Reliability of commercially available sleep and activity trackers with manual switch-to-sleep mode activation in free-living healthy individuals
Reliability of commercially available sleep and activity trackers with manual switch-to-sleep mode activation in free-living healthy individuals
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DOI：
10.1016/j.ijmedinf.2017.03.008
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发表时间：
2017-06-01
影响因子：
4.9
通讯作者：
Bruyneel, Marie
中科院分区：
文献类型：
--
作者：
Gruwez, Alexia;Libert, Walter;Bruyneel, Marie
简介：可穿戴健康设备已经成为消费者的时尚，但目前尚不清楚它们是否能准确测量睡眠和身体活动参数。为了解决这个问题，我们研究了两个消费者级别活动监测器的测量数据（Up Move Jawbone（R）（U）和Withings Pulse 02（R）（W））进行比较，并将其与睡眠和活动记录的参考方法进行比较，即Bodymedia SenseWear Pro Armband（R）活动记录仪（SWA）和家庭多导睡眠图（H-PSG）。20名健康患者在家中睡眠期间使用四种设备进行评估。然后计划进行额外的24小时记录，在此期间，他们佩戴了2个跟踪器和SWA。结果：所有设备的总睡眠时间（TST）与H-PSG显著相关：W的r = 0.48（p = 0.04），U的r = 0.63（p = 0.002），SWA的r = 0.7（p = 0.0003）。SWA法的系数最大。U和SWA与PSG的卧床时间（TIB）也有显著相关性（r = 0.79和r = 0.76，p < 0.0001），但与W无显著相关性（r = 0.45，p = 0.07）。深度睡眠、轻度睡眠和睡眠效率（SE）测量值与W、U和SWA之间没有显着相关性。睡眠潜伏期（SL）仅在与SWA测量时与H-PSG相关体力活动评估显示U和W与SWA的步数（r = 0.95和p < 0.0001）和主动能量消耗（EE）（r = 0.65和0.54; p = 0.0006和p < 0.0001）显著相关。总EE也正确估计（r = 0.75和0.52; p < 0.0001和p = 0.001）。结论：睡眠和活动监测仪只能产生有限的一组可靠的测量，如TST，步数，和活动EE，与U的偏好，表现更好的整体。尽管手动激活到睡眠模式，U和W不适合给出正确的数据，如睡眠结构，SE和SL。在未来，为了提高这种监测器的准确性，研究人员和供应商必须合作编写基于睡眠生理学的可靠算法。这样可以避免误导消费者。(C)2017年爱思唯尔B。V.保留所有权利。
Introduction: Wearable health devices have become trendy among consumers, but it is not known whether they accurately measure sleep and physical activity parameters. To address this question, we have studied the measured data of two consumer-level activity monitors (Up Move Jawbone (R) (U) and Withings Pulse 02 (R) (W)) and compared it with reference methods for sleep and activity recordings, namely the Bodymedia SenseWear Pro Armband (R) actigraph (SWA) and home-polysomnography (H-PSG).Methods: Twenty healthy patients were assessed at home, during sleep, with the four devices. An additional 24-h period of recording was then planned during which they wore the 2 trackers and the SWA. Physical activity and sleep parameters obtained with the 4 devices were analyzed.Results: Significant correlations with H-PSG were obtained for total sleep time (TST) for all the devices: r = 0.48 for W (p = 0.04), r = 0.63 for U (p = 0.002), r = 0.7 for SWA (p = 0.0003). The best coefficient was obtained with SWA. Significant correlations were also obtained for time in bed (TIB) for U and SWA vs PSG (r = 0.79 and r = 0.76, p < 0.0001 for both) but not for W (r = 0.45, p = 0.07). No significant correlations were obtained for deep sleep, light sleep, and sleep efficiency (SE) measurements with W, U and SWA. Sleep latency (SL) correlated with H-PSG only when measured against SWA (r = 0.5, p = 0.02).Physical activity assessment revealed significant correlations for U and W with SWA for step count (both r = 0.95 and p < 0.0001) and active energy expenditure (EE) (r = 0.65 and 0.54; p = 0.0006 and p < 0.0001). Total EE was also correctly estimated (r = 0.75 and 0.52; p < 0.0001 and p = 0.001).Conclusion: Sleep and activity monitors are only able to produce a limited set of reliable measurements, such as TST, step count, and active EE, with a preference for U which performs globally better. Despite the manual activation to sleep mode, U and W were not suitable for giving correct data such as sleep architecture, SE, and SL. In the future, to enhance accuracy of such monitors, researchers and providers have to collaborate to write algorithms based reliably on sleep physiology. It could avoid misleading the consumer. (C) 2017 Elsevier B. V. All rights reserved.