Deepti Adlakha

Emerging technologies: webcams and crowd-sourcing to identify active transportation.

PA behaviors and the ability to eliminate researcher and respondent bias in assessing post-change environments. A novel transdisciplinary collaboration between public health and computer science is presented here with the goal of automatically analyzing existing public data feeds in innovative ways to quantify BE intervention effectiveness.

Use of Emerging Technologies to Assess Differences in Outdoor Physical Activity in St. Louis, Missouri

Introduction: Abundant evidence shows that regular physical activity (PA) is an effective strategy for preventing obesity in people of diverse socioeconomic status (SES) and racial groups. The proportion of PA performed in parks and how this differs by proximate neighborhood SES has not been thoroughly investigated. The present project analyzes online public web data feeds to assess differences in outdoor PA by neighborhood SES in St. Louis, MO, USA. Methods: First, running and walking routes submitted by users of the website MapMyRun.com were downloaded. The website enables participants to plan, map, record, and share their exercise routes and outdoor activities like runs, walks, and hikes in an online database. Next, the routes were visually illustrated using geographic information systems. Thereafter, using park data and 2010 Missouri census poverty data, the odds of running and walking routes traversing a low-SES neighborhood, and traversing a park in a low-SES neighborhood were examined in comparison to the odds of routes traversing higher-SES neighborhoods and higher-SES parks. Results: Results show that a majority of running and walking routes occur in or at least traverse through a park. However, this finding does not hold when comparing low-SES neighborhoods to higher-SES neighborhoods in St. Louis. The odds of running in a park in a low-SES neighborhood were 54% lower than running in a park in a higher-SES neighborhood (OR = 0.46, CI = 0.17–1.23). The odds of walking in a park in a low-SES neighborhood were 17% lower than walking in a park in a higher-SES neighborhood (OR = 0.83, CI = 0.26–2.61). Conclusion: The novel methods of this study include the use of inexpensive, unobtrusive, and publicly available web data feeds to examine PA in parks and differences by neighborhood SES. Emerging technologies like MapMyRun.com present significant advantages to enhance tracking of user-defined PA across large geographic and temporal settings.

Adaptation and Evaluation of the Neighborhood Environment Walkability Scale in India (NEWS-India)

Physical inactivity is the fourth leading risk factor for global mortality, with most of these deaths occurring in low and middle-income countries (LMICs) like India. Research from developed countries has consistently demonstrated associations between built environment features and physical activity levels of populations. The development of culturally sensitive and reliable measures of the built environment is a necessary first step for accurate analysis of environmental correlates of physical activity in LMICs. This study systematically adapted the Neighborhood Environment Walkability Scale (NEWS) for India and evaluated aspects of test-retest reliability of the adapted version among Indian adults. Cultural adaptation of the NEWS was conducted by Indian and international experts. Semi-structured interviews were conducted with local residents and key informants in the city of Chennai, India. At baseline, participants (N = 370; female = 47.2%) from Chennai completed the adapted NEWS-India surveys on perceived residential density, land use mix-diversity, land use mix-access, street connectivity, infrastructure and safety for walking and cycling, aesthetics, traffic safety, and safety from crime. NEWS-India was administered for a second time to consenting participants (N = 62; female = 53.2%) with a gap of 2–3 weeks between successive administrations. Qualitative findings demonstrated that built environment barriers and constraints to active commuting and physical activity behaviors intersected with social ecological systems. The adapted NEWS subscales had moderate to high test-retest reliability (ICC range 0.48–0.99). The NEWS-India demonstrated acceptable measurement properties among Indian adults and may be a useful tool for evaluation of built environment attributes in India. Further adaptation and evaluation in rural and suburban settings in India is essential to create a version that could be used throughout India.

“Can we walk?” environmental supports for physical activity in India

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.

Do you see what I see: crowdsource annotation of captured scenes

The Archive of Many Outdoor Scenes has captured 400 million images. Many of these cameras and images are of street intersections, a subset of which has experienced built environment improvements during the past seven years. We identified six cameras in Washington, DC, and uploaded 120 images from each before a built environment change (2007) and after (2010) to the crowdsourcing website Amazon Mechanical Turk (n=1,440). Five unique MTurk workers annotated each image, counting the number of pedestrians, cyclists, and vehicles. Two trained Research Assistants completed the same tasks. Reliability and validity statistics of MTurk workers revealed substantial agreement in annotating captured images of pedestrians and vehicles. Using the mean annotation of four MTurk workers proved most parsimonious for valid results. Crowdsourcing was shown to be a reliable and valid workforce for annotating images of outdoor human behavior.

Learning from Outdoor Webcams: Surveillance of Physical Activity Across Environments

Publicly available, outdoor webcams continuously view the world and share images. These cameras include traffic cams, campus cams, ski-resort cams, etc. The Archive of Many Outdoor Scenes (AMOS) is a project aiming to geolocate, annotate, archive, and visualize these cameras and images to serve as a resource for a wide variety of scientific applications. The AMOS dataset has archived over 750 million images of outdoor environments from 27,000 webcams since 2006. Our goal is to utilize the AMOS image dataset and crowdsourcing to develop reliable and valid tools to improve physical activity assessment via online, outdoor webcam capture of global physical activity patterns and urban built environment characteristics.

Pokémon GO or Pokémon Gone: How can cities respond to trends in technology linking people and space?

Abstract Pokemon GO and its ability to get people moving highlights new beginnings for gamification of health and social life of urban spaces with implications for people–space interaction.

Exploring associations between perceived home and work neighborhood environments, diet behaviors, and obesity: Results from a survey of employed adults in Missouri

Dietary behaviors are associated with obesity, and may be influenced by the environment. The objective of the current work was to investigate whether perceptions of built environment factors related to eating in the residential neighborhood will have different, independent associations with BMI and dietary behaviors than perceived built environment factors in the worksite neighborhood. In 2012–2013, a cross-sectional telephone-survey of Missouri adults (n = 2015) assessed perceptions of home and workplace built environment factors related to eating, dietary behaviors, and height and weight. Logistic regression models explored associations between perceived neighborhood built environment variables, diet, and obesity. The only variable associated with any of the outcomes explored in the fully adjusted models was the home neighborhood composite scale. None of the work environment variables were significantly associated with any of the health/behavior outcomes after adjustment. Few associations were found after adjustment for personal and job-related characteristics, and none were identified with the workplace neighborhood environment. While few home environment associations were found after adjustment, and none were identified with the perceived workplace neighborhood environment, the current study adds to the limited literature looking at associations between the perceived neighborhood around the workplace neighborhood and the perceived neighborhood around the home and dietary behaviors and obesity in adults. Future studies are needed to determine whether relationships between these environments and behavior exist, and if so, if they are causal and warrant intervention attempts.

Neighbourhood Environment Features and Active Commuting in Chennai, India

1School of Natural and Built Environment, Queen’s University Belfast, UK. 2Department of Parks, Recreation, and Tourism Management, Centre for Geospatial Analytics, Centre for Human Health and the Environment, North Carolina State University, Raleigh, NC 27695-8004, USA. 3Department of Family Medicine and Public Health, University of California, San Diego, CA, USA, and Australian Catholic University, Melbourne, Australia. 4Prevention Research Centre in St. Louis, Brown School, Washington University in St. Louis, St. Louis, MO, USA. 5Department of Surgery (Division of Public Health Sciences) and Alvin J. Siteman Cancer Centre, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA.

Exploring Neighborhood Environments and Active Commuting in Chennai, India

Few studies assess built environment correlates of active commuting in low-and-middle-income countries (LMICs), but the different context could yield distinct findings. Policies and investments to promote active commuting remain under-developed in LMICs like India, which grapples with traffic congestion, lack of activity-supportive infrastructure, poor enforcement of traffic rules and regulations, air pollution, and overcrowding. This cross-sectional study investigated associations between home neighborhood environment characteristics and active commuting in Chennai, India. Adults (N = 370, 47.2% female, mean age = 37.9 years) were recruited from 155 wards in the metropolitan area of Chennai in southern India between January and June 2015. Participants self-reported their usual mode of commute to work, with responses recoded into three categories: (1) multi-modal or active commuting (walking and bicycling; n = 56); (2) public transit (n = 52); and (3) private transport (n = 111). Environmental attributes around participants’ homes were assessed using the Neighborhood Environment Walkability Scale for India (NEWS-India). Associations between environmental characteristics and likelihood of active commuting and public transit use were modeled using logistic regression with private transport (driving alone or carpool) as the reference category, adjusting for age, gender, and household car ownership. Consistent with other international studies, participants living in neighborhoods with a mix of land uses and a transit stop within a 10-minute walk from home were more likely to use active commuting (both p < 0.01). Land-use mix was significantly associated with the use of public transit compared to private transport (adjusted odds ratio (aOR) = 5.2, p = 0.002). Contrary to findings in high-income countries, the odds of active commuting were reduced with improved safety from crime (aOR = 0.2, p = 0.003), aesthetics (aOR = 0.2, p = 0.05), and street connectivity (aOR = 0.2, p = 0.003). Different environmental attributes were associated with active commuting, suggesting that these relationships are complex and may distinctly differ from those in high-income countries. Unexpected inverse associations of perceived safety from crime and aesthetics with active commuting emphasize the need for high-quality epidemiologic studies with greater context specificity in the study of physical activity in LMICs. Findings have public health implications for India and suggest that caution should be taken when translating evidence across countries.