Associations between the combined physical activity environment, socioeconomic status, and obesity: a cross-sectional study

Aims: This study investigates associations between the combined physical activity environment and obesity and explores any sub-group effects by individual-level socioeconomic status. Methods: In a large cross-sectional cohort ( n = 22,889) from the Yorkshire Health Study, body mass index was calculated using self-reported height and weight and obesity was defined as a body mass index ≥ 30. The physical activity environment was split into ‘unfavourable physical activity’, ‘moderately favourable physical activity’ and ‘favourable physical activity’ environments. This was based on the count of parks and physical activity facilities within a 2 km radial buffer centred on home addresses. A favourable physical activity environment was defined as having ≥1 physical activity facility and ≥1 park, unfavourable as having no physical activity facility and park and any other combinations defined as moderately favourable. Logistic regression (odds ratios) identified associations with obesity. Results: Relative to ‘unfavourable physical activity environments’, individuals within favourable physical activity environments were less likely to be obese (odds ratio = 0.90; 95% confidence interval = 0.82–0.97), and there was no effect for moderately favourable environment. Furthermore, once stratified by education level, this relationship was only present for those of higher education. Conclusion: Our findings provide novel UK evidence and is one of the first papers internationally that highlights the importance of considering the interplay of individual-level socioeconomic factors when investigating associations between the physical activity environment and obesity.

Abstract MP38: Local Population Income, Geographic Space and Interactions Predict Increased Presence of Physical Activity Facilities in New York City Metropolitan Area Census Tracts, 1990-2010

Introduction: Physical activity is associated with improved health and is supported, in part, by the presence of facilities that provide space and equipment to pursue a variety of physical activities. We assessed the hypothesis that socio-geographic characteristics predict increased local availability of commercial physical activity facilities over time. Longitudinal examination of physical activity facility distribution can inform our response to current disparities in access to public and private physical activity venues. Methods: We used data from the National Establishment Time-Series (NETS), a longitudinal database of U.S. businesses, focusing on 4528 census tracts (23 counties) in the New York City metropolitan area and on decennial intervals for which population data were also available through the Census or American Community Survey (1990, 2000, 2010). Commercial physical activity facilities (e.g., gyms, tennis courts, martial arts studios) were defined based on Standard Industrial Classification (SIC) codes and name searches. Facility counts were aggregated to 2010 census tract boundaries and linked to local population characteristics. Comparisons across decennial intervals were used to define increasing count of physical activity facilities and shifting population demographics. Associations were evaluated using lasso logistic regression to estimate relationships with predictor variables and their interactions with model shrinkage and variable subset selection through 10-fold cross-validation for minimization of test set model deviance. Results: Census tracts with at least one physical activity facility increased over time (1990=1172, 2000=2295, 2010=2365). Greater tract-level median income, larger land area, and higher previous total physical activity facilities at start of decade were positively associated with greater odds for local increase in physical activity facilities (OR=1.27 per SD median income; OR=1.30 per SD land area; OR=1.14 per SD lagged facility count). Inclusion of two-way interaction terms increased R2 estimates from 0.30 to 0.33, suggesting explanation of an additional 3% of the variation in facility count increase. Subset selection through lasso to minimize cross-validation error resulted in retention of 11 of 21 possible two-way predictor interactions. The association between 10-year increase in median income with increased physical activity facility count was stronger in geographically larger census tracts (interaction OR=1.05); similarly, a stronger relationship was found for 10-year population count increase with physical facility count increase in larger census tracts (interaction OR=1.05). Conclusion: Local population, geographic, and business environment characteristics are associated with change in physical activity facilities. Inclusion of interaction terms improved prediction.