SummarySleep loss, which affects about one-third of the US population, can severely impair physical and neurobehavioural performance. Although caffeine, the most widely used stimulant in the world, can mitigate these effects, currently there are no tools to guide the timing and amount of caffeine consumption to optimize its benefits. In this work, we provide an optimization algorithm, suited for mobile computing platforms, to determine when and how much caffeine to consume, so as to safely maximize neurobehavioural performance at the desired time of the day, under any sleeploss condition. The algorithm is based on our previously validated Unified Model of Performance, which predicts the effect of caffeine consumption on a psychomotor vigilance task. We assessed the algorithm by comparing the caffeine-dosing strategies (timing and amount) it identified with the dosing strategies used in four experimental studies, involving total and partial sleep loss. Through computer simulations, we showed that the algorithm yielded caffeine-dosing strategies that enhanced performance of the predicted psychomotor vigilance task by up to 64% while using the same total amount of caffeine as in the original studies. In addition, the algorithm identified strategies that resulted in equivalent performance to that in the experimental studies while reducing caffeine consumption by up to 65%. Our work provides the first quantitative caffeine optimization tool for designing effective strategies to maximize neurobehavioural performance and to avoid excessive caffeine consumption during any arbitrary sleep-loss condition.
K E Y W O R D Salertness, fatigue, mathematical model, response time, vigilance
| INTRODUCTIONSleep loss, which is a common stressor for both civilians and military personnel, can severely impair cognitive and physical performance, and thereby diminish productivity and compromise safety. Several studies have demonstrated that, when safely used, caffeine can help to sustain cognitive performance during prolonged periods of restricted sleep (Doty et al., 2017;Kamimori et al., 2015;Killgore et al., 2008;Mclellan, Bell, & Kamimori, 2004;Mclellan et al., 2005;Wesensten, Killgore, & Balkin, 2005). However, these investigations offer caffeine countermeasure guidance that is study-specific, and which cannot be readily adaptable to any arbitrary sleep-loss condition. Providing a foundation for addressing this need, our group has previously developed and validated a mathematical model, theThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.