Early Childhood Physical Activity: A Dynamic Systems Approach to Reducing Health Disparities

项目总结

PROJECT SUMMARY Almost half of American adults have a preventable chronic disease, most of which could be improved with regular physical activity (PA). These proportions are even higher for racially/ethnically diverse populations where disparities emerge in both chronic disease and PA behavior. Importantly, adults with ~20 minutes/day of physical activity have a 33% lower risk for all-cause mortality than those who are inactive We know that physical activity patterns for adults have their developmental beginnings in childhood. Although we know roughly when, specifically how to affect these patterns is multi-factorial. Direct “cause and effect” models are insufficient to accommodate the layers of complexity involved in pattern formation. Such complexity includes multiple dynamic systems with inter- and intra- interactions that influence children’s PA behaviors, including the built environment, the social environment (both inside and outside the home), and cognitive processes that vary during- child development. Providing a deeper understanding of these dynamics can advance interventions and policies for childhood PA behaviors and long-term health disparities reduction. To accomplish this task, we will leverage approaches more commonly used outside of biomedical research, in fields such as ecology and social science, and bring together a trans-disciplinary and cross-sector team of experts in complex systems modeling approaches (Brookings Institution) and pediatric PA and health disparities (Vanderbilt) to build an etiologic Agent-Based Model (ABM) that identifies which modifiable determinants may have the biggest impact on PA pattern formation for diverse young children. This project will utilize an independent dataset collected by the Growing Right Onto Wellness (GROW) Trial of child-parent pairs to inform the ABM. All of these families represented diverse under-served populations in Tennessee. The GROW Trial (total N=610 children ages 3-8) collected detailed objective PA data (via accelerometry) at four annual time- points over the study period (for child-parent pairs), as well as data on the child’s social environment, built environment, and cognitive processes. Using ABM in this context leverages the diversity and richness of this longitudinal dataset to build a model with empirically derived parameter estimates to generate new insights into supporting early childhood PA in diverse populations. ABMs allows us to examine how, when, and for whom PA behaviors are dynamically shaped by macro-level influences such as the built environment in which children reside, meso-level influences such as social environments both in and out of the home, and micro-level influences such as individual cognitive processes in early childhood development. We will examine the potential heterogeneity in these influences across child characteristics including gender, race/ethnicity, BMI, and BMI change over time. The result of this project will be a set of data-driven policy recommendations, based on a complex systems approach to studying childhood PA behaviors, that can be applied in real-world community settings.