Should We Use Activity Tracker Data From Smartphones and Wearables to Understand Population Physical Activity Patterns?

J. Mair, L. Hayes, A. Campbell, N. Sculthorpe
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引用次数: 9

Abstract

Researchers, practitioners, and public health organizations from around the world are becoming increasingly interested in using data from consumer-grade devices such as smartphones and wearable activity trackers to measure physical activity (PA). Indeed, large-scale, easily accessible, and autonomous data collection concerning PA as well as other health behaviors is becoming ever more attractive. There are several benefits of using consumer-grade devices to collect PA data including the ability to obtain big data, retrospectively as well as prospectively, and to understand individual-level PA patterns over time and in response to natural events. However, there are challenges related to representativeness, data access, and proprietary algorithms that, at present, limit the utility of this data in understanding population-level PA. In this brief report we aim to highlight the benefits, as well as the limitations, of using existing data from smartphones and wearable activity trackers to understand large-scale PA patterns and stimulate discussion among the scientific community on what the future holds with respect to PA measurement and surveillance.
我们应该使用智能手机和可穿戴设备的活动跟踪数据来了解人口的体育活动模式吗?
来自世界各地的研究人员、从业人员和公共卫生组织对使用来自消费级设备(如智能手机和可穿戴活动追踪器)的数据来测量身体活动(PA)越来越感兴趣。事实上,大规模的、易于获取的、自主的关于PA以及其他健康行为的数据收集正变得越来越有吸引力。使用消费级设备收集PA数据有几个好处,包括能够获得回顾性和前瞻性的大数据,以及了解个人层面的PA模式随时间的变化和对自然事件的响应。然而,目前存在与代表性、数据访问和专有算法相关的挑战,这些挑战限制了这些数据在理解人口水平PA方面的效用。在这份简短的报告中,我们旨在强调利用智能手机和可穿戴活动追踪器的现有数据来了解大规模PA模式的好处和局限性,并激发科学界对PA测量和监测的未来的讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.90
自引率
0.00%
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