Real-Time Caffeine Optimization During Total Sleep Deprivation

Study Summary

Sleep deprivation (SD) has a powerful degrading effect on cognitive performance, particularly psychomotor vigilance (PV) and reaction time. Caffeine is well known to be an effective countermeasure to the effects of SD. However, individuals differ in both their response to SD and to the administration of caffeine. This has made it difficult to provide individualized recommendations regarding the use of caffeine to sustain alertness when needed. For the past two decades, the Army's Biotechnology HPC Institute (BHSAI), in collaboration with the Walter Reed Army Institute of Research, have been developing statistical models to predict individual performance during prolonged SD. Recently, this resulted in the publication of the 2B-Alert app, a computer algorithm based on large datasets that can learn an individual's response to SD by combining actigraphic sleep data with simultaneously acquired PV performance data. The 2B-Alert algorithm can predict an individual's sleep need and performance after \~2 weeks of training the model. Recently, the model has been extended to incorporate individualized responses to caffeine. This was recently validated in a retrospective study published by BHSAI in 2019. The present study is designed to test the predictive capacity of the 2B-Alert app in real time. During Phase 1 a total of 21 healthy participants will wear an actigraph \& complete multiple daily PV tests on a personal cell phone. After 2 weeks, these individuals will attend Phase 2 involving an in-laboratory stay \& SD. Participants will have an 8-hour period of sleep in the laboratory, followed by 62 hours of continuous wakefulness. During these 62 hours, participants will complete PV and mood testing every 3 hours. The 2B-Alert app will be used to predict individual caffeine need to sustain performance at near-baseline levels based on the statistical model. At 44 hours SD, participants will undergo a 6-hour "alertness window" where they may receive individualized doses of c

Primary Outcome

Assesses the effects of sleep loss on visual reaction time as a behavioral measure of sleepiness Subjects continuously monitor a blank display and touch the smart phone screen as quickly as possible in response to a visual stimulus (i.e. a millisecond counter that begins at zero and stops when you press the button). Outcome measures: Mean RT (ms), Mean Speed (s-1), Lapses (#) The primary objective of this study is to determine if the real-time 2B-Alert Caffeine Optimization algorithm can produce

Interventions

• DIETARY_SUPPLEMENT Caffeine