PREDICTIONS OF SLEEP TIMING DURING LAYOVERS ON INTERNATIONAL FLIGHT PATTERNS USING SOCIAL AND CIRCADIAN FACTORS

Transcription

1 - 1 - PREDICTIONS OF SLEEP TIMING DURING LAYOVERS ON INTERNATIONAL FLIGHT PATTERNS USING SOCIAL AND CIRCADIAN FACTORS Katie J. Kandelaars 1, Guy Eitzen 2, Adam Fletcher 3, Gregory D. Roach 1, Drew Dawson 1 1 Centre for Sleep Research University of South Australia Adelaide, Australia 2 Centre for Industrial and Applied Mathematics University of South Australia Adelaide, Australia 3 Department of Behavioral Biology Walter Reed Army Institute of Research Silver Spring, Maryland

2 - 2 - LIST OF FIGURES Figure 1 Schematic diagram showing the three phases defined during a layover period; Recovery, Personal and Preparation...7 Figure 2 A schematic diagram comparing the Alternate, Actual and Two-Process sleep data for the SYD-LAX dataset...9 Figure 3 A schematic diagram comparing the Alternate, Actual and Two-Process sleep data for the SYD-LHR dataset...10 Figure 4 Diagram showing the validation of the alternate model compared to the actual data and two-step model predictions...11

3 - 3 - INTRODUCTION Organisations are turning towards 24-hour operations in order to satisfy customer demands. One problem in such settings is the impact these schedules have on the lives of the employees and increased fatigue and/or decreased alertness levels while at work. These have been shown to increase the risk of accidents and incidents in the workplace (1-3). In order to control this risk, many organizations are now implementing fatigue risk management procedures (4, 5) using over-lapping, redundant systems designed to limit the number of fatigue related accidents and incidents (6). Bio-mathematical models of fatigue, performance and/or alertness are often implemented as one level of these systems to determine whether sufficient opportunity for sleep is being given (6). Recent investigations into the similarities and differences between these biomathematical models have been conducted (7-9). Most of the current models in the literature were formulated from the two process model of sleep regulation proposed by Borbély (10). This model posits that the timing of sleep and wake is regulated by a combination of circadian (Process C) and sleep homeostasis (Process S) components. Results indicate that the main difference between all models lies in their input variables. Some models currently use actual sleep and wake timing to predict fatigue (one-step approach) (11) whereas others use hours of work to estimate sleep/wake and then subsequent fatigue levels (two-step approach) (12, 13). The sleep regulation portion of these models were created using physiological variables and data collected in laboratory settings (10-14) with some since further validated in operational settings (13, 15, 16). It has recently been shown that the estimates of sleep and wake from such models are inaccurate as they do not account for factors such as sleep strategy selection and anticipatory sleep behaviour designed to prepare for work periods. Following the predictions of fatigue, alertness and/or performance are often incorrect, especially in a field setting (7). A further problem with current bio-mathematical models of fatigue, alertness and/or performance is that break lengths are considered to be homogenous time periods during which time sleep strategies do not change. It has however been reported in the literature that the sleep strategies selected by employees during breaks is a dynamic process which changes over the course of the break (17, 18). In this paper we will introduce and develop a method to predict the timing of sleep and wake during layovers following duty periods of transmeridian flight. Unlike

4 - 4 - previous modelling attempts the model presented here will be constructed in such a way that this dynamic selection of sleep strategies can be exploited. Our aim is to introduce a modeling technique that uses information gathered from airline schedule information rather than physiological parameters to predict the onset and duration of individual sleep episodes. These estimations could then be used to predict the resulting alertness, performance and/or fatigue scores using existing models. APPROACH Participants In total 32 flight crew personnel consisting of Captains (n=17), First Officers (n=10) and Second Officers (n=5) flying either; Sydney-Los Angeles-Auckland-Sydney (SYD-LAX) or Sydney-Bangkok-London-Singapore-Sydney (SYD-LHR) were included in the analysis. The database currently includes 16 SYD-LAX and 21 SYD-LHR patterns as some crew members completed both patterns. This is a subset of the group involved in data collection for a Fatigue Risk Management System project by Qantas Airways Limited, the Civil Aviation Safety Authority, the Australian and International Pilots Association and the University of South Australia s Centre for Sleep Research. To date only pilot data has been published on this project (19-21). The data sets were chosen as they represent two flight patterns regularly undertaken by the airline s flight crew. The individual trips selected to form these sets of data have the same flight pattern, with constant scheduled arrival and departure times and layover lengths in all destinations. The SYD-LAX pattern contains one flight from Sydney to Los Angeles (LA) followed by a short layover lasting 35.9 ±1.08 hours (mean ± st. dev.). Following this, the second duty period from LA to Auckland, New Zealand was conducted. A short layover (23.7 hours ± 0.87) was spent in Auckland, before returning to Melbourne or Brisbane, Australia followed by a short tag-flight to Sydney. The SYD-LHR data set consisted of three separate layovers; one in Bangkok while outbound from Sydney (23.84 ± 0.4 hours), one after landing at London Heathrow (63.45 ± 1.20 hours) and one in Singapore while inbound to Melbourne (48.34 ± 0.66 hours). After a short stop in Melbourne, flight crews then fly to Sydney to complete the pattern.

5 - 5 - Recruitment At the beginning of the data collection period Qantas flight crew personnel were informed of the proposed study by and by poster. Interested parties were then invited to information sessions to explain the study s purpose and protocol in full. All individuals received an information sheet, and it was explained that participation was entirely voluntary and that they were free to withdraw at any stage during the study without explanation. Participants gave their informed consent and were not paid for taking part in the study, other than their usual salary while at work. Based on their duty periods and patterns flown subjects were then selected to wear activity monitors and complete sleep and work diaries during either the SYD-LAX or SYD-LHR patterns. The study had approval from the University of South Australia s Human Research Ethics Committee. Work Setting Duty lengths varied greatly depending on the origin and destination of flights and the number of layovers within each trip. Each duty period contained at least three international flights with crew spending time recovering between flights during layover periods. During these layover periods flight crew were free to do as they wished, and were not required to undertake any work-related duties. During periods of duty, flight crews engaged in a variety of tasks with the highest periods of workload occurring at takeoff and landing. While the aircraft is at cruising altitude, the individual members of the flight crew were given the opportunity to take in-flight naps in designated crew rest areas situated away from the flight deck. These naps were coordinated to ensure the flight deck is attended by either the Captain or First Officer at all times. All crew members had the opportunity to take at least one nap, possibly more, depending on the length of the flight. Procedure When selected for an international flight to the destinations in question participants were sent an activity monitor by mail and asked to wear it for a minimum of 15 days, beginning four days prior to departure. During the study period participants continued their regular work schedule except for the additional requirement to complete the sleep and work diaries as described in the Measurements section and to wear the activity monitor.

6 - 6 - Measurements Duty Length: Participants also completed a duty diary, which required them to provide information about the start and end time of each flight. This also included the Samn-Perelli Fatigue scale (22) to enable the crew members to rate their levels of fatigue before and after each flight. Objective Sleep Duration: The sleep/wake behaviour of flight crew was monitored objectively using wrist activity monitors (Mini Mitter, Sunriver, Oregon) and Sleepwatch software (Actiware-SleepTM, Cambridge Neurotechnology Ltd.). The actiwatch contained a piezo-electric accelerometer that detected movement with a resultant force above 0.01g. Flight crew wore the activity monitor at all times during the participation period, unless showering or in situations where the monitor was likely to be damaged. Model Development The methodology constructed herein is designed to predict the timing and duration of sleep during international layover periods. Currently, sleep regulation functions do not account for differences in sleep strategies or anticipatory sleep behaviour in their calculations and are therefore poorly suited to complex operational environments (7). There is indication in the existing literature that the level of sleep disturbance experienced by transmeridian flight crew changes over the length of the layover (17, 18). Existing models currently consider layover or break periods as one homogenous time period rather than a dynamic duration where sleep strategies, and therefore its timing changes as a function of time. As a solution to this problem we have devised a sleep regulation model which splits layover periods into three distinct time periods. The stages considered are shown below (Figure 1); Recovery Phase - lasting from flight arrival until the onset of the first local night (2100 hours). Personal Phase lasting from the first local night onset until the offset of the final local night period. Preparation Phase lasting from the offset of the last local night period until flight departure.

7 - 7 - Figure 1. Schematic diagram showing the three phases defined during a layover period; Recovery, Personal and Preparation. Shading indicates flights into and out of the layover location. Black boxes indicate sleep episodes during the layover. White areas indicate periods of wakefulness. The model uses information specific to each layover phase to predict the time of onset and duration of each sleep period within that particular phase. The following steps are carried out in order to systematically predict the timing and duration of all sleep episodes during the layover periods. These steps include the formation of a number of smaller, sub-models designed for different time periods and using different inputs. Recovery Model: Predict the sleep onset time and duration of the first sleep period of a layover using the input variables; layover length, layover start time (in both local and domicile time) and preceding flight length. Preparation Model: Predict the sleep onset time and duration of the final sleep period of a layover using the input variables; layover length, layover end time (in both local and domicile time), and proceeding flight length. Personal Model: Systematically and iteratively predict the remaining sleep periods occurring during the Personal phase. This model uses a combination of pre- and retrodiction using previously estimated sleep information. That is, use the predicted timing and duration of the first sleep period as inputs to estimate the timing and duration of the second sleep. Likewise, use the predicted timing and duration of the final sleep period as inputs to estimate the timing and duration of the penultimate sleep period.

8 - 8 - This method uses a combination of prediction and retrodiction to ensure that the variance accounted for in the model is as large as possible. The method of prediction uses information from past events such as prior flight information to estimate sleep episodes in the future. Retrodiction, on the other hand, uses events which are due to take place in the future to estimate the timing and duration of sleep in the past. Throughout the Personal phase, two estimates of the timing and duration of all sleep periods are made; one using prediction and one using retrodiction. The technique resulting in the solution that accounts for the greatest amount of variance is then chosen and used as input for other sleep episodes. Statistical Analysis The modeling procedure outlined in the previous section was applied to the SYD-LAX and SYD-LHR datasets. For each set of data, models described in the previous section were constructed using mixed modeling techniques. Predictions of the onset time and duration were made for all sleep episodes during all layover periods (alternate model) and compared to i) the actual data as obtained using actigraphy (actual data) and ii) the sleep and wake estimations as obtained by an existing model of fatigue in the literature (two-step model). Correlation coefficients comparing the alternate model and the actual data were calculated and are reported where appropriate. It was possible to calculate 95% Confidence Intervals (95% CI) for the actual data as obtained from actigraphy, as well as for the alternate model showing the level of variability within the data set and are depicted in all graphical results. The two-step model on the other hand does not allow for estimates on inter-individual variability to be made and therefore 95% CI are not included for this method. A detailed discussion of mixed modelling procedures is beyond the scope of this paper, however for a more detailed description on their formulation readers are directed to (23-25). The models created and tested during this analysis only include fixed intercept and coefficient terms for all factors included in the models. It is also possible to include random intercept and coefficient terms within mixed models, however they are not included in the present analysis as the aim of the research is to predict the timing and duration of sleep for an average population member, rather than fit a model to specific individuals.

9 - 9 - RESULTS SYD-LAX Implementation of the alternate modelling approach resulted in estimations of the sleep and wake times undertaken during the Los Angeles and Auckland layovers to be made (Figure 2). Time series analysis indicated that there is a significant strong correlation between the alternate model results and the actual data (r=0.683). Similarly, there is a significant relationship between the actual data and the sleep estimations obtained from the two-process model (r=0.418). Interestingly, the existing two process model is unable to correctly predict the anticipatory sleep behaviour undertaken prior to departure from Los Angeles. The alternate model on the other hand, uses retrodiction to estimate the final anticipatory sleep episode (that is, it uses events that are due to occur in the near future to estimate sleep patterns occurring now). Alternate Actual Two Step 10:20 22:20 10:20 22:20 10:20 22:20 10:20 22:20 Mean Sleep Duration 95% CI of Sleep Timing Australian Eastern Standard Time Figure 2. A schematic diagram comparing the Alternate, Actual and Two-Process sleep data for the SYD- LAX dataset. The white blocks indicate duty periods (flights). The black boxes represent sleep duration and the grey bars represent the 95% CI of sleep timing (i.e. left grey bars represent 95% CI of sleep onset time and right grey bars represent 95% CI of sleep duration) SYD-LHR Implementation of the alternate modelling approach resulted in a good estimation of the actual sleep and wake times undertaken during the Bangkok, London and Singapore (Figure 3). Time series analysis indicated that there is a significant strong correlation between the alternate model results and the actual data (r=0.912). Similarly, there is a significant, albeit a weak, relationship between the actual data and the sleep estimations obtained from the two-process

10 model (r=0.198). Again, the existing two process model is unable to correctly predict the anticipatory sleep behaviour undertaken prior to departure from London and again in Singapore. The alternate model uses retrodiction to estimate the final anticipatory sleep episodes. Alternate Actual Two Step 11:15 23:15 11:15 23:15 11:15 23:15 11:15 23:15 11:15 23:15 11:15 23:15 11:15 23:15 11:15 Mean Sleep Duration 95% CI of Sleep Timing Australian Eastern Standard Time Figure 3. A schematic diagram comparing the Alternate, Actual and Two-Process sleep data for the SYD- LHR dataset. The white blocks indicate duty periods (flights). The black boxes represent sleep duration and the grey bars represent the 95% CI of sleep timing (i.e. left grey bars represent 95% CI of sleep onset time and right grey bars represent 95% CI of sleep duration) Initial Model Validation Protocol In order to fully test the alternate model described in this paper it was important to use the parameter estimates derived using one (or both) of the above data sets to predict rather than model the sleep periods during a similar layover. The data chosen for this task was sleep and wake information obtained from another series of layovers in London collected concurrently with the previously described information (LHR-VAL). In total seven layovers were selected for this initial validation protocol, including one Captain, three First Officers and two Second Officers (one of which participated in data collection twice). This data set possesses very similar characteristics as the previously described SYD-LHR pattern, including layover lengths and flight timing. The only significant difference between the two data sets is that the LHR-VAL has a second flight undertaken upon return to Australia. That is, crews on this pattern return straight to Sydney from Singapore rather than returning via Melbourne. The parameters obtained for the SYD-LHR data were used to estimate the sleep timing and duration for the sleep episodes in the LHR-VAL dataset. A schematic diagram comparing the alternate, actual and two process predictions are shown in Figure 4. Time series analysis indicated that there was a significant strong relationship between the alternate predictions and the

11 actual sleep data (r=.670). On the other hand, relationship between the actual and two process predictions was weak (r=0.123). Predicted Actual Two Step 04:15 10:15 16:15 22:15 04:15 10:15 16:15 22:15 04:15 10:15 16:15 22:15 04:15 10:15 16:15 Mean Sleep Duration 95% CI of Sleep Timing Figure 4. Diagram showing the validation of the alternate model compared to the actual data and two-step model predictions. The white blocks indicate duty periods (flights). The black boxes represent sleep duration and the grey bars represent the 95% CI of sleep timing (i.e. left grey bars represent 95% CI of sleep onset time and light grey bars represent 95% CI of sleep duration). DISCUSSION In this paper a method to estimate sleep timing and duration was presented. This method uses mixed model and a procedure of prediction and retrodiction to determine the sleep onset times and duration of all sleep periods. This method splits the layovers of flight crew into three distinct periods of time; Recovery Phase, Personal Phase and Preparation Phase, exploiting specific information about each. The method described in this paper was used to estimate the sleep timing and duration for layovers conducted on flight patterns to the United Kingdom and United States from Australia s eastern seaboard. It was found that the correlation connecting the actual and alternate model estimates ranged between (United States) and (United Kingdom). This significantly strong relationship compares well to the relationship between the actual data and the sleep estimations obtained using existing two process modeling found in the current literature (r=0.418 for the United States, r=0.198 for the United Kingdom). The results obtained from the SYD-LHR simulation indicated that retrodiction is better at predicting sleep onset and duration for the final sleep period when anticipatory sleep behaviour occurs. The flights from London to Singapore leave at approximately 22:25 ± 0.85 hours local time and last for approximately ± 0.85 hours. To combat the effects of fatigue caused by this long night flight, crossing eight time zones, many flight crew members choose to nap in the

12 afternoon immediately prior to departure, even though the timing of this nap does not occur at a socially nor biologically appropriate time of day. As is currently stands, existing models of fatigue, alertness and/or performance are unable to predict sleep during such periods as it contradicts the fundamental principles of these models. In fact, such models only predict sleep periods during times of high sleep drive (Process S) and/or at times where it is biologically appropriate to do so (Process C). When we considered the raw sleep data obtained from the flight crew it was found that this anticipatory nap usually occurs at 16:00 hours local time (corresponding to 02:00 hours AEST). From 21 participants the raw data shows that 18 chose to have such a nap, again indicating that this strategy is quite common within this employee group. When considering the outbound layover in Bangkok, both the two process and alternate models fit the actual data quite well for a number of reasons. Firstly, the flights from Sydney to Bangkok occur during domicile day time causing minimal disruptions to routine sleep patterns. Secondly, the time zone difference between Sydney and Bangkok is small (three hours West) delaying the biologically appropriate sleep zone by three hours. Thirdly, the layover in Bangkok occurs at the beginning of the SYD-LHR flight pattern therefore fatigue caused by long duty periods and circadian de-synchronization is negligible. On the other hand, the results obtained for the inbound layover in Singapore using the two process model do not fit the actual sleep and wake data as well as the alternate model. This is again, for a number of reasons. Firstly, the sleep patterns of the flight crews by this stage of the duty period are quite fragmented due to its complexity and length. Secondly, social interactions and anticipatory impact on the timing and duration of sleep meaning that it is taking place at times where it is socially rather than biologically appropriate. It is interesting to note that the layovers in Los Angeles and Auckland do not show evidence of the same anticipatory sleep strategies employed in London. In particular, better estimations of the final sleep onset time and duration in Los Angeles was found using prediction rather than retrodiction. One reason for this is that the layover period in LA is too short for flight crews to implement any anticipatory sleep behavior. During a layover of this length, crew members have the choice to either: adapt to LA time, retain a domicile sleep strategy or choose a combination of both. The raw data obtained from the 16 participants conducting the SYD-LAX pattern indicates those crews are split on this issue and rather choose to sleep when they can, for as long

13 as they can. This strategy serves to both recuperate from the ± 0.45 hour flight from Sydney and prepare for the ± 0.21 hour flight from LA to Auckland, New Zealand. Likewise in the case of the layover in Auckland, because the layover is short (23.63 ± 0.95 hours), flight crews do not have the opportunity to split their layovers into the three separate time periods described in this paper. Rather their Post-Flight and Pre-Flight phases are combined and the Personal phase is discarded. As an initial validation attempt we have used the parameter estimates obtained from the SYD-LHR model and applied these to a similar flight pattern s layover in London. It was found that there was a strong fit between this prediction result and the actual data (r=0.670) compared to the fit between the actual data and the two process model (r=0.123). Further validation of this modelling technique is however required using data sets in both a transmeridian and simpler shiftwork environments. Current work includes creating a general set of parameters to fit multiple city pairs in the aviation setting and multiple industries in the shiftwork setting. There are also a number of improvements currently being made to this method of sleep estimation. For example, the results reported in this paper use domicile or Australia Eastern Standard Time (AEST) as a proxy for biological clock time. In the current data collection program biological clock time (in the form of salivary melatonin data) was not measured due to logistical limitations. Initially salivary melatonin samples were to be collected from flight crew to indicate their circadian phase. After attempting this in a pilot study, it was decided to cease the collection as it was difficult to create a collection protocol that did not interfere with the recovery of flight crew while obtaining meaningful data. Rather than using AEST, the biological clock time of flight crew could be estimated using findings found in the literature regarding the time taken to re-entrain the body to a new time zone (26, 27). By gradually adjusting the biological clock time from AEST to local time by the order of one day per hour of time crossed in a westerly direction and one and a half days per hour for easterly travel (28), the sleep onset time and duration estimations from middle-end of the layover could be improved. However, it was thought that this would only replace one estimate of circadian phase with another rather than significantly altering the results and so was not undertaken. The circadian rhythms of long haul flight crew conducting frequent transmeridian travel are constantly changing and re-adjusting. In fact it is doubtful that these rhythms could fully entrain during layovers and on short recovery periods in Australia. For this

14 reason it is very difficult to accurately estimate the circadian phase of the flight crew members included in our analysis. Implications for fatigue and alertness models Currently, most fatigue and alertness models make estimations of sleep and wake times based on Borbély s two process of sleep regulation (1982). This approach uses the physiological parameters circadian phase (C) and homeostatic drive for sleep (S) to make these predictions. The method described in this paper has the potential to be used as an external resource for such models. The sleep and wake times predicted using this new approach could be used to predict subsequent fatigue and/or alertness levels. The subsequent fatigue predictions could then be compared to those obtained using existing models such as those described in the literature (12, 13, 28, 29) The results presented here are for the average number of sleep periods for each layover (as determined from the raw data). This method also allows predictions of sleep times to be made for a number of different sleep episodes per layover (that is, for different sleep strategies). For example in the short Los Angeles layover, it is possible to estimate the sleep times of crew who choose to have a nap upon arrival and one long sleep during the local night period and compare this solution to crew members who choose to have three shorter sleep periods. These estimations could then be used as input into existing fatigue models to determine the differences in fatigue and/or alertness resulting from different sleep strategies. ACKNOWLEDGEMENTS We thank the Qantas flight crew who volunteered to take part in this study and those who assisted with data collection and analysis, in particular David Darwent and Tracey Sletten. This work was supported by Qantas Airways Limited, the Australian Civil Aviation Safety Authority, the Australian and International Pilots Association and the Australian Research Council. Thanks also to Dr Jill Dorrian for her helpful suggestions regarding analysis and manuscript.

15 REFERENCES 1. Folkard S, Akerstedt T. Trends in the risk of accidents and injuries and their implications for models of fatigue and performance. Aviat Space Environ Med 2004;75(3 Suppl):A Folkard S. Report to the Civil Aviation Authority on Work Hours of Aircraft Maintenance Personnel. Swansea, Wales: Body Rhythms and Shiftwork Centre; Folkard S, Hill J. Can we predict perceived risk? J Hum Ergol (Tokyo) 2001;30(1-2): McCulloch K, Sletten T, Baker A, et al. The Management of Workplace Fatigue. Safety in Australia 2002;24(2): McCulloch K, Fletcher A, Dawson D. Moving towards a non-prescriptive approach to fatigue management in Australian aviation: A field validation: The Civil Aviation Safety Authority; 2003 August. 6. Dawson D, McCulloch K. Managing Fatigue: It's about sleep - stupid. Sleep Medicine Reviews (In Press). 7. Kandelaars K, Dorrian J, Roach G, et al. A Review of Bio-Mathematical Fatigue Models: Where to from here? In: Proceedings of the 2005 International Conference on Fatigue Management in Transport Operations; 2005 Sep 11-15; Seattle, USA. 8. Van Dongen HPA. Comparison of Mathematical model predictions to experimental data of fatigue and performance. Aviat Space Environ Med 2004;75(3 Suppl):A Mallis MM, Mejdal S, Nguyen TT, et al. Summary of the key features of seven biomathematical models of human fatigue and performance. Aviat Space Environ Med 2004;75(3 Suppl):A Borbély AA. A two process model of sleep regulation. Hum Neurobiol 1982;1(3): Jewett ME, Kronauer RE. Interactive Mathematical Models of Subjective Alertness and Cognitive Throughput in Humans. J Biol Rhythms 1999;14(6): Folkard S, Åkerstedt T. A Three-Process Model of the Regulation of Alertness- Sleepiness. In: Broughton R, Wilkinson RT, Ogilvie RD, editors. Sleep, Arousal and Performance: Problems and Promises. Boston: Birkheauser; p Belyavin AJ, Spencer MB. Modeling Performance and Alertness: The QinetiQ Approach. Aviat Space Environ Med 2004;75(3 Suppl):A Daan S, Beersma DG, Borbély AA. Timing of human sleep: recovery process gated by a circadian pacemaker. Am J Physiol 1984;246(2 Pt 2):R Åkerstedt T, Folkard S. Validation of the S and C Components of the Three-Process Model of Alertness Regulation. Sleep 1995;18(1): Fletcher A, Dawson D. Field-Based Validations of Work-Related Fatigue Model Based on Hours of Work. Transportation Research Part F 2001;4: Samel A, Wegmann HM. Sleep and circadian rhythms of an airline pilot operating on the polar route: a case study. Aviat Space Environ Med 1988;59(5): Samel A, Wegmann HM, Summa W, et al. Sleep patterns in aircrew operating on the polar route between Germany and east Asia. Aviat Space Environ Med 1991;62(7): Sletten T, Darwent D, Roach G, et al. How well do international aircrew sleep in onboard rest facilities? In: The fifth International Conference on fatigue and transportation; 2003; Fremantle, WA.

16 Darwent D, Sletten T, Roach G, et al. The timing of pilots' sleep following international flight. In: Proceedings of the fifth International Conference on Fatigue and Transportation; 2003; Fremantle, WA. p Roach G, Sletten T, Darwent D, et al. How well do international aircrew sleep during layovers? In: The fifth International Conference on Fatigue and Transportation; 2003; Fremantle, WA. 22. Samn SW, Perelli LP. Estimating aircrew fatigue: a technique with application to airlift operations: Brooks AFB, USAF School of Aerospace Medicine; Report No.: SAM-TR Van Dongen HP, Olofsen E, Dinges DF, et al. Mixed-model regression analysis and dealing with interindividual differences. Methods Enzymol 2004;384: Skrondal A, Rabe-Hesketh S. Generalized Latent Variable Modeling. Boca Raton, FL: Chapman and Hall/CRC; Ingre M, Kecklund G, Akerstedt T, et al. Variation in sleepiness during early morning shifts: A mixed model approach to an experimental field study of train drivers. Chronobiol Int 2004;21(6): Gander PH, Kronauer RE, Graeber RC. Phase shifting two coupled circadian pacemakers: implications for jet lag. Am J Physiol 1985;249(6 Pt 2):R Klein KE, Wegmann HM. The effect of transmeridian and transequatorial air travel on psychological well-being and performance. In: Scheving LF, Halberg F, editors. Chronobiology: Principles and applications to shifts in schedules. The Netherlands: Sitjhoff & Noordhoff; p Hursh SR, Redmond DP, Johnson ML, et al. Fatigue models for applied research in warfighting. Aviat Space Environ Med 2004;75(3 Suppl):A44-53; discussion A Achermann P, Borbély AA. Simulation of daytime vigilance by the additive interaction of a homeostatic and a circadian process. Biological Cybernetics 1994;71(2):