Department of Geography and Geographic Information Science, Natural History Building, 1301 W Green Street University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Int J Environ Res Public Health. 2018 Sep 15;15(9):2022. doi: 10.3390/ijerph15092022.
In past studies, individual environmental exposures were largely measured in a static manner. In this study, we develop and implement an analytical framework that dynamically represents environmental context (the environmental context cube) and effectively integrates individual daily movement (individual space-time tunnel) for accurately deriving individual environmental exposures (the environmental context exposure index). The framework is applied to examine the relationship between food environment exposures and the overweight status of 46 participants using data collected with global positioning systems (GPS) in Columbus, Ohio, and binary logistic regression models. The results indicate that the proposed framework generates more reliable measurements of individual food environment exposures when compared to other widely used methods. Taking into account the complex spatial and temporal dynamics of individual environmental exposures, the proposed framework also helps to mitigate the uncertain geographic context problem (UGCoP). It can be used in other environmental health studies concerning environmental influences on a wide range of health behaviors and outcomes.
在过去的研究中,个体环境暴露主要以静态方式进行测量。在这项研究中,我们开发并实施了一个分析框架,该框架以动态方式表示环境背景(环境背景立方体),并有效地整合了个体日常活动(个体时空隧道),从而准确推导出个体环境暴露(环境背景暴露指数)。该框架应用于俄亥俄州哥伦布市使用全球定位系统 (GPS) 收集的数据,使用二元逻辑回归模型来研究食物环境暴露与 46 名参与者超重状况之间的关系。结果表明,与其他广泛使用的方法相比,所提出的框架在生成个体食物环境暴露的更可靠测量方面具有优势。考虑到个体环境暴露的复杂时空动态,所提出的框架还有助于缓解不确定地理背景问题 (UGCoP)。它可以用于其他环境健康研究,研究环境对广泛的健康行为和结果的影响。