2B-Alert Web (This version has an updated graphical user interface and the ability to optimize caffeine.)
Predict the effects of sleep/wake and caffeine on alertness
This software tool predicts the alertness of an "average" individual as a function of sleep/wake schedule, caffeine consumption, and time of day. Optionally, it also provides optimal caffeine schedules for user-provided periods of desired peak alertness. Specifically, it allows users to manually enter a sleep/wake/peak alertness schedule, as well as caffeine dosing and timing, and displays the corresponding predictions for three different statistics of alertness on the psychomotor vigilance task (PVT). If the user provides the desired period of peak alertness, the system will output the estimated optimal caffeine schedules. The tool predicts alertness for the duration of the given schedule and 48 hours of subsequent total sleep deprivation.

This tool can be used to:
  1. Assess the effect of different sleep/wake schedules and caffeine consumption
  2. Design sleep/wake and caffeine schedules to optimize alertness
  3. Generate hypotheses that can be experimentally tested
  4. Optimize the benefits of caffeine use
Disclaimer: The 2B-Alert Web tool is for educational and informational purposes only. It should not be used or relied upon to predict the performance of any specific individual or the likelihood of errors or accidents by any specific individual or group of individuals.

Key References:
  1. Rajdev, P., D. Thorsley, S. Rajaraman, T. L. Rupp, N. J. Wesensten, T. J. Balkin, and J. Reifman. A unified mathematical model to quantify performance impairment for both chronic sleep restriction and total sleep deprivation. Journal of Theoretical Biology. 2013 April 24; 331:66-77. (PubMed ID: 23623949)
  2. Ramakrishnan, S., S. Laxminarayan, N. J. Wesensten, G. H. Kamimori, T. J. Balkin, and J. Reifman. Dose-dependent model of caffeine effects on human vigilance during total sleep deprivation. Journal of Theoretical Biology. 2014 October 7; 358:11-24. (PubMed ID: 24859426)
  3. Ramakrishnan, S., N. J. Wesensten, T. J. Balkin, and J. Reifman. A unified model of performance: validation of its predictions across different sleep/wake schedules. Sleep. 2016 January 1; 39(1):249-262. (PubMed ID: 26518594)
  4. Ramakrishnan, S., N. J. Wesensten, G. H. Kamimori, J. E. Moon, T. J. Balkin, and J. Reifman. A unified model of performance for predicting the effects of sleep and caffeine. Sleep. 2016 October 1; 39(10):1827-1841. (PubMed ID: 27397562)
  5. Reifman, J., K. Kumar, N. J. Wesensten, N. A. Tountas, T. J. Balkin, and S. Ramakrishnan. 2B-Alert Web: An open-access tool for predicting the effects of sleep/wake schedules and caffeine consumption on neurobehavioral performance. Sleep. 2016 December 1; 39(12):2157-2159. (PubMed ID: 27634801)
  6. Vital-Lopez, F. G., S. Ramakrishnan, T. J. Doty, T. J. Balkin, and J. Reifman. Caffeine dosing strategies to optimize alertness during sleep loss. Journal of Sleep Research. 2018 May 28; e12711. (PubMed ID: 29808510)
  7. Reifman, J., S. Ramakrishnan, J. Liu, A. Kapela, T. J. Doty, T. J. Balkin, K. Kumar, and M. Y. Khitrov. 2B-Alert App: A mobile application for real-time individualized prediction of alertness. Journal of Sleep Research. 2018 July 23:e12725. (PubMed ID: 30033688)
Version 2.0