0034 When Should You Sleep to Maximize Alertness?. (27th May 2020)

• Record Type: Journal Article
• Authors: Vital-Lopez, F; Doty, T J; Balkin, T J; Reifman, J
• Is Part Of: Sleep. Volume 43(2020)Supplement 1
• Page Start: A14
• Page End: A14
• Publication Date: 2020-05-27
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsaa056.033
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Working under sleep-restricted conditions may curtail safety and productivity. We could potentially minimize the negative effects of sleep restriction by optimizing the timing of sleep. However, to date, there are no algorithms that can determine the optimal sleep time to maximize alertness when most needed. Methods: Our previously validated unified model of performance predicts the recuperative effects of sleep on alertness. Here, we extended this model to predict the likelihood of an individual falling and remaining asleep at any given moment, as a function of recent sleep history and time of day. Then, we combined the model with an optimization algorithm to provide optimal sleep recommendations for a given work/rest schedule. Specifically, using the model to predict the effectiveness of different sleep schedules, the algorithm determines when to sleep and for how long, so as to maximize alertness at desired times. The algorithm takes as inputs the 1) user-provided sleep history, 2) periods when the user has an opportunity to sleep, and 3) desired periods for maximum alertness, and provides as outputs sleep recommendations that are physiologically feasible and optimize alertness for the desired period. We assessed the algorithm by computing and comparing sleep recommendations for five previously published experimental studies of sleep restriction, including diurnal and nocturnal sleep. Results: Compared to the original sleep schedules in the studies, our algorithm identified sleep recommendations that increased the predicted alertness by up to 33% and by 18% on average. These results suggest that the algorithm can tailor the timing of sleep to each specific sleep-restriction condition so as to maximize its benefits. Conclusion: Our algorithm provides automated, customized guidance to enhance the recuperative benefits of limited sleep opportunities to maximize alertness at the most needed times. As such, it is the first quantitative sleep optimization tool for fatigue-management systems. Support: This work was sponsored by the Military Operational Medicine Research Area Directorate of the U.S. Army Medical Research and Development Command, Ft. Detrick, MD.