title: A Unified Model of Performance: Validation of its Predictions across Different Sleep/Wake Schedules.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD.
authors.forename: Sridhar
authors.initials: S
authors.lastname: Ramakrishnan
authors.affiliation: Department of Behavioral Biology, Walter Reed Army Institute of Research, Silver Spring, MD.
authors.forename: Nancy J
authors.initials: NJ
authors.lastname: Wesensten
authors.affiliation: Department of Behavioral Biology, Walter Reed Army Institute of Research, Silver Spring, MD.
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Balkin
authors.affiliation: Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD.
authors.forename: Jaques
authors.initials: J
authors.lastname: Reifman
mesh_terms: D000293:Adolescent; D000328:Adult; D001288:Attention / Q000502:physiology; D002940:Circadian Rhythm / Q000502:physiology*; D006801:Humans; D008875:Middle Aged; D008959:Models, Neurological; D008960:Models, Psychological; D017286:Polysomnography; D011597:Psychomotor Performance; D015203:Reproducibility of Results; D012890:Sleep / Q000502:physiology*; D012892:Sleep Deprivation / Q000503:physiopathology* / Q000523:psychology; D007319:Sleep Initiation and Maintenance Disorders / Q000503:physiopathology* / Q000523:psychology; D013997:Time Factors; D014851:Wakefulness / Q000502:physiology*; D055815:Young Adult
keywords: PVT; biomathematical model; chronic sleep restriction; naps; total sleep deprivation; two-process model