首页> 美国政府科技报告 >Flight Attendant Fatigue Recommendation 2: Flight Attendant Work/Rest Patterns, Alertness, and Performance Assessment
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Flight Attendant Fatigue Recommendation 2: Flight Attendant Work/Rest Patterns, Alertness, and Performance Assessment

机译:空乘人员疲劳建议2:空乘人员工作/休息模式,警觉性和绩效评估

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Impaired performance induced by fatigue may compromise safety in commercial aviation. Given the direct role flight attendants play in passenger safety, the U.S. Congress ordered a comprehensive examination of fatigue in cabin crew, including a field study of actual flight operations. This report provides an overview of the field study results, focusing on objective measures of sleep patterns and neurocognitive performance (Psychomotor Vigilance Test, PVT) over a 3-4 week period in 202 U.S.-based flight attendants of all seniority levels working for network, low-cost, and regional carriers embarking on domestic and international flight operations. On average, flight attendants slept 6.3 hr on days off and 5.7 hr on work days, fell asleep 29 min after going to bed, awoke four times per sleep episode, and spent 77% of each episode actually sleeping. After controlling for reserve status, gender, and age, junior-level flight attendants had the shortest sleep latencies on their days off. Those working international operations slept significantly less (4.9 hr vs. 5.9 hr) and less efficiently (75% vs. 79%), compared with their colleagues in domestic operations. All flight attendants exhibited significant impairments during pre-work PVT tests when compared to their own optimum baseline performance. Across the workday, regional flight attendants committed fewer premature PVT responses, junior-level participants produced significantly higher post-work reaction times, and those working international flights produced better pre-work reaction times but had a greater increase in lapses. These objective data are consistent with other shift work research and echo subjective survey findings across the U.S. flight attendant community. Additional planned analyses of this dataset may identify the precise operational variables that contribute to fatigue in cabin crew.

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