For individual use only. Duplication or distribution prohibited by law.
Weight Control; Youth
Predictors for Persistent Overweight, Deteriorated Weight Status, and Improved Weight Status During 18 Months in a School-Based Longitudinal Cohort Dong-Chul Seo, PhD; Mindy H. King, PhD; Nayoung Kim, MA; Danielle Sovinski, MPH; Rhonda Meade, MS; Alyssa M. Lederer, MPH Abstract Purpose. To examine predictors for persistent overweight/obesity, deteriorated weight status, and improved weight status among students who participated in a school-based obesity prevention intervention from fall 2009 to spring 2011. Design. Longitudinal assessment of a school-based cohort was conducted to determine the characteristics of students who remained overweight/obese, improved their weight status, or showed deteriorated weight status during an 18-month period. Setting. Eleven schools in southern Indiana, northwestern Kentucky, and southeastern Illinois. Subjects. N ¼ 5309 students in 4th through 12th grade. Measures. Weight, height, and self-reported physical activity and nutrition behaviors of students were measured at baseline and 6, 12, and 18 months. Analysis. SAS 9.3 was employed to examine predictors for the three different weight categories using logistic regression. Results. Low socioeconomic status (SES) (adjusted odds ratio [AOR] ¼ 1.56 and p , .001, AOR ¼ 1.35 and p ¼ .0069, respectively) and higher soda intake (AOR ¼ 1.07 and p ¼ .0016, AOR ¼ 1.08 and p ¼ .0278, respectively) increased the odds of belonging to persistent overweight/obesity (30.6%) and deteriorated weight status (6.9%), compared to the persistent nonoverweight status group. Conclusion. While SES is an important determinant of weight category change, students’ screen time and soda consumption may be important factors. Schools and families may be able to successfully focus on these modifiable risk factors, decreasing the burden of childhood obesity. (Am J Health Promot 2015;30[1]: 22–27.)
Key Words: Obesity, Weight, School Health, Longitudinal Cohort, Children, Prevention Research. Manuscript format: research; Research purpose: modeling/ relationship testing; Study design: longitudinal observation design; Outcome measure: biometric; Setting: school; Health focus: weight control; Strategy: culture change; Target population age: adolescents; Target population circumstances: 4th through 12th grade students, southern Indiana, northwestern Kentucky, southeastern Illinois, all races/ethnicities
INTRODUCTION The prevalence of childhood obesity in the United States has increased significantly during the past 30 years.1 Although recent data suggest that the levels of childhood obesity appear to have reached a plateau,2 the prevalence of childhood obesity still remains high.3 Childhood obesity is associated with high blood pressure, type 2 diabetes, metabolic disorders, and asthma.4 Although researchers have made efforts to understand the determinants of obesity and many different interventions and policies have been developed to combat childhood obesity, national rates are still unacceptably high. It is well known that schools are a rational setting where interventions can be implemented to help deter this obesity epidemic. A recent meta-analysis of childhood obesity interventions illustrates a higher level of efficacy for multilevel and multicomponent interventions.5 The Healthy, Energetic, Ready, Outstanding, Enthusiastic Schools (HEROES) Initiative is a grant-
Dong-Chul Seo, PhD, is with the College of Health Sciences, Ewha Womans University, Seoul, South Korea. Nayoung Kim, MA, and Alyssa M. Lederer, MPH, are with the Indiana University School of Public Health, Bloomington, Indiana. Mindy H. King, PhD; Danielle Sovinski, MPH; and Alyssa M. Lederer, MPH, are with the Center on Education and Lifelong Learning, Indiana University, Bloomington, Indiana. Rhonda Meade, MS, is with the Welborn Baptist Foundation, Evansville, Indiana. Send reprint requests to Dong-Chul Seo, PhD, College of Health Sciences, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul, 120750, South Korea; [email protected]. This manuscript was submitted November 18, 2013; revisions were requested February 21, 2014; the manuscript was accepted for publication June 2, 2014. Copyright Ó 2015 by American Journal of Health Promotion, Inc. 0890-1171/15/$5.00 þ 0 DOI: 10.4278/ajhp.131118-QUAN-585
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For individual use only. Duplication or distribution prohibited by law. funded multilevel intervention intended to help schools change their culture by implementing the coordinated school health approach, recommended by the U.S. Centers for Disease Control and Prevention (CDC) to decrease childhood obesity and increase healthy life habits for students, their families, and school staff.6 Results of a recent evaluation of the HEROES Initiative show that it successfully reduced the percentage of overweight children in participating schools and healthfully modified their dietary, physical activity, and sedentary behaviors.7 Detailed information about the HEROES Initiative can be found elsewhere.7 Literature suggests that race/ethnicity3 and socioeconomic status (SES) of individual families and schools8–10 are factors related to childhood obesity. Children of racial/ethnic minority have a higher risk of obesity development at most ages than their white counterparts.3 Children with low family or school-level SES tend to show a higher prevalence of obesity than their counterparts.10 School-based studies found that low SES at school level increases the risk of being overweight and obese in school-aged children.8,9 In terms of lifestyle factors, screen time, including TV viewing and playing video games,11 was conducive to weight gain, whereas physical activity such as participation in team sports was significantly associated with weight loss.12 A recent study published in the Lancet reports that 80% of adolescents are at a high risk of developing diseases from lack of physical activity.13 Electronic pastimes are increasingly taking the place of outdoor activities and fixating sedentary lifestyle patterns among youth.14 Research also indicates that drinking sugar-sweetened beverages (SSBs) increases and having frequent meals decreases the risk for childhood obesity.15–17 Despite these findings on the determinants of childhood obesity, there is a paucity of data on differential predictors for weight status changes in a longitudinal cohort of students. Although the National Longitudinal Study of Adolescent Health has been a source of data to provide information about adolescent obesity, it includes only secondary school students.18 Kim
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et al.19 examined longitudinal association between incidence or remission of overweight and sociodemographic characteristic at the student level, but the authors did not examine the relationship between students’ weight status changes and their lifestyle behaviors, let alone school-level factors. This study is the first to investigate differential long-term predictors, including both sociodemographic and behavioral factors, for persistent overweight/obesity, deteriorated weight status, and improved weight status among elementary and secondary students in a school-based longitudinal cohort who were exposed to the HEROES Initiative for 18 months.
METHODS Participants and Data Collection A total of 5309 students in 4th through 12th grade in 11 elementary or secondary schools in southern Indiana, northwestern Kentucky, and southeastern Illinois that participated in the HEROES Initiative for at least two waves between fall 2009 and spring 2011 comprised the sample for this study. Schools included in the HEROES Initiative completed a competitive application process or were invited to participate. All the data were used if participants provided data for at least two waves. School wellness coordinators, nurses, and nursing students from local colleges measured students’ physiological data such as height and weight. In addition, students completed the Student Health Assessment Questionnaire (SHAQ), an online survey developed for the initiative, at baseline (fall 2009), 6 months (spring 2010), 12 months (fall 2010), and 18 months (spring 2011). Almost all the question items were adopted from the already validated Schools Physical Activity and Nutrition Survey Questionnaire. For more information on the validation and reproducibility process for this instrument, refer to the works of Hoelscher et al.20 and Penkilo et al.21 The instrument was pilot tested in two elementary schools and it was determined that students were able to successfully access and respond to the survey items. The SHAQ included questions related to dietary habits, physical activity, and sedentary behaviors of individual stu-
dents. Demographic data were collected by school administrators and entered into an Excel spreadsheet. The study protocol was approved by the Institutional Review Board of the investigators’ institution. Measures Weight Status. Two trained staff, one who completed the measurements of physiological data and one who recorded the data, participated in the measurement process. Weight and height were directly measured by trained staff, using a digital scale and stadiometer, respectively, at each time point. Body mass index (BMI) was computed based upon measured height and weight (kg/m2). Using the BMI percentiles for sex and age suggested by the CDC growth charts,22 participants were categorized into four groups: underweight (,5th percentile), normal (5th–84.9th percentile), overweight (85th–94.9th percentile), and obesity (95th percentile).23 Then, students were further grouped into four groups based on the between-weight status category changes: (1) persistent nonoverweight/nonobese (‘‘nonoverweight’’ hereafter), (2) persistent overweight/obese (‘‘overweight’’ hereafter), (3) deteriorated weight, and (4) improved weight during the 18 months of the study period. Student who stayed normal or underweight throughout the 18 months of the study period were included in the persistent nonoverweight group. Students who stayed overweight or obese throughout the 18 months of study period were included in the persistent overweight group. Students who moved from normal/ underweight to overweight/obesity and then stayed in overweight/obesity or from overweight to obesity and then remained in obesity were categorized into the deteriorated weight group. Those whose weight status showed improvement and then reverted back to overweight/obesity were categorized into the deteriorated weight group. Of the overweight or obese students at entry to the study, those who lost weight and moved to and maintained normal/underweight status were categorized into the improved weight group. As such, obese students who lost
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For individual use only. Duplication or distribution prohibited by law.
Table 1 Characteristics of Sample by Different Patterns of Weight Status Categories Among Elementary and Secondary School Students Who Participated in the HEROES Initiative During 18 Months (N ¼ 5309)* Persistent Nonoverweight
Persistent Overweight
(n ¼ 2929)
Deteriorated Weight
(n ¼ 1627)
(n ¼ 364)
Improved Weight (n ¼ 330)
Variable
Total
No.
%
No.
%
No.
%
No.
%
Total Sex Female Male Race White Nonwhite Lunch status Free or reduced Paid meal School area Urban Rural
5309
2929
55.2
1627
30.6
364
6.9
330
6.2
2649 2660
1492 1437
56.3 54.0
804 823
30.4 30.9
161 203
6.1 7.6
162 168
6.1 6.3
4282 1024
2413 515
56.4 50.3
1254 371
29.3 36.2
298 66
7.0 6.5
273 57
6.4 5.6
1804 1761
905 1005
50.2 57.1
592 464
32.8 26.4
170 139
9.4 7.9
109 123
6.0 7.0
2708 2601
1466 1463
54.1 56.3
919 708
33.9 27.2
149 215
5.5 8.3
151 179
5.6 6.9
* The total of the four categories above does not add up to 100% because of missing values and 59 students who did not belong to any of the above categories. Overweight includes obesity. Deteriorated weight refers to changes from normal/underweight to overweight/obesity or from overweight to obesity. Improved weight refers to changes from overweight/obesity to normal or from obesity to overweight. HEROES indicates Healthy, Energetic, Ready, Outstanding, Enthusiastic Schools.
weight and moved to and maintained overweight status during the study period were also categorized into the improved weight group. Sociodemographics. Race/ethnicity was self-defined and dichotomized into whites and nonwhites. The nonwhite category included American Indian/ Alaskan Native, African American/ black, Asian/Pacific Islander, Hispanic, multiracial, and other. Sex was measured by asking whether participants were male or female. School lunch program status served as a proxy measure of individual-level SES and was categorized into (1) those who were eligible for free/reduced-price lunch and (2) those who were not eligible for free/reduced-price lunch. The proportion of students in each school who were eligible for free/ reduced-price lunch was computed and used as a school-level SES variable. Schools were also divided into rural versus urban schools. Lifestyle Factors (Dietary Habits, Physical Activity, and Sedentary Behavior). Students’ soda consumption was measured by the question, ‘‘Yesterday, how many times did you drink a full serving of regular (not diet) soda?’’ The
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number of times family eat a full meal together was measured by the question, ‘‘In the past 7 days, how many times did you sit down and eat a full meal with your family?’’ The average number of meals per day was measured by the question, ‘‘How many meals do you usually eat per day? Count breakfast, lunch, dinner, and supper.’’ In terms of sedentary behavior, screen time was measured by adding hours spent on watching TV/movies and hours spent on playing video/computer games. Participants were also asked about the number of team sports they played during the past 12 months, including any after-school team sports. Data Analysis All analyses were conducted using SAS version 9.3 (SAS Institute Inc., Cary, NC). Descriptive statistics of these four weight status groups were computed. Prior to selecting variables to be submitted to logistic regression analyses predicting differential long-term factors for weight status groups, we examined bivariate associations of each variable with three outcome variables (i.e., persistent overweight, deteriorated weight, and improved weight) using a likelihood ratio v2 test. The variables with a bivariate p value , .25 were
candidates for multivariate logistic models as recommended by Hosmer and Lemeshow.24 Multiple logistic regression analyses for each of the three outcome variables were performed to obtain odds ratios and 95% confidence intervals (CIs), controlling for age. Clustering effects within the same school and correlated errors for repeated measures were accounted for using the SURVEYLOGISTIC procedure.
RESULTS Of the 5309 participants (50.1% males), the mean of age and BMI percentile at baseline were 10.8 years (SD ¼ 3.2) and 68.4 (SD ¼ 28.0), respectively. Dropout rates were 3% at wave 2, 14% at wave 3, and 5% at wave 4. As shown in Table 1, of the 5309 students, 55.2% (n ¼ 2929) remained nonoverweight, 30.6% (n ¼ 1627) remained overweight, 6.9% (n ¼ 364) showed deteriorated weight, 6.2 % (n ¼ 330) showed improved weight, and 1.1% (n ¼ 59) did not fall into any of the four categories during the 18 months of the study period. The most typical pattern of these 59 students was being normal/underweight at baseline
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Table 2 Logistic Regression Analyses Predicting Persistent Overweight, Deteriorated Weight, and Improved Weight Status Among Elementary and Secondary School Students Who Participated in the HEROES Initiative During 18 Months (N ¼ 5309)* Persistent Overweight
Deteriorated Weight
(n ¼ 1627) Variable Sex Female (vs. male) Race White (vs. nonwhite) Lunch status Free or reduced (vs. paid meal) Soda consumption Times family eat a full meal together Average number of meals per day Screen time (TV and video games) No. of sports teams played a year School area Urban (vs. rural) % of students on a free or reduced lunch plan at school level
AOR
95% CI
0.96
0.85–1.10
0.72
Improved Weight
(n ¼ 364) p
(n ¼ 330)
AOR
95% CI
p
AOR
95% CI
p
0.5669
0.79
0.63–0.99
0.0437
0.97
0.75–1.25
0.8078
0.62–0.85
,0.0001
0.95
0.71–1.27
0.7298
1.52
1.10–2.10
0.0120
1.56 1.07 0.98 0.82 1.03 0.83
1.38–1.77 1.03–1.11 0.95–1.00 0.76–0.89 1.01–1.06 0.79–0.88
,0.0001 0.0016 0.0657 ,0.0001 0.0136 ,0.0001
1.35 1.08 0.99 0.86 1.04 0.95
1.09–1.69 1.01–1.15 0.94–1.03 0.76–0.97 1.00–1.09 0.85–1.06
0.0069 0.0278 0.5691 0.0182 0.0588 0.3165
0.63 0.93 1.01 1.14 0.98 1.13
0.49–0.80 0.85–1.02 0.96–1.06 0.99–1.33 0.93–1.04 1.00–1.28
0.0002 0.1356 0.7486 0.0787 0.5453 0.0470
1.22
1.03–1.45
0.0200
0.86
0.63–1.16
0.3149
0.71
0.52–0.97
0.0313
1.01
1.01–1.01
,0.0001
1.00
1.00–1.01
0.7554
0.99
0.98–1.00
0.0002
* Overweight includes obesity. Deteriorated weight refers to changes from normal/underweight to overweight/obesity or from overweight to obesity. Improved weight refers to changes from overweight/obesity to normal or from obesity to overweight. The persistent nonoverweight group is used as a reference group in the logistic regression analyses predicting persistent overweight and deteriorated weight status. For categorical predictor variables, the reference category is shown after ‘‘vs.’’ HEROES indicates Healthy, Energetic, Ready, Outstanding, Enthusiastic Schools; AOR, odds ratio adjusted for age; and CI, confidence interval.
but becoming overweight in the middle and then returning to normal/ underweight at their last assessment in the study. A higher percentage of nonwhite students (36.2%) than white students (29.3%) and of those attending urban schools (33.9%) than rural schools (27.2%), respectively, belonged to the persistent overweight group. As for the deteriorated weight group, more male students (7.6%) than female students (6.1%) and more rural school students (8.3%) than urban school students (5.5%) belonged to the deteriorated weight group. Interestingly, more rural school students (6.9%) than urban school students (5.6%) showed improvement in their weight. Results of the logistic regression analyses to predict weight change categories are found in Table 2. Whites were less likely than nonwhites (adjusted odds ratio [AOR] ¼ .72, 95% CI ¼ .62–.85) and students on free/ reduced-price lunch were more likely than their counterparts (AOR ¼ 1.56, 95% CI ¼ 1.38–1.77) to be persistently overweight. Higher soda consumption, more screen time, and going to schools
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with a higher percentage of students on free/reduced-price lunch were associated with persistent overweight. Race and student-level SES (i.e., free/ reduced-price lunch status) were not correlated in this sample (u coefficient ¼ .04). For the deteriorated weight category, significant predictors showed exactly the same directionality as that of predictors for the persistent overweight group. Students who used free/reduced-price lunch were more likely than their counterparts (AOR ¼ 1.35, 95% CI ¼ 1.09–1.69) to show deteriorated weight status. Higher soda consumption and more screen time were associated with deteriorated weight status, whereas the more frequently students ate meals in a day, the less likely students belonged to the deteriorated weight group. For the improved weight category, whites were more likely than nonwhites (AOR ¼ 1.52, 95% CI ¼ 1.10–2.10) and students on free/reduced-price lunch were less likely than their counterparts (AOR ¼ .63, 95% CI ¼ .49–.80) to show improved weight status. Playing on more sports teams per year and attending schools with
lower rates of students eligible for free/ reduced-price lunch were associated with improved weight status. When student-level SES and school-level SES were entered into the same model, both variables maintained their statistical significance at the .05 level.
DISCUSSION This study in a cohort of children in select U.S. schools provides evidence of behavioral and nonbehavioral factors associated with 18-month changes in weight status. Specifically, it investigated differential long-term predictors for persistent overweight/obesity, deteriorated weight status, and improved weight status. Racial disparities were observed in persistent overweight and improved weight categories. Nonwhite overweight students (59% of whom were black) were more likely to stay overweight than white students, whereas white overweight students were more likely to have improved weight status than nonwhite students. This may have to do with different attitudes toward
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For individual use only. Duplication or distribution prohibited by law. body shape and different weight perceptions. White adolescents are less likely to be satisfied with their body shape and thus more likely to make an endeavor to reduce their weight than their nonwhite counterparts.25 By contrast, black adolescents tend to prefer a heavier body figure and are less likely to engage in weight management practices than their white counterparts.26 Moreover, black and Hispanic mothers are less likely to recognize their children as being overweight or obese than white mothers.27 Lack of parents’ concern about their children’s overweight status may reduce children’s motivation to lose weight. Future school-based childhood obesity prevention programs might benefit from tailoring their programs to cultural and racial/ethnic differences, especially with regard to perceived body weight and body image among students and their parents. It is noteworthy that both studentlevel and school-level low SES were positively associated with persistent overweight status and were negatively related to improved weight status. As shown by previous studies,10,19 this suggests that low SES at student level increases the risk of being overweight among children. The finding that school-level SES predicted individual student’s persistent overweight and improved weight status independently of individual-level SES deserves further mention. We suggest the following explanations for this finding. First, it is plausible that there are differences in school nutrition and physical activity policies and their implementation. Nanney et al.28 report that low-SES schools are less likely to provide students with healthy food choices and physical activity opportunities than high-SES schools. Specifically, low-SES schools tend to provide less nutritious foods in school lunches than high-SES schools.29 Second, peer influence might play a role in this phenomenon. Social contagion theory and research on health and social networks indicate that interdependence among peers helps establish shared norms in adolescence.30,31 O’Malley et al.9 report that low-SES schools have a larger proportion of overweight students than high-SES schools. Our study also found that low-SES schools had a
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higher prevalence of overweight students than high-SES schools (34.9% vs. 27.2%) whereas high-SES schools showed a higher proportion of improved weight status students than lowSES schools (6.8% vs. 5.9%). A high prevalence of overweight students in low-SES schools may build favorable norms regarding overweight and may not provide a strong motivation to lose weight. In terms of behavioral factors, soda consumption and the frequency of having meals per day were significant factors to predict persistent overweight and deteriorated weight status. A systematic review study reports strong evidence of a linear relation between weight gain and SSB consumption.32 In a different 3-year cohort study, Berkey et al.15 observed that an increase in soda intake led to excessive weight gain among children ages 9 to 17 years. Our study also found that the more frequently students ate meals in a day, the less likely they were to stay overweight and gain weight. Franko et al.17 found that regular meal patterns, including at least three meals a day, significantly slowed BMI increases and reduced overweight prevalence among girls ages 9 to 19 years. Previous studies also have shown that skipping breakfast, lunch, dinner, or supper increases the risk for weight gain.33 Thus, underscoring the importance of reducing SSB intake and encouraging students to maintain a regular meal pattern with three meals or more appears to be a good strategy to help students achieve healthy weight. As noted, screen time, including television and video/computer games, was also found to be a significant risk factor, whereas participating in team sports predicted weight improvement. Randomized controlled school-based interventions with a focus on reducing screen time showed a significant reduction in adiposity changes among elementary and middle school children.34,35 Recently emerging activitybased games that have children standing and moving may be a feasible alternative to sedentary-based games, although the ability of these games to facilitate increased physical activity is currently unclear.36 In addition, schools should find ways to encourage classroom breaks that incorporate
SO WHAT? Implications for Health Promotion Practitioners and Researchers What is already known on this topic? Literature has suggested that sociodemographic and lifestyle factors are associated with children’s weight gain. However, there is a paucity of studies that have examined differential long-term predictors for weight status changes in a longitudinal cohort of school-aged children. What does this article add? This study contributes to the literature by revealing long-term predictors for persistent overweight/ obesity, deteriorated weight status, and improved weight status among a large group of students participating in a school-based childhood obesity prevention initiative. What are the implications for health promotion practice or research? The finding of this study warrants further research on the mechanism by which school-level SES contributes to individual student’s persistent overweight and improved weight status independent of individual-level SES. It also deserves further research if a high prevalence of overweight students in low-SES schools may build favorable norms regarding overweight and may not provide a strong motivation to lose weight. Health promotion practitioners can focus on modifiable behavioral risk factors, such as soda consumption, the frequency of having meals per day, and screen time, when designing interventions that aim to decrease the burden of childhood obesity.
physical activity or activities that combine standards-based curriculum (e.g., math, science, reading) with physical activities. The findings of this research are subject to limitations. First, although body weight and height were actually measured and school-level information was obtained from objective measures, other sociodemographic and behavioral information of students was based on self-reported measures that might have introduced bias such as responses due to social desirability. Second, the participants in this study were schoolchildren in 4th through 12th grade in southern Indiana, northwestern Kentucky, and southeastern Illinois. The
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For individual use only. Duplication or distribution prohibited by law. results may not be applicable to children in other regions. It is also possible that some findings may be due to the intervention that participants were exposed to. For example, while HEROES was intended to be culturally competent, if it was not successful in this realm, this could have influenced the racial disparities identified. Further, although previously published implementation findings for the HEROES Initiative found that schools were able to implement the program with fidelity, the intervention might not have been implemented the same in all schools.7 Despite these limitations, the findings of this study contribute to the literature by revealing long-term predictors for different patterns of weightcategory changes among a large group of students participating in a schoolbased childhood obesity prevention initiative. Recognition of the predictors and designing interventions to incorporate them may help to reduce obesity rates, leading to improved health and well-being among children and adolescents.
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Acknowledgments The project described was supported by grants from the Welborn Baptist Foundation (WBF), grant numbers 4440311, 4443010, and 4443011 as well as the National Research Foundation of Korea Grant funded by the Korean government, grant number NRF – 2012S1A3A2033416.
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among fourth-grade students in Texas. J Nutr Educ Behav. 2008;40:20–27. Centers for Disease Control and Prevention. Clinical growth charts, 2009. Available at: http://www.cdc.gov/ growthcharts/clinical_charts.htm. Accessed August 14, 2012. Centers for Disease Control and Prevention. PedNSS health indicators, 2009. Available at: http://www.cdc.gov/ pednss/what_is/pednss_health_indicators. htm. Accessed August 14, 2012. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. New York, NY: John Wiley & Sons Inc; 2000. Grabe S, Hyde JS. Ethnicity and body dissatisfaction among women in the United States: a meta-analysis. Psychol Bull. 2006;132:622–640. Neumark-Sztainer D, Croll J, Story M, et al. Ethnic/racial differences in weight-related concerns and behaviors among adolescent girls and boys: findings from Project EAT. J Psychosom Res. 2002;53:963–974. Jain A, Sherman SN, Chamberlin LA, et al. Why don’t low-income mothers worry about their preschoolers being overweight? Pediatrics. 2001;107:1138– 1146. Nanney MS, Bohner C, Friedrichs M. Poverty-related factors associated with obesity prevention policies in Utah secondary schools. J Am Diet Assoc. 2008; 108:1210–1215. Gould R, Russell J, Barker ME. School lunch menus and 11 to 12 year old children’s food choice in three secondary schools in England—are the nutritional standards being met? Appetite. 2006;46:86– 92. Christakis NA, Fowler JH. Social contagion theory: examining dynamic social networks and human behavior. Stat Med. 2013;32:566–577. Smith KP, Christakis NA. Social networks and health. Annu Rev Sociol. 2008;34:405– 429. Malik VS, Schulze MB, Hu FB. Intake of sugar-sweetened beverages and weight gain: a systematic review. Am J Clin Nutr. 2006;84:274–288. Timlin MT, Pereira MA, Story M, Neumark-Sztainer D. Breakfast eating and weight change in a 5-year prospective analysis of adolescents: Project EAT (Eating Among Teens). Pediatrics. 2008; 121:e638–e645. Gortmaker SL, Peterson K, Wiecha J. Reducing obesity via a school-based interdisciplinary intervention among youth: Planet Health. Arch Pediatr Adolesc Med. 1999;153:409–418. Robinson TN. Reducing children’s television viewing to prevent obesity. JAMA. 1999;282:1561–1567. Baranowski T, Abdelsamad D, Baranowski J, et al. Impact of an active video game on healthy children’s physical activity. Pediatrics. 2012;129:e636–e642.
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Weight Control; Youth
Predictors for Persistent Overweight, Deteriorated Weight Status, and Improved Weight Status During 18 Months in a School-Based Longitudinal Cohort Dong-Chul Seo, PhD; Mindy H. King, PhD; Nayoung Kim, MA; Danielle Sovinski, MPH; Rhonda Meade, MS; Alyssa M. Lederer, MPH Abstract Purpose. To examine predictors for persistent overweight/obesity, deteriorated weight status, and improved weight status among students who participated in a school-based obesity prevention intervention from fall 2009 to spring 2011. Design. Longitudinal assessment of a school-based cohort was conducted to determine the characteristics of students who remained overweight/obese, improved their weight status, or showed deteriorated weight status during an 18-month period. Setting. Eleven schools in southern Indiana, northwestern Kentucky, and southeastern Illinois. Subjects. N ¼ 5309 students in 4th through 12th grade. Measures. Weight, height, and self-reported physical activity and nutrition behaviors of students were measured at baseline and 6, 12, and 18 months. Analysis. SAS 9.3 was employed to examine predictors for the three different weight categories using logistic regression. Results. Low socioeconomic status (SES) (adjusted odds ratio [AOR] ¼ 1.56 and p , .001, AOR ¼ 1.35 and p ¼ .0069, respectively) and higher soda intake (AOR ¼ 1.07 and p ¼ .0016, AOR ¼ 1.08 and p ¼ .0278, respectively) increased the odds of belonging to persistent overweight/obesity (30.6%) and deteriorated weight status (6.9%), compared to the persistent nonoverweight status group. Conclusion. While SES is an important determinant of weight category change, students’ screen time and soda consumption may be important factors. Schools and families may be able to successfully focus on these modifiable risk factors, decreasing the burden of childhood obesity. (Am J Health Promot 2015;30[1]: 22–27.)
Key Words: Obesity, Weight, School Health, Longitudinal Cohort, Children, Prevention Research. Manuscript format: research; Research purpose: modeling/ relationship testing; Study design: longitudinal observation design; Outcome measure: biometric; Setting: school; Health focus: weight control; Strategy: culture change; Target population age: adolescents; Target population circumstances: 4th through 12th grade students, southern Indiana, northwestern Kentucky, southeastern Illinois, all races/ethnicities
INTRODUCTION The prevalence of childhood obesity in the United States has increased significantly during the past 30 years.1 Although recent data suggest that the levels of childhood obesity appear to have reached a plateau,2 the prevalence of childhood obesity still remains high.3 Childhood obesity is associated with high blood pressure, type 2 diabetes, metabolic disorders, and asthma.4 Although researchers have made efforts to understand the determinants of obesity and many different interventions and policies have been developed to combat childhood obesity, national rates are still unacceptably high. It is well known that schools are a rational setting where interventions can be implemented to help deter this obesity epidemic. A recent meta-analysis of childhood obesity interventions illustrates a higher level of efficacy for multilevel and multicomponent interventions.5 The Healthy, Energetic, Ready, Outstanding, Enthusiastic Schools (HEROES) Initiative is a grant-
Dong-Chul Seo, PhD, is with the College of Health Sciences, Ewha Womans University, Seoul, South Korea. Nayoung Kim, MA, and Alyssa M. Lederer, MPH, are with the Indiana University School of Public Health, Bloomington, Indiana. Mindy H. King, PhD; Danielle Sovinski, MPH; and Alyssa M. Lederer, MPH, are with the Center on Education and Lifelong Learning, Indiana University, Bloomington, Indiana. Rhonda Meade, MS, is with the Welborn Baptist Foundation, Evansville, Indiana. Send reprint requests to Dong-Chul Seo, PhD, College of Health Sciences, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul, 120750, South Korea; [email protected]. This manuscript was submitted November 18, 2013; revisions were requested February 21, 2014; the manuscript was accepted for publication June 2, 2014. Copyright Ó 2015 by American Journal of Health Promotion, Inc. 0890-1171/15/$5.00 þ 0 DOI: 10.4278/ajhp.131118-QUAN-585
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For individual use only. Duplication or distribution prohibited by law. funded multilevel intervention intended to help schools change their culture by implementing the coordinated school health approach, recommended by the U.S. Centers for Disease Control and Prevention (CDC) to decrease childhood obesity and increase healthy life habits for students, their families, and school staff.6 Results of a recent evaluation of the HEROES Initiative show that it successfully reduced the percentage of overweight children in participating schools and healthfully modified their dietary, physical activity, and sedentary behaviors.7 Detailed information about the HEROES Initiative can be found elsewhere.7 Literature suggests that race/ethnicity3 and socioeconomic status (SES) of individual families and schools8–10 are factors related to childhood obesity. Children of racial/ethnic minority have a higher risk of obesity development at most ages than their white counterparts.3 Children with low family or school-level SES tend to show a higher prevalence of obesity than their counterparts.10 School-based studies found that low SES at school level increases the risk of being overweight and obese in school-aged children.8,9 In terms of lifestyle factors, screen time, including TV viewing and playing video games,11 was conducive to weight gain, whereas physical activity such as participation in team sports was significantly associated with weight loss.12 A recent study published in the Lancet reports that 80% of adolescents are at a high risk of developing diseases from lack of physical activity.13 Electronic pastimes are increasingly taking the place of outdoor activities and fixating sedentary lifestyle patterns among youth.14 Research also indicates that drinking sugar-sweetened beverages (SSBs) increases and having frequent meals decreases the risk for childhood obesity.15–17 Despite these findings on the determinants of childhood obesity, there is a paucity of data on differential predictors for weight status changes in a longitudinal cohort of students. Although the National Longitudinal Study of Adolescent Health has been a source of data to provide information about adolescent obesity, it includes only secondary school students.18 Kim
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et al.19 examined longitudinal association between incidence or remission of overweight and sociodemographic characteristic at the student level, but the authors did not examine the relationship between students’ weight status changes and their lifestyle behaviors, let alone school-level factors. This study is the first to investigate differential long-term predictors, including both sociodemographic and behavioral factors, for persistent overweight/obesity, deteriorated weight status, and improved weight status among elementary and secondary students in a school-based longitudinal cohort who were exposed to the HEROES Initiative for 18 months.
METHODS Participants and Data Collection A total of 5309 students in 4th through 12th grade in 11 elementary or secondary schools in southern Indiana, northwestern Kentucky, and southeastern Illinois that participated in the HEROES Initiative for at least two waves between fall 2009 and spring 2011 comprised the sample for this study. Schools included in the HEROES Initiative completed a competitive application process or were invited to participate. All the data were used if participants provided data for at least two waves. School wellness coordinators, nurses, and nursing students from local colleges measured students’ physiological data such as height and weight. In addition, students completed the Student Health Assessment Questionnaire (SHAQ), an online survey developed for the initiative, at baseline (fall 2009), 6 months (spring 2010), 12 months (fall 2010), and 18 months (spring 2011). Almost all the question items were adopted from the already validated Schools Physical Activity and Nutrition Survey Questionnaire. For more information on the validation and reproducibility process for this instrument, refer to the works of Hoelscher et al.20 and Penkilo et al.21 The instrument was pilot tested in two elementary schools and it was determined that students were able to successfully access and respond to the survey items. The SHAQ included questions related to dietary habits, physical activity, and sedentary behaviors of individual stu-
dents. Demographic data were collected by school administrators and entered into an Excel spreadsheet. The study protocol was approved by the Institutional Review Board of the investigators’ institution. Measures Weight Status. Two trained staff, one who completed the measurements of physiological data and one who recorded the data, participated in the measurement process. Weight and height were directly measured by trained staff, using a digital scale and stadiometer, respectively, at each time point. Body mass index (BMI) was computed based upon measured height and weight (kg/m2). Using the BMI percentiles for sex and age suggested by the CDC growth charts,22 participants were categorized into four groups: underweight (,5th percentile), normal (5th–84.9th percentile), overweight (85th–94.9th percentile), and obesity (95th percentile).23 Then, students were further grouped into four groups based on the between-weight status category changes: (1) persistent nonoverweight/nonobese (‘‘nonoverweight’’ hereafter), (2) persistent overweight/obese (‘‘overweight’’ hereafter), (3) deteriorated weight, and (4) improved weight during the 18 months of the study period. Student who stayed normal or underweight throughout the 18 months of the study period were included in the persistent nonoverweight group. Students who stayed overweight or obese throughout the 18 months of study period were included in the persistent overweight group. Students who moved from normal/ underweight to overweight/obesity and then stayed in overweight/obesity or from overweight to obesity and then remained in obesity were categorized into the deteriorated weight group. Those whose weight status showed improvement and then reverted back to overweight/obesity were categorized into the deteriorated weight group. Of the overweight or obese students at entry to the study, those who lost weight and moved to and maintained normal/underweight status were categorized into the improved weight group. As such, obese students who lost
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For individual use only. Duplication or distribution prohibited by law.
Table 1 Characteristics of Sample by Different Patterns of Weight Status Categories Among Elementary and Secondary School Students Who Participated in the HEROES Initiative During 18 Months (N ¼ 5309)* Persistent Nonoverweight
Persistent Overweight
(n ¼ 2929)
Deteriorated Weight
(n ¼ 1627)
(n ¼ 364)
Improved Weight (n ¼ 330)
Variable
Total
No.
%
No.
%
No.
%
No.
%
Total Sex Female Male Race White Nonwhite Lunch status Free or reduced Paid meal School area Urban Rural
5309
2929
55.2
1627
30.6
364
6.9
330
6.2
2649 2660
1492 1437
56.3 54.0
804 823
30.4 30.9
161 203
6.1 7.6
162 168
6.1 6.3
4282 1024
2413 515
56.4 50.3
1254 371
29.3 36.2
298 66
7.0 6.5
273 57
6.4 5.6
1804 1761
905 1005
50.2 57.1
592 464
32.8 26.4
170 139
9.4 7.9
109 123
6.0 7.0
2708 2601
1466 1463
54.1 56.3
919 708
33.9 27.2
149 215
5.5 8.3
151 179
5.6 6.9
* The total of the four categories above does not add up to 100% because of missing values and 59 students who did not belong to any of the above categories. Overweight includes obesity. Deteriorated weight refers to changes from normal/underweight to overweight/obesity or from overweight to obesity. Improved weight refers to changes from overweight/obesity to normal or from obesity to overweight. HEROES indicates Healthy, Energetic, Ready, Outstanding, Enthusiastic Schools.
weight and moved to and maintained overweight status during the study period were also categorized into the improved weight group. Sociodemographics. Race/ethnicity was self-defined and dichotomized into whites and nonwhites. The nonwhite category included American Indian/ Alaskan Native, African American/ black, Asian/Pacific Islander, Hispanic, multiracial, and other. Sex was measured by asking whether participants were male or female. School lunch program status served as a proxy measure of individual-level SES and was categorized into (1) those who were eligible for free/reduced-price lunch and (2) those who were not eligible for free/reduced-price lunch. The proportion of students in each school who were eligible for free/ reduced-price lunch was computed and used as a school-level SES variable. Schools were also divided into rural versus urban schools. Lifestyle Factors (Dietary Habits, Physical Activity, and Sedentary Behavior). Students’ soda consumption was measured by the question, ‘‘Yesterday, how many times did you drink a full serving of regular (not diet) soda?’’ The
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number of times family eat a full meal together was measured by the question, ‘‘In the past 7 days, how many times did you sit down and eat a full meal with your family?’’ The average number of meals per day was measured by the question, ‘‘How many meals do you usually eat per day? Count breakfast, lunch, dinner, and supper.’’ In terms of sedentary behavior, screen time was measured by adding hours spent on watching TV/movies and hours spent on playing video/computer games. Participants were also asked about the number of team sports they played during the past 12 months, including any after-school team sports. Data Analysis All analyses were conducted using SAS version 9.3 (SAS Institute Inc., Cary, NC). Descriptive statistics of these four weight status groups were computed. Prior to selecting variables to be submitted to logistic regression analyses predicting differential long-term factors for weight status groups, we examined bivariate associations of each variable with three outcome variables (i.e., persistent overweight, deteriorated weight, and improved weight) using a likelihood ratio v2 test. The variables with a bivariate p value , .25 were
candidates for multivariate logistic models as recommended by Hosmer and Lemeshow.24 Multiple logistic regression analyses for each of the three outcome variables were performed to obtain odds ratios and 95% confidence intervals (CIs), controlling for age. Clustering effects within the same school and correlated errors for repeated measures were accounted for using the SURVEYLOGISTIC procedure.
RESULTS Of the 5309 participants (50.1% males), the mean of age and BMI percentile at baseline were 10.8 years (SD ¼ 3.2) and 68.4 (SD ¼ 28.0), respectively. Dropout rates were 3% at wave 2, 14% at wave 3, and 5% at wave 4. As shown in Table 1, of the 5309 students, 55.2% (n ¼ 2929) remained nonoverweight, 30.6% (n ¼ 1627) remained overweight, 6.9% (n ¼ 364) showed deteriorated weight, 6.2 % (n ¼ 330) showed improved weight, and 1.1% (n ¼ 59) did not fall into any of the four categories during the 18 months of the study period. The most typical pattern of these 59 students was being normal/underweight at baseline
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For individual use only. Duplication or distribution prohibited by law.
Table 2 Logistic Regression Analyses Predicting Persistent Overweight, Deteriorated Weight, and Improved Weight Status Among Elementary and Secondary School Students Who Participated in the HEROES Initiative During 18 Months (N ¼ 5309)* Persistent Overweight
Deteriorated Weight
(n ¼ 1627) Variable Sex Female (vs. male) Race White (vs. nonwhite) Lunch status Free or reduced (vs. paid meal) Soda consumption Times family eat a full meal together Average number of meals per day Screen time (TV and video games) No. of sports teams played a year School area Urban (vs. rural) % of students on a free or reduced lunch plan at school level
AOR
95% CI
0.96
0.85–1.10
0.72
Improved Weight
(n ¼ 364) p
(n ¼ 330)
AOR
95% CI
p
AOR
95% CI
p
0.5669
0.79
0.63–0.99
0.0437
0.97
0.75–1.25
0.8078
0.62–0.85
,0.0001
0.95
0.71–1.27
0.7298
1.52
1.10–2.10
0.0120
1.56 1.07 0.98 0.82 1.03 0.83
1.38–1.77 1.03–1.11 0.95–1.00 0.76–0.89 1.01–1.06 0.79–0.88
,0.0001 0.0016 0.0657 ,0.0001 0.0136 ,0.0001
1.35 1.08 0.99 0.86 1.04 0.95
1.09–1.69 1.01–1.15 0.94–1.03 0.76–0.97 1.00–1.09 0.85–1.06
0.0069 0.0278 0.5691 0.0182 0.0588 0.3165
0.63 0.93 1.01 1.14 0.98 1.13
0.49–0.80 0.85–1.02 0.96–1.06 0.99–1.33 0.93–1.04 1.00–1.28
0.0002 0.1356 0.7486 0.0787 0.5453 0.0470
1.22
1.03–1.45
0.0200
0.86
0.63–1.16
0.3149
0.71
0.52–0.97
0.0313
1.01
1.01–1.01
,0.0001
1.00
1.00–1.01
0.7554
0.99
0.98–1.00
0.0002
* Overweight includes obesity. Deteriorated weight refers to changes from normal/underweight to overweight/obesity or from overweight to obesity. Improved weight refers to changes from overweight/obesity to normal or from obesity to overweight. The persistent nonoverweight group is used as a reference group in the logistic regression analyses predicting persistent overweight and deteriorated weight status. For categorical predictor variables, the reference category is shown after ‘‘vs.’’ HEROES indicates Healthy, Energetic, Ready, Outstanding, Enthusiastic Schools; AOR, odds ratio adjusted for age; and CI, confidence interval.
but becoming overweight in the middle and then returning to normal/ underweight at their last assessment in the study. A higher percentage of nonwhite students (36.2%) than white students (29.3%) and of those attending urban schools (33.9%) than rural schools (27.2%), respectively, belonged to the persistent overweight group. As for the deteriorated weight group, more male students (7.6%) than female students (6.1%) and more rural school students (8.3%) than urban school students (5.5%) belonged to the deteriorated weight group. Interestingly, more rural school students (6.9%) than urban school students (5.6%) showed improvement in their weight. Results of the logistic regression analyses to predict weight change categories are found in Table 2. Whites were less likely than nonwhites (adjusted odds ratio [AOR] ¼ .72, 95% CI ¼ .62–.85) and students on free/ reduced-price lunch were more likely than their counterparts (AOR ¼ 1.56, 95% CI ¼ 1.38–1.77) to be persistently overweight. Higher soda consumption, more screen time, and going to schools
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with a higher percentage of students on free/reduced-price lunch were associated with persistent overweight. Race and student-level SES (i.e., free/ reduced-price lunch status) were not correlated in this sample (u coefficient ¼ .04). For the deteriorated weight category, significant predictors showed exactly the same directionality as that of predictors for the persistent overweight group. Students who used free/reduced-price lunch were more likely than their counterparts (AOR ¼ 1.35, 95% CI ¼ 1.09–1.69) to show deteriorated weight status. Higher soda consumption and more screen time were associated with deteriorated weight status, whereas the more frequently students ate meals in a day, the less likely students belonged to the deteriorated weight group. For the improved weight category, whites were more likely than nonwhites (AOR ¼ 1.52, 95% CI ¼ 1.10–2.10) and students on free/reduced-price lunch were less likely than their counterparts (AOR ¼ .63, 95% CI ¼ .49–.80) to show improved weight status. Playing on more sports teams per year and attending schools with
lower rates of students eligible for free/ reduced-price lunch were associated with improved weight status. When student-level SES and school-level SES were entered into the same model, both variables maintained their statistical significance at the .05 level.
DISCUSSION This study in a cohort of children in select U.S. schools provides evidence of behavioral and nonbehavioral factors associated with 18-month changes in weight status. Specifically, it investigated differential long-term predictors for persistent overweight/obesity, deteriorated weight status, and improved weight status. Racial disparities were observed in persistent overweight and improved weight categories. Nonwhite overweight students (59% of whom were black) were more likely to stay overweight than white students, whereas white overweight students were more likely to have improved weight status than nonwhite students. This may have to do with different attitudes toward
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For individual use only. Duplication or distribution prohibited by law. body shape and different weight perceptions. White adolescents are less likely to be satisfied with their body shape and thus more likely to make an endeavor to reduce their weight than their nonwhite counterparts.25 By contrast, black adolescents tend to prefer a heavier body figure and are less likely to engage in weight management practices than their white counterparts.26 Moreover, black and Hispanic mothers are less likely to recognize their children as being overweight or obese than white mothers.27 Lack of parents’ concern about their children’s overweight status may reduce children’s motivation to lose weight. Future school-based childhood obesity prevention programs might benefit from tailoring their programs to cultural and racial/ethnic differences, especially with regard to perceived body weight and body image among students and their parents. It is noteworthy that both studentlevel and school-level low SES were positively associated with persistent overweight status and were negatively related to improved weight status. As shown by previous studies,10,19 this suggests that low SES at student level increases the risk of being overweight among children. The finding that school-level SES predicted individual student’s persistent overweight and improved weight status independently of individual-level SES deserves further mention. We suggest the following explanations for this finding. First, it is plausible that there are differences in school nutrition and physical activity policies and their implementation. Nanney et al.28 report that low-SES schools are less likely to provide students with healthy food choices and physical activity opportunities than high-SES schools. Specifically, low-SES schools tend to provide less nutritious foods in school lunches than high-SES schools.29 Second, peer influence might play a role in this phenomenon. Social contagion theory and research on health and social networks indicate that interdependence among peers helps establish shared norms in adolescence.30,31 O’Malley et al.9 report that low-SES schools have a larger proportion of overweight students than high-SES schools. Our study also found that low-SES schools had a
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higher prevalence of overweight students than high-SES schools (34.9% vs. 27.2%) whereas high-SES schools showed a higher proportion of improved weight status students than lowSES schools (6.8% vs. 5.9%). A high prevalence of overweight students in low-SES schools may build favorable norms regarding overweight and may not provide a strong motivation to lose weight. In terms of behavioral factors, soda consumption and the frequency of having meals per day were significant factors to predict persistent overweight and deteriorated weight status. A systematic review study reports strong evidence of a linear relation between weight gain and SSB consumption.32 In a different 3-year cohort study, Berkey et al.15 observed that an increase in soda intake led to excessive weight gain among children ages 9 to 17 years. Our study also found that the more frequently students ate meals in a day, the less likely they were to stay overweight and gain weight. Franko et al.17 found that regular meal patterns, including at least three meals a day, significantly slowed BMI increases and reduced overweight prevalence among girls ages 9 to 19 years. Previous studies also have shown that skipping breakfast, lunch, dinner, or supper increases the risk for weight gain.33 Thus, underscoring the importance of reducing SSB intake and encouraging students to maintain a regular meal pattern with three meals or more appears to be a good strategy to help students achieve healthy weight. As noted, screen time, including television and video/computer games, was also found to be a significant risk factor, whereas participating in team sports predicted weight improvement. Randomized controlled school-based interventions with a focus on reducing screen time showed a significant reduction in adiposity changes among elementary and middle school children.34,35 Recently emerging activitybased games that have children standing and moving may be a feasible alternative to sedentary-based games, although the ability of these games to facilitate increased physical activity is currently unclear.36 In addition, schools should find ways to encourage classroom breaks that incorporate
SO WHAT? Implications for Health Promotion Practitioners and Researchers What is already known on this topic? Literature has suggested that sociodemographic and lifestyle factors are associated with children’s weight gain. However, there is a paucity of studies that have examined differential long-term predictors for weight status changes in a longitudinal cohort of school-aged children. What does this article add? This study contributes to the literature by revealing long-term predictors for persistent overweight/ obesity, deteriorated weight status, and improved weight status among a large group of students participating in a school-based childhood obesity prevention initiative. What are the implications for health promotion practice or research? The finding of this study warrants further research on the mechanism by which school-level SES contributes to individual student’s persistent overweight and improved weight status independent of individual-level SES. It also deserves further research if a high prevalence of overweight students in low-SES schools may build favorable norms regarding overweight and may not provide a strong motivation to lose weight. Health promotion practitioners can focus on modifiable behavioral risk factors, such as soda consumption, the frequency of having meals per day, and screen time, when designing interventions that aim to decrease the burden of childhood obesity.
physical activity or activities that combine standards-based curriculum (e.g., math, science, reading) with physical activities. The findings of this research are subject to limitations. First, although body weight and height were actually measured and school-level information was obtained from objective measures, other sociodemographic and behavioral information of students was based on self-reported measures that might have introduced bias such as responses due to social desirability. Second, the participants in this study were schoolchildren in 4th through 12th grade in southern Indiana, northwestern Kentucky, and southeastern Illinois. The
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For individual use only. Duplication or distribution prohibited by law. results may not be applicable to children in other regions. It is also possible that some findings may be due to the intervention that participants were exposed to. For example, while HEROES was intended to be culturally competent, if it was not successful in this realm, this could have influenced the racial disparities identified. Further, although previously published implementation findings for the HEROES Initiative found that schools were able to implement the program with fidelity, the intervention might not have been implemented the same in all schools.7 Despite these limitations, the findings of this study contribute to the literature by revealing long-term predictors for different patterns of weightcategory changes among a large group of students participating in a schoolbased childhood obesity prevention initiative. Recognition of the predictors and designing interventions to incorporate them may help to reduce obesity rates, leading to improved health and well-being among children and adolescents.
8.
9.
10.
11.
12.
13.
14.
Acknowledgments The project described was supported by grants from the Welborn Baptist Foundation (WBF), grant numbers 4440311, 4443010, and 4443011 as well as the National Research Foundation of Korea Grant funded by the Korean government, grant number NRF – 2012S1A3A2033416.
15.
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