Australian Long Haul Fatigue Study

Matthew J. W. Thomas, Renée M Petrilli, Nicole Lamond, Drew Dawson, and Gregory D. Roach

Centre for Sleep Research, University of South Australia, Adelaide, AUSTRALIA 5000

Address all correspondence to Matthew Thomas:

The Centre for Sleep Research, University of South Australia, City East Campus, Level

7-35 Playford Building, Frome Rd, Adelaide SA 5000, AUSTRALIA

Phone: +61 8 8302 6624. Fax: +61 8 83026623

Email: matthew.thomas@unisa.edu.au

This paper was presented at the:

59th Annual International Air Safety Seminar (IASS), 23-26 October 2006

A Joint Meeting of Flight Safety Foundation, International Federation of Airworthiness

and International Air Transport Association.

Background Functional impairment associated with human fatigue is increasingly being recognized as a significant risk in commercial airline flight operations. Fatigue experienced by domestic and international pilots is largely due to sleep irregularities, long duty days, early starts and night flying (Caldwell, 2005; Graeber, 1988; K. E. Klein et al., 1972). Furthermore, international pilots must also deal with multiple time zone changes, which can lead to circadian desynchrony (Graeber et al., 1986; Orlady & Orlady, 1999). Importantly, relatively little is known about the effects of fatigue on operational performance, within the complex domain of long-haul flight operations. It is well-known that safe flight operations require the detection and management of various operational events and errors (O'Hare, 2003; Thomas, 2004). Defined as situations, events or errors that occur outside the flight-deck, threats are conditions that have the potential to impact negatively on the safety of a flight. In turn, defined as crew action or inaction that leads to a deviation from crew or organisational intentions or expectations, errors are taken to be an unavoidable and ubiquitous aspect of normal operations (Helmreich et al., 1999). As these two factors form fundamental causal components of incidents and accidents, it is argued that the management of threat and error must form the focus of any organisation’s attempts to effectively maintain safety in high-risk operations (Klinect et al., 1999). Effective threat and error management is therefore a critical component of safe flight operations, and offers an extremely powerful framework with which to analyse and measure the performance of a crew. Importantly, no previous research has examined the impact of fatigue associated with international flying patterns on the threat and error management of flight crew. One of the main skills underlying the management of threats is crew decision-making. In general, previous research suggests that decision-making ability declines as fatigue levels increase (Harrison & Horne, 2000; Neri et al., 1992). Notably however, studies investigating operational performance of flight crew have typically used low-level cognitive performance tasks or low-fidelity flight simulators that may only be partially generalisable to the commercial airline environment. Moreover these studies have examined the behaviour of the individual and not the behaviour of the crew. The implications of fatigue, such as the occurrence of a fatigue-related incident or accident, may be quite different in an individual setting compared to a team environment such as a two-pilot crew (Foushee et al., 1986). With respect to the limitations of research relating to fatigue complex crew behaviour, the primary aim of the current study was to investigate the effects of fatigue on the operational performance of international long-haul flight crew. The study utilised trained expert observers to analyse and evaluate crews’ threat and error management behaviours and decision-making performance during a simulated flight operation. The simulator scenario was designed to present crews with a “normal” flight operation which included a series of operational threats, designed to provide additional workload for the crews, yet not extend outside the parameters of normal flight operations. To guide the threat and error analysis and interpretation of results, we used a modified version of the Threat and Error Management Methodology framework developed by Helmreich and colleagues at

the University of Texas (Helmreich et al., 1999). To guide the decision-making analysis, we employed a prototypical model of naturalistic decision making- Klein’s (1993) recognition-primed decision (RPD) model. Method Participants A total of 134 aircrew (67 Captains, 67 First Officers) participated in the study after bidding for a pattern that concluded with a study-related simulator session. All aircrew participated voluntarily in the study and were assigned a unique identification code to ensure their anonymity. Participants were paid at the standard rate for the simulator session that they attended in fulfillment of the experimental protocol. The study was approved by the University of South Australia Human Research Ethics Committee using guidelines established by the National Health and Medical Research Council of Australia. A summary of crew demographic variables is provided in Table 1.

Table 1 Demographic Characteristics of the Captains and First Officers. Demographic Captains First Officers Gender 67 male, 0 female. 65 male, 2 female. Age 50.4 (± 4.6) years; range = 40 to 58. 42.9 (± 8.6) years; range = 33 to 59. Body Mass Index 26.8 (± 2.9) kg/m2;

range = 21.5 to 36.4 kg/m2. 25.7 (± 3.0) kg/m2; range = 18.9 to 32.9 kg/m2.

Marital Status 60 married or living with partner, 3 separated or divorced, 3 single, 1 unknown.

58 married or living with partner, 1 separated or divorced, 7 single, 1 unknown.

Number of Children 1.3 (± 1.2); range = 0 to 4. 1.1 (± 1.1); range = 0 to 4. Years on B747-400 4.9 (± 3.8); range = 0.5 to 15.0. 4.7 (± 4.6); range = 0.5 to 20.0. B747-400 Flying Hours

3,294 (± 2,257); range = 300 to 10,000 3,221 (± 2,855); range = 290 to 10,600.

Total Flying Hours 16,576 (± 2,851); range = 11,300 to 23,800.

10,758 (± 4,189); range = 4,500 to 21,700.

Legend. Numbers represent Mean (± S.D.).

Materials Flight Simulator Crew performance was assessed in a Boeing 747-400 full flight simulator (CAE Electronics Ltd), certified to the Civil Aviation Safety Authority (CASA) Level D prescribed in the FSD-1 (Operational Standards and Requirements-Approved Flight Simulators) Issue 4. At the time of the study, the commercial airline used these simulators for training and license renewals.

The Flight Scenario In the simulator scenario, crews were required to fly a full simulated flight from Sydney to Melbourne, Australia which has a flying time of approximately 60-70 minutes. The scenario included the normal pre-flight checks, take-off, cruise, descent, approach, and landing as well as various operational threats that the crew needed to manage. The threats that crews were required to manage are presented in Table 2. During the scenario, crews were required to resolve a critical decision event (CDE) which occurred at approximately 10 minutes prior to the top of descent. The CDE was designed to assess the important skill dimensions underlying decision-making. There was no single (in)correct resolution to the CDE, which is described below.

• At the outset, crews plan to land on Runway 16 in Melbourne. The aircraft is

dispatched with the Engine No. 3 Thrust Reverser “locked out” which means the plane has reduced braking capability on the runway. Crews receive a valid terminal area forecast (TAF) for Melbourne that indicates that the visibility and cloudbase are acceptable and above the alternate criteria for landing.

• At approximately 10 minutes prior to the top of descent, Air Traffic Control (ATC)

issues a revised Aerodrome Terminal Information Service (ATIS) (i.e., change of weather conditions) for Melbourne, informing the crew that the weather conditions have changed in Melbourne.

• The new ATIS indicates that winds in Melbourne have increased, with the

implication they are now above crosswind limitations for Runway 16 such that landing on this runway is no longer legal.

Dependent variables Threat and Error Management Trained expert observers recorded details of pilot performance in the simulator based on an observational methodology. The observational methodology was based on the Threat and Error Management Model developed by the University of Texas for the analysis of flight crew performance during normal operations (Helmreich, 2000).

Table 2 Operational “Threats” Embedded within the Simulator Scenario. Threat Event Description

Dispatch with Thrust Reverser Locked Out

The first operational event included within the simulator scenario involved the dispatch of the aircraft with the Thrust Reverser on Engine Number Three unserviceable, and therefore “locked out” of operation by engineering.

Change in Loadsheet The second operational event involved a number of subtle changes between the provisional and final loadsheet provided to the crew. In keeping with regular occurrences on the line, this threat provided a realistic operational event that required active management by the crew to ensure safe operation of the aircraft.

Change in Duty Runway

The third operational event involved a change in the duty runway and subsequent departure clearance at Sydney. This change was included in the scenario after the crew had completed all initial take-off and departure planning, had completed push-back from their departure gate, completed the engine start sequence, and requested clearance to taxi to the runway.

Clearance to Lower Altitude on Departure

The fourth operational event involved the crew being cleared to a lower initial level-off altitude on departure. The initial clearance was for the crew to maintain 5000’. However, as the crew were lining up on runway 16R they were provided with the following clearance: Qantas 2, Wind 190/10, maintain 4000, RWY 16R cleared for Take-Off.

Clearance to Higher Cruising Altitude and Expedite Climb

The fifth operational event involved a request from ATC for a higher cruising altitude of FL400, and a clearance to expedite climb through FL230.

High Speed Descent and Track Shortening

The sixth operational event involved ATC asking the crew to maximise speed on descent and cancelling the speed restrictions below 10000’ due to sequencing requirements. Also associated with this threat was some track shortening below 10000’, keeping the crew slightly above their VNAV descent profile, and consequentially having to capture the Glide Slope from above.

ATC QNH Error The seventh operational event involved ATC providing the crew with an incorrect QNH. On first contact with MEL approach, the approach controller states “Qantas 2, Identified continue descent 8000’ QNH 1013”. The crew had previously received a QNH of 1003 as part of MEL ATIS SIERRA.

Failure to Provide Clearance to Land

The eighth, and final, operational event involved a failure of ATC to provide the crew with a clearance to land. This was an unusual threat in as much as it involved the absence of an ATC clearance that in the vast majority of instances would be provided to crews without any prompt.

Decision-Making To assess crew decision-making performance during the CDE, an observer viewed and coded each simulator session from the video recordings. Several variables extracted from a list of measures of crew performance were chosen for the analyses. It was proposed

that the chosen variables had the highest relevancy to crew decision-making that readily tap the following areas of crew decision-making based on Klein’s (1993) recognition-primed decision (RPD) model: (i) situation awareness; (ii) options and planning; (iii) decision implementation; and (iv) evaluation. The decision-making variables are presented within the framework of the RPD (Table 3). Table 3. The Dependent Variables Presented Within Klein’s (1993) RPD Model Decision-Making Stage Dependent Variables

Situation Awareness Cross-check Figures? (categorical) - Did crews cross-checked the landing figures?

Obtain Melbourne Weathers/Trend Forecasts (categorical) – Did crews request weather information including forecasts and trends?

Determine Melbourne below alternate criteria (categorical) - Did crews determine that Melbourne was below alternate criteria such that an alternate airport was necessary?

Options and Planning

Number of Options (continuous) – Number of options considered by crews.

Determine Runway 16 Available (categorical) - Did crews determine that Runway 16 would be available if the wind dropped when heading for Runway 27?

Decision Implementation

Time to Finalise Decision (continuous) – Time taken for crews to verbally decide their plan of action.

Time to Positive Action (continuous) - Time taken for crews to take positive action towards the decision (e.g., execution of FMC or request diversion).

Divert from MEL? (categorical) - Did crews divert to another airport instead of continuing to Melbourne?

Decision Evaluation

Review decision? (categorical) –Did the crews review the decision?

Independent Variables Sleep/Wake Schedules Pilots’ sleep/wake schedules were obtained using sleep diaries and activity monitors. Pilots kept self-recorded sleep diaries for the entire study period (i.e., approximately 15 days) where they recorded information for each sleep period (including all in-flight sleep periods) including self-rating their level of fatigue using the Samn-Perelli Fatigue Checklist, and perceived sleep quality using a 5-point Likert Scale. Objective sleep/wake schedules were assessed using activity monitors (Mini MitterTM, Sunriver, Oregon), which were also worn by each pilot for the entire study period. Activity monitors are devices worn like a wristwatch on the wrist that allow for 24-hour recordings of activity (see Ancoli-Israel et al., 2003). The independent variable derived from the sleep diaries and activity monitors was Sleep in prior 24h (in hours) - the amount of sleep that a participant had obtained in the 24 hours prior to the start of the simulator session.

Work/Rest Schedules Pilots’ work/rest schedules were obtained from duty diaries that were kept for the entire study period (i.e., approximately 15 days). In the duty diaries, participants recorded information for every work period, which included the on-blocks and off-blocks time (i.e., start and end) of each flight, the origin and destination ports, and subjective fatigue using the Samn-Perelli Fatigue Checklist before and after each flight. Time was recorded as universal time, coordinated (UTC). The independent variable derived from the duty diary was duty – whether the crews were rested or non-rested (see below). Psychomotor Vigilance Task Pilots’ response times were measured using a PalmPilot version of the psychomotor vigilance task (PVT, Ambulatory Monitoring Inc.), developed by the Walter Reed Army Institute of Research (see Thorne et al., 2003). This version of the task has been validated in several studies (Belenky et al., 2003; Lamond et al., 2005). The independent measure derived from the PVT was mean response speed, expressed as the mean reciprocal response time multiplied by 1000, as per standard methodology. Lower scores indicated a greater level of impairment. Subjective Fatigue Subjective fatigue was assessed using the Samn-Perelli Fatigue Checklist (Samn & Perelli, 1982). The Samn-Perelli is a 7-point Likert scale with: 1 = “Fully alert, wide awake”; 2 = “Very lively, responsive, but not at peak”; 3 = “Okay, somewhat fresh”; 4 = “A little tired, less than fresh”; 5 = “Moderately tired, let down”; 6 = “Extremely tired, very difficult to concentrate”; and 7 = “Completely exhausted, unable to function effectively”. The independent measure derived from the Samn-Perelli Fatigue Checklist was self-rated fatigue. Higher scores indicated a higher level of subjective fatigue. Design and Procedure The study employed a completely randomised design. Sixty-seven crews (Captain and First Officer) operated a B747-400 simulator either:

• after having at least four consecutive days free of duty after an international pattern (rested), OR

• immediately after the final landing in Sydney at the end of an international pattern (non-rested).

Ultimately, there were:

• 21 rested crews • 22 non-rested crews that had returned from patterns to Europe • 21 non-rested crews that had returned from patterns to the United States • 3 non-rested crews that had returned from patterns to South Africa

Simulator Session The simulator sessions were identical for the rested and non-rested crews (see Figure 1).

• Pre-flight Testing. At the start of the simulator session, participants completed the pre-simulator questionnaires and a 5-minute PVT (approximately 15 minutes).

• Pre-flight Planning. Participants began their pre-flight planning (i.e. fuel, route, weather, etc.) approximately 30-minutes prior to entering the simulator.

• Simulator Session. The simulator session involved a single flight sector (Sydney-Melbourne) with all stages of flight – pre-flight, take-off and climb, cruise, descent and approach, and landing (approximately 2 hours). In all cases, the Captain was the pilot flying.

• Post-flight Testing. At the end of the simulator session, participants completed the post-simulator questionnaires and a 5-minute PVT (approximately 10 minutes).

Figure 1 Simulator Study Protocol.

Legend. PR = PVT practice. BL = PVT baseline. SIM = simulator session.

Analyses To assess the impact of fatigue on the threat and error management, and decision-making of flight crew, independent variables corresponded to one of the first three levels of the fatigue hazard trajectory (Dawson & McCulloch, 2005).

• Level 1: Recent Duty History - prior sleep opportunity based on recent duty history.

• Level 2: Actual seep – amount of sleep obtained in the 24 hours prior to the start of the simulator session.

• Level 3: Self-rated Fatigue – self-rated fatigue using the Samn-Perelli Fatigue Checklist prior to the start of the simulator session.

• Level 3: PVT Response Speed – inverse reaction time (relative to baseline) on the Psychomotor Vigilance Task prior to the start of the simulator session.

For the primary analyses, crews were allocated to groups (low-, moderate-, and high-fatigue) on the basis of the independent variables. Unpaired samples t-tests and chi-squared cross-tabulations were used to examine the impact of the independent variables on crew performance. For the secondary analyses, multiple and logistical regression analyses were conducted to determine whether the operational behaviours of flight crew could be predicted by the independent variables. The current paper will report a summary of the primary results only. Regression analyses are published elsewhere.

-4 -3 -2 -1 +4+3+2+1

PR BL SIM

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International Flight Pattern

Sleep/Duty Diaries + Activity Monitor

-4 -3 -2 -1 +4+3+2+1

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Sleep/Duty Diaries + Activity MonitorNon-rested

Rested

Day of Study (relative to pattern)

RESULTS Threat and Error Management Threat Detection In general, threat detection was consistently good across all phases of flight. Overall, only 3.7% (N=19) of threats were not detected by crews. There were no statistically significant effects of fatigue on threat detection with respect to any of the fatigue-related independent variables. Threat Response and Outcome On average a total of 67.7% (N=343) of threats were detected and effectively managed by crews, without error, to an inconsequential outcome. Accordingly, the remaining 32.3% (N=164) of threats resulted in one or more errors being committed by crews. Analyses found a strong effect of sleep in the prior 24 hours on the occurrence of mismanaged threats leading to one or more errors. Analysis of the relative rates of error occurrence indicated that 40.3% (N=50) of threat events faced by crews who both had obtained less than 5 hours of sleep in the prior 24 hours resulted in errors, compared to only 26.0% (N=57) of threat events faced by crews who had obtained more than 5 hours of sleep in the prior 24 hours. This indicates a 14.3% higher incidence of error in crews who had obtained less than 5 hours sleep, a finding that was significant, χ2 (1,343) = 7.538 p=.006. Error Rate In total, 578 errors were observed and coded during the 67 simulator sessions observed as part of the study. Of the fatigue-related variables, only sleep in the prior 24 hours was found to have a significant effect with respect to error rate. Crews who had both obtained less than four hours sleep in the prior 24 hours committed on average 11.71 (SD = 4.50) errors per simulated flight sector, compared to only 8.37 (SD = 3.51) errors per sector observed in crews who had both obtained more than four hours sleep in the prior 24 hours. Accordingly, crews who had both obtained less than four hours sleep in the prior 24 hours committed on average 3.34 more errors per flight sector, F(1, 41) = 4.832, p=.034. Error Detection Of the 578 errors committed by crews, a total of 52.4% (N=303) were detected by a crew-member. Crews who participated in the study immediately after sign-off from an international flight sector detected 56.9% of their errors, compared to 42.9% of errors detected by crews in the “rested” condition. Accordingly, the nominally fatigued non-rested crews detected significantly more errors that the rested crews χ2 (1,578) = 9.714 p=.002. Similarly, of the errors committed by crews in which both crew-members had obtained less than six-hours sleep in the prior 24 hours, 56.2% were detected by those crews, compared to only 44.3% of errors committed by crews in which both crew members had obtained six or more hours of sleep in the prior 24 hours. Accordingly, the nominally

fatigued low-sleep crews detected significantly more of their errors, than the high sleep group χ2 (1,411) = 5.657 p=.017. Error Outcome The final dependent variable related to the eventual outcome of the error, whether the outcome was: a) inconsequential, with no operational significance; b) led to an additional error; or c) resulted in an undesired aircraft state. Of the fatigue-related variables, there were no significant effects found with respect to the distribution of inconsequential errors, additional errors, or errors that led to undesired aircraft states. However, multivariate analyses, which are reported elsewhere, indicate a number of relationships between fatigue-related variables and error outcome. Decision-Making The results relating to crew decision-making are organised based on the four stages of the decision-making process: (i) situation awareness, (ii) options and planning, (iii) decision implementation, and (iv) evaluation. A summary of the significant descriptive analyses are shown in Figure 2.

Figure 2 Effects of Fatigue on Crew Decision-Making (Descriptive Analyses).

Recent Duty Sleep in Prior 24h Self-rated Fatigue PVT Performance

Decision Variable Duty Trip FAID Capt FO Crew Capt FO Crew Capt FO Crew

Situation Awareness

Cross-check?

MEL WX/TTF?

Below Alternate?

Options/Planning

Number of options

Rwy16 Available?

Decision

Divert from MEL?

Time to Decision

Time to Action

Evaluation

Review Decision?

Performance improves as fatigue increases.

Performance degrades as fatigue increases. Greater occurrence as fatigue increases.

Legend. Figure shows summary of results for significant independent samples t-tests and chi-square cross-tabulations (all p<.05) considering effects of Level 1 (recent duty history), Level 2 (sleep obtained in the prior 24 hours), and Level 3 (self-rated fatigue, PVT Performance) fatigue indicators on the decision variables.

+ + + + + + + +

> > >

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Situation Awareness Interestingly, in all analyses moderate- and high-fatigue crews indicated better performance compared to low-fatigue crews, suggesting that fatigued flight crew may have employed a high-effort strategy to cope with fatigue. Analyses indicated that whether crews cross-checked the landing figures (Cross-check Figures?) was significantly affected by recent duty (X2[1]=6.2, p<.05), and the amount of sleep in the prior 24 hours of the Captain (X2[2]=7.0, p<.05), and the self-rated fatigue of the both the Captain and First Officer (X2[2]=6.7, p<.05). Whether crews obtained information regarding Melbourne weathers or trend forecasts (Obtain Mel Weathers/TTF?) was significantly affected by recent duty (X2[2]=9.8, p<.01). Notably however, whether crews determined Melbourne was below alternate criteria (i.e., it was necessary to establish an alternate airport before landing in Melbourne- Determine MEL below alternate?) was not affected by fatigue. Options and Planning The options and planning stage of the decision-making process remained unaffected by the Level 1, Level 2, or Level 3 fatigue indicators. Specifically, the analyses indicated no significant differences between groups in terms of the number of options considered (Number of Options) or whether crews determined that Runway 16 would be available if the wind dropped (Determine RWY16 Available?). Decision Implementation The analyses revealed that the implementation of the decision was highly sensitive the effects of recent duty history (t33=2.0, p<.05), and the self-rated fatigue of the Captain (t24=2.1, p<.05). Accordingly, fatigued crews tended to take longer to finalise their decision (Time to Finalise Decision). Figure 3 illustrates the time to finalise the decision with respect to the self-rated fatigue of the Captain. The approximate time at which crews should commence the descent-approach-landing phase of flight is included in the figure to illustrate the operational significance of taking longer to resolve the critical decision event. Furthermore, with respect to crew diversion (Divert from Melbourne), a greater proportion of non-rested crews chose to divert from the destination airport, Melbourne compared to rested crews (X2[2]=6.7, p<.05). This may indicate that fatigued crews may tend to opt for more conservative options during complex decision-making. Interestingly however, the time to take positive action towards the decision (Time to Positive Action) was not significantly affected by fatigue. Evaluation Interestingly, the analyses indicated that whether crews evaluated the decision (Review Decision?) was not significantly affected by the Level 1, Level 2, or Level 3 fatigue indicators.

Figure 3 Time Taken for Crews to Finalise the Critical Decision Event.

Legend. Figure represents the time taken for crews to finalise the critical decision event

when allocated to groups on the basis of the self-rated fatigue of the Captain. Asterisk indicates significant difference from low-fatigue crews

(Samn Perelli [SP] of 1-3; p<.05).

!

Self-rated fatigue (Captain)

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3

6

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15

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SP1-3 SP4 SP5 SP6

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Discussion Threat and Error Management The results of this study highlight a number of significant changes in flight crew operational performance associated with fatigue. While there was some consistency with respect to changes in performance, it was evident that fatigue was not simply associated with performance impairment. Firstly, fatigue was found to be associated with degraded crew performance. By way of general summary, fatigue was found to be associated with increased mismanagement of operational threats, higher rates of error occurrence overall, and the subsequent mismanagement of errors that the crew had detected. Multivariate analyses also highlighted the increased likelihood of operationally consequential outcomes of poor threat and error management associated with increased levels of fatigue. Secondly, the results of this study also highlight a number of areas in which fatigue was associated with aspects of enhanced operational performance, and accordingly, the results of this study provide considerable new evidence with respect to the complex interactions between fatigue and performance in real-world high-risk work environments. First, fatigue was found to be associated with improved cross-checking performance by crews. Of note, fatigued crews were found to make noticeably fewer errors in procedural cross-checking than non-fatigued crews. This finding from the simulator study suggests that fatigued crews paid more attention to detail, and were in general more disciplined, in their cross-checking behaviours. Second, fatigue was found to be associated with increases in the rates of error detection by crews, with both descriptive and multivariate analyses highlighting this relationship. This relatively counter-intuitive finding suggests that fatigue is associated with increased monitoring of performance by crews, and suggests the deployment of specific adaptive strategies to compensate for the increased likelihood of error in fatigued crews. It could be argued that fatigued crews anticipated the occurrence of error, and thus devoted more cognitive resources, and targeted behavioural strategies, towards the detection of fatigue-related error. Decision-Making Overall, our findings indicate that Level 1, Level 2, and Level 3 fatigue indicators of the fatigue hazard trajectory, as described in the methods section of this paper, influence some aspects of crew decision-making. Specifically, crews take longer to make decisions if they have obtained a small opportunity to sleep based on recent duty history (Level 1), have obtained a small amount of actual sleep in the prior 24 hours (Level 2), are experiencing high levels of subjective fatigue (Level 3), and/or have slow response times (Level 3). Taking longer to make decisions may have negative implications for operational safety as this could lead to greater time pressures, which may enhance the risk of errors during the later stages of flight (i.e., descent, approach, and landing). This study has also revealed that high-fatigue crews tend to make more conservative decisions (i.e., decide to divert) compared to low-fatigue crews. This may be associated with a greater aversion to risk of crewmembers under conditions of fatigue.

Finally, this study has highlighted the importance of Level 2 and Level 3 fatigue indicators of the fatigue hazard trajectory. While providing crewmembers with adequate opportunity to sleep is vital to the management of fatigue in commercial airline flight operations (Level 1 control), the amount of sleep actually obtained by crewmembers (Level 2 control), and their subjective fatigue levels (Level 3 control) appear to be the primary factors influencing flight crew decision-making. Furthermore, fatigue indicators associated with the Captain were the main predictors of crew performance in the current study. This is reasonable given that the Captain is generally accountable for overall crew performance. However, given that the Captain was also the pilot flying, this must be acknowledged as a possible confounding factor. Implications for Fatigue Risk Management In the first instance, the results of this study have reinforced the conventional wisdom that fatigue is a real issue within commercial flight operations, with significant implications for the operational performance of flight crew, and the overall safety of flight operations. Within the simulator study, there was consistent evidence of fatigue associated with long-haul flying operations in terms of reduced prior sleep, reduced neurobehavioural performance, and increased self-rated fatigue in crews who had recently operated an international flying pattern compared to the control group of rested participants. Throughout the study, these fatigue-related variables were found to be associated with significant changes in operational performance. More importantly, these fatigue-related variables were also found to have some implications for operational safety. While reduced sleep in the prior 24 hours was most consistently associated with changes in crew performance, the other fatigue-related variables were also found to be associated with changes in operational performance. From the perspective of fatigue risk management, sleep has been reinforced as the critical factor, and this study emphasises the role of both traditional, and innovative strategies that focus specifically on providing crews with sufficient opportunity for sleep, and ensuring (to the greatest extent possible) that crews obtain enough sleep. Within the operational context, the study has highlighted the role of generic countermeasures, such as napping and strategic caffeine use, as appropriate mechanisms. However, and perhaps more importantly, this study has highlighted the role of specific strategies such as, increased monitoring and cross-checking, strategic time management, automation use, and decisions relating to crew complement and utilisation, as essential components of operationalised fatigue risk management. These themes will be explored in more detail in subsequent publications, and form the future directions of fatigue-related research.

References Ancoli-Israel, S., Cole, R., Alessi, C., Chambers, M., Moorcroft, W., & Pollak, C. P.

(2003). The role of actigraphy in the study of sleep and circadian rhythms. Sleep, 26(3), 342-392.

Belenky, G., Wesensten, N. J., Thorne, D. R., Thomas, M. L., Sing, H. C., Redmond, D. P., et al. (2003). Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: a sleep dose-response study. Journal of Sleep Research, 12, 1-12.

Caldwell, J. A. (2005). Fatigue in aviation. Travel Medicine and Infectious Disease, 3(2), 85-96.

Dawson, D., & McCulloch, K. (2005). Managing Fatigue: It’s about sleep. Sleep Medicine Reviews, 9(5), 365-380.

Graeber, R. C. (1988). Fatigue and Circadian Rhythmicity. In E. L. Weiner & D. C. Nagel (Eds.), Aircrew fatigue and circadian rhythmicity (pp. 305-344). New York: Academic Press.

Graeber, R. C., Lauber, J. K., Connell, L. J., & Gander, P. H. (1986). International aircrew sleep and wakefulness after multiple time-zone flights: A cooperative study. Aviation, Space, and Environmental Medicine, 57(12), B3-B9.

Harrison, Y., & Horne, J. A. (2000). The impact of sleep deprivation on decision making: A review. Journal of Experimental Psychology: Applied, 6(3), 236-249.

Helmreich, R. L. (2000). On error management: lessons from aviation. British Medical Journal, 320, 781-785.

Helmreich, R. L., Klinect, J. R., & Wilhelm, J. A. (1999). Models of threat, error and CRM in flight operations. In R. S. Jensen (Ed.), Proceedings of the Tenth International Symposium on Aviation Psychology (pp. 677-682). Columbus, OH: Ohio State University.

Klein, G. (1993). A recognition-primed decision (RPD) of rapid decision making. In G. A. Klein, J. Orasanu, R. Calderwood & C. E. Zsambok (Eds.), Decision making in action: models and methods (pp. 138-147). Noorwood, CT: Ablex.

Klein, K. E., Wegmann, H. M., & Hunt, B. I. (1972). Desynchronisatization of body temperature and performance circadian rhythm as a result of outgoing and homegoing transmeridian flights. Aerospace Medicine, 43(2), 119-132.

Klinect, J. R., Wilhelm, J. A., & Helmreich, R. L. (1999). Threat and Error Management: Data from Line Operations Safety Audits. In R. S. Jensen (Ed.), Proceedings of the Tenth International Symposium on Aviation Psychology (pp. 683-688). Columbus, OH: Ohio State University.

Lamond, N., Dawson, D., & Roach, G. D. (2005). Fatigue assessment in the field: Validation of a hand-held electronic psychomotor vigilance task. Aviation, Space, and Environmental Medicine, 4, 486-489.

Neri, D. F., Shappell, S. A., & DeJohn, C. A. (1992). Simulated Sustained Flight Operations and Performance, Part 1: Effects of Fatigue. Military Psychology, 4(3), 137-155.

O'Hare, D. (2003). Aeronautical decision making. In P. S. Tsang & M. A. Vidulich (Eds.), Principles and Practice of Aviation Psychology (pp. 201-237). Mahwah, NJ: Lawrence Erlbaum Associates.

Orlady, H. W., & Orlady, L. M. (1999). Human Factors in Multi-Crew Flight Operations. Aldershot: Ashgate.

Samn, S. W., & Perelli, L. P. (1982). Estimating aircraft fatigue: a technique with application to airline operations (Technical Report No. SAM-TR-82-21): USAF School of Medicine, Brooks AFB, TX.

Thomas, M. J. W. (2004). Predictors of Threat and Error Management: Identification of core non-technical skills and implications for training systems design. International Journal of Aviation Psychology, 14(2), 207-231.

Thorne, D. R., Johnson, D. E., Redmond, D. P., Sing, H. C., Belenky, G., & Shapiro, H. M. (2003). The Walter Reed Palm-held psychomotor vigilance test. Behaviour Research Methods, 37, 111-118.