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).

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.

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.