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，以进行基于现场的建筑环境审计，并对该方法预测体力活动行为的能力进行初步验证。近年来开发的实地审计工具越来越多，反映出人们对建筑环境特征作为体力活动和相关健康结果的决定因素的兴趣与日俱增。高分辨率全方位图像(以谷歌街景为代表的一种新兴技术)为详细评估建筑环境提供了一种潜在的替代现场方法。这种类型的图像正变得越来越广泛，并提供了从虚拟漫游或免下车的角度查看建筑环境的能力。因此，本研究将审查全方位图像和实地审计之间的一致性。所有审计将使用现役社区核对表，该核对表衡量71个详细的建筑环境特征。具体目标是：1.检查从公开可获取的图像得出的建筑环境的详细测量与现场实地审计之间的一致性；以及2.检查从可公开获取的图像得出的建筑环境的详细测量与现场实地审计与体力活动行为之间的关系。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科恩的卡帕统计量(K)来比较每个核对表项目。Kappa将用于比较二分项目(即是/否回答选择)，而单向模型组内相关系数(ICC)将用于多项选择题。由于Kappa在审计师对数据中类别的分布存在分歧时会产生更高的一致性度量，而当他们达成一致时会产生更低的一致性度量，因此我们也将使用Krippendorffer的1，它不会受到这一缺点的影响。我们将使用皮尔逊乘积相关性和T检验来评估这种方法预测身体活动行为的能力。通过改善对更大和地理上更分散的队列进行建成环境变化的纵向研究的可行性，成像方法可能会加强对建造环境决定体力活动和相关健康结果的更全面的研究。 与公共健康相关：先前的研究表明，建筑环境的特征会影响身体活动和相关的健康结果。这项研究的目标是开发一种使用创新的基于图像的技术(例如谷歌街景)来评估建筑环境的新方法。如果有效，这一新方法可以通过允许对建筑环境的体力活动和相关健康结果进行多区域和/或纵向研究来推动这一领域的发展。

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.