Camille Perchoux1, Yan Kestens2, Ruben Brondeel3, Basile Chaix4. 1. Sorbonne Universités, UPMC Université Paris 06, UMR_S 1136, Institut Pierre Louis, d'Epidémiologie et de Santé Publique, 75012 Paris, France; INSERM, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique,F-75012 Paris, France; Département de médecine sociale et préventive, Université de Montréal, Montreal, QC, Canada; Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada. Electronic address: camille.perchoux@umontreal.ca. 2. Département de médecine sociale et préventive, Université de Montréal, Montreal, QC, Canada; Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada. 3. Sorbonne Universités, UPMC Université Paris 06, UMR_S 1136, Institut Pierre Louis, d'Epidémiologie et de Santé Publique, 75012 Paris, France; INSERM, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique,F-75012 Paris, France; Ecole des Hautes Etudes en Santé Publique (EHESP), 35043 Rennes, France. 4. Sorbonne Universités, UPMC Université Paris 06, UMR_S 1136, Institut Pierre Louis, d'Epidémiologie et de Santé Publique, 75012 Paris, France; INSERM, UMR_S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique,F-75012 Paris, France.
Abstract
BACKGROUND: Understanding how built environment characteristics influence recreational walking is of the utmost importance to develop population-level strategies to increase levels of physical activity in a sustainable manner. PURPOSE: This study analyzes the residential and non-residential environmental correlates of recreational walking, using precisely geocoded activity space data. METHODS: The point-based locations regularly visited by 4365 participants of the RECORD Cohort Study (Residential Environment and CORonary heart Disease) were collected between 2011 and 2013 in the Paris region using the VERITAS software (Visualization and Evaluation of Regular Individual Travel destinations and Activity Spaces). Zero-inflated negative binomial regressions were used to investigate associations between both residential and non-residential environmental exposure and overall self-reported recreational walking over 7 days. RESULTS: Density of destinations, presence of a lake or waterway, and neighborhood education were associated with an increase in the odds of reporting any recreational walking time. Only the density of destinations was associated with an increase in time spent walking for recreational purpose. Considering the recreational locations visited (i.e., sports and cultural destinations) in addition to the residential neighborhood in the calculation of exposure improved the model fit and increased the environment-walking associations, compared to a model accounting only for the residential space (Akaike Information Criterion equal to 52797 compared to 52815). CONCLUSIONS: Creating an environment supportive to walking around recreational locations may particularly stimulate recreational walking among people willing to use these facilities.
BACKGROUND: Understanding how built environment characteristics influence recreational walking is of the utmost importance to develop population-level strategies to increase levels of physical activity in a sustainable manner. PURPOSE: This study analyzes the residential and non-residential environmental correlates of recreational walking, using precisely geocoded activity space data. METHODS: The point-based locations regularly visited by 4365 participants of the RECORD Cohort Study (Residential Environment and CORonary heart Disease) were collected between 2011 and 2013 in the Paris region using the VERITAS software (Visualization and Evaluation of Regular Individual Travel destinations and Activity Spaces). Zero-inflated negative binomial regressions were used to investigate associations between both residential and non-residential environmental exposure and overall self-reported recreational walking over 7 days. RESULTS: Density of destinations, presence of a lake or waterway, and neighborhood education were associated with an increase in the odds of reporting any recreational walking time. Only the density of destinations was associated with an increase in time spent walking for recreational purpose. Considering the recreational locations visited (i.e., sports and cultural destinations) in addition to the residential neighborhood in the calculation of exposure improved the model fit and increased the environment-walking associations, compared to a model accounting only for the residential space (Akaike Information Criterion equal to 52797 compared to 52815). CONCLUSIONS: Creating an environment supportive to walking around recreational locations may particularly stimulate recreational walking among people willing to use these facilities.
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