Paperdoi:10.1093/sleep/zsad288
Can we rely on wearable sleep-tracker devices for fatigue management?
Sleep 2024;47(3):zsad288 (advance access 8 Nov 2023)
TL;DR
Characterised sleep-measurement error vs PSG for 18 sleep-tracker devices from the literature (mean sleep-duration overestimate 19 min, SD 44) and propagated it through UMP simulations (5, 8, 9 h/night or irregular, 30 days). Acceptance criterion: predicted PVT mean-RT difference < 30 ms (within-subject variability). Predicted differences were < 30 ms nearly 80% of the time.
- Form factor: software (sleep/schedule input; wrist actigraphy optional)
Abstract
Study Objectives
Wearable sleep-tracker devices are ubiquitously used to measure sleep; however, the estimated sleep parameters often differ from the gold-standard polysomnography (PSG). It is unclear to what extent we can tolerate these errors within the context of a particular clinical or operational application. Here, we sought to develop a method to quantitatively determine whether a sleep tracker yields acceptable sleep-parameter estimates for assessing alertness impairment.
Methods
Using literature data, we characterized sleep-measurement errors of 18 unique sleep-tracker devices with respect to PSG. Then, using predictions based on the unified model of performance, we compared the temporal variation of alertness in terms of the psychomotor vigilance test mean response time for simulations with and without added PSG-device sleep-measurement errors, for nominal schedules of 5, 8, or 9 hours of sleep/night or an irregular sleep schedule each night for 30 consecutive days. Finally, we deemed a device error acceptable when the predicted differences were smaller than the within-subject variability of 30 milliseconds. We also established the capability to estimate the extent to which a specific sleep-tracker device meets this acceptance criterion.
Results
On average, the 18 sleep-tracker devices overestimated sleep duration by 19 (standard deviation = 44) minutes. Using these errors for 30 consecutive days, we found that, regardless of sleep schedule, in nearly 80% of the time the resulting predicted alertness differences were smaller than 30 milliseconds.
Conclusions
We provide a method to quantitatively determine whether a sleep-tracker device produces sleep measurements that are operationally acceptable for fatigue management.
Verbatim from the publisher's DOI record (opens in a new tab) · Abstract as deposited by the publisher with Crossref; © the authors/publisher
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Cite this paper
Reifman, J., Priezjev, N. V., & Vital-Lopez, F. G. (2023). Can we rely on wearable sleep-tracker devices for fatigue management? Sleep 2024. https://doi.org/10.1093/sleep/zsad288
Reifman, Jaques, Nikolai V. Priezjev, and Francisco G. Vital-Lopez. 2023. "Can we rely on wearable sleep-tracker devices for fatigue management?" Sleep 2024. https://doi.org/10.1093/sleep/zsad288.
Reifman, J., Priezjev, N. V. and Vital-Lopez, F. G. (2023) 'Can we rely on wearable sleep-tracker devices for fatigue management?', Sleep 2024. doi: 10.1093/sleep/zsad288.
Reifman, Jaques, et al. "Can we rely on wearable sleep-tracker devices for fatigue management?" Sleep 2024, 2023. https://doi.org/10.1093/sleep/zsad288.
J. Reifman, N. V. Priezjev, and F. G. Vital-Lopez, "Can we rely on wearable sleep-tracker devices for fatigue management?" Sleep 2024, 2023, doi: 10.1093/sleep/zsad288.
@article{reifman2023rely,
title = {{Can we rely on wearable sleep-tracker devices for fatigue management?}},
author = {Reifman, Jaques and Priezjev, Nikolai V. and Vital-Lopez, Francisco G.},
year = {2023},
journal = {Sleep 2024},
doi = {10.1093/sleep/zsad288}
}Added to the ChatPPG research hub
