Public transit generates new physical activity: Evidence from individual GPS and accelerometer data before and after light rail construction in a neighborhood of Salt Lake City,Utah, USA |
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| Affiliation: | 1. Department of Geography, The Ohio State University, 1036 Derby Hall/154 North Oval Mall, Columbus, OH 43210, USA;2. Center for Urban and Regional Analysis (CURA), The Ohio State University, USA;3. Department of Family and Consumer Studies, University of Utah, USA;4. Huntsman Cancer Institute, University of Utah, USA;5. Department of Psychology, University of Utah, USA;6. Westat, Inc., USA;1. Division of Biology, 421 Ackert Hall, Kansas State University, Manhattan, KS 66506, USA;2. Center for Forest Disturbance Science, USDA Forest Service, Southern Research Station, 320 Green Street, Athens, GA 30602, USA;1. Texas Advanced Computing Center, University of Texas, Austin, TX, United States;2. Urban Form Lab and the College of Built Environments Department of Urban Design and Planning, University of Washington, Seattle, WA, United States;3. Seattle Children''s Research Institute and School of Medicine/Department of Pediatrics, University of Washington, Seattle, WA, United States;1. Centre for Research in Environmental Epidemiology, Barcelona, Spain;2. CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain;3. Universitat Pompeu Fabra, Departament de Ciències Experimentals i de la Salut, Barcelona, Spain;4. Physical Activity and Sports Sciences Department, Fundació Blanquerna, Barcelona, Spain;5. Center for Environmental Policy, Imperial College London, London, United Kingdom;6. Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina;7. Environmental Health Sciences, School of Public Health, University of California, Berkeley, California;1. Department of Social and Preventive Medicine, École de Santé Publique de l’Université de Montréal (ESPUM), Canada;2. University of Montreal Hospital Research Centre (Centre de recherche du Centre Hospitalier de l’Université de Montréal, CRHUM), Canada;3. Luxembourg Institute of Socio-Economic Research, Esch/Alzette, Luxembourg;4. INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, Nemesis team, Paris, France;5. Faculty of Nursing, Université de Montréal, Canada;6. Department of Medicine, Université de Montréal, Canada;7. Institut de recherche en santé publique, Université de Montréal (IRSPUM), Canada;1. Keble College, University of Oxford, Parks Road, Oxford OX1 3PG, United Kingdom;2. School of Environment & Life Sciences, University of Salford, Peel Building, Salford M5 4WT, United Kingdom;1. McGill University, Department of Civil Engineering and Applied Mechanics, 817 Sherbrooke Street W, Montreal, Quebec H3A 0C3, Canada;2. McGill University, Department of Civil Engineering and Applied Mechanics, Room 268, 817 Sherbrooke Street W, Montreal, Quebec H3A 0C3, Canada;3. University of Minnesota, Twin Cities, Humphrey School of Public Affairs, 301 19th Avenue S, Minneapolis, MN 55455, United States;4. World Resources Institute, WRI Ross Center for Sustainable Cities, Suite 800, 10 G Street NE, Washington, DC 20002, United States |
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| Abstract: | Poor health outcomes from insufficient physical activity (PA) are a persistent public health issue. Public transit is often promoted for positive influence on PA. Although there is cross-sectional evidence that transit users have higher PA levels, this may be coincidental or shifted from activities such as recreational walking. We use a quasi-experimental design to test if light rail transit (LRT) generated new PA in a neighborhood of Salt Lake City, Utah, USA. Participants (n=536) wore Global Positioning System (GPS) receivers and accelerometers before (2012) and after (2013) LRT construction. We test within-person differences in individuals’ PA time based on changes in transit usage pre- versus post-intervention. We map transit-related PA to detect spatial clustering of PA around the new transit stops. We analyze within-person differences in PA time based on daily transit use and estimate the effect of daily transit use on PA time controlling for socio-demographic variables. Results suggest that transit use directly generates new PA that is not shifted from other PA. This supports the public health benefits from new high quality public transit such as LRT. |
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| Keywords: | Physical activity Public transit Quasi-experiment Global positioning system Accelerometer |
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