Evaluation of Personal and Built Environment Attributes to Physical Activity: A Multilevel Analysis on Multiple Population-Based Data Sources
Author(s) -
Wei Yang,
Karen Spears,
Fan Zhang,
Wai Lee,
Heidi L. Himler
Publication year - 2012
Publication title -
journal of obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.756
H-Index - 53
eISSN - 2090-0716
pISSN - 2090-0708
DOI - 10.1155/2012/548910
Subject(s) - recreation , behavioral risk factor surveillance system , environmental health , built environment , residence , geographic information system , medicine , urbanization , land use , logistic regression , population , gerontology , level design , geography , demography , cartography , civil engineering , computer science , human–computer interaction , game design , sociology , economic growth , political science , law , economics , engineering
Background. Studies have documented that built environment factors potentially promote or impede leisure time physical activity (LTPA). This study explored the relationship between multiple built environment factors and individual characteristics on LTPA. Methods. Multiple data sources were utilized including individual level data for health behaviors and health status from the Nevada Behavioral Risk Factor Surveillance System (BRFSS) and community level data from different data sources including indicators for recreation facilities, safety, air quality, commute time, urbanization, population density, and land mix level. Mixed model logistic regression and geographic information system (GIS) spatial analysis were conducted. Results. Among 6,311 respondents, 24.4% reported no LTPA engagement during the past 30 days. No engagement in LTPA was significantly associated with (1) individual factors: older age, less education, lower income, being obesity, and low life satisfaction and (2) community factors: more commute time, higher crime rate, urban residence, higher population density, but not for density and distance to recreation facilities, air quality, and land mix. Conclusions. Multiple data systems including complex population survey and spatial analysis are valuable tools on health and built environment studies
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