difference between White

and non-White parents’ tendency to describe their overweight child as “overweight.”

Similarly, Maynard et al., (2000) found non-significant outcomes for race and parental

perceptions of children’s weight. In general, research on parental perceptions of

children’s weight seems to focus mostly on whether parents perceive their child’s weight

to be a problem while often omitting an assessment of parent’s ability to accurately

identify their child’s weight status. Moreover, even when studies have assessed

33

parent’s ability to accurately identify their children’s weight status, results regarding

levels of accuracy across race are mixed. More research on the impact race may have

on parental perceptual is warranted.

Socioeconomic status. Childhood obesity is a public health crisis deeply

connected to poverty (Kaufman & Karpati, 2007). A qualitative examination of parental

perceptions of children’s weight status, found that 66% of low-income mothers of

overweight children labeled their child as either a “little” or “very overweight” while only

13% of them were concerned about health risks (Jain et al., 2001). The mothers voiced

an inability to say “no” if a child claimed to be hungry and requested food, and they

believed that saying “no” to the child’s request was equivalent to starving the child. The

provision of sufficient nourishment to their children was an important and emotionally

rewarding aspect of parenting that low-income mothers seemingly were reluctant to

relinquish. Some low-income mothers are unwilling to limit certain snacks because they

are proud to be able to afford “treats”. This behavior seemingly is driven by their desire

to “makeup” for previous occasions in which food supply was scarce.

Low-income mothers emphasized the need to bolster a child’s self-esteem if he

or she was teased because of weight issues. Importantly, they often promoted this

approach instead of preventing obesity itself. Mothers considered their children at a

healthy weight as long as activity levels and social functioning were unimpaired. In fact,

many respondents described overweight children as “solid” or “thick” rather than fat.

Pediatric growth charts had little impact on how parents determined their children’s

weight status.

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Although, this study (Jain et al., 2001) offers potentially useful insights regarding

the beliefs, values, and practices of a specific group of mothers, several limitations

should be noted. First, a very small sample of mothers was used (n=18). Second,

there was not much racial variation across participants (72% were Black). Third, the

study excluded suburban and rural participants. Fourth, the study had very few normal

weight respondents (89% of mothers were overweight or obese). Finally, parental

perceptions were evaluated in a focus group setting that may have inhibited some of the

participants from expressing their views honestly.

Not all studies have reported an inverse relationship between SES and accuracy

of perceptions. For example in a study that examined the perceptions of parents of

children enrolled in grades 4 to 6, low family income was not associated with parents’

accuracy in identifying their children’s weight status (He & Evans, 2007). However, the

way in which weight categories were created may have masked potential differences

across income levels. For example, He and Evans created three income categories (1=

< $23,000; 2= $23,001 - $38,999; and 3 = > $39,000) that appear very closely clustered

and thus shows little variance. One could argue that the difference between $23,000

(the low-income group) and $39,000 (the high-income group) is relatively small and

perhaps qualitatively negligible. Furthermore, the range for the middle-income group

appears relatively narrow. Thus, the questionable categorization system may have

created an illusion of consistency in weight-perception-accuracy across socioeconomic

groups because the income ranges were not adequately spread apart. Secondly, the

sample surveyed was not very diverse as nearly 80% of the participants were White and

the other participants were only described as “non-White” so it is unclear whether the

35

non-White participants were Black, Hispanic, Asian, Mexican, etc. In addition, these

findings may not generalize to U.S. families because the data were collected on

individuals living in London, Ontario. Consequently, there is still much to be researched

and learned about the influence SES has on weight perception.

An investigation of the intersection of gender, race and SES on perceptions of

body weight concluded that high SES may represent greater lifetime access to health

information that has encouraged weight control and a healthier lifestyle (Schieman,

Pudrovska, & Eccles, 2007). Thus, higher SES individuals, regardless of race, may be

more inclined to accurately estimate weight status and report a desire to be a healthy

weight than their lower SES peers. Higher SES minorities are likely to experience

greater pressure to acculturate to a mainstream ideology (that values thinness) than

their lower SES peers. These findings reiterate the importance of understanding one’s

value system, which often develops based on SES, before making judgments or

developing interventions for families with overweight children.

Child’s weight. Numerous studies indicate that parents of overweight children

misclassify their children’s weight more often than parents of normal weight children.

Weight status misclassification usually occurs due to parents underestimating their

children’s body weight. While this tendency has been reported in numerous studies, the

extent to which parents of overweight children underestimate weight differs markedly

across investigations. For example, Maynard et al., (2003) found that 33% of mothers

of overweight children misclassified their child as being a lower weight. However, these

findings are somewhat limited because they represent parental perceptions that are

quite dated as the data were obtained from a household survey conducted about two

36

decades ago (i.e.1988-1994). A study of parents with children age 4 to 8 found that

nearly 90% of parents of overweight children misperceived their children’s weight

compared to 40% of parents of normal weight children (Etelson, Brand, Patrick, &

Sharali, 2003). Etelson et al., (2003) utilized visual analog scales (VAS) to assess

parental perceptions of their children’s weight; whereas, Maynard et al., (2003) required

parents to answer closed-ended questions. Although VAS approaches have been

applied to various health-related measurements including pain assessment, functional

status, and psychological measurement it has been used significantly less to assess

perceptions about weight. A limitation with this design exists because it is possible that

parents’ inaccurate estimates may have occurred, in part, from difficulty interpreting the

VAS which may have led to uncertainty about how to place a mark at a sensible position

on the scale. It is worth noting; however, that the authors provided a large margin of

error to address that potential which may have minimized the problem.

Some studies of parent perception found more alarming results. For example,

less than 2% of parents of overweight children and 17% of parents of obese children

accurately classified their children as above-normal weight (Carnell et al., 2005). This

study has one notable limitation; however, because only children age 3 to 5 years were

included, so the results may not generalize to U.S. families with older children. Taken

together, in all three studies, parents of overweight children invariably underestimated

their child’s weight status.

Child’s sex. A number of studies reveal that mothers of overweight children are

more likely to identify daughters as overweight than they are sons (Boutelle et al., 2004;

Fisher et al., 2006; Holm-Denoma et al., 2005; Jeffery et al., 2005; Maynard et al.,

37

2003). In fact, mothers are nearly three times as likely to classify at-risk daughters as

overweight as compared with at-risk sons (Maynard et al., 2003). In addition,

overweight daughters are more likely to elicit maternal concern about weight than

overweight sons (Campbell et al., 2006).

Another study found 27% of overweight boys were identified as overweight by

their parents compared with 54% of overweight daughters (Jeffery et al., 2005). These

findings suggest that sons often are viewed with a different standard. While this pattern

may be associated to sex differences in body composition (Sopher, Thornton, & Wang,

2004), it seems more likely to reflect social values; mothers may be more sensitive to

size and body image issues for girls than for boys. Conversely, larger boys may be

more likely to be viewed as having a physical advantage (Campbell et al., 2006). This

may play out in various social contexts such as organized sports, especially football, in

which larger, often overweight players, are praised for their physical supremacy (e.g.

ability to withstand blocks and push around smaller opponents). In contrast, girls

seldom are praised for being larger than their peers. Despite these compelling findings,

nine studies reported that child’s sex did not affect parental classification of their child’s

weight (Adams et al., 2005; Baughcum et al., 2000; Carnell et al., 2005; Contendo et al.,

2003; Genovesi et al., 2005; Jackson et al., 1990; Rhee et al., 2005; Rich et al., 2005;

Young-Hyman et al., 2000). Therefore, future research should continue to explain the

role of sex in weight status perception.

Child’s age. A number of studies have examined how a child’s age influences

parents’ perceptions of their children’s weight status. Five studies report no age effect

on parents’ ability to classify their child as overweight (Adams et al., 2005; Baughcum et

38

al., 2000; Boutelle et al., 2004; Carnell et al., 2005; Jackson et al., 1990) while six

studies report parents are less likely to consider younger children as overweight than

older children (Genovesi et al., 2005; Jain et al., 2001; Maynard et al., 2003; Rhee et

al., 2005; Rich et al., 2005; Young-Hyman et al., 2000). Some parents expressed

beliefs that younger children were not really overweight because the fat would go away

as the child grew older or the child’s weight was simply due to being tall or big-boned

(Jain et al., 2001 & Rich et al., 2005). Clearly, the research related to the influence of a

child’s age on parents’ perceptions of weight status is mixed and in need of further

investigation.

Non-Demographic Variables

In addition to demographic variables such as race, family SES, child’s sex, child’s

weight, and child’s age, non-demographic variables may influence parents’ perceptions

of their children’s weight status. The following section discusses two non-demographic

variables that are germane to the current research proposal: (1) Health-Related Quality

of Life (HRQL) and (2) information acquired from health professionals.

Defining Health-Related Quality of Life

During the past 25 years, significant progress has been made in defining and

measuring health-related quality of life (HRQOL) and in recognizing its significance as a

health outcome (Palermo et al., 2008; Quittner, Davis, & Modi, 2003). About 65 years

ago, the World Health Organization proposed the first definition of HRQOL as “a state of

complete physical, mental, and social well-being, and not merely the absence of

disease or infirmity” (World Health Organization [WHO], 1947, p. 29). A consensus

definition of HRQOL eventually emerged, with agreement that it is multidimensional and

includes four core domains: (1) disease state and physical symptoms, (2) functional

39

status (e.g., performing daily activities), (3) psychological and emotional functioning,

and (4) social functioning (Hays, 2005; Rothman et al., 2007). Health-related quality of

life usually is assessed from the individual’s perspective, although parent proxy-reports

often are utilized when assessing the health status of young children or those with

severe disabilities (Palemo, Long, Lewandowski, Drotar, Quittner, & Walker, 2008).

Health-related quality of life may be a particularly useful population health outcome

measure in the school setting (Varni et al., 2006). Specifically, this measure can aid in

identifying subgroups of children who are at-risk for health issues, in determining the

burden of a specific disease or disability, and in enhancing efforts aimed at prevention

and intervention (Kaplan, 2001). Furthermore, HRQOL data may assist in the

evaluation of the health needs of a school district, and results can be used to inform

public policy, including the development of strategic healthcare plans and school health

clinics, identifying health disparities, promoting policies and legislation related to school

health, and aiding in the allocation of healthcare resources (CDC, 2000). For example,

a district whose students score exceptionally low on measures of emotional functioning

may need to enhance the mental health supports offered during and after school by

hiring additional school psychologists and extending counseling hours.

Health-Related Quality of Life and Childhood Obesity

Several studies published between 2003-2007 reveal that children who are

obese report lower HRQOL compared to their normal-weight peers (Fallon, Tanofsky-

Kraff, Norman et al., 2005; Pinhas-Hamiel, Singer, Pilpel et al., 2006; Hughes, Farewill,

Harris, & Reilly, 2007; Friedlander, Larkin, Rosen, Palermo, & Redline, 2003;

Schwimmer, Burwinkle, &Varni, 2003). Specifically, increased weight has a moderate

to strong negative influence on overall HRQOL in children when their BMI falls in the

40

overweight range (Tsiro et al., 2008). While this relationship has been found in multiple

investigations, a more recent study by O’Neil et al., (2010) did not find a significant

relationship between children’s BMIs and psychosocial factors (PedsQL) so it is

possible that other variables make certain overweight children impervious to reductions

in psychosocial functioning.

With regard to parent ratings of their child’s HRQOL, parents who recognize an

association between their child’s weight and negative health condition may be more

motivated to promote healthy changes than parents who do not recognize the link

between weight and health (Prochasak, Redding & Evans, 2002; Wu, Yu, Wei, & Yin,

2003). Thus, it seems reasonable to conjecture that children who are overweight and

perceived by parents to have low quality of life may be more likely to recognize the

condition (i.e. obesity) that has impacted their child’s functioning than parents of

children who are overweight and display better quality of life. This possibility is

supported in a study in which mothers were found to be more likely to consider their

child being teased about weight (i.e. social functioning) or developing limitations in

physical activity (i.e. physical functioning) as indicators of being overweight. These

cues were used instead of their child’s measurements on standard growth charts (Jain

et al., 2001). Growth charts consist of a series of percentile curves that illustrate the

distribution of selected body measurements in children and have been used by

pediatricians and nurses to track the growth of youth in the United States since 1977

(CDC, 2010). The findings by Jain et al., (2001) highlight the influence that a child’s

perceived quality of life can have on parents’ perceptions of their child’s weight status.

41

Relationships between children’s HRQOL and parents’ perceptions of their

children’s weight status have not been studied directly. However, some studies

included these variables in broader investigations. For example, relationships between

weight status (BMI), health perceptions, and psychosocial characteristics of children,

parents, and parent-child dyads were examined (O’Neil et al., 2010). However, the

purpose of the research was to determine whether there was a relationship between

children’s HRQOL and the stage in which parents fell in the stages of change model.

They did not investigate whether there was a relationship between children’s HRQOL

and parental perceptions of their children’s weight status. Therefore, no conclusions

can be drawn as to whether children’s HRQOL influenced parental perceptions of their

children’s weight status from this study.

A study of qualities that may influence the degree of concern parents feel about

their children’s weight found that higher parental concern was associated with higher

child BMI, lower parental underestimation of child body size, and lower child HRQL

(Lampard, Byrne, Zubrick, & Davis, 2008). As a complement to this research, it will be

useful to know the extent to which HRQL impacts parental estimation of body size.

A study of weight stigma, self-perception of weight status, and qualities that may

contribute to accurate self-perception of weight status in children who are obese found

that older children and greater HRQOL impairment predicted correct self-perception

(Zeller, Ingerski, Wilson, & Modi, 2010). It will be interesting to compare these findings

on children’s self-perceptions with parental perceptions. These studies are described in

more detail below given their relevance to the current research proposal.

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Relevant Studies

Parent-child study. Relationships between weight status (BMI), health

perceptions, and psychosocial characteristics in children, parents, and parent-child

dyads using 114 families were examined in a study of above-normal weight children, the

vast majority of whom were Black or Hispanic (O’Neil et al., 2010).

Questionnaires included the Parenting Stress-Index Short-Form (PSI), the

Parents’ Stage of Change (SOC) Questionnaire, and the PedsQL. In addition, parents

and children completed demographic and health behavior questionnaires developed

specifically for the study because there was no existing measure that included all items

of interest. Items on the parent questionnaire included: family structure, parent and

child race, parent education level, zip code, parent perceptions of their own weight and

their children’s weight, parent and child health behaviors for physical activity, sedentary

behavior, fruit and vegetable consumption, and neighborhood environmental

characteristics. Items on the child questionnaire were similar to the parent version with

additional items on perception of health and weight, desire to be healthier and time

spent in healthy behaviors at home and school. The health behavior items for both

questionnaires were from the Youth Risk Behavior Surveillance Survey (YRBSS) for

high school and middle school-aged children. The health behavior items included time

spent weekly in moderate-to-vigorous physical activity, time spent daily in sedentary

behaviors, and average daily fruit and vegetable consumption. The weight status and

description items also were from the YRBSS. The neighborhood items on the parent

questionnaire were neighborhood safety and resources for outdoor play or physical

activity.

43

The results indicated that child BMI increased with age, weight status, and with

decreased health. Results among variables for the parent group were less clear.

Specifically, parent BMI increased with increased ratings of weight status.

Relationships among parent-child variables were not significant.

Nearly 87% of parents reported that their children were overweight, objective

data showed that 90% of the children were overweight. Thus the vast majority of

parents possessed accurate perceptions of their child’s weight status. This degree of

parent accuracy is far greater than what previous studies have reported. It is likely that

parental-perception-accuracy was higher in this study compared to others because

participants were recruited from an outpatient pediatric practice. Accordingly, parents

may have been informed of their child’s unhealthy weight status by virtue of receiving

treatment at a pediatric practice and being referred by their physician to a study about

obesity. Thus, the process by which participants were acquired leads to considerable

limitations when interpreting the findings and also may explain why there were no

significant correlations between children’s BMI and psychosocial factors because there

were no healthy-weight children in the sample to make comparisons. Perhaps a more

representative participant pool would yield relationships between HRQOL and weight

status perception. A more focused study design and analysis plan may have helped

reduce the number of tests run to determine significant relationships.

Parent study. Research has examined the degree of concern parents feel about

their children’s weight and characteristics associated with this concern (Lampard,

Byrne, Zubrick, & Davis, 2008). The participants included a mixture of normal-weight,

overweight, and obese children age 6-13 years and their parents. Results revealed that

44

34% and 48% of parents of overweight children reported little concern or no concern,

respectively for their children’s weight. Additionally, 13% and 5% of parents of obese

children reported little concern and no concern for their children’s weight. In reference

to parent perception of their child’s weight status, 51% of parents of obese children and

44% of parents of overweight children underestimated their child’s weight status.

Under-estimation of child size, child BMI, and child health-related quality of life were

found to predict parental concern. In other words, parents were more concerned if their

child was at the upper limit of the obese range (e.g. morbidly obese), had accurate

perceptions about their child’s weight status, and if their child was perceived to have low

quality of life.

While these findings are helpful, the study did not explicitly investigate the

potential relationship between parents’ estimation of their child’s weight status and

quality of life. Instead, the focus was placed on exploring parents’ concern about their

child’s size and quality of life as well as parents’ concern about their child’s size and

weight estimation, omitting an examination of the impact quality of life may have had on

parents’ perception of their child’s weight status. Thus, it will be important for future

research to directly examine the potential relationship between the latter variables.

Child study. Researchers have assessed weight stigma, self-perception of

weight status, and factors contributing to accurate self-perceptions of weight status in

obese children age 5 to 11 years (Zeller, Ingerski, Wilson, & Modi, 2010). The

participants completed the Perception of Weight Scale (POW) which is a sex-specified

figural rating scale designed to assess young school-age children’s perceptions of

45

weight stigma, their own weight status, as well as their ideal weight status. The POW

was administered orally by an interviewer who read the following instructions.

Here is a picture of 3 boys/girls. I am going to ask you some questions about the pictures. There are no right or wrong answers; just pick the one that you think is best.

Children were asked to select which of the three pictures best characterizes

items that describe activities and attributes typical of all children regardless of weight

status (e.g. “Which one of these kids sings the best?”) and attributes that are

stereotypically applied to overweight/obese children (e.g. “teased the most,” “runs the

slowest”). Participants also were asked to choose the figure drawing that was “most like

you” and “the one you would most like to be.”

Children completed an obesity-specific HRQOL scale called Sizing Me Up

developed and validated for youth 5 to 13 years. The measure includes 22 items that

prompt children to respond to questions in context of their size (e.g. “…because of my

size”). Sizing Me Up consists of five subscales, including Emotional Functioning,

Physical Functioning, Teasing/Marginalization, Positive Social Attributes, and Social

Distress/Avoidance and Total Quality of Life.

A logistic regression analysis was completed to determine the odds of the child

being correct or incorrect in their self-perceptions of weight status based on child sex,

race, BMI, SES, and self-reported HRQOL scores. Results reveled that a larger

percentage of children endorsed negative attributes for the obese body type and

positive attributes for the average weight and underweight body types. Sixty-one

percent of children accurately estimated their weight status based on figural drawings.

All children reported an ideal body as either underweight or average weight. Child sex,

SES, and race did not predict weight perception accuracy. This is in contrast to studies

46

that suggest that certain non-White groups are less likely to perceive themselves as

overweight. Although, the initial analysis suggested that greater obesity increased the

odds of children’s correct self-perception of obesity status, degree of obesity became

irrelevant when a child’s self-perceptions of obesity-specific quality of life was

considered. In general, older children and children with lower quality of life were more

likely to correctly perceive themselves as being obese. This study is the first to

demonstrate relationships between the social, emotional, physical, and school impact of

a child’s obesity and a child’s subjective understanding (i.e. self-perception) of his or her

obesity. Whether this level of association exists between children’s psychosocial

functioning and parents’ subjective understanding of their child’s obesity remains to be

seen.

Several limitations of the study are worth noting. First, all of the children were

obese and had received treatment at a hospital-based multidisciplinary weight

management program and were referred to the study by their physician. This means

that the parent and referring physician, to some degree, had already acknowledged that

the child’s weight was a problem. As a result, these children likely had more exposure

to information on health and received more messages that emphasized maintenance of

a healthy body size compared to non-treatment seekers. This possible exposure may

help explain why such a high percentage of participants were able to correctly identify

their body size when presented with the body figures. Whether these findings are

generalizable to obese children who do not or cannot access an obesity treatment

program remains unknown. Second, the study did not address whether children’s

HRQOL impacts parents’ perceptions of their child’s weight status. Given the significant

47

role parents play in initiating, implementing, and maintaining healthy lifestyle changes to

prevent or reverse childhood obesity, it will be important to better understand factors

that influence parents’ subjective understanding of their child’s weight status. It is

unlikely that parents will seek treatment if they do not perceive their overweight child as

being above-normal weight. Furthermore, the study suggested that children may have

received information from their parents or physician about their weight status or need for

intervention but children were not asked whether this was indeed the case. Finally, the

mean age of children in this study was 8.56 +1.66 years. This limited age range omits

young children and those in middle school. In order to draw reliable conclusions about

younger and older children it will be important to broaden the age range of the sample in

future studies.

Information from Health Professionals

Health professionals’ acknowledgement of children’s weight status is the second

non-demographic variable of interest in the current research proposal. Physician

feedback generally encourages parents of overweight children to make lifestyle

changes to reduce health risks for their family. Several studies that have examined

physician feedback concerning smoking cessation, diet, exercise, and immunization

have shown that patients are more likely to participate in these behaviors if the

physician provided guidance (Frank et al., 1991; Greenlund et al., 2002; Wray et al.,

2007). In terms of weight management, physician feedback has been shown to be an

effective tool in shaping patients’ weight loss intentions (Gillespie et al., 2010, Huang et

al., 2004, Kant & Miner, 2007).

Parent-child study. Research that investigated demographic factors and

parental perceptions associated with parents’ readiness to make weight-reducing-

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lifestyle changes for their overweight children among 151 parents of children ages 2 to

12 who had BMIs in the 85th percentile or higher found that many parents who thought

their child’s weight was a health problem also reported that their physician had made a

comment about their child’s weight status. Specifically, parents were ready to make a

change when the child’s physician had commented that the child’s weight was an issue.

Fifty-six percent of parents who believed that their child’s weight was an issue reported

that their physician made a comment about their child’s weight, whereas only 8% of

parents who did not think their child’s weight was an issue reported that the physician

had made a comment. Thus, health professionals may have an important influence on

whether parents of overweight children understand that their child is above healthy

weight and appreciate the health risks associated with obesity.

The survey used in this study was designed to obtain demographic information

about the child and the parent as well as information about parental beliefs and

behaviors. Parental perceptions regarding the child’s weight and their own weight were

rated on a 5-point Likert scale ranging from “very underweight” to “very overweight.”

Additional questions determined whether parents thought that their child’s weight was a

health problem. They also were asked whether they felt obesity in general was a health

problem. Lastly, parents were asked to recall whether their physician had given them

any feedback about their child’s weight.

This study provides important information concerning the role health

professionals play in influencing parents’ perceptions of their children’s weight status.

However, a few limitations are worthy of mentioning. First, the study used a fairly

homogeneous sample of participants as most were from the inner-city and Latino or

49

Black. This may limit the generalizability of the results to other populations. It would be

interesting to know more on this matter for other races and those from rural or suburban

areas. Second, the study included 151 children between the ages of 2 to 12 years,

which means that on average each age was represented by just 15 children. A larger

sample of children may be needed to uncover meaningful differences on the outcome

variable across ages. Third, the multivariate logistic-regression model could explain only

24.6% of the variance. This indicates that variables not measured in the study

contribute to a parent’s readiness to make lifestyle behavioral changes to improve their

child’s weight and health status. For instance, one can hypothesize that the child’s

perceived quality of life also will influence parents’ perceptions of their child’s weight

and readiness to change.

Adult study. Post and his colleagues (2011) evaluated whether patient reports

of physician acknowledgement of overweight patients’ weight status was associated

with patients’ perceptions of their own weight and desire to lose weight. The

researchers analyzed the 2005-2008 National Health and Nutrition Examination Survey

data on adults ages 20 to 64 with a BMI of at least 25. The primary outcome measure

was the proportion of participants who considered themselves as “not overweight” vs.

“overweight” in two different BMI classifications: overweight and obese. Participants

with obesity, defined as a BMI of 30 or greater, were included in the overweight

classification because obese participants also are overweight. Therefore, the

categories are not mutually exclusive.

Among participants with BMIs of 25 or greater, 45% reported that they had been

told by their physician that they were overweight. Among participants with BMIs of 30 or

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greater, 66% reported that they had been told by their physician that they were

overweight. Overall, participants who reported that they had been told by a physician

they were overweight, compared with those who were not told, were more likely to

accurately identify themselves as overweight, whether they were overweight (94.0% vs.

63.1%) or obese (96.7% vs. 81.4%). In other words, almost 37% of overweight

participants and almost 19% of obese participants did not consider themselves to be

overweight if a physician had never told them that they were overweight. Among

participants who had been told by their physicians that they were overweight, these

misperceptions were considerably lower: only 6% of overweight participants and about

3% of obese participants did not consider themselves to be overweight. This finding

again suggests that physicians’ feedback regarding weight issues influence perceptions

of adults.

Further analysis that included individuals with a BMI of 25 or greater was

conducted to examine other aspects of the data. A model considered age, sex,

education, poverty to income ratio, race, marital status, having a place for routine care,

and number of physician visits in the last year. Several variables were associated with

patient reports of being told that they were overweight; those who were older, female,

had at least one routine place of health care, and had at least one physician visit in the

previous 12 months had an increased likelihood of reporting that they were told by a

physician that they were overweight. Conversely, respondents who were married or

living with a partner were less likely to report being told that they were overweight.

Among individuals with a BMI of 30 or greater (i.e. the obese participants), the same

qualities were associated with an increased or decreased likelihood of reporting having

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been told they were overweight. In addition, non-Hispanic Blacks had a lower likelihood

of reporting being told that they were overweight compared to non-Hispanic Whites.

Again, the results of this study underscore the important role health professionals play

in shaping adult patients’ perceptions of their own weight status. It would be useful to

know the affect health professionals have on perceptions of parents who have

overweight children. Information that includes children is crucial for early intervention

efforts.

Summary

Obesity has reached epidemic proportions and shows no sign of abating

(Pomeranz, 2011). The physical and psychological sequelae of obesity are well

documented (Daniel, Arnett, &Eckel, 2005; Din-Dzietham, Lui, Beilo, &Shamsa, 2007),

as well as its financial burden on overweight individuals and society at large (WIN,

2010). Many experts agree that prevention could be a key strategy for controlling the

current epidemic of obesity. Prevention may include primary prevention of overweight

or obesity, secondary prevention of weight regains following weight loss, and avoidance

of more weight increase in obese persons unable to lose weight (Dehghan, Akhtar-

Danesh, &Merchant, 2005).

Most research on treating obesity has focused on changing individuals’ diet and

exercise behaviors. However, these strategies seemingly have had little impact on the

increase of the obesity epidemic (Dehgan, Akhtar-Danesh, & Merchant, 2005). In 2000,

a Healthy People 2010 objective was established to reduce the prevalence of obesity

among adults in the U.S. to 15%. Disappointingly in 2009, not a single state met this

target (MMWRW, 2010).

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In 2007-2008, the prevalence of obesity among adults in the U.S. was 34%

overall, 32% among men, and 36% among women. The corresponding prevalence

estimates for overweight and obesity combined were 68%, 72%, and 64% (Flegal,

Carroll, Ogden, & Curtin, 2010). While these are staggering figures worthy of attention,

attempts to reduce excessive weight once it becomes established are difficult.

Furthermore, adults who were obese during childhood have a higher risk of developing

hypertension, dyslipidemia, metabolic syndrome, diabetes, and coronary heart disease

than those who were not obese during childhood (Pratt, Stevens, & Daniels, 2008).

Therefore, children should be considered the priority population for intervention

strategies (Dehgan et al., 2005).

Overweight children represent a vulnerable group that requires intervention.

They rely heavily on support from adults, particularly parents, in order to improve their

health. Viewed within the transtheoretical model of behavior change for weight control

(Andres, Saldana, & Gomez-Benito, 2009), parents of overweight children who do not

see their child as above healthy weight are at the precontemplation stage, which means

they seemingly have no desire to help their child become a healthier weight.

This literature review leads to the following conclusions. Several demographic

variables may be expected to affect parental perceptions of their children’s weight

status, including race, family SES, child’s sex, child’s weight, and child’s age. Non-

demographic variables also may affect parents’ perceptions of their child’s weight

status, including the child’s health-related quality of life, timing of last physician visit, and

weight-related information received from health professionals.

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While O’Neil and colleagues’ (2010) study is noteworthy because it examined the

relationships among BMI, health perceptions, and psychosocial characteristics in

children and parents, the findings may not generalize to society at large because the

participants were recruited from an outpatient pediatric practice. Accordingly, the

parents were likely made aware of their child’s unhealthy weight status by virtue of

receiving treatment in that type of health setting. Unfortunately, however, parents were

not directly asked whether their child’s physician had discussed weight with them so it is

impossible to quantify the affect this information may have had on perceptions. In

addition, the study’s sample size was relatively small, all children were overweight or

obese, and most of them were Black and Hispanic. Thus, a more diverse sample of

participants is needed before drawing conclusions about characteristics that may predict

parents’ weight-perception-accuracy. The study by Lampard and colleagues’ (2008)

provides more insight regarding the affect quality of life has on parents’ views of their

children. However, it focused primarily on whether parents were concerned about their

child’s weight. A parent of an overweight child may recognize their child is overweight

but not be concerned about the child’s size. Therefore the two concepts (accuracy of

perception and concern) are not synonymous. Thus, research is needed that directly

examines the potential relationship between children’s quality of life and parents’

perceptions of their children’s weight status. Zeller and colleagues (2010) directly

examined the relationship between quality of life and perceptions of weight status by

measuring only children’s self-perceptions. They found a relationship between quality

of life and children’s self-perceptions of their weigh status. However, similar to the

study by O’Neil et al., (2010), all children were obese and had received treatment at a

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hospital-based multidisciplinary weight management program. Thus, the findings may

not be generalizable to obese children who have not received help at an obesity

treatment center. Future research may build on this study by examining a more diverse

sample of participants who includes children of various weight categories who are non-

treatment seekers and include parents’ perceptions of children’s weight. Parents play a

crucial role in the initiation of both medical and psychosocial services. Therefore, their

views are both informative and critical (Campo, Comer, Jensen-McWilliams, Gardner, &

Keller, 2002; Janicke, Varni, &Kurtin, 2001; Seid, Varni, &Kurtin, 2000).

The influence health professionals may have on parents’ perceptions of their

children’s weight status constitutes another potentially critical piece of research on

childhood obesity prevention. Rhee et al., (2005) found that many parents who thought

their child’s weight was a health problem also reported that their physician informed

them of their child’s weight status. However, this study used a relatively small and

narrow sample of participants as most were Latino and Black from the inner-city. More

information on the effects of race and community-type (e.g. urban, suburban, and rural)

on weight status is important. The variables in the study explained just 25% of the

variance. This suggests that other variables have an important influence on parents’

readiness to make lifestyle behavioral changes to improve their child’s weight and

health status. The child’s perceived quality of life also may influence parents’

perceptions of their child’s weight status and readiness to change. Post et al., (2011)

found that overweight adults who were informed about their weight status by a health

professional were more accurate at pinpointing their weight status. However, this study

did not explore whether parental accuracy increases if a health professional informs

55

them of their child’s weight status – clearly, this gap in the literature needs to be

addressed.

This review of the literature demonstrates that race, SES, child’s sex, child’s

weight, child’s age, child’s quality of life, and weight-related information from health

professionals appear to be important variables to consider when examining parental

perceptions of children’s weight status. Although, the potential impact of these

variables on parental weight-perception-accuracy seems plausible, a comprehensive

model that includes each variable has not been tested. Thus, a primary goal of this

study was to investigate the extent to which these identified variables predict weight-

perception-accuracy in parents of children age 5-14. A primary research question for

this proposed study was the following: How well do demographic variables, health-

related quality of life, and weight-related information from health professionals predict

parental perceptions of their child’s weight status?

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CHAPTER 3 METHODS AND PROCEDURES

Participants and Setting

A convenience sample of 277 parent-child dyads was recruited from PK Yonge

(PKY) Developmental Research School located in North Central Florida. Child

participants ranged in age from 5 through 14 (i.e. grades K-8). The mean age for child

participants was 9.21 (SD 2.5) years. The majority of child participants were female

(53%). There also were more female parent participants (81%) than male. With regard

to race, participants were White (53%), Black (21%), Hispanic (17%), Asian (4%), other

(3%), and Native American (1%). The household income levels of participants were

distributed across four categories: low (21%), middle (26%), high (23%), and very high

(30%). Parent participants had varying levels of education with the majority possessing

at least a 4-year college degree (63%). More details regarding participant

demographics are summarized in Table 3-1.

Procedures

The primary investigator (PI) submitted the research protocol to the University of

Florida’s (UF) Institutional Review Board (IRB) to obtain necessary approval to

complete the study. Upon approval from UF’s IRB, a copy of the documentation was

provided to administrators at PKY to gain permission to conduct the study at their site.

Once this paperwork was processed, the following steps were carried out to collect

data.

Questionnaire Data

Two procedures were utilized for soliciting parent volunteers and collecting

parent questionnaires. For the first procedure, the PI invited parents to complete

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research packets at the school’s fall carnival. A table was setup at this event and

parents were invited to complete all paperwork including consent forms and

questionnaires. The consent forms ensured that parent volunteers understood the

general purpose of the study and were aware that their child’s height and weight would

be collected during school hours.

For the second procedure, the PI sent research packets home via weekly

classroom information folders. These packets were not sent to the homes of families

who had completed paperwork at the fall carnival. The PI provided all classroom

teachers with enough packets for their students to take home. Parents of children in

elementary school were instructed to submit completed forms, in sealed envelopes, to

their child’s homeroom teacher within two weeks. Parents of children in middle school

were instructed to submit completed forms, in sealed envelopes, to their child’s PE

teacher during the same time span. Reminders were sent to parents via notices in

weekly classroom information folders and periodic announcements provided by PE

teachers. The PI collected completed packets from teachers weekly. A class-wide

incentive was offered to classes that demonstrated at least 75% participation to improve

the return rate.

These two combined procedures produced an overall response rate of 48% (i.e.

277 returned questionnaire packets out of 528 participants) – not including

questionnaires that were partially completed because incomplete packets were

disregarded. The first procedure accounted for 18% of total packets collected and the

second procedure accounted for the remaining 82%. The next paragraph will explain

steps taken to protect participants’ identities.

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To maintain the confidentiality of the data, returned packets were opened only by

the PI. In addition, identification numbers were assigned to every parent-child dyad.

Accordingly, the PI entered identification numbers, in lieu of names, into an electronic

database. All forms with identifying child and parent names were stored in a secure

location and electronically protected by passwords. Parents were informed that they

could withdraw from the study at any time without penalty. In addition, participants were

provided with the contact information for UF’s IRB, the PI, and faculty supervisor in case

questions or concerns arose.

Height and Weight Measurements

This section describes the steps carried out to collect children’s height and

weight measurements. Elementary and middle school students at PKY are required to

participate in physical education (PE) and teachers annually measure students’ height

and weight during PE class. Thus, this grade range was recruited for the study. The PI

provided PE teachers with a list that displayed the names of students whose parents

had provided consent for them to participate in the study to ensure that correct student

data were collected. The PE teachers submitted a copy of these measurements to the

PI once height and weight measurements were collected for these specific students.

Consistent with storage methods utilized for parent questionnaires, height and weight

measurements also were kept in a secure location and de-identified to maintain

confidentiality.

Sociodemographic Data

Each child’s date of birth, sex (male/female), race (Asian, Black, Hispanic, Mixed,

White), and family income as an indicator of socioeconomic status (SES) were gathered

from the school’s administration electronic record system. The annual household

59

income distribution for PKY families was as follows: 30% = $97,750 or more; 22% =

$69,000 to 97,749; 24% = $39,250 to 68,999; and 24% = $0 to 39,249. These ranges

were labeled very high-income; high-income; middle-income; and low-income for

descriptive purposes.

Measures

The measures used in this study included the following: (1) Parent Demographics

and Perceptions Questionnaire (PDPQ) (2) Pediatric Quality of Life Inventory (PedsQL)

and (3) a Detecto Physician Scale equipped with a height rod for measuring children’s

height and weight. Parents who consented to participate in the study completed the

abovementioned questionnaires to gather background information and assess various

aspects of their child’s health and quality of life. Based on parental consent, children’s

heights and weights were collected during school as part of routine fitness assessments

by PE teachers and then provided to the PI. Additional sociodemographic data were

gathered from the school’s electronic database. These measures are discussed in

greater detail below.

Parent Demographics and Perceptions Questionnaire

A parent questionnaire (i.e. the PDPQ) was developed by the PI to collect data

on parent characteristics and perceptions of their children’s health. The first section of

the questionnaire included six items. Specifically, parents provided basic demographic

information, including their name, sex, race, age, and education level. The second

section of the questionnaire included five items. Specifically, parents indicated whether

their child had any chronic health conditions (i.e. anxiety, asthma, ADD/ADHD,

autoimmune disease, cancer, cerebral palsy, congenital heart problems, cystic fibrosis,

depression, diabetes, epilepsy, gastro intestinal tract problems, obsessive compulsive

60

disorder, sickle cell anemia, spina bifida, other). Parents also provided their perceptions

of their child’s weight status. Specifically, they were asked, “compared with other

children the same age, do you feel your child is underweight, about the right weight,

overweight, or obese?” This question was adapted from He and Evans (2007).

Following that item, parents provided information about their experiences with health

professionals. Specifically, parents were asked, (a) “At your child’s last visit, did a

physician or other health professional inform you of your child’s weight status?” (b) “At

your child’s last visit, did a physician or other health professional tell you that your child

was overweight?” These questions were adapted from Post et al., (2011). Parents also

indicated the time of their child’s last physician visit (within the past 6 months; 7-12

months; 13-18 months; 19-24 months; more than 2 years). This questionnaire took

parents approximately five minutes to complete.

Pediatric Quality of Life Inventory

The Pediatric Quality of Life Inventory TM 4.0 Generic Core Scales (PedsQL)

were completed to measure children’s health-related quality of life. The PedsQL

encompasses: physical functioning (8 items), emotional functioning (5 items), social

functioning (5 items), and school functioning (5 items). The domains were developed

through focus groups, interviews, pre-testing, and field testing measurement

development protocols (Varni et al., 2001). A 5-point response scale is utilized for

parent proxy-report (0 = never a problem; -1= almost never a problem; 2 = sometimes a

problem; 3 = often a problem; 4 = almost always a problem). Items are reverse-scored

and linearly transformed to a 0–100 scale so that higher scores indicate better quality of

life. The PedsQL has undergone considerable field-testing nationally and internationally

and has been utilized in hundreds of studies that have examined various pediatric

61

health problems (e.g. obesity, sickle cell disease, attention-deficit/hyperactivity disorder,

Duchenne muscular dystrophy, autism spectrum disorders, vesicoureteral reflux,

diabetes, inflammatory bowel disease, heart disease, cerebral palsy, asthma, cancer,

and liver transplant recipients). The PedsQL 4.0 builds on programmatic instrument

development research during the past two decades, beginning with the measurement of

pain and functional status (Varni, Thompson, & Hanson, 1987; Varni, Wilcox, Hanson,

et al., 1988). The PedsQL 1.0 (Varni, Seid, & Rode, 1999), derived from a cancer

database (Varni, Katz, & Seid, 1998, Varni, Katz, Seid et al., 1998), was designed as a

generic instrument to be utilized across pediatric populations. Compared to the original

PedsQL, the PedsQL 2.0 and 3.0 included additional constructs and items, a more

sensitive scaling range, and a broader age range. The PedsQL 4.0 was designed to

measure the core health dimension delineated by the World Health Organization (WHO,

1948), including role (school) functioning.

The PedsQL is a popular instrument used primarily in medical settings.

However, research also supports the feasibility, reliability and validity of the PedsQL as

a health related quality of life (HRQOL) measurement instrument for health of youth

(Varni, Burwinkle, & Seid, 2006). For example, in a study conducted in 18 elementary

schools, 4 middle schools, and 3 high schools, Varni et al., (2006) found that the

internal consistency reliability of the PedsQL was suitable across age, with alpha for the

parent proxy-report Generic Core Scales exceeding the 0.70 standard. Furthermore,

alpha for the full 23-item scale approached or exceeded 0.90 recommended for

individual patient analysis (Nunnaley & Bernstein, 1994), making the Total Scale Score

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suitable as a summary score for the primary analysis of HRQOL outcome in school

population health analyses.

An investigation of the reliability and validity of the PedsQL Generic Core Scales

for use with healthy and patient populations found that the internal consistency reliability

for the PedsQL parent proxy-report Total Scale Score (a = .90), physical health

summary score (a = 0.88), and psychosocial health summary score (a = 0.86) were

acceptable for group comparisons (Varni, Seid, & Kurtin, 2001). Validity was

demonstrated using the known-groups method, correlations with indicators of morbidity

and illness burden, and factor analysis. The PedsQL distinguished between healthy

children and pediatric patients with acute or chronic health conditions and was related to

indicators of morbidity and illness burden. The results demonstrate that the PedsQL

may be applicable in clinical trials, research, clinical practice, school health settings, and

community populations.

Furthermore, the internal consistency reliability of each subscale is strong. For

example, Varni et al., (2001) found the following Parent Proxy-Report internal

consistency reliabilities: Physical functioning 0.88; Emotional functioning 0.77; Social

functioning 0.75; and School functioning 0.76. Thus, the Physical, Emotional, Social,

and School Functioning Subscales maybe independently utilized to examine specific

domains of functioning.

A more recent study compared generic HRQOL across ten chronic disease

clusters and 33 disease categories/severities from the perspective of parents (Varni,

Limbers, & Burwinkle, 2007). Results showed sensitivity to different pediatric chronic

conditions on patient HRQOL across ten pediatric chronic disease categories from the

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perspective of parents who completed the PedsQL. Specifically, parents of children

with obesity, cardiac conditions, diabetes, gastrointestinal conditions, end stage renal

disease, asthma, rheumatologic conditions, cancer, and psychiatric conditions report

that their children have progressively more impaired overall HRQOL than healthy

children, respectively, with medium to large effect sizes.

The PI completed an application provided by MAPI Research Trust Education

Information Dissemination to obtain permission and materials to administer the PedsQL.

There is no license fee for using the PedsQL scales, modules, and translations for non-

funded academic research.

Detecto Physician Scale

Children’s weight status was assessed based on height and weight

measurements. Students were weighed wearing light indoor-clothing without shoes

with a Detecto Physician Scale equipped with a height rod. Students who wore more

than one layer of clothing removed items such as jackets, sweaters, and sweatshirts to

increase the accuracy of the weight measurement. Height was measured to the nearest

0.25 inch and weight was measured to the nearest 0.5 pound. Body mass index (BMI)

was calculated and categorized based on the Center for Disease Control and

Prevention (CDC) protocol (CDC; 2011). Based on CDC age norms, children whose

BMI (based on age and sex) were equal to or greater than the 95th percentile were

classified as obese; children whose BMI was between the 85th and 94th percentile were

classified as overweight; children whose BMI was between the 6th and 84th percentile

were classified as normal weight (i.e. healthy weight); and children with a BMI less than

the 6th percentile were classified as underweight.

64

Table 3-1 Demographic characteristics: frequencies and percentages

Demographic Variables Frequency Percentages

Parent sex Male Female

52

225

18.8 81.2

Child sex Male Female

130 147

46.9 53.1

Race White Black Hispanic Asian Other American Indian

148

59 47 12 8 3

53.4 21.3 16.9

4.3 2.9 1.1

Income Low Medium High Very High

57 72 65 83

20.6 26.0 23.5 30.0

Parent Education Less than H.S. Some H.S. H.S. Diploma Some College Associate Degree Bachelor Degree Advanced Degree

1 1

18 47 33 97 80

.36 .36

6.5 17.0 11.9 35.0 28.9

65

CHAPTER 4 RESULTS

The purpose of this study was to investigate parental perceptions of children’s

weight status. Independent variables consisted of both demographic and non-

demographic data. The demographic variables included race, family income, child age,

child weight, child sex, parent age, parent sex, and parent education. The non-

demographic variables included mental health, physical health, other health, physical

functioning, emotional functioning, social functioning, school functioning, timing of last

physician visit, and weight-related information received during the physician visit. The

main goal was to investigate the extent to which these identified variables predicted

weight-perception-accuracy in parents of children ages 5-14. Thus, the dependent

variable was accuracy of parental perceptions of their child’s weight status. The primary

question for this study was the following: How well do demographic variables, health-

related quality of life, and weight-related information from health professionals predict

parental perceptions of their child’s weight status? Based on existing literature, it was

hypothesized that White race (Dorsey et al., 2009; Paeratakul et al., 2002); high SES

(Jain et al., 2001), young child age (Genovesi et al., 2005, Jain et al., 2001, Maynard et

al., 2003; Rhee et al., 2005; Rich et al., 2005; Young-Hyman et al., 2000) normal child

weight (Etelson, 2003; Carnell et al., 2005), female child sex (Boutelle et al., 2004;

Fisher et al., 2006; Holm-Denoma, 2005; Jeffery et al., 2005; Maynard et al., 2003),

visiting a physician recently (Post et al., 2011), and receiving-weight related information

from a health professional (Rhee et al., 2005) would be associated with greater

accuracy in parental-weight-perception. In addition, it was hypothesized that low

psychosocial functioning would be associated with accurate perceptions because

66

limitations in social, emotional, physical, and school functioning may raise parents’

awareness of their child’s weight problems (Zeller et al., 2010). All variables were

tested to ensure that assumptions were not violated. An alpha level of .05 was used for

all statistical tests.

Data Analysis

Descriptive analyses were conducted among variables of interest. Data

comparing underweight, normal weight, overweight, and obese children on the measure

of HRQOL (i.e. PedsQL) also were analyzed. The primary analysis focused on parental

accuracy in estimating their child’s weight status. Based on responses to the question,

“compared with other children the same age, do you feel your child is underweight,

about the right weight, overweight, or obese?” parents were categorized as

overestimating, accurately estimating, or underestimating their child’s weight status. A

logistic regression analysis was completed to determine the odds of parents

overestimating, accurately estimating, or underestimating their child’s weight status

based on race, family SES, child’s sex, child’s age, and child’s BMI, parent-report of the

child’s HRQOL (PedsQL scores), and whether a health professional informed the parent

of their child’s weight status (yes or no). Logistic regression generally is well suited for

describing and testing hypotheses about relationships between a categorical outcome

variable and one or more categorical or continuous predictor variables. Logistic

regression can be a powerful analytical technique for use when the outcome variable is

dichotomous (Peng, Lee, & Ingersoll, 2002) and has been applied increasingly in

educational research. The independent variables in the study included race, family

SES, child’s age, child’s BMI, physical health, mental health, other health, physical

functioning, social functioning, emotional functioning, school functioning, and weight-

67

related information received from health professionals. The dependent variable is

accuracy of parental perceptions of their child’s weight status (i.e. overestimate,

accurately estimate, underestimate). Data were examined for outliers and other

anomalies, and the sample size was large enough to support the number of variables in

the regression equation (Algina, 2007; Fields, 2005; George & Mallery, 2006).

Preliminary Analyses

Preliminary analyses were conducted to determine the normative distribution of

each variable and to examine whether there were any statistically significant

associations between demographic variables. Linear and logistic regression analyses

were then performed to examine whether any of the independent variables predicted

various outcomes related to children’s health, functioning, and parental perceptions of

children’s weight status. Nagelkerke’s R2, an indicator of the power of explanation to

predict categorical responses for a model derived from selected explanatory variables,

was used due to its ability to generate interpretations similar to the R squared of simple

regression (Nagelkerke, 1991).

Demographics

A total of 277 parent-child dyads participated in this study. There were 52 male

and 225 female parent participants. There were 130 male and 147 female child

participants. Children ranged in age from 5 to 14 years (M = 9.21, SD 2.50).

Participants were White (53%), Black (21%), Hispanic (17%), Asian (4%), other (3%),

and Native American (1%). Asian, other, and Native American were combined into one

category called “other race” due to low representation (n=23). Participants were

categorized across four income groups: 0 to $39,249 (21%), $39,250 to $68,999 (26%),

$69,000 to $97,749 (23%), and >$97,750 (30%). For descriptive purposes, these

68

groups were labeled (1) low-income (2) middle-income (3) high-income and (4) very-

high income. With regard to weight status, 60% of children were normal weight, 34%

were overweight or obese, and 6% were underweight. Data on health obtained through

parent-report revealed that 13% of children had a physical disorder, 8% of children had

a mental disorder, and 8% of children had a disorder classified as “other”. Sixty-three

percent of parents had at least a 4-year college degree, 17% had some college, 12%

had an associate degree, 6% had a high school diploma, and less than 1% had below

high school education. With regard to physician visits, approximately 67% of children

visited a pediatrician in the past six months, 26% in the past 7 to 12 months, 4% in the

past 13 to 18 months, 2% in the past 19 to 24 months, and 2% had not visited a

physician in the past 24 months. As a result of low representation in the latter options,

two categories were established: (1) visited a physician within the past six months (2)

had not visited a physician within the past six months.

Eighty-five percent of parents reported that a health professional informed them

of their child’s weight status during the last medical exam. While 34% of children were

overweight or obese, 9% of parents reported that a health professional informed them

that their child was indeed above normal weight. Approximately 58% of parents

accurately estimated the weight status of their child. About 37% of parents estimated

their child’s weight status by committing an error of underestimating. In other words,

approximately 1 in 3 parents of overweight or obese children erroneously believed that

their child fell in a lighter weight category. Finally, less than 5% of parents over-

perceived their child’s weight status (Table 4-1). Additional frequencies and

percentages for key demographic data are reported in Table 4-2.

69

Statistical Analyses

The following section describes results of linear and logistic regression analyses

for the primary research question as well as second-level questions of interest. A

detailed summary of key findings is provided at the end of the chapter.

Primary Research Question

Predictors of parental perceptions. Logistic regression analysis was

completed to examine the relationship of parental perceptions of children’s weight

status and both demographic and non-demographic variables (Tables 4-3 and 4-4). In

the sample, parents were categorized as overestimating, accurately estimating, or

underestimating their child’s weight status. The first category (overestimating) had very

low representation (n=14); therefore, this group was omitted from further analyses.

Sixteen variables were included in this analysis: BMI percentile, race, income, child sex,

child age, parent education, parent sex, school functioning, emotional functioning,

physical functioning, school functioning, physical disorder, mental disorder, other

disorder, told overweight, and told weight status. The regression coefficient for BMI

percentile was 0.08 and was significant t = 7.47, p = .00. Thus, high BMI was

associated with a greater chance of parents underestimating their child’s weight status.

The regression coefficient for mental disorder was 2.04 and was significant t = 2.58, p =

.01. Thus, having a child with a mental disorder was associated with a greater chance

of parents underestimating their child’s weight status. The regression coefficient for told

weight status was 1.78 and was significant t = -2.91, p=.00. Thus, being informed of the

child’s weight status by a health professional was associated with a decreased chance

of parents underestimating their child’s weight status. Additional tests revealed that

when each of the abovementioned variables were systematically removed from the

70

model, each were good predictors of parental weight-perception-accuracy; albeit, the

variance explained by BMI was greater than that explained by mental disorder and told

weight status. Parental perception of children’s weight status was not significantly

related to any of the other independent variables. Results revealed pseudo R2 = 0.637.

Second-Level Questions

Predictors of BMI. Linear regression analysis was completed to examine the

relationship of BMI percentile and both demographic and non-demographic variables

(Tables 4-5 and 4-6). Twelve independent variables were included in this analysis:

race, income, child sex, child age, parent education, physical disorder, mental disorder,

other disorder, physical functioning, emotional functioning, social functioning, and

school functioning. The regression coefficient for physical functioning was -2.02 and

was significant t (257) = -2.85, p = .00. Thus, higher physical functioning scores were

associated with lower BMI percentile. The regression coefficient for physical disorder

was 11.47 and was significant t (257) = 1.93, p = .05. Thus, having a physical disorder

was associated with higher BMI percentile. BMI percentile was not significantly related

to any of the other independent variables. The squared multiple correlation was R2 =

0.097 (adjusted R2 = 0.031).

Predictors of physical functioning. Linear regression analysis was completed

to examine the relationship of physical functioning and both demographic and non-

demographic variables (Tables 4-7 and 4-8). Ten variables were included in this

analysis: BMI percentile, race, income, child sex, child age, parent education, parent

sex, physical disorder, mental disorder, and other disorder. The regression coefficient

for BMI percentile was -0.02 and was significant t (259) = -2.87, p = .00. Thus, higher

BMI percentile was associated with lower physical functioning scores. The regression

71

coefficient for other disorder was -1.74 and was significant t (259) = -2.29, p = .02.

Thus, having a health issue classified as “other disorder” was associated with lower

physical functioning scores. Physical functioning scores were not significantly related to

any of the other independent variables. The squared multiple correlation was R2 =

0.1244 (adjusted R2 = 0.0669).

Predictors of emotional functioning. Linear regression analysis was

completed to examine the relationship of emotional functioning and both demographic

and non-demographic variables (Tables 4-9 and 4-10). Ten variables were included in

this analysis: BMI percentile, race, income, child sex, child age, parent education,

parent sex, physical disorder, mental disorder, and other disorder. The regression

coefficient for mental disorder was -1.56 and was significant t (259) = -2.28, p = .02.

Thus, having a mental disorder was associated with lower emotional functioning scores.

The regression coefficient for other disorder was -1.47 and was significant t (259) = -

2.09, p = .04. Thus, having a health issue classified as “other disorder” was associated

with lower emotional functioning scores. Emotional functioning scores were not

significantly related to any of the other independent variables. The squared multiple

correlation was R2 = 0.1645 (adjusted R2 = 0.1097).

Predictors of social functioning. Linear regression analysis was completed to

examine the relationship of social functioning and both demographic and non-

demographic variables (Tables 4-11 and 4-12). Ten variables were included in this

analysis: BMI percentile, race, income, child sex, child age, parent education, parent

sex, physical disorder, mental disorder, and other disorder. The F test for the income

categories was significant indicating that social functioning differed among the four

72

income categories. The regression coefficient for income level four was 1.37 and was

significant t (259) = 2.49, p = .01. Thus, higher income was associated with higher

social functioning scores. The regression coefficient for physical disorder was -1.05 and

was significant t (259) = -1.96, p = .05. Thus having a physical disorder was associated

with lower social functioning scores. The regression coefficient for other disorder was -

1.66 and was significant t (259) = -2.48, p = .01. Thus, having a health issue classified

as “other disorder” was associated with lower social functioning scores. Social

functioning scores were not significantly related to any of the other independent

variables. The squared multiple correlation was R2 = 0.1466 (adjusted R2 = 0.09057).

Predictors of school functioning. Regression analysis was completed to

examine the relationship of school functioning and both demographic and non-

demographic variables (Tables 4-13 and 4-14). Ten variables were included in this

analysis: BMI percentile, race, income, child sex, child age, parent education, parent

sex, physical disorder, mental disorder, and other disorder. The F test for the income

categories was significant indicating that school functioning differed among the four

income categories. The regression coefficient for income level four was 1.76 and was

significant t (259) = 3.11, p = .00. Thus, higher income was associated with higher

school functioning scores. The regression coefficient for other disorder was -1.55 and

was significant t (259) = -2.24, p = .03. Thus, having a health issue classified as “other

disorder” was associated with lower school functioning scores. School functioning

scores were not significantly related to any of the other independent variables. The

squared multiple correlation was R2 = 0.1673 (adjusted R2 = 0.1127).

73

Predictors of physician visit. Logistic regression analysis was completed to

examine the relationship between whether a child had visited a physician in the last six

months and demographic variables (Table 4-15 and 4-16). Fourteen variables were

included in this analysis: BMI percentile, race, income, child sex, child age, parent

education, parent sex, school functioning, emotional functioning, physical functioning,

school functioning, physical disorder, mental disorder, and other disorder. The F test for

the income categories was significant indicating that timing of last physician visit differed

among the four income categories. The regression coefficient for income level two was

1.04 and was significant t = 2.43, p = .02. Thus, children in middle income level families

($39,250-$68,999) were more likely to have visited a physician within the past six

months than those in low income level families (0-$39,249). The F test for the

education levels was significant indicating that education differed among the four

income categories The regression coefficients for parent education levels four and five

were -1.69 and -1.33 respectively, and significant t = -2.74, p = .01 and t = -2.07, p =

.04. Thus, possessing lower education was associated with a decreased chance of

parents taking their child to see a physician in the past six months. The regression

coefficient for social functioning was -0.13 and was significant t = -1.93, p = .05. Thus,

children with low social functioning scores had a decreased chance of visiting a

physician in the past six months. Timing of children’s last physician visit was not

significantly related to any of the other independent variables. Results revealed pseudo

R2 = 0.158.

Predictors of physicians notifying parents. Logistic regression analysis was

completed to examine the relationship between physicians notifying parents of their

74

child’s weight status and both demographic and non-demographic variables (Tables 4-

17 and 4-18). Fourteen variables were included in this analysis: BMI percentile, race,

income, child sex, child age, parent education, parent sex, school functioning, emotional

functioning, physical functioning, school functioning, physical disorder, mental disorder,

and other disorder. The regression coefficient for child age was -0.21 and was

significant t = -2.62, p = .01. Thus, parents of older children had a decreased chance of

being notified of their child’s weight status. Physician disclosure of child’s weight status

was not significantly related to any of the other independent variables. Results revealed

pseudo R2 = 0.157.

Predictors of physician diagnosing overweight. Logistic regression analysis

was completed to examine the relationship between physicians telling parents that their

child was overweight and both demographic and non-demographic variables (Tables 4-

19 and 4-20). Fourteen variables were included in this analysis: BMI percentile, race,

income, child sex, child age, parent education, parent sex, school functioning, emotional

functioning, physical functioning, school functioning, physical disorder, mental disorder,

and other disorder. The F test for the education levels was significant indicating that

physician diagnosing overweight differed among the four income categories. The

regression coefficient for parent education levels four and seven were -3.14 and -3.40

respectively, and both were significant t = -2.28, p = .02 and t = -2.43, p = .02. Thus,

parents at the lowest level and highest level for income had a greater chance of being

told by a physician that their overweight child was indeed overweight. The regression

coefficient for school functioning was 0.36 and was significant t = 2.34, p = .02. Thus,

overweight children with low school functioning had a decreased chance of receiving an

75

overweight diagnosis from physicians. Receiving a diagnosis of overweight was not

significantly related to any of the other independent variables. Results revealed pseudo

R2 = 0.606.

Summary

In general, the strength of most associations was weak; therefore, the following

summation of results should be interpreted cautiously so as to not overstate the

findings. The existence of weak correlations was especially consistent across second-

level questions. While correlations for the primary research question was moderate.

This section will summarize noteworthy findings beginning with the primary research

question and concluding with second-level questions.

With regard to the primary research question, parents of children who were

overweight or obese were more likely to display misperceptions about their child’s

weight status. Specifically, many parents of overweight children erroneously believed

that their child was normal weight. Similarly, parents of obese children erroneously

believed that their child was overweight or normal weight. These are considered to be

errors of underestimation. Furthermore, an association was found between mental

health and weight misperception. Specifically, if a child who was overweight or obese

had a mental health issue parents were more likely to underestimate their child’s weight

status. An association also was found between being informed of the child’s weight

status during a physician visit and parental perceptions. Specifically, if parents reported

being informed of their child’s weight status by a health professional, errors of

underestimation decreased (R2 = 0.64).

With regard to second-level research questions, as anticipated BMI percentile

and physical functioning were negatively correlated; albeit, as previously noted this

76

relationship was weak (R2 = 0.10). Not surprisingly, having a mental disorder or other

disorder was associated with lower emotional functioning in children but this correlation

was also weak (R2 = 0.16). Interestingly, a positive correlation was found between

income and social functioning. Specifically, children from high-income families

displayed more favorable social functioning ratings. Conversely, children with physical

disorders displayed poorer social functioning ratings; albeit, this link was also weak (R2

= 0.15). High-income was also positively correlated with school functioning in children

(R2 = 0.17). It seems that parents with less formal education were less likely to have

taken their child to see a physician within the past six months of completing the

questionnaire, but similar to abovementioned relationships, the strength of the

correlation was weak (R2 =0.16). Additionally, health professionals reportedly were

more likely to provide weight status information with parents of younger age children (R2

= 0.16). Several of these key findings and their implications are discussed further in the

next chapter.

77

Table 4-1 Weight perceptions: frequencies and percentages according to type of estimate

Parental Perceptions Frequency Percentages

Accurate estimation 160 58% Underestimation 103 37% Overestimation 14 5%

Table 4-2 Demographic characteristics: frequencies and percentages according to

race and weight

Demographic Variables Frequency Percentages

Normal weight children White Black Hispanic Other Total

92 33 25 15

165

33.2 11.9

9.0 5.4

59.6 Overweight/obese children White Black Hispanic Other Total

45 24 18 7

94

16.2

8.7 6.5 2.5

33.9 Underweight children White Black Hispanic Other Total

11 2 4 1

18

4.0 0.7 1.4 0.4 6.5

Accurately estimated child’s weight White Black Hispanic Other Total

87 34 24 15

160

31.4 12.3

8.7 5.4

57.8 Underestimated child’s weight White Black Hispanic Other Total

52 24 20 7

103

18.8

8.7 7.2 2.5

37.2 Overestimated child’s weight White Black Hispanic Other Total

9 1 3 1

14

3.2 0.3 1.1 0.3 4.9

78

Table 4-3 Summary of tests of multiple degree of freedom categorical independent variables in logistic regression for parental perceptions of children’s weight status

Df LRT Pr(>Chi)

Race 3 0.08 .994 Income 3 2.37 .499

Parent Education 4 1.99 .737

Table 4-4 Summary of logistic regression analysis for parental perceptions of

children’s weight status Estimate Std. Error z value Pr(>|z|)

Intercept -5.63 2.05 --- --- BMI Percentile 0.08 0.01 6.91 .000

Black 0.04 0.53 0.08 .935 Hispanic 0.24 0.55 0.43 .664

Other Race -0.18 0.85 -0.21 .832 Middle Income 0.45 0.59 0.76 .447 High Income 1.03 0.60 0.63 .099

Very High Income 0.61 0.65 0.94 .347 Female Child Sex -0.43 0.42 -1.00 .315

Child Age -0.09 0.08 -1.13 .260 Female Parent Sex -0.34 0.50 -0.67 .500

Some College -0.13 0.87 -0.15 .883 Associates Degree 0.75 0.90 0.83 .405 Bachelor Degree 0.17 0.79 0.21 .830 Advanced Degree 0.16 0.80 0.20 .845 Physical Disorder 0.15 0.59 0.25 .801 Mental Disorder 2.04 0.80 2.58 .009 Other Disorder -1.10 0.82 -1.33 .183

Physical Functioning -0.08 0.82 -1.09 .277 Emotional Functioning 0.02 0.07 0.21 .831

Social Functioning -0.02 0.09 -0.20 .837 School Functioning 0.08 0.08 1.00 .315

Told Overweight 1.30 0.87 1.49 .135 Told Weight Status -1.78 0.61 -2.91 .003

79

Table 4-5 F statistics for multiple degree of freedom categorical independent variables for body mass index percentile

df F Pr(>F)

Race 3.00 1.00 .919 Income 3.00 0.56 .608

`Parent Education 4.00 1.32 .553

Table 4-6 Summary of linear regression analysis for body mass index percentile Estimate Std. Error t value Pr(>|t|)

Intercept 83.27 17.70 --- --- Black 2.56 5.17 0.50 .620

Hispanic 2.65 5.38 0.49 .623 Other Race -1.40 7.23 -0.19 .846

Middle Income -0.94 5.72 -0.16 .869 High Income 1.92 5.96 0.32 .747

Very High Income -4.83 6.02 -0.80 .423 Female Child Sex 3.48 3.88 0.90 .370

Child Age 0.02 0.78 0.03 .975 Some College -6.21 8.52 -0.73 .466

Associate Degree -1.27 8.96 -0.14 .887 Bachelor Degree -9.81 7.88 -1.24 .214 Advanced Degree -7.99 8.04 -0.99 .321 Physical Disorder 11.47 5.94 1.93 .054* Mental Disorder 3.50 7.20 0.49 .627 Other Disorder -9.64 7.32 -1.32 .189

Physical Functioning -2.02 0.71 -2.85 .004 Emotional Functioning 0.83 0.75 1.10 .271

Social Functioning -0.23 0.90 -0.26 .795 School Functioning 0.58 0.79 0.73 .464

80

Table 4-7 F statistics for multiple degree of freedom categorical independent variables for physical functioning

df F Pr(>F)

Race 3.00 2.02 .417 Income 3.00 1.05 .084

`Parent Education 4.00 2.01 .082

Table 4-8 Summary of linear regression analysis for physical functioning Estimate Std. Error t value Pr(>|t|)

Intercept 18.61 1.31 -- -- BMI Percentile -0.02 0.01 -2.87 .004

Black -0.65 0.54 -1.21 .226 Hispanic -1.04 0.56 -1.87 .062

Other Race 0.56 0.75 0.75 .451 Middle Income -0.02 0.60 -0.03 .974 High Income 0.55 0.62 0.89 .376

Very High Income 0.24 0.62 0.39 .697 Female Child Sex 0.05 0.40 0.13 .898

Child Age -0.03 0.08 -0.38 .703 Some College -0.44 0.89 -0.50 .618

Associate Degree 1.16 0.93 1.25 .213 Bachelor Degree 0.99 0.82 1.20 .230 Advanced Degree 1.02 0.84 1.22 .225

Female Parent Sex 0.10 0.51 0.20 .841 Physical Disorder -0.71 0.60 -1.18 .239 Mental Disorder -0.26 0.74 -0.35 .728 Other Disorder -1.74 0.76 -2.29 .022

81

Table 4-9 F statistics for multiple degree of freedom categorical independent

variables for emotional functioning df F Pr(>F)

Race 3.00 2.45 .067 Income 3.00 2.69 .680

`Parent Education 4.00 0.80 .029

Table 4-10 Results of linear regression analysis for emotional functioning Estimate Std. Error t value Pr(>|t|)

Intercept 16.03 1.21 --- --- BMI Percentile 0.00 0.01 0.41 .608

Black 0.76 0.50 1.54 .125 Hispanic 0.69 0.52 1.34 .180

Other Race 1.43 0.69 2.07 .039 Middle Income 0.13 0.56 0.22 .822 High Income 0.90 0.58 1.55 .123

Very High Income 0.71 0.58 1.23 .218 Female Child Sex -0.37 0.38 -0.99 .324

Child Age 0.03 0.07 0.43 .667 Some College -0.55 0.83 -0.67 .503

Associate Degree 0.58 0.87 0.67 .504 Bachelor Degree -0.71 0.76 -0.94 .349 Advanced Degree -0.41 0.78 -0.53 .595

Female Parent Sex 0.04 0.48 0.09 .928 Physical Disorder -2.36 0.56 -4.22 .000 Mental Disorder -1.56 0.69 -2.28 .023 Other Disorder -1.47 0.70 -2.09 .037

82

Table 4-11 F statistics for multiple degree of freedom categorical independent variables for social functioning

df F Pr(>F)

Race 3.00 1.41 .687 Income 3.00 6.16 .010

`Parent Education 4.00 0.98 .420

Table 4-12 Results of linear regression analysis for social functioning Estimate Std. Error t value Pr(>|t|)

Intercept 16.38 1.16 --- --- BMI Percentile -0.01 0.01 -1.14 .255

Black 0.14 0.47 0.30 .766 Hispanic 0.21 0.49 0.42 .673

Other Race 0.77 0.66 1.16 .246 Middle Income -0.01 0.53 -0.02 .986 High Income 0.92 0.55 1.68 .095

Very High Income 1.37 0.55 2.49 .013 Female Child Sex 0.12 0.36 0.32 .746

Child Age 0.07 0.07 1.02 .310 Some College -0.45 0.79 -0.57 .571

Associate Degree 0.23 0.83 0.28 .778 Bachelor Degree 0.43 0.73 0.59 .554 Advanced Degree 0.57 0.74 0.77 .440

Female Parent Sex -0.05 0.45 -0.12 .904 Physical Disorder -1.05 0.53 -1.96 .050 Mental Disorder -1.16 0.65 -1.78 .076 Other Disorder -1.66 0.67 -2.48 .013

83

Table 4-13 F statistics for multiple degree of freedom categorical independent

variables for school functioning df F Pr(>F)

Race 3.00 2.50 .118 Income 3.00 5.65 .011

`Parent Education 4.00 0.76 .587

Table 4-14 Results of linear regression analysis for school functioning Estimate Std. Error t value Pr(>|t|)

Intercept 15.56 1.20 --- --- BMI Percentile 0.00 0.01 -0.12 .904

Black 0.54 0.49 1.10 .273 Hispanic 0.33 0.51 0.65 .517

Other Race 1.54 0.68 2.25 .025 Middle Income 0.62 0.55 1.13 .257 High Income 0.93 0.57 1.63 .104

Very High Income 1.76 0.57 3.11 .002 Female Child Sex 0.65 0.37 1.75 .082

Child Age -0.14 0.07 -1.86 .063 Some College 0.42 0.81 0.52 .605

Associate Degree 0.98 0.86 1.15 .250 Bachelor Degree 0.64 0.75 0.85 .397 Advanced Degree 1.04 0.77 1.35 .177

Female Parent Sex 0.32 0.47 0.68 .499 Physical Disorder -0.32 0.55 -0.58 .565 Mental Disorder -1.93 0.68 -2.85 .004 Other Disorder -1.55 0.69 -2.24 .026

84

Table 4-15 Summary of tests of multiple degree of freedom categorical independent

variables in logistic regression for doctor’s visit in the last six months Df LRT Pr(>Chi)

Race 3 1.80 .613 Income 3 9.45 .023

Parent Education 4 10.40 .034

Table 4-16 Summary of logistic regression analysis for doctor’s visit in the last six

months Estimate Std. Error t value Pr(>|z|)

Intercept 0.46 1.34 --- --- Black 0.13 0.38 0.33 .739

Hispanic 0.52 0.38 1.35 .177 Other Race 0.17 0.53 0.33 .741

Middle Income 1.04 0.43 2.43 .015 High Income 0.13 0.45 0.29 .772

Very High Income 0.13 0.46 0.29 .770 Female Child Sex 0.07 0.29 0.25 .805

Age 0.09 0.06 1.59 .112 Female Parent Sex -0.22 0.36 -0.60 .545

Some College -1.69 0.62 -2.74 .006 Associate Degree -1.33 0.64 -2.07 .038 Bachelor Degree -0.90 0.54 -1.68 .093 Advanced Degree -0.60 0.55 -1.10 .272

BMI Percentile 0.01 0.00 1.24 .216 Physical Disorder -0.04 0.44 -0.09 .928 Mental Disorder -0.75 0.59 -1.27 .205 Other Disorder 0.13 0.53 0.25 .801

Physical Functioning 0.02 0.05 0.36 .716 Emotional Functioning 0.04 0.06 0.70 .483

Social Functioning -0.13 0.07 -1.93 .053 School Functioning -0.03 0.06 -0.57 .569

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Table 4-17 Summary of tests of multiple degree of freedom categorical independent

variables in logistic regression for disclosure of child’s weight status Df LRT Pr(>Chi)

Race 3 2.84 .417 Income 3 2.45 .484

Parent Education 4 3.00 .557

Table 4-18 Summary of logistic regression analysis for physician disclosure of

child’s weight Estimate Std. Error t value Pr(>|z|)

Intercept 1.82 1.87 --- --- Black 0.52 0.52 1.02 .308

Hispanic 0.81 0.60 1.37 .171 Other Race 0.54 0.72 0.75 .454

Middle Income 0.52 0.54 0.95 .340 High Income 0.88 0.60 1.48 .139

Very High Income 0.35 0.56 0.63 .529 Female Child Sex -0.07 0.38 -0.19 .846

Age -0.21 0.08 -2.62 .013 Female Parent Sex 0.32 0.44 0.72 .472

Some College 0.23 0.86 0.27 .789 Associate Degree 0.56 0.97 0.57 .566 Bachelor Degree -0.44 0.79 -0.56 .578 Advanced Degree -0.44 0.81 -0.54 .588

BMI Percentile 0.00 0.01 0.37 .709 Physical Disorder -0.06 0.56 -0.10 .921 Mental Disorder 0.29 0.64 0.46 .647 Other Disorder -0.95 0.59 -1.61 .107

Physical Functioning -0.02 0.08 -0.32 .752 Emotional Functioning 0.11 0.07 1.61 .107

Social Functioning -0.08 0.09 -0.89 .373 School Functioning 0.08 0.07 1.04 .299

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Table 4-19 Summary of tests of multiple degree of freedom categorical independent variables in logistic regression for physician giving overweight diagnosis

Df LRT Pr(>Chi)

Race 3 2.86 .413 Income 3 2.46 .483

Parent Education 4 9.85 .042

Table 4-20 Summary of logistic regression analysis for physician giving overweight

diagnosis Estimate Std. Error t value Pr(>|z|)

Intercept -21.81 7.39 --- --- Black -0.92 0.85 -1.09 .280

Hispanic 0.69 0.90 0.76 .445 Other Race 0.76 1.21 0.63 .531

Middle Income -0.01 1.11 -0.01 .992 High Income 0.04 1.09 0.04 .967

Very High Income -1.23 1.16 -1.07 .286 Female Child Sex -0.59 0.73 -0.81 .419

Child Age 0.13 0.15 0.86 .390 Female Parent Sex -0.60 0.96 -0.63 .528

Some College -3.14 1.38 -2.28 .022 Associate Degree -1.07 1.18 -0.91 .363 Bachelor Degree -1.31 1.00 -1.32 187 Advanced Degree -3.40 1.40 -2.43 .015

BMI Percentile 0.20 0.07 2.95 .003 Physical Disorder 0.82 0.85 0.96 .335 Mental Disorder -0.54 1.27 -0.43 .669 Other Disorder 0.66 1.44 0.46 .645

Physical Functioning -0.11 0.10 -1.07 .282 Emotional Functioning 0.09 0.12 0.72 .474

Social Functioning -0.17 0.15 -1.12 .261 School Functioning 0.36 0.16 2.34 .019

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CHAPTER 5

DISCUSSION

The purpose of this study was to identify variables that predicted parental

perceptions of children’s weight status. Parents play an important role in the fight to

prevent childhood obesity and thus their perception of children’s weight status is critical.

For example, parents who perceive their children as being overweight are more likely to

model healthy behaviors (Parry et al., 2008). The impact of children’s health-related

quality of life and weight-related information parents receive during physician visits on

parental perceptions of children’s weight status has received little attention in the

literature. To date, no study investigating parental-perception-accuracy of children’s

weight status has examined these variables simultaneously. This study sought to

address the following question: How well do demographics, health-related quality of life,

and information parents receive during physician visits predict the accuracy of parental

perceptions of their child’s weight status? The following second-level questions also

were examined. What variables predict children’s BMI? What variables predict

children’s physical, social, emotional, and school functioning? What variables predict

the timing of children’s last physician visit? What variables predict whether health

professionals told parents their child’s weight status? For children who were overweight

or obese, what variables predict whether health professionals informed parents of this

diagnosis? This chapter will address the aforementioned questions, offer implications

for clinical practice and research, discuss the limitations of the current study, and offer

future directions for research.

In this study, 37% of parents with children who were overweight or obese

underestimated their child’s weight status. Specifically, these parents believed that their

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children fell within the normal weight range for age and sex, when in fact, their children

weighed much more than the standard endorsed by the Center for Disease Control and

Prevention (CDC) as well as the American Academy of Pediatrics (AAP). Astonishingly,

despite there being 56 children whose BMI fell in the 95th percentile or higher (i.e. the

obese range), not a single parent-participant rated their child as obese. Instead, these

parents rated their obese children as overweight or normal weight. The overall degree

of parental misperception of children’s weight status across weight groups in this

investigation is similar to several previous studies (Carnell et al., 2005, Etelson et al.,

2003; Hearst et al., 2011; Jain et al., 2001, Jeffery et al., 2005, Maynard, et al., 2003).

Interestingly, parental misperception of children’s weight status was associated

with children’s BMI percentile, mental health status, and health professional disclosure

of child weight status. Specifically, parents of children who were overweight or obese,

who also had a mental disorder, were more likely to make errors of underestimation.

The exact reason for this association is unknown but may be related to the amount of

time, resources, and energy parents expend to care for children with mental health

issues. Research indicates that caregiver strain is extremely high amongst parents

whose children display mental health problems (Angold, Messer, Strangl, Farmer,

Costello, & Burns, 1998; Early, Gregoire & McDonald, 2002; Kenny & McGilloway,

2007; Tan & Rey, 2005; van Wijngaarden, Schene, & Koeter, 2004). Therefore, parents

may be more attentive to the child’s mental health needs and overlook body weight

issues. Given the discernible behavioral manifestations (e.g. defiance, aggression,

impulsivity, withdrawal, detachment, anxiety, and moodiness) commonly associated

with pediatric mental health disorders, one can easily imagine how mental health needs

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may take precedence over less acute health concerns (i.e. obesity). In other words,

these behavioral problems may distract parents from recognizing silent health issues.

Silent health issues are medical conditions that are deleterious to one’s wellbeing yet

man not cause immediate limitations to daily functioning. It may take several years for a

child with obesity to exhibit marked impairment that sufficiently alerts parents to the

child’s unhealthy condition (Raman, 2002); thus, obesity could be considered a silent

health issue. For example in this study, only one of the four health-related quality of life

subscales predicted BMI percentile. Consequently, parents may downplay or ignore

their child’s excess weight because weight problems in youth don’t always lead to

immediate glaring functional impairments. In fact, there appears to be increasing

evidence that adolescence may be the onset period in which declines in HRQOL

become noticeable in overweight and obese youths (Arif & Rohrer, 2006; Ravens-

Sieber et al., 2001; Simon et al., 2008; Swallen et al., 2005; Tsiros, et al., 2009;

Williams et al., 2005). Given that the mean age of participants in the current study was

9 and no children were over the age of 14, the sample possibly was too young to reveal

significant differences in HRQOL across weight categories. Conversely, children with

mental health issues (e.g. mood disorder, adjustment disorder, oppositional defiance

disorder, conduct disorder, ADHD), typically exhibit problem-behaviors that require

immediate and ongoing supervision, redirection, correcting, shaping, and prompting

from parents (Maughan, Christiansen, Jenson, Olympia, & Clark, 2005). As a result,

these parents may be hyper-focused on their children’s mental health needs.

There also was a significant association between health professional disclosure

of child weight status and parental-perception-accuracy. Specifically, parents of

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children who are overweight who were not informed of their child’s weight status by a

health professional during the last physician visit were more likely to underestimate their

child’s weight status. This association suggests that parents’ awareness of their child’s

weight status improves when they are provided weight-related information from health

professionals. This finding is consistent with research by Post et al., (2011) who found

that adults who were overweight or obese and told by a physician that they were

overweight displayed more accurate perceptions of their weight. The current study

suggests that weight-related information offered by health professionals also helps

parents of children who are overweight form more realistic perceptions of their child’s

weight status.

In this study, physical disorder and physical functioning predicted BMI; albeit, the

correlation was somewhat weak. Specifically, children with at least one physical

disorder were more likely to have higher BMI. This finding is logical given the

challenges persons with physical disorders may face on a daily basis. According to the

CDC, people with physical disorders can find it more difficult to eat healthy, control their

weight, and be physically active. This may be due to medications that can contribute to

weight gain, weight loss, and changes in appetite; physical limitations that can reduce a

person’s ability to exercise; pain; a lack of energy; a lack of accessible environments

(e.g. sidewalks, parks, and fitness equipment) needed to enable exercise; and a lack of

resources (e.g. money, transportation, and social support from family, friends,

neighbors, and community members; CDC, 2011).

Not surprisingly, children with low physical functioning also were more likely to

have higher BMI. These children were more likely to have problems walking more than

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one block, running, participating in sports or exercise, lifting heavy objects, taking a bath

or shower independently, and doing household chores. They also were more likely to

have hurts, aches, and low energy levels. Thus, their physical functioning limitations

permeated nearly every facet of their lives.

The relationship between BMI and physical functioning in children is well

documented. Numerous studies have reported an association between obesity and

physical functioning in children and adolescents (Beer, Hofsteenge, Koot, Hirasing,

Waal, & Gemke, 2007; Swallen, Reither, Haas, & Meier, 2005; Williams, Wake,

Hesketh, Maher, & Waters, 2005; Wake, Salmon, Waters, Wright, & Hesketh, 2002;

Friedlander, Larkin, Rosen, Palermo, & Redline, 2003; Pinhas-Hamiel, Singer, Pilpel,

Fradkin, Modan, & Reichman, 2006). The current study provides additional data

supporting this relationship, although, the strength of this relationship is not strong.

This study also revealed that child age helped predict whether physicians

informed parents of their child’s weight status; albeit, the correlation was somewhat

weak. Specifically, parents of older children were less likely to be informed of their

child’s weight status. This may occur because parents of younger children (ages 0-5)

are often particularly concerned about their children meeting developmental milestones

in key areas, including height and weight (Mesibov, Schroeder, & Wesson, 1977). This

concern may fade as children age, unless they exhibit glaring deficits, abnormalities, or

delays. Consequently, pediatricians may be more inclined to provide height and weight-

related information for younger children to appease inquisitive parents. Furthermore,

being overweight as a child does not always present immediate and noticeable

limitations to functioning. Thus, parents in this study may not have proactively sought

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weight-related information during the physician visit. This is unfortunate because

pediatricians are more likely to notice problems and make needed referrals when

parents express concerns. For example, Glascoe and Dworkin (1995) found that

physicians were 13 times more likely to notice problems and make needed referrals

when parents raised concerns about various aspects of their children’s health.

Regardless of whether parents do their part in inquiring about their children’s

weight, physicians should provide this feedback. According to the American Academy

of Pediatrics (AAP), pediatricians should measure children’s BMI at every well-child visit

and provide a “prescription for healthy, active living” (AAP, 2012). The AAP set this

goal in response to data that revealed that pediatric providers were not regularly

informing families that their children were overweight (CDC, 2005) and often were not

calculating BMI (Klein, Sesselberg, Johnson, et al., 2010). The current study reveals

new and encouraging findings indicating that physicians may be improving in this area.

For example, 85% of parents in the current study reported that a physician informed

them of their child’s weight status during the child’s last physician visit. However, only

11% of these parents reported that a physician gave their child a diagnosis of

overweight or obese (9% of total sample). This is concerning because 37% of children

in the current study were overweight or obese. Thus approximately 74% of parents of

children who were overweight or obese reported that they were not explicitly told that

their child’s weight fell in the overweight or obese range. It’s possible that parents were

told their child’s weight in pounds but were not told that the weight category to which

that amount corresponded to. This would explain why there is a gap between being told

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weight status and being diagnosed overweight or obese for children who were above

normal weight.

Implications for Practice

The Role of Parents

Even though the AAP encourages pediatricians to provide parents with weight

status information and BMI, changing pediatricians’ behavior may be difficult because

many pediatricians have reported feeling that such efforts were futile (Story, Neumark-

Stzainer, Sherwood, et al., 2002), despite some evidence to the contrary (Rhee, De

Lago, Arscott-Mills, Mehta, & Davis, 2005). Encouragingly, West et al., (2008) found

strong evidence suggesting that it is possible to change the accuracy of parental

perceptions of their child’s weight status. Specifically, when provided with a child report

consisting of BMI and weight status classification along with general dietary and

physical activity recommendations, parents were more likely to correctly classify their

child’s weight status. Given varying attitudes pediatricians appear to have with regard to

conveying weight-related information with parents; Parents should be more assertive

and inquisitive during their child’s checkup. For example, parents should directly ask

about their child’s weight, BMI percentile, and weight category. After obtaining this

information, parents should seek advice on specific lifestyle behavioral changes they

should make if necessary.

Another issue in this area appears to exist related to parents’ ability; or lack

thereof, to accept feedback. While parents report that the physician’s office is the best

place to seek treatment for their children’s weight problems (Puhl, Peterson, &

Luedicke, 2011) many feel that providers blame them for their children’s excess weight

(Eneli, Kalogiros, McDonald, & Totem, 2007; Hernandez, Cheng, & Serwint, 2010).

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This issue reveals that even when health professionals provide candid feedback to

parents regarding their child’s overweight status, parents may not be emotionally ready

to accept it. In addition, a number of parents feel that providers offer vague advice or

unhelpful suggestions (Edmunds, 2005; Mikhailovich & Morrison, 2007). These issues

suggest that health professionals should consider developing more effective strategies

for communicating weight-related information to parents. Parents may be more

receptive to communication styles that demonstrates sensitivity and provides useful

information (Lumeng, Castle, & Lumeng, 2010). At the same time, parents should

regularly inquire about their child’s weight status and not rely on health professionals to

initiate the provision of this information.

The Role of Schools

In addition to the obvious responsibility pediatricians have by virtue of their

profession, schools also can provide weight-related information to parents. Schools

provide a logical measurement site because they reach virtually all youth (U.S.

Department of Commerce, 2007). In 2005, the Institute of Medicine (IOM) called on the

federal government to develop guidance for BMI measurement programs in schools

(IOM, 2005). BMI measurement programs in schools may be conducted for

surveillance and/or screening purposes. Surveillance programs assess the weight

status of students to identify the percentage of youth who may be at risk for weight-

related health problems. Surveillance data are typically anonymous and can be used

for many purposes, including identifying population trends and monitoring the outcomes

of interventions. Screening programs, on the other hand, assess the weight status of

individual students to identify those at risk and provide parents with individualized

information to help them take appropriate action (CDC, 2012). Arkansas and California

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have initiated BMI measurement programs across districts for several years. However,

very little is known about the outcomes of BMI measurement programs, including effects

on attitudes, weight-related knowledge, and behaviors of youth and their families (CDC,

2012). Consequently, no consensus exists on the effectiveness of BMI screening

programs for children and adolescents.

The U.S. Preventative Services Task Force (USPSTF) concluded that insufficient

evidence exists to recommend for or against BMI screening programs in clinical settings

as a means to prevent adverse health outcomes (USPSTF, 2005). However, the AAP

recommends that BMI should be calculated and plotted annually on all youth as part of

normal health supervision within the child’s primary care setting (Murray, 2007). The

IOM builds on the AAP’s guideline by recommending annual school-based BMI

screenings (IOM, 2005). Although more evaluation is needed to determine whether BMI

screening programs are an effective approach for addressing obesity, they are

inexpensive, simple to conduct, and have the potential to provide critical information to

parents. Thus, they should not be dismissed.

Helping Parents

In this study, approximately 1 in 3 parents of overweight and obese children

underestimated their child’s weight status. Needless to say, more needs to be done to

help these parents form more realistic perceptions of their children’s weight status.

Given that parents may distrust growth charts (Jain et al., 2001) health professionals

should consider enhancing these charts by placing more emphasis on informing parents

of the obesity-related diseases that correspond to high BMI percentile. In other words,

growth charts should describe the diseases that one is at risk for based on their BMI

percentile. As BMI increases, more diseases would be listed and the likelihood of

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acquiring them would also increase. This type of chart may be more attention-grabbing,

meaningful, and memorable to parents. In addition, this type of chart would emphasize

health over physical aesthetics which parents may be more willing to receive.

Furthermore, the emphasis would be taken off of labeling the condition (i.e. overweight

and obese) which may be a welcomed change since these labels are often resented by

individuals who are sensitive about their weight or their child’s weight.

Limitations

Several cautionary statements are warranted to assist readers in recognizing the

limitations of this study. First, threats to external validity may have emerged due to the

lack of random sampling. For example, all participants meeting eligibility criteria, who

had parents submit research packets, and had their heights and weights collected were

eligible for study participation. Convenience samples, like the one in this study, may

limit generalizability to all parents of children with weight problems. In addition, the

participants in this study attended the same school. Consequently, the population may

be more homogeneous than other groups included in similar studies that examined

parental perceptions of children’s weight status (Carnell et al., 2005, Etelson et al.,

2003, Jain et al., 2001, Jeffery et al., 2005, Maynard, et al., 2003). Furthermore,

although a relatively strong response rate was obtained (48%) from the general student

population, it is not known whether participants differ in meaningful ways from

individuals who were contacted yet did not provide data.

Threats to internal validity also may have emerged due to recall demands placed

on participants. Specifically, this study relied on parents to recall whether their child’s

physician informed them of weight-related information during the last physician visit.

Research suggests that participants’ recall is generally unreliable especially when they

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are required to remember details of an event after several months have passed

(Schacter, Chiao, & Mitchell, 2003). For decades, cognitive psychologists have warned

that the human memory is fallible (Schacter, 1999) and thus the reliability of self-

reported data is tenuous. Furthermore, Cook and Campbell (1979) revealed that

participants may report what they believe the researcher expects to see, or report what

reflects positively on their own abilities, knowledge or opinions which may further

threaten the validity of the findings. Furthermore, this study collapsed multiple physical

and mental health disorders into two variables. It is conceivable that having multiple

physical disorders or mental disorders may yield changes in the level of weight-

perception-accuracy exhibited by parents. However, based on the methodology

employed in this study there was no way to decipher if children presenting with

comorbid disorders impacted parental perceptions differently than those presenting with

only one mental disorder or physical disorder. Finally, the cross-sectional design may

have further limited internal validity given that data were collected on one occasion

rather than longitudinally. Moreover, cross-sectional data do not indicate causality.

Thus, these data do not allow us to conclude that higher BMI, the presence of a mental

disorder, or not receiving weight status information from physicians produced parental

misperceptions of children’s weight status.

Conclusions

The current study adds to the growing literature on parental perceptions of

children’s weight status. Despite its limitations, this study provides support for the

effects of BMI, mental health, and health professional disclosure of weight-related

information on parental-perception-accuracy of children’s weight status. Previous

studies also found an association between BMI (Carnell et al., 2005; Etelson et al.,

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2003; Maynard et al., 2003), health professional disclosure of weight-related information

(Post et al., 2011), and accuracy of weight perception. However, this study seems to be

the first that reveals a relationship between children’s mental health status and parental

perceptions of children’s weight status.

Future research in this area may be enhanced by employing methodology that

decreases reliance on participants’ long-term recall abilities to determine the type of

weight-related information health professionals provide during pediatrician visits. As

previously stated, for years cognitive psychologists have warned us about the fallibility

of human memory, especially after a long period of time has elapsed. Future research

on this topic should survey parents immediately following a pediatrician visit in order to

collect more reliable data than surveying parents several months after the visit.

Furthermore, health professionals also should be surveyed to get their perspectives on

how often they provide weight-related information to parents so as to reduce the one-

sidedness of data sources.

Future research may also be enhanced by utilizing a mixed-methods (also known

as multiple methodologies) approach to gathering data on parental perceptions of

children’s weight status. For example, the current study relied solely on quantitative

data collection methods. While these methods have a number of strengths, they also

possess inherent limitations. For example, typically quantitative approaches do not

provide opportunities for researchers to probe answers. Furthermore, the structure of

quantitative research questionnaires may lead participants to endorse responses that

do not precisely align with their true thoughts or feelings. These limitations could be

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minimized by including qualitative data collection procedures such as interviews or

focus groups.

Over the years, health research has paid increased attention to the growing role

played by qualitative methods. This is consistent with developments in the social and

policy sciences at large, reflecting the need for understanding context and gaining a

greater understanding of naturalistic settings (Shortell, 1999). In the social sciences,

the use of multiple methodologies can be traced to Campbell and Fiske (1959) who

developed the idea of “multiple operationism.” They argued that more than one method

should be used in the validation process to ensure that the variance reflected is that of

the trait and not of the method (Jick, 1979). Health researchers have been especially

interested in the possibility of combining qualitative and quantitative methods (Carey,

1993; Goering & Steiner, 1996; McKeganey, 1995; Miller & Crabtree, 1994; Morse,

1991; O’Conner & Zyzanski, 1994) due to the complexity of the many different factors

that influence health. According to Jones (1995), qualitative research has the potential

to close the gap between the sciences of discovery and implementation.

Future research should also explore the relationship between weight-perception-

accuracy and mental health more closely by identifying specific mental health disorders

that may be more impactful to parental perceptions than others. In addition, knowing

whether mental health and physical health comorbidities impact parental perception

outcomes is of interest. In the current study, parents with younger children seemed to

be more likely to receive weight-related information than parents with older children

during physician visits. Therefore, pediatricians and other health professionals should

be mindful of behaviors that unfairly favor one group over another so that concerted

100

efforts can be made to reduce bias in service delivery and treatment. At the same time,

parents should be more proactive in asking weight-related questions and expressing

their weight-related concerns during physician visits.

While a number of public health campaigns exist to raise parental awareness of

childhood obesity, parents may not take these messages personally and believe they

are targeted at a more vulnerable group (i.e. optimistic bias; Weinstein, 1980). This

study reveals that 37% of parents misperceived their child’s weight status due to

underestimating. As a result, these parents fall in the pre-contemplative stage of

behavior change (DiClemente & Prochaska, 1982) because they are unaware of their

child’s unhealthy weight status. Parents in this stage are unlikely to introduce and

model healthy lifestyle behaviors because of their ignorance regarding their child’s

unhealthy weight status (Towns & D’Auria, 2009).

Outcomes from this study may inform school and health professionals of the lack

of awareness many parents have of their children’s weight status. Suggestions were

offered regarding ways pediatricians and school personnel can help improve parental-

perception- accuracy of children’s weight status. Additional research is needed to

determine whether BMI screening programs are as effective as they appear to be on the

surface for preventing childhood obesity. Research also is needed to determine the

cost-effectiveness of BMI screening programs in schools to address the feasibility of this

approach.

In conclusion, the task of improving parental-perception-accuracy of children’s

weight status requires a deep understanding of variables that promote or demote weight

status awareness and perception formation. This task will not be achieved with most

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current approaches because as this study and other studies reveal, a large percentage

of parents continue to underestimate their children’s weight despite numerous public

health campaigns to prevent childhood obesity. Therefore, results of this study and

similar research will hopefully help those in leadership positions to adequately educate

parents before the childhood obesity epidemic worsens.

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BIOGRAPHICAL SKETCH

Robert Joshua Wingfield was born in Lynchburg, Virginia and raised in Prince

George’s County, Maryland. He obtained his undergraduate education at McDaniel

College, where he majored in psychology. Upon receiving his Bachelor of Arts in

psychology, Robert was admitted as a master’s degree student in the school

psychology program at Towson University in 2004. After receiving his Master of Arts,

Robert worked for the Office of Psychological Services in Prince George’s County

Public Schools from 2006-2008. Robert began his doctoral studies in school

psychology in August 2008 at the University of Florida. During his doctoral studies,

Robert primarily focused on the research and treatment of youth with anxiety spectrum

disorders, specifically obsessive compulsive disorder. He also focused on the research

and treatment of youth and families with weight control issues, primarily those with

obesity. Robert is currently completing an APA accredited internship in professional

psychology at the Center for Behavioral Health at Father Flanagan’s Boys Town. He

has accepted a postdoctoral fellowship in the Department of Behavioral Psychology at

Johns Hopkins University where he will provide outpatient therapy within Kennedy

Krieger Institute. Robert’s short term goals are to become licensed as a psychologist

and seek employment in a mental health setting helping children, adolescents, and

families with emotional and behavioral problems.