Associations between body mass index, physical activity and the built environment in disadvantaged, minority neighborhoods: Predictive validity of GigaPan® imagery

Abstract

Background: The built environment has been shown to influence health in studies of disadvantaged populations using different measurement methods. This study determined whether environmental exposures derived from GigaPan® images could serve as valid predictors of body mass index (BMI), walking and moderate to vigorous physical activity (MVPA) in a longitudinal study of low-income adults living in two primarily African American neighborhoods in Pittsburgh, Pennsylvania, USA. GigaPan® is a robotic system used to obtain high-resolution, panoramic images of environments. Methods: Microscale environmental features along 481 streets were audited in 2015-2016 using an audit form. Environmental exposures were estimated for 731 adult participants, using a sample of street segments within a 0.4 km (0.25 mile) network distance from each participant's residential address. Summary environmental exposures were constructed using factor analysis. We tested associations between participant-level environmental exposures and objectively measured BMI, self-reported walking and objectively measured MVPA in regression models controlling for baseline health and demographic variables. Results: Three factors representing participants' environmental exposures were constructed: pedestrian bicycle-amenities; hilly-vacant-boarded; physical activity-recreation/low housing density. Environments with infrastructure and amenities supportive of walking and bicycling were associated with lower BMI (Coef. ¼ -0.47, p ¼ 0.02). Frequent walking was less likely in environments with more physical activity and recreation venues/low housing density (OR ¼ 0.81, 95% CI [0.67, 0.96]). MVPA was not associated with any of the environmental measures and the hillyvacant-boarded factor was not associated with any of the outcomes. Conclusions: Predictive validity was demonstrated for an environmental exposure factor that captured features supportive of walking and cycling in a model predicting BMI, using built environment audit data from GigaPan® imagery. A complementary analysis found lower odds of frequent walking in the neighborhood among participants with exposure to more physical activity and recreational features, but fewer types and lower density of housing.