Paperdoi:10.1093/sleep/zsad080
2B-Alert App 2.0: personalized caffeine recommendations for optimal alertness
Sleep 2023;46(7):zsad080
TL;DR
62-h TSD study, n = 21: the app learned each participant's sleep-loss phenotype from PVTs over the first 36 h and recommended caffeine (0–800 mg) to hold mean RT at 270 ms for 6 h from 44 h awake. Participants sustained the target ~80% of the time regardless of dose.
- Form factor: software (sleep/schedule input; wrist actigraphy optional)
Abstract
Study Objectives
If properly consumed, caffeine can safely and effectively mitigate the effects of sleep loss on alertness. However, there are no tools to determine the amount and time to consume caffeine to maximize its effectiveness. Here, we extended the capabilities of the 2B-Alert app, a unique smartphone application that learns an individual’s trait-like response to sleep loss, to provide personalized caffeine recommendations to optimize alertness.
Methods
We prospectively validated 2B-Alert’s capabilities in a 62-hour total sleep deprivation study in which 21 participants used the app to measure their alertness throughout the study via the psychomotor vigilance test (PVT). Using PVT data collected during the first 36 hours of the sleep challenge, the app learned the participant’s sleep-loss response and provided personalized caffeine recommendations so that each participant would sustain alertness at a pre-specified target level (mean response time of 270 milliseconds) during a 6-hour period starting at 44 hours of wakefulness, using the least amount of caffeine possible. Starting at 42 hours, participants consumed 0 to 800 mg of caffeine, per the app recommendation.
Results
2B-Alert recommended no caffeine to five participants, 100–400 mg to 11 participants, and 500–800 mg to five participants. Regardless of the consumed amount, participants sustained the target alertness level ~80% of the time.
Conclusions
2B-Alert automatically learns an individual’s phenotype and provides personalized caffeine recommendations in real time so that individuals achieve a desired alertness level regardless of their sleep-loss susceptibility.
Verbatim from the publisher's DOI record (opens in a new tab) · Abstract as deposited by the publisher with Crossref; © the authors/publisher
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Cite this paper
Vital-Lopez, F. G., Doty, T. J., Anlap, I., Killgore, W. D. S., & Reifman, J. (2023). 2B-Alert App 2.0: personalized caffeine recommendations for optimal alertness. Sleep 2023. https://doi.org/10.1093/sleep/zsad080
Vital-Lopez, Francisco G., Tracy J. Doty, Ian Anlap, William D. S. Killgore, and Jaques Reifman. 2023. "2B-Alert App 2.0: personalized caffeine recommendations for optimal alertness." Sleep 2023. https://doi.org/10.1093/sleep/zsad080.
Vital-Lopez, F. G., Doty, T. J., Anlap, I., Killgore, W. D. S. and Reifman, J. (2023) '2B-Alert App 2.0: personalized caffeine recommendations for optimal alertness', Sleep 2023. doi: 10.1093/sleep/zsad080.
Vital-Lopez, Francisco G., et al. "2B-Alert App 2.0: personalized caffeine recommendations for optimal alertness." Sleep 2023, 2023. https://doi.org/10.1093/sleep/zsad080.
F. G. Vital-Lopez, T. J. Doty, I. Anlap, W. D. S. Killgore, and J. Reifman, "2B-Alert App 2.0: personalized caffeine recommendations for optimal alertness," Sleep 2023, 2023, doi: 10.1093/sleep/zsad080.
@article{vitallopez20232balert,
title = {{2B-Alert App 2.0: personalized caffeine recommendations for optimal alertness}},
author = {Vital-Lopez, Francisco G. and Doty, Tracy J. and Anlap, Ian and Killgore, William D. S. and Reifman, Jaques},
year = {2023},
journal = {Sleep 2023},
doi = {10.1093/sleep/zsad080}
}Added to the ChatPPG research hub
