Study Objectives: Existing 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. Methods: We 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). Results: The UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% < error < 27%), yielding greater accuracy for mild and moderate sleep loss conditions than for more severe cases. Overall, accounting for the effects of caffeine resulted in improved predictions (after caffeine consumption) by up to 70%.
Conclusions:The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance. Keywords: biomathematical model, caffeine model, chronic sleep restriction, PVT, total sleep deprivation Citation: Ramakrishnan S, Wesensten NJ, Kamimori GH, Moon JE, Balkin TJ, Reifman J. A unified model of performance for predicting the effects of sleep and caffeine. SLEEP 2016;39(10):1827-1841.
INTRODUCTIONMathematical models that accurately predict the effects of sleep/wake schedules on human neurobehavioral performance are valuable tools for effective management of operational alertness and fatigue. However, to be of practical use, they must also be able to predict the alertness-and performance-enhancing effects of caffeine, the most widely used stimulant compound. Caffeine is available in a wide range of concentrations in myriad foods and beverages, including coffee, tea, soft drinks, and energy drinks, providing a spectrum of performance-improving effects. Nevertheless, the most thoroughly validated predictive models of performance do not account for the effects of caffeine, 1,2 and the few that do have limitations. For example, the models proposed by Benitez et al. 3 and by Ramakrishnan et al.