Study objectives: Working outside the conventional "9-to-5" shift may lead to reduced sleep and alertness impairment. Here, we developed an optimization algorithm to identify sleep and work schedules that minimize alertness impairment during work hours, while reducing impairment during non-work hours.
Methods: The optimization algorithm searches among a large number of possible sleep and work schedules and estimates their effectiveness in mitigating alertness impairment using the Unified Model of Performance (UMP). To this end, the UMP, and its extensions to estimate sleep latency and sleep duration, predicts the time course of alertness of each potential schedule and their physiological feasibility. We assessed the algorithm by simulating four experimental studies, where we compared alertness levels during work periods for sleep schedules proposed by the algorithm against those used in the studies. In addition, in one of the studies we assessed the algorithm's ability to simultaneously optimize sleep and work schedules.
Results: Using the same amount of sleep as in the studies but distributing it optimally, the sleep schedules proposed by the optimization algorithm reduced alertness impairment during work periods by an average of 29%. Similarly, simultaneously optimized sleep and work schedules, for a recovery period following a chronic sleep restriction challenge, accelerated the return to baseline levels by two days when compared to the conventional 9-to-5 work schedule.
Conclusions: Our work provides the first quantitative tool to optimize sleep and work schedules and extends the capabilities of existing fatigue-management tools.
Keywords: mathematical models; neurobehavioral performance; shift work; sleep deprivation.
Published by Oxford University Press on behalf of Sleep Research Society (SRS) 2021.