Abstract
Sleep loss can cause cognitive impairments that increase the risk of mistakes and accidents. However, existing guidelines to counteract the effects of sleep loss are generic and are not designed to address individual-specific conditions, leading to suboptimal alertness levels. Here, we developed an optimization algorithm that automatically identifies sleep schedules and caffeine-dosing strategies to minimize alertness impairment due to sleep loss for desired times of the day. We combined our previous algorithms that separately optimize sleep or caffeine to simultaneously identify the best sleep schedules and caffeine doses that minimize alertness impairment at desired times. The optimization algorithm uses the predictions of the well-validated Unified Model of Performance to estimate the effectiveness and physiological feasibility of a large number of possible solutions and identify the best one. To assess the optimization algorithm, we used it to identify the best sleep schedules and caffeine-dosing strategies for four studies that exemplify common sleep-loss conditions and compared the predicted alertness-impairment reduction achieved by using the algorithm's recommendations against that achieved by following the U.S. Army caffeine guidelines. Compared to the alertness-impairment levels in the original studies, the algorithm's recommendations reduced alertness impairment on average by 63%, an improvement of 24 percentage points over the U.S. Army caffeine guidelines. We provide an optimization algorithm that simultaneously identifies effective and safe sleep schedules and caffeine-dosing strategies to minimize alertness impairment at user-specified times.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.