The built environment predicts observed physical activity.
The built environment predicts observed physical activity.
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建筑环境预测了观察到的体育锻炼。
DOI：
10.3389/fpubh.2014.00052
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发表时间：
2014
影响因子：
5.2
通讯作者：
Miller DK
中科院分区：
文献类型：
--
作者：
Kelly C;Wilson JS;Schootman M;Clennin M;Baker EA;Miller DK
背景：为了提高我们对建筑环境与体育活动之间关系的理解，确定特定地理特征与体育活动行为之间的联系是很重要的。目的：研究在印第安纳波利斯和圣路易斯的291条街道收集的观察到的身体活动行为和建筑环境测量之间的关系。方法：采用分层地理抽样设计选择街道段，以确保具有不同土地利用和社会经济特征的社区的代表性。采用两种方法对街道段内的建筑环境特征进行审计：现场审计和基于谷歌街景图像解释的审计，每种方法对另一种方法的结果都是盲的。根据两种审计方法分别将路段分为具有特定特征（例如，人行道存在与否）的路段。采用直接观察的方法对每个时间段从事不同形式体育活动的个体计数进行评估。使用非参数统计来比较每个区段上体力活动个体的计数与建筑环境特征。结果：在混合土地利用或所有非住宅用地的路段，以及有行人基础设施（如人行横道和人行道）和公共交通的路段，从事体育活动的个人数量显著较高。结论：几个微观层面的建成环境特征与体育活动相关。这些数据为改变建筑环境和相关政策可能鼓励更多体育活动的理论提供了支持。
Background: In order to improve our understanding of the relationship between the built environment and physical activity, it is important to identify associations between specific geographic characteristics and physical activity behaviors. Purpose: Examine relationships between observed physical activity behavior and measures of the built environment collected on 291 street segments in Indianapolis and St. Louis. Methods: Street segments were selected using a stratified geographic sampling design to ensure representation of neighborhoods with different land use and socioeconomic characteristics. Characteristics of the built environment on-street segments were audited using two methods: in-person field audits and audits based on interpretation of Google Street View imagery with each method blinded to results from the other. Segments were dichotomized as having a particular characteristic (e.g., sidewalk present or not) based on the two auditing methods separately. Counts of individuals engaged in different forms of physical activity on each segment were assessed using direct observation. Non-parametric statistics were used to compare counts of physically active individuals on each segment with built environment characteristic. Results: Counts of individuals engaged in physical activity were significantly higher on segments with mixed land use or all non-residential land use, and on segments with pedestrian infrastructure (e.g., crosswalks and sidewalks) and public transit. Conclusion: Several micro-level built environment characteristics were associated with physical activity. These data provide support for theories that suggest changing the built environment and related policies may encourage more physical activity.