Patterns of neighborhood environment attributes related to physical activity across 11 countries: a latent class analysis

International Journal of Behavioral Nutrition and Physical Activity

Adrian Bauman³  Vida Volbekiene⁶  Heidi Tomten^1,10  Michael Sjöström⁴  Norio Murase¹  Grant McLean^1,17  Victor Matsudo^1,16  Sandra Matsudo^1,16  Duncan J Macfarlane⁵  Johan Lefevre⁷  Shigeru Inoue¹  Lena Klasson-Heggebø^1,15  Maria Hagströmer⁴  Luis Fernando Gomez²  Ilse De Bourdeaudhuij⁸  Cora L Craig^1,11  Harriette Carr^1,17  Fiona C Bull^1,13  Patrick Bergman⁹  Barbara E Ainsworth^1,12  Heather R Bowles^1,14  James F Sallis^1,18  Ding Ding³  Marc A Adams^1,12 

[1] Department of Preventive Medicine and Public Health, Tokyo Medical University, Tokyo, Japan;Pontificia Universidad Javeriana, Bogota, Colombia;Prevention Research Collaboration, University of Sydney, Sydney, Australia;Unit for Preventive Nutrition, Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden;Institute of Human Performance, The University of Hong Kong, Hong Kong, China;Department of Sport Science, Lithuanian Academy of Physical Education, Kaunas, Lithuania;Department of Kinesiology, Katholic University, Leuven, Belgium;Department of Movement and Sport Sciences, Ghent University, Ghent, Belgium;School of Education, Psychology and Sports Science, Linnaeus University, Kalmar, Sweden;Oppegård Municipality, Oppegård County, Norway;Canadian Fitness and Lifestyle Research Institute, School of Public Health, Ottawa, Canada;Exercise and Wellness, School of Nutrition and Health Promotion, Arizona State University, Phoenix, AZ, USA;School of Population Health, The University of Western Australia, Crawley, WA, Australia;Risk Factor Monitoring and Methods Branch, Applied Research Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA;Valnesfjord Rehabilitation Centre, Valnesfjord, Norway;Center of Studies of the Physical Fitness Research Center from São Caetano do Sul, CELAFISCS, Sao Paulo, Brazil;Sport New Zealand, Ministry of Health, Wellington, New Zealand;Active Living Research, University of California, San Diego, CA, USA

关键词: Exercise;    Surveillance;    Recreation;    International;    Built environment;   

Others  :  810664
DOI  :  10.1186/1479-5868-10-34

 received in 2012-09-11, accepted in 2013-03-07,  发布年份 2013

【 摘 要 】

Background

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. The purpose of this analysis was to: 1) detect international neighborhood typologies based on participants’ response patterns to an environment survey and 2) to estimate associations between neighborhood environment patterns and PA.

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 practical differences in PA. These observational results support WHO/UN recommendations for programs and policies targeted to improve features of the neighborhood environment for PA.

【 授权许可】

   
2013 Adams et al.; licensee BioMed Central Ltd.

【 图 表 】

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