Abstract

Performance prediction models based on the classical two-process model of sleep regulation are reasonably effective at predicting alertness and neurocognitive performance during total sleep deprivation (TSD). However, during sleep restriction (partial sleep loss) performance predictions based on such models have been found to be less accurate. Because most modern operational environments are predominantly characterized by chronic sleep restriction (CSR) rather than by episodic TSD, the practical utility of this class of models has been limited.To better quantify performance during both CSR and TSD, we developed a unified mathematical model that incorporates extant sleep debt as a function of a known sleep/wake history, with recent history exerting greater influence. This incorporation of sleep/wake history into the classical two-process model captures an individual's capacity to recover during sleep as a function of sleep debt and naturally bridges the continuum from CSR to TSD by reducing to the classical two-process model in the case of TSD. We validated the proposed unified model using psychomotor vigilance task data from three prior studies involving TSD, CSR, and sleep extension. We compared and contrasted the fits, within-study predictions, and across-study predictions from the unified model against predictions generated by two previously published models, and found that the unified model more accurately represented multiple experimental studies and consistently predicted sleep restriction scenarios better than the existing models. In addition, we found that the model parameters obtained by fitting TSD data could be used to predict performance in other sleep restriction scenarios for the same study populations, and vice versa. Furthermore, this model better accounted for the relatively slow recovery process that is known to characterize CSR, as well as the enhanced performance that has been shown to result from sleep banking.

Highlights

  • Sleepiness increases the risk of human error and accidents

  • A similar notion of fading memory is used in the Fatigue Audit InterDyne (FAID) model developed by Dawson and Fletcher (2001); the fading-memory filter we propose in this paper goes beyond the FAID model in that it incorporates the possible beneficial effects of sleep banking (Rupp et al, 2009)

  • We present a unified model that incorporates sleep debt to quantify performance impairment during both chronic sleep restriction (CSR) and total sleep deprivation (TSD)

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Summary

Introduction

Sleepiness increases the risk of human error and accidents. It affects the health, safety, and quality of life of military and civilian personnel who are regularly exposed to work schedules that preclude adequate daily sleep duration and timing (Mallis et al, 2004). Critical to effective management of operational alertness and performance is the ability to accurately predict the impact of various work/rest schedules on individual operators. Biomathematical modeling provides the most promising strategy for addressing the problem of helping manage alertness and neurocognitive performance in operational environments (Friedl et al, 2004), thereby enhancing the safety and productivity of both military and civilian operators

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