Fatigue risk management for cabin crew: the importance of company support and sufficient rest for work-life balance-a qualitative study.

Knowledge about cabin crew fatigue associated with ultra-long range (ULR) flights is still limited. Current ULR scheduling for cabin crew is therefore predominantly based on flight crew data. Cabin crews' views on fatigue, and their strategies for mitigating it, have seldom been sought. To better understand the causes and consequences of cabin crew fatigue, semi-structured focus group discussions were held. Thematic analysis was undertaken with data from 25 cabin crew. Participants indicated that the consequences of fatigue are twofold, affecting 1) cabin crew health and wellbeing and 2) safety (cabin, passenger and personal) and cabin service. While the primary causes of fatigue were sleep loss and circadian disruption, participants also identified other key factors including: insufficient rest, high workload, the work environment, a lack of company support, and insufficient fatigue management training. They highlighted the importance of sufficient rest, not only for obtaining adequate recovery sleep but also for achieving a work-life balance. They also highlighted the need for company support, effective communication, and management's engagement with cabin crew in general. We recommend that priority is given to fatigue management training for cabin crew, which may also enhance perceived company support and assist with achieving a better work-life balance.

Personal and Work Factors That Predict Fatigue-Related Errors in Aircraft Maintenance Engineering.

BACKGROUND: The study aimed to identify factors associated with an increased likelihood of aircraft maintenance personnel reporting a fatigue-related error.METHODS: There were 966 maintenance engineering personnel (mean age = 42 yr, 98% male) who completed a survey with items on personal factors, work factors and a question asking whether during the last month they had made an error in their work due to tiredness. Logistic regression analyses were used to determine factors independently associated with making an error at work due to tiredness.RESULTS: Respondents obtained on average 7.0 h sleep and nearly half (45%) reported that they had felt close to falling asleep while driving home from work in the past 12 mo. Most respondents (70%) had received no education on strategies for coping with shift work. Among respondents, 22% agreed/strongly agreed with the statement "During the last month, I have made an error in my work due to tiredness." Unexpected roster changes independently predicted the likelihood of reporting an error in work due to tiredness and for certain groups of aircraft maintenance personnel, < 6.5 h sleep increased the odds of an error in work due to tiredness fivefold, whereas > 7.5 h sleep almost halved the odds of reporting such an error.DISCUSSION: These findings indicate the importance of stable and predictable work patterns to minimize the risk of fatigue-related errors in this safety critical environment, and also the need for education on coping with shift work to ensure the workforce are best placed to manage their sleep away from work.Signal TL, van den Berg MJ, Mulrine HM. Personal and work factors that predict fatigue-related errors in aircraft maintenance engineering. Aerosp Med Hum Perform. 2019; 90(10):860-866.

Subjective Measurements of In-Flight Sleep, Circadian Variation, and Their Relationship with Fatigue.

BACKGROUND: This study examined whether subjective measurements of in-flight sleep could be a reliable alternative to actigraphic measurements for monitoring pilot fatigue in a large-scale survey. METHODS: Pilots (3-pilot crews) completed a 1-page survey on outbound and inbound long-haul flights crossing 1-7 time zones (N = 586 surveys) between 53 city pairs with 1-d layovers. Across each flight, pilots documented flight start and end times, break times, and in-flight sleep duration and quality if they attempted sleep. They also rated their fatigue (Samn-Perelli Crew Status Check) and sleepiness (Karolinska Sleepiness Scale) at top of descent (TOD). Mixed model ANCOVA was used to identify independent factors associated with sleep duration, quality, and TOD measures. Domicile time was used as a surrogate measure of circadian phase. RESULTS: Sleep duration increased by 10.2 min for every 1-h increase in flight duration. Sleep duration and quality varied by break start time, with significantly more sleep obtained during breaks starting between (domicile) 22:00-01:59 and 02:00-05:59 compared to earlier breaks. Pilots were more fatigued and sleepy at TOD on flights arriving between 02:00-05:59 and 06:00-09:59 domicile time compared to other flights. With every 1-h increase in sleep duration, sleepiness ratings at TOD decreased by 0.6 points and fatigue ratings decreased by 0.4 points. DISCUSSION: The present findings are consistent with previous actigraphic studies, suggesting that self-reported sleep duration is a reliable alternative to actigraphic sleep in this type of study, with use of validated measures, sufficiently large sample sizes, and where fatigue risk is expected to be low. van den Berg MJ, Wu LJ, Gander PH. Subjective measurements of in-flight sleep, circadian variation, and their relationship with fatigue. Aerosp Med Hum Perform. 2016; 87(10):869-875.

Does the circadian clock drift when pilots fly multiple transpacific flights with 1- to 2-day layovers?

On trips with multiple transmeridian flights, pilots experience successive non-24 h day/night cycles with circadian and sleep disruption. One study across a 9-day sequence of transpacific flights (no in-flight sleep, 1-day layovers between flights) reported an average period in the core body temperature rhythm of 24.6 h (circadian drift). Consequently, pilots were sometimes flying through the circadian performance nadir and had to readapt to home base time at the end of the trip. The present study examined circadian drift in trip patterns with longer flights and in-flight sleep. Thirty-nine B747-400 pilots (19 captains, 20 first officers, mean age = 55.5 years) were monitored on 9- to 13-day trips with multiple return flights between East Coast USA and Japan (in 4-pilot crews) and between Japan and Hawaii (in 3-pilot crews), with 1-day layovers between each flight. Measures included total in-flight sleep (actigraphy, log books) and top of descent (TOD) measures of sleepiness (Karolinska Sleepiness Scale), fatigue (Samn-Perelli Crew Status Check) and psychomotor vigilance task (PVT) performance. Circadian rhythms of individual pilots were not monitored. To detect circadian drift, mixed-model analysis of variance examined whether for a given flight, total in-flight sleep and TOD measures varied according to when the flight occurred in the trip sequence. In addition, sleep propensity curves for pre-trip and post-trip days were examined (Chi-square periodogram analyses). Limited data suggest that total in-flight sleep of relief crew at landing may have decreased across successive East Coast USA-Japan (flights 1, 3, 5 or 7; median arrival 03:45 Eastern Daylight Time (EDT)). However, PVT response speed at TOD was faster on East Coast USA-Japan flights later in the trip. On these flights, circadian drift would result in flights later in the trip landing closer to the evening wake maintenance zone, when sleep is difficult and PVT response speeds are fastest. On Japan-East Coast USA flights (flights 2, 4, 6 or 8; median arrival time 14:52 EDT), PVT response speeds were slower on flight 8 than on flight 2. Circadian drift would move these arrivals progressively earlier in the SCN pacemaker cycle, where PVT response speeds are slower. Across the five post-trip days, 12 pilots (Group A) immediately resumed their pre-trip sleep pattern of a single nocturnal sleep episode; 9 pilots (Group B) had a daytime nap on most days that moved progressively earlier until it merged with nocturnal sleep and 17 pilots (Group C) had nocturnal sleep and intermittent naps. Chi-square periodogram analyses of the sleep propensity curves for each group across baseline and post-trip days suggest full adaptation to EDT from post-trip day 1 (dominant period = 24 h). However, in Groups B and C, the patterns of split sleep post-trip compared to pre-trip suggest that this may be misleading. We conclude that the trends in total in-flight sleep and significant changes in PVT performance speed at TOD provide preliminary evidence for circadian drift, as do persistent patterns of split sleep post-trip. However, new measures to track circadian rhythms in individual pilots are needed to confirm these findings.

Estimating long-haul airline pilots' at-home baseline sleep duration.

OBJECTIVE: Characterize the baseline sleep of long-haul airline pilots. METHODS: Sleep of 332 pilots (median age = 51 years, range = 23-64 years) from 4 airlines was measured by actigraphy while at home and off-duty and by retrospective estimate of the total amount of nighttime sleep usually obtained at home. RESULTS: Mean actigraphic sleep per 24 hours during baseline periods was 6.8 hours (SD = 1.0 hour), 52 minutes shorter than mean self-reported usual nighttime sleep (7.6 hours, SD = 1.1 hours). CONCLUSIONS: Pilots' self-reported sleep duration was comparable to weekend sleep of men in general population samples, but their actigraphic baseline sleep was longer than objectively monitored sleep of other samples. Long-haul pilots routinely experience sleep restriction and circadian disruption across trips, both of which are implicated in increased health risks. We recommend that they be educated about the long-term importance for health of obtaining adequate sleep on off-duty days.

Monitoring and Managing Cabin Crew Sleep and Fatigue During an Ultra-Long Range Trip.

BACKGROUND: The aims of this study were to monitor cabin crew fatigue, sleep, and performance on an ultra-long range (ULR) trip and to evaluate the appropriateness of applying data collection methods developed for flight crew to cabin crew operations under a fatigue risk management system (FRMS). METHODS: Prior to, throughout, and following the ULR trip (outbound flight ULR; mean layover duration=52.6 h; inbound flight long range), 55 cabin crew (29 women; mean age 36.5 yr; 25 men; mean age 36.6 yr; one missing data) completed a sleep/duty diary and wore an actigraph. Across each flight, crewmembers rated their fatigue (Samn-Perelli Crew Status Check) and sleepiness (Karolinska Sleepiness Scale) and completed a 5-min Psychomotor Vigilance Task (PVT) at key times. RESULTS: Of crewmembers approached, 73% (N=134) agreed to participate and 41% (N=55) provided data of suitable quality for analysis. In the 24 h before departure, sleep averaged 7.0 h and 40% took a preflight nap. All crewmembers slept in flight (mean total sleep time=3.6 h outbound, 2.9 h inbound). Sleepiness and fatigue were lower, and performance better, on the longer outbound flight than on the inbound flight. Post-trip, crewmembers slept more on day 1 (mean=7.9 h) compared to baseline days, but there was no difference from day 2 onwards. DISCUSSION: The present study demonstrates that cabin crew fatigue can be managed effectively on a ULR flight and that FRMS data collection is feasible for cabin crew, but operational differences between cabin crew and flight crew need to be considered.

Effects of sleep/wake history and circadian phase on proposed pilot fatigue safety performance indicators.

The Karolinska Sleepiness Scale and Samn-Perelli fatigue ratings, and psychomotor vigilance task performance are proposed as measures for monitoring commercial pilot fatigue. In laboratory studies, they are sensitive to sleep/wake history and circadian phase. The present analyses examined whether they reliably reflect sleep/wake history and circadian phase during transmeridian flight operations. Data were combined from four studies (237 pilots, 730 out-and-back flights between 13 city pairs, 1-3-day layovers). Sleep was monitored (wrist actigraphy, logbooks) before, during and after trips. On duty days, sleepiness, fatigue and mean response speed were measured pre-flight and at the top of the descent. Mixed-model analysis of variance examined associations between these measures and sleep/wake history, after controlling for operational factors. Circadian phase was approximated by local (domicile) time in the city where each trip began and ended. More sleep in the 24 h prior to duty was associated with lower pre-flight sleepiness and fatigue and faster response speed. Sleepiness and fatigue were greater before flights departing during the domicile night and early morning. At the top of the descent, pilots felt less sleepy and fatigued after more in-flight sleep and less time awake. Flights arriving in the early-mid-morning (domicile time) had greater sleepiness and fatigue and slower response speeds than flights arriving later. Subjective ratings showed expected associations with sleep/wake history and circadian phase. The response speed showed expected circadian variation but was not associated with sleep/wake history at the top of the descent. This may reflect moderate levels of fatigue at this time and/or atypically fast responses among pilots.

Mitigating and monitoring flight crew fatigue on a westward ultra-long-range flight.

BACKGROUND: This study examined the uptake and effectiveness of fatigue mitigation guidance material including sleep recommendations for a trip with a westward ultra-long-range flight and return long-range flight. METHODS: There were 52 flight crew (4-pilot crews, mean age 55 yr) who completed a sleep/duty diary and wore an actigraph prior to, during, and after the trip. Primary crew flew the takeoff and landing, while relief crew flew the aircraft during the Primary crew's breaks. At key times in flight, crewmembers rated their fatigue (Samn-Perelli fatigue scale) and sleepiness (Karolinska Sleepiness Scale) and completed a 5-min Psychomotor Vigilance Task. RESULTS: Napping was common prior to the outbound flight (54%) and did not affect the quantity or quality of in-flight sleep (mean 4.3 h). Primary crew obtained a similar amount on the inbound flight (mean 4.0 h), but Secondary crew had less sleep (mean 2.9 h). Subjective fatigue and sleepiness increased and performance slowed across flights. Performance was faster on the outbound than inbound flight. On both flights, Primary crew were less fatigued and sleepy than Secondary crew, particularly at top of descent and after landing. Crewmembers slept more frequently and had more sleep in the first 24 h of the layover than the last, and had shifted their main sleep to the local night by the second night. DISCUSSION: The suggested sleep mitigations were employed by the majority of crewmembers. Fatigue levels were no worse on the outbound ultra-long-range flight than on the return long-range flight.

Pilot fatigue: relationships with departure and arrival times, flight duration, and direction.

INTRODUCTION: Flight timing is expected to influence pilot fatigue because it determines the part of the circadian body clock cycle that is traversed during a flight. However the effects of flight timing are not well-characterized because field studies typically focus on specific flights with a limited range of departure times and have small sample sizes. The present project combined data from four studies, including 13 long-range and ultra-long range out-and-back trips across a range of departure and arrival times (237 pilots in 4-person crews, 730 flight segments, 1-3 d layovers). METHODS: All studies had tripartite support and underwent independent ethical review. Sleep was monitored (actigraphy) from 3 d prior to ≥ 3 d post-trip. Preflight and at top of descent (TOD), pilots rated their sleepiness (Karolinska Sleepiness Scale) and fatigue (Samn-Perelli scale), and completed a psychomotor vigilance task (PVT) test. Mixed model ANOVA identified independent associations between fatigue measures and operational factors (domicile times of departure and arrival, flight duration and direction, landing versus relief crew). RESULTS: Preflight subjective fatigue and sleepiness were lowest for flights departing 14:00-17:59. Total in-flight sleep was longest on flights departing 18:00-01:59. At TOD, fatigue and sleepiness were higher and PVT response speeds were slower on flights arriving 06:00-09:59 than on flights arriving later. PVT response speed at TOD was also faster on longer flights. DISCUSSION: The findings indicate the influence of flight timing (interacting with the circadian body clock cycle), as well as flight duration, on in-flight sleep and fatigue measures at TOD.

Crew fatigue safety performance indicators for fatigue risk management systems.

INTRODUCTION: Implementation of Fatigue Risk Management Systems (FRMS) is gaining momentum; however, agreed safety performance indicators (SPIs) are lacking. This paper proposes an initial set of SPIs based on measures of crewmember sleep, performance, and subjective fatigue and sleepiness, together with methods for interpreting them. METHODS: Data were included from 133 landing crewmembers on 2 long-range and 3 ultra-long-range trips (4-person crews, 3 airlines, 220 flights). Studies had airline, labor, and regulatory support, and underwent independent ethical review. SPIs evaluated preflight and at top of descent (TOD) were: total sleep in the prior 24 h and time awake at duty start and at TOD (actigraphy); subjective sleepiness (Karolinska Sleepiness Scale) and fatigue (Samn-Perelli scale); and psychomotor vigilance task (PVT) performance. Kruskal-Wallis nonparametric ANOVA with post hoc tests was used to identify significant differences between flights for each SPI. RESULTS: Visual and preliminary quantitative comparisons of SPIs between flights were made using box plots and bar graphs. Statistical analyses identified significant differences between flights across a range of SPls. DISCUSSION: In an FRMS, crew fatigue SPIs are envisaged as a decision aid alongside operational SPIs, which need to reflect the relevant causes of fatigue in different operations. We advocate comparing multiple SPIs between flights rather than defining safe/unsafe thresholds on individual SPIs. More comprehensive data sets are needed to identify the operational and biological factors contributing to the differences between flights reported here. Global sharing of an agreed core set of SPIs would greatly facilitate implementation and improvement of FRMS.

In-flight sleep, pilot fatigue and Psychomotor Vigilance Task performance on ultra-long range versus long range flights.

This study evaluated whether pilot fatigue was greater on ultra-long range (ULR) trips (flights >16 h on 10% of trips in a 90-day period) than on long range (LR) trips. The within-subjects design controlled for crew complement, pattern of in-flight breaks, flight direction and departure time. Thirty male Captains (mean age = 54.5 years) and 40 male First officers (mean age = 48.0 years) were monitored on commercial passenger flights (Boeing 777 aircraft). Sleep was monitored (actigraphy, duty/sleep diaries) from 3 days before the first study trip to 3 days after the second study trip. Karolinska Sleepiness Scale, Samn-Perelli fatigue ratings and a 5-min Psychomotor Vigilance Task were completed before, during and after every flight. Total sleep in the 24 h before outbound flights and before inbound flights after 2-day layovers was comparable for ULR and LR flights. All pilots slept on all flights. For each additional hour of flight time, they obtained an estimated additional 12.3 min of sleep. Estimated mean total sleep was longer on ULR flights (3 h 53 min) than LR flights (3 h 15 min; P(F) = 0.0004). Sleepiness ratings were lower and mean reaction speed was faster at the end of ULR flights. Findings suggest that additional in-flight sleep mitigated fatigue effectively on longer flights. Further research is needed to clarify the contributions to fatigue of in-flight sleep versus time awake at top of descent. The study design was limited to eastward outbound flights with two Captains and two First Officers. Caution must be exercised when extrapolating to different operations.

In-flight sleep of flight crew during a 7-hour rest break: implications for research and flight safety.

STUDY OBJECTIVES: To assess the amount and quality of sleep that flight crew are able to obtain during flight, and identify factors that influence the sleep obtained. DESIGN: Flight crew operating flights between Everett, WA, USA and Asia had their sleep recorded polysomnographically for 1 night in a layover hotel and during a 7-h in-flight rest opportunity on flights averaging 15.7 h. SETTING: Layover hotel and in-flight crew rest facilities onboard the Boeing 777-200ER aircraft. PARTICIPANTS: Twenty-one male flight crew (11 Captains, mean age 48 yr and 10 First Officers, mean age 35 yr). INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: Sleep was recorded using actigraphy during the entire tour of duty, and polysomnographically in a layover hotel and during the flight. Mixed model analysis of covariance was used to determine the factors affecting in-flight sleep. In-flight sleep was less efficient (70% vs. 88%), with more nonrapid eye movement Stage 1/Stage 2 and more frequent awakenings per h (7.7/h vs. 4.6/h) than sleep in the layover hotel. In-flight sleep included very little slow wave sleep (median 0.5%). Less time was spent trying to sleep and less sleep was obtained when sleep opportunities occurred during the first half of the flight. Multivariate analyses suggest age is the most consistent factor affecting in-flight sleep duration and quality. CONCLUSIONS: This study confirms that even during long sleep opportunities, in-flight sleep is of poorer quality than sleep on the ground. With longer flight times, the quality and recuperative value of in-flight sleep is increasingly important for flight safety. Because the age limit for flight crew is being challenged, the consequences of age adversely affecting sleep quantity and quality need to be evaluated.

Post-sleep inertia performance benefits of longer naps in simulated nightwork and extended operations.

Operational settings involving shiftwork or extended operations require periods of prolonged wakefulness, which in conjunction with sleep loss and circadian factors, can have a negative impact on performance, alertness, and workplace safety. Napping has been shown to improve performance and alertness after periods of prolonged wakefulness and sleep loss. Longer naps may not only result in longer-lasting benefits but also increase the risk of sleep inertia immediately upon waking. The time course of performance after naps of differing durations is thus an important consideration in weighing the benefits and risks of napping in workplace settings. The objective of this study was to evaluate the effectiveness of nap opportunities of 20, 40, or 60 min for maintaining alertness and performance 1.5-6 h post-nap in simulated nightwork (P1) or extended operations (P2). Each protocol included 12 participants in a within-subjects design in a controlled laboratory environment. After a baseline 8 h time-in-bed, healthy young males (P1 mean age 25.1 yr; P2 mean age 23.2 yr) underwent either ≈ 20 h (P1) or ≈ 30 h (P2) of sleep deprivation on four separate occasions, followed by nap opportunities of 0, 20, 40, and 60 min. Sleep on the baseline night and during the naps was recorded polysomnographically. During the nap opportunities, sleep onset latency was short and sleep efficiency was high. A greater proportion of slow-wave sleep (SWS) was obtained in nap opportunities of 40 and 60 min compared with 20 min. Rapid eye movement (REM) sleep occurred infrequently. A subjective sleepiness rating (Karolinska Sleepiness Scale, KSS), 2-Back Working Memory Task (WMT), and Psychomotor Vigilance Task (PVT) were completed 1.5, 2, 2.5, 3, 4, 5, and 6 h post-nap. The slowest 10% of PVT responses were significantly faster after 40 and 60 min naps compared with a 20 min (P1) or no (P2) nap. There were significantly fewer PVT lapses after 40 and 60 min naps compared with no nap (P2), and after 60 min naps compared with 20 min naps (P1). Participants felt significantly less sleepy and made more correct responses and fewer omissions on the WMT after 60 min naps compared with no nap (P2). Subjective sleepiness and WMT performance were not related to the amount of nap-time spent in SWS. However, PVT response speed was significantly slower when time in SWS was <10 min compared with 20-29.9 min. In conclusion, in operationally relevant scenarios, nap opportunities of 40 and 60 min show more prolonged benefits 1.5-6 h post-nap, than a 20 min or no nap opportunity. Benefits were more apparent when the homeostatic pressure for sleep was high and post-nap performance testing occurred across the afternoon (P2). For sustained improvement in cognitive performance, naps of 40-60 min are recommended.

Duration of sleep inertia after napping during simulated night work and in extended operations.

Due to the mixed findings of previous studies, it is still difficult to provide guidance on how to best manage sleep inertia after waking from naps in operational settings. One of the few factors that can be manipulated is the duration of the nap opportunity. The aim of the present study was to investigate the magnitude and time course of sleep inertia after waking from short (20-, 40- or 60-min) naps during simulated night work and extended operations. In addition, the effect of sleep stage on awakening and duration of slow wave sleep (SWS) on sleep inertia was assessed. Two within-subject protocols were conducted in a controlled laboratory setting. Twenty-four healthy young men (Protocol 1: n = 12, mean age = 25.1 yrs; Protocol 2: n = 12, mean age = 23.2 yrs) were provided with nap opportunities of 20-, 40-, and 60-min (and a control condition of no nap) ending at 02:00 h after ∼20 h of wakefulness (Protocol 1 [P1]: simulated night work) or ending at 12:00 h after ∼30 h of wakefulness (Protocol 2 [P2]: simulated extended operations). A 6-min test battery, including the Karolinska Sleepiness Scale (KSS) and the 4-min 2-Back Working Memory Task (WMT), was repeated every 15 min the first hour after waking. Nap sleep was recorded polysomnographically, and in all nap opportunities sleep onset latency was short and sleep efficiency high. Mixed-model analyses of variance (ANOVA) for repeated measures were calculated and included the factors time (time post-nap), nap opportunity (duration of nap provided), order (order in which the four protocols were completed), and the interaction of these terms. Results showed no test x nap opportunity effect (i.e., no effect of sleep inertia) on KSS. However, WMT performance was impaired (slower reaction time, fewer correct responses, and increased omissions) on the first test post-nap, primarily after a 40- or 60-min nap. In P2 only, performance improvement was evident 45 min post-awakening for naps of 40 min or more. In ANOVAs where sleep stage on awakening was included, the test x nap opportunity interaction was significant, but differences were between wake and non-REM Stage 1/Stage 2 or wake and SWS. A further series of ANOVAs showed no effect of the duration of SWS on sleep inertia. The results of this study demonstrate that no more than 15 min is required for performance decrements due to sleep inertia to dissipate after nap opportunities of 60 min or less, but subjective sleepiness is not a reliable indicator of this effect. Under conditions where sleep is short, these findings also suggest that SWS, per se, does not contribute to more severe sleep inertia. When wakefulness is extended and napping occurs at midday (i.e., P2), nap opportunities of 40- and 60-min have the advantage over shorter duration sleep periods, as they result in performance benefits ∼45 min after waking.