fatigue and performance in heavy truck drivers working day shift

FATIGUE AND PERFORMANCE IN HEAVY TRUCK DRIVERS

WORKING DAY SHIFT, NIGHT SHIFT OR ROTATING SHIFTS

RESEARCH REPORT

December 2004

Prepared by

Ann Williamson (IRMRC), Rena Friswell (IRMRC), Anne-Marie Feyer (PwC)

National Transport Commission Fatigue and Performance in Heavy Truck Drivers Working Day Shift, Night Shift or Rotating Shifts

Report Prepared by: Ann Williamson (IRMRC), Rena Friswell (IRMRC), Anne-Marie Feyer (Pwc) ISBN: 1 877093 77 7

REPORT OUTLINE

Date: December 2004

ISBN: 1 877093 77 7

Title: Fatigue and Performance in Heavy Truck Drivers Working Day Shift, Night Shift or Rotating Shifts

Address: National Transport Commission Level 15/628 Bourke Street MELBOURNE VIC 3000

E-mail: [email protected] Website: www.ntc.gov.au

Type of report: Research Report

Objectives: This project was designed to:

1) directly compare the impact of day and night driving on the fatigue and performance of heavy vehicle drivers under real working conditions; and

2) determine whether levels of fatigue engendered by regular night driving pose a threat to road and occupational safety

Abstract: Research on shiftwork has shown that fatigue and related performance problems increase at night. In Australia, this has lead to calls to limit night work for long distance heavy vehicle drivers. However the evidence for diminished performance at night among heavy vehicle drivers is equivocal, and the risks posed need to be weighed against different safety risks inherent in daytime driving. This study sought to directly compare the impact of day and night shift rosters on the fatigue and performance of heavy vehicle drivers going about their normal work. Self-reported fatigue increased similarly over the week for all drivers, but more over individual night shifts than individual day shifts. There were also indications of slowing response speed over the week but this effect was not different for night and day drivers. In short, night shifts made drivers feel more tired than day shifts, but did not produce significantly poorer performance, suggesting that night drivers can manage their fatigue. Whether these results would also apply to drivers working the types of irregular shifts common in the long-haul road transport industry requires further investigation.

Purpose: For information

Key words: fatigue, driving, performance, long distance road transport, trucking

FOREWORD

The National Transport Commission’s role is to lead transport regulatory reform nationally to meet the needs of transport users and the broader Australian community for safe efficient and sustainable land transport.

Improving the safety of Australian transport operations is a key objective and is clearly highlighted in this report. The Commission has recognized the importance of fatigue as a direct or indirect cause of crashes in the long distance road transport industry. It has undertaken a significant research and policy program in recent years to better understand the role of fatigue in these crashes and to develop well based regulatory arrangements that will reduce fatigue levels in heavy vehicle drivers.

This project was designed to provide important insights into the relative fatigue levels of drivers who operate day shifts and those who drive at night and into the ways in which drivers manage their sleep arrangements .to minimize the impacts of fatigue.

The project was undertaken by Dr Ann Williamson and Ms Rena Friswell of the Independent Risk Management Research Center of the University of New South Wales in conjunction with Associate Professor Anne-Marie Feyer of Price Waterhouse Coopers. The project relied on getting information from drivers as the went about their business rested and performed their family and social responsibiities. This in itself proved a challenge for the project team and demonstrated the diversity of operations in the heavy vehicle transport industry and the difficulties of matching the working arrangements of drivers who perform their tasks at different times of the day.

The report provides interesting insights into the similarities and differences in the ways fatigue impacts on drivers who perform day, night or rotating day and night shifts. It will also assist in designing future “on-road” studies which aim to obtain information direct from the workers themselves. More importantly, the findings of this report will feed into the Commission’s overall current and future knowledge of how fatigue affects the driving performance of long distance heavy vehicle drivers.

The Commission wishes to thank all who participated.

ACKNOWLEDGMENTS

This study was undertaken with financial support from the National Road Transport Commission (NRTC), the Australian Transport Safety Bureau (ATSB), the NSW Roads and Traffic Authority (RTA) and the New Zealand Land Transport Safety Authority (LTSA NZ).

The study would not have been possible without the support of the participating transport companies and the individual drivers who kindly took part.

We would like to thank Professor Philippa Gander for providing the PVTs used in the study and for advice on their use.

We were fortunate to have some very dedicated and capable people assisting with data collection. Helen Gardiner and Kristin Rogers managed data collection with drivers working out of Melbourne. Peter Hardy, Emma Grove, Therese Ma and Alex Symonds played a similar role in Sydney. Emma, Therese and Alex also made an invaluable contribution to the production of this report.

We would also like to thank Hugh McMaster at the NSW Road Transport Association for his help in finding companies employing suitable shift rosters.

SUMMARY

This study took up the issue of whether there is a differential effect of night work on fatigue experiences and effects in long distance road transport driving compared to day work. The study involved on-road evaluation of 22 drivers doing permanent day shifts and 21 drivers doing permanent night shifts. This allowed comparison between drivers of levels of subjective ratings of fatigue and its effects on performance. In addition, a group of 11 drivers who were doing weekly rotating day and night shifts were also included in the study allowing examination of the effects of night and day driving in the same people. Each permanent shift driver and each rotating shift driver under each type of shift was followed for two full weeks while doing their normal work-rest schedules. The evaluation involved questionnaires asking about work and rest activities leading up to the start of the study period, demographic characteristics and details of consumption of social drugs and sleep characteristics. Drivers kept a diary of work-rest activities during the study weeks and to make ratings of their subjective fatigue and quality of last sleep at the beginning and end of each period of work separated by breaks of at least 15 minutes. Objective measures of timing and quality of sleep were taken using actigraphs worn for the two week study time. Drivers completed tests of concentration and reaction speed at the start of the study period, and at the end of the last work shift in the first and second week of the study. Drivers also self-administered shorter versions of the tests at the start and end of each work shift and at the start of one mid-shift break in each shift.

The results showed increasing fatigue ratings across the work week which were clearly more pronounced for night shift in both permanent shift and rotating shift drivers. There was also evidence of performance decrements, especially slowing of response speed, between the beginning and end of the work week, but this was shown for all drivers. Night driving did not have any clear differential effects on performance compared to day driving in either permanent or rotating shift drivers.

The results suggest that in practice, night driving may not be as different from day driving as previous research might suggest. This might be at least partly due to the fact that all drivers in this study did similar long hours of work and reported roughly equal degrees of fatigue across the work weeks. There were some differences between night and day drivers in this study, but these differences were also likely to have the effect of balancing the effect of night work with effective sleep. While drivers on night shifts did more driving work and permanent night drivers did longer trips compared to day drivers, night drivers used their rest differently. Night drivers apparently balanced the greater demands of night work by taking their rests earlier in the break period and taking longer rests and incorporating more naps into their days off when there was time to do so. Rotating shift drivers on day shift who needed to adapt to very early morning starts showed a similar response.

In conclusion, the study indicates that consecutive night driving shifts in a regular work-rest schedule clearly make drivers feel more tired than day driving shifts, but they do not necessarily produce significantly poorer or unacceptable levels of performance decrement in professional drivers who are accustomed to night work. It may be misleading, however, to extend these results to other work-rest schedules, especially where the schedule is irregular, or where work, break and sleep times differ from those undertaken by the drivers in this study.

The full report of this research can be found at www.ntc.gov.au

CONTENTS

1. INTRODUCTION ............................................................................................................. 1

2. MEASURING THE IMPACT OF SHIFT ROSTERS ON FATIGUE ........................ 3 2.1 Subjective measures ...............................................................................................................3

2.1.1 Current Fatigue ..................................................................................................................... 3 2.1.2 Sleep problems and typical sleepiness................................................................................... 4

2.2 Performance ...........................................................................................................................6 2.3 Sleep.......................................................................................................................................9

3. METHOD......................................................................................................................... 11 3.1 Design ..................................................................................................................................11 3.2 Participants ...........................................................................................................................11

3.2.1 Participation overview......................................................................................................... 11 3.2.2 Description of driver groups ............................................................................................... 12

3.3 Materials and measures ........................................................................................................16 3.3.1 Paper and pencil measures.................................................................................................. 16 3.3.2 Performance tests ................................................................................................................ 16 3.3.3 Actigraphy ........................................................................................................................... 18

3.4 Procedure..............................................................................................................................18 3.5 Analysis................................................................................................................................19

4. RESULTS......................................................................................................................... 21 4.1 Events preceding the study period .......................................................................................21

4.1.1 Work .................................................................................................................................... 21 4.1.2 Rest ...................................................................................................................................... 23 4.1.3 Eating and drinking ............................................................................................................. 26

4.2 Events during the selected study period ...............................................................................28 4.2.1 Work during the selected study week................................................................................... 29 4.2.2 Rest during the selected work week ..................................................................................... 33

4.3 Comparison of subjective fatigue between beginning and end of selected study period....................................................................................................................................43

4.4 Comparison of performance at the beginning and end of the selected study period....................................................................................................................................45 4.4.1 Simple Reaction Time task................................................................................................... 45 4.4.2 Mackworth Clock Vigilance task ......................................................................................... 46 4.4.3 Psychomotor Vigilance task (PVT)...................................................................................... 48

4.5 Subjective fatigue changes from shift to shift across the selected study period...................51 4.6 Performance changes from shift to shift across the selected study period ...........................55

4.6.1 Simple Reaction Time task................................................................................................... 55 4.6.2 Mackworth Clock Vigilance task ......................................................................................... 60

5. DISCUSSION................................................................................................................... 67

6. CONCLUSIONS.............................................................................................................. 73

7. REFERENCES ................................................................................................................ 75

8. APPENDICES.................................................................................................................. 79 8.1 Consent form........................................................................................................................80 8.2 Background questionnaire....................................................................................................84 8.3 Excerpt from work and rest diary.........................................................................................95 8.4 Palmtop instruction sheet ...................................................................................................100 8.5 Actiwatch instruction sheet ................................................................................................101

LIST OF TABLES

Table 1. Summary of population samples completing the Epworth Sleepiness Scale ............................. 5 Table 2. Demographic characteristics of participants.............................................................................. 12 Table 3. Health problems reported by participants ................................................................................. 13 Table 4. Smoking, caffeine and alcohol use reported by participants .................................................... 14 Table 5. Sleep problems and sleepiness reported by participants........................................................... 15 Table 6. Characteristics of the last shift before the start of the study and of the last shift

before the selected week for analysis........................................................................................... 22 Table 7. Characteristics of rest before the start of the study................................................................... 24 Table 8. Characteristics of rest before the selected work weeks of the study......................................... 26 Table 9. Food and drug intake before the start of the study.................................................................... 28 Table 10. Workload during study work week for drivers working under different shift

conditions....................................................................................................................................... 29 Table 11. Mean shift durations over the selected work week .................................................................... 32 Table 12. Mean (SD) hours driving and doing other work during shifts in the selected work

week ............................................................................................................................................... 32 Table 13. Mean duration of long breaks between shifts over the selected work week ............................ 34 Table 14. Mean duration of reported short breaks within shifts over the selected work week .............. 34 Table 15. Numbers of drivers with actigraph sleep data between work shifts in selected work

week ............................................................................................................................................... 35 Table 16. Mean (SD, n) hours sleep obtained in the first sleep in each break between shifts for

drivers in each group.................................................................................................................... 38 Table 17. Mean (SD, n) rated sleep quality and refreshingness of the last sleep in each break

between shifts in the selected work week for drivers in the different shift conditions............ 41 Table 18. Actual sleep obtained by drivers during the mid-study weekend............................................. 42 Table 19. Percentage of midstudy weekend break time spent in actual sleep.......................................... 43 Table 20. Mean subjective fatigue ratings across the selected study period............................................. 44 Table 21. Simple Reaction Time responses across the selected study period ........................................... 45 Table 22. Mackworth Clock Vigilance responses across the study period ............................................... 47 Table 23. Psychomotor Vigilance Task responses across the study period for day and night

shift drivers ................................................................................................................................... 49 Table 24. Psychomotor Vigilance Task responses across the selected study week for rotating

shift drivers ................................................................................................................................... 50 Table 25. Pre- and post-PVT sleepiness ratings for tests at baseline and the end of the selected

study week ..................................................................................................................................... 51 Table 26. Percentage of drivers in each shift condition with subjective fatigue ratings at the

start and end of breaks during the week..................................................................................... 53 Table 27. Number of drivers in each shift condition with palmtop Reaction time test data for

each shift in the week.................................................................................................................... 56 Table 28. Number of drivers in each shift condition with palmtop Mackworth Clock Vigilance

test data for each shift in the selected study week...................................................................... 60

LIST OF FIGURES

Figure 1. Start and end times of consecutive shifts over the selected week for permanent day shift drivers (left panel) and night shift drivers (right panel; with 95% confidence intervals) .............................................................................. 30

Figure 2. Start and end times of consecutive shifts over the selected work week for rotating shift drivers during their day week (left panel) and their night week (right panel; with 95% confidence intervals) ........................................................ 31

Figure 3. Time at sleep onset for the first sleep in each break between shifts in selected work week – percent of drivers in each group ...................................................... 36

Figure 4. Time at waking for the first sleep in each break between shifts in selected work week – percent of drivers in each group ...................................................... 37

Figure 5. Total hours of sleep scored in each break between shifts in the selected work week (with 95% confidence intervals) ................................................................... 39

Figure 6. Mean percentage of time between sleep onset and waking that scored as sleep, for the first sleep in each break between shifts in the selected work week for drivers in each group (with 95% confidence intervals) ........................ 40

Figure 7. Averaged subjective fatigue ratings at the starts(S) and ends(E) of shifts and at the starts and ends of midshift (_1) breaks (with 95% confidence intervals) ................................................................................................................... 54

Figure 8. Mean reaction time (ms) on the palmtop Reaction Time test at the start (S), middle (M) and end (E) of consecutive shifts in the week (with 95% confidence intervals)................................................................................................ 57

Figure 9. Mean variability (SD) in reaction time (ms) on the palmtop Reaction Time test at the start (S), middle (M) and end (E) of consecutive shifts in the week (with 95% confidence intervals) ............................................................................. 58

Figure 10. Mean number of missed responses on the palmtop Reaction Time test at the start (S), middle (M) and end (E) of consecutive shifts in the week (with 95% confidence intervals)................................................................................................ 59

Figure 11. Mean reaction time (ms) on the palmtop Mackworth Clock Vigilance test at the start (S), middle (M) and end (E) of consecutive shifts in the week (with 95% confidence intervals)....................................................................................... 62

Figure 12. Mean standard deviation on the palmtop Mackworth Clock Vigilance test at the start (S), middle (M) and end (E) of consecutive shifts in the week (with 95% confidence intervals)....................................................................................... 63

Figure 13. Mean number of misses on the palmtop Mackworth Clock Vigilance test at the start (S), middle (M) and end (E) of consecutiveshifts in the week (with 95% confidence intervals)....................................................................................... 64

Figure 14. Mean number of false alarms on the palmtop Mackworth Clock Vigilance test at the start (S), middle (M) and end (E) of consecutive shifts in the week (with 95% confidence intervals) ............................................................................. 65

Fatigue and Performance in Heavy Truck Drivers Working Day Shift, Night Shift or Rotating Shifts 1

1. INTRODUCTION

The long distance road transport industry is a 24 hour industry, requiring drivers to work at all times of the day and night. The demand for round-the-clock service is one of the major challenges for the industry along with the need to drive for long hours. There is considerable evidence that work performance is poorer in the midnight to dawn period compared to day time hours (Folkard & Monk, 1979), especially for tasks like driving that require sustained attention over long periods. This effect is due to biological or circadian rhythms, which play a role in ensuring that the body obtains sufficient rest and recovery between waking periods. This rhythm also causes changes in the capacity to maintain alertness and safe performance over the 24 hour period and therefore has implications for safety. These biological influences can also be seen in the higher risk of road traffic crashes in the midnight to dawn period compared to other times across the 24 hour period (US Department of Transportation, 2000).

Traditionally there has been little acknowledgement of circadian influences on work performance in the regulated approaches to work-rest scheduling in the road transport sector. Working hours regulations have always focused solely on the hours that drivers were allowed to work, but not on the time of day in which the work was being done. The recent report from the Fatigue Expert Group convened by the NRTC emphasised the importance of accounting for time of day in the development of work-rest schedules in order to reduce fatigue for drivers and increase safety in the long distance road transport industry. This issue has been taken up in the NRTC’s review of the regulatory approach to work-rest scheduling, and circadian influences are one of the issues that they are attempting to address.

The round-the-clock demands of the industry, however, mean that the problem may not be just a simple matter of reducing night driving in the industry. It is argued that reductions in night driving may be unnecessary as the evidence for the adverse effects of night driving on performance have not been demonstrated conclusively in the long distance road transport industry. One research project in the industry compared day and night driving and showed that for rested drivers, there was no difference between day and night driving over a 14 hour shift for either subjective fatigue or performance (Williamson, Feyer, Finlay-Brown and Friswell, 2000a). This was only over a single shift, however. As long distance drivers are currently permitted to do up to six shifts in succession under the regulated regime and up to 12 under the transitional fatigue management scheme (TFMS), it is important to compare day and night driving over more than a single shift. In addition, research highlights the critical importance of the accumulation of fatigue over consecutive shifts (Dinges, Pack, Williams, Gillen, Powell, Ott, Aptowicz, & Pack, 1997; Rosekind, Neri, & Dinges, 1997). Furthermore, it has been argued that there would be adverse consequences of moving most heavy vehicle travel into the day time periods because of the increased exposure to other traffic also using the road in the day time (Buxton, Hartley and Buxton, 2001). It is argued that the risk of crashing at night would need to be significantly higher than during the day in order to justify reducing night travel for heavy vehicles. There is clearly a need for direct evidence on the impact of night driving compared to day driving on fatigue and work performance and road safety for long distance heavy vehicle drivers under operational conditions.

The aims of this project therefore were:

2 Fatigue and Performance in Heavy Truck Drivers Working Day Shift, Night Shift or Rotating Shifts

• To understand the relative impact of night driving on heavy vehicle driver fatigue and performance compared to similar hours of driving during the day.

• To determine whether the level of fatigue for drivers involved in regular night shifts is a road safety problem.


2. MEASURING THE IMPACT OF SHIFT ROSTERS ON FATIGUE

Measuring driver fatigue is challenging for a number of reasons. Theoretically, fatigue is not well understood. As a result, it is typically measured as a multidimensional phenomenon with subjective, performance and physiological indicators, each of which may reflect some different combination of sleep need and task fatigue and each of which may be affected by motivational and environmental factors. This, in turn, means that the relationships between subjective, performance and physiological indicators of fatigue at any given point in time and across time are not straightforward. Indeed, different types of measures make different assumptions about how fatigue behaves over time. Subjective measures, for example, implicitly assume that fatigue is a relatively slowly changing tonic state, however some researchers have argued that fatigue (or at least sleepiness) may also undergo fluctuations from moment to moment with important consequences for a person’s ability to operate effectively and equally important implications for the most informative type of measures (Dinges & Kribbs, 1991).

Current approaches to driver fatigue measurement have been strongly shaped by pressure to apply them to real-world problems in the workplace. Within occupational settings, the need to minimise measurement time and to maximise compliance by ensuring that measures are simple, easy and have face validity for the work at hand is a strong influence on the selection of measures and the measurement regime.

In view of the considerable gaps in our basic understanding about fatigue, the constraints imposed by the workplace and the impact that applied research findings may have on people’s working lives, the choice of fatigue measures is important. This section outlines the rationale for the measurement instruments used in the current study.

2.1 Subjective measures

2.1.1 Current Fatigue

Subjective experience is one tool that individuals in the workplace are likely to use to judge their own fatigue level and to make fatigue management decisions. In this sense, subjective measures are an important component of any fatigue measurement battery. There is also some evidence to suggest that subjective experience may be more sensitive to fatigue (or at least sleepiness) than performance or physiological measures because sleepiness ratings have been shown to respond to sleep deprivation before effects on other the measures become evident (e.g., Akerstedt & Gillberg, 1990; Dinges et al., 1997; Jewett, Dijk, Kronauer and Dinges, 1999; Williamson, Feyer, Mattick, Friswell, & Finlay-Brown, 2001b). However, subjective measures suffer a number of drawbacks. Establishing the validity of subjective measures of fatigue is problematic because fatigue is not a clear theoretical concept. So, for example, we do not understand why subjective measures do not always relate strongly to performance and physiological measures of fatigue. The lack of concordance between measures may occur because people are poor at making absolute judgements about their fatigue but rather, may perceive their current level of fatigue relative to their typical recent experience or indeed recent events such as the amount of sleep obtained the previous night. Of course, subjective measures of fatigue have the added disadvantage that responses can be easily manipulated by respondents, which is an issue particularly pertinent in a workplace setting where participants may have a vested interest in the outcome of a study.


A number of different measures of current (‘state’) fatigue have been used in studies of driver fatigue, in particular, the Stanford Sleepiness Scale (SSS; Hoddes, Zarcone, Smythe, Phillips, & Dement, 1973), the Karolinska Sleepiness Scale (KSS; Akerstedt & Gillberg, 1990) and Visual Analogue Scales (VAS; e.g., Williamson, Feyer, Friswell & Finlay-Brown, 2000b; Williamson et al., 2001b). All of these measures have been validated to an extent. For example, scores increase with continuous time awake and vary with time of day (Williamson et al., 2000b; Akerstedt & Gillberg, 1990) but there is little direct comparative evidence for choosing between them. In studies where more than one of the measures has been used, the results tend to be similar. For example, Dinges, Maislin, Kuo, Carlin, Powell, van Dongen and Mullington (1999) used all three types of measures in their study of sleep restricted to 4, 6 or 8 hours in bed over a 14 day period. The measures were consistent but none were sensitive to the degree of sleep restriction. Dinges et al. (1997) also found agreement between the SSS and a sleepy-alert VAS over 7 days of sleep restricted to approximately 5 hours per night.

The SSS and KSS were both designed specifically to capture sleepiness. The SSS consists of 7 sets of statements graded according to the level of sleepiness they portray. The statements were selected empirically using a sort technique and Thurstone scaling. Hoddes et al. (1973) reported that the scale is sensitive to sleepiness changes over 15 minute intervals and differentiated a rested day from a sleep deprived day. Interestingly, Balkin, Thorne, Sing, Thomas, Redmond, Wesensten, Russo, Williams, Hall and Belenky (2000) recently reported that the SSS was affected by a 3 hour restriction on time in bed but showed no effect of sleep restriction regimes of 5 or 7 hours in bed per night over 7 nights. This suggests either that the SSS is not sensitive enough to detect the resulting changes in sleepiness or that such a regime was insufficient to produce noticeable alterations in subjective fatigue. Indeed, these results are consistent with those of Dinges et al. (1999), whose sleep restriction regimes were all longer than 3 hours.

The KSS is a 9 point Likert scale ranging from 1 (Extremely Alert) to 9 (Extremely Sleepy – fighting sleep) with intermediate anchor terms placed at each or alternate points. However, Akerstedt and Gillberg (1990) reported that the KSS and a 100mm VAS scale using the Extremely Alert and Extremely Sleepy end anchor terms behaved almost identically over a night of sleep deprivation.

In Australia, Williamson and colleagues have typically employed three 100mm VAS scales, with anchors labelled Fresh – Tired, Clear headed – Muzzy headed, and Very alert – Very drowsy, to capture different aspects of the driver fatigue experience (Williamson, Feyer, & Friswell, 1996; Feyer, Williamson, & Friswell, 1997; Williamson et al., 2001b; Williamson et al., 2000a; Williamson, Feyer, Friswell, & Finlay-Brown, 2000c). The last scale mirrors the alert-sleepy dimension used in the KSS. Both the alert-drowsy and clear headed-muzzy headed dimensions (devised by Norris, 1971) loaded on the same factor (general alertness) when analysed together with other mood scales, which suggests construct overlap (Bond & Lader, 1974; Herbert, Johns & Dore, 1976). VAS scales have the advantage that they only require people to negotiate 2 anchor terms when deciding their rating, and the use of multiple scales helps to ensure that ratings are not tied solely to sleepiness. Consequently, these VAS scales were used in the current study.

2.1.2 Sleep problems and typical sleepiness

It is important to measure drivers’ usual levels of sleepiness to properly understand the impact of their shift rosters on subjective fatigue and performance. The Epworth


Sleepiness Scale (ESS; Johns, 1991; 1992) was designed as a subjective measure of habitual daytime sleepiness levels. It was developed as a portable, quick and inexpensive alternative measure of chronic sleepiness to EEG-based sleep latency measures, in order to identify people with excessive sleepiness who may be at risk for sleep disorders. Importantly, however, the ESS does not directly measure sleep disturbance or symptoms of sleep disorders so that scores may reflect chronic sleepiness resulting from situational as well as medical circumstances (Pack, Dinges & Maislin, 2002). Further, unlike objective sleepiness measures like the Multiple Sleep Latency Test (MSLT) which is limited to measuring sleepiness at particular points in time, the ESS attempts to measure usual sleepiness. Of course, being a self-report measure, the ESS is open to the same sorts of problems noted above for current fatigue ratings. That is, responses may be inaccurate either through intentional misrepresentation or simply because the ability to make judgements about one’s typical sleepiness varies. In either case, ESS results deserve the same cautions applied to all subjective measures.

The ESS consists of eight situations from daily life and respondents are asked to rate, on a scale from 0 to 3, how likely they are to doze or sleep in each situation. According to self-reports, the situations are differentially likely to support dozing in people with non-excessive sleepiness. Responses to the eight individual situations are summed to yield the overall score for the scale (/24) with higher scores reflecting higher levels of typical daytime sleepiness. Scores of 10 or less have been shown to typify people without sleep pathology (i.e., ‘normal’ levels of daytime sleepiness), and higher scores increasingly typify people suffering some form of ongoing sleep disruption (Johns, 1991; Johns & Hocking, 1997).

With growing interest in the road safety implications of excessive sleepiness, ESS profiles have been reported for a number of driver samples (unselected for sleep disorders). Table 1 summarises a number of these profiles together with those of other occupational groups. There is considerable variation between the samples in the mean Epworth scores and also in the proportion exceeding 10. Interestingly, however, reports from two of the large surveys of Australian drivers (Swann, 2000; Williamson et al., 2001a) produced quite similar sleepiness profiles for truck drivers. Excessively sleepy truck drivers seemed to comprise about 16 to 18% of the truck driver population in Australia on the basis of these studies. A third study of randomly selected drivers in the states of Victoria, NSW and Queensland found that 24% of drivers scored over 10 (Sleep Disorders in Australian Transport Drivers Study, 1999-2002). Despite the variation between the three large Australian surveys, truck drivers do not appear to have an aberrant sleepiness profile compared to samples of car drivers or other occupational groups.

Table 1. Summary of population samples completing the Epworth Sleepiness Scale ESS profile (%)

Study Sample (n) Country Mean (SD)

≤10 11-15 ≥16

Williamson, Feyer, Friswell & Sadural (2001a)

Truck drivers (942)

Aus 7.0 (3.8) 82.1 15.9 2.0

Swann (2000) Truck drivers (>3500)

Aus - 84 13.1 2.9

Sleep Disorders in Australian Transport Drivers Study, 1999-2002 1

Truck drivers (2164)

Aus 2 7.6 (4.3) 75.9 24.1

Desai, Newcombe, Bartlett, Joffe, & Grunstein (2000)

Truck Drivers (158)

Aus - 66 (<10)

34 (≥10)


ESS profile (%)

Study Sample (n) Country Mean (SD)

≤10 11-15 ≥16

Maycock (1997) 3 Male car drivers (4621)

UK - 86.75 12.0 1.25

Philip, Taillard, Quera-Salva, Bioulac, Akerstedt (1999)

Car drivers <30yrs (101) ≥30yrs (193)

France 6.4 (3.2) 6.6 (3.5)

- -

- -

- -

Cummings, Koepsell, Moffat, Rivara (2001)

Highway car drivers (199)

USA 77.5 22.5

Melamed & Oksenberg (2002) Factory day workers (532)

Israel 9.2 (4.7) 77.4 22.6

Johns (1992) Medical students (104)

Aus 7.6 (3.9) 75.0 23.1 1.9

Johns & Hocking (1997) Company employees (331)

Aus 5.8 (4.0) 89.1 9.4 1.5

1 Dr Mark Howard, Austin Hospital. Personal Communication, 15 Mar 2004; data not yet published 2 Australia 3 Percentages were estimated from graphed data

Self report measures of symptoms of sleep disorders are useful as a gross screening tool for study participants who may be fatigued for reasons unrelated to their work pattern. Stoohs, Bingham, Itoi, Guilleminault and Dement (1995) for example surveyed 388 truck drivers working for a large American company and then, for 159 of these drivers, monitored at least 6 hours of their sleep at the truck depot. These researchers collected self-reports of the frequency of daytime sleepiness, daytime napping, restless sleep, difficulty falling asleep, difficulty maintaining sleep, snoring and breathing disruptions using 5-point response scales anchored with the terms ‘never’, ‘rarely’, ‘sometimes’, ‘often’, ‘always’. Only a minority of the full sample of drivers reported experiencing the sleep difficulties ‘often’ or ‘always’ (daytime sleepiness 9.3%, daytime napping 9.5%, restless sleep 12.4%, difficulty falling asleep 9.2%, difficulty maintaining sleep 7.0%, snoring 20.6%, and breathing lapses 3.6%). However, drivers reporting regular symptoms of daytime sleepiness showed more objective evidence of respiratory disturbance during sleep than drivers reporting less frequent daytime sleepiness.

Both the ESS and measures of sleep symptomatology were used in the current study to identify habitual driver sleepiness and to isolate potential causes.

2.2 Performance

Performance measures tapping driver fatigue fall into two main classes: those that capture some aspect of driving itself and those that attempt to measure more fundamental cognitive and motor functioning. Although evidence, especially from simulator studies, suggests that driving performance indices of, for example, steering integrity are reliably affected by fatigue (Balkin, Belenky, Bliese & Wesensten, 2003), such driving measures can be costly and difficult to implement in situ, especially where multiple workplaces are involved. Consequently, the current study used psychomotor test measures to index performance.

Some psychomotor performance tests tap very basic abilities which are clearly important for many tasks, including driving (e.g., reaction speed, the ability to sustain attention). Other performance tasks tap abilities which are not obviously related to driving, but which


may nonetheless index fatigue or sleepiness (e.g., memory). These tasks have lower face validity for drivers but also typically require a more complex cluster of abilities, which may make them less reliable measures of fatigue if not all of the component abilities are affected equally. In their review of the literature on sleepiness and performance, Dinges and Kribbs (1991) concluded that effects of sleep deprivation have indeed been seen more consistently on simpler tasks than on more complex tasks. However, this may also be a reflection of the fact that task complexity and interestingness are often confounded (e.g., Wilkinson, 1968), so that complex tasks are more likely to motivate respondents thus masking fatigue or sleepiness effects. Assuming that tasks are suitably dull, the more complex the task, the more difficult it becomes to site the source of a performance change. Performance on a memory task, for example, may decline because respondents are not able to pay full attention when to-be-remembered items are presented so that attention, not memory per se, or some more general de-arousal, may be the critical component (see Dinges & Kribbs, 1991). All of these issues point to simple, dull tasks as providing the most unambiguous measures of psychomotor performance for capturing fatigue. Until we know more about the fundamental nature of fatigue and its effects, these issues cannot be resolved.

This study used the tests with the strongest evidence of sensitivity to fatigue. Three such tests were selected - the Psychomotor Vigilance Task (PVT), the Mackworth Clock Vigilance task (MAK), and a Simple Reaction Time task (SRT). These tasks are all variants on the simple visual reaction time paradigm. That is, all three require respondents to indicate that they have witnessed a particular type of stimulus event by pressing a response key as quickly as possible. The PVT and the MAK are both prolonged tasks lasting 10 and 15 minutes respectively, whereas the SRT is a short task with a high stimulus rate (40 stimuli in 2 minutes). The PVT task typically provides approximately 100 stimuli whereas the stimulus rate for the MAK is much lower, with only one stimulus occurring in each minute.

The PVT has been used extensively in studies of sleep loss and fatigue (e.g., Balkin et al., 2000; Dinges et al., 1999; Dinges et al., 1997 Pack et al., 2002). The test requires people to observe a small display screen and to respond to the appearance of digits on the screen by pressing a button as quickly as possible. The digits increment until the test taker responds, thus providing trial by trial feedback on response speed. Because the task is run for 10 minutes, declines over a period of extended attention and performance (the vigilance decrement) can be captured. The task also appears to be largely unaffected by practice (Kribbs & Dinges, 1994). Another advantage of the test is that commercial production of PVT testing units and automated scoring (e.g., Dinges & Powell, 1985) have encouraged the adoption of a standard set of task parameters allowing the results from different studies to be compared. Standard PVT measures include the mean and variation in raw reaction times, in reciprocal reaction times and in the upper and lower deciles of the reaction times. Scoring also extracts the number of long (slow) reaction times, or “lapses”, and characteristics of the vigilance decrement such as the slope of reaction times across the task duration. Dinges and Kribbs (1991) have argued that performance decrements resulting from sleepiness can occur either as sporadic lapses associated with EEG changes indicative of (micro)sleep onset or as sustained reductions in performance perhaps due to some more overarching cognitive or response slowing or an increased susceptibility to habituation. The numerous measures derived from PVT performance reflect these views about the nature of fatigue.


A considerable body of research has shown that the PVT is sensitive to sleep deprivation and sleep restriction. Of most relevance to the current research are studies of the effect of cumulative sleep restriction on performance because these studies most closely parallel real world sleep loss. Dinges et al. (1999) restricted time in bed to 4, 6 or 8 hours per night for a period of 14 days. The rate of decline in PVT responding (number and duration of lapses) over days was greater the less the participants had been allowed to sleep. Similarly, Drake, Roehrs, Burduvali, Bonahoom, Rosekind and Roth (2001) reported deteriorating median RT after two nights of sleep restricted to 4, but not 6, hours in bed.

Recently, Balkin et al. (2000) reported a dose-response study looking at the effects on PVT measures of graded sleep restriction over 7 nights. The 66 heavy vehicle drivers who participated were subjected to a regime of either 9, 7, 5, or 3 hours in bed each night during the sleep restriction phase of the study. The results confirmed that PVT performance (reciprocal RT and transformed lapses) declined across days for the 3, 5 and 7 hour regimes with the 3 and 5 hour regimes being generally more affected than the 7 hour regime. Performance decline was continuous across the 7 days for the 3 hour regime but occurred early in the study then plateaued in the 5 and 7 hour regimes (Belenky, Wesensten, Thorne, Thomas, Sing, Redmond, Russo & Balkin, 2003). Interestingly, the researchers observed incomplete recovery of function in all except the 9 hour regime even after 3 recovery nights. Dinges et al. (1997) measured 16 people across 2 baseline days (sleep mean 7.4hrs per night), 7 sleep restriction days (sleep mean 4.98hrs per night), and 1 recovery day (sleep mean 7.94hrs per day). The number of PVT lapses increased and the fastest decile of RTs slowed after the second night of sleep restriction and continued to deteriorate over the week, particularly following the 7th night of restricted sleep. The slowest decile of RTs slowed significantly after the first and second nights of sleep restriction and again after the 7th night. Performance did not return to baseline levels after a single recovery night, but further monitoring showed full recovery after two unrestricted sleep nights.

Together, these studies indicate dose dependent declines in PVT performance over consecutive days following moderate as well as severe sleep restriction. Marked effects of sleep restriction appear to occur early in the sleep restriction period (the first two days) for moderate restriction regimes and recovery from even moderate restriction regimes is surprisingly slow.

Both the SRT and MAK are used in the computer-based PIPS Test Battery. In the MAK task, respondents are presented with a series of dots arranged in a circle on a computer screen. Each consecutive dot flashes in turn for the duration of the task. The stimulus event occurs when the flash skips a dot. Measures of reaction time, the number of missed responses and the number of false alarm responses are scored. Gillberg & Akerstedt (1998) used a similar task with the dots presented in a horizontal line rather than a circle and 32 stimulus events presented over 34 minutes. The SRT presents respondents with a circle moving around a computer screen. The stimulus event is a change in the colour of the circle. Reaction time and the number of missed responses are scored.

The MAK and SRT tasks have been used in previous studies of driver fatigue by the current authors (Williamson et al. (2000a; 2000c). Williamson et al. (2000b; 2001b) identified these tasks from among eight as being most sensitive to a night of sleep deprivation. In their study, 20 long distance truck drivers and 19 non-driver controls completed tests from the PIPS test battery at 2 hourly intervals over a period of approximately 28 hours. All performance measures on the MAK and SRT declined with


time awake, but particularly between 9pm and 7am. The poorest performance was observed during the 5am test session. Gillberg & Akerstedt (1998) also observed a decline in accuracy over 64 hours of total sleep deprivation on their variant of the MAK task. Circadian variations in accuracy were apparent but the lowest accuracy was observed in the 3 hour window between 8am and 11am on both test days, somewhat later than the nadir reported by Williamson et al. (2001b). Accuracy also declined within each test session, but this time-on-task effect did not change with increasing sleep deprivation, perhaps because performance at the start of the task was already impaired. Interestingly, Gillberg & Akerstedt (1998) observed missed responses in the presence and absence of EEG sleep indices and both types of misses showed the same relationships with time-on-task, time awake and time of day.

Williamson et al. (2000b; Williamson & Feyer, 2000) attempted to benchmark performance during sleep deprivation against performance under differing levels of alcohol intoxication. The same participants who had undertaken the 28 hours of sleep deprivation reported above were also tested on a different day after consuming 4 alcohol doses designed to raise blood alcohol concentrations (BAC) in 0.025% steps. Performance under the influence of alcohol and sleep deprivation were compared to determine the time awake required to reduce performance to the same level as that observed at a BAC considered unsafe for driving. MAK and SRT performance typically reached levels equivalent those seen at 0.05%BAC 17 to 18 hours after waking. This result is very similar to that reported for a co-ordination task by Dawson & Reid (1997).

One of the advantages of using all three performance tasks in the current study is the opportunity to examine how well the results obtained on the MAK and SRT tasks concur with those obtained on the PVT task.

2.3 Sleep

The extent to which different shift systems limit sleep is critical to understanding their effects on driver fatigue. It is common wisdom that work systems often limit truck drivers’ sleep to undesirable levels. This belief was graphically confirmed by Mitler, Miller, Lipsitz, Walsh and Wylie (1997). Mitler et al. (1997) recorded EEG around the clock for a week in 80 long distance truck drivers working in the US and Canada. The drivers worked either: 1) Day shift - five 10 hour shifts with a standard start time, or 2) Night shift - four 13 hour shifts with a standard start time, or 3) Advancing night shift - five 10 hour shifts with start time getting earlier each day, or 4) Delaying evening shift - four 13 hour shifts with start time getting later each day. According to polysomnography, the mean duration of the principle sleeps in each break between shifts was extremely short, ranging from 3.83 hours for night shift drivers to 5.38 hours for day shift drivers. Although these sleep durations differed significantly suggesting a differential effect of day and night shift on sleep time, the day and night shifts varied in length as well as time of day, so that it is not possible to conclude unambiguously that night work per se was responsible for the sleep difference. Further, more naps were taken on the night shift roster (22) than on the day shift roster (13) despite the night roster having fewer shifts. Nap durations were variable but averaged approximately 45 minutes. Unfortunately, Mitler et al. (1997) did not report the total amount of sleep (principle and naps) obtained by drivers during each break between the shifts so it is not clear whether the rosters differentially limited total sleep time. Regardless of the problems comparing day and night rosters, the amount of sleep obtained by the drivers in all the shift conditions was less than optimal for maintaining alertness, performance and safety and certainly less than the 6.22 hours sleep recorded via


actigraph for members of the general population in a large American city (Jean-Louis, Kripke, Ancoli-Israel, Klauber & Sepulveda, 2000).

Although EEG recording is recognised as the most accurate way to measure sleep, it is generally too costly to be practical in most field research. The alternatives are self-reports of sleep, generally gathered via some form of sleep diary, and actigraphy.

Sleep diaries are systematic records of the timing and perceived characteristics of sleep made by the individual. Typically, people are asked to record information about each sleep as accurately as possible and at the time. However, forgetfulness, delayed recording, lack of precision, errors, misrepresentations and waning motivation over time all threaten the validity of self-reported sleep information in a person’s own environment away from direct monitoring. In addition, people may not always be able to accurately determine their sleep onset and waking times, despite their best attempt. For these reasons, objective measures of sleep such as actigraphy are clearly preferable.

Actigraphs record how frequently a person moves. This in turn can be scored as indicating wakefulness or sleep. Actigraphs are typically designed as a watch-like device to be worn on the wrist continuously for the measurement period. They are relatively unobtrusive, require no additional instrumentation in situ and allow a person’s sleep to be measured objectively in their own environment. In addition, actigraphic data do not appear to be affected by an initial aclimatisation period (see Sadeh, Hauri, Kripke & Lavie, 1995). Actigraphic identification of sleep and wake periods have shown an acceptably high concordance (approximately 90%) with polysomnography in normal sleepers across a range of scoring systems (Cole, Kripke, Guen, Mullaney & Gillin, 1992). However actigraphy may consistently overestimate total ‘nightly’ sleep time somewhat (by 5.6 to 7.0% in the data reported by Cole et al., 1992). This is most likely due to the inherent difficulty of distinguishing quiet rest from sleep on the basis of movement alone (Sadeh et al., 1995). The problem of correctly distinguishing sleep from periods of quiet rest both during the time spent in bed and during the day has led Sadeh et al. (1995) to recommend that actigraphy always be used concurrently with sleep diaries. Diary information can then be used to improve the validity of the actigraph data by clarifying the nature of ambiguous movement periods in the actigraphic record as well as identifying times when the actigraph has been taken off. Heeding this advice, both types of sleep measures were employed in the current study.

Recently, Balkin et al. (2000) trialled actigraphy for measuring the sleep of long-haul and short-haul truck drivers in the field. Twenty five volunteer drivers working in each sector wore actigraph watches for 20 consecutive days while living and working as usual. For this sample of drivers, sleep between shifts was found to be much shorter for long-haul drivers (4.32 hours) than short-haul drivers (7.46 hours), but long haul drivers obtained more sleep during their shifts (2.99 vs 0.2 hours), so that total sleep obtained per 24 hour period was very similar for the two groups (long-haul 7.31 hours vs short-haul 7.66 hours). Given the difference between these sleep times and those observed by Mitler et al. (1997), they should not be assumed to be representative of the broader population of truck drivers. However, Balkin et al. (2000) concluded that actigraphy was indeed practical for use with commercial drivers but reiterated the need for good quality diary information to accompany actigraph data.


3. METHOD

3.1 Design

This study was designed to allow the subjective fatigue, performance, sleep and work of drivers working night and day shifts to be compared. Three groups of drivers were sampled - a group working permanent day shift, a group working permanent night shift and a group working alternating weeks of day and night shifts (rotating roster). All drivers participated for two weeks to attempt to obtain a reliable sample of their work. For the rotating shift drivers, the two week measurement period covered one week of day work and one week of night work. As far as possible, the order in which the rotating shift drivers completed their day and night shift weeks was counterbalanced.

Each driver was measured repeatedly over the two week period. Drivers were asked to complete tests of concentration and reaction speed (PVT, Mackworth Clock Vigilance task and Simple Reaction Time task) at the start of the first shift of the study fortnight (baseline), and at the end of the last shift in week 1 and week 2. Baseline measures were scheduled for the end of a break from work of at least 24 hours. In addition, drivers were asked to self-administer the Simple Reaction Time test and a short version of the Mackworth Clock Vigilance task at the start and end of each shift during the fortnight, and also at the start of one midshift break in each shift. As well as completing performance tests, drivers kept diaries of their work, break and sleep times. The diaries were also used to record drivers’ ratings of their subjective fatigue at the start and end of each period of work bounded by a break of at least 15 minutes duration as well as ratings of the quality and restorative impact of the last sleep taken prior to each work shift. Together with the performance and diary measures, drivers were asked to wear actigraphs for the duration of the fortnight study period. These were used to provide objective measures of the timing and quality of drivers’ sleep, to complement the self-report measures. A questionnaire completed at the baseline testing session provided additional background information, about the drivers’ lifestyles, health, recent sleep and work.

3.2 Participants

3.2.1 Participation overview

Drivers from seven companies operating in NSW and/or Victoria participated in the study. Permanent day shift drivers were drawn from six of these companies, permanent night shift drivers from four companies, and rotating drivers were drawn from a single Sydney-based company. In total, 26 day shift drivers began the study, 20 from the Sydney-Newcastle area and 6 from Melbourne-based companies. Four day shift drivers withdrew during the study, leaving 22 permanent day shift drivers in the final sample. Thirty (30) permanent night shift drivers began the study, 18 from the Sydney-Newcastle area and 12 from Melbourne-based companies. Three NSW and six Victorian drivers withdrew or had data that could not be used (because, for example, they worked unusual shift patterns), leaving 21 permanent night shift drivers in the final sample. Eleven rotating shift drivers participated although one of these drivers only participated during the day shift week.

Originally, only drivers working at least five shifts per week on the route between Sydney and Melbourne were to be sampled to standardise the driving task and shift length as much as possible. However participant recruitment, when these criteria were applied, was so


unsuccessful that this strategy was abandoned and any willing heavy vehicle drivers working at least 11 hour shifts on a permanent roster were included in the study. Consequently, almost half of the permanent day (n=9) and night (n=11) shift drivers in the study worked a continuous short distance circuit between loading and delivery depots.

3.2.2 Description of driver groups

3.2.2.1 Demographic information

Table 2 summarises the main demographic characteristics of the participating drivers in the three shift groups. The three groups were similar on most characteristics and none of the differences between the groups reached statistical significance. All the drivers (n = 54) were men. The majority were older, falling in the 40-49 age group for day shift and night shift drivers. Rotating shift drivers, however, were slightly younger than the other groups, with the majority of drivers aged between 30-39 years of age. The majority of participants in all groups were living in on-going relationships. Most drivers in all groups had high-school education, up to year ten, but night shift and rotating shift drivers were somewhat more likely to have completed TAFE studies. Personal computer experience was also similar between the groups, generally being split between having no experience and a little. The three groups of drivers each averaged approximately 21 years of professional driving experience and 3 to 4 years experience of their current shift roster.

Table 2. Demographic characteristics of participants Driver Group Characteristic Day

shift Night shift

Rotating shifts

Age (%) (n=22) (n=21) (n=11) 30-39 22.7 33.3 45.5 40-49 50.0 33.3 27.3 50-59 27.3 28.6 27.3 60+ 0.0 4.8 0.0 Marital status (%) (n=22) (n=21) (n=11) Partnered 68.2 66.7 90.9 Widower, separated,

or divorced 13.6 19.0 9.1

Single 18.2 14.3 0.0 Education (%) (n=22) (n=21) (n=11) High School to yr 10 90.9 71.4 63.6 High School to yr 12 4.5 4.8 9.1 Tafe 4.5 19.0 27.3 College/University 0.0 4.8 0.0 PC experience (%) (n=22) (n=21) (n=11) None 54.5 57.1 45.5 A little 40.9 38.1 54.5 Frequent user 4.5 4.8 0.0 Professional driving experience (n=22) (n=21) (n=11) Mean (SD) years 20.77 (7.41) 21.19 (8.05) 21.18 (10.57) Range 8-35 10-37 5-35 Experience on current roster (n=21) (n=20) (n=11) Mean (SD) years 3.69 (3.53) 4.69 (3.62) 5.11 (6.78) Median 2.0 4.5 3.8 Range 0.1-10.0 0.5-11.0 0.67-25.0


3.2.2.2 Health

Table 3 summarises the types of health problems reported by the participating drivers in the three shift groups. In general, few drivers in any group reported chronic health conditions. The only notable difference was that night shift drivers reported a significantly higher number of problems (Kruskal-Wallis χ2

(2)=14.63, p=0.001). A high percentage (42.9%) of night drivers reported asthma/hayfever and sleep and stomach/digestive problems were also somewhat more common in the night shift group.

Table 3. Health problems reported by participants Driver Group Health problems Day shift Night shift Rotating shifts Type of problem (%) (n=22) (n=21) (n=11) Diabetes 4.5 9.5 0.0 Asthma/Hayfever 9.1 42.9 9.1 Sleep disorders 4.5 14.3 0.0 Stomach/digestive problems 4.5 14.3 0.0 Liver/kidney problems 0.0 4.8 0.0 Heart/vascular problems 13.6 14.3 0.0 Headaches/migraines 14.3 19.0 0.0 Number of problems Mean (SD) problems 0.5 (0.60) 1.19 (1.03) 0.91 (0.3) Median 0.0 1 0.0 Range 0-2 0-4 0-1

3.2.2.3 Social drug use

Table 3.2.3 summarises the usual levels of smoking, caffeine and alcohol use for the drivers in the three shift groups. Overall, the groups were similar in their use of these drugs.

Day drivers were significantly more likely to smoke. Fifty per cent of day drivers were smokers compared to 9.5 percent of night shift drivers and 18.2 percent of rotating drivers (χ2

(4)=10.20, p=0.037). Interestingly, smoking behaviour was not significantly related to drivers’ reports of asthma/hayfever (χ2

(2)=1.40, p=0.50).

Almost all the drivers, regardless of group, regularly consumed some form of caffeinated beverage, with night shift drivers consuming the highest mean number of such drinks per day. However, the difference between the groups was not statistically significant (F(2,50)=0.66, p=0.52).

Of the three groups, the rotating shift drivers drank alcohol most frequently, with almost three quarters of rotating drivers consuming alcohol at least twice a week. In contrast, night shift drivers tended to drink least often, with the majority drinking once a week or less. To meet the assumptions of the chi square test, the frequency of drinking was collapsed into 3 categories (‘never/rarely’, ‘1-2 times a month/weekly’ and ‘2-3 times a week/daily’). Chi square revealed that the difference between the groups in frequency of drinking alcohol was significant (χ2

(4)=16.36, p=0.003). While the largest number of drivers in each group reported consuming two to three drinks on any occasion, night shift


drivers appeared more likely to report drinking more than five drinks compared to the other two groups. This difference was not statistically significant, however, and may be a function of the lower frequency of drinking in this group.

Table 4. Smoking, caffeine and alcohol use reported by participants Driver Group Smoking, caffeine and alcohol use Day shift Night shift Rotating shifts Smoking (n=22) (n=21) (n=11) Smokers (%) 50.0 9.5 18.2 Mean (SD) cigarettes/day 22.5 (6.22) 25.0 (7.07) 25.0 (0.0) Caffeine drinks (n=22) (n=21) (n=11) Caffeine drinkers (%) 95.5 100.0 100.0 Mean (SD) drinks/day 4.02 (1.66) 4.76 (2.82) 4.13 (1.58) Alcoholic drinks Frequency (n=22) (n=21) (n=11) Daily 0.0 0.0 36.4 2-3 times a week 45.5 19.0 36.4 Weekly 9.1 57.1 27.3 1-2 times a month 4.5 0.0 0.0 Rarely 22.7 23.8 0.0 Never 18.2 0.0 0.0 Drinks per occasion (n=18) (n=19) (n=11) 1 5.6 10.5 0.0 2-3 61.1 42.1 63.6 4-5 22.2 15.8 27.3 >5 11.1 31.6 9.1

3.2.2.4 Sleep problems and sleepiness

Table 5 summarises self reported sleepiness and sleep problems for the drivers in the three shift groups. Despite their different work and sleep patterns, the groups were similar in their reporting of sleep problems and sleepiness.

The drivers in the three groups reported similar frequencies of stopping breathing during sleeping with approximately 80% in each group rarely or never experiencing this problem. Most day and night drivers (between 50% and 60%) sometimes moved a lot during sleep, but rotating shift drivers were less consistent. This difference between the groups was not statistically significant. Similarly, although day drivers were most likely and night drivers least likely to report loud snoring, the group differences did not reach statistical significance (χ2

(4)=8.46, p=0.08). Indeed, the majority of drivers reported snoring infrequently. Similar numbers of drivers from each group had had their adenoids removed – 13.6 per cent of day shift drivers, 9.5 per cent of night shift drivers, and 18.2 per cent of rotating shift drivers. Frequency of loud snoring and stopping breathing were unrelated to whether drivers had had their adenoids removed.


Table 5. Sleep problems and sleepiness reported by participants Driver Group Sleep problems Day shift Night shift Rotating shifts Stopping breathing (%) (n=21) (n=20) (n=9)

Always 0.0 0.0 0.0 Often 0.0 0.0 0.0

Sometimes 23.8 20.0 22.2 Rarely 19.0 10.0 0.0 Never 57.1 70.0 77.8

Move a lot (%) (n=22) (n=21) (n=11)

Always 13.6 9.5 0.0 Often 13.6 19.0 36.4


Loud snoring (%) (n=22) (n=21) (n=11)

Always 9.1 0.0 0.0 Often 27.3 19.0 9.1


Difficulties (n=22) (n=21) (n=11) Getting to sleep (%) 18.2 9.5 27.3 Staying asleep (%) 27.3 23.8 9.1 Staying awake in the day (%)

Always 0.0 0.0 0.0 Often 0.0 0.0 0.0


Epworth Sleepiness Score (/24) (n=22) (n=21) (n=11)

Mean (SD) 5.72 (3.86) 5.30 (3.68) 6.36 (2.66) Median 4.5 5.0 6.0

Range 0-15 0-15 2-11

There were no statistically significant differences between the group reports of problems getting to sleep, staying asleep, or staying awake during the day, and the majority of drivers either did not experience these problems, or did so infrequently.

Typical daytime sleepiness as measured by the Epworth Sleepiness Scale (/24) was similar for the three groups with median scores falling in the 4 to 6 range. The groups did not differ statistically. All three groups averaged within the normal range of values on this scale (≤ 10). Two drivers in each group scored above 10. Of these, four drivers’ scores were just outside the normal range (11), and only one day shift driver (4.5%) and one night shift driver (4.8%) scored at levels suggesting excessive daytime sleepiness (15).


3.3 Materials and measures

3.3.1 Paper and pencil measures

Participants completed a formal consent form before commencing the study (Appendix 8.1). At the first test session, they were asked to complete a Background Questionnaire (Appendix 8.2). This contained sections addressing basic demographic information, health issues, sleep or sleepiness problems, and work and rest activities leading up to the start of the study.

Drivers were given a diary in which to record their work and rest hours for the duration of the study period (Appendix 8.3). The diary consisted of a page for the start of each work period, another for the end of each work period and another for each break of at least 15 minutes from work. At the start of each work period, drivers recorded the time and date and rated their current fatigue on three 10cm Visual Analogue Scales (Fresh-Tired, Clear-headed-Muzzy-headed, Alert-Drowsy). During the work period they recorded any times when they took off their actigraphs. At the end of the work period, drivers again recorded the time and date and made subjective fatigue ratings. They also provided a summary of the time spent driving or doing other work during the work period. For each break, the diary asked drivers to record when they took their actigraphs off, the time they went to bed and arose, and an estimate of when they actually fell asleep and woke. The diary sleep information was used to supplement and clarify the data from direct actigraphic recordings where necessary. Finally, drivers rated the quality and refreshingness of the last sleep taken before beginning the next work period on 10cm Visual analogue scales. At the end of the diary, drivers were asked to report the number of trips and kilometres driven in each week of the study and to provide a global assessment of how typical their workload and fatigue had been during each week.

3.3.2 Performance tests

Two commercially available Psychomotor Vigilance Task (PVT) testers (CWE PVT-192) were used together with accompanying analysis software. The testers presented participants with two response buttons (left and right), a 2-line alphanumeric LED display window and a 4-digit red LED display window. The PVT stimulus event, a millisecond counter, appeared in the red LED display and participants responded as quickly as possible by pressing the button corresponding to their preferred hand. The millisecond count at the time of the response then remained on the screen for approximately one second before the display was cleared ready for the next stimulus event. At the start and end of the PVT task a 10 point rating scale was displayed in the alphanumeric window, anchored with the terms ‘No’ at left and ‘Yes’ at right. The scale descriptor was displayed above the scale. Participants positioned a cursor at the point on the scale appropriate for them using the response buttons. In the current study, the standard rating scale descriptor (‘Sleepy?’) was used together with the standard task parameters for the visual form of the PVT - duration was 10 minutes (600sec) and interstimulus interval ranged between limits of 2sec and 10sec. These parameters typically yield approximately 100 stimulus events. A timeout period of 30sec operates for very long responses.

A number of measures of PVT performance were scored including the mean and standard deviation of reaction time and its reciprocal, the mean and standard deviation of the slowest 10% of reaction times and reciprocal reaction times, the number of responses exceeding 500msec (lapses), the number of false alarm responses and the slope of the


relationship between reciprocal reaction time and time on task. Reciprocal RT (1/RT) is a transformation often used to reduce the lopsided variability (skewness) in RTs that occurs because there is a natural lower limit on response time but no comparable upper limit. The 1/RT transformation has the effect of reducing the variance in the RT scores so that significant mean differences are not obscured. However, because one of the effects of fatigue may be to increase the size or frequency of slow scores rather than to produce an overall increase in the mean RT score, there are theoretical arguments for and against using raw RT versus reciprocal RT. Dinges and colleagues routinely use 1/RT for their analyses of PVT data, coupled with separate examinations of slow responses and lapses (defined as any response slower than 500ms). The analyses reported here include both raw RT and reciprocal RT together with analyses of slow responding. In addition, the degree to which drivers show a drop off in performance during the PVT task (the vigilance decrement) is captured using the slope of the reciprocal reaction time plotted against time on task. A zero slope indicates no change in performance over the 10 minute task, whereas a negative slope indicates performance degradation. The larger (steeper) the negative slope, the faster performance is degrading.

The two PIPs tasks (Simple Reaction Time and Mackworth Clock Vigilance) were computerised and run from an MSDOS platform. Two versions of the tasks were used – a version run from laptop computers and a version run from Hewlett Packard 200LX Palmtop computers. The use of palmtops allowed drivers to self-administer the tests on multiple occasions throughout the work week. Each driver was issued with a palmtop kit for the duration of the study. The internal palmtop batteries were supplemented with external 12V lead acid gel batteries to prolong operation time. Drivers responded on external serial Micropads (Genovation #623) which provided larger keys than the palmtop’s own keyboard. The cables linking the external battery and keypad to the palmtop were held secure by a custom-built aluminium bracket that was velcroed to the base of palmtop. Each palmtop kit also contained a small clip-on reading light (Mighty Bright) to improve illumination in the event that ambient lighting (e.g., in the truck cab) was poor and an instruction sheet for using the palmtops (Appendix 8.4). The laptop versions of the tests were run on laptop computers and responses were recorded via a standard serial mouse and a PS2 Genovation Micropad AT (#622). The laptop tasks were used because they have been validated directly against time awake and against an alcohol comparison (Williamson et al., 2000b; Williamson & Feyer, 2000) and because, for the Mackworth Clock Vigilance task, the laptop task provides a longer test of concentration.

The Simple Reaction Time task involved a line drawing of a circle which moved continuously around the screen in an anticlockwise direction. Participants responded as quickly as possible by pressing the zero key on the keypad whenever the circle changed colour (from yellow to red on the laptop version of the test) or changed form (from a solid line to dashed line on the monochrome palmtop version of the test). The changes to the circle were cancelled either when the participant responded or when the timeout period was reached. The task was 2 minutes in duration. Forty stimuli (circle changes) were presented with a minimum interstimulus interval of 2 sec. The timeout period was 1 sec. Mean and standard deviation reaction times across the 40 stimuli and the number of missed responses were recorded.

The Mackworth Clock Vigilance task was identical on the laptop and palmtop computers except that the laptop task ran for 15 min with 15 stimulus presentations whereas the palmtop task ran for 5 min with 5 stimulus presentations. During the task, a circle comprised of 24 equally-spaced dots was presented on the screen. Consecutive dots


flashed in a continuous circuit for the duration of the task. Participants responded as quickly as possible by pressing the zero key on the keypad whenever a dot did not flash in its turn. These skipped flashes occurred at least 45sec apart. The maximum response time recorded (timeout period) was 10sec. Mean and standard deviation reaction time across the stimuli, the number of missed responses and the number of false alarm responses were recorded.

3.3.3 Actigraphy

Ten MiniMitter Actiwatch 64s with event marker buttons were employed to measure drivers’ movement levels during the study. The watches were worn on the drivers’ non-preferred wrist and in a standard orientation. Drivers were asked to wear them continuously around the clock except for times when the watches were likely to be immersed in water (e.g., while bathing). In addition, drivers were asked to press the event marker button whenever they started trying to sleep and when they arose. These two times (“bedtime” and “get up time”) were designed to provide the outer limits for scoring and analysis of individual sleep periods. An instruction sheet for the use of the Actiwatches was provided to the drivers (Appendix 8.5). The Actiwatches were programmed to record activity counts over 15-second epochs throughout the study. At this sampling rate, Actiwatch memory was sufficient for approximately 11 days of continuous recording. Consequently, the watches needed to be downloaded and reset during the two week study period for each driver.

Data were downloaded and scored using Actiware-Sleep v3.2, the proprietary software provided with the Actiwatches. Sleep scoring was carried out using the recommended medium sensitivity level, where at least 40 total activity counts were required before an epoch was scored as waking. The software calculated the total activity counts for each epoch by summing the activity counts in: 1) the epoch of interest, weighted by a factor of 4, 2) the 4 epochs in the minute above and the 4 epochs in the minute below the epoch of interest, weighted by a factor of 0.2, and 3) the 4 epochs in the second minute above and the 4 epochs in the second minute below the epoch of interest, weighted by a factor of 0.04. The Actiware-Sleep software scored sleep onset and waking times in the following way. Each 15-second epoch was scored as immobile if it contained no activity counts. Sleep onset was said to have occurred at the start of the first 10 minute period containing no more than 1 mobile epoch. Wake up time was scored as the last immobile epoch in a 10 minute period prior to rising.

3.4 Procedure

Drivers were alerted to the study via information sheets distributed at their workplace. Testing was typically conducted at the company depot in the drivers’ tea rooms because they were accessible around the clock. Researchers met interested drivers at the company depot at the start of their first shift of the fortnight study period (the baseline testing session). The purpose and requirements of the study were explained and those drivers willing to participate gave formal consent.

Participants completed the background questionnaire and were then introduced to the performance tests (PVT and PIPs). The order in which the PVT and PIPs tests were completed was reversed for half the drivers in each shift group and this order was maintained at the testing sessions at the ends of the work weeks too. Before completing the baseline PVT task, drivers completed a 1 minute demonstration of the task for


familiarisation and then proceeded to complete the full 10 minute task. Before completing the laptop PIPs tasks, drivers completed the shorter palmtop version of the tests. This served both as an introduction to the palmtop testing equipment and provided familiarisation and practice of the tests. Once both PVT and laptop tasks were completed, drivers completed their baseline palmtop test relying on the written instruction page to initiate the tests. At the end of these tests, drivers were fitted with an actiwatch and instructed in its use. Drivers were introduced to the diary and completed the first page, relating to the start of the first work period.

Drivers were advised that they could access 24 hour telephone assistance should they have any problems with the equipment or any further questions about the procedure. Research staff contacted participating drivers in the middle of each study week to schedule the end of week testing sessions, providing another opportunity for drivers to seek assistance or clarification.

At the end of the last shift in both weeks of the study, research staff again met the participating drivers at the depot. Drivers first completed their end-of-shift palmtop tests and relevant diary entries, including the global assessments of the week’s work. They then completed the laptop and PVT tests in the same order as at the baseline session. At the end of the first week of the study, drivers’ actigraphs were downloaded and reset and their palmtop computer batteries recharged or replaced by research staff.

3.5 Analysis

Although the study was designed to examine data across 2 work weeks, the extent of missing data for many drivers made this impractical. Instead, for those working permanent day and night shifts, each drivers’ data profile was examined and the week with the most complete data across all measures was selected for analysis. Where neither week was more complete, the week with the most consecutive shifts was selected or, if both weeks had the same number of shifts, the first week was selected by default. Regardless of which week was selected, data collected at the first test session of week 1 was used as the rested ‘baseline’ data. For rotating shift drivers, the data from both weeks were necessary, regardless of completeness. Data collected at the start of week 1 were again used as the baseline measures for both weeks.

Comparison of the different shift rosters was usually conducted using 2 separate analyses – one that compared the permanent day and permanent night drivers groups and another which compared the day shift and night shift weeks of the rotating shift drivers.

Because the study was concerned with the cumulative effects of day and night work, a work week was defined as an uninterrupted sequence of shifts on consecutive days. Where a driver had a shift off in mid week, only the longest run of shifts before or after the time off were included in the analyses of the week. Performance and other data collected at baseline and at the ends of the work weeks were only included in analyses if they occurred at the start and end of an uninterrupted sequence of shifts.

For measures taken at the start and ends of weeks only, repeated measures MANOVA was used to compare the performance and subjective ratings for day shift drivers and night shift drivers at the start and ends of the work week and to compare performance and subjective ratings at baseline, end-of-day-shift week, and end-of-night-shift week for rotating shift drivers. Multiple comparison t-tests, with alpha adjusted for the number of tests, were used to clarify the nature of significant MANOVA effects. MANOVA, and where


necessary follow-up t-tests, were also used to examine differences across shifts between day and night groups and between day and night weeks for rotating drivers.

Typically, results were considered significant at p<0.05, however non-significant trends to p<0.1 were also considered. Where corrections to the alpha level were made for multiple tests, these are reported in the text. Where t-test data violated equality of variance assumptions, corrected degrees of freedom are reported. Similarly, where MANOVA analyses violated sphericity, the degrees of freedom were adjusted using the Greenhouse-Geisser correction.


4. RESULTS

The results of this study are divided into three main sections. The first section is a description of the events preceding the study period, including details of work and rest. The second section examines the events occurring during the study period, again including work and rest. The final section covers fatigue and performance changes between the beginning and end of the selected study week and across the individual shifts in the selected study week.

4.1 Events preceding the study period

The objective of the first part of the analysis was to compare the patterns of work and rest for the last shift before the study period as well as the food, caffeine and alcohol intake before the study period for each of the groups. Because the final analyses in this study were conducted on the week of work that had the most complete data, the preceding week may have been the first study period week rather than the week before the study period. However, rested (baseline) recordings were always taken at the start of the first week of the study period. For this reason, the comparison of the work and rest patterns for each of the groups looked at both the shift preceding the study period and the shift preceding the week selected for analysis.

The characteristics of the last shift worked and the last rest obtained prior to the fortnight study period were examined first for all permanent day and night shift drivers, regardless of which week was used for the later analyses. Rotating drivers were split into two groups depending on whether they were about to start their day shift week, and had therefore just finished a week of night shifts, or were about to start their night shift so that their prior week had been day shift.

The characteristics of the last shift and the last rest prior to the week selected for analysis were then examined. For rotating drivers, these results relate to the week prior to their day shift week (i.e. the last shift of a night shift week) and prior to their night shift week (i.e. the last shift of a day shift week).

4.1.1 Work

As would be expected, the time that the last shift before the study period finished differed significantly between the four driver groups, (F(3,50)=30.35, p<0.001). Permanent night drivers finished work in the morning and rotating drivers finished night work in the early morning hours. In contrast, day drivers and the rotating drivers who had just finished their day week (i.e. were doing night week first in the study) finished at roughly the same time in the mid-afternoon (Table 6). No significant differences were found between the groups for the length of their last shift, or the lag time between the end of the last shift and beginning of the study.


Table 6. Characteristics of the last shift before the start of the study and of the last shift before the selected week for analysis

Driver Group Characteristics of last shift Permanent

Day Permanent

Night Rotating – Day

week first Rotating – Night week

first Shift preceding the study period Mean Time Ended (SD) (n = 22) (n = 21) (n = 7) (n = 4) 15:20 (3:10) 8:35 (4:30)1 00:23 (5:06)2 15:03 (0:52) Mean Length in hours (SD) (n =20) (n = 21) (n = 7) (n = 4) 10.55 (3.38) 11.81 (1.19) 11.43 (0.97) 11.88 (0.66) Mean Lag between last shift and start of study in hours (SD)

(n = 22) (n = 21) (n = 7) (n = 4)

53.58 (13.34) 53.35 (26.87) 61.96 (26.72) 66.61 (12.47) Shift preceding the selected week for analysis

Day week Night week

Mean Time Ended (SD) (n = 22) (n = 21) (n = 11) (n = 10) 15:54 (2:54) 8:27 (4:37) 1:22 (5:31) 15:34 (2:11) Mean Length in hours (SD) (n = 20) (n = 21) (n = 11) (n = 10) 11.29 (2.91) 11.83 (1.1) 10.09 (3.13) 11.1 (3.23) Mean Lag between last shift and start of week in hours (SD)

(n = 22) (n = 21) (n = 11) (n = 10)

59.61 (14.53) 48.81 (19.94) 55.86 (22.93) 64.29 (12.35) 1 Includes 5 permanent night-time drivers whose last shift was an overtime day shift (71.4% finished between 03-09, 28.6% finished between 06-09). 2 One driver finished at 13:45; Mean (SD) excluding this driver is 2:10 (2:12). Group differences were unchanged.

When the last shift prior to the week selected for analysis was examined (Table 6), the end times of the last shift for the permanent day and night drivers differed significantly (t(33.45)=6.29, p=0.01), with permanent day drivers finishing in the mid-afternoon and permanent night drivers finishing in the early morning, but there were no differences between day and night drivers on the duration of their last shifts. There was a significant difference between the groups on the lag between the last shift and the beginning of the studied week (t(36.48)=2.02, p=0.05). The permanent day shift drivers averaged 10.8 hours more time off than the night drivers before their first shift of the selected study week.

No significant differences were found between the day and night shift weeks for rotating drivers on the duration of their last shift, or the lag between their last shift and the beginning of the study week. Not surprisingly, however, a significant difference was found between the weeks for rotating drivers on the time their last shift ended before the studied week (t(9)=4.95, p=0.001).

The results in Table 6 also show little difference between the work-rest patterns leading up to the study fortnight and the week selected for analysis. Within each of the four shift types, the time of day at the end of the previous shift differed, on average, by one hour or less before the study fortnight compared to the selected week. The average durations of the last pre-study and pre-week shifts differed by 1.5 hours or less, and the length of the


weekend breaks leading up to the study fortnight and the week selected for analysis differed by 6 hours or less. These results suggest that the week selected for analysis was not atypical in terms of the immediately preceding work and rest pattern, and therefore, that the data collected at the start of the study fortnight are appropriate to use as baseline data for the selected week.

4.1.2 Rest

There were no significant differences between the groups on the amount of sleep reported by drivers in the weekend before the study (Table 7). Significant differences were found, however, in the length of the drivers’ last substantial sleep before the study period (F(3,48)=4.01, p=0.01). Day drivers had significantly shorter last substantial sleep than both night drivers, and rotating drivers doing night shift in their first week.

Significant differences were also found in the lag between drivers’ last substantial sleep and the start of the study (Kruskal-Wallis χ2

(3)=23.63, p<0.001). The night shift group had the longest lag between the last substantial sleep and the beginning of the study. A significant difference was also found in the lag between drivers’ last sleep (substantial or nap) before the study (F(3,46) =3.56, p=0.02). That is, day drivers had a significantly shorter lag than rotating drivers undertaking their night week first.

Night drivers were also significantly more likely to have taken a nap compared with the other driver groups (Kruskal-Wallis χ2

(3)=18.39, p<0.001). There were no significant differences, however, between drivers on ratings of the quality or refreshingness of their last sleep or nap before the first study week.


Table 7. Characteristics of rest before the start of the study Driver Group Characteristics Day Night Rotating – Day


first Sleep since last shift (hours) (n = 21) (n = 20) (n = 7) (n = 4) Mean (SD) 18.38 (6.3) 18.1 (9.36) 23.29 (9.03) 22 (4.97) Type of last sleep (%) (n = 22) (n = 21) (n = 7) (n = 4) Nap 0.0 52.4 14.3 0.0 Sleep 100.0 47.6 85.7 100.0 Length of last substantial sleep (n = 21) (n = 20) (n = 7) (n = 4) Mean (SD) 6.36 (1.42) 8.29 (2.34) 6.93 (1.84) 8.38 (1.8) Lag between last substantial sleep and study start (n = 20) (n = 18) (n = 7) (n = 4) Mean (SD) 2.38 (2.72) 8.63 (3.17) 6.79 (8.54) 7.80 (1.56) Length of last nap (n = 0) (n = 11) (n = 1) (n = 0) Mean (SD) - 2.07 (0.9) 3.5 (-) 1 - Lag between last nap and study start (n =0) (n = 11) - (n = 0) Mean (SD) 0.0 2.64 (1.0) - 2 0.0 Lag between last sleep (substantial or nap) and start work (n = 20) (n = 20) (n = 6) (n = 4) Mean (SD) 2.38 (2.72) 4.73 (3.09) 4.38 (6.23) 7.79 (1.56) Rated quality of last substantial sleep (n = 20) (n = 17) (n = 7) (n = 4) Mean (SD) 76.45 (17.38) 78.59 (20.48) 76.29 (11.46) 79.5 (23.97) Rated refreshingness of last substantial sleep (n = 20) (n = 17) (n = 7) (n = 4) Mean (SD) 68.85 (14.19) 79.65 (21.89) 77.29 (14.56) 80.5 (14.39) 1 Due to n = 1, SD cannot be calculated. 2 Lag information for nap was missing from driver’s records.

The characteristics of the rest obtained just prior to the study week selected for analysis were examined next (Table 8). For rotating drivers, these results related to the week prior to their day shift week (i.e. the last shift of a night shift week) and prior to their night shift week (i.e. the last shift of a day shift week).

Day and night drivers did not differ significantly on the total amount of sleep since the last shift, but night drivers had significantly longer mean sleep time in their last substantial sleep before beginning the selected study week compared to day drivers (t(39)=2.84, p =0.007). Around half of the night drivers had taken a nap as their last sleep whereas none of the day drivers had taken a nap. For day drivers the last substantial sleep occurred


around two to three hours before the start of work compared to more than eight hours on average for night drivers (t(36)=4.85, p<0.001). Where the night driver had napped, however, the nap typically occurred around two to three hours before the start of work.

Rotating drivers had a significantly longer substantial sleep before the start of their night week than their day week (8.61 and 6.35 hours, respectively, n=9; t(8)=2.56, p =0.034)). These drivers also differed significantly in the length of their last sleep (substantial or nap) before each week of the study (t(8)=2.87, p = 0.02). Again, more sleep was reported prior to the first night shift than the first day shift (8.23 and 5.57, respectively). There were no other significant differences on rest characteristics before the day and night shift weeks for the rotating shift drivers.

The sleep patterns recorded in the break before the week selected for analysis were similar to those recorded before the study fortnight as a whole, again suggesting that baseline measures taken at the start of the study fortnight are appropriate for the week selected for analysis. In summary, the main distinguishing characteristic of rest-taking between the groups was that permanent night drivers had longer sleeps and tended to supplement this with a nap in the longer period between sleep and work that also characterised night drivers. Rotating shift drivers were also characterised by longer sleep before night work.


Table 8. Characteristics of rest before the selected work weeks of the study Driver Group Characteristics Day Night Rotating –

Day week Rotating – Night week

Sleep since last shift (hours) (n = 21) (n = 19) (n =11) (n = 9) Mean amount

(SD)

20.73 (7.86)

17.1 (7.0)

21.01 (7.9)

22.47 (3.77) Type of last sleep (%) (n = 22) (n = 20) (n = 11) (n = 9) Nap 0.0 40.0 9.1 11.1 Sleep 100.0 60.0 90.9 88.9 Length of last substantial sleep (n = 22) (n = 19) (n =11) (n = 9) Mean (SD) 6.57 (1.74) 8.37 (2.32) 6.29 (1.79) 8.61 (1.43) Lag between last substantial sleep and study start (n = 20) (n = 18) (n = 11) (n = 9) Mean (SD) 2.32 (2.74) 7.56 (3.88) 4.68 (7.24) 7.15 (1.4) Length of last nap (n = 0) (n = 8) (n = 1) (n = 1) Mean (SD) - 2.16 (0.72) 3.5 (-) 3.83 (-) Lag between last nap and study start Mean (SD) - 2.51 (0.71) - 1.5 (-) Lag between last sleep (substantial and nap) and start work (n = 20) (n = 20) (n = 6) (n = 4) Mean (SD) 2.32 (2.74) 4.74 (3.2) 4.38 (6.23) 7.79 (1.56) Rated quality of last substantial sleep (n = 19) (n = 18) (n = 10) (n = 6) Mean (SD) 77.63 (17.2) 72.44 (23.72) 70.7 (14.7) 76.33 (19.46) Rated refreshingness of last substantial sleep Mean (SD) 69.89 (15.16) 77.0 (20.06) 71.1 (17.3) 78.0 (11.82)

4.1.3 Eating and drinking

Data on food and drink intake were only collected at the baseline testing session and so are only available for the period leading up to the first week of the study fortnight (Table 9).

Drivers differed significantly on the time they consumed their last meal before the study period (Kruskal-Wallis χ2

(3)=34.61, p<0.001), and on the time between the last meal and starting work (Kruskal-Wallis χ2

(3)=10.15, p=0.02). Drivers about to start a day shift tended to eat on the previous evening (8 to 9 hours before work), whereas drivers about to start a night shift typically last ate at about lunchtime (3 to 4 hours before work). There was no difference, however in the size of the meal consumed.


The time a caffeinated drink was last consumed was significantly different for day and night drivers, and for rotating day week drivers compared to all other groups (F(3,49)=45.29, p<0.001). Again, drivers about to begin a night shift (permanent and rotating) had their last caffeine at about lunch time, whereas day drivers did so about breakfast time, and rotating drivers starting day shift consumed their last drink very early in the morning. Rotating day week drivers drank significantly fewer caffeinated drinks than night drivers and rotating night week drivers (F(3,49)=5.63, p=0.002).

No significant differences were observed between the drivers in the time of their last alcoholic drink, the number of alcoholic drinks consumed on the last occasion, or the lag between the last alcoholic drink and the beginning of the study. All groups reported consuming alcohol in the late afternoon and consuming between four and five drinks. There was a very wide range in time since the last alcoholic drink for each group, but the median values show that drivers typically drank in the day or two preceding the start of the study (i.e., during their last weekend break).

In summary, work, rest and consumption prior to the first week of the study and prior to the week selected for analysis show differences between the groups consistent with the time of day at which they were working.


Table 9. Food and drug intake before the start of the study Driver Group Day Night Rotating – Day


first Last meal consumed (n = 21) (n = 21) (n = 7) (n = 4) Time Mean (SD) 22:43 (5:35) 14:15 (2:25) 22:30 (5:50) 12:07 (1:10) Type (%) Light 57.1 19.0 14.3 75.0 Moderate 38.1 57.1 57.1 25.0 Large 4.8 23.8 28.6 0.0 Hours before study

Mean (SD) 8.18 (7.14) 2.94 (1.45) 8.96 (5.84) 3.54 (1.89)

Last caffeinated beverage consumed (n = 21) (n = 21) (n = 7) (n = 4) Time Mean (SD) 7:08 (5:07) 14:51 (3:48) 4:58 (4:14) 13:07 (1:01) Number before study

Mean (SD) 1.31 (0.81) 2.05 (1.12) 0.71 (0.756) 2.5 (0.577)

Hours before study

Mean (SD) 3.15 (8.54) 2.62 (2.52) 9.42 (9.244) 2.54 (1.86)

Last alcoholic beverages consumed (n = 14) (n = 17) (n = 7) (n = 4) Time Mean (SD) 18:57 (3:43) 18:07 (3:12) 15:51 (5:21) 21:00 (1:49) Number Mean (SD) 4.07 (2.76) 3.97 (2.85) 5.0 (3.06) 4.5 (2.65) Hours before study

(n = 15) (n = 17) (n = 7) (n = 4)

Mean (SD) 87.45 (181.93) 50.85 (46.58) 19.04 (9.81) 30.67 (15.15) Median 28.0 38.5 15.5 30.38

4.2 Events during the selected study period

Because the study was interested in the accumulating effects over consecutive shifts on fatigue, the study week for each driver was defined as an uninterrupted sequence of shifts on consecutive days. Where drivers took a ‘day’ off before their week was scheduled to end, only those shifts leading up to the day off were included in the work week. Although this strategy artificially shortened the work week for some drivers, it meant that any changes observed as the week progressed were not affected by the additional recovery time available during the day off. In total, five drivers took days off before the end of the study week. They were equally spread across permanent (1 day and 2 night drivers) and rotating (2 drivers) shift conditions.


4.2.1 Work during the selected study week

Table 10 summarises the week’s workload for drivers operating under the four shift conditions. Drivers typically spent between 50 and 60 hours at work on consecutive days during the week. Total time at work did not differ significantly for permanent day shift drivers and permanent night shift drivers (t(41)=0.48, p=0.63). Similarly, rotating shift drivers spent equivalent amounts of time at work during their day shift week and their night shift week (t(9)=0.08, p=0.94). Most drivers worked five shifts on consecutive days and the mean number of consecutive shifts worked did not differ between permanent day and night drivers nor between rotating drivers on their day and night shift weeks.

Table 10. Workload during study work week for drivers working under different shift conditions

Day shift Night shift Rotating – Day week

Rotating – Night week

(n=22) (n=21) (n=11) (n=10) Total time at work Mean (SD) 55:12 (8:58) 56:34 (9:32) 50:49 (14:03) 51:32 (9:20) Number of shifts (%) 3 13.6 9.5 9.1 0 4 0 9.5 9.1 9.1 5 72.7 76.2 72.7 90.9 6 13.6 4.8

(n=1) 9.1

(n=1) 0

Mean (SD) 4.86 (0.83) 4.76 (0.70) 4.82 (0.75) 4.90 (0.32) Number of return trips 1 (n=19) (n=17) (n=8) (n=8) Mean (SD) 22.4 (28.5) 34.9 (39.6) 4.0 (1.7) 4.1 (2.4) 0-10 trips (%) 68.4 58.8 100 100 > 10 trips (%) 31.6 41.2 0 0 Km driven (n=18) (n=15) (n=7) (n=7) Mean (SD) 2088 (744) 3559 (1131) 2014 (1029) 2279 (899) Global workload (n=20) (n=15) (n=8) (n=8) Usual level (%) 70.0 66.7 50.0 62.5 Less than usual (%) 20.0 20.0 50.0 25.0 Global fatigue Usual level (%) 85.0 86.7 50.0 75.0 Less than usual (%) 10.0 0 37.5 25.0 1 The high number of trips reported by permanent day and night shift drivers is due largely to the subgroup of drivers who worked a continuous shorthaul operation rather than a longhaul operation. These drivers drove a continuous circuit from depot to cargo sites.

At the end of the week, drivers reported the total kilometres driven and the number of return trips made. They also rated whether their workload and fatigue during the week were unusual. These reports are also summarised in Table 10 above. Permanent day and night drivers did not differ in the number of return trips driven during the week (t(28.8)=1.07,p=0.29) but night drivers travelled 1500 kilometres more in making these trips (t(23.4)=4.32, p<0.001). Rotating shift drivers completed similar numbers of trips during their day shift and night shift weeks (t(7)=0.24, p=0.82) and also covered similar distances


(t(8)=1.54, p=0.18). The rotating drivers also reported travelling a similar number of kilometres to permanent day shift drivers. The majority of drivers in all the shift conditions assessed their workload and fatigue during the week to be at usual levels. However, the results suggest that some drivers working the day week of a rotating roster had a lighter workload week than usual with correspondingly lower global fatigue ratings.

Figure 1 shows the mean time of day at the start and end of consecutive shifts worked during the week by permanent day and night shift drivers. The data for the sixth consecutive night shift is not shown because only one driver completed this shift.

Typical shifts for permanent day shift drivers began between 4am and 6am and finished between 3pm and 6pm. Night shift drivers typically started work between 5pm and 6pm and finished between 4am and 6am.

Figure 1. Start and end times of consecutive shifts over the selected week for permanent day shift drivers (left panel) and night shift drivers (right panel; with 95% confidence intervals)

Day shift

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

1 2 3 4 5 6Consecutive shift in week

Tim

e of

day

Start End

Night shift

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00


Tim

e of

day

Start End

Figure 2 presents the time of day at the start and end of consecutive shifts for the day and night shift weeks worked by rotating shift drivers. The data for the sixth consecutive day shift is not shown because only one driver completed this shift.

On their day shift week, rotating shift drivers typically started work between 3am and 4am and finished between 2pm and 3pm (Figure 2). This is slightly earlier than the shift times reported by permanent day shift drivers. On their night shift week, rotating shift drivers usually began work between 2pm and 3pm and finished between 1am and 2am. Again, these times, particularly at the end of the shift, are earlier than those reported by permanent night shift drivers.


Figure 2. Start and end times of consecutive shifts over the selected work week for rotating shift drivers during their day week (left panel) and their night week (right panel; with 95% confidence intervals)

Rotating day shift

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00


Tim

e of

day

Start End

Rotating night shift

0:001:002:003:004:005:006:007:008:009:00

10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00


Tim

e of

day

Start End

The durations of shifts across the week for drivers working under the different shift conditions are shown in Table 11. Permanent day and night shifts typically spanned 11 to 12 hours (including meal times). MANOVA analysis was conducted with shift number as a repeated measures factor and the first 5 shifts included to maximise the number of drivers contributing data. Nineteen day shift drivers and 17 night shift drivers had data for all 5 shifts. The analysis revealed that night shifts were longer than day shifts (F(1,34)=4.38, p=0.04) but there were no significant differences between the five shifts and no interaction effect.

Analysis of the rotating shift drivers showed that the day and night shifts were somewhat shorter on average than the permanent roster shifts, spanning 9.5 to 11.5 hours. A 2 (week type) x 5 (shift number) MANOVA analysis with repeated measures on both factors was conducted on the shift durations of the rotating shift drivers. Only 7 drivers had data at all occasions so some caution is needed when interpreting the results. No significant differences were found between the day and night shift weeks nor between shifts within each week. Interestingly, when the analysis was rerun on fewer shifts (4), so that 9 drivers were included, a marginally significant decrease in shift duration was observed over the work week (F(3,6)=4.94, p=0.05).


Table 11. Mean shift durations over the selected work week Consecutive shift number Driver group 1 2 3 4 5 6 Day shift Mean 11:45 11:44 11:12 11:19 10:45 10:30 (n=22) SD 1:28 1:50 1:21 1:14 1:45 2:10 n 22 22 22 19 19 3 Night shift Mean 12:27 11:40 11:25 11:38 12:14 12:00 (n=21) SD 0:59 0:52 1:33 0:44 2:33 - n 21 21 21 19 17 1 Rotating - day week Mean 10:56 10:23 10:02 9:43 11:21 14:30 (n=11) SD 1:45 3:05 1:32 2:07 2:13 - n 11 11 11 10 9 1 Rotating - night week Mean 11:15 11:14 9:34 10:27 10:01 - (n=10) SD 2:02 1:28 3:27 1:32 2:48 - n 10 10 10 10 9 0

Drivers were asked to report the amount of time they spent driving and doing other work during each shift. Table 12 shows the average time spent in each shift for these activities. Total hours spent working during the week appeared similar for drivers in all shift conditions but there were clear differences in the make-up of work activities. Permanent night drivers spent the most time driving and this was consistently 9 to10 hours per shift. Permanent day drivers spent 1 to 2 hours less per shift driving, but did more non-driving work. Rotating shift drivers did, by far, the least driving and the most other work during their shifts, averaging between 2 to 4 hours less driving per shift than permanent night drivers.

Table 12. Mean (SD) hours driving and doing other work during shifts in the selected work week

Consecutive shift number 2 Week’s

total 1 1 2 3 4 5 6

Day (n=21) (n=22) (n=22) (n=21) (n=19) (n=19) (n=3) Driving 39.0 (12.7) 7.9 (2.8) 7.9 (3.1) 8.0 (2.5) 8.7 (2.6) 8.0 (2.8) 8.0 (4.0) Other work 11.6 (8.3) 2.8 (2.4) 2.8 (2.6) 2.4 (1.6) 1.9 (1.9) 1.9 (1.7) 1.5 (1.8) Total work 50.7 (9.5) 10.7 (1.4) 10.8 (1.5) 10.4 (1.5) 10.6 (1.4) 10.0 (1.8) 9.5 (2.2) Night (n=21) (n=21) (n=21) (n=21) (n=19) (n=17) (n=1) Driving 46.9 (10.9) 9.9 (1.2) 9.7 (1.7) 9.4 (2.7) 10.2 (1.2) 10.1 (1.7) 10.2 (-) Other work 5.4 (3.7) 1.6 (1.2) 1.0 (0.8) 1.3 (2.6) 0.7 (0.7) 1.1 (1.2) 1.0 (-) Total work 52.3 (9.7) 11.5 (1.1) 10.7 (1.3) 10.6 (1.8) 10.9 (0.7) 11.2 (2.1) 11.2 (-) Rotating -Day (n=8) (n=11) (n=11) (n=10) (n=10) (n=7) (n=1) Driving 32.4 (18.8) 6.0 (2.8) 5.9 (3.5) 6.4 (2.6) 5.3 (3.8) 7.3 (3.5) 14.0 (-) Other work 18.8 (11.7) 4.0 (2.5) 3.6 (2.1) 3.3 (2.7) 3.8 (2.5) 3.9 (2.8) 0 (-) Total work 51.2 (13.6) 10.1 (1.7) 9.5 (3.3) 9.7 (1.7) 9.1 (2.1) 11.2 (2.6) 14.0 (-) Rotating - Night (n=9) (n=10) (n=10) (n=9) (n=10) (n=9) (n=0) Driving 35.3 (8.7) 6.5 (2.0) 7.9 (2.0) 7.3 (2.5) 7.0 (2.3) 7.1 (2.0) - Other work 13.0 (5.2) 2.7 (1.2) 2.7 (1.3) 1.9 (1.0) 3.0 (1.1) 2.9 (1.7) - Total work 48.2 (7.3) 9.2 (2.0) 10.6 (1.5) 9.2 (2.4) 10.0 (1.5) 10.0 (1.3) - 1 Only includes drivers with complete work data for all their shifts 2 Includes drivers with complete work data for each single shift


The eighteen permanent day shift drivers and 17 permanent night shift drivers with complete work data for all their shifts were compared statistically. Analysis of the total work time for the week showed that permanent night shift drivers spent a significantly longer total time driving than permanent day shift drivers (t(40)=2.17, p=0.04), but the reverse was true for other work (t(27.61)=3.21, p=0.003) so that in total, day and night drivers did not differ significantly in the total time spent working over the week (p=0.60).

Analysis across shifts by 2 (group) x 5 (shift) MANOVA’s with repeated measures on the shift factor confirmed that permanent night drivers spent more time driving than day drivers (F(1,33)=10.56, p=0.003) but there was no significant change in driving time over shifts (F(4,30)=2.05, p=0.11) and no interaction between group and shift (F(4,30)=1.12, p=0.37). Further, although day drivers spent more time doing other work than night drivers (F(1,33)=10.12, p=0.003), both groups spent more time on other work in the earlier shifts of the week (F(2.8,30)=2.91, p=0.04). There was no interaction between group and change over shifts for the amount of other work (F(2.8,30)=1.39, p=0.25). Analysis of the total overall hours work by shift revealed a non-significant trend (F(1,33)=3.63,p=0.07) for night shift drivers to work more than day shift drivers, but there was no effect of shift (F(4,30)=0.62, p=0.65) nor was there an interaction between shift and group (F(4,30)=1.35, p=0.27).

Seven of the rotating shift drivers had complete work data for all their shifts in both day and night shift weeks. Paired-samples t-tests revealed no significant difference between the weeks in the amount of driving or other work reported for the week, nor in the total work reported.

4.2.2 Rest during the selected work week

This section covers the length of breaks both long and short then examines the characteristics of sleep in each break. This includes the time of sleep onset, time of waking, amount of sleep obtained in breaks and the quality of this sleep.

4.2.2.1 Length of breaks

The duration of long breaks between shifts in the selected work week are summarised in Table 13. Breaks between shifts were typically 12 to 13 hours long for all groups. In all shift conditions, the shortest break occurred after the first shift of the week. When day and night drivers’ break lengths were compared using a 2 (day v night) x 4 (break number) MANOVA with repeated measures on the break factor, there were no significant differences between the groups or the breaks and the interaction between these factors was also not significant.


Table 13. Mean duration of long breaks between shifts over the selected work week Consecutive break number Driver group 1 2 3 4 5 Day shift Mean (hr) 11.88 11.99 12.68 12.38 13.17 (n=22) SD 1.61 2.78 1.17 1.28 2.02 n 22 22 19 19 3 Night shift Mean (hr) 11.86 12.49 12.53 12.48 - (n=21) SD 1.15 1.30 1.40 1.14 - n 21 21 19 17 0 Rotating - day week Mean (hr) 12.97 13.84 14.15 13.31 14.0 (n=11) SD 1.96 3.00 1.44 2.02 - n 11 11 10 8 1 Rotating - night week Mean (hr) 13.02 13.28 14.69 13.63 - (n=10) SD 2.40 2.35 2.31 1.43 - n 10 10 10 9 0

Table 14 summarises the patterns of shorter breaks of at least 15 minutes duration within the shifts of the selected work week for drivers in the various shift conditions. Many of the drivers did not report taking breaks within their work shifts. Just over one third of drivers on permanent day shift reported midshift breaks (36%), compared to around half of drivers on permanent night shift (57.1%). Among rotating shift drivers, nearly half reported breaks early in the week, but this dropped to around one-fifth of drivers at the end of the work week. For all groups, the midshift breaks were typically between 0.5 to 1 hour per shift.

Table 14. Mean duration of reported short breaks within shifts over the selected work week

Consecutive shift number Driver group Break 1 2 3 4 5 6 Day shift 1 Mean (hr) 0.68 0.55 0.59 0.67 0.77 - (n=22) SD 0.33 0.07 0.11 0.23 0.29 - n 8 6 7 6 5 0 2 Mean (hr) 0.57 0.72 0.73 0.87 - - SD 0.12 0.21 0.18 - - - n 3 3 2 1 0 0 Total Mean (hr) 0.89 0.92 0.79 0.82 0.77 - SD 0.60 0.43 0.36 0.47 0.29 - n 8 6 7 6 5 0 % 1 36.4 27.3 31.8 31.6 26.3 0 Night shift 1 Mean (hr) 0.75 0.79 0.58 0.56 0.72 0.5 (n=21) SD 0.49 0.54 0.22 0.21 0.29 - n 12 10 9 9 9 1 2 Mean (hr) - 0.75 - 3.00 - - SD - - - - - - n 0 1 0 1 0 0 Total Mean (hr) 0.75 0.87 0.58 0.89 0.72 0.5


SD 0.49 0.62 0.22 1.00 0.29 - n 12 10 9 9 9 1 % 1 57.1 47.6 42.9 47.4 52.9 100 Rotating - day week 1 Mean (hr) 0.58 0.90 0.63 0.53 0.79 - (n=11) SD 0.54 0.65 0.32 0.38 0.06 - n 5 5 4 3 2 0 % 1 45.5 45.5 36.4 30.0 22.2 0 Rotating - night week 1 Mean (hr) 0.63 0.56 0.63 0.75 0.50 - (n=10) SD 0.31 0.16 0.18 .035 0.00 - n 5 4 2 2 2 0 % 1 50.0 40.0 20.0 20.0 22.2 - 1 Percentages are of the number of drivers working each shift, not the total number of drivers in each shift condition.

4.2.2.2 Characteristics of sleep

Available actigraph data on sleeps taken during the breaks between work shifts within the selected week for day and night shift drivers and in the day and night shift weeks of rotating shift drivers are summarised in Table 15. Several factors contributed to the availability of data and changes in available sleep data across the week’s breaks. Not all drivers completed the same number of shifts in the week. Very few drivers, for example, completed six shifts in the week so the numbers of drivers represented in break five is small. The regularity with which drivers wore the actiwatch varied. This reflected occasional forgetfulness and, sometimes, drivers finding the watch too uncomfortable to wear. In addition, there was some occasional data loss due to actigraph malfunction.

In general, most drivers in each group have actigraph data for one sleep between each pair of consecutive shifts. Only a very small number of drivers reported an additional sleep/nap (day n = 2, night n = 3, rotating day n = 1, rotating night n = 2). Of interest is the napping behaviour of permanent night shift drivers. Only 14.3% (3) of these drivers napped between shifts during the work week. This contrasts with the 40 to 50% who napped before the first shift of the week (see section 4.1.2).

Table 15. Numbers of drivers with actigraph sleep data between work shifts in selected work week

Break number in week Driver group Sleep # 1 2 3 4 5 Day shift 1st 21 20 16 15 3 (n=22) 2nd 1 0 0 1 0 Night shift 1st 19 20 17 16 1 (n=21) 2nd 2 1 2 1 0 Rotating - day week 1st 9 9 9 8 1 (n=11) 2nd 1 0 0 0 0 Rotating - night week 1st 9 10 10 9 0 (n=10) 2nd 2 1 1 1 0


Time at sleep onset

The time at sleep onset, as determined by the Actiwatch Sleep Analysis software, is plotted below (Figure 3) for the first sleep in each break between shifts in the week. As noted above, few drivers in the study worked six shifts in a week so that the values of 100% in Figure 3 for Break 5 reflect a single case each.

Figure 3. Time at sleep onset for the first sleep in each break between shifts in selected work week – percent of drivers in each group

0%

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00:00-2:59 03:00-5:59 06:00-08:59 09:00-11:59

12:00-14:59 15:00-17:59 18:00-20:59 21:00-23:59

Not surprisingly, almost all drivers working day shifts (either permanently or on a rotating roster) fell asleep between 6:00pm and midnight. Within this period, rotating roster drivers were more likely to fall asleep earlier than permanent day drivers, reflecting their earlier shift start times. Both permanent and rotating night drivers fell asleep in the early hours of the morning with rotating drivers falling asleep earlier than permanent night shift drivers (00:00-5:59 v 03:00-09:00). There was little systematic change in time of sleep onset across the breaks in the week for drivers on day shift. Permanent night drivers were the most variable in their sleep onset time.

Time of waking

The time of waking as determined by the Actiwatch Sleep Analysis for each break between shifts in the selected week is plotted in Figure 4. Again, values of 100% for Break 5 reflect a single case each. The data showed that drivers working permanent day shifts woke between midnight and 6:00am with about half waking between 03:00 and 06:00. Consistent with their earlier bedtimes, rotating drivers tended to wake earlier than permanent day drivers with most waking between 00:00 and 03:00. Drivers working night shifts whether permanent or rotating woke between 06:00 and 12:00 with permanent night shift drivers tending to wake later than rotating night shift drivers (typically 9:00am-3:00pm v 6:00am-12:00pm). Permanent night drivers were most variable in their waking


time compared to the other groups which is consistent with the pattern of sleep onset times, and rotating shift drivers on night shift tended to wake later in the morning in the later half of the week.

A few drivers in each group had naps or second sleeps in breaks during the study week, but mainly night drivers. Analysis of the onset and waking times for naps showed that naps for day time drivers were extremely sporadic. Night shift drivers tended to nap in the afternoon (between midday and 6pm) leading up to the start of the next shift. Similarly, rotating night shift drivers tended to nap in the period before starting their next shift and this occurred earlier in the day than for permanent night shift drivers (between 9am and 3pm) again consistent with their earlier work start times.

Figure 4. Time at waking for the first sleep in each break between shifts in selected work week – percent of drivers in each group

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Break number within the week and shift type(D = Day, RD = Rotating - Day week, N = Night, RN = Rotating - Night week)

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00:00-2:59 03:00-5:59 06:00-08:59 09:00-11:59

12:00-14:59 15:00-17:59 18:00-20:59 21:00-23:59

Amount of sleep obtained in breaks

Analysis of the amount of time scored as sleep in each first break using the Actiwatch Sleep Analysis software (actual sleep) is shown in Table 16. The results showed little change in the amount of actual sleep obtained across the week for permanent day drivers who averaged approximately 5.5 hours per break in their first sleep of each break. Permanent night shift drivers tended to get 0.5 to 1 hour less sleep than permanent day shift drivers (F(1,23)=6.30, p=0.02), with the biggest difference following the second shift (Break 2) of the week. The interaction between shift and group was not significant, however. There was little evidence of systematic change in sleep across the week for permanent day or night shift drivers. Rotating drivers on day shift averaged the least sleep of all the conditions on their first sleeps of the breaks, significantly less than on their night week (F(1,5)=18.46, p=0.008), with sleep tending to increase slightly across the week. This pattern of increasing sleep over the week was also evident for rotating drivers on night shift, but for both weeks the increase in mean sleep was less than 1 hour over the week.


The second sleeps recorded in each break between shifts contained from 1:34 to 4:08 of actual sleep. Fifty percent (50.0%) of second sleeps contained between 3 and 4 hours of actual sleep. However, very few drivers recorded second sleeps. In the permanent day shift group, two drivers had one second sleep each and in the permanent night shift group three drivers had six second sleeps between them. In the rotating group, one driver had a second sleep during the day shift week and two drivers had five such sleeps during the night shift week.

Table 16. Mean (SD, n) hours sleep obtained in the first sleep in each break between shifts for drivers in each group

Break Day shift Night shift Rotating - Day Rotating - Night

1 5:49 (1:26, 21) 4:55 (1:02, 19) 4:19 (1:13, 9) 5:07 (1:30, 9) 2 5:36 (1:07, 20) 4:24 (1:27, 20) 4:56 (0:41, 9) 5:25 (1:31, 10) 3 5:37 (1:02, 16) 5:13 (1:24, 17) 5:11 (0:50, 9) 5:46 (1:31, 10) 4 5:35 (0:59, 15) 4:43 (1:03, 16) 4:56 (0:48, 8) 5:55 (1:32, 9) 5 5:49 (0:46, 3) 5:19 (-, 1) 6:22 (-, 1)

The additional sleep hours were added to the first sleep to yield the total sleep obtained in each break (Figure 5). Analysis of the total sleep data for permanent day and night drivers by 2 (day v night shift group) by 4 (breaks) MANOVA with repeated measures on the break factor showed a significant effect of group (F(1,25)=4.97, p=0.04), with day shift drivers obtaining significantly more sleep than night shift drivers. There was no significant change in amount of sleep across the 4 breaks and the interaction between group and change over breaks was not significant.

The same analysis for rotating drivers during their day and night weeks, by 2 (week type) by 4 (breaks) MANOVA with repeated measures on both factors confirmed that drivers obtained significantly more sleep on night shift than on day shift (F(1,5)=18.46,p=0.008), but there was no significant change over breaks and no significant interaction between week and breaks. Only six drivers had data at all eight occasions, so the results should be accepted cautiously.


Figure 5. Total hours of sleep scored in each break between shifts in the selected work week (with 95% confidence intervals)

0:000:301:001:302:002:303:003:304:004:305:005:306:006:307:007:308:008:309:009:30

Day Night Rotating - Day Rotating - Night

Shift condition

Tot

al ti

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scor

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Break 1 Break 2 Break 3 Break 4 Break 5

Quality of sleep

Estimates of sleep quality for the first sleep in each break between shifts can be obtained from Actigraph data by calculating the actual amount of sleep obtained as a percentage of the time between sleep onset and waking (Figure 6). The lower the percentage figures, the more disrupted the driver’s sleep. Drivers were asleep for approximately 90 to 95% of the time between sleep onset and waking, regardless of their shift roster.

Analysis of the percentage of sleep time for permanent day and permanent night shift drivers was by a 2 (group) by 4 (breaks) MANOVA with repeated measures on the break factor. No significant differences were found between the day and night drivers or between the 4 breaks and there was no interaction between group and change over breaks. For rotating drivers, the percentage of sleep time was compared for their day and night weeks using a 2 (week type) by 4 (breaks) MANOVA with repeated measures on both factors. Again, only six drivers had data at all eight occasions, so the results should be treated cautiously. Analysis revealed a marginal statistically significant effect of week type (F(1,5,)=6.47, p=0.052) such that drivers had a significantly higher percentage sleep time on the day shifts than on the night shifts. There was no significant change over breaks and no interaction between week type and change over breaks. Taken together with the analysis of actual sleep time, this result shows that although rotating drivers get more sleep on night shift, they may get it somewhat less efficiently than when on day shift.

Where drivers had additional sleeps during the breaks between shifts, the sleep quality also showed little sleep disruption (92.9% of naps were more than 90% sleep and 42.9% of naps were more than 95% sleep).


Figure 6. Mean percentage of time between sleep onset and waking that scored as sleep, for the first sleep in each break between shifts in the selected work week for drivers in each group (with 95% confidence intervals)

50.00

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100.00

Day Night Rotating - Day Rotating - Night

Shift condition

%

Break 1 Break 2 Break 3 Break 4 Break 5

Table 17 summarises the subjective sleep quality and refreshingness of the last sleep in each break between shifts for drivers in the different shift conditions. The higher the ratings, the better drivers perceived the sleep. Drivers consistently averaged ratings of between 60 and 75 for most sleeps. For permanent day and night drivers, a 2 (group) x 5 (break) MANOVA with repeated measures on the break factor was conducted on the quality and refreshingness ratings for the last sleep in each break between shifts. The five breaks included the first four breaks between shifts in the week, but also the last break prior to the first shift of the selected week. Inclusion of the rating for the last weekend sleep will reveal whether the subjective quality of sleep changed with the onset of the selected work week. The analysis showed no differences in subjective sleep quality between day and night drivers and no significant changes across the five breaks. In contrast, rated refreshedness upon waking was higher among permanent night drivers than day drivers (F(1,24)=6.06, p=0.02), but there was no indication of any significant change across breaks. Analysis of rotating shift drivers’ sleep ratings were hampered by the small number of drivers involved. Only four rotating drivers had data across the five breaks employed in the previous analysis so no further analysis was conducted. Nevertheless, rotating night drivers also reported slightly higher mean refreshedness at each break than rotating day drivers.


Table 17. Mean (SD, n) rated sleep quality and refreshingness of the last sleep in each break between shifts in the selected work week for drivers in the different shift conditions

Break Day Night Rotating - Day Rotating - Night

Sleep Quality rating (/100)

Last break preshift 78.2 (16.9, 20) 70.8 (22.5, 18) 69.1 (14.6, 9) 74.7 (18.3, 7)

1 65.8 (20.2, 18) 60.0 (23.3, 20) 67.7 (23.8, 10) 69.8 (12.6, 8) 2 61.6 (20.3, 19) 68.0 (21.0, 17) 70.4 (18.8, 10) 68.1 (11.1, 8) 3 66.6 (24.2, 15) 72.3 (18.0, 18) 67.11 (13.7, 9) 73.7 (7.6, 7) 4 66.4 (19.3, 16) 74.2 (21.1, 15) 67.75 (12.1, 8) 74.1 (8.8, 7) 5 77.5 (0.7, 2) 77.0 (-, 1) 66.0 (-, 1) -

Refreshedness rating (/100)

Last break preshift 70.8 (15.2, 20) 77.9 (17.8, 18) 68.3 (15.8, 9) 71.1 (21.1, 7) 1 64.0 (20.9, 18) 71.9 (17.5, 20) 58.8 (21.7, 10) 68.9 (13.8, 8) 2 61.6 (19.7, 19) 71.5 (17.6, 17) 66.9 (20.6, 10) 72.4 (6.6, 8) 3 66.1 (21.7, 15) 73.4 (13.0, 18) 69.0 (9.1, 9) 71.3 (10.1, 7) 4 59.5 (20.9, 16) 75.9 (17.6, 15) 64.8 (17.5, 8) 73.4 (9.5, 7) 5 81.5 (5.0, 2) 76.0 (-, 1) 62.0 (-, 1) -

Drivers’ actigraphic sleep data for the weekend period occurring in the middle of the two week data collection period were compared for differences in recovery sleep between the different shift conditions. Table 18 shows the actual sleep obtained by drivers during the course of the weekend break in the middle of the study period. Comparison of permanent day and permanent night drivers revealed no difference in total sleep obtained over the mid study weekend. Statistical comparison of rotating drivers completing day and night weeks first was not attempted because of the small numbers of drivers involved. Nonetheless, the means for the whole weekend period showed that drivers finishing a day week got around four hours more sleep on the weekend than drivers finishing a night week.


Table 18. Actual sleep obtained by drivers during the mid-study weekend Consecutive sleep number in mid study

weekend2 Group Total actual

sleep hours1 1 2 3 4 5

Day shift Mean 16:02 6:57 5:47 5:47 6:13 7:08 (n=22) SD 5:20 1:20 2:43 0:51 2:53 - n 11 16 12 9 4 1 Night shift Mean 17:12 4:55 6:35 7:32 3:52 2:33 (n=21) SD 4:46 2:29 2:17 2:49 4:09 - n 16 18 16 13 5 1 Rotating – Day shift 1st Mean 18:47 7:13 4:07 7:02 5:35 - (n=6) SD 0:28 2:21 3:45 1:44 3:56 - n 3 5 4 4 2 0 Rotating – Night shift 1st Mean 14:38 5:21 5:55 4:29 - - (n=4) SD 3:22 1:42 1:22 1:13 - - n 4 4 4 3 0 0 All Rotating drivers Mean 16:25 6:23 5:01 5:56 5:35 - (n=10) SD 3:16 2:12 2:47 1:57 3:56 - n 7 9 8 7 2 0 1 Only includes drivers with complete sleep data for the whole break. 2 Includes drivers with data for any individual sleep.

Because the amount of sleep obtained by drivers partly reflects the length of the break in which sleep is taken, the amount of actual sleep recorded by the actigraphs was expressed as a percentage of the length of the weekend break. When permanent day and night shift drivers were compared, there was no significant difference between groups on the percentage of weekend time spent asleep (Table 19). The means for the rotating shift drivers are almost identical as well. Regardless of roster, the drivers spent about one third of their weekend time asleep. In 24 hours of weekend time, then, drivers would be expected to average about 8 hours of sleep. This contrasts with the average weekday sleep time of between 5 and 6 hours sleep.


Table 19. Percentage of midstudy weekend break time spent in actual sleep Group n Mean % SD Day shift (n=22) 11 32.06 4.03 Night shift (n=21) 16 37.38 12.52 Rotating – Day shift 1st (n=6) 3 32.18 10.12 Rotating – Night shift 1st (n=4) 4 32.33 2.18 All Rotating drivers (n=10) 7 32.26 6.05

4.3 Comparison of subjective fatigue between beginning and end of selected study period

In addition to other ratings made during the study, drivers rated their subjective fatigue at the time they completed the long performance tests – at the start of the first week of participation (baseline) and at the ends of the study weeks. Ratings at the end of the selected work week were compared with baseline ratings for day and night shift drivers using 2 (group) x 2 (occasion) repeated measures MANOVA. For rotating shift drivers, baseline ratings were compared with the ratings at the end of the day shift week and the end of the night shift week using one-way repeated measures MANOVA. Overall ratings were obtained by averaging the three separate fatigue ratings (tiredness, muzzy-headedness, and drowsiness). The results are shown in Table 20. The higher the rating, the greater the subjective fatigue reported.

For permanent shift drivers, 2 (group) by 2 (occasion) repeated measures MANOVA showed a significant occasion effect, with the overall subjective fatigue rating higher at the end of the working week compared to baseline (F(1,35)=12.71, p=0.001). Although the mean scores suggest a greater increase in rated fatigue across the week for night shift drivers, no statistically significant difference in overall subjective fatigue was found between day shift drivers and night shift drivers and there was no significant group by occasion interaction effect.

Similar results were found when the three fatigue scales were analysed separately. Repeated measures MANOVA’s showed significant occasion effects for each of the three fatigue scales (p’s ≤ 0.044) but there were no differences between the day and night driver groups for any of the separate fatigue scales and no significant group by occasion interaction effects.


Table 20. Mean subjective fatigue ratings across the selected study period Permanent Shift Day Night (n=20) (n=17) Mean SD Mean SD Overall average (/100) Baseline 23.50 16.36 24.22 18.42 End of week 1 32.75 21.16 44.41 18.90 Tiredness (/100) Baseline 27.75 23.31 28.82 27.13 End of week 1 35.00 22.18 52.35 21.11 Muzzy-headedness (/100) Baseline 22.75 20.03 26.76 27.72 End of week 1 30.75 23.80 39.71 18.41 Drowsiness (/100) Baseline 20.00 18.21 17.06 18.21 End of week 1 32.50 21.49 41.18 19.25 Rotating Shift (n=6) Mean SD Overall average (/100) Baseline 12.50 15.52 End of day week 55.56 20.27 End of night week 57.22 22.10 Tiredness (/100) Baseline 22.50 29.11 End of day week 64.17 22.23 End of night week 61.67 32.66 Muzzy-headedness (/100) Baseline 5.00 4.47 End of day week 53.33 21.60 End of night week 54.17 15.94 Drowsiness (/100) Baseline 10.00 14.83 End of day week 49.17 21.78 End of night week 55.83 26.16 1 Significant occasion main effect (p<.05)

Repeated measures MANOVA for rotating drivers revealed a significant difference on overall subjective fatigue ratings between baseline, end-of-day-shift week, and end-of-night-shift week (F(2,4)=24.99, p=0.005). Follow-up multiple comparison t-tests confirmed that overall subjective ratings of fatigue were significantly higher than baseline after a week of day shift (t(1,5)=7.25, p=0.001), and between baseline and after a week of night shift (t(1,5) =-5.56, p=0.003). There was no difference in end-of-week fatigue ratings between rotating day or night shift weeks. Subsequent repeated measures MANOVAs for each fatigue scale also indicated significantly higher end-of-day-shift week, and end-of-night-shift week ratings compared to baseline on the muzzy-headedness and drowsy scale (p’s ≤ 0.006). For the tiredness scale, the higher end of day shift week, and end of night shift week ratings were marginally statistically significant compared to baseline (F(2,4)=6.41, p=0.057). The follow-up multiple comparison t-tests confirmed that ratings on the three separate scales were significantly higher compared to baseline after a week of day shifts (p’s = 0.01) and after a week of night shifts (p≤0.03). The ratings were not statistically significantly different between end of day and end of night weeks for any of the scales.


4.4 Comparison of performance at the beginning and end of the selected study period

4.4.1 Simple Reaction Time task

Table 21 summarises Simple Reaction Time task results for each of the four groups at the start and end of the selected study week showing mean reaction time (RT), standard deviation of reaction times (RT_SD) and missed responses (MIS).

Table 21. Simple Reaction Time responses across the selected study period Permanent Shift Day Night (n=20) (n=18) Mean SD Mean SD

Baseline 481.00 50.06 497.44 42.86 Reaction Time (msec) End of week 1 492.95 41.26 509.56 41.54

Baseline 74.80 32.43 75.72 25.42 Reaction Time–Standard

Deviation (msec) End of week 90.95 31.41 73.22 28.08 Missed signals Baseline 0.80 1.77 0.33 0.59 End of week 0.55 1.32 0.56 1.46 Rotating Shift (n=5) Mean SD Reaction Time (msec) Baseline 505.00 37.19 End of day week 502.80 37.77 End of night week 498.40 58.08

Baseline 85.00 25.42 Reaction Time–Standard

Deviation (msec) End of day week 95.00 24.69 End of night week 77.40 21.21 Missed signals Baseline 0.40 0.55 End of day week 0.20 0.45 End of night week 0.60 0.55 1 Non-significant trend for an occasion main effect

Repeated measures MANOVA showed that permanent day and night shift drivers did not differ on any of the Simple Reaction Time task measures. RT tended to be higher at the end-of-week compared to baseline (F(1,36)=3.84, p=0.058) but there was no significant interaction on any performance measures between type shift and test occasion. These results indicate that both day and night drivers showed slowing of reaction speed over the week, but to an equal extent.

Repeated measures MANOVA’s comparing RT, RT_SD and MIS for rotating drivers indicated no significant overall differences between baseline, end-of-day-shift week, and end-of-night-shift week.

Williamson et al. (2000) examined performance on the Simple Reaction Time task when blood alcohol levels were at 0.05%BAC up to 0.1%BAC. The mean test scores at 0.05%BAC were RT = 534msec, RT_SD = 95msec and MIS = 1.17. These scores provide


a performance benchmark linked to community-accepted standards of unsafe driving and have been shown to occur in groups of people who have been awake for more than about 17 hours. Drivers in the current study averaged scores below these benchmarks at all test occasions, except for response variability (RT_SD) at the end of the day shift week for rotating drivers. However, 95% confidence intervals about the drivers’ mean RT_SD scores incorporated the 0.05%BAC estimate at the end of the week for day shift drivers. Similarly, confidence interval around the mean MIS scores incorporated the 0.05% BAC estimate at baseline for day shift drivers and at the end of the week for night shift drivers. For rotating shift drivers, 95% confidence interval about their mean score always incorporated the 0.05% BAC estimates for RT and RT_SD but not for MIS. It should be noted, however, that the rotating shift drivers represented a small sample.

4.4.2 Mackworth Clock Vigilance task

Table 22 summarises Mackworth Clock Vigilance task performance at the start and end of study weeks for permanent day shift drivers, permanent night shift drivers, rotating drivers on day shift and rotating drivers on night shift. Measures included mean reaction time (RT), standard deviation of reaction times (RT_SD), number of false alarm responses (FA) and missed responses (MIS).

On average, there was no significant difference between permanent day shift drivers and permanent night shift drivers on any of the Mackworth Clock Vigilance performance measures. Similarly, there was no overall difference in baseline performance compared to the end-of-week performance for RT, RT_SD and MIS. However, significantly more false alarms were observed at the beginning of the study period compared to the end of the selected week (F(1,36)=5.83, p=0.02). Lastly, there were also no significant group by occasion interactions for any of the four Mackworth Clock Vigilance performance measures for permanent day and night shift drivers.

For rotating shift drivers, repeated measures MANOVA’s showed that there were no significant differences in performance at the end of the day shift week compared to the night shift week on any of the performance measures or between baseline and end of shift for day shift or night shift weeks for any of the measures.


Table 22. Mackworth Clock Vigilance responses across the study period Permanent Shift Day Night (n=20) (n=18) Mean SD Mean SD Reaction Time (msec) Baseline 835.55 73.79 888.11 138.28 End of-week 857.75 100.25 946.83 298.77

Baseline 98.65 49.77 204.78 440.77 Reaction Time-Standard Deviation (msec) End of week 127.20 104.68 228.28 522.24 False Alarms Baseline 3.10 3.29 5.78 9.50 End of week 1 2.05 3.39 1.39 1.85 Missed signals Baseline 2.55 2.96 1.33 1.75 End of week 2.85 2.64 2.33 1.68 Rotating Shift (n=5) Mean SD Reaction Time (msec) Baseline 829.00 27.67 End of day week 854.20 96.17 End of night week 873.60 138.24

Baseline 95.40 9.94 Reaction Time-Standard Deviation (msec) End of day week 120.60 39.48 End of night week 102.80 49.77 False Alarms Baseline 1.00 1.41 End of day week 1.40 2.61 End of night week 0.80 0.84 Missed signals Baseline 3.00 2.83 End of day week 3.20 3.27 End of night week 3.80 3.63 1 Significant occasion main effect (α<0.05)

The 0.05% BAC equivalence for mean Mackworth Clock Vigilance test scores among truck drivers were RT = 1094msec, RT_SD = 304msec, MIS = 4.09 and FA = 1.63 (Williamson et al., 2000b). Drivers in the current study averaged scores below these benchmarks at all test occasions, except that false alarm responses were almost always higher than the 0.05% BAC estimate for all shift conditions. When 95% confidence intervals were calculated around the drivers’ mean performance scores, RT_SD incorporated the 0.05% BAC estimate at both the start and the end of the week for night shift drivers, and false alarms incorporated the 0.05% BAC estimate for both day and night drivers at all occasions. Rotating shift drivers MIS confidence intervals enclosed the 0.05% BAC estimate at all occasions and for false alarms, at baseline and the end of the day shift week. It should be noted again that the rotating shift drivers represented a small sample.


4.4.3 Psychomotor Vigilance task (PVT)

Table 23 summarises Psychomotor Vigilance task (PVT) results at the start and end of study weeks for permanent day shift drivers and night shift drivers. A wide range of measures were analysed including the traditional direct measures of mean reaction time (RT), standard deviation of the reaction times (SD_RT), number of responses made more than 500ms after presentation of the stimulus (LAPSES) and number of false alarms (FA). In addition, a number of derived measures were also analysed to attempt to detect any differences in more specific patterns of responding between groups. These derived measures include measures which reduce the effect of the skewness in reaction time measures; mean reciprocal reaction time (RRT) and standard deviation of the reciprocal reaction times (SD_RRT) and measures that look specifically at the patterns of the slowest responses including the mean of the slowest 10% of reaction time responses (RT_SLOW), standard deviation of the slowest 10% of reaction time responses (SD_RT_SLOW), mean reciprocal of slowest 10% of reaction time responses (RRT_SLOW), and standard deviation of the reciprocal of slowest 10% of reaction time responses (SD_RRT_SLOW). Lastly, the overall pattern of responses across each test session were compared at the start and end of the selected study period using the slope of the regression line of the reciprocal reaction time over the 10 minutes of the task (RRT_SLOPE). (In extended tasks requiring attention, performance often becomes poorer as the time on task increases. This is the “vigilance decrement” phenomenon and is reflected in negative values of RRT_SLOPE.) Comparable data for rotating drivers on day shift and rotating drivers on night shift are presented in Table 24.

Analysis of PVT performance measures for permanent day and night shift drivers used 2 group (day v night) by 2 occasions (baseline v end of week) MANOVA with repeated measures on the occasion factor. Only the SD_RRT measure showed a significant difference between day and night drivers, with day drivers showing significantly more variability in inverse reaction times then night shift drivers (F(1,33)=5.79, p=0.02). There were no significant differences in any PVT performance measures between the beginning of the week and the end of the selected study week. Analysis of interaction effects showed two non-significant trends for interaction between driver group and test occasion. First, at baseline, day shift drivers had higher RT compared to night shift drivers, but at the end of the study week, day shift drivers were performing faster and night shift drivers were performing slower than their respective baseline RT. At the end of the study period day shift drivers were faster than night shift drivers (F(1,33)=3.03, p=0.09). This was reinforced by the finding of a similar interaction trend for the reciprocal of the mean RT (RRT). Day shift drivers showed an increase in RRT across the study period, while night shift drivers showed a decrease in RRT across the study period (F(1,33)=3.49, p=0.07).


Table 23. Psychomotor Vigilance Task responses across the study period for day and night shift drivers

Permanent Shift Day Night (n=19) (n=16) Mean SD Mean SD RT (ms) Baseline 312.59 172.06 270.65 28.74 End of-week 269.98 45.82 305.53 117.45 SD_RT (ms) Baseline 162.95 287.70 71.33 30.12 End of week 91.68 45.18 254.92 757.58 RRT (s) Baseline 3.88 0.60 3.91 0.36 End of week 4.04 0.55 3.80 0.33 SD_RRT (s) Baseline 0.85 0.25 0.72 0.14 End of week 1 0.81 0.16 0.69 0.13 RT_SLOW (ms) Baseline 688.31 882.56 423.59 94.08 End of week 467.85 135.33 721.26 1205.48 SD_RT_SLOW (ms) Baseline 267.10 481.00 107.50 81.57 End of week 159.95 108.29 663.95 2313.27 RRT_SLOW (s) Baseline 2.30 0.80 2.57 0.47 End of week 2.47 0.60 2.50 0.45 SD_RRT_SLOW (s) Baseline 0.45 0.25 0.40 0.17 End of week 0.52 0.18 0.41 0.27 RRT_SLOPE Baseline -0.01 0.07 -0.03 0.06 End of week -0.04 0.05 -0.03 0.08 LAPSES Baseline 5.00 9.62 1.56 1.79 End of week 2.47 2.65 1.56 2.03 FA Baseline 2.74 5.82 1.56 3.03 End of week 2.47 3.12 1.63 3.42 1 Significant group main effect (p<0.05)

Frequency analysis indicated that two participants responded atypically on the PVT, one in the day shift group and one in the night shift group. The repeated measures MANOVA’s were run again excluding these outlying scores with the following results:

1) The group by occasion interaction trends for both RT (F(1,31)=2.50, p=0.12) and reciprocal RT (F(1,31)=3.13, p=0.09) were weaker so there was less evidence for a difference between permanent day and night drivers in the change in RT over the week.

2) Day drivers still presented more variable reciprocal reaction times (RRT) than night drivers (F(1,31)=4.70, p=0.04) but the same effect trend also emerged on the variability of raw RT (F(1,31)=3.46, p=0.07), and reached significance on the variability of the slowest 10% of raw (F(1,31)=4.29, p=0.047) and reciprocal reaction times(F(1,31)=7.56, p=0.01). These results


indicate that permanent day drivers had more variable reaction time performance overall than permanent night drivers.

Table 24 summarises the PVT performance of drivers working rotating shifts. Repeated measures MANOVA showed that the PVT performance of rotating shift drivers who completed a day shift week first did not differ at baseline from rotating drivers who completed a night shift week first. Comparison of baseline PVT responding to performance at the end of the day and night shift weeks for rotating drivers showed no significant differences on any measure, but the sample size was very small which may compromise this analysis.

Table 24. Psychomotor Vigilance Task responses across the selected study week for rotating shift drivers

Test occasion Baseline End of day

week End of night

week (n=5) RT (ms) Mean 290.31 271.94 274.13 SD 41.92 23.40 27.26 SD_RT (ms) Mean 86.58 58.61 56.46 SD 48.72 17.57 19.97 RRT (s) Mean 3.68 3.83 3.80 SD 0.46 0.37 0.37 SD_RRT (s) Mean 0.64 0.63 0.63 SD 0.14 0.20 0.15 RT_SLOW (ms) Mean 483.39 391.04 390.96 SD 144.63 45.17 66.10 SD_RT_SLOW (ms) Mean 137.30 93.50 59.90 SD 130.37 48.33 52.52 RRT_SLOW (s) Mean 2.36 2.68 2.67 SD 0.67 0.29 0.45 SD_RRT_SLOW (s) Mean 0.39 0.43 0.30 SD 0.24 0.11 0.16 RRT_SLOPE Mean -.02 -0.04 -0.06 SD 0.04 0.09 0.08 LAPSES Mean 3.40 1.20 0.60 SD 3.58 0.84 0.89 FA Mean 0.40 0.40 0.40 SD 0.55 0.55 0.89

Baseline and end-of-week pre-test and post-test ratings of sleepiness were also obtained for the PVT. These are summarised in Table 25.


A 2 group (day v night) x 2 occasion (baseline v end of week) x 2 rating time (pre-test v post-test) MANOVA with repeated measures on the occasion and rating time factors revealed a marginally significant 3-way interaction between all factors (F(1,33)=4.05, p=0.052). Post hoc multiple comparisons with alpha adjusted in a family-wise fashion revealed that sleepiness ratings did not change significantly between the start and end of the PVT test at baseline for either day or night shift drivers. However, night shift drivers, had higher sleepiness ratings at the end of the week before the PVT test compared to day shift drivers at the same time (t(33)=2.98,p =0.005). In addition, night shift drivers’ sleepiness ratings at the end of the week were higher before the PVT than after it (t(15)=3.11, p=0.007) and higher than their baseline pre-test ratings (t(15)=3.50, p=0.003).

Table 25. Pre- and post-PVT sleepiness ratings for tests at baseline and the end of the selected study week

Permanent Shift 1 Day Night (n=19) (n=16) Mean SD Mean SD Baseline Pre-test 3.05 2.39 2.75 2.35 Post-test 3.95 2.78 3.75 2.70 End-of-week Pre-test 3.37 2.22 5.63 2.25 Post-test 3.95 2.55 4.81 1.97 Rotating Shift 2 (n=5) Mean SD Baseline Pre-test 1.20 0.45 Post-test 2.80 3.03 End of day shift week Pre-test 4.40 3.21 Post-test 6.00 3.32 End of day shift week Pre-test 5.60 3.44 Post-test 6.40 3.71 1 Three-way interaction (p=0.052) 2 Pre-test < post-test (p=0.051)

Rotating drivers’ sleepiness ratings were subjected to a 3 occasion (baseline v end day week v end night week) x 2 rating time (pre-test v post-test) MANOVA with repeated measures on both factors. Pre-test sleepiness ratings were marginally lower than post-test ratings (F(1,4)=7.62, p=0.051), but with so few drivers contributing data, the difference between baseline and end of week ratings for each type of shift did not reach significance (F(2,3)=5.01, p=0.11). There was also no significant interaction between occasion and rating time.

4.5 Subjective fatigue changes from shift to shift across the selected study period

During the study period, drivers were asked to rate their fatigue in diaries at the start and end of every break from work lasting at least 15 minutes. Table 26 shows the percentage


of drivers in each shift condition with subjective fatigue ratings at each occasion during the study week. Most drivers did not report their subjective fatigue within shifts although, during the first shift of the week, up to half of the drivers reported fatigue at the start and/or end of one midshift break. The percentage of drivers reporting fatigue at a midshift break then decreased across subsequent shifts. Only a small percentage of drivers reported on their fatigue at a second break within shifts and these were typically day shift drivers. No rotating shift drivers reported fatigue for a second midshift break. The numbers of drivers represented at each occasion varies because of differences in the number of consecutive shifts worked, the number of midshift breaks taken, and the completeness of drivers’ reporting. As so few drivers reported a second break between shifts this data is not presented in Figure 7.

The average scores for the combined fatigue rating scales for drivers with data at each occasion during the week are plotted in Figure 7.

Statistical comparison of the permanent day and night shift drivers was only conducted using the subjective fatigue ratings at the start and end of each shift and across the first five shifts of the week to maximise the number of drivers contributing data points to the analysis. Sixteen of the day shift drivers and 15 of the night shift drivers had sufficient data for this analysis. A 2 (group) x 2 (start vs end of shift) x 5 (shift number) MANOVA with repeated measures on both shift factors confirmed that fatigue ratings were significantly higher at the end of shifts than at the start for both groups (start vs end main effect; F(1,29)=70.51,p<0.001) and that night shift drivers showed a significantly larger increase in ratings across a shift than day shift drivers. This latter difference was due to the fact that night shift drivers started the shift with lower fatigue ratings than day shift drivers (mean fatigue ratings, night shift=16.7, day shift=26.3) but had similar ratings at the end of shifts (mean fatigue ratings, night shift=38.6, day shift=38.1; start vs end x group interaction effect; F(1,29)=6.28, p=0.018). The analysis also confirmed that this pattern was stable across the shifts in the week (all effects involving shift number; p>0.31).

Subjective fatigue at the starts and ends of shifts during the day and night weeks for rotating shift drivers were compared using a 2 (type of week) x 2 (start vs end of shift) x 4 (shift number) MANOVA with repeated measures on all factors. Only four shifts were used for the analysis of rotating drivers in order to maximise the number of drivers with sufficient data across the shifts although the resulting sample across four shifts (n=8) is still small and this may have influenced the findings. Like the permanent day and night drivers, the subjective fatigue rating scales for rotating shift drivers were significantly higher at the end of shifts than at the start (F(1,7)=68.85, p<0.001), and the increase over a shift was greater during the night shift week than the day shift week (F(1,7)=11.40, p=0.01). For these drivers, subjective ratings differed at the end of the shifts rather than at the start, with end-of-shift ratings during the night week being higher than end-of-shift ratings during the day week (mean fatigue rating for day preshift=22.9, day post-shift=43.7, for night preshift=16.7, for night post-shift=59.2). No other effects approached significance.


Table 26. Percentage of drivers in each shift condition with subjective fatigue ratings at the start and end of breaks during the week

Day shift Night shift Rotating - day week

Rotating – night week

(n=22) (n=21) (n=11) (n=10) Shift 1 Start shift 95.45 100 100 100 Start midshift break 1 36.36 52.38 45.45 50.00 End midshift break 1 36.36 52.38 45.45 40.00 Start midshift break 2 13.64 0 0 0 End midshift break 2 13.64 0 0 0 End shift 95.45 100 100 100 Shift 2 Start shift 100 100 100 100 Start midshift break 1 27.27 47.62 45.45 40.00 End midshift break 1 27.27 33.33 45.45 30.00 Start midshift break 2 13.64 4.76 0 0 End midshift break 2 13.64 4.76 0 0 End shift 100 95.24 100 100 Shift 3 Start shift 100 100 90.91 100 Start midshift break 1 31.82 42.86 36.36 20.00 End midshift break 1 27.27 33.33 18.18 20.00 Start midshift break 2 9.09 0 0 0 End midshift break 2 9.09 0 0 0 End shift 100 95.24 90.91 90.00 Shift 4 Start shift 86.36 90.48 90.91 90.00 Start midshift break 1 27.27 42.86 27.27 20.00 End midshift break 1 27.27 33.33 18.18 10.00 Start midshift break 2 4.55 4.76 0 0 End midshift break 2 4.55 4.76 0 0 End shift 86.36 85.71 90.91 100 Shift 5 0 Start shift 81.82 80.95 72.73 90.00 Start midshift break 1 22.73 38.10 18.18 20.00 End midshift break 1 13.64 33.33 0 20.00 Start midshift break 2 0 0 0 0 End midshift break 2 0 0 0 0 End shift 86.36 71.43 63.64 90.00 Shift 6 Start shift 13.64 4.76 9.09 0 Start midshift break 1 0 4.76 0 0 End midshift break 1 0 4.76 0 0 Start midshift break 2 0 0 0 0 End midshift break 2 0 0 0 0 End shift 13.64 4.76 9.09 0


Figure 7. Averaged subjective fatigue ratings at the starts(S) and ends(E) of shifts and at the starts and ends of midshift (_1) breaks (with 95% confidence intervals)

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4.6 Performance changes from shift to shift across the selected study period

4.6.1 Simple Reaction Time task

Table 27 presents the number of drivers who completed palmtop computer reaction time tests at each occasion throughout the study week. Around half to two-thirds of drivers in all groups completed start and end shift tests, but relatively few completed the midshift tests, although night drivers were slightly more compliant than day and rotating drivers in completing this test. Technical problems with the palmtops and driver compliance contributed to the extent of missing data. Data also diminished across the shifts as drivers completed their week’s work and very few had a sixth shift. Because few of the drivers had complete data across sufficient occasions, inferential analysis of the palmtop test measures for the day and night shift drivers was confined to 2 (group) x 2 (start v end of shift) MANOVA’s for each of shifts 1-5. Alpha rate was adjusted to 0.01 for the use of multiple tests. As the sample size for rotating shift drivers was small, statistical testing was not conducted on the palmtop data for the rotating shift drivers.

Figure 8 plots the mean reaction times over shifts for drivers in the different shift conditions. The number of drivers contributing data at each point varied in accordance with Table 27. Repeated measures MANOVA results indicated no significant differences between permanent day and night drivers or between shifts across the week. Visual inspection of the results for the rotating drivers showed little evidence of systematic changes in reaction time between their day and night weeks, within shifts or between shifts.

Figure 9 presents the average variability in reaction times on the palmtop Reaction Time test for drivers in the different shift conditions. MANOVA analyses comparing permanent day and night drivers at the start and end of each shift revealed a trend for day shift drivers to have more variable reaction times at shift 1 than night shift drivers (F(1,24)=4.75, p=0.04) and a trend for night shift drivers to become more variable at the end of shift 3 compared to the start whereas day shift drivers became less variable across shift 3 (F(1,24)=4.46, p=0.045). Visual inspection of the results for rotating drivers showed no clear patterns.

The mean number of missed responses on the palmtop Reaction Time task for drivers in the different shift conditions are plotted in Figure 10 across shifts in the week. MANOVA comparing permanent day and night drivers at the start and end of each shift revealed no differences significant at p<0.01. There was a trend for day shift drivers, but not night shift drivers, to have elevated numbers of missed responses at the start of shift 1 (F(1,24)=5.70, p=0.025) but no other results approached significance. No clear patterns were evident for rotating shift drivers.


Table 27. Number of drivers in each shift condition with palmtop Reaction time test data for each shift in the week

Simple Reaction Time task



Shift 1 Start shift 15 13 7 4 Mid shift 7 12 2 3 End shift 14 14 5 4 Shift 2 Start shift 15 13 6 4 Mid shift 5 10 3 4 End shift 15 14 7 4 Shift 3 Start shift 13 13 6 4 Mid shift 6 9 2 4 End shift 14 14 7 4 Shift 4 Start shift 11 13 6 4 Mid shift 4 7 1 4 End shift 8 12 7 4 Shift 5 Start shift 9 12 7 4 Mid shift 6 6 1 3 End shift 12 11 5 4 Shift 6 Start shift 3 - 1 - Mid shift 1 - - - End shift 3 - 1 -


Figure 8. Mean reaction time (ms) on the palmtop Reaction Time test at the start (S), middle (M) and end (E) of consecutive shifts in the week (with 95% confidence intervals)

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Figure 9. Mean variability (SD) in reaction time (ms) on the palmtop Reaction Time test at the start (S), middle (M) and end (E) of consecutive shifts in the week (with 95% confidence intervals)

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4.6.2 Mackworth Clock Vigilance task

Table 28 details the available data from the palmtop Mackworth Clock Vigilance test for drivers in the different shift conditions and for shifts across the work week. This information should be used in conjunction with Figures 11 to 14. Not surprisingly, the number of data points is similar to those for the Reaction Time test. Again, data were available for approximately two thirds of drivers at the starts and ends of shifts, with fewer drivers having data at midshift. Consequently, analysis involved MANOVA comparing day and night drivers at the start and end of each separate shift for permanent day and night shift drivers. Once again, no inferential statistical comparisons were attempted on the data from the rotating shift drivers.

Table 28. Number of drivers in each shift condition with palmtop Mackworth Clock Vigilance test data for each shift in the selected study week

Mackworth Clock Vigilance task



Shift 1 Start shift 15 13 7 4 Mid shift 6 12 2 3 End shift 14 14 6 4 Shift 2 Start shift 14 13 6 4 Mid shift 6 9 3 4 End shift 14 14 7 4 Shift 3 Start shift 13 13 6 4 Mid shift 7 9 2 4 End shift 14 14 7 4 Shift 4 Start shift 11 13 6 4 Mid shift 4 7 1 4 End shift 8 12 7 4 Shift 5 Start shift 9 12 7 4 Mid shift 6 7 1 3 End shift 12 11 6 4 Shift 6 Start shift 3 - 1 - Mid shift 1 - - - End shift 3 - 1 -

The mean reaction times on the palmtop Mackworth Clock Vigilance task are plotted in Figure 11. MANOVA analyses of reaction speed for permanent day and night shift drivers at the starts and ends of shifts revealed no changes across any shift and no group differences significant at p<0.01. However, there was a trend for RT to be slower at the end of shift 2 than at the start (F(1,22)=4.53, p=0.045). Rotating shift drivers showed more variable reaction speeds across the selected study week, but there were no clear patterns.


The variability in drivers’ reaction times on the palmtop Mackworth Clock Vigilance task are plotted in Figure 12. There were no consistent patterns in the data for either the day, night or rotating shift groups, except for some elevation in variability at shifts 1 and 5 for all groups. None of the MANOVA comparisons of permanent day and night shift driver performance at the start and end of each shift revealed any effects approaching significance.

The mean number of missed responses on the palmtop Mackworth Clock Vigilance task are plotted in Figure 13. Analysis by MANOVA did not reveal any significant differences between permanent day and night drivers presumably because of the large variation between individuals. Rotating shift drivers working night shift showed an apparent increase in missed responses towards the end of the week, which was evident at all test occasions in the shifts but was associated with large variation between individual drivers.

The average number of false alarm responses made by drivers on the palmtop Mackworth Clock Vigilance task are shown in Figure 14. Across shifts 1 to 3, night shift drivers made higher mean false alarms than day shift drivers, but the 95% confidence intervals indicate considerable variation between individual drivers within each group. Analysis by MANOVA comparing day and night shift drivers at the start and ends of shifts revealed no statistically significant differences in false alarm rates. For rotating shift drivers false alarm rates were consistently higher during the day shift week than the night shift week.


Figure 11. Mean reaction time (ms) on the palmtop Mackworth Clock Vigilance test at the start (S), middle (M) and end (E) of consecutive shifts in the week (with 95% confidence intervals)

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Figure 12. Mean standard deviation on the palmtop Mackworth Clock Vigilance test at the start (S), middle (M) and end (E) of consecutive shifts in the week (with 95% confidence intervals)

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Figure 13. Mean number of misses on the palmtop Mackworth Clock Vigilance test at the start (S), middle (M) and end (E) of consecutiveshifts in the week (with 95% confidence intervals)

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Figure 14. Mean number of false alarms on the palmtop Mackworth Clock Vigilance test at the start (S), middle (M) and end (E) of consecutive shifts in the week (with 95% confidence intervals)

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5. DISCUSSION

The purpose of this study was to compare the fatigue experiences and effects of night and day driving in professional truck drivers. The results showed that over a typical work week of five consecutive 10 to 12 hour shifts there was a significant increase in subjective ratings of fatigue by all drivers. The effect was seen both across individual drivers in the comparisons between permanent day and night drivers and within individual drivers who were doing consecutive weeks of rotating day and night shifts. These findings lead to the conclusion that their work pattern made drivers significantly more tired by the end of the work week than they were at the beginning. The results are not consistent with predictions from previous research that cumulative fatigue across a week of work would be higher for night drivers compared to day drivers.

Rated fatigue also increased between the start and end of each shift within the work week, with night drivers showing greater increases in fatigue than day drivers. This was seen for both drivers working permanent night shifts and drivers on rotating night shifts. For both types of shifts night drivers rated their fatigue as markedly higher at the end of the shift than at the beginning, although for permanent shift night drivers the start of shift ratings were lower than the ratings of day shift drivers at the beginning of their shifts. It is possible that the higher fatigue ratings at the start of the shift for permanent day drivers were at least partly due to the time of day, since around half woke between 03:00 and 06:00, coinciding with the low point in the circadian rhythm. Overall, these results are consistent with predictions from previous research that night work augments the development of fatigue.

Changes in performance due to night work were not as expected based on previous research. Similar to the findings on the experience of fatigue, simple reaction time test performance tended to be slower at the end of the week compared to the beginning of the week, suggesting that the work week had effects of decreasing the capacity to react quickly in addition to increasing feelings of fatigue. As for subjective fatigue, this effect was shown for all drivers; that is, there were no differences between day and night shift drivers. There was some suggestion from the results of the PVT of a slowing in reaction speed across the work week for night drivers while day drivers showed faster response time at the end of the week. This was not a strong finding, as the changes were non-significant trends, but it is consistent with predictions from previous research that night work should have a greater adverse effect on performance than day work. It could be argued therefore, that slowing of PVT response is an indication that night work is having some adverse effects that are not being picked up well by the measures used in this study.

There are some problems with this argument, however. First, the evidence of slowing reaction speed across the work week for all drivers shows that the measures are able to detect change that is consistent with fatigue, yet no changes specifically due to night driving were detected. Second, all the measures used in this study were selected on the basis of their demonstrated sensitivity for detecting fatigue. The Simple Reaction time and Mackworth Vigilance tests showed clear decrements with increasing levels of sleep deprivation (Williamson et al., 2000b; 2001b) and the PVT has extensive evidence of its sensitivity to conditions that are likely to cause fatigue (Dinges and Kribbs, 1991). It is unlikely, therefore that lack of sensitivity of measures is the main reason for failing to show that night work has greater negative effects on fatigue and performance than day work. On the contrary, it could be argued that some of the problems of conducting real-


world studies of fatigue mean that the effects on performance are not seen when fatigue is at its worst. During the study period, a considerable proportion of drivers missed test occasions, at least partly, perhaps, because they were too tired to complete the tests at that time. This was increasingly notable across the work week, for both day and night drivers. This would have the effect of underestimating the level of fatigue and its effect on performance especially later in the study week.

A number of other factors could account for the lack of expected effects due to night work. The major problem with night work, and which prompted this study, is the demonstrated adverse effect on fatigue and performance of the time of day in the midnight to dawn period. It could be argued that the absence of strong differences between day and night drivers in this study reflects the testing timetable for night drivers. That is, permanent night shift drivers tended to complete their end-of-week tests around dawn, when their alertness might be increasing after the early morning low. As a result, the full impact of their work schedule on fatigue and performance may have been masked by circadian effects. However, this argument does not explain the absence of differences between rotating day and night shifts. For rotating shift drivers, the end-of-week testing occurred much earlier in the morning and unambiguously during the circadian low.

An alternative explanation for failing to find the predicted effect for night drivers in this study could be that for all drivers in this study the work-rest pressures were more important than the circadian influences experienced in night work. All drivers in this study were experiencing similar levels of work pressure and doing similar long hours with comparatively little sleep which may eclipse any effects due to circadian or time of day influences. The majority of drivers in this study were doing work weeks of over 50 to 55 hours work typically arranged in five 10 to 12 hour shifts. While this amount of work corresponds to the mean work week reported by drivers in previous surveys of the long distance road transport industry, (Williamson et al., 2001a), it represents a considerably higher workload than seen for most other industries. Average weekly hours worked by males in full-time work in Australia was estimated to be around 42 hours per week based on 2001 figures (Australian Bureau of Statistics, 2002).

All drivers in this study also had short sleep. On average, their sleep between work shifts was between four and six hours duration which is far shorter than the currently recommended sleep length (Ferrara and De Gennaro, 2001), especially over a sustained period of five or six nights. Studies of restricting sleep length to five hours over a similar period show adverse effects on fatigue experiences and the capacity to perform (Dinges et al., 1997; Belenky et al., 2003). Drivers in this study clearly attempted to make up for their sleep restriction during the week by having more sleep in their rest time between work weeks. Recent results also show, however, that sleep restricted people failed to completely recover performance capacity after only one longer rest period (Belenky et al., 2003) and even after three longer restorative sleeps, performance decrements had not recovered. Based on the results of previous studies therefore, it could be concluded that all drivers in this study were being affected by restricted sleep and that any differential effects of night work may be overshadowed by this effect. By this argument, therefore, it may be that for drivers who do shorter shifts or shorter runs of shifts than in this study and have consequently lower overall fatigue levels, performance decrements due to night work may be more obvious.

Sleep disorders and other, demographic factors did not differ greatly between the groups of drivers. This means that the failure to demonstrate effects of shift rosters on fatigue and


performance cannot be attributed to group differences in these background factors. However, a further possible reason for failing to show clear differences between night and day drivers could be that all drivers in this study were experienced professional long distance drivers who were well-suited to cope with the demands of the road transport industry by organising their work-rest. Other research has suggested that individuals differ in their ability to maintain performance in the face of sleep deprivation (Van Dongen, Baynard, Maislin, & Dinges, 2004). Further, experienced professional drivers have been found to respond differently to sleep deprivation compared to non-professional drivers by being more conservative and pacing themselves in performance tests over long periods without sleep (Williamson et al., 2001b). This research suggests that professional truck drivers are self-selected or develop the facility for managing the pressures of working with little sleep and maintaining good performance levels.

Examination of the work and sleep patterns of the drivers working night shifts lends some support to the idea that they may be especially tolerant of fatigue or skilled at managing fatigue, which might explain why they did not perform differently to day shift drivers in this study. Night shift drivers worked longer shifts than day shift drivers and spent much more of their working time driving than day shift drivers which might predict that night shifts would be more tiring than day shifts. Night drivers also obtained less sleep between shifts during the work week. During their weekend break, night and day drivers each increased their sleep time but to an equivalent amount. Night drivers did not compensate for their greater sleep debt accumulated during the week by taking more weekend sleep than day drivers. However, there is some evidence that night drivers organised their sleep differently and this may partly explain how they could maintain performance. During the weekend breaks, the last substantial sleep taken by night drivers before returning to work was taken at night which would maximise its benefit, and this sleep was significantly longer than that taken by day drivers. In addition, for permanent night shift drivers at least, a significant proportion (up to half) napped in the hours before their first shift of the week During the week, night drivers also arranged their rest to maximise its effectiveness by sleeping when sleep would be most likely and of most benefit, that is as soon as possible after they had finished work in the early mornings. It seems that night drivers endeavoured to capitalise on the sleep propensity influences of the circadian rhythm by getting as much sleep as they could as close as possible to the early morning circadian trough when sleep is most likely. This appears to have been of limited success for permanent night shift drivers as the sleep taken in the morning after consecutive night shifts was significantly shorter than sleep following day shift. It is well-known that sleep taken in the early morning is typically truncated, because the body’s circadian rhythms make it increasingly difficult to maintain sleep as the morning progresses (Akerstedt, 1985). Surprisingly, and in contrast to their use of napping on the weekend, very few night drivers used afternoon napping during the week to supplement their main sleep period. Whether these drivers felt sufficiently refreshed that a nap was not needed or whether the demands of daily life made weekday napping difficult is not clear. Overall, then, it seems that the night drivers in this study were organising their rest to attempt to cope with the negative consequences of high work loads and long hours and this may have been sufficient for them to overcome some of the potential differential effects of night work. Drivers sleep management could, nonetheless, be improved by greater exploitation of napping.

The study highlighted another work-rest pattern of concern. Rotating shift drivers on day shift started work very early in the morning with many starting work in the 03:00 to 04:00 period. Consequently waking time for this group was also very much earlier than any of the other groups as around three-quarters of these drivers woke between midnight and


03:00. In contrast, the corresponding sleep onset time was not greatly different to the permanent shift day drivers. While most rotating shift drivers on day shift went to sleep in the period 18:00 to 21:00, well over one-third did not sleep until the 21:00 to 24:00 period. It is not surprising then, that on day shift, rotating drivers obtained the least amount of sleep in the breaks between shifts although their sleep efficiency tended to be significantly higher than when they were on rotating night shifts. Other studies have also noted similar problems with early morning starts (Tucker, Smith, MacDonald and Folkard, 1999).

There was some indication that performance may have been affected in this group in terms of the variability of their response time on the Simple Reaction Time test. There was no significant difference between baseline levels of response time variability on this test and the end of either day week or night week. By the end of the day week, however, the response time of rotating shift drivers on day shift had become sufficiently inconsistent that the variability measure was above the 0.05%BAC alcohol-equivalence level. This suggests that the early starts and abbreviated sleep between shifts while on day shift were beginning to have adverse effects on the drivers’ performance capacity.

Fortunately, the nature of the rotating shift meant drivers only did one week of such early starts before moving on to a week of night shifts. It is notable that on the weekend following day shift, rotating drivers obtained around four hours more in sleep presumably as they needed to discharge the sleep debt developed over the previous week. It is also notable that for rotating shift drivers, the week of day work seems to have produced more adverse consequences than the week of night work. It seems likely that the difference is mainly to do with the earlier starts, which were not compensated for sufficiently by earlier sleep times and resulted in smaller amounts of sleep between day shifts.

The results of this study support those of previous research evaluating work weeks of 12 – 14 hour shifts for long distance road transport drivers (Williamson et al., 2000a). Both studies showed little performance change across individual shifts, but some evidence that towards the end of a series of five long work shifts (in the range 10 to 14 hours) drivers may not be performing as well as they were earlier in the week. This could compromise their capacity to drive reliably and safely. The findings also suggest that with comparatively long hours of work and short periods of sleep, an additional long 24 hour break might be beneficial in order to break up the week.

This study again showed different results for subjective fatigue and for performance effects attributable to fatigue. The divergence of subjective and performance measures of fatigue has been a consistent finding of a number of similar studies (Williamson et al., 2000a; 2000c), and supports the idea that self-reports may capture fatigue earlier or at lower levels than are necessary to compromise performance (e.g., Akerstedt & Gillberg, 1990; Dinges et al, 1997; Jewett et al., 1999, Williamson et al., 2001b). It highlights the importance of not relying only on subjective fatigue ratings to estimate fatigue. This also means that fatigue management cannot simply rely on drivers reporting and responding to feelings of fatigue. Clearly, effective fatigue management needs to concentrate on achieving a balance between work and rest.

The current study was limited by a number of practical and methodological issues that need to be considered in designing future studies on the impact of work practices on fatigue. Two issues are of particular importance: 1) driver recruitment and 2) missing data. These will be discussed in turn.


The current study was designed to examine the impact of day and night shift rosters on subjective fatigue and performance among long distance drivers. The particular design selected was a naturalistic experiment where the primary variable of interest, type of roster, was not directly manipulated but was sampled from the range of “naturally occurring” work practices. Groups of drivers were selected on the basis that they did a particular shift roster. This method has the advantage that drivers can be measured during the normal course of their work, which improves the ecological validity of the study and may also minimise the cost of the research. Unfortunately, the heterogeneous nature of work practices in the long distance road transport industry meant that it was difficult to recruit a sufficient sample of drivers working to a particular regime. This was particularly problematic in the current study where the regimes of interest were initially defined by multiple constraints: on the route used (Sydney-Melbourne), shift length (11 hours or more), shift timing (ideally 7am-7pm and 7pm-7am), roster type (identical shifts on consecutive days), week length (5 days or more) and roster length (2 weeks). This level of standardisation proved unattainable and in the end, the constraints were relaxed considerably in order to recruit sufficient participants. The final sample included any driver working permanent day or night shifts that were rostered to be 11 or more hours long. As a result, in order to obtain a sufficiently large sample of drivers who did day shifts, the study also included drivers who worked rotating day shifts.

The problem is complicated further by the fact that there is an enormous variation in rostering practices. For example, as the typical work routine for long distance drivers is based on the trip rather than the shift, a driver’s work often incorporates both day and night work. Some industry sectors specialising in overnight or express freight may work almost exclusively at night, and night driving is often preferred over day driving by both companies and drivers on the basis that problems due to road sharing are minimised. However, depending on the freight and customer requirements, drivers’ starting times on night shifts may vary from anywhere between 3pm in the afternoon to 2am in the morning. Furthermore, with growing concern for fatigue management in the industry, many companies who work their drivers predominantly at night have introduced innovative rosters designed to reduce continuous night work and to increase opportunities for recovery. Such rosters include, for example, short runs of night shifts, or combinations of day and night shifts. In addition trip timing may be altered at any time within work weeks in response to personal and commercial necessities. The net result of all this variety in scheduling practices, for fatigue and shiftwork research, is to introduce variability into the data and reduce the degree of control that can be exercised over extraneous factors. A solution to these problems would be to use a more experiment-based approach to the study design in which companies and drivers are contracted to undertake the work-rest schedules required for the study design. This approach would increase the commitment by participating companies and improve the reliability and quality of the data collected, although it would probably increase the overall cost of the research. In addition, overall rates of volunteering by drivers might be increased if participants were reimbursed for time spent completing research activities where this time cannot be incorporated into paid working time.

Missing data posed a serious problem for the current study, limiting the type of data analyses that could be conducted and the strength of conclusions that could be drawn. This is a common problem in field research, especially studies that attempt to examine changes over time within the same people (repeated measures designs). Traditional statistical analysis techniques do not handle missing data very well with the result that the number of people in the sample and the power of the statistical tests to detect real differences are


reduced. Clearly, the analysis of the data on rotating shift drivers in the current study was hampered by having a small initial sample (due to the problem of finding sufficient day shift drivers) which was reduced further by missing data. The problem of missing data becomes even greater if the pattern of missing data is not random, that is, if the reason that data are missing is related to a variable of interest in the study or to a particular type of participant. In this case missing data may bias the results. As discussed earlier, this was a problem for the current study because drivers may have been less motivated to complete tests or provide self reports when they were very fatigued, so that the results of this study may underestimate the experience and effects of fatigue. In the same way, drivers may have been more likely to take days off work when they were more tired.

There were a number of other reasons for missing data in this study that limited the final study analysis. These include drivers forgetting to bring their study materials, drivers forgetting to complete their self reports or performance tests, lack of motivation to complete the tests, last minute changes to drivers’ work schedules and depot destinations, unanticipated leave days, industrial strike action affecting suppliers, an electricity blackout, two instances of freeway closures in response to major road accidents that prevented researchers from keeping testing appointments with drivers, and some technical difficulties with the testing equipment. Clearly the adoption of more strictly controlled experimental designs may help to reduce the incidence of missing data, but several other strategies are also likely to be useful in reducing the incidence of missing data. These include ensuring that study requirements are treated in the same way as commercial demands by operations staff at the participating companies through contracting companies to participate in the study. In addition, drivers who are potential study participants need to be identified well in advance so that their participation can be scheduled at a time when they are unlikely to request leave. This means that research timelines need to make provision for planning at each participating organisation. Continuous researcher presence at testing premises throughout the data collection period would also help to ensure that drivers provide responses at all appropriate occasions and this strategy would also allow technical problems to be solved immediately without loss of data. Potential strategies to improve driver motivation to complete all data collection requirements might include monetary payments as an incentive to provide complete data. Lastly, reliable in-vehicle technologies that prompt drivers to provide measures at appropriate times when they are on the road might also assist in obtaining data from drivers when they cannot be prompted to do so by research staff.


6. CONCLUSIONS

This study of five consecutive 10 to 12 hour periods of work showed higher fatigue ratings and some indication of poorer performance at the end of the work week compared to the start of the week but there was little evidence that night drivers were more affected than day drivers. Night drivers did report greater increases in fatigue between the start and end of their shifts, but this effect did not appear to accumulate across shifts and no effects on performance were observed. The results suggest that, at least for drivers doing this sort of regular work pattern, night work does not produce any greater adverse effects on performance than day work, even though it makes drivers feel more tired. It is not clear, however, how well these findings would extrapolate to work-rest patterns that are different; whether longer, shorter, or less regular than those in this study. For example, it is conceivable that under the work-rest conditions in this study, drivers needed to adapt their sleep and rest to accommodate the demands of the schedule, which they were mostly able to do. If the work-rest schedule was lighter and involved less sleep pressure, drivers may not be required to make such adaptations and the effects of night work might be more evident. Where the work-rest schedule was heavier, with longer shifts or longer runs of shifts without a break, the effectiveness of the sleep adaptations might be challenged, and under these conditions too the night work effect may be more evident. While the results of this study suggest that night work may not be a particular problem for professional drivers, further work would be needed to explore the effects under different types of work-rest schedules. Such studies would be best conducted under more controlled experimental conditions.

In conclusion, this study attempted to address the question of whether night work in practice produces different effects on fatigue and performance than day driving work. Drivers doing night work experienced greater increases in fatigue across a shift than day drivers, but it seems that when working such regular shifts as worked in this study, night shift drivers may be particularly tolerant or able to adapt their rest schedules to accommodate the effects of night work so that fatigue does not accumulate across the working week any more than for day shift drivers. It is not clear, however, how the capacity to adapt changes under different work-rest schedules to those examined in this study.


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Rosekind, M.R., Neri, D.F., & Dinges, D.F. (1997). From laboratory to flight deck: Promoting operational alertness. Fatigue and duty time limitations – An international review. London: The Royal Aeronautical Society

Sadeh, A., Hauri, P.J., Kripke, D.F., & Lavie, P. (1995). The role of actigraphy in the evaluation of sleep disorders. Sleep, 18:4, 288-302.

Sleep Disorders in Australian Transport Drivers Study, 1999-2002. Personal communication with Dr Mark Howard, Austin Hospital, Melbourne, 15 March 2004.

Stoohs, R.A., Bingham, L.A., Itoi, A., Guilleminault, C., & Dement, W.C. (1995). Sleep and sleep-disordered breathing in commercial long-haul truck drivers. Chest, 107:5, 1275-1282.

Swann, P. (2000). Heavy Vehicle Driver Health and Sleep Disorders. AP-148/00. Sydney: Austroads.

Tucker, P., Smith, L., Macdonald, I., & Folkard, S. (1999). Distribution of rest days in 12 hour shift systems: impacts on health, wellbeing, and on shift alertness. Occupational and Environmental Medicine. 56, 206-214.

US Department of Transportation Federal Motor Carrier Safety Administration. (2000). 49 CFR Parts 350, et al. Hours of service of drivers; driver rest and sleep for safe operations; Proposed rule. Federal Register, 65:85, pp.25539-25611.

Van Dongen, H.P.A., Baynard, M.D., Maislin, G., & Dinges, D.F. (2004). Systematic interindividual differences in neurpbehavioral impairment from sleep loss: Evidence of trait-like differential vulnerability. Sleep, 27:3, 423-433.


Wilkinson, R.T. (1968). Sleep deprivation: Performance tests for partial and selective sleep deprivation. In L.A. Abt & B.F. Riess (Eds.), Progress in Clinical Psychology (vol. 8). New York: Grune and Stratton. (pp. 28-43)

Williamson, A.M., & Feyer, A.-M. (2000). Moderate sleep deprivation produces impairments in cognitive and motor performance equivalent to legally prescribed levels of alcohol intoxication. Occupational and Environmental Medicine, 57, 649-655.

Williamson, A., Feyer, A.-M., Finlay-Brown, S., & Friswell, R. (2000a). Evaluating a regulated hours regime on-road and an alternative compliance regime under simulated conditions. CR 190. Canberra: Australian Transport Safety Bureau.

Williamson, A., Feyer. A.-M., & Friswell, R. (1996). The impact of work practices on fatigue in long distance truck drivers. Accident Analysis and Prevention, 28:6, 709-719.

Williamson, A., Feyer, A.-M., Friswell, R., & Finlay-Brown, S. (2000b). Development of measures of fatigue: Using an alcohol comparison to validate the effects of fatigue on performance. CR 189. Canberra: Australian Transport Safety Bureau.

Williamson, A., Feyer, A.-M., Friswell, R., & Finlay-Brown, S. (2000c). On-road evaluations of a regulated hours regime and an alternative compliance regime. CR 191. Canberra: Australian Transport Safety Bureau.

Williamson, A., Feyer, A.-M., Friswell, R., & Sadural, S. (2001a). Driver fatigue: A survey of long distance heavy vehicle drivers in Australia. CR 198. Canberra: Australian Transport Safety Bureau.

Williamson, A., Feyer, A.-M., Mattick, R.P., Friswell, R., and Finlay-Brown, S. (2001b). Developing measures of fatigue using an alcohol comparison to validate the effects of fatigue on performance. Accident Analysis and Prevention, 33, 313-326.


8. APPENDICES

8.1 Consent form

8.2 Background questionnaire

8.3 Excerpt from work and rest diary

8.4 Palmtop instruction sheet

8.5 Actiwatch instruction sheet


8.1 Consent form

PARTICIPANT INFORMATION AND CONSENT FORM

STUDY OF DIFFERENCES IN HEAVY VEHICLE DRIVER FATIGUE LEVELS UNDER DAY AND NIGHT DRIVING

2001-2002

University of New South Wales, NSW Injury Risk Management Research Centre and School of Psychology,

New Zealand Environmental and Occupational Health Research Centre


STUDY OF DAY AND NIGHT DRIVING 2001-2002

Driver fatigue is a major safety issue in the long distance road transport industry in Australia mainly because of the long distances that have to be travelled. As you know, there are a number of options currently being explored to help drivers manage fatigue better. This study is part of a number of project attempting to identify the best ways of managing work and rest for the long distance road transport industry generally, in order to minimise driver fatigue. This study is looking at whether drivers experience more fatigue when their trips are at night compared to driving in the daytime. The study involves looking at drivers' experiences of fatigue on day trip and on night trips. By doing this we hope to have some real evidence about the times when fatigue is most problem for drivers. This information, we hope, will help the industry design better and more flexible ways of arranging work and rest schedules. What is involved? We would like to measure your sleep and fatigue over a 14 day period working on day trips or on night trips. Some people will be asked to swap from day trips to night trips or vice versa and participate in a further 14 days of measurements. On the first day of participation, you will be asked to do two tests on a laptop computer and one on a small hand-held device (total 27 minutes) at the company depot prior to their trip. You will also be asked to complete a short questionnaire providing some background information about your lifestyle, health, and recent work and sleep patterns. These tests will be repeated at the depot at the end of each weeks work before the 24 hour break. The tests are simple tasks similar to computer games and measure concentration and reaction speed which are both affected by fatigue. You will then be given a palmtop computer to take with you for the next two weeks. You will be asked to do shorter versions of the two tests (total 7 minutes) on the palmtop computer at the beginning and end of each day’s work and at the start and end of each long break in the trip and any sleep break taken during trips. We will ensure that you have some practice using the palmtop computer before leaving on their first trip, and 24-hour help will be available by phone should you need it. We would also ask that you complete a simple diary during the fortnight to record how fatigued you feel at the start and end of each work period. To measure sleep we would like you to wear a movement monitor for the duration of the study. The monitor is similar in size and shape to a watch and is worn on the wrist. If you are participating in the additional 14 days of measurement, this procedure would then be repeated. All the information you provide will be confidential. In fact, once all the information has been collected, we will not be keeping your name at all. Your participation in this project is voluntary and you are free to withdraw at any time without penalty or prejudice. Please note that your decision to participate will have no bearing on your employment and your personal results will not be shown to your employer. If you have any questions about the study please do not hesitate to contact Ann Williamson on (02) 9385 4599, or Samantha Sadural on (02) 9385 4207, or Rena Friswell on (02) 9385 1646.

If you wish to complain about any aspect of the conduct of this research project please contact, Executive Officer, Ethics Secretariat, University of New South Wales on (02) 9385 4234.


STUDY OF DAY AND NIGHT DRIVING

2001-2002

You are invited to participate in the evaluation of the effects on fatigue and performance of day and night driving. If you wish to participate, please complete the consent form below.

Consent Form

I, ____________________________________________________, agree to participate in the evaluation of the effects on fatigue and performance of day and night driving.

I acknowledge that I have read the statement above outlining the study, and that the statement has been explained to my satisfaction. I have been given the opportunity to ask any questions relating to any possible physical or mental harm I might suffer as a result of my participation, and have received satisfactory answers.

I understand the information that I provide will be strictly confidential, and that only the study’s research team will have access to information that identifies me with my responses.

I also understand that I am free to withdraw my consent and stop my participation at any time without prejudice.

_____________________________ ____________________________

(Signature of participant) (Signature of witness)

__________________ _________________

(Date) (Date)


STUDY OF DAY AND NIGHT DRIVING

2001-2002

REVOCATION OF CONSENT

I hereby wish to WITHDRAW my consent to participate in the research proposal described above and understand that such withdrawal WILL NOT jeopardise any treatment or my relationship with the University of New South Wales.

Signature Date

Please PRINT Name

The section for Revocation of Consent should be forwarded to Dr Ann Williamson, NSW Injury Risk Management Research Centre, UNSW, Sydney, 2052.


8.2 Background questionnaire

Code Number:

PARTICIPANT

BACKGROUND INFORMATION

DAY AND NIGHT DRIVING STUDY

2001-2002


As part of our research on fatigue in day and night long distance driving, we need

to find out about the people participating in the study. In particular we need to

collect some general information on your lifestyle, health and work history.

All information you give to us will be CONFIDENTIAL and ANONYMOUS. You

will be assigned a code number so that your name will not appear on any of your

results.

On the following pages there are some questions about these matters that we

would appreciate you filling in as carefully as possible.

THANK YOU FOR YOUR HELP


Today’s date: ____________ Current time: ____________ am / pm ? 1. What is your: Age: (Please tick) < 20 years ( ) 20 – 29 years ( ) 30 - 39 years ( ) 40 – 49 years ( ) 50 –59 years ( ) 60 or more years ( ) Sex: (Please circle) M F Please tick 2. Are you: married or living in a defacto relationship? ( ) widowed, separated or divorced? ( ) single? ( ) 3. How long have you been driving a truck for a living? ________ years 4. How far did you continue with formal education? (Please tick) To Primary school level ( ) To High school Year 7, 8, 9, or 10 level ( ) To High school Year 11 or 12 level ( ) To Tafe level ( ) To College or University level ( ) 5. How much experience have you had using personal computers? Please tick None ( )

A little ( ) Frequent user ( )


6. Do you suffer any of the following health problems? (Please circle) Diabetes Yes No Asthma/Hayfever Yes No Sleep disorders eg sleep apnea Yes No Stomach or digestive problems Yes No Liver or kidney problems Yes No Heart or circulation problems Yes No eg angina, high blood pressure Headaches or migraines Yes No 7. Do you smoke cigarettes?

No ( ) Given up ( ) How long ago did you give up? ______________ years/months Yes ( )

How many do you smoke on average per day? __________ cigarettes

8. Do you drink caffeinated drinks? Yes ( ) (e.g., tea, coffee, coke)

No ( )

If YES, what sorts of caffeinated drinks do you usually consume?

_______________________________________________________________

How many of these drinks do you have on average per day?

_______________________________________________________________


9. How often do you usually drink alcohol? (Please tick) Every day ( ) 2-3 times a week ( ) Once a week ( ) 1-2 times a month ( ) Rarely ( ) Never ( )

If you do drink alcohol, how many standard drinks do you usually drink at one time? (Please tick)

One drink ( )

2-3 drinks ( )

4-5 drinks ( )

more than 5 drinks ( )

10. When you are sleeping, how often do you: Please tick Snore loudly ? always ( ) often ( ) sometimes ( ) rarely ( ) never ( ) Stop breathing ? always ( ) often ( ) sometimes ( ) rarely ( ) never ( ) Move around a lot ? always ( ) often ( ) sometimes ( ) rarely ( ) never ( )

1 drink =

1 middy beer or

1 glass wine or

1 nip spirits

1 can beer = 1.5 drinks


11. Do you have difficulty getting to sleep ? Yes ( ) No ( )

12. Do you have difficulty staying asleep once you are asleep ?

Yes ( ) No ( )

13. Do you have difficulty preventing yourself from falling asleep during the day ? always ( ) often ( ) sometimes ( ) rarely ( ) never ( ) 14. Have you had your adenoids removed ? Yes ( )

No ( )

Please continue to next page


15. How likely are you to DOZE OFF OR FALL ASLEEP, in contrast to just feeling tired, in the following situations? These situations refer to your usual way of life in recent times. Even if you have not done some of these things recently try to work out how they would have affected you.

Use the following scale to choose the MOST APPROPRIATE NUMBER for

indicating how likely it is you would have dozed off in each situation

0 Would never doze 1 Slight chance of dozing 2 Moderate chance of dozing 3 High chance of dozing

Situation

Chance of Dozing

Sitting and reading

_____

Watching TV

_____

Sitting inactive in a public place (eg. In a movie theatre or at a meeting)

_____

As a passenger in a car for an hour without a break

_____

Lying down to rest in the afternoon when circumstances permit

_____

Sitting and talking to someone

_____

Sitting quietly after a lunch without alcohol

_____

In a car, while stopped for a few minutes in traffic _____


16. Do you usually work: (Please tick) Mostly days? (6:00 to 18:00) ( ) Mostly nights (18:00 to 6:00)? ( ) Days and nights about equally? ( ) 17. Was last week a typical working week for you? Yes ( )

No ( ) If No, what was unusual about it? (e.g., on holidays, sick, on light duties etc) _______________________________________________________________

18. In the last 7 days (not counting today):

How many hours did you work? _________ hours

How many of these were at night (i.e. 18:00 to 6:00)? _________ hours

How many trips did you drive? _________ trips How long was your last trip in terms of:

Kilometres? _________ km

Total duration? _________ hours Hours spent working? _________ hours 19. When did your last work day/shift end? Time: ___________ am/pm Day: __________ Date: ___________ How long was your last work day/shift in terms of:

Total duration? _________ hours Hours spent working? _________ hours

20. In total, how much sleep have you had since then? _________ hours


21. How long was your last substantial sleep ? _________ hours 22. When did this sleep end? Time: ___________ am/pm Day: __________ Date: ___________ 23. How would you rate the quality of this sleep? (Please draw a cross at the point which most closely describes the quality of your sleep) Very poor Very good quality quality

24. How did you feel when you awoke from this sleep? (Please draw a cross at the point which most closely describes how refreshed you felt ) Not at all Very refreshed refreshed

25. Have you had any naps since your last substantial sleep ?

Yes ( )

No ( ) If No, go to question 28

If Yes, please record the length of the nap and the time you woke up in the table below. (If you have had more than 2 naps, please record the others on the back of this page.)

Length of nap End of nap hours : minutes date time Nap 1 : am/pm Nap 2 : am/pm


26. How would you rate the quality of your last nap ? (Please draw a cross at the point which most closely describes the quality of your sleep) Very poor Very good quality quality

27. How did you feel when you awoke from your last nap ? (Please draw a cross at the point which most closely describes how refreshed you felt ) Not at all Very refreshed refreshed

28. When did you last eat a meal? Time: ___________ am/pm Day: __________ Date: ___________ Was this meal (Please tick): Light ( ) Moderate ( ) Large ( ) Have you snacked since then? Yes ( ) No ( ) 29. If applicable, when did you last have a caffeinated drink? (eg. Coffee, tea, coke) Time: ___________ am/pm Day: __________ Date: ___________ How many caffeinated drinks have you had today? ___________ drinks


30. If applicable, when did you last have an alcoholic drink? Time: ___________ am/pm Day: __________ Date: ___________

How many alcoholic drinks did you have on that occasion?

__________ drinks 31. Are you currently taking any medication? Yes ( )

No ( )

If Yes, what? __________________________________________________ 32. How long have you been working under your current shift roster?

__________ weeks/months/years


8.3 Excerpt from work and rest diary

Code:

ON ROAD DIARY

DAY & NIGHT DRIVING STUDY

2001-2002

Instructions

Work, breaks and fatigue: On the following pages, we would like you to record some basic details about your work and breaks over the next two weeks, and to rate how tired you feel at the beginning and end of each work period. This means making ratings at the start and end of every sleep break including the start and end of every work day and also at the start of any non-sleep break you take of 15 min or more. It is important that you make the ratings at the time rather than doing it later from memory. To make the ratings, simply put a mark somewhere on each scale line to show how you feel. For example, on the scale of happiness below, if you were only a bit happy you might put the mark as shown Very happy Very sad

Sleep: We would also like you to record the times you sleep during your breaks and to rate how good the sleep was. This includes any sleeps you take during days off. If you have a break with more than one sleep, record the times for each sleep, but only rate the last sleep you took before returning to work. Tests and monitor: Lastly, we would like you to note any times when you take the motion monitor on and off during work or break time, and whether you did the handheld tests at the start and end of each break. Remember: • Fill in this diary for ALL BREAKS • Out of these breaks, only do the handheld tests at the start and end of

SLEEP BREAKS (including the start and end of each day) and at the start of your LONGEST MIDSHIFT break.


MOTION MONITOR ON: _________ am/pm Date: ________ WORK PERIOD 1 START WORK TIME: _________ am or pm Date: ________ PLACE: _____________________________________________ Did you do the handheld tests at the start of this work period? (Please tick) Yes ( ) No ( ) Please rate how you feel now on the following scales Fresh Tired

Clear-headed Muzzy-headed

Very alert Very drowsy

Please record any times during this work period when you take the motion monitor off and put it back on again

Time you took monitor off Time you put monitor back on am / pm

date am / pm

date

am / pm date

am / pm date

am / pm date

am / pm date

END WORK TIME: _________ am or pm Date: ________ PLACE: _____________________________________________ During this work period, how long did you spend: Driving? __________ hours Doing other work? __________ hours Taking short breaks? __________ hours Please rate how you feel now on the following scales? Fresh Tired



Did you do the handheld tests at the end of this work period? (Please tick) Yes ( ) No ( )


BREAK 1 Please record any times during this break when you take the motion monitor off and put it back on again


date am / pm

date

am / pm date

am / pm date

am / pm date

am / pm date

Did you sleep during your break ? Yes ( ) No ( ) If yes, please record any times during this break when you slept

Time you start trying to sleep

(press star button on monitor)

Time you think you fell asleep

Time you think you woke up

Time you got up (press star button

on monitor)

am / pm date

am / pm date

am / pm date

am / pm date

am / pm date

am / pm date

am / pm date

am / pm date

am / pm date

am / pm date

am / pm date

am / pm date

How would you rate the quality of your last sleep during this break? Very poor Very good quality quality

How did you feel when you woke from the last sleep in this break? Not at all Very Refreshed Refreshed


WORK PERIOD 2 START WORK TIME: _________ am or pm Date: ________ PLACE: _____________________________________________ Did you do the handheld tests at the start of this work period? (Please tick) Yes ( ) No ( ) Please rate how you feel now on the following scales Fresh Tired



Please record any times during this work period when you take the motion monitor off and put it back on again


date am / pm

date

am / pm date

am / pm date

am / pm date

am / pm date

END WORK TIME: _________ am or pm Date: ________ PLACE: _____________________________________________ During this work period, how long did you spend: Driving? __________ hours Doing other work? __________ hours Taking short breaks? __________ hours Please rate how you feel now on the following scales? Fresh Tired



Did you do the handheld tests at the end of this work period? (Please tick) Yes ( ) No ( ) The diary continued in this format until work period thirty.


END OF STUDY WEEKS How many trips did you drive in each week of the study? Week 1: ________ trips Week 2: ________ trips How many kilometres did you drive in each week of the study? Week 1: ________ km Week 2: ________ km How would you describe your workload each week? (please tick) Week 1 Week 2

Much less than usual ( ) ( ) Less than usual ( ) ( ) About the usual level ( ) ( ) Greater than usual ( ) ( ) Much greater than usual ( ) (…)

Overall, how would you describe your fatigue levels this week? (please tick) Week 1 Week 2

Much less than usual ( ) ( ) Less than usual ( ) ( ) About the usual level ( ) ( ) Greater than usual ( ) ( ) Much greater than usual ( ) (…)

Do you have any other comments that you would like to make about your work or your fatigue during the study? _____________________________________________________ _____________________________________________________ _____________________________________________________ _____________________________________________________ _____________________________________________________ _____________________________________________________ _____________________________________________________ Many thanks for your participation MOTION MONITOR OFF: _________ am/pm Date: ________


8.4 Palmtop instruction sheet

Instructions for using handheld testers

1. Turn the white switch on the battery cable ON (orange dot showing). 2. Press the ON/OFF button in the top right corner of the handheld tester keyboard.

If the tester is ready for use, the last line of writing on the screen should be: E:\PIPS> 3. To run the tests, use the keyboard on the handheld tester to:

type TEST

then leave a space by pressing the long white key at the bottom of the keyboard

then type your code number as shown on the front of your On Road Diary.

Make sure there is a space between the word TEST and your subject code. If you make a typing mistake, use the ← key (above the letter P) to erase the problem. Then re-type.

4. Press the ENTER key on the grey keypad to start the tests, then follow the instructions on the screen.

It is important to use the grey keypad to do the actual tests. If, instead of the test instructions, a “bad command” message appears on the screen followed by the E:\PIPS> message, you have made a typing mistake. Just redo step 3.

5. When the tests are finished the E:\PIPS> message will return. 6. Switch the handheld tester OFF by pressing the ON/OFF button. 7. Turn the white switch on the battery cable OFF. Please do the handheld tests: at the start and end of each work shift ('day'), and at the start of your longest break from work during the shift, and at the start and end of any sleep breaks during the shift.

Troubleshooting

If the tester gives you a message about low batteries or if you have any other problems using the tester, please contact:


8.5 Actiwatch instruction sheet

Instructions for using motion monitors 1. We would like you to wear the monitor 24 hours a day for two weeks (including non-work days). 2. Wear the monitor on your non-dominant wrist (the hand you don’t write with).

Don’t change wrists during the study as this may change the results. 3. Place the monitor on the outside of your wrist like a watch, with the star button closest to your thumb

and the word "Actiwatch" at the top. Make sure the band is done up fairly tightly so the monitor does not move around on your wrist. 4. The monitor is water-resistant but not waterproof, so we would prefer if you do not cover it in water.

Just take the monitor off before showering or swimming or any other time when it is likely to get wet. Then put it on again when you are finished.

If you forget to put the monitor back on straight away, put it back on as soon as you remember. 5. Whenever you put the monitor on or off, please note the time in your On-Road Diary.

It is important that the times you record in the diary are accurate. 6. Each time you begin trying to go to sleep or when you get up, press the star button on the monitor. If

your press has been recorded, the button will click. 7. Note the time that you press the button in your On-Road Diary. If you wake up for a short time (less than 10 minutes), maybe to get a drink or go to the toilet, do not worry about filling in the diary. If you get up for longer than 10 minutes, treat the next sleep as a new sleep.

Troubleshooting

If you have any problems using the monitor, please contact:

fatigue and performance in heavy truck drivers working day shift

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