Using built environment characteristics to predict walking for exercise.
Using built environment characteristics to predict walking for exercise.
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DOI：
10.1186/1476-072x-7-10
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发表时间：
2008-02-29
影响因子：
4.9
通讯作者：
Psaty BM
中科院分区：
文献类型：
--
作者：
Lovasi GS;Moudon AV;Pearson AL;Hurvitz PM;Larson EB;Siscovick DS;Berke EM;Lumley T;Psaty BM
有利于步行的环境可以帮助人们避免久坐的生活方式和相关疾病。最近的研究开发了步行性模型，结合了几种建筑环境特征来最佳地预测步行。使用相同的数据开发和测试此类模型可能会导致高估一个人在独立人口样本中预测步行的能力。通过将单个研究群体分成训练集和验证集（保留方法）或通过在不同群体中开发和评估模型，可以获得更准确的模型拟合估计。我们使用这两种方法来测试家庭附近的建筑环境特征是否可以预测步行锻炼。研究参与者居住在华盛顿州西部，是健康维护组织的成年成员。本研究中使用的体力活动数据是通过电话采访收集的，并根据其与心血管疾病的相关性进行选择。为了限制先前健康状况的混淆，样本仅限于自我报告健康状况良好且没有心血管疾病病史的参与者。对于符合纳入标准的 1,608 名参与者，平均年龄为 64 岁，90% 是白人，37% 拥有大学学位，62% 的参与者表示他们步行是为了锻炼身体。单一建筑环境特征，例如住宅密度或连通性，并不能显着预测步行锻炼的情况。使用多个建筑环境特征来预测步行的回归模型未能成功预测独立人群样本中的锻炼步行。在验证集中，没有一个逻辑模型具有排除零值 0.5 的 C 统计置信区间，并且没有一个线性模型可以解释超过 1% 的步行锻炼时间方差。我们没有发现人口普查区或邮政编码之间步行锻炼的显着差异，这些地区或邮政编码被用作社区的代表。当使用坚持方法进行测试时，没有任何建筑环境特征能够显着预测步行锻炼，这些特征的组合也不能预测步行锻炼。这些结果反映出，所研究人群的步行锻炼缺乏社区层面的差异。
Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach) or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease. For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not detect significant differences in walking for exercise among census areas or postal codes, which were used as proxies for neighborhoods. None of the built environment characteristics significantly predicted walking for exercise, nor did combinations of these characteristics predict walking for exercise when tested using a holdout approach. These results reflect a lack of neighborhood-level variation in walking for exercise for the population studied.