Fatigue risk management based on self-reported fatigue: Expanding a biomathematical model of fatigue-related performance deficits to also predict subjective sleepiness |
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| Affiliation: | 1. Sleep and Performance Research Center, Washington State University Health Sciences Spokane, USA;2. Elson S. Floyd College of Medicine, Washington State University Health Sciences Spokane, USA;3. Unit for Experimental Psychiatry, Division of Sleep and Chronobiology, University of Pennsylvania Perelman School of Medicine, USA;4. Behaviour-Brain-Body Research Centre, University of South Australia, Australia;5. Department of Mathematical Sciences, University of Montana, USA;6. Federal Express Corporation, USA;1. Institutes for Behavior Resources, Baltimore, MD, USA;2. Johns Hopkins University School of Medicine, Baltimore, MD, USA;3. MedStar Georgetown University Hospital, Washington, DC, USA;4. Department of Emergency Medicine, Georgetown University School of Medicine, Washington, DC, USA;5. MedStar Institute for Innovation, Washington, DC, USA;6. Department of Surgery, MedStar Georgetown University Hospital, Washington, DC, USA;1. Department of Industrial Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China;2. Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing 102616, China;3. Department of Industrial Engineering, Tsinghua University, Beijing 100084, China;1. Pulsar Informatics, Inc., United States;2. Sleep and Performance Research Center, Washington State University, United States;1. Institutes for Behavior Resources, Inc., Baltimore, MD 21218, United States;2. Southeastern Oklahoma State University, Department of Occupational Safety and Health, Durant, OK 74701, United States;3. Johns Hopkins University School of Medicine, Psychiatry and Behavioral Sciences, Baltimore, MD, 21218, United States;1. College of Nursing, The University of Tennessee Knoxville, TN, USA;2. School of Nursing, University of Maryland Baltimore, MD, USA;3. Department of Pediatrics, George Washington University, Washington, DC, USA;4. Department of Nursing Research and Quality Outcomes, Children''s National Medical Center, Washington, DC, USA;5. Children''s National Medical Center, Washington, DC, USA;6. School of Nursing, George Washington University, Washington, DC, USA |
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| Abstract: | Biomathematical models of fatigue can be used to predict neurobehavioral deficits during sleep/wake or work/rest schedules. Current models make predictions for objective performance deficits and/or subjective sleepiness, but known differences in the temporal dynamics of objective versus subjective outcomes have not been addressed. We expanded a biomathematical model of fatigue previously developed to predict objective performance deficits as measured on the Psychomotor Vigilance Test (PVT) to also predict subjective sleepiness as self-reported on the Karolinska Sleepiness Scale (KSS). Four model parameters were re-estimated to capture the distinct dynamics of the KSS and account for the scale difference between KSS and PVT. Two separate ensembles of datasets – drawn from laboratory studies of sleep deprivation, sleep restriction, simulated night work, napping, and recovery sleep – were used for calibration and subsequent validation of the model for subjective sleepiness. The expanded model was found to exhibit high prediction accuracy for subjective sleepiness, while retaining high prediction accuracy for objective performance deficits. Application of the validated model to an example scenario based on cargo aviation operations revealed divergence between predictions for objective and subjective outcomes, with subjective sleepiness substantially underestimating accumulating objective impairment, which has important real-world implications. In safety-sensitive operations such as commercial aviation, where self-ratings of sleepiness are used as part of fatigue risk management, the systematic differences in the temporal dynamics of objective versus subjective measures of functional impairment point to a potentially significant risk evaluation sensitivity gap. The expanded biomathematical model of fatigue presented here provides a useful quantitative tool to bridge this previously unrecognized gap. |
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| Keywords: | Alertness Fatigue and performance models Fatigue risk management Karolinska Sleepiness Scale Psychomotor Vigilance Test Self-rated sleepiness |
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