Sleep–wake behaviors exhibited by shift workers in normal operations and predicted by a biomathematical model of fatigue

Mar 28, 2020Sleep

Sleep and Wake Patterns of Shift Workers During Normal Work and Predicted by a Fatigue Model

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗

Abstract

Consolidated-sleep behaviors were observed during 1,441 breaks, with correct predictions made for 1,359 of those breaks.

Actual sleep-wake behaviors of 354 rail workers were monitored during 1,722 breaks lasting 8-24 hours.
Split-sleep behaviors were observed during 280 breaks, but only 182 of those were correctly predicted.
Incorrect predictions of sleep-wake behavior led to misestimation of hours of sleep during breaks.
Misestimation of sleepiness during subsequent shifts was also linked to predicting the wrong type of sleep-wake behavior.
Group-level predictions failed to account for individual variability in sleep-wake behaviors among workers.

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STUDY OBJECTIVES: To compare rail workers' actual sleep-wake behaviors in normal operations to those predicted by a biomathematical model of fatigue (BMMF). To determine whether there are group-level residual sources of error in sleep predictions that could be modeled to improve group-level sleep predictions.

METHODS: The sleep-wake behaviors of 354 rail workers were examined during 1,722 breaks that were 8-24 h in duration. Sleep-wake patterns were continuously monitored using wrist-actigraphy and predicted from the work-rest schedule using a BMMF. Rail workers' actual and predicted sleep-wake behaviors were defined as split-sleep (i.e. ≥2 sleep periods in a break) and consolidated-sleep (i.e. one sleep period in a break) behaviors. Sleepiness was predicted from the actual and predicted sleep-wake data.

RESULTS: Consolidated-sleep behaviors were observed during 1,441 breaks and correctly predicted during 1,359 breaks. Split-sleep behaviors were observed during 280 breaks and correctly predicted during 182 breaks. Predicting the wrong type of sleep-wake behavior resulted in a misestimation of hours of sleep during a break. Relative to sleepiness predictions derived from actual sleep-wake data, predicting the wrong type of sleep-wake behavior resulted in a misestimation of sleepiness predictions during the subsequent shift.

CONCLUSIONS: All workers with the same work-rest schedule have the same predicted sleep-wake behaviors; however, these workers do not all exhibit the same sleep-wake behaviors in real-world operations. Future models could account for this group-level residual variance with a new approach to modeling sleep, whereby sub-group(s) may be predicted to exhibit one of a number of sleep-wake behaviors.

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Sleep–wake behaviors exhibited by shift workers in normal operations and predicted by a biomathematical model of fatigue

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