Improving built environment measurement in physical activity research with omnidi

描述（由申请人提供）：该项目的目标是开发一种替代方法来进行基于现场的建筑环境审计，并对该方法预测身体活动行为的能力进行初步验证。近年来开发的越来越多的实地审计工具反映了人们对建筑环境特征作为身体活动和相关健康结果的决定因素的日益关注。高分辨率全方位图像（谷歌街景为例的新兴技术）为建筑环境的详细评估提供了基于现场的方法的潜在替代方案。这种类型的图像正变得越来越广泛，并提供了从虚拟步行或驾车视角观察建筑环境的能力。因此，本研究将审查全方位图像和实地审计之间的一致性。所有审计都将使用“活跃社区清单”，该清单测量了71个详细的建筑环境特征。具体目标是：1。审查从公众可获得的图像中得出的建筑环境的详细措施与实地审计之间的一致性；和2。检查建筑环境的详细措施之间的关系，这些措施来源于公开可用的图像和实地审计与身体活动行为。为了衡量实地审计结果与通过图像解释获得的结果之间的一致性，我们将使用观察到的一致性和Cohen的kappa统计量(K)来比较每个检查表项目。Kappa将用于比较二分类项目（即是/否回答选择），而单向模型类内相关系数（ICC）将用于多项选择题。因为当审计师不同意数据中类别的分布时，kappa产生更高的一致性度量，而当他们同意时，kappa产生更低的度量，我们也将使用Krippendorffer的1，它没有这个缺点。我们将使用皮尔逊产品相关和t检验来评估这种方法预测身体活动行为的能力。通过提高对更大、地理位置更分散的人群进行建筑环境变化纵向研究的可行性，图像方法可以加强对建筑环境对身体活动和相关健康结果决定因素的更全面的研究。

DESCRIPTION (provided by applicant): The goal of this project is to develop an alternative method to conducting field-based audits of the built environment and conduct preliminary validation of the approach's ability to predict physical activity behavior. The mounting number of field audit tools developed in recent years reflects increasing interest in built environment characteristics as determinants of physical activity and related health outcomes. High-resolution omnidirectional imagery (an emerging technology exemplified by Google Street View) provides a potential alternative to field-based approaches for detailed assessment of the built environment. This type of imagery is becoming more widely available and provides the ability to view the built environment from a virtual walk- through or drive-through perspective. Therefore, this study will examine the agreement between omnidirectional imagery and field-based audits. All audits will use the Active Neighborhood Checklist that measures 71 detailed built environment characteristics. The specific aims are: 1. examine the agreement between detailed measures of the built environment derived from publicly accessible imagery and in-person field audits; and 2. examine the relationship between detailed measures of the built environment derived from publicly available imagery and in-person field audits with physical activity behavior. To measure the agreement between results of field-based audits and those obtained through interpretation of the imagery, we will use observed agreement and Cohen's kappa statistic (K) to compare each checklist item across. Kappa will be used to compare dichotomous items (i.e. yes/no response choice), while the 1-way model intraclass correlation coefficient (ICC) will be used for multiple-choice questions. Because kappa yields higher agreement measures when auditors disagree on the distribution of categories in the data and lower measures when they agree, we also will use Krippendorffer's 1 which does not suffer from this disadvantage. We will use Pearson Product Correlation and T-tests to assess the ability of this approach to predict physical activity behavior. Imagery methods may enhance more comprehensive research on built environment determinants of physical activity and related health outcomes by improving the feasibility of conducting longitudinal studies of changes in the built environment for larger and more geographically dispersed cohorts. PUBLIC HEALTH RELEVANCE: Previous research suggests characteristics of the built environment influence physical activity and related health outcome. The goal of this study is to develop a new method of assessing the built environment that uses innovative image-based technology (e.g. Google Street View). If effective, this new method could advance the field by allowing for multi-regional and/or longitudinal studies of the built environment on physical activity and related health outcomes.