Accession Number:

ADA582727

Title:

A Unified Mathematical Model to Quantify Performance Impairment for Both Chronic Sleep Restriction and Total Sleep Deprivation

Descriptive Note:

Journal article

Corporate Author:

BIOTECHNOLOGY HIGH PERFORMANCE COMPUTING SOFTWARE APPLICATIONS INST FREDERICK MD

Report Date:

2013-04-24

Pagination or Media Count:

14.0

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 201TSD202. However,during sleep restriction201partial sleep loss202performance predictions based on such models have been found to be less accurate. Because most modern operational environments are predominantly characterized by chronic sleep restriction201CSR202rather 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 sleepwake history, with recent history exerting greater influence. This incorporation of sleepwake history into the classical two-process model captures an individuals 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.

Subject Categories:

  • Stress Physiology
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE