Associations between the built environment and physical activity in public housing residents
Associations between the built environment and physical activity in public housing residents
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DOI：
10.1186/1479-5868-4-56
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发表时间：
2007-11-12
影响因子：
8.7
通讯作者：
Ahluwalia, Jasjit S.
中科院分区：
文献类型：
--
作者：
Heinrich, Katie M.;Lee, Rebecca E.;Ahluwalia, Jasjit S.
背景：环境因素可能影响非裔美国人和低收入成年人的低体育活动率。这项横断面研究调查了低收入公共住房开发项目居民的测量环境因素与自我报告的步行和剧烈体育活动之间的关系。方法：将居住在12个低收入住房开发项目的452名成年居民的身体活动数据与每个住房开发项目周围（800 m半径缓冲区）的实测环境数据相结合。采用聚合生态和多级回归模型进行分析。结果：参与者主要是女性（72.8%），非裔美国人（79.6%），高中及以上学历（59.0%）。总体而言，体育锻炼率很低，只有21%的参与者符合适度体育锻炼指南。生态模型显示，更少的不文明行为和更大的街道连通性预测了83%的每周步行天数方差，p < 0.001，在多层次分析中，性别和连通性都预测了每周步行天数，p < 0.05。更强的连通性和更少的体力活动资源预测了90%的符合适度体力活动指南的方差，p < 0.001，性别和连通性是多级预测因子，p分别< 0.05和0.01。在生态模型中，资源可及性越高，每周剧烈运动天数的变化预测率为34%，p < 0.05，但多层次分析没有发现显著的预测因子。结论：这些结果表明，低收入公共住房居民的身体活动与建筑环境的可修改方面有关。有更多机会获得更多的体育活动资源，而不那么文明，以及更大的街道连通性的个人，更有可能进行体育活动。
Background: Environmental factors may influence the particularly low rates of physical activity in African American and low-income adults. This cross-sectional study investigated how measured environmental factors were related to self-reported walking and vigorous physical activity for residents of low-income public housing developments.Methods: Physical activity data from 452 adult residents residing in 12 low-income housing developments were combined with measured environmental data that examined the neighborhood ( 800 m radius buffer) around each housing development. Aggregated ecological and multilevel regression models were used for analysis.Results: Participants were predominately female ( 72.8%), African American (79.6%) and had a high school education or more (59.0%). Overall, physical activity rates were low, with only 21% of participants meeting moderate physical activity guidelines. Ecological models showed that fewer incivilities and greater street connectivity predicted 83% of the variance in days walked per week, p < 0.001, with both gender and connectivity predicting days walked per week in the multi-level analysis, p < 0.05. Greater connectivity and fewer physical activity resources predicted 90% of the variance in meeting moderate physical activity guidelines, p < 0.001, and gender and connectivity were the multi-level predictors, p < 0.05 and 0.01, respectively. Greater resource accessibility predicted 34% of the variance in days per week of vigorous physical activity in the ecological model, p < 0.05, but the multi-level analysis found no significant predictors.Conclusion: These results indicate that the physical activity of low-income residents of public housing is related to modifiable aspects of the built environment. Individuals with greater access to more physical activity resources with fewincivilities, as well as, greater street connectivity, are more likely to be physically active.