|Abstract or Summary
- In the U.S., childhood overweight and obesity has grown to epidemic levels. The National Survey of Children’s Health (NSCH) found that nationwide, approximately 32% of children 10–17 years old are identified as overweight, over half of them (nearly 17%) are obese. This is a concern because these rates continue to climb. In the last 25 years, obesity in children has tripled and presaged a reduction in overall health for this population. When examining risk factors for cardiovascular disease in children, 70% of obese children had at least one illness and 39% had at least two. This struggle can transcend into adulthood where it can develop into heart disease, diabetes, and cancer. For the first time in history, the current generation of children could have a shorter projected life expectancy than their parents.The main factors that influence weight are physical activity (PA) and dietary intake (DI). Many factors influence the levels of physical activity and types of food ingested. Several of these factors from the social ecological model can influence BMI, including acculturation, family structure, socioeconomic status (SES), built environment, technology use, adolescent physical activity, and adolescent dietary intake. Potential moderators are ethnicity, age, and gender. The specific aims of this project were to 1) determine if significant associations of the exogenous variables with PA behavior and DI occur among adolescents in the 12–17-year-old age group, 2) test if acculturation, family structure, socioeconomic status, built environment, and technology use, had indirect effects on BMI; that is, functioned through PA and DI as mediators, and 3) examine the association of the exogenous variables with PA and DI by ethnicity, age, and gender and how the mediated pathways predicting BMI vary between levels of the moderators.I used the 2009 California Health Interview Survey (CHIS) in a cross-sectional design. I conducted tests for means, differences, proportions, and group differences to examine the study sample characteristics. To conduct path analysis, I identified indicators to represent the exogenous and mediator variables. For the exogenous variables, acculturation was created from the indicators of citizenship, primary language, and country of birth. For family structure, the only indicator was marital status of parents. For SES, only household poverty was considered. For built environment, neighborhood and park safety were the indicators. For technology use, it was the indicators of television video game use and computer use. For the mediators, PA was created from the indicator of physical activity in the past week. Lastly, fruits, vegetables, fast food, and soda intake were the indicators for DI. To examine the initial relationships of these variables with each other and on BMI, I used a combination of association matrixes and analysis of variance (ANOVA).I conducted path analyses for the entire sample and individually for ethnic, age, and gender groups to examine the associations of the exogenous variables with the mediators, and BMI. Invariance testing examined group differences among the parameters in the models. The likelihood ratio test examined further the group differences among ethnic, age, and gender groups. The mediation test examined the direct and indirect effects of the variables on BMI and the likelihood ratio test determined which variables (PA or DI) significantly mediated the effect of the exogenous variables on BMI.The first hypothesis was confirmed with the association of the several exogenous variables with PA and DI for the entire study sample. The variables associated with PA were SES, built environment, and technology use. SES, built environment, and technology use were also associated with DI. None of the paths from the mediators to BMI were significant. The second hypothesis was not confirmed because none of the associations of exogenous variables with BMI were mediated by PA or DI.The third hypothesis was partially confirmed in that the associations with PA and DI differed by ethnic group (Whites, Hispanics, Asians, and Others), age (12 – 14 year-olds and 15 – 17 year-olds), and gender (males and females), and the indirect effect occurred for ethnicity, age, and gender but for certain mediators. For ethnicity, the associations with PA were family structure (significantly for Asians only), SES (for Whites only), built environment (for Whites and Hispanics), and technology use (for Whites, Hispanics, and Others). The associations with DI were acculturation (for Whites, Hispanics, and Asians), family structure (for Hispanics only), SES (for Whites, Hispanics, and Asians), built environment (for Whites only), and technology use (for all groups). For age, the variables associated with PA were SES (for young adolescents only),built environment (for older adolescents only), and technology use (for both groups). The variables associated with DI were family structure (for older adolescents only), SES (for both groups), built environment (for both groups), and technology use (for both groups). For gender, the association with PA were SES (for females only), built environment (for males only), and technology use (for both groups). The variables associated with DI were acculturation (for females only), and SES, built environment, and technology use for both gender groups.The test for mediation resulted in indirect effects of the exogenous variables on BMI depending on the moderator and group. For ethnicity, the indirect effect occurred for SES and technology use for only Whites mediated by PA. For age, the indirect effect occurred for SES, built environment, and technology but only for young adolescents; and the effect were mediated by DI. For gender, the indirect effect occurred for technology use for males; the effect was mediated through only PA.The outcomes show that some variables have a greater influence on BMI compared to others. For this study, acculturation, family structure, SES, built environment, and technology use all have influence on PA and DI. However, the strength of the association varies when considering the entire sample and then by levels of the moderators. The different ethnic, age, and gender backgrounds, practices, and lifestyles make it challenging for effective healthy approaches. The different lifestyles and practices of each group present challenges for developing effective interventions for overweight and obese adolescents.The findings suggest that both PA and DI are mediators to BMI for the exogenous variables depending on the moderator. This suggests that specific groups and exogenous variables can be targeted for interventions. Diversity in the U.S. adolescent populationpresents a challenge in developing effective approaches but also provides opportunities to learn about each group’s habits, beliefs, and lifestyles.For future analyses, my hope is for better developed programs to promote healthy lifestyles for adolescents because of the consideration and awareness of group differences. The influences on adolescent BMI are different for each ethnic group and further differentiated by age and gender. I believe that better understanding of group differences with respect to cultural values and practices, family makeup, financial situation, access in neighborhoods, and media device usage will provide a foundation for intervention development. The hope is that the associations reported in this dissertation can provide a foundation for tailored interventions targeted to different ethnic, age, and gender groups of adolescents.