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 walkable/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-H-H). All measures were higher in the H-H-H than the L-L-L 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.