NCT04399083

Real-Time Caffeine Optimization During Total Sleep Deprivation

Completed NA Last updated 17 September 2021

What this trial tests

NA trial testing Caffeine in Caffeine in 60 participants. Completed in 31 July 2021.

Timeline

19 February 2021

Primary endpoint
31 July 2021

31 July 2021

Quick facts

Lead sponsor	University of Arizona
Phase	NA
Status	Completed
Study type	INTERVENTIONAL
Allocation	na
Design	single group
Masking	none
Primary purpose	other
Enrollment	60
Start date	19 February 2021
Primary completion	31 July 2021
Estimated completion	31 July 2021
Sites	1 location across United States

Drugs / interventions tested

• Caffeine (caffeine) — full drug profile →

Conditions studied

• Caffeine — all drugs for Caffeine →
• Sleep Deprivation — all drugs for Sleep Deprivation →
• Shift-Work Related Sleep Disturbance — all drugs for Shift-Work Related Sleep Disturbance →

Sponsor

University of Arizona

Who can join

Adults 18 to 39, any sex, with Caffeine or Sleep Deprivation. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

1 peer-reviewed publication reference this trial (live from Europe PMC):

1. Personalized alertness prediction using video-based ocular and facial features.
   Subramaniyan M, Vital-Lopez FG, Doty TJ, Anlap I, et al · · 2025 · cited 1× · PMID 40457721 · DOI 10.1093/sleep/zsaf149