Fatigue risk management based on self-reported fatigue: Expanding a biomathematical model of fatigue-related performance deficits to also predict subjective sleepiness.

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.

Predictive and proactive fatigue risk management approaches in commercial aviation.

In commercial aviation, sharing best practices of fatigue risk management (FRM) is important for the industry, its employees, and the community. Chronobiologists and sleep scientists have elucidated the impact of the biological clock and sleep/wake schedules on fatigue and captured their contributions in biomathematical models. The application of these models and other aspects of FRM requires expertise to which not all operators have access. We, therefore, describe some predictive and proactive approaches to FRM, including a collaborative process for evaluating and revising duty schedules to reduce fatigue risk and an innovative wake-up call program to better utilize planned napping opportunities.

Quantifying fatigue risk in model-based fatigue risk management.

The question of what is a maximally acceptable level of fatigue risk is hotly debated in model-based fatigue risk management in commercial aviation and other transportation modes. A quantitative approach to addressing this issue, referred to by the Federal Aviation Administration with regard to its final rule for commercial aviation "Flightcrew Member Duty and Rest Requirements," is to compare predictions from a mathematical fatigue model against a fatigue threshold. While this accounts for duty time spent at elevated fatigue risk, it does not account for the degree of fatigue risk and may, therefore, result in misleading schedule assessments. We propose an alternative approach based on the first-order approximation that fatigue risk is proportional to both the duty time spent below the fatigue threshold and the distance of the fatigue predictions to the threshold--that is, the area under the curve (AUC). The AUC approach is straightforward to implement for schedule assessments in commercial aviation and also provides a useful fatigue metric for evaluating thousands of scheduling options in industrial schedule optimization tools.