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Imputational modeling of spatial context and social environmental predictors of walking in an underserved community: The PATH trial

机译:服务欠缺社区中行走的空间背景和社会环境预测因素的估算模型:PATH试验

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Background: This study examined imputational modeling effects of spatial proximity and social factors of walking in African American adults. Purpose: Models were compared that examined relationships between household proximity to a walking trail and social factors in determining walking status. Methods: Participants (N=133; 66% female; mean age = 55 years) were recruited to a police-supported walking and social marketing intervention. Bayesian modeling was used to identify predictors of walking at 12. months. Results: Sensitivity analysis using different imputation approaches, and spatial contextual effects, were compared. All the imputation methods showed social life and income were significant predictors of walking, however, the complete data approach was the best model indicating Age (1.04, 95% OR: 1.00, 1.08), Social Life (0.83, 95% OR: 0.69, 0.98) and Income <$10,000 (0.10, 95% OR: 0.01, 0.97) were all predictors of walking. Conclusions: The complete data approach was the best model of predictors of walking in African Americans. ? 2012 Elsevier Ltd.
机译:背景:本研究探讨了非裔美国人成年人行走的空间接近性和社会因素的推论建模效果。目的:比较了模型,该模型检查了家庭在步行道上的距离与确定步行状态的社会因素之间的关系。方法:招募参与者(N = 133;女性66%;平均年龄= 55岁)接受警察支持的步行和社会营销干预。贝叶斯模型被用来识别12个月步行的预测因素。结果:比较了使用不同插补方法的敏感性分析和空间上下文效应。所有推算方法均显示社交生活和收入是步行的重要预测指标,但是,完整的数据方法是表明年龄(1.04,95%OR:1.00,1.08),社交生活(0.83,95%OR:0.69, 0.98)和收入$ 10,000以下(0.10,95%OR:0.01,0.97)都是步行的预测指标。结论:完整的数据方法是非洲裔美国人步行预测的最佳模型。 ? 2012爱思唯尔有限公司

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