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Updated: Oct 27, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
Diurnal variations in multi-sensor wearable-derived sleep characteristics in morning- and evening-type shift workers
Ian Clark1, Benjamin Stucky1,2, Yasmine Azza3,4
1Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
Abstract:
Consumer-grade, multi-sensor, rest-activity trackers may be powerful tools, to help optimize rest-activity management in shiftwork populations undergoing circadian misalignment. Nevertheless, performance testing of such devices under field conditions is scarce. We previously validated Fitbit Charge 2TM against home polysomnography and now evaluated the potential of this device to document differences in rest-activity behavior, including sleep macrostructure, in first-responder shift workers in an operational setting. We continuously monitored 89 individuals (54% females; mean age: 33.9 ± 7.7 years) for 32.5 ± 9.3 days and collected 2,974 individual sleep episodes scattered around the clock. We stratified the study participants according to their self-reported circadian preference on the reduced Horne-Östberg Morningness-Evening Questionnaire (rMEQ; the scores from 4 participants were missing). Fitbit estimates of sleep duration, wakefulness after sleep onset (WASO), REM sleep percentage in the first NREM-REM sleep cycle, and REM sleep latency formed approximately sinusoidal oscillations across 24 hours. Generalized additive mixed model analyses revealed that the phase position of sleep duration minimum was delayed by 2.8 h in evening types (ET; rMEQ ≤ 11; n = 20) and by 2.6 h in intermediate types (IT; 11 < rMEQ < 18; n = 45) when compared to morning types (MT; rMEQ ≥ 18; n = 20). Similarly, the phase position of WASO was delayed by 2.7 h in ET compared to MT. While nocturnal sleep duration did not differ among the three groups, sleep episodes during the biological day decreased in duration from ET to IT to MT. Together, the findings support the notion that a consumer-grade, rest-activity tracker allows estimation of behavioral sleep/wake cycles and sleep macrostructure in shift workers under naturalistic conditions that are consistent with their self-reported chronotype.
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