The United States has experienced a two to three fold increase in pediatric obesity since the 1970’s. To date, school-based interventions to prevent and treat overweight and obesity have realized only limited success.
A growing body of research suggests that friends tend to share similar weight status as well as weight-related behaviors such as physical activity, screen time, and diet. However, the mechanisms underlying this clustering of behaviors and outcomes among friends remain unclear. Similar students may become friends, friends may be exposed to similar activities and environments, or friends may affect each other’s behavior directly. A better understanding of these phenomena would facilitate design of more effective health interventions tailored to the social environments of adolescents.
The purpose of this study is to help us understand patterns of weight related behaviors (and health outcomes) across diverse cohorts of students. The research team will collect and analyze friendship and health behavior data among 6th to 8th grade students in four middle schools. Data will be collected several times each academic year allowing researchers to observe the interplay of health behavior and the social environment over time.
Once the data are collected, cutting edge statistical models will be used to rigorously analyze the co-evolution of patterns of friendships and weight-related behaviors. Using findings from those analyses, computer models will be developed to simulate these processes operating over time. Those models will provide a test bed to explore potential intervention scenarios on the weight related behaviors and adolescent health in schools.
The FAST Study is a cross-campus collaboration fostered by the Computational Social Science Institute (CSSI) and includes Co-Principal Investigators John Sirard (Kinesiology) and James Kitts (Sociology), with Co-Investigators Mark Pachucki (Sociology), Lindiwe Sibeko (Nutrition), and Krista Gile (Mathematics & Statistics).
The FAST Study is funded by a five-year, $2.8 million grant from the National Institutes of Health.