T J Doty

Limited Efficacy of Caffeine and Recovery Costs During and Following 5 Days of Chronic Sleep Restriction

ObjectivesnTo investigate the effects of caffeine on psychomotor vigilance and sleepiness during sleep restriction and following subsequent recovery sleep.nnnMethodsnParticipants were N = 48 healthy good sleepers. All participants underwent five nights of sleep satiation (time-in-bed [TIB]: 10 hours), followed by five nights of sleep restriction (TIB: 5 hours), and three nights of recovery sleep (TIB: 8 hours) in a sleep laboratory. Caffeine (200 mg) or placebo was administered in the form of chewing gum at 08:00 am and 12:00 pm each day during the sleep restriction phase. Participants completed hourly 10-minute psychomotor vigilance tests and a modified Maintenance of Wakefulness Test approximately every 4 hours during the sleep restriction and recovery phases.nnnResultsnCaffeine maintained objective alertness compared to placebo across the first 3 days of sleep restriction, but this effect was no longer evident by the fourth day. A similar pattern of results was found for Maintenance of Wakefulness Test sleep latencies, such that those in the caffeine group (compared to placebo) did not show maintenance of wakefulness relative to baseline after the second night of restriction. Compared to placebo, participants in the caffeine condition displayed slower return to baseline in alertness and wakefulness across the recovery sleep period. Finally, the caffeine group showed greater N3 sleep duration during recovery.nnnConclusionsnCaffeine appears to have limited efficacy for maintaining alertness and wakefulness across 5 days of sleep restriction. Perhaps more importantly, there may be recovery costs associated with caffeine use following conditions of prolonged sleep loss.

Caffeine dosing strategies to optimize alertness during sleep loss

Sleep 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 sleep‐loss 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.

2B-Alert App: A mobile application for real-time individualized prediction of alertness

Knowing how an individual responds to sleep deprivation is a requirement for developing personalized fatigue management strategies. Here we describe and validate the 2B‐Alert App, the first mobile application that progressively learns an individual’s trait‐like response to sleep deprivation in real time, to generate increasingly more accurate individualized predictions of alertness. We incorporated a Bayesian learning algorithm within the validated Unified Model of Performance to automatically and gradually adapt the model parameters to an individual after each psychomotor vigilance test. We implemented the resulting model and the psychomotor vigilance test as a smartphone application (2B‐Alert App), and prospectively validated its performance in a 62‐hr total sleep deprivation study in which 21 participants used the app to perform psychomotor vigilance tests every 3 hr and obtain real‐time individualized predictions after each test. The temporal profiles of reaction times on the app‐conducted psychomotor vigilance tests were well correlated with and as sensitive as those obtained with a previously characterized psychomotor vigilance test device. The app progressively learned each individual’s trait‐like response to sleep deprivation throughout the study, yielding increasingly more accurate predictions of alertness for the last 24 hr of total sleep deprivation as the number of psychomotor vigilance tests increased. After only 12 psychomotor vigilance tests, the accuracy of the model predictions was comparable to the peak accuracy obtained using all psychomotor vigilance tests. With the ability to make real‐time individualized predictions of the effects of sleep deprivation on future alertness, the 2B‐Alert App can be used to tailor personalized fatigue management strategies, facilitating self‐management of alertness and safety in operational and non‐operational settings.