How well do pilots sleep during long-haul flights?

Pilot fatigue survey: A study of the mutual influence among fatigue factors in the "work" dimension

Background

Fatigue risk management for pilots has received increasing attention. The existing fatigue management systems have detailed descriptions of the factors and the mutual influences among the factors that affect the dimension of "sleep", which is one of the most important causes of fatigue. However, the analysis of the influencing factors of the "work" dimension of fatigue causes has not been very detailed or accurate, especially the exploration of the mutual influence among many fatigue-influencing factors in the "work" dimension.

Objective

The purpose of this study was to explore the mutual influence among fatigue-influencing factors related to the "work" dimension in the analysis of pilot fatigue causes.

Methods

This study designed a questionnaire on the dimension of "work" in the causes of pilot fatigue and collected a total of 270 feedback data points from international flight pilots. Based on the questionnaires and data, descriptive statistical analysis, exploratory factor analysis and confirmatory factor analysis were performed to explore the influencing factors and their mutual influences on the "work" dimension of pilot fatigue.

Results

There is a strong, mutual influence relationship among the fatigue causes of long-haul flight pilots - working status, working conditions and working schedules - in the dimension of "work". The workload only has a strong correlation with the working schedule, and the interaction relationships with the working status or working conditions are weak.

Conclusion

This study analyses the mutual influence among the influencing factors of the "work" dimension of pilot fatigue, and we expect to provide empirical data for pilot fatigue risk management and to help improve fatigue risk management systems.

Forecasting crew fatigue risk on international flights under different policies in China during the COVID-19 outbreak

To predict the risk of fatigue for flight crews on international flights under the new operating model policy of the civil aviation exemption approach policy during the COVID-19 outbreak, and to provide scientific validation methods and ideas for the exemption approach policy. This paper uses the change in flight crew alertness as a validation indicator, and then constructs an alertness assessment model to predict flight crew fatigue risk based on the SAFTE model theory. Then, the corresponding in-flight rotation plans for the flight is designed according to the exemption approach policy issued by the CAAC, the CCAR-121 part policy and the real operational requirements of the airline, respectively, and finally the simulation results is compared by comparing the pilot alertness and cockpit crew alertness under the exemption approach policy and the CCAR-121 part policy with the flight duration. The results show that the flight crew alertness level for the flight in-flight rotation plan simulation designed under the exemption approach policy is higher or closer to the pilot alertness level for operational flights under the CCAR-121 Part policy. This validates the reasonableness and safety of the exemption approach policy issued by the CAAC to meet the requirements of epidemic prevention and control, and provides scientific support and solutions for fatigue monitoring and management.

Predictive Biomathematical Modeling Compared to Objective Sleep During COVID-19 Humanitarian Flights

BACKGROUND: Biomathematical modeling software like the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model and Fatigue Avoidance Scheduling Tool (FAST) help carriers predict fatigue risk for planned rosters. The ability of a biomathematical model to accurately estimate fatigue risk during unprecedented operations, such as COVID-19 humanitarian ultra-long-range flights, is unknown. Azul Cargo Express organized and conducted five separate humanitarian missions to China between May and July 2020. Prior to conducting the missions, a sleep-prediction algorithm (AutoSleep) within SAFTE-FAST was used to predict in-flight sleep duration and pilot effectiveness. Here we compare AutoSleep predictions against pilots' sleep diary and a sleep-tracking actigraphy device (Zulu watch, Institutes for Behavior Resources) from Azul's COVID-19 humanitarian missions.METHODS: Pilots wore Zulu watches throughout the mission period and reported sleep duration for their in-flight rest periods using a sleep diary. Agreement between AutoSleep, diary, and Zulu watch measures was compared using intraclass correlation coefficients (ICC). Goodness-of-fit between predicted effectiveness distribution between scenarios was evaluated using the R² statistic.RESULTS: A total of 20 (N = 20) pilots flying across 5 humanitarian missions provided sleep diary and actigraphy data. ICC and R² values were >0.90, indicating excellent agreement between sleep measures and predicted effectiveness distribution, respectively.DISCUSSION: Biomathematical predictions of in-flight sleep during unprecedented humanitarian missions were in agreement with actual sleep patterns during flights. These findings indicate that biomathematical models may retain accuracy even under extreme circumstances. Pilots may overestimate the amount of sleep that they receive during extreme flight-duty periods, which could constitute a fatigue risk.Devine JK, Garcia CR, Simoes AS, Guelere MR, de Godoy B, Silva DS, Pacheco PC, Choynowski J, Hursh SR. Predictive biomathematical modeling compared to objective sleep during COVID-19 humanitarian flights. Aerosp Med Hum Perform. 2022; 93(1):4-12.

Fatigue in Aviation: Safety Risks, Preventive Strategies and Pharmacological Interventions

Fatigue poses an important safety risk to civil and military aviation. In addition to decreasing performance in-flight (chronic) fatigue has negative long-term health effects. Possible causes of fatigue include sleep loss, extended time awake, circadian phase irregularities and work load. Despite regulations limiting flight time and enabling optimal rostering, fatigue cannot be prevented completely. Especially in military operations, where limits may be extended due to operational necessities, it is impossible to rely solely on regulations to prevent fatigue. Fatigue management, consisting of preventive strategies and operational countermeasures, such as pre-flight naps and pharmaceuticals that either promote adequate sleep (hypnotics or chronobiotics) or enhance performance (stimulants), may be required to mitigate fatigue in challenging (military) aviation operations. This review describes the pathophysiology, epidemiology and effects of fatigue and its impact on aviation, as well as several aspects of fatigue management and recommendations for future research in this field.

Pilot Sleep Behavior across Time during Ultra-Long-Range Flights

Fatigue risk to the pilot has been a deterrent for conducting direct flights longer than 12 h under normal conditions, but such flights were a necessity during the COVID-19 pandemic. Twenty (N = 20) pilots flying across five humanitarian missions between Brazil and China wore a sleep-tracking device (the Zulu watch), which has been validated for the estimation of sleep timing (sleep onset and offset), duration, efficiency, and sleep score (wake, interrupted, light, or deep Sleep) throughout the mission period. Pilots also reported sleep timing, duration, and subjective quality of their in-flight rest periods using a sleep diary. To our knowledge, this is the first report of commercial pilot sleep behavior during ultra-long-range operations under COVID-19 pandemic conditions. Moreover, these analyses provide an estimate of sleep score during in-flight sleep, which has not been reported previously in the literature.

Flat-out napping: The quantity and quality of sleep obtained in a seat during the daytime increase as the angle of recline of the seat increases

Some shiftwokers in the long-haul transportation industries (i.e. road, rail, sea, air) have the opportunity to sleep in on-board rest facilities during duty periods. These rest facilities are typically fitted with a seat with a maximum back angle to the vertical of 20°, 40°, or 90°. The aim of this study was to examine the impact of "back angle" on the quantity and quality of sleep obtained in a seat during a daytime nap. Six healthy adults (3 females aged 27.0 years and 3 males aged 22.7 years) each participated in three conditions. For each condition, participants had a 4-h sleep opportunity in a bed (02:00-06:00 h) followed by a 4-h sleep opportunity in a seat (13:00-17:00 h). The only difference between conditions was in the back angle of the seat to the vertical during the seat-based sleep periods: 20° (upright), 40° (reclined), and 90° (flat). Polysomnographic data were collected during all sleep episodes. For the seat-based sleep episodes, there was a significant effect of back angle on three of four measures of sleep quantity, i.e. total sleep time, slow-wave sleep, and rapid eye movement (REM) sleep, and three of four measures of sleep quality, i.e. latency to REM sleep, arousals, and stage shifts. In general, the quantity and quality of sleep obtained in the reclined and flat seats were better than those obtained in the upright seat. In particular, compared to the flat seat, the reclined seat resulted in similar amounts of total sleep and slow-wave sleep, but 37% less REM sleep; and the upright seat resulted in 29% less total sleep, 30% less slow-wave sleep, and 79% less REM sleep. There are two main mechanisms that may explain the results. First, it is difficult to maintain the head in a comfortable position for sleep when sitting upright, and this is likely exacerbated during REM sleep, when muscle tone is very low. Second, an upright posture increases sympathetic activity and decreases parasympathetic activity, resulting in a heightened level of physiological arousal.

Sleep, Health, and Society

Biological needs for sleep are met by engaging in behaviors that are largely influenced by the environment, social norms and demands, and societal influences and pressures. Insufficient sleep duration and sleep disorders such as insomnia and sleep apnea are highly prevalent in the US population. This article outlines some of these downstream factors, including cardiovascular and metabolic disease risk, neurocognitive dysfunction, and mortality, as well as societal factors such as age, sex, race/ethnicity, and socioeconomics. This review also discusses societal factors related to sleep, such as globalization, health disparities, public policy, public safety, and changing patterns of use of technology.

Fatigue on the flight deck: the consequences of sleep loss and the benefits of napping

The detrimental effects of fatigue in aviation are well established, as evidenced by both the number of fatigue-related mishaps and numerous studies which have found that most pilots experience a deterioration in cognitive performance as well as increased stress during the course of a flight. Further, due to the nature of the average pilot's work schedule, with frequent changes in duty schedule, early morning starts, and extended duty periods, fatigue may be impossible to avoid. Thus, it is critical that fatigue countermeasures be available which can help to combat the often overwhelming effects of sleep loss or sleep disruption. While stimulants such as caffeine are typically effective at maintaining alertness and performance, such countermeasures do nothing to address the actual source of fatigue - insufficient sleep. Consequently, strategic naps are considered an efficacious means of maintaining performance while also reducing the individual's sleep debt. These types of naps have been advocated for pilots in particular, as opportunities to sleep either in the designated rest facilities or on the flight deck may be beneficial in reducing both the performance and alertness impairments associated with fatigue, as well as the subjective feelings of sleepiness. Evidence suggests that strategic naps can reduce subjective feelings of fatigue and improve performance and alertness. Despite some contraindications to implementing strategic naps while on duty, such as sleep inertia experienced upon awakening, both researchers and pilots agree that the benefits associated with these naps far outweigh the potential risks. This article is a literature review detailing both the health and safety concerns of fatigue among commercial pilots as well as benefits and risks associated with strategic napping to alleviate this fatigue.

Can a short nap and bright light function as implicit learning and visual search enhancers?

The present study examined effects of a short nap (20 min) and/or bright light (2000 lux) on visual search and implicit learning in a contextual cueing task. Fifteen participants performed a contextual cueing task twice a day (1200-1330 h and 1430-1600 h) and scored subjective sleepiness before and after a short afternoon nap or a break period. Participants served a total of four experimental conditions (control, short nap, bright light and short nap with bright light). During the second task, bright light treatment (BLT) was applied in the two of the four conditions. Participants performed both tasks in a dimly lit environment except during the light treatment. Results showed that a short nap reduced subjective sleepiness and improved visual search time, but it did not affect implicit learning. Bright light reduced subjective sleepiness. A short nap in the afternoon could be a countermeasure against sleepiness and an enhancer for visual search. Practitioner Summary: The study examined effects of a short afternoon nap (20 min) and/or bright light (2000 lux) on visual search and implicit learning. A short nap is a powerful countermeasure against sleepiness compared to bright light exposure in the afternoon.

A model of shiftworker sleep/wake behaviour

Software-based biomathematical models of alertness provide a means to estimate fatigue-related risk in advance of a schedule being worked. Obtaining a good estimate of employees' sleep/wake behaviour during non-work periods is critical in obtaining accurate estimates of alertness. This is because estimates of alertness are generated based on estimated sleep and wake times, not rest and work times per se. The purpose of the current analysis was to evaluate the predictive validity of a novel version of a previously published sleep predictor model. This model was originally designed to predict sleep probability for aviation pilots in connection with long-haul flight operations. It has since been modified to predict sleep periods for industrial shiftwork rosters in non-transmeridian environments. The algorithm uses two procedures to predict sleep timing and duration: (1) estimate the total amount of sleep likely to be obtained in a given rest period; and then (2) estimate the timing and duration of sleep periods within that rest period. The sleep periods predicted in the second procedure are generated such that their combined sum is a priori equivalent to the total amount of sleep predicted in the first procedure. The model was parameterized and validated based on a sample of 225 train drivers who collected work/rest and sleep/wake data for two weeks during normal commercial operations. Agreement between observed and predicted sleep periods was robust (percent agreement=85%) and compared favourably with agreement levels between sleep behaviours exhibited by the same individual on distinct occasions but where shift sequences were repeated. These results are discussed within the context of ongoing efforts to develop individualized biomathematical models of alertness.

Duty periods with early start times restrict the amount of sleep obtained by short-haul airline pilots

Most of the research related to human fatigue in the aviation industry has focussed on long-haul pilots, but short-haul pilots also experience elevated levels of fatigue. The aim of this study was to examine the impact of early start times on the amount of sleep obtained prior to duty and on fatigue levels at the start of duty. Seventy short-haul pilots collected data regarding their duty schedule and sleep/wake behaviour for at least two weeks. Data were collected using self-report duty/sleep diaries and wrist activity monitors. Mixed-effects regression analyses were used to examine the effects of duty start time (04:00-10:00 h) on (i) the total amount of sleep obtained in the 12h prior to the start of duty and (ii) self-rated fatigue level at the start of duty. Both analyses indicated significant main effects of duty start time. In particular, the amount of sleep obtained in the 12h prior to duty was lowest for duty periods that commenced between 04:00 and 05:00 h (i.e. 5.4h), and greatest for duty periods that commenced between 09:00 and 10:00 h (i.e. 6.6h). These data indicate that approximately 15 min of sleep is lost for every hour that the start of duty is advanced prior to 09:00 h. In addition, self-rated fatigue at the start of duty was highest for duty periods that commenced between 04:00 and 05:00 h, and lowest for duty periods that commenced between 09:00 and 10:00 h. Airlines should implement a fatigue risk management system (FRMS) for short-haul pilots required to work early-morning shifts. One component of the FRMS should be focussed on the production of 'fatigue-friendly' rosters. A second component of the FRMS should be focussed on training pilots to optimise sleep opportunities, to identify circumstances where the likelihood of fatigue is elevated, and to manage the risks associated with fatigue-related impairment.

Predicting pilot's sleep during layovers using their own behaviour or data from colleagues: implications for biomathematical models

Biomathematical models are used in industry to estimate how much sleep people are likely to get on different work patterns, and how efficient and safe people are likely to be at work. Since there is evidence to suggest that individuals respond differently to sleep loss, there has been a recent focus on trying to account for individual differences. One possible approach could use past behaviour to predict future responses to similar working conditions. This study investigated the predictive value of sleep timing and duration data for a particular individual on a break between shifts relative to data from their colleagues. Sleep diaries and wrist actigraphy were collected from 306 international long-haul pilots for at least 2-weeks. Fifty layovers, equivalent in origin and destination, length and timing, were completed twice by individual pilots. Matched layovers done by other pilots (n=2311) were also identified. Layover periods were analysed for minute-by-minute correspondence of sleep or wake (yes/no), and total sleep time (TST). Using an individual's own data improved concordance by approximately 5% relative to using a large sample of different pilots, and by 10% relative to using a random sample of 50 different pilots. Using an individual's own TST to predict their TST on an equivalent layover yielded an r value of 0.83, compared to r=0.78 when data from a colleague was used, and r=0.73 using different pilots in a random sample of equivalent size. The mean difference in TST using pilots' own data was <20 min, compared to <40 min using data from colleagues. However, the confidence limits on these differences were large (up to 8h). Results suggest that for international pilots on specific layover patterns, knowing the past behaviour of an individual may only represent a modest improvement over knowing the length and timing of a colleague's sleep, when it comes to predicting their sleep behaviour.