Frontiers in Environmental Health (Apr 2024)
The dynamics of neurobehavioral impairment and recovery sleep: improved biomathematical modeling for fatigue risk management in operational settings
Abstract
Adequate sleep is essential for maintaining health, safety, and neurobehavioral functioning in 24/7 operational settings. Loss of sleep causes fatigue, which results in neurobehavioral impairment. Neurobehavioral impairment builds up disproportionately under conditions of total sleep deprivation compared to sustained sleep restriction; and recuperation due to recovery sleep is disproportionately fast after total sleep deprivation compared to sustained sleep restriction. These sleep schedule-dependent dynamics have been captured in a previously published, biomathematical model of fatigue, which includes positive feedback regulation of a relatively fast (hours to days) sleep/wake homeostatic process by a slower (days to weeks) allostatic process—a feature that suggests adenosinergic mechanisms are involved. However, the previously published model underestimates the rate of recuperation due to recovery sleep after acute total sleep deprivation as well as after consecutive days of sleep restriction. The objective of the present research is to modify the model to improve the accuracy of its predictions for recuperation due to recovery sleep. This can be accomplished by including in the model an additional, reciprocal feedback mechanism, presumed to be predominantly adenosinergic in nature, which provides feedback from the faster homeostatic process back onto the slower allostatic process. Adding a single new model parameter and refitting three existing model parameters significantly improves the predictions for recuperation due to recovery sleep after both acute total sleep deprivation and sustained sleep restriction. This model modification also improves the predictions of the build-up of neurobehavioral impairment across days of sustained sleep restriction, without adversely affecting the accuracy of the model in other scenarios including circadian misalignment and sleep inertia. The modified model preserves the previously developed capability to predict the differential dynamics of fatigue for objective performance impairment and subjective sleepiness. With the improved predictions for recuperation due to recovery sleep, the expanded model can be used to provide quantitative estimates for potentially impactful work scheduling decisions, such as the duration of time off needed before workers would be safe to return to the work floor. This enhances the usefulness of the model as a tool for predicting and managing neurobehavioral functioning and safety in 24/7 operational settings.
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