Best practices for analyzing large-scale health data from wearables and smartphone apps
Best practices for analyzing large-scale health data from wearables and smartphone apps
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DOI：
10.1038/s41746-019-0121-1
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发表时间：
2019-06-03
影响因子：
15.2
通讯作者：
Delp, Scott L.
中科院分区：
文献类型：
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作者：
Hicks, Jennifer L.;Althoff, Tim;Delp, Scott L.
近年来，用于跟踪身体活动和其他健康行为的智能手机应用程序和可穿戴设备变得流行起来，为自由生活环境中的健康行为提供了一个基本上未被开发的数据来源。这些数据规模庞大，在野外以低成本收集，并经常以自动方式记录，为传统的监测研究和对照试验提供了强有力的补充。例如，这些数据有助于揭示环境和社会对身体活动的影响的新见解。然而，数据集和通过商业设备和应用程序收集的观测性质构成了挑战，包括潜在的测量、总体和/或选择偏差，以及丢失的数据。在这篇文章中，我们回顾了从这些数据集收集的见解，并提出了解决来自应用程序和可穿戴设备的大规模数据限制的最佳实践。我们的目标是使研究人员能够有效地利用来自智能手机应用程序和可穿戴设备的数据，以更好地了解是什么驱动着身体活动和其他健康行为。
Smartphone apps and wearable devices for tracking physical activity and other health behaviors have become popular in recent years and provide a largely untapped source of data about health behaviors in the free-living environment. The data are large in scale, collected at low cost in the "wild", and often recorded in an automatic fashion, providing a powerful complement to traditional surveillance studies and controlled trials. These data are helping to reveal, for example, new insights about environmental and social influences on physical activity. The observational nature of the datasets and collection via commercial devices and apps pose challenges, however, including the potential for measurement, population, and/or selection bias, as well as missing data. In this article, we review insights gleaned from these datasets and propose best practices for addressing the limitations of large-scale data from apps and wearables. Our goal is to enable researchers to effectively harness the data from smartphone apps and wearable devices to better understand what drives physical activity and other health behaviors.