Paperdoi:10.5665/sleep.6164
A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine
Sleep 2016;39(10):1827–1841
TL;DR
Integrates a dose-dependent caffeine factor (multiplicative on performance) into the Unified Model of Performance (UMP). Assessed on 14 lab/field conditions: 7 sleep-loss schedules (5 h/night to 85 h continuous wake) and 6 caffeine doses (placebo to 600 mg). Group-average PVT predictions had 6%–27% error; adding caffeine improved post-caffeine predictions by up to 70%.
- Form factor: software (sleep/schedule input; wrist actigraphy optional)
Abstract
Study objectivesExisting mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies.MethodsWe hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg).ResultsThe UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% ConclusionsThe UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance.
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Cite this paper
Ramakrishnan, S., Wesensten, N. J., Kamimori, G. H., Moon, J. E., Balkin, T. J., & Reifman, J. (2016). A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine. Sleep 2016, 39(10), 1827–1841. https://doi.org/10.5665/sleep.6164
Ramakrishnan, Sridhar, Nancy J. Wesensten, Gary H. Kamimori, James E. Moon, Thomas J. Balkin, and Jaques Reifman. 2016. "A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine." Sleep 2016 39 (10): 1827–1841. https://doi.org/10.5665/sleep.6164.
Ramakrishnan, S., Wesensten, N. J., Kamimori, G. H., Moon, J. E., Balkin, T. J. and Reifman, J. (2016) 'A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine', Sleep 2016, 39(10), pp. 1827–1841. doi: 10.5665/sleep.6164.
Ramakrishnan, Sridhar, et al. "A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine." Sleep 2016, vol. 39, no. 10, 2016, pp. 1827–1841. https://doi.org/10.5665/sleep.6164.
S. Ramakrishnan, 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, vol. 39, no. 10, pp. 1827–1841, 2016, doi: 10.5665/sleep.6164.
@article{ramakrishnan2016unified,
title = {{A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine}},
author = {Ramakrishnan, Sridhar and Wesensten, Nancy J. and Kamimori, Gary H. and Moon, James E. and Balkin, Thomas J. and Reifman, Jaques},
year = {2016},
journal = {Sleep 2016},
volume = {39},
number = {10},
pages = {1827--1841},
doi = {10.5665/sleep.6164}
}Added to the ChatPPG research hub
