An Investigation of Variables That Predict Parental Perceptions of Children's Weight Status

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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
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english
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Wingfield, Robert J
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Doctorate ( Ph.D.)
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University of Florida
Degree Disciplines:
School Psychology, Special Education, School Psychology and Early Childhood Studies
Committee Chair:
Waldron, Nancy L
Committee Members:
Algina, James J
Oakland, Thomas David
Janicke, David

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children -- demographics -- perceptions -- weight
Special Education, School Psychology and Early Childhood Studies -- Dissertations, Academic -- UF
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School Psychology thesis, Ph.D.
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Abstract:
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  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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Includes vita.
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by Robert J Wingfield.
Thesis:
Thesis (Ph.D.)--University of Florida, 2013.
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Adviser: Waldron, Nancy L.
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RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2014-08-31

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1 AN INVESTIGATION OF VARIABLES THAT PREDI CT PARENTAL PERCEPTI ONS STATUS: THE ROLE OF DEMOGRAPHICS, HEALTH RELATED QUALITY OF L IFE, AND WEIGHT RELATED INFORMATION RECEIVED FROM HEALTH PROFESSI ONALS 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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2 2013 Robert Joshua Wingfield

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

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4 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 c larity 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, Yoland L. Wingfield. S he has consist ently 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 times for many years. Without the fruition of this dream would not h ave occurred.

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5 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 ................................ .................. 29 Demographic Variables ................................ ................................ .................... 30 Non Demogr aphic 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 So ciodemographic Data ................................ ................................ ................... 58

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6 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 Demogr aphics ................................ ................................ ................................ .. 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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7 LIST OF TABLES Table page 3 1 Demographic characteristics: f requencies and percentages .............................. 64 4 1 Weight perceptions: frequencies and percentages according to t ype of estimates ................................ ................................ ................................ ........... 77 4 2 Demographic characteristics: frequencies and percentages a ccording to race and weight ................................ ................................ ................................ 77 4 3 Summary of tests of multiple degree of freedom c atego rical independent s tatus ................................ ................................ ................................ .................. 78 4 4 Summary of logistic regression analysis for parental p erceptions of tatus ................................ ................................ ...................... 78 4 5 F statistics for multiple d egree of freedom categorical i ndependent variables for body mass index p ercentile ................................ ................................ ........... 79 4 6 Summary of linear regression analysis for body mass index p ercentile ............. 79 4 7 F statistics for multiple degree of freedom categorical independent variables for physical f unctioning ................................ ................................ ....................... 80 4 8 Summary of linear regression analysis for physical f unctioning ......................... 80 4 9 F statistics for multiple degree of freedom categorical independent variables for emotional f unctioning ................................ ................................ .................... 81 4 10 Results of linear regression analysis for emotional f unctioning .......................... 81 4 11 F statistics for multiple degree of freedom categorical independent variables for social f unctioning ................................ ................................ ........................... 82 4 12 Results of linear regression analysis for social f unctioning ................................ 82 4 13 F statistics for multiple degree of freedom categorical indepen dent variables for school f unctioning ................................ ................................ .......................... 83 4 14 Results of linear regression a na lysis for school f unctioning ............................... 83 4 15 Summary of tests of multiple degree of freedom categorical independent variables in l ogi onths ................ 84 4 16 isit in the last six m onths ................................ ................................ ................................ ................ 84

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8 4 17 Summary of tests of multiple degree of freedom categorical independent variables in l ogistic tatus ................. 85 4 18 Summary of logistic regression analysis for physician d isclosure w eight ................................ ................................ ................................ ................. 85 4 19 Summary of tests of multiple degree of freedom categorical independent variables in logistic regression for physician giving overweight d iagnosis .......... 86 4 20 Summary of logistic regression analysis for physician giving o ve rweight d iagnosis ................................ ................................ ................................ ............ 86

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9 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 PERCEP TIONS : 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: S chool 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 childr en 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 eral demographic 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 com prehensive 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 resear ch question for this

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10 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 Measures included the P arental Demographics and Perceptions Questionnaire (PDPQ), the Pediatric Quality of Life Logistic regression analysis was completed to examine the relationship of weight status and both demographic and non demographic variables. Results revealed that parents of children who were overweight Specifically, many parents of overweigh t 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 associa tion was found between mental health and weight misperception. Specifically, if a child who was overweight or obese had a mental association also was found between bei physician visit and parental perceptions. Specifically, if parents reported being informed perception accuracy increased.

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11 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 pregnan cy, 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 childr en 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 Obesi ty 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 campaign

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12 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, hypert ension, 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 Hoffm an, 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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13 the victims of bullying behaviors (Janseen, Boyce, Craig, & Pickett, 2004). It seems that obesity predicts bullying involvement for both g irls 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, Inge rski, 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). L andmark 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 a nd 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, 2 004), 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 pr oblems (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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14 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 associate d 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 norma l 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 proc edure 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). ublic health issue that is 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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15 childhood ob esity 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 ition 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 pres ence 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 systemati cally 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 p erson (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 processe s within the immediate setting in which the developing person lives.

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16 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, be lief 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 a nd 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 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 constitu te some factors known to (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 ris k 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). Furtherm ore, the heritability of body weight is high, and genetic variation plays a significant role in determining the inter individual differences in

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17 susceptibility or resistance to the obesogenic environment (Ramachandrappa, 2011). For example, if a child has t wo 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). H 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 gen es, 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 sati ety (e.g. feeling full), not just to the biology of fat storage 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 terme eating and inactivity (Maziak, Ward, & Stockton, 2008). Factors contributing to sedentary lifestyles in children (e.g. television time, computer usage, unavailability of playgrounds, neighborhood structu re 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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18 occurred in how people obtain and consume their food. Popular foods among children have become more energ y 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 ina dvertently 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, Bre ner, & 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 m ore directly linked to academic goals (Martin & Chalmers, 2007). Disparities The foll owing section discusses factors associated with disparities in prevalence rates of childhood obesity. These factors include race, eating patterns and environment, socioec onomic status, and weight perceptions.

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19 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 c hildren, 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, Bl ack 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 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 exam ple, 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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20 occurs in a recently identified cluster of states in the southeastern portion of the country, 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 la rgely 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 con trol and a system, which often develops based on SES, before making judgments or developing interventions for families with overweight children. The high cost of nut ritionally 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, foun d 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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21 poultry. The healthier b asket 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 veget ables, 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 p erceptions. Weight percept ion 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, His panic 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, m others 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 (Campb ell, 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 demon strated that they were good parents (Baughcum et al., 2000).

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22 ing 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 under standing of why certain groups perceive weight differently may assist professionals in developing interventions that minimize misperceptions.

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23 CHAPTER 2 REVIEW OF LITERATURE Factors Influencing We ight Perception Accuracy The following review discusses the importance of weight perception and factors that influence weight perception accuracy. Research on parental perceptions of their ods 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 morbidly obese children). In addition, this review discusses the transtheoretica l 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 wh y 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 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 percepti on into the management of childhood weight

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24 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, & Gree ne, 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 m odel has been validated in et al., 2005). Parents are more likely to be ready to make changes if they believe the an 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 bec ause 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 characte rized as unmotivated. During the contemplation stage, people do intend to change their behaviors during 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,

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25 people intend to take action soon (i.e. within the next month). Indivi duals 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, p eople 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 c hildren 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 9). 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

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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 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 optimis tic 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, g etting 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 & Smi th, 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

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27 indicates that optimistic bias is n ot 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 auto mobile 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 bein g 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 unrealistic ally 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 (Gordon et al., 2002). Unrealistically high levels of optimism intersect with self e 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

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28 concept or 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 th e bias and invited to acknowledge its influence (Pronin, 2007). Parental Perceptual Bias Parental perceptual bias occurs when parents hold excessively favorable views the purportedly provide an objective measure of intellectual functioning or academic achievement to support their belief. Parental perceptual bias is not limited to percepti ons 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 behavi characteristics (Luby & Steiner, 1993). A number of theories have been offered to suscep tibility 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 ob esity, 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 hig to be too distressing to accept, especially if they lack control and knowledge on how to

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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 s ignificantly hinder acknowledgment of risks and efforts to promote risk reducing behaviors, such as exercise and hea lthful eating (Weistein, 1989). 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 t falls within the 6 th to 84 th percentile on growth charts endorsed by the Center for Disease Control and Prevention (CDC), as well as the American Association of Pediatricians (AAP). An race) as well a s 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 Casti llon, & 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 th eir own children are overweight. Some believe that stereotypes of overweight children portrayed in the media tend to be at the extreme end of the

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30 continuum and may distort non many overweight childre n blend in with the crowd (Campbell et al., 2006). Demographic Variables The following section discusses several variables that research indicates may variables include race The non related quality of life and information received from health professionals. Race. Racial differences exist in weight misperceptio n. 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 A mericans 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 overweight. A recent study found that both White and Black women desire a curv aceous 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

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31 are more likel y to have mothers and role models who are overweight. These older With regard to motivation for thinness, research suggests that Black females may be less persuaded by majority cultural standa rds 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, th us 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 (Kumanyi ka 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 b 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 personal acceptance of larger body sizes is likely to extend to an acceptance of larger body sizes in their children. related health risks in g obese, only

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32 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 their child was overweight but failed to see the health risks associated with their overweight status. Thus, accuracy of parental perceptions of chi 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 compare d 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 covered. example, Baughcum et al., (2000) did not detect a significant difference between White and non Similarly, Maynard et al., (2000) found non significant outcomes for race and parental eight

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33 rding 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 qua litative examination of parental income mothers of 13% of them were concerned about health risks (Ja in et al., 2001). The mothers voiced provision of sufficient nourishment to t heir 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 This behavior seemingly is driven by their desire Low 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, weight status.

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34 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 p articipants. 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 way in which weight categories were created may have masked potential differences across income levels. For example, He and Evans created three income categ ories (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 s mall 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 socioec onomic 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 r the

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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 rese arched 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 enc ouraged 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 value system, which ofte n develops based on SES, before making judgments or developing interventions for families with overweight children. Numerous studies indicate that parents of overweight children f normal weight children. Weight status misclassification usually occurs due to parents underestimating their extent to which parents of overweight children underestim ate 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

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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 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 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 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 e xample, 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 A number of studies re veal 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.,

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37 2003). In fact, mothers are nea rly 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., 20 06). 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 patte rn 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 b e 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 suprem acy (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, 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. et al., 2005; Baughcum et

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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 boned (Ja in et al., 2001 & Rich et al., 2005). Clearly, the research related to the influence of a investigation. Non Demographic Variables In addition to demographic variables su demographic variables that are germane to the cur rent research proposal: (1) Health Related Quality of Life (HRQL) and (2) information acquired from health professionals. Defining H ealth R elated Q uality of L ife 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 complete physical, mental, and social well being, and not merely the absence of definition of HRQOL eventually emerged, with agreement that it is multidimensional and includes four core domains: (1) disease state and physical symptoms, (2) functional

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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 us reports often are utilized when assessing the health status of young children or those with severe disabilities (Palemo, Long, Lewandowski, Drotar, Quittner, & Walker, 2008). Healt h 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. H ealth R elated Q uality of L ife 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 child ren when their BMI falls in the

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40 overweight range (Tsiro et al., 2008). While this relationship has been found in multiple et al., (2010) did not find a significant hosocial factors (PedsQL) so it is possible that other variables make certain overweight children impervious to reductions in psychosocial functioning. association between their 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 children who are overweight and displ ay 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 fun ctioning) as indicators of being overweight. These et al., 2001). Growth charts consist of a series of percentile curves that illustrate the distribution of selected bod y 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., perceived quality of life can ha

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41 included these variables in broader investig ations. For example, relationships between weight status (BMI), health perceptions, and psychosocial characteristics of children, parents, and parent et al., 2010). However, the purpose of the research was to determine w hether there was a relationship between ght status. Therefore, no conclusions A study of qualities that may influence the degree of concern parents feel about their chi 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 t o 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 perceptions with parental perceptions. These studies are described in mor e detail below given their relevance to the current research proposal

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42 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 et al., 2010). Questionnaires included the Parenting Stress Index Short Form (PSI), the naire, 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 question naire included: family structure, parent and child race, parent education level, zip code, parent perceptions of their own weight and 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 a nd 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 physi cal 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 re sources for outdoor play or physical activity.

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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 increa sed 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 p 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 becau se participants were recruited from an outpatient pediatric practice. Accordingly, parents 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 ause 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 help ed reduce the number of tests run to determine significant relationships. Parent study. Research has examined the degree of concern parents feel about Byrne, Zubrick, & Dav is, 2008). The participants included a mixture of normal weight, overweight, and obese children age 6 13 years and their parents. Results revealed that

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44 34% and 48% of parents of overweight children reported little concern or no concern, respectively for 44% 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 quality of life. While these findings are helpful, the study did not explicitly investigate the weight estimation, omitting an examination of the impact quality of life may have had on research to directly examine the potential relati onship 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, Wils on, & Modi, 2010). The participants completed the Perception of Weight Scale (POW) which is a sex specified figural rating scale designed to assess young school

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45 weight stigma, their own weight status, as well as their ideal w eight 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 s that are 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 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 c hild 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 po sitive 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

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46 that suggest that certain non White groups are less likely to perceive themselves as overweight. Although, the initial analysis suggested that greater obesity incr eased the perception of obesity status, degree of obesity became perceptions of obesity specific quality of life was considered. In general, older children and children with lower quality of l ife 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 pe rception) of his or her seen. Several limitations of the study are worth noting. Fi rst, 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 alrea dy acknowledged that to information on health and received more messages that emphasized maintenance of a healthy body size compared to non treatment seekers. This pos sible 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 obes ity treatment

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47 role parents play in initiating, implementing, and maintaining healthy li festyle changes to prevent or reverse childhood obesity, it will be important to better understand factors unlikely that parents will seek treatment if they do not perc eive 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 t his 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 impo rtant to broaden the age range of the sample in future studies. Information from Health Professionals non demographic variable of interest in the current research proposal. Ph ysician 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 ha ve 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 et al., 2010, Huang et al., 2004, Kant & Miner, 2007). Parent child study. Research that investigated demographic factors and reducing

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48 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 Fifty sue 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 ob esity. 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 rat ed on a 5 health problem. They also were asked whether they felt obesity in general was a hea lth problem. Lastly, parents were asked to recall whether their physician had given them This study provides important information concerning the role health 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

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49 Black. This may limit the generalizability of the res ults 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 rep resented 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 perceived quality of life also will influen and readiness to change. Adult study. Post and his colleagues (2011) evaluated whether patient reports ions 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 particip with obesity, defined as a BMI of 30 or greater, were included in the overweight classification because obese part icipants 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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50 gr eater, 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 phys ician 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 di d not consider themselves to be overweight. This finding of adults. Further analysis that included individuals with a BMI of 25 or greater was conducted to examine othe r 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 bein g 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 t hey 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 a ssociated with an increased or decreased likelihood of reporting having

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51 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 r esults of this study underscore the important role health professionals play know the affect health professionals have on perceptions of parents who have overweight c hildren. 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 epi demic 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, &Merc hant, 2005). 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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52 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 atte ntion, 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 re quires 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 overw eight 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. Severa l demographic related quality of life, timing of last physician visit, and weight related information received from health professionals.

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53 rel ationships 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 receiving treatment in that type of health setting. Unfortunately, however, parents were is impossible to quantify the affect this information may have had on perceptions. In 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 perception heir children. However, it focused primarily on whether parents were concerned about their ccuracy of perception and concern) are not synonymous. Thus, research is needed that directly directly examined the relationship between quality of life and perceptions of weight status by perceptions. They found a relationship between quality perceptions of their weigh status. However, s imilar to the et al., (2010), all children were obese and had received treatment at a

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54 hospital based multidisciplinary weight management program. Thus, the findings may not be generalizable to obese children who have not received help at a n 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 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, &Kurt in, 2000). childhood obesity prevention. Rhee et al., (2005) found that many parents who th ought narrow sample of participants as most were Latino and Black from the inner cit y. 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 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 professi onal informs

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55 clearly, this gap in the literature needs to be addressed. related i nformation from health professionals appear to be important variables to consider when examining parental 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 resear ch 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

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56 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. grad es 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 edu cation 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 provided to administrators at PKY to gain permission to conduct the study at their site. Once this p aperwork 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 comple te

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57 parents were invited to complete all paperwork including consent forms and questionnaires. The consent forms ensured that parent volunteers understood the general purp 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 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 r eturn 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

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58 To maintain the confidentiality of the data, return ed 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 U questions or concerns arose. Height and Weight Measurements weight measurements. Elementary and middle school students at PKY are required to 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 student s 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 spe cific 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 le/female), race (Asian, Black, Hispanic, Mixed, White), and family income as an indicator of socioeconomic status (SES) were gathered

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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 th is study included the following: (1) Parent Demographics and Perceptions Questionnaire (PDPQ) (2) Pediatric Quality of Life Inventory (PedsQL) height and weight. Parents who consented to participate in the study completed the abovementioned questionnaires to gather background information and assess various 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 greater detail below. Parent Demographics and Perceptions Questionnaire A parent questionnaire (i.e. the PDPQ) was developed by the PI to collect data the questionnaire included six items. Specifically, parents provided basic demograph ic 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

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60 disorder, sickle cell anemia, spina bifida, other). Parents also provided their perceptions children the same age, do you feel your child is underweight, about the right weight, ). Following that item, parents provided information about their experiences with health et al., (2011). Parents also hin 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 Sca les (PedsQL) 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 d eveloped 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 int ernationally and has been utilized in hundreds of studies that have examined various pediatric

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61 health problems (e.g. obesity, sickle cell disease, attention deficit/hyperactivity disorder, Duchenne muscular dystrophy, autism spectrum disorders, vesicourete ral 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 measur ement 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 designe d 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, reli ability 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, V arni 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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62 suitable as a summary score for the primary analysis of HRQOL outcome in school population health analyses. An investigation of the reliabi lity 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 p sychosocial 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. T he 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, re search, 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 reli abilities: 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 fun ctioning. 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 co nditions on patient HRQOL across ten pediatric chronic disease categories from the

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63 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 Physic ian Scale 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 BM I 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 classifie d as underweight.

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64 Table 3 1 Demographic characteristics: f requencies 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

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65 CHAPTER 4 RESULTS 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 vis it, 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 wa 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 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

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66 et al., 201 0). 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 weigh t, overweight, and obese children on the measure of HRQOL (i.e. PedsQL) also were analyzed. The primary analysis focused on parental same age, do you feel your child is underweight, logistic regression analysis was complete d to determine the odds of parents report of the health professional informed the parent 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 varia bles in the study included race, family functioning, social functioning, emotional functioning, school functioning, and weight

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67 related information received from health pr ofessionals. The dependent variable is accurately estimate, underestimate). Data were examined for outliers and other anomalies, and the sample size was large enough to s upport 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 ning, and parental perceptions of predict categorical responses for a model derived from selected explanatory variables, was used due to its ability to generate interpr etations 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 particip ants. 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 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

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68 groups were labeled (1) l ow 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 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 physi c ian within the past six months. Eighty five percent of parents reported that a health professional informed them overweight or obese, 9% of parents reported that a he alth 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 restimating. 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 1). A dditional frequencies and percentages for key demographic data are reported in Table 4 2.

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69 Statistical Analyses The following section describes results of linear and logistic regression analyses for the primary research question as well as second level que stions of interest. A detailed summary of key findings is prov ided at the end of the chapter. Primary Research Question Predictors of parental perceptions. Logistic regression analysis was 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 low representation (n=14); therefore, this group was omitted from further analyses. Sixteen variables were included in this analysis: BM I 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 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 weight status was 1.78 and was signi ficant t = 2.91, p=.00. Thus, being informed of the when each of the abovementio ned variables were systematically removed from the

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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 perceptio 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 B MI 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, p hysical 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 w ith 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 demo graphic 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 regres sion 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

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71 coefficient for other disorder was 1.74 and was significant t ( 259) = 2.29, p = .02. physical functioning scores. Physical functioning scores were not significantly related to any of the other independent variables. The squared mu ltiple 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 an d 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 i 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 anal ysis: 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

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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 1.0 5 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 hea lth issue classified 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: BM I 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) = scores were not significantly related to any of the other independent variables. The squared multiple correlation was R2 = 0.1673 (adjusted R2 = 0.1127).

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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 demograp hic 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 1.0 4 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 educ ation 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. Th us, children with low social functioning scores had a decreased chance of visiting a significantly related to any of the other independent variables. Results revealed pse udo R2 = 0.158. Predictors of physicians notifying parents. Logistic regression analysis was completed to examine the relationship between physicians notifying parents of their

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74 demographic variables (Tabl es 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 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 b etween 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 educ ation, 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 overwe ight 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 leve l 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 childre n with low school functioning had a decreased chance of receiving an

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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 resea rch question, parents of children who were weight status. Specifically, many parents of overweight children erroneously believed that their child was normal weight. Simila rly, 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. Specificall y, if a child who was overweight or obese status during a physician visit and paren tal perceptions. Specifically, if parents reported underestimation decreased (R 2 = 0.64). With regard to second level research questions, as anticipated BMI percentile and p hysical functioning were negatively correlated; albeit, as previously noted this

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76 relationship was weak (R 2 = 0.10). Not surprisingly, having a mental disorder or other disorder was associated with lower emotional functioning in children but this correlati on was also weak (R 2 = 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 (R 2 = 0.15). High income was also positively correlated with school functioning in children (R 2 = 0.17). It seems that parents with less formal education were less li kely 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 (R 2 =0.16). Additionally, health professionals reportedly w ere more likely to provide weight status information with parents of younger age children (R 2 = 0.16). Several of these key findings and their implications are discussed further in the next chapter.

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77 Table 4 1 Weight perceptions: frequencies and p e rcentages according to t ype of e stimate Parental Perceptions Frequency Percentages Accurate estimation 160 58% Underestimation 103 37% Overestimation 14 5% Table 4 2 Demographic c h aracteristics: frequencies and percentages a ccording to race and w eight 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 White Black Hispanic Other Tot al 87 34 24 15 160 31.4 12.3 8.7 5.4 57.8 White Black Hispanic Other Total 52 24 20 7 103 18.8 8.7 7.2 2.5 37.2 White Black Hispanic Other Total 9 1 3 1 14 3.2 0.3 1.1 0.3 4.9

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78 Table 4 3 Summary of tests of multiple degree of freedom c ategorical independent s tatus 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 p erceptions of tatus 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

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79 Table 4 5 F statistics for m ult iple degree of freedom categorical independent variables for body mass index p ercentile 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 p ercentile 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

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80 Table 4 7 F statistics for multiple degree of freedom categorical independent variables for physical f unctioning 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 r egr ession analysis for physical f unctioning 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

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81 Table 4 9 F statistics for m ultipl e degree of freedom c ategorical i ndependent variables for emotional f unctioning 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 f unctioning 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

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82 Table 4 11 F statistics for multiple degree of freedom categorical i ndependent v ariables f or social f unctioning 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 f unctioning 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

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83 Table 4 13 F statistics for multiple degree of freedom categorical i ndependent variables for school f unctioning 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 f unctioning 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

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84 Table 4 15 Summary of tests of multiple degree of freedom c ategorical independent onths 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 ix m onths 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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85 Table 4 17 Summary of tests of multiple degree of freedom c ategorical independent variables in tatus 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 d isclosure of eight 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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86 Table 4 19 Summary of tests of multiple d egree of freedom c ategorical independent variables in logistic regression for physician giving overweight d iagnosis 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 o verweight d iagnosis 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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87 CHAPTER 5 DISCUSSION The purpose of this study was to identify variables that predicted parental prevent childhood obesity and thus their perception For example, parents who perceive their children as being overweight are more likely to model healthy behaviors (Parry et al., 2008 related quality of life and weight related inf ormation parents receive during physician visits on literature. To date, no study investigating parental perception weight status has examined the se 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 level questions also physician visit? What variables predict whether health or obese, what variables predict whether health professionals informed parents of this diagnosis? This chapt er 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

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88 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 Dis ease Control and Prevention (CDC) as well as the American Academy of Pediatrics (AAP). Astonishingly, despite there being 56 children whose BMI fell in the 95 th 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 investigation is similar to several previous studies (Carne ll et al., 2005, Etelson et al., 2003; Hearst et al., 2011; Jain et al., 2001, Jeffery et al., 2005, Maynard, et al., 2003). us, 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 un known 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 d 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 m ental health needs

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89 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 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 ty 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 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 & Rohre r, 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 wa s 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 be haviors 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 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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90 health professional during the last physician visit were more likely to underestimate their 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 curr ent study suggests that weight related information offered by health professionals also helps weight status. In this study, physical disorder and physical functioning p redicted 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 basi s. 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; ph ysical limitations that can reduce a (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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91 one bl ock, 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 limitati ons permeated nea rly 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, Si nger, 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 weak. Specifically, parents of older children were less likely to be informed of their 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, abnorma lities, 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 notic eable limitations to functioning. Thus, parents in this study may not have proactively sought

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92 weight related information during the physician visit. This is unfortunate because pediatricians are more likely to notice problems and make needed referrals wh en parents express concerns. For example, Glascoe and Dworkin (1995) found that physicians were 13 times more likely to notice problems and make needed referrals Regardless of weight, physicians should provide this feedback. According to the American Academy child visit 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 thei 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 curr ent 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 to that amount corresponded to. This would explain why there is a gap between being told

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93 weight status and being diagnosed overweight or obese for children who were above n ormal weight. Implications for Practice The Role of Parents Even though the AAP encourages pediatricians to provide parents with weight many pediatricians have reported feeling that such efforts were futile (Story, Neumark Stzaine r, 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 consisting of BMI and weight status classification along with gener al dietary and physical activity recommendations, parents were more likely to correctly classify their conveying weight related information with parents; Parents sho uld be more assertive information, parents should seek advice on specific lifesty le behavioral changes they should make if necessary. place to seek treatment for the (Eneli, Kalogiros, McDonald, & Totem, 2007; Hernandez, Cheng, & Serwint, 2010).

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94 This issue reveals that even when healt h professionals provide candid feedback to to accept it. In addition, a number of parents feel that providers offer vague advice or unhelpful suggestions (Edmunds, 200 5; 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 dem onstrates sensitivity and provides useful information (Lumeng, Castle, & Lumeng, 2010). At the same time, parents should 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 y outh (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 surveill ance 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 purpo ses, 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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95 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 atti tudes, 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) con cluded 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 o n all youth as part of 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 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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96 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) whi ch may be a welcomed change since these labels are often resented by 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 et al., 2005, Etelson et al., 2003, Jain et al., 2001, Jeffery et al., 2005, Maynard, et al., 2003). Furthermore, al though 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 physician informed them of weight related information during the last physician visit. Research suggests that partic

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97 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 fallib le (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 abi lities, 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 perception s differently than those presenting with only one mental d isorder 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, cr oss 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 status. Conclusions The current study adds to the growing literature on parental perceptions of effects of BMI, mental health, and health professional disclosure of we ight related information on parental perception studies also found an association between BMI (Carnell et al., 2005; Etelson et al.,

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98 2003; Maynard et al., 2003), health professional disclosure of weight relat ed information (Post et al., 2011), and accuracy of weight perception. However, this study seems to be Future research in this area may be enhanced by employing methodology that term recall abilities to determine the type of weight related information health professionals provide during pediatrician visits. As previously stated, for y ears 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 d ata 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 ive 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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99 minimized by including qualitative data collection procedures such as intervie ws or focus groups. Over the years, health research has paid increased attention to the growing role p layed by qualitative methods. This is consistent with developments in the social and policy sciences at large, reflecting the need for understanding cont ext 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 re than one method should be used in the validation process to ensure that the variance reflected is that of the trait and no t of the method (Jick, 1979). Health researchers have been especially interested in the possibility of combining qualitative and q uantitative methods (Carey, 1993; Goering & Steiner, 1996; McKeganey, 1995; Miller & Crabtree, 1994; Morse, 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 percep tion 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

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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. optim istic bias; Weinstein, 1980). This study reveals that 37 underestimating. As a result, these parents fall in the pre contemplative stage of behavior change (DiClemente & Prochaska, 1982) because they are unaware of their s. Parents in this stage are unlikely to introduce and Outcomes from this study may inform school and health professional s of the lack offered regarding ways pediatricians and school personnel can help improve parental perception 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 feasi bility of this approach. In conclusion, the task of improving parental perception weight status requires a deep understanding of variables that promote or demote weight status awareness and perception formation. This task will not b e achieved with most

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101 current approaches because as this study and other studies reveal, a large percentage health campaigns to prevent childhood obesity. Therefore, resul ts 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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117 BIOGRAPHICAL SKETCH Robert Joshua Wingfield was born in Lynchburg, Virginia and raised in Prince College, where he majored in ps ychology. Upon rec eiving his Bachelor of Arts in p sychology, R psychology p rogram at Towson University in 2004. After receiving his Master of Arts Robert worked for the Office of Psychological 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 accredi ted internship in professional psychology at the Center for Behavioral Health 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. and seek employment in a mental health setting helping children, adolescents, and families with emotional and behavioral problems.