Performance of consumer wrist-worn type sleep tracking devices compared to polysomnography: a meta-analysis.

IF 3.5 3区 医学 Q1 CLINICAL NEUROLOGY
Young Jeong Lee, Jae Yong Lee, Jae Hoon Cho, Yun Jin Kang, Ji Ho Choi
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Abstract

Study objectives: The use of sleep tracking devices is increasing as people become more aware of the importance of sleep and interested in monitoring their patterns. With many devices on the market, we conducted a meta-analysis comparing sleep-scoring data from consumer wrist-worn sleep tracking devices with polysomnography to validate the accuracy of devices.

Methods: We retrieved studies from the databases of SCOPUS, EMBASE, Cochrane Library, PubMed, Web of Science, and KoreaMed, and OVID Medline up to March 2024. We compared personal data about participants and information on objective sleep parameters.

Results: From 24 studies, data of 798 patient using Fitbit, Jawbone, myCadian watch, WHOOP strap, Garmin, Basis B1, Zulu Watch, Huami Arc, E4 wristband, Fatigue Science Readiband, Apple Watch, or Xiaomi Mi Band 5 were analyzed. There were significant differences in total sleep time {mean difference (MD) -16.854, 95% confidence interval (CI) [-26.332; -7.375]}, sleep efficiency (MD -4.691, 95% CI [-7.079; -2.302]), sleep latency (MD 2.574, 95% CI [0.606; 4.542]), and wake after sleep onset (MD 13.255, 95% CI [4.522; 21.988]) between all consumer sleep tracking devices and polysomnography. In subgroup analysis, there was no significant difference of wake after sleep onset between Fitbit and polysomnography. There was also no significant difference sleep latency between other devices and polysomnography. Fitbit measured sleep latency longer than other devices, and other devices measured wake after sleep onset longer. Based on Begg and Egger's test, there was no publication bias in total sleep time and sleep efficiency.

Conclusions: Wrist-worn sleep tracking devices, while popular, are not as reliable as polysomnography in measuring key sleep parameters like total sleep time, sleep efficiency, and sleep latency. Physicians and consumers should be aware of their limitations and interpret results carefully, though they can still be useful for tracking general sleep patterns. Further improvements and clinical studies are needed to enhance their accuracy.

消费者腕戴式睡眠追踪设备与多导睡眠监测仪的性能比较:一项荟萃分析。
研究目的随着人们越来越意识到睡眠的重要性,并对监测自己的睡眠模式越来越感兴趣,睡眠跟踪设备的使用也在不断增加。市场上有许多睡眠追踪设备,我们对消费者腕戴式睡眠追踪设备和多导睡眠监测仪的睡眠评分数据进行了荟萃分析,以验证睡眠追踪设备的准确性:我们从 SCOPUS、EMBASE、Cochrane Library、PubMed、Web of Science、KoreaMed 和 OVID Medline 等数据库中检索了截至 2024 年 3 月的研究。我们比较了参与者的个人数据和客观睡眠参数信息:我们分析了24项研究中798名患者使用Fitbit、Jawbone、myCadian手表、WHOOP表带、Garmin、Basis B1、Zulu Watch、Huami Arc、E4腕带、Fatigue Science Readiband、Apple Watch或小米手环5的数据。总睡眠时间{平均差(MD)-16.854,95% 置信区间(CI)[-26.332; -7.375]}、睡眠效率(MD -4.691,95% CI [-7.079; -2.302])、睡眠潜伏期(MD 2.574,95% CI [0.606;4.542])和睡眠开始后的唤醒(MD 13.255,95% CI [4.522;21.988])。在亚组分析中,Fitbit 和多导睡眠图在睡眠开始后唤醒方面没有显著差异。其他设备与多导睡眠监测仪的睡眠潜伏期也无明显差异。Fitbit 比其他设备测量的睡眠潜伏期更长,而其他设备测量的睡眠开始后唤醒时间更长。根据Begg和Egger检验,总睡眠时间和睡眠效率不存在发表偏差:结论:腕戴式睡眠追踪设备虽然很流行,但在测量总睡眠时间、睡眠效率和睡眠潜伏期等关键睡眠参数方面不如多导睡眠图可靠。医生和消费者应认识到它们的局限性,并谨慎解释结果,尽管它们对跟踪一般睡眠模式仍然有用。要提高其准确性,还需要进一步的改进和临床研究。
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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来源期刊
CiteScore
6.20
自引率
7.00%
发文量
321
审稿时长
1 months
期刊介绍: Journal of Clinical Sleep Medicine focuses on clinical sleep medicine. Its emphasis is publication of papers with direct applicability and/or relevance to the clinical practice of sleep medicine. This includes clinical trials, clinical reviews, clinical commentary and debate, medical economic/practice perspectives, case series and novel/interesting case reports. In addition, the journal will publish proceedings from conferences, workshops and symposia sponsored by the American Academy of Sleep Medicine or other organizations related to improving the practice of sleep medicine.
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