Emerging technologies for assessing physical activity behaviors in space and time.
Emerging technologies for assessing physical activity behaviors in space and time.
复制标题
DOI：
10.3389/fpubh.2014.00002
复制
发表时间：
2014
影响因子：
5.2
通讯作者：
Duncan GE
中科院分区：
文献类型：
--
作者：
Hurvitz PM;Moudon AV;Kang B;Saelens BE;Duncan GE
身体活动的精确测量对于健康研究非常重要，可以更好地了解活动的位置，类型，持续时间和强度。本文介绍了一套新的工具来测量和分析身体活动行为的空间流行病学研究。我们使用个人层面的，高分辨率的，在时空框架中收集的客观数据来调查建筑和社会环境对活动的影响。首先，我们通过加速度计、全球定位系统以及基于智能手机的数字旅行和照片日记收集数据，以克服自我报告数据固有的许多局限性。行为是在日常生活中的全方位环境暴露中连续测量的，而不是只关注家庭邻里。第二，数据流使用共同的时间戳集成到一个单一的数据结构，“生活日志”。图形界面工具“LifeLog View”可同时显示所有LifeLog数据流。最后，我们使用地理信息系统SmartMap栅格来测量空间连续的环境变量，以捕获与LifeLog中相同的空间和时间尺度的暴露。这些技术能够精确测量其空间和时间设置中的行为，但也会生成非常大的数据集;我们讨论了当前处理和分析此类大型数据集的局限性和有前途的方法。最后，我们提供了这些方法在面向空间的研究中的应用，包括一个自然的实验，以评估新的交通基础设施对活动水平的影响，并使用双胞胎作为准因果控制，以克服自我选择和反向因果关系的问题，邻里环境对活动的影响的研究。总之，Lifecycle和SmartMaps中包含的大型数据集的综合特性为推进空间流行病学研究以促进健康行为带来了巨大的希望。
Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Second, data streams are integrated using common timestamps into a single data structure, the “LifeLog.” A graphic interface tool, “LifeLog View,” enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors.