Real-Time Caffeine Optimization During Total Sleep Deprivation

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 caffeine based on the recommendations of the model. After 62 hours of SD, Phase 3 begins, involving a night of monitored recovery sleep and additional sessions of PV and mood testing until release from the study at 6 pm on the final day. It is hypothesized that the 2B-Alert app will be effective at providing caffeine dosing recommendations that return PV and mood performance to normal levels during the alertness window.

Conditions

• Caffeine
• Sleep Deprivation
• Shift-Work Related Sleep Disturbance

Interventions

DIETARY_SUPPLEMENT

Caffeine

Caffeine will be administered to each participant following an individualized optimal dosing schedule created by the 2B-Alert app. Individualized caffeine dosing schedules will be created by the 2BAlert app on the smartphone the participant uses to do Smart-PVT tests. Study staff will run the optimization at 1900 on Day 15. The algorithm will recommend caffeine dosing to optimize performance during the Peak Alertness window (e.g. from 0300-0900 on Day 16, 44-50 hours of continuous sleep loss). Doses could be recommended at any hour from 2000 on Day 15 to 0800 on Day 16. HOWEVER, the algorithm will not exceed 800 mg of caffeine across the entire study AND it will not dose more than 300 mg of caffeine at a time. Gum will be chewed for a total of 10 minutes by each participant and then discarded. As there will be no placebo in this protocol, randomization and blinding procedures are not necessary. IT IS IMPORTANT TO NOTE THAT SOME PARTICIPANTS MAY NOT RECEIVE CAFFEINE AT ALL.

Sponsors & Collaborators

• Walter Reed Army Institute of Research (WRAIR)

  collaborator FED

• University of Arizona

  lead OTHER

Study Design

Allocation

NA

Purpose

OTHER

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Max Age

39 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2021-02-19

Primary Completion

2021-07-31

Completion

2021-07-31

Countries

• United States

Study Locations