A Unified Model of Performance: Validation of its Predictions across Different Sleep/Wake Schedules

Abstract

Historically, mathematical models of human neurobehavioral performance developed on data from one sleep study were limited to predicting performance in similar studies, restricting their practical utility. We recently developed a unified model of performance (UMP) to predict the effects of the continuum of sleep loss—from chronic sleep restriction (CSR) to total sleep deprivation (TSD) challenges—and validated it using data from two studies of one laboratory. Here, we significantly extended this effort by validating the UMP predictions across a wide range of sleep/wake schedules from different studies and laboratories. We developed the UMP on psychomotor vigilance task (PVT) lapse data from one study encompassing four different CSR conditions (7 d of 3, 5, 7, and 9 h of sleep/night), and predicted performance in five other studies (from four laboratories), including different combinations of TSD (40 to 88 h), CSR (2 to 6 h of sleep/night), control (8 to 10 h of sleep/night), and nap (nocturnal and diurnal) schedules. The UMP accurately predicted PVT performance trends across 14 different sleep/wake conditions, yielding average prediction errors between 7% and 36%, with the predictions lying within 2 standard errors of the measured data 87% of the time. In addition, the UMP accurately predicted performance impairment (average error of 15%) for schedules (TSD and naps) not used in model development. The unified model of performance can be used as a tool to help design sleep/wake schedules to optimize the extent and duration of neurobehavioral performance and to accelerate recovery after sleep loss. We showed that, given the sleep/wake schedule of a group of individuals, one mathematical model can accurately predict neurobehavioral performance across a whole host of sleep-loss conditions, including conditions considerably different from those used in model development. In particular, the model accurately predicted the effects of sleep loss (total and partial) and countermeasure strategies, such as extended sleep and short daytime sleep episodes, with errors in model predictions no greater than those observed in the experimental performance data. Such a validated model can be used to generate hypotheses that can be experimentally tested, and to design optimal sleep/wake schedules for maintaining performance at desired levels at specified times of the day or for accelerating recovery following sleep loss.

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