India is currently facing a non-communicable disease epidemic. Physical activity (PA) is a preventative factor for non-communicable diseases. Understanding the role of the built environment (BE) to facilitate or constrain PA is essential for public health interventions to increase population PA. The objective of this study was to understand BEs associations with PA occurring in two major life domains or life areas—travel and leisure—in urban India. Between December 2014 and April 2015, in-person surveys were conducted with participants (N = 370; female = 47.2%) in Chennai, India. Perceived BE characteristics regarding residential density, land use mix-diversity, land use mix-access, street connectivity, infrastructure for walking and bicycling, aesthetics, traffic safety, and safety from crime were measured using the adapted Neighborhood Environment Walkability Scale-India (NEWS-India). Self-reported PA was measured the International Physical Activity Questionnaire. High residential density was associated with greater odds of travel PA (aOR = 1.9, 95% CI = 1.2, 3.2). Land use mix-diversity was positively related to travel PA (aOR = 2.1, 95%CI = 1.2, 3.6), but not associated with leisure or total PA. The aggregate NEWS-India score predicted a two-fold increase in odds of travel PA (aOR = 1.9, 95% CI = 1.1, 3.1) and a 40% decrease in odds of leisure PA (aOR = 0.6, 95% CI = 0.4, 1.0). However, the association of the aggregated score with leisure PA was not significant. Results suggest that relationships between BE and PA in low-and-middle income countries may be context-specific, and may differ markedly from higher income countries. Findings have public health implications for India suggesting that caution should be taken when translating evidence across countries.

Introduction: Fewer than 50% of adults and 40% of youth meet US CDC guidelines for physical activity (PA) with the built environment (BE) a culprit for limited PA. A challenge in evaluating policy and BE change is the forethought to capture a priori PA behaviors and the ability to eliminate bias in post-change environments. The present objective was to analyze existing public data feeds to quantify effectiveness of BE interventions. The Archive of Many Outdoor Scenes (AMOS) has collected 135 million images of outdoor environments from 12,000 webcams since 2006. Many of these environments have experienced BE change. Methods: One example of BE change is the addition of protected bike lanes and a bike share program in Washington, DC. We selected an AMOS webcam that captured this change. AMOS captures a photograph from each webcam every half hour. AMOS captured the 120 webcam photographs between 0700 and 1900 during the first work week of June 2009 and the 120 photographs from the same week in 2010. We used the Amazon Mechanical Turk (MTurk) website to crowd-source the image annotation. MTurk workers were paid US$0.01 to mark each pedestrian, cyclist and vehicle in a photograph. Each image was coded 5 unique times (n=1200). The data, counts of transportation mode, was downloaded to SPSS for analysis. Results: The number of cyclists per scene increased four-fold between 2009 and 2010 (F=36.72, p=0.002). There was no significant increase in pedestrians between the two years, however there was a significant increase in number of vehicles per scene (F=16.81, p<0.001). Discussion: This novel research presents unobtrusive surveillance of PA policy and BE intervention effectiveness. Washington, DC, added bike lanes during 2009 and the use of webcam data allowed for a pre- and post-bike lane analysis of travel. The addition of the bike lane is associated with a significant four-fold increase in number of cyclist per scene. The number of vehicles per scene also increased. A portion of this increase may be associated with compressed lanes due to lane removal for the bike lane, thus increasing vehicles per remaining lane. Captured public webcam scenes and MTurks offer an inexpensive (US$12.00), discreet means to evaluate effectiveness of BE policy change and interventions. This abstract presents an initial effort. We continue to test methodology with additional scenes, including parks, school playgrounds, and safe routes to schools, and are working to automate the MTurk process.