Investigating Links Between Urban Residential Streetscapes and Physical Activity Using Deep Learning of Google Street View Imagery Applied to the Washington State Twin Registry
Rattachement africain : us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
The evidence linking urban greenspace to individual's physical activity (PA) levels is mixed. This study examines relationships between street-level and satellite-derived greenspace measures with PA outcomes. Our sample included 7855 adult twins enrolled in the Washington State Twin Registry from 2009 to 2020 living in urban areas; 14,095 total survey observations were analyzed. We applied a deep learning segmentation algorithm to Google Street View images sampled from 100 m around residential addresses to quantify street-level greenspace. Bouts and duration of PA, including moderate to vigorous PA and neighborhood walking were self-reported. We applied mixed-effects linear regression models to determine relationships between greenspace measures and PA outcomes, overall and stratified by residential population density. Adjusted models included age, body mass index, sex, race, education, income, neighborhood deprivation, urban sprawl, and seasonality. A series of sequential models was constructed to test associations between various greenspace exposures and PA outcomes. Overall, we found no consistent associations between greenspace exposures and PA outcomes. We found that the summer normalized difference vegetation index was associated with an increase in moderate to vigorous PA in low population density areas, but this was not significant when controlling for seasonality. Both Google Street View and normalized difference vegetation index were associated with lower total walking for those residing in areas with high population density only. Findings highlight the importance of seasonality and the need to address where PA is actually done.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Investigating Links Between Urban Residential Streetscapes and Physical Activity Using Deep Learning of Google Street View Imagery Applied to the Washington State Twin Registry
- Date Crossref
- 01/11/2025
- Éditeur
- Human Kinetics
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Center for Science in the Public Interest (United States) pays non établi dans la noticeOrganisation à but non lucratif
-
Washington State University Spokane Paul G. Allen School for Global Animal Health pays non établi dans la noticeUniversité ou école supérieure
-
Washington State University Department of Nutrition and Exercise Physiology pays non établi dans la noticeUniversité ou école supérieure
-
Oregon State University pays non établi dans la noticeUniversité ou école supérieure
-
Engagement and Capacity Building Team pays non établi dans la noticeInstitution
-
School of Nutrition and Public Health pays non établi dans la noticeUniversité ou école supérieure
Center for Science in the Public Interest (United States), Paul G. Allen School for Global Animal Health — Washington State University Spokane et Department of Nutrition and Exercise Physiology — Washington State University, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.