Citation

Rodrigues TE, Fischer FM, Helene O, Antunes E, Furlan E, Morteo E, Menquini A, Lisboa J, Frank A, Simões A, Papazian K, Helene AF. Safety Sci. 2023; 157: e105905.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.ssci.2022.105905

PMID

unavailable

Abstract

This work evaluates the potential root causes of fatigue and its relationships with accident risks using a bio-mathematical model approach and a robust sample (N = 8476) of aircrew rosters from the Brazilian regular aviation, extracted from the Fadigômetro database. The fatigue outcomes derive from the software Sleep, Activity, Fatigue, and Task Effectiveness Fatigue Avoidance Scheduling Tool (SAFTE-FAST), which considers the homeostatic process, circadian rhythms and the sleep inertia. The analyses include data from January 2019 until March 2020 and show relevant group effects comparing early 2019 and 2020, with the latter presenting lower fatigue outcomes in most cases. The average minimum SAFTE-FAST effectiveness during critical phases of flight (departures and arrivals) decreases cubically with the number of shifts that elapse totally or partially between mid-night and 6 a.m. within a 30-day period (NNS). As a consequence, the relative fatigue risk increases by 23.3% (95% CI, 20.4-26.2%) when increasing NNS from 1 to 13. The average maximum equivalent wakefulness in critical phases also increases cubically with the number of night shifts and exceeds 24 h for rosters with NNS above 10. The average fatigue hazard area in critical phases of flight varies quadratically with the number of departures and arrivals within 2 and 6 a.m. (NWocl). These findings demonstrate that both NNS and NWocl represent potential root causes of fatigue and therefore should be considered key performance indicators and kept as low as reasonably practical when building aircrew rosters, in order to properly manage the fatigue risk. The effectiveness scores obtained at 30-minute time intervals allowed a model estimate for the relative fatigue risk as a function of the time of the day, whose averaged values show reasonable qualitative agreement with previous measurements of pilot errors in the cockpit. Moreover, the 2019 data revealed a risk exposure factor two times (14%) higher than the figures reported by de Mello et al. (2008) (7%), within 0h00 do 05h59. Tailored analyses of the SAFTE-FAST inputs for afternoon naps before night shifts, commuting from home to station and vice-versa, and bedtime before early-start shifts were carried out using the responses of a questionnaire. The average fatigue hazard area in critical phases of flight increases by 43 to 63% switching off the afternoon naps, 14 to 21% increasing the commuting from one to two hours and 35 to 54% switching off the advanced bedtime criterion, with significant group effects in all cases (p<0.001), evidencing the need of a better and more accurate understanding of these parameters when modelling fatigue risk factors. The non-linear relationships between SAFTE-FAST outcomes and key roster's parameters, such as NNS and NWocl, provided a novel statistical approach for risk assessment and led to few safety recommendations to the aviation sector regarding regulatory reviews and fatigue risk management and mitigation policies.

Language: en

Keywords

Aviation; Fatigue; Fatigue risk assessment; Fatigue risk management; Modelling; Rostering