Patterns of walkability, transit, and recreation environment for physical activity

Introduction: Diverse combinations of built environment (BE) features for physical activity (PA) are understudied. This study explored whether patterns of GIS-derived BE features explained objective and self-reported PA, sedentary behavior, and BMI. Methods: Neighborhood Quality of Life Study participants (N=2,199, aged 20–65 years, 48.2% female, 26% ethnic minority) were sampled in 2001–2005 from Seattle / King County WA and Baltimore MD / Washington DC regions. Their addresses were geocoded to compute net residential density, land use mix, retail floor area ratio, intersection density, public transit, and public park and private recreation facility densities using a 1-km network buffer. Latent profile analyses (LPAs) were estimated from these variables. Multilevel regression models compared profiles on accelerometer-measured moderate to vigorous PA (MVPA) and self-reported PA, adjusting for covariates and clustering. Analyses were conducted in 2013–2014. Results: Seattle region LPAs yielded four profiles, including low walkability/transit/recreation (L-L-L); mean walkability/transit/recreation (M-M-M); moderately high walkability/transit/recreation (MH-MH-MH); and high walkability/transit/recreation (H-HH). All measures were higher in the HHH than the LLL profile (difference of 17.1 minutes/day for MVPA, 146.5 minutes/week for walking for transportation, 58.2 minutes/week for leisure-time PA, and 2.2 BMI points; all p < 0.05). Baltimore region LPAs yielded four profiles, including L-L-L; M-M-M; high land use mix, transit, and recreation (HLU-HT-HRA); and high intersection density, high retail floor area ratio (HID-HRFAR). HLU-HT-HRA and L-L-L differed by 12.3 MVPA minutes/day; HID-HRFAR and L-L-L differed by 157.4 minutes/week for walking for transportation (all p < 0.05). Conclusions: Patterns of environmental features explain greater differences in adults’ PA than the four-component walkability index.