Evaluations of Commercial Sleep Technologies for Objective Monitoring During Routine Sleeping Conditions

• Published in: Nature and science of sleep 2020-01, Vol.12, p.821-842
• Main Authors: Stone, Jason D, Rentz, Lauren E, Forsey, Jillian, Ramadan, Jad, Markwald, Rachel R, Finomore, Victor S, Galster, Scott M, Rezai, Ali, Hagen, Joshua A
• Format: Article
• Language: English
• Subjects: Accuracy; consumer sleep technologies; Data collection; Diaries; Electroencephalography; Electromyography; Heart rate; Laboratories; Medical screening; Original Research; Patient monitoring equipment; Physiology; Population; Sleep; Sleep disorders; sleep duration; sleep efficiency; sleep stagingintroduction; Third party; Trends; wearables
• ISSN: 1179-1608; 1179-1608
• DOI: 10.2147/nss.s270705

The commercial market is saturated with technologies that claim to collect proficient, free-living sleep measurements despite a severe lack of independent third-party evaluations. Therefore, the present study evaluated the accuracy of various commercial sleep technologies during in-home sleeping conditions. Data collection spanned 98 separate nights of sleep from five healthy adults. Prior to bedtime, participants utilized nine popular sleep devices while concurrently wearing a previously validated electroencephalography (EEG)-based device. Data collected from the commercial devices were extracted for later comparison against EEG to determine degrees of accuracy. Sleep and wake summary outcomes as well as sleep staging metrics were evaluated, where available, for each device. Total sleep time (TST), total wake time (TWT), and sleep efficiency (SE) were measured with greater accuracy (lower percent errors) and limited bias by Fitbit Ionic [mean absolute percent error, bias (95% confidence interval); TST: 9.90%, 0.25 (-0.11, 0.61); TWT: 25.64%, -0.17 (-0.28, -0.06); SE: 3.49%, 0.65 (-0.82, 2.12)] and Oura smart ring [TST: 7.39%, 0.19 (0.04, 0.35); TWT: 36.29%, -0.18 (-0.31, -0.04); SE: 5.42%, 1.66 (0.17, 3.15)], whereas all other devices demonstrated a propensity to over or underestimate at least one if not all of the aforementioned sleep metrics. No commercial sleep technology appeared to accurately quantify sleep stages. Generally speaking, commercial sleep technologies displayed lower error and bias values when quantifying sleep/wake states as compared to sleep staging durations. Still, these findings revealed that there is a remarkably high degree of variability in the accuracy of commercial sleep technologies, which further emphasizes that continuous evaluations of newly developed sleep technologies are vital. End-users may then be able to determine more accurately which sleep device is most suited for their desired application(s).