Validation of a nonwearable device in healthy adults with normal and short sleep durations

Hsiou, David; Gao, Chenlu; Matlock, Robert C.; Scullin, Michael K.

doi:10.5664/jcsm.9700

Cited by 7 publications

(10 citation statements)

References 28 publications

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“…The examination of individual titles and abstracts yielded 137 publications for the full-text appraisal. However, after scrutinizing each of them according to a priori inclusion and exclusion criteria, only 26 articles were qualified for systematic review [ 17 , 20 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. Out of the 26 articles, one did not evaluate PSG and CCSTD simultaneously [ 30 ], which likely led to a bias; three did not include data of meta-analysis [ 30 , 39 , 41 , 46 ].…”

Section: Resultsmentioning

confidence: 99%

“…Out of the 26 articles, one did not evaluate PSG and CCSTD simultaneously [ 30 ], which likely led to a bias; three did not include data of meta-analysis [ 30 , 39 , 41 , 46 ]. Finally, 22 articles qualified for meta-analysis [ 17 , 20 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 42 , 43 , 44 , 45 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ].…”

Section: Resultsmentioning

confidence: 99%

“…These articles described 32 studies of CCSTDs. Among the studies that used PSG as the standard test, five studies compared CCSTDs and actigraphy using the same cohort [ 34 , 35 , 42 , 49 , 53 ]. Their performance was tested against that of PSG by making participants wear both CCSTD and an actigraphy device.…”

Section: Resultsmentioning

confidence: 99%

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Evaluation of the Accuracy of Contactless Consumer Sleep-Tracking Devices Application in Human Experiment: A Systematic Review and Meta-Analysis

Zhai

Yan

et al. 2023

Sensors

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Compared with the gold standard, polysomnography (PSG), and silver standard, actigraphy, contactless consumer sleep-tracking devices (CCSTDs) are more advantageous for implementing large-sample and long-period experiments in the field and out of the laboratory due to their low price, convenience, and unobtrusiveness. This review aimed to examine the effectiveness of CCSTDs application in human experiments. A systematic review and meta-analysis (PRISMA) of their performance in monitoring sleep parameters were conducted (PROSPERO: CRD42022342378). PubMed, EMBASE, Cochrane CENTRALE, and Web of Science were searched, and 26 articles were qualified for systematic review, of which 22 provided quantitative data for meta-analysis. The findings show that CCSTDs had a better accuracy in the experimental group of healthy participants who wore mattress-based devices with piezoelectric sensors. CCSTDs’ performance in distinguishing waking from sleeping epochs is as good as that of actigraphy. Moreover, CCSTDs provide data on sleep stages that are not available when actigraphy is used. Therefore, CCSTDs could be an effective alternative tool to PSG and actigraphy in human experiments.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Evaluation of the Accuracy of Contactless Consumer Sleep-Tracking Devices Application in Human Experiment: A Systematic Review and Meta-Analysis

Zhai

Yan

et al. 2023

Sensors

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“…Last, we only evaluated two actigraphy algorithms using activity counts collected with the Actiwatch Spectrum devices that automatically detect and exclude off-wrist periods. 49 Future studies are needed to examine other actigraphy-scoring algorithms (eg, the Sadeh algorithm) 50 or other sleep-monitoring devices (eg, other wristband accelerometers or non-wearables) 51 especially those without on-wrist/in-use detection.…”

Section: Discussionmentioning

confidence: 99%

Actigraphy-Based Sleep Detection: Validation with Polysomnography and Comparison of Performance for Nighttime and Daytime Sleep During Simulated Shift Work

Gao¹,

P²,

Morris³

et al. 2022

NSS

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Purpose Actigraphy-based sleep detection algorithms were mostly validated using nighttime sleep, and their performance in detecting daytime sleep is unclear. We evaluated and compared the performance of Actiware and the Cole-Kripke algorithm (C-K) – two commonly used actigraphy-based algorithms – in detecting daytime and nighttime sleep. Participants and Methods Twenty-five healthy young adults were monitored by polysomnography and actigraphy during two in-lab protocols with scheduled nighttime and/or daytime sleep (within-subject design). Mixed-effect models were conducted to compare the sensitivity, specificity, and F1 score (a less-biased measure of accuracy) of Actiware (with low/medium/high threshold setting, separately) and C-K in detecting sleep epochs from actigraphy recordings during nighttime/daytime. t -tests and intraclass correlation coefficients were used to assess the agreement between actigraphy-based algorithms and polysomnography in scoring total sleep time (TST). Results Sensitivity was similar between nighttime (Actiware: 0.93–0.99 across threshold settings; C-K: 0.61) and daytime sleep (Actiware: 0.93–0.99; C-K: 0.66) for both the C-K and Actiware (daytime/nighttime×algorithm interaction: p > 0.1). Specificity for daytime sleep was lower (Actiware: 0.35–0.54; C-K: 0.91) than that for nighttime sleep (Actiware: 0.37–0.62; C-K: 0.93; p = 0.001). Specificity was also higher for C-K than Actiware (p < 0.001), with no daytime/nighttime×algorithm interaction (p > 0.1). C-K had lower F1 (nighttime = 0.74; daytime = 0.77) than Actiware (nighttime = 0.95–0.98; daytime = 0.90–0.91) for both nighttime and daytime sleep (all p < 0.05). The daytime-nighttime difference in F1 was opposite for Actiware (daytime: 0.90–0.91; nighttime: 0.95–0.98) and C-K (daytime: 0.77; nighttime: 0.74; interaction p = 0.003). Bias in TST was lowest in Actiware (with medium-threshold) for nighttime sleep (underestimation of 5.99 min/8h) and in Actiware (with low-threshold) for daytime sleep (overestimation of 17.75 min/8h). Conclusion Daytime/nighttime sleep affected specificity and F1 but not sensitivity of actigraphy-based sleep scoring. Overall, Actiware performed better than the C-K algorithm. Actiware with medium-threshold was the least biased in estimating nighttime TST, and Actiware with low-threshold was the least biased in estimating daytime TST.

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“…When compared to PSG for the two-state categorisation of sleep/wake, EarlySense, SleepScore Max, and ResMed S+ showed a very high sensitivity (all ≥0.93) with specificities of 0.47, 0.50, and 0.51, respectively [ 23 ]. In addition, the Beddit device identified total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset with an excellent agreement compared to PSG (all ICC values ≥ 0.92) [ 47 ]. However, when compared with PSG, the EmFit failed to recognise sleep stages, and for the two-state categorisation of sleep/wake, the EmFit resulted in a low agreement, with a kappa value of 0.13 [ 48 ].…”

Section: Introductionmentioning

confidence: 99%

Pyjamas, Polysomnography and Professional Athletes: The Role of Sleep Tracking Technology in Sport

Driller

Dunican

Omond

et al. 2023

Sports

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Technological advances in sleep monitoring have seen an explosion of devices used to gather important sleep metrics. These devices range from instrumented ‘smart pyjamas’ through to at-home polysomnography devices. Alongside these developments in sleep technologies, there have been concomitant increases in sleep monitoring in athletic populations, both in the research and in practical settings. The increase in sleep monitoring in sport is likely due to the increased knowledge of the importance of sleep in the recovery process and performance of an athlete, as well as the well-reported challenges that athletes can face with their sleep. This narrative review will discuss: (1) the importance of sleep to athletes; (2) the various wearable tools and technologies being used to monitor sleep in the sport setting; (3) the role that sleep tracking devices may play in gathering information about sleep; (4) the reliability and validity of sleep tracking devices; (5) the limitations and cautions associated with sleep trackers; and, (6) the use of sleep trackers to guide behaviour change in athletes. We also provide some practical recommendations for practitioners working with athletes to ensure that the selection of such devices and technology will meet the goals and requirements of the athlete.

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Validation of a nonwearable device in healthy adults with normal and short sleep durations

Cited by 7 publications

References 28 publications

Evaluation of the Accuracy of Contactless Consumer Sleep-Tracking Devices Application in Human Experiment: A Systematic Review and Meta-Analysis

Evaluation of the Accuracy of Contactless Consumer Sleep-Tracking Devices Application in Human Experiment: A Systematic Review and Meta-Analysis

Actigraphy-Based Sleep Detection: Validation with Polysomnography and Comparison of Performance for Nighttime and Daytime Sleep During Simulated Shift Work

Pyjamas, Polysomnography and Professional Athletes: The Role of Sleep Tracking Technology in Sport

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