Evaluating the root causes of fatigue and associated risk factors in the Brazilian regular aviation industry
This work evaluates the potential root causes of fatigue using a biomathematical model and a robust sample of aircrew rosters from the Brazilian regular aviation. The fatigue outcomes derive from the software Sleep, Activity, Fatigue, and Task Effectiveness Fatigue Avoidance Scheduling Tool (SAFTE-F...
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Veröffentlicht in: | arXiv.org 2022-02 |
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Sprache: | eng |
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Zusammenfassung: | This work evaluates the potential root causes of fatigue using a biomathematical model and a robust sample of aircrew rosters from the Brazilian regular aviation. The fatigue outcomes derive from the software Sleep, Activity, Fatigue, and Task Effectiveness Fatigue Avoidance Scheduling Tool (SAFTE-FAST). The average minimum SAFTE-FAST effectiveness during critical phases of flight decreases cubically with the number of shifts that elapse totally or partially between mid-night and 6 a.m. within a 30-day period (\(N_{NS}\)). As a consequence, the relative fatigue risk increases by 23.3% (95% CI, 20.4-26.2%) when increasing \(N_{NS}\) from 1 to 13. The average maximum equivalent wakefulness in critical phases also increases cubically with the number of night shifts and exceeds 24 hours for rosters with \(N_{NS}\) above 10. The average fatigue hazard area in critical phases of flight varies quadratically with the number of departures and landings within 2 and 6 a.m. (\(N_{Wocl}\)). These findings demonstrate that both \(N_{NS}\) and \( N_{Wocl}\) should be considered as key performance indicators and be kept as low as reasonably practical when building aircrew rosters. The effectiveness scores 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. 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 show relevant group effects (\(p < 0.001\)) comparing the groups with and without afternoon naps, with one or two hours of commuting and with or without the advanced bedtime feature of the SAFTE-FAST software, evidencing the need of a better and more accurate understanding of these parameters when modelling fatigue risk factors. |
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ISSN: | 2331-8422 |