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

McCauley, Mark; McCauley, Peter; Riedy, Samantha M.; Banks, Siobhan; Ecker, Adrian J.; Kalachev, Leonid; Rangan, Suresh; Dinges, David F.; Dongen, Hans P. A. Van · 2021 · Transportation Research Part F Traffic Psychology and Behaviour

DOI: 10.1016/j.trf.2021.04.006

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Summary

This paper addresses a critical gap in fatigue risk management systems, particularly in commercial aviation, where subjective self-ratings of sleepiness (e.g., the Karolinska Sleepiness Scale, KSS) are frequently used for operational decisions, yet existing biomathematical models primarily predict objective performance deficits (e.g., the Psychomotor Vigilance Test, PVT). The authors argue that the temporal dynamics of subjective sleepiness differ from objective impairment, creating a potential "risk evaluation sensitivity gap" where self-reported fatigue may underestimate actual cognitive decline. The study aims to expand a state-of-the-art biomathematical model of fatigue to simultaneously predict both objective PVT performance and subjective KSS sleepiness, thereby providing a quantitative tool to bridge this gap. The methodology involved expanding a previously developed model based on coupled first-order ordinary differential equations that capture homeostatic and circadian processes. The authors re-estimated four metric-specific parameters—homeostatic build-up and dissipation rates ($\alpha_w, \alpha_s$), circadian amplitude supremum ($\xi$), and the bifurcation threshold ($W_c$)—while keeping other neurobiological parameters fixed. Calibration was performed using Markov chain Monte Carlo fitting on three laboratory datasets (A1–A3) involving total sleep deprivation, sustained sleep restriction, simulated night shifts, and napping. Validation was conducted on three separate datasets (B1–B3) covering total sleep deprivation with recovery, extended wakefulness with napping, and dose-response recovery sleep. The model was then applied to a simulated cargo aviation scenario involving early morning out-and-back duty schedules. Results indicated that the expanded model achieved high prediction accuracy for subjective sleepiness, explaining 40.4% of the variance in KSS data with a root-mean-square error of 1.01, while retaining high accuracy for PVT predictions. Crucially, the application to the cargo aviation scenario revealed a significant divergence between the two metrics: subjective sleepiness substantially underestimated the accumulating objective performance impairment. This suggests that pilots relying solely on self-reported sleepiness may not perceive the extent of their cognitive deficits. The study concludes that the expanded model provides a versatile tool for fatigue risk management, highlighting that systematic differences in the dynamics of subjective versus objective measures necessitate careful interpretation in safety-sensitive operations to avoid underestimating fatigue-related risks.

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Topics

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• drowsiness detection algorithms
• truck driver fatigue
• drowsiness
• sleep deprivation