Patterns Of Neighborhood Environment Attributes Related To Physical Activity Across 11 Countries: A Latent Class Analysis
Artigo publicado na International Journal of Behavioral Nutrition and Physical Activity em 2013. Escrito por Marc A Adams, Ding Ding, James F Sallis, Heather R Bowles, Barbara E Ainsworth, Patrick Bergman, Fiona C Bull, Harriette Carr, Cora L Craig, Ilse De Bourdeaudhuij, Luis Fernando Gomez, Maria Hagströmer, Lena Klasson-Heggebø, Shigeru Inoue, Johan Lefevre, Duncan J Macfarlane, Sandra Marcela Mahecha Matsudo, Victor Keihan Rodrigues Matsudo, Grant McLean, Norio Murase, Michael Sjöström, Heidi Tomten16, Vida Volbekiene e Adrian Bauman. O objetivo desta análise foi: 1) detectar tipologias de bairros internacionais com base nos padrões de resposta dos participantes a uma pesquisa ambiental e 2) estimar associações entre os padrões ambientais do bairro e a atividade física.
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Neighborhood environment studies of physical activity (PA) have been mainly single-country focused. The International Prevalence Study (IPS) presented a rare opportunity to examine neighborhood features across countries. Methods: A Latent Class Analysis (LCA) was conducted on pooled IPS adults (N=11,541) aged 18 to 64 years old (mean=37.5 ±12.8 yrs; 55.6% women) from 11 countries including Belgium, Brazil, Canada, Colombia, Hong Kong, Japan, Lithuania, New Zealand, Norway, Sweden, and the U.S. This subset used the Physical Activity Neighborhood Environment Survey (PANES) that briefly assessed 7 attributes within 10–15 minutes walk of participants’ residences, including residential density, access to shops/services, recreational facilities, public transit facilities, presence of sidewalks and bike paths, and personal safety. LCA derived meaningful subgroups from participants’ response patterns to PANES items, and participants were assigned to neighborhood types. The validated short-form International Physical Activity Questionnaire (IPAQ) measured likelihood of meeting the 150 minutes/week PA guideline. To validate derived classes, meeting the guideline either by walking or total PA was regressed on neighborhood types using a weighted generalized linear regression model, adjusting for gender, age and country. Results: A 5-subgroup solution fitted the dataset and was interpretable. Neighborhood types were labeled, “Overall Activity Supportive (52% of sample)”, “High Walkable and Unsafe with Few Recreation Facilities (16%)”, “Safe with Active Transport Facilities (12%)”, “Transit and Shops Dense with Few Amenities (15%)”, and “Safe but Activity Unsupportive (5%)”. Country representation differed by type (e.g., U.S. disproportionally represented “Safe but Activity Unsupportive”). Compared to the Safe but Activity Unsupportive, two types showed greater odds of meeting PA guideline for walking outcome (High Walkable and Unsafe with Few Recreation Facilities, OR= 2.26 (95% CI 1.18-4.31); Overall Activity Supportive, OR= 1.90 (95% CI 1.13-3.21). Significant but smaller odds ratios were also found for total PA. Conclusions: Meaningful neighborhood patterns generalized across countries and explained practicaldifferences in PA. These observational results support WHO/UN recommendations for programs and policies targeted to improve features of the neighborhood environment for PA.
