Queensland University of Technology, Australia.
Am J Health Promot. 2011 Mar-Apr;25(4):e12-21.
PURPOSE: Explore the role of the neighborhood environment in supporting walking. DESIGN: Cross-sectional study of 10,286 residents of 200 neighborhoods. Participants were selected using a stratified two-stage cluster design. Data were collected by mail survey (68.5% response rate). SETTING: Brisbane City Local Government Area, Australia, 2007. SUBJECTS: Brisbane residents aged 40 to 65 years. MEASURES: Environmental: street connectivity, residential density, hilliness, tree coverage, bikeways, and streetlights within a 1-km circular buffer from each resident's home; and network distance to nearest river or coast, public transport, shop, and park. Walking: minutes walked in the previous week: < 30 minutes, ≥ 30 to < 90 minutes, ≥ 90 to < 150 minutes, ≥ 150 to < 300 minutes, and ≥ 300 minutes. ANALYSIS: The association between each neighborhood characteristic and walking was examined using multilevel multinomial logistic regression, and the model parameters were estimated using Markov chain Monte Carlo simulation. RESULTS: After adjustment for individual factors, the likelihood of walking for more than 300 minutes (relative to < 30 minutes) was highest in areas with the most connectivity (odds ratio [OR] 5 1.93; 99% confidence intervals [CI], 1.32-2.80), greatest residential density (OR 5 1.47; 99% CI, 1.02-2.12), least tree coverage (OR 5 1.69; 99% CI, 1.13-2.51), most bikeways (OR 5 1.60; 99% CI, 1.16-2.21), and most streetlights (OR 5 1.50; 99% CI, 1.07-2.11). The likelihood of walking for more than 300 minutes was also higher among those who lived closest to a river or the coast (OR 5 2.06; 99% CI, 1.41-3.02). CONCLUSION: The likelihood of meeting (and exceeding) physical activity recommendations on the basis of walking was higher in neighborhoods with greater street connectivity and residential density, more streetlights and bikeways, closer proximity to waterways, and less tree coverage. Interventions targeting these neighborhood characteristics may lead to improved environmental quality as well as lower rates of overweight and obesity and associated chromic disease.
目的:探讨邻里环境在支持步行方面的作用。
设计:对 200 个街区的 10286 名居民进行横断面研究。参与者采用分层两阶段聚类设计选择。通过邮件调查收集数据(68.5%的回应率)。
地点:澳大利亚布里斯班市地方政府区,2007 年。
受试者:年龄在 40 至 65 岁之间的布里斯班居民。
措施:环境:从每个居民家的 1 公里圆形缓冲区测量街道连通性、居住密度、坡度、树木覆盖率、自行车道和路灯;以及到最近的河流或海岸、公共交通、商店和公园的网络距离。步行:上周步行的时间:<30 分钟,≥30 至<90 分钟,≥90 至<150 分钟,≥150 至<300 分钟,≥300 分钟。
分析:使用多层次多项逻辑回归模型检验每个邻里特征与步行的关系,并使用马尔可夫链蒙特卡罗模拟估计模型参数。
结果:在调整个体因素后,与(<30 分钟)相比,连接度最高(优势比[OR]51.93;99%置信区间[CI],1.32-2.80)、居住密度最大(OR51.47;99%CI,1.02-2.12)、树木覆盖率最低(OR51.69;99%CI,1.13-2.51)、自行车道最多(OR51.60;99%CI,1.16-2.21)和路灯最多(OR51.50;99%CI,1.07-2.11)的区域步行超过 300 分钟的可能性最高。住在离河流或海岸最近的地方(OR52.06;99%CI,1.41-3.02)的人,步行超过 300 分钟的可能性也更高。
结论:在街道连通性和居住密度更高、路灯和自行车道更多、靠近水道、树木覆盖率更低的社区,步行达到(并超过)身体活动建议的可能性更高。针对这些邻里特征的干预措施可能会提高环境质量,降低超重和肥胖以及相关慢性病的发病率。
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