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

Sleep assessment using EEG-based wearables - A systematic review

Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.

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

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.

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

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.

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

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.