Gruwez Alexia, Libert Walter, Ameye Lieveke, Bruyneel Marie
Chest Service, Saint-Pierre University Hospital, 322 rue haute, Brussels, Belgium.
Data Center, Bordet Institute, 121 Boulevard de Waterloo, Brussels, Belgium.
Int J Med Inform. 2017 Jun;102:87-92. doi: 10.1016/j.ijmedinf.2017.03.008. Epub 2017 Mar 21.
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 (U) and Withings Pulse 02 (W)) and compared it with reference methods for sleep and activity recordings, namely the Bodymedia SenseWear Pro Armband actigraph (SWA) and home-polysomnography (H-PSG).
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.
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).
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.
可穿戴健康设备在消费者中已变得流行，但尚不清楚它们是否能准确测量睡眠和身体活动参数。为解决这个问题，我们研究了两款消费级活动监测器（Up Move Jawbone（U）和Withings Pulse 02（W））的测量数据，并将其与睡眠和活动记录的参考方法进行比较，即Bodymedia SenseWear Pro臂式活动记录仪（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这四种设备进行评估。然后计划进行另外24小时的记录，在此期间他们佩戴2个追踪器和SWA。分析了用这4种设备获得的身体活动和睡眠参数。
所有设备的总睡眠时间（TST）与H - PSG均有显著相关性：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获得的系数最佳。U和SWA的卧床时间（TIB）与PSG也有显著相关性（r分别为0.79和0.76，两者p均<0.0001），但W没有（r = 0.45，p = 0.07）。W、U和SWA在测量深度睡眠、浅睡眠和睡眠效率（SE）方面未获得显著相关性。仅在与SWA测量时，睡眠潜伏期（SL）与H - PSG相关（r = 0.5，p = 0.02）。身体活动评估显示，U和W与SWA在步数计数（两者r = 0.95且p均<0.0001）和活动能量消耗（EE）方面有显著相关性（r分别为0.65和0.54；p = 0.0006和p<0.0001）。总EE也得到了正确估计（r分别为0.75和0.52；p<0.0001和p = 0.001）。
睡眠和活动监测器仅能产生有限的一组可靠测量值，如TST、步数计数和活动EE，总体上更倾向于U。尽管手动激活了睡眠模式，但U和W仍不适用于给出诸如睡眠结构、SE和SL等正确数据。未来，为提高此类监测器的准确性，研究人员和供应商必须合作，基于睡眠生理学可靠地编写算法。这可以避免误导消费者。
