# Investigation into Motor Carrier Practices to Achieve Optimal Commercial Motor Vehicle Driver Performance: Phase I

April 2010

### FOREWORD A laboratory study was conducted t o examine the duration of the restart period for commercial motor vehicle drivers that would b e needed to recycle to the work force with optimal performance. This r eport presents the design, methods, research findings, and conclusions of the study. This r eport may be of interest to anyone interested in fatigue and its management in commercial motor vehicle operations and other modes of transportation. This is the final report of the study.



### NOTICE This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The United States Government assumes no liability for its contents or the use thereof. The contents of this R eport reflect the views of the contractor , who is responsible for the accuracy of the data presented herein. The contents do not necessarily reflect the official po licy of the U.S. Department of Transportation. This R eport does not constitute a standard, specification, or regulation. The United States Government does not endorse products or manufacturers named herein. Trade or manufacturers’ names appear herein solel y because they are considered essential to the object of this R eport
.Technical Report Documentation Page 1. Report No. FMCSA - RRR - 10 - 005 2. Government Accession No. 3. Recipient's Catalog No.

4. Title and Subtitle INVESTIGATION INTO MOTOR CARRIER PRACT ICES TO ACHIEVE OPTIMAL COMMERCIAL MOTOR VEHICLE DRIVER PERFORMANCE: PHASE I 5. Report Date April 2010
6

. Performing Organization Code
7. Author(s) Hans P.A. Van Dongen, PhD, Gregory Belenky , MD 8. Performing Organization Report No.
9. Performing O rganization Name and Address Sleep and Performance Research Center Washington State University Spokane, WA 10. Work Unit No. (TRAIS) 11. Contract or Grant No.
12. Sponsoring Agency Name and Address U.S. Department of Transportation Federal Motor Carr ier Safety Administration 1200 New Jersey Ave SE Washington, DC 20 590 13. Type of Report and Period Covered Final Report September 2008

– November 2009 14. Sponsoring Agency Code 15. Supplementary Notes This program was sponsored by the Federal Motor Ca rrier Safety Administra tion . Albert Alvarez served as the Contracting Officer ’s Technical Representative . 16. Abstract The objective of this research project was to determine the effectiveness of the 34 - hour restart provision in the H ours - of - S ervice regu lations governing property - carrying commercial motor vehicle drivers. A sample of 27 healthy subjects was studied in an in - residence laboratory study with frequent testing of cognitive performance and driving performance on a high - fidelity driving simulato r . A “ worst - case ”

– ” best - case” between - groups comparison was made of two 5 - day (14 - hour/day) work periods separated by a 34 - hour restart period. Half the sample was randomized to the “ best - case” condition, which entailed daytime wakefulness and work (and ni ghttime sleep) throughout the study. The other half was randomized to the “ worst - case” condition, which entailed nighttime wakefulness and work (and daytime sleep) during the two 5 - day work periods, while transitioning back to a daytime schedule during the 34 - hour restart period. In the “ best - case” condition, performance was the same before and after the 34 - hour restart. In the “ worst - case” condition, however, performance after the 34 - hour restart was impaired relative to performance before the restart, ind icating that the restart period was not effective at maintaining performance in th is condition. Driver fatigue could be reduced by establishing work schedules and adapting HOS regulations that allow greater flexibility (e.g., taking into account strategic napping and circadian timing).

17. Key Word s 34 - hour restart provision, circadian rhythm, c ommercial motor vehicle, CMV, fatigue, Hours - of - Ser vice regulations, performance, sleep, worker recycling 18. Distribution Statement No restrictions
19. Security Classif. (of this report) Unclassified 20. Security Classif. (of this page) Unclassified 21. No. of Pages 72 22. Price Form DOT F 1700.7 (8
- 72) Reproduction of completed page authorized.ii 

### SI* (MODERN METRIC) CONVERSI ON FACTORS Table of APPROXIMATE CONVERSIONS TO SI UNITS Symbol When You Know Multiply By To Find Symbol LENGTH in inches 25.4 millimeters mm ft feet 0.305 meters m yd yards 0.914 meters m mi miles 1.61 kilometers km AREA in2 square inches 64 5.2 square millimeters mm2 ft2 square feet 0.093 square meters m2 yd2 square yards 0.836 square meters m2 ac acres 0.405 hectares ha mi2 square miles 2.59 square kilometers km2 VOLUME Note: Volumes greater than 1000 L shall be shown in m3 fl oz fl uid ounces 29.57 milliliters mL gal gallons 3.785 liters L ft3 cubic feet 0.028 cubic meters m3 yd3 cubic yards 0.765 cubic meters m3 MASS oz ounces 28.35 grams g lb pounds 0.454 kilograms kg T short tons (2000 lb) 0.907 megagrams (or “metric to n”) Mg (or “t”) TEMPERATURE Temperature is in exact degrees °F Fahrenheit 5 × (F
- 32) ÷ 9 or (F
- 32) ÷ 1.8 Celsius °C ILLUMINATION fc foot
- candles 10.76 lux lx fl foot
- Lamberts 3.426 candela/m2 cd/m2 Force and Pressure or Stress lbf poundfo rce 4.45 newtons N lbf/in2 poundforce per square inch 6.89 kilopascals kPa Table of APPROXIMATE CONVERSIONS FROM SI UNITS Symbol When You Know Multiply By To Find Symbol LENGTH mm millimeters 0.039 inches in m meters 3.28 feet ft m meters 1.09 ya rds yd km kilometers 0.621 miles mi AREA mm2 square millimeters 0.0016 square inches in2 m2 square meters 10.764 square feet ft2 m2 square meters 1.195 square yards yd2 ha hectares 2.47 acres ac km2 square kilometers 0.386 square miles mi2 VO LUME mL milliliters 0.034 fluid ounces fl oz L liters 0.264 gallons gal m3 cubic meters 35.314 cubic feet ft3 m3 cubic meters 1.307 cubic yards yd3 MASS g grams 0.035 ounces oz kg kilograms 2.202 pounds lb Mg (or “t”) megagrams (or “metric to n”) 1.103 short tons (2000 lb) T TEMPERATURE Temperature is in exact degrees °C Celsius 1.8C + 32 Fahrenheit °F ILLUMINATION lx lux 0.0929 foot
- candles fc cd/m2 candela/m2 0.2919 foot
- Lamberts fl Force & Pressure or Stress N newtons 0.225 poundforce lbf kPa kilopascals 0.145 poundforce per square inch lbf/in2 * SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. (Revised March 2003, Sectio n 508

- accessible ver sion September 2009)iii 

### TABLE OF CONTENTS LIST OF ABBREVIATION S

................................ ................................ ................................ . VIII EXECUTIVE SUMMARY

................................ ................................ ................................ ........ IX 1. INTRODUCTION

................................ ................................ ................................ ................. 1 1.1 OBJECTIVE

................................ ................................ ................................ .................. 1 1.2 BACKGROUND

................................ ................................ ................................ ........... 1 1.3 PHASE I PROJECT TASK S

................................ ................................ ......................... 3 2. METHODS

................................ ................................ ................................ ............................ 5 2.1 EXPERIMENTAL DESIGN

................................ ................................ ......................... 5 2.2 MEASUREMENTS

................................ ................................ ................................ ....... 7 2.3 STATISTICAL METHODS AND POWER CALCULATIO N

................................ .... 9 2.4 SUBJECT RECRUITMENT AND SC REENING

................................ ...................... 11 3. RESULTS

................................ ................................ ................................ ............................ 13 3.1 SUBJECTS

................................ ................................ ................................ .................. 13 3.2 PERFORMANCE OUTCOME S

................................ ................................ ................. 13 3.2.1 Primary Performance Outcome

................................ ................................ ....... 13 3.2.2 Secondary Performance Outcomes

................................ ................................ . 16 3.2.3 Simulator Driving Performance

................................ ................................ ...... 32 3.3 SLEEP AND POLYSOMNOG RAPHY

................................ ................................ ...... 40 4. CONCLUSIONS

................................ ................................ ................................ ................. 51 4.1 KEY FINDINGS

................................ ................................ ................................ .......... 51 4.2 ANCILLARY FINDINGS

................................ ................................ ........................... 53 5. RECOMMENDATIONS

................................ ................................ ................................ .... 55 ACKNOWLEDGEMENTS

................................ ................................ ................................ ........ 57 REFERENCES

................................ ................................ ................................ ............................. 59iv 

### LIST OF FIGURES F igure 1. Graph. Time on task, time of day, and time awake interact to degrade/ modulate cognitive performance over 38 hours of total sleep deprivation in 50 healthy volunteer subjects.

................................ ................................ ................................ ............................ 2 Figur e 2. Graph. Core body temperature rises and falls in an approximately 24

- hour (circadian) rhythm.

................................ ................................ ................................ ............................. 2 Figure 3. Chart. Study design for the “worst

- case” (top schematic) and “best

- case” (bottom s chematic) conditions of the study, with hours of the day (midnight to midnight) progressing from left to right (see top numbers), days of the study progressing from top to bottom (see right side), gray indicating scheduled wakefulness, and black indicating scheduled sleep periods.

................................ ................................ ................................ ... 6 Figure 4. Chart. Measurement scheme for the “worst

- case” (top schematic) and “best

- case” (bottom schematic) conditions of the study, with hours of the day (midnigh t to midnight) progressing from left to right (see top numbers); days of the study progressing from top to bottom (see right side); gray indicating scheduled wakefulness; black indicating scheduled sleep periods; triple asterisks marking 40

- minute driving simulator sessions each preceded and followed by a 10

- minute PVT; lower

- case x marking brief neurobehavioral performance test bouts; capital X marking brief neurobehavioral performance test bouts augmented with a PVT (during off

- duty days); and PSG marking polysomnography (i.e., sleep recordings).

................................ .......... 9 Figure 5. Graph. Lapses (number of reaction times greater than 500 ms) on the 10

- minute PVT in the pre

- restart 5

- day work period (session 1) as com pared to the post

- restart 5

- day work period (session 2) for the “worst

- case” and “best

- case” conditions (error bars indicate standard error derived from mixed

- effects ANOVA).

................................ ..... 14 Figure 6. Grap h. Lapses on the 10

- minute PVT as a function of days in the 5

- day work periods before and after the 34

- hour restart period, for the “worst

- case” and “best

- case” conditions (vertical error bars indicate standard error derived from mixed

- effects ANOVA).

................................ ................................ ................................ ....................... 15 Figure 7. Graph. Lapses on the 10

- minute PVT as a function of time of day, collapsed over the 5

- day work periods before and after the 34

- hour restart period (error bars indicate standard error derived from mixed

- effects ANOVA). Times of day are through the night (09:05 pm

– 06:55 am) for the “worst

- case” condition and through the day (09:05 am

– 06:55 pm) for the “best

- case” condition; 1 hour should be added for subjects who were assigned to perform ance testing first and driving second.

................................ .... 16 Figure 8. Graph. Subjective sleepiness score on the KSS in the pre

- restart 5

- day work period (session 1) as compared to the post

- restart 5

- day work pe riod (session 2) for the “worst

- case” and “best

- case” conditions (error bars indicate standard error derived from mixed

- effects ANOVA).

................................ ................................ ....................... 17 Figure 9. Graph. Subjective sleepiness on the KSS as a function of days in the 5

- day work periods before and after the 34

- hour restart period, for the “worst

- case” and “best

- case” conditions (error bars indicate standard error derived from mixed

- effects ANOVA).

.. 18v Figure 10. Graph. Subjective sleepiness on the KSS as a function of time of day, collapsed over the 5

- day work periods before and after the 34

- hour restart period (error bars indicate standard error derived from mixed

- effects ANOVA). Ti mes of day are through the night (09:05 pm

– 06:05 am) for the “worst

- case” condition and through the day (09:05 am

– 06:05 pm) for the “best

- case” condition; 1 hour and 50 minutes should be added to each time point for subjects who were assigned to driving fi rst and performance testing second.

................................ ................................ ................................ ............................ 19 Figure 11. Graph. Subjective mood score on the VASM in the pre

- restart 5

- day work period (session 1) as compared to the post

- restart 5

- day work period (s ession 2) for the “worst

- case” and “best

- case” conditions.

................................ ................................ ....... 20 Figure 12. Graph. Subjective mood on the VASM as a function of time of day, collapsed over the 5

- day work periods before and after the 34

- hour restart period. Times of day are through the night (09:05 pm

– 06:05 am) for the “worst

- case” condition and through the day (09:05 am

– 06:05 pm) for the “best

- case” condition; 1 hour and 50 minutes should be added to each time point for subjects who were assigned to driving first and performance testing second.

................................ ................................ ........................... 21 Figure 13. Graph. Positive affect score on the PANAS in the pre

- restart 5

- day work period (session 1) as compared to the p ost

- restart 5

- day work period (session 2) for the “worst

- case” and “best

- case” conditions (the vertical scale is inverted; upward direction corresponds to less positive affect).

................................ ................................ 22 Figure 14. Graph. Negative affect score on the PANAS as a function of days in the 5

- day work periods before and after the 34

- hour restart period, for the “worst

- case” and “best

- case” conditions.

................................ ................................ ................................ ...................... 23 Figur e 15. Graph. Number of correct responses on DSST in the pre

- restart 5

- day work period (session 1) as compared to the post

- restart 5

- day work period (session 2) for the “worst

- case” and “best

- case” conditions (the vertical scale is inverted; downward direct ion corresponds to performance improvement).

................................ ................... 24 Figure 16. Graph. Number of correct responses on DSST as a function of days in the 5

- day work periods before and after the 34

- hour restart perio d, for the “worst

- case” and “best

- case” conditions (the vertical scale is inverted; downward direction corresponds to performance improvement).

................................ ................................ ........................... 25 Figure 17. Graph. Number of correct responses on the DSST as a function of time of day, collapsed over the 5

- day work periods before and after the 34

- hour restart period (the vertical scale is inverted; downward direction corresponds to performance improvement). Times of day are through the night (09 :05 pm

– 06:05 am) for the “worst

- case” condition and through the day (09:05 am

– 06:05 pm) for the “best

- case” condition; 1 hour and 50 minutes should be added to each time point for subjects who were assigned to driving first and performance testing second.

................................ ..... 26 Figure 18. Graph. Retrospective self

- evaluation score on the PERF in the pre

- restart 5

- day work period (session 1) as compared to the post

- restart 5

- day work period (session 2) for the “wor st

- case” and “best

- case” conditions (upward direction corresponds to poorer performance).

................................ ................................ ................................ ................. 27 Figure 19. Graph. Retrospective self

- evaluation score on the PERF as a function of days in the 5

- da y work periods before and after the 34

- hour restart period, for the “worst

- case” and “best

- case” conditions (upward direction corresponds to poorer performance).

........... 28vi Figure 20. Graph. Retrospec tive self

- evaluation score on the PERF as a function of time of day, collapsed over the 5

- day work periods before and after the 34

- hour restart period (upward direction corresponds to poorer performance). Times of day are through the night (09:05 pm

– 06:05 am) for the “worst

- case” condition and through the day (09:05 am

– 06:05 pm) for the “best

- case” condition; 1 hour and 50 minutes should be added to each time point for subjects who were assigned to driving first and performance testing second.

................................ ................................ ................................ ............................ 29 Figure 21. Graph. Retrospective self

- evaluation score on the EFFR in the pre

- restart 5

- day work period (session 1) as compared to the post

- restart 5

- day work period (session 2) for the “worst

- case” and “best

- case” conditions (upward direction corresponds to greater subjective effort needed to complete the neurobehavioral test bouts).

.......................... 30 Figure 22. Graph. Number of error responses on the CDDT in the pre

- restart 5

- day work period (session 1) as compared to the post

- restart 5

- day work period (session 2) for the “worst

- case” and “best

- case” conditions.

................................ ................................ ....... 31 Figure 23. Graph. Number of error responses on the CDDT as a function of days in the 5

- day work periods before and after the 34

- hour restart period, for the “worst

- case” and “best

- case” conditions.

................................ ................................ ................................ ... 32 Figure 24. Graph. A verage simulator driving speed in the pre

- restart 5

- day work period (session 1) as compared to the post

- restart 5

- day work period (session 2) for the “worst

- case” and “best

- case” conditions.

................................ ................................ ............................ 33 Figure 25. Graph. Average simulator driving speed as a function of days in the 5

- day work periods before and after the 34

- hour restart period, for the “worst

- case” and “best

- case” conditions.

................................ ................................ ................................ ...................... 34 Figure 26. Graph. Variability (standard deviation) of simulator driving speed as a function of days in the 5

- day work periods before and after the 34

- hour restart period, for the “worst

- case” and “best

- case” conditions (upward direction corresponds to driving less steadily).

................................ ................................ ................................ ......................... 35 Figure 27. Graph. Variability (standard deviation) of simulator driving speed as a function of time of day, collapsed over the 5

- day work periods before and after t he 34

- hour restart period (upwards corresponds to driving less steadily). Times of day are through the night (09:30 pm

– 06:30 am) for the “worst

- case” condition and through the day (09:30 am

– 06:30 pm) for the “best

- case” condition; 1 hour should be added to each time point for subjects who were assigned to performance testing first and driving second.

......... 36 Figure 28. Graph. Lane deviation (standard deviation of lane position) on the driving simula tor in the pre

- restart 5

- day work period (session 1) as compared to the post

- restart 5

- day work period (session 2) for the “worst

- case” and “best

- case” conditions.

.................... 37 Figure 29. Graph. Lane de viation (standard deviation of lane position) on the driving simulator as a function of time of day, collapsed over the 5

- day work periods before and after the 34

- hour restart period. Times of day are through the night (09:30 pm

– 06:30 am) for the “worst

- ca se” condition and through the day (09:30 am

– 06:30 pm) for the “best

- case” condition; 1 hour should be added to each time point for subjects who were assigned to performance testing first and driving second.

................................ ................................ 38vii Figure 30. Graph. Computed fuel use on the driving simulator as a function of time of day, collapsed over the 5

- day work periods before and after the 34

- hour restart period. Times of day are through the night (09:30 pm

– 06:30 am) for the “ worst

- case” condition and through the day (09:30 am

– 06:30 pm) for the “best

- case” condition; 1 hour should be added to each time point for subjects who were assigned to performance testing first and driving second.

................................ ................................ 40 Figure 31. Chart. Comparison scheme for the polysomnographically recorded sleep periods in the “worst

- case” (top schematic) and “best

- case” (bottom schematic) conditions of the study, with hours of the day progressing from left to right (see top numbers), days of the study progressing from top to bottom (see right side), gray indicating scheduled wakefulness, black indicating scheduled sleep periods, and ××× marking sleep periods that were not recorded polysomnographically. Days ar e shown from 22:00 (10:00 pm) to 22:00 (rather than from midnight to midnight) for easier visual comparison of the scheduled sleep periods between the two conditions. Text labels indicate which sleep periods were combined to form the Baseline, Session 1, R estart, Session 2, and Recovery periods that were being compared between the two conditions.

.................... 41 Figure 32. Graph. Combined TIB during the Baseline, Session 1, Restart, Session 2, and Recovery sets of sleep periods, comparing them for “worst

- case” and “best

- case” conditions.

................................ ................................ ................................ ...................... 42 Figure 33. Graph. Combined TST during the Baseline, Session 1, Restart, Session 2, and Recovery sets of sl eep periods, comparing them for “worst

- case” and “best

- case” conditions.

................................ ................................ ................................ ...................... 43 Figure 34. Graph. Combined slow

- wave sleep (non

- REM sleep stage N3) during the Baseline, Session 1, Restart, Session 2, and Recovery sets of sleep periods, comparing them for “worst

- case” and “best

- case” conditions.

................................ ................................ ....... 44 Figure 35. Graph. Combined REM sleep during the Baseline, Session 1, Restart, Session 2, and Recovery sets of sleep periods, comparing them for “worst

- case” and “best

- case” conditions.

................................ ................................ ................................ ...................... 45 Figure 36. Graph. Combined non

- REM sleep stage N2 during the Baseline, Session 1, Restart, Se ssion 2, and Recovery sets of sleep periods, comparing them for “worst

- case” and “best

- case” conditions.

................................ ................................ ................................ ... 46 Figure 37. Graph. Combined non

- REM sleep stage N1 during the Baseline, Session 1, Re start, Session 2, and Recovery sets of sleep periods, comparing them for “worst

- case” and “best

- case” conditions.

................................ ................................ ................................ ... 47 Figure 38. Graph. Average SL during the Baseline, Session 1, Restart, Sessi on 2, and Recovery sets of sleep periods, comparing them for “worst

- case” and “best

- case” conditions.

.... 48 Figure 39. Graph. Average SWSL during the Baseline, Session 1, Restart, Session 2, and Recovery sets of sleep periods, comparing them for “worst

- case” and “best

- case” conditions.

................................ ................................ ................................ ...................... 49 Figure 40. Graph. REML during the Baseline, Session 1, Restart, Session 2, and Recovery sets of s leep periods, comparing them for “worst

- case” and “best

- case” conditions.

............... 50viii 

### LIST OF ABBREVIATIONS Acronym Definition ANOVA analysis of variance CDDT cardinal direction decision task CMV commercial motor vehicle DSST d i git

- s ymbol s ubstitution t ask EEG electroencephalogram EFFR e ffort r ating s cale FMCSA Federal Motor Carrier Safety Administration HOS Hours of Service KSS Karolinska Sleepiness Scale N1 non - REM s leep s tage 1 N2 non - REM s leep s tage 2 N3 slow

- wave sle ep ( non - REM s leep s tage 3) PANAS Positive Affect Negative Affect Schedule PERF p erformance r ating s cale PSG p olysomnography PVT p sychomotor v igilance t est REM rapid eye movement (sleep ) REML REM latency SL s leep l atency SWS slow

- wave sleep SWSL sl ow - wave sleep latency TIB time in bed TST total sleep time VASM v isual a nalog s cale of m ood WRAIR Walter Reed Army Institute of Researchix 

### EXECUTIVE SUMMARY The objective of this Phase I research project was to determine the recuperative effectiveness of the current 34

- hour restart provision in the H ours - of

- S ervice (HOS) regulations governing freight

- carrying commercial motor vehicle (CMV) drivers

. An in

- residence laboratory study was conducted to address this issue. A sample of 27 healthy subjects was studied in a “worst

- case ”

– ” best

- case ” between

- groups comparison of two 5

- day (14

- hour/day) work periods separated by a 34

- hour restart period. Half the sample was randomized to the “ best

- case” condition, which entailed daytime wakefulness and work (and nig httime sleep) throughout the study. The other half was randomized to the
“ worst

- case” condition, which entailed nighttime wakefulness and work (and daytime sleep) during the two 5

- day work periods, while transitioning back to a daytime schedule during the 34

- hour restart period. The main goal of the study was to evaluate whether the 34

- hour restart period was effective at maintaining performance in both conditions. To this end, performance on a variety of neurobehavioral tasks and on a high

- fidelity driving simulator was measured throughout the study. The primary performance outcome measure was the number of lapses (reaction times greater than 500 ms) on a 10

- minute psychomotor vigilance test (PVT), which is a validated metric of the performance consequences of fatigue. In the “ best

- case” condition, average PVT performance in the 5

- day work period after the 34

- hour restart was the same as that in the 5

- day work period before the 34

- hour restart, indicating that the restart period was effective at maintaining performance in this condition. In the “worst

case” condition, however, average PVT performance in the 5

- day work period after the 34

- hour restart was impaired relative to performance in the 5

- day work period before the 34

- hour restart, indicating that the restart period was not effective at maintaining performance in the “worst

case” condition. Subjects in the “worst

- case” condition displayed a progressive increase in lane deviation over the hours of the night, which was accompanied by an increase of up to 1 percent in fuel use. These and other indices of driving impairment were predicted by performance on the PVT administered just before each of the simulator driving bouts. The objective observations of performance impairment during the study were not refle cted in subjective measures of sleepiness and mood, which inaccurately suggested that some adaptation to the “worst

- case” condition would occur. Despite equal durations of sleep opportunity in the two study conditions, subjects in the “ worst

case” conditio n did not manage to get enough sleep , due to the adverse circadian placement of the sleep periods. Perhaps due to the transitioning back to a normal nighttime sleep schedule during the 34

- hour restart period, the restart intervention was not effective at m itigating the sleep loss and consequent performance impairment in the “worst

- case” condition. The study findings highlight the importance of considering circadian effects on sleep and performance in HOS regulations. This final report describes the methodol ogy and the results of the research study, and provides further conclusions and recommendations.x1 

### 1. INTRODUCTION
1.1 OBJECTIVE The objective of the project was to determine the effectiveness of the current 34

- hour restart provision in the H ours - of

- S ervice (HOS) regulations governing freight

- carrying commercial motor vehicle (CMV) drivers with regard to restoring performance

. Specifically, the effectiveness of the 34

- hour restart provision was evaluated using a “ worst

- case ”

– ” best

- case” in

laboratory experimental study design with testing of neurobehavioral task and driving simulator performance.
1.2 BACKGROUND The current Federal Motor Carrier Safety Administration (FMCSA) HOS regulation s for freight

carrying CMV drivers prescribe that drivers 1) may drive 11 hours wi thin a 14

- hour window after coming on duty following 10 consecutive hours off duty; 2) m ay not drive after 60/70 hours on duty in 7/8 consecutive days; 3) may r estart a 60/70

- hour period after taking 34 or more consecutive hours off duty ( the 34

- hour resta rt provision ). The scientific evidence supporting the 34

- hour restart provision is limited , and the provision may present problems in light of the current state of sleep science

. Specifically, the provision does not take into account the well

described cir cadian rhythms in both performance and sleep propensity. Depending on circadian placement of both the sleep opportunity and the period of on

- duty time, the current restart provision may be (in the case of adverse placement of sleep and work relative to cir cadian phase) inadequate to restore performance and may be (in the case of optimal placement of sleep and work relative to circadian phase) adequate or even unnecessary to sustain performance. The FMCSA has commissioned scientific studies to evaluate the e fficacy of the current 34

- hour restart provision and to provide information in support of possible revisions to this provision

. Fatigue

— as measured by both self

- report and objective measures indicating degraded performance

— is a function of sleep/wake histo ry (time awake), circadian phase (time of day), and workload (time on task). These factors are clearly seen in laboratory studies of sleep loss

. ( 1

– 3 ) The effects of all three factors during 38 hours of total sleep deprivation in 50 young healthy human volu nteers can be seen in Figure 1. The circadian rhythm (the 24

- hour rhythm of the endogenous biological clock) modulates alertness, performance, and sleep propensity

. ( 4

– 7 ) The circadian rhythm in core body temperature is depicted in Figure 2. Sleep propensit y displays the inverse of this rhythm in core body temperature. Sleep propensity is high when core body temperature is falling or low. Sleep propensity is low when core body temperature is rising or high. Similarly, but approximately 180° out of phase with sleep propensity, performance is high when body temperature is rising or high and low when body temperature is falling or low. Light exposure is the primary factor s etting circadian rhythm phase. Humans, being diurnal (daytime

waking) animals, are most pr oductive and sleep best when they work during the day and sleep during the night. Conversely, human beings perform poorly and sleep poorly when working through the night and attempting to sleep during the day. Given the normal diurnal light exposure, even most permanent night

- shift workers do not resynchronize their circadian rhythms in core body temperature and hence never adapt to night

- shift work. The average night

- shift2 worker is able to sleep no more than ~5 hours/day , ( 8 ) because he or she is attemptin g to sleep at a time when sleep propensity is falling

.

Figure 1 . Graph. Time on task, time of day, and time awake interact to degrade/ modulate cognitive performance over 38 hours of total sleep deprivation in 50 healthy voluntee r subjects. Source: Wesensten et al. (3 )

Figure 2 . Graph. C ore body temperature rises and falls in an approximately 24 - hour (circadian) rhythm
. Source: Mistlbe rger and Rusak (9

)3 1.3 PHASE I PROJECT TASK S The following tasks were comple ted for this project : Task 1: Conduct kick - off meeting. The investigators confer red with the G overnment sponsors and provide d an overview of the project and the planned strategy. A Power P oint presentation was presented outlining the project tasks and the e stimated timeline (October 22, 2008)

. Task 2: Submi t work plan. The investigators wro te a work plan for the project providing details for each of the project tasks , including the formation of a peer review committee of at least three members

. The work plan specified the laboratory research in detail and provide a basis for Tasks 3 and 4 (October 22, 2008)

. Task 3: Conduct peer review committee meeting. The investigators convened a meeting of the peer review committee via teleconference. The committee consis ted of Dr. Thomas Balkin (Walter Reed Army Institute of Research), Dr. Siobhan Banks (University of Pennsylvania), and Dr. Melissa Mallis (Institutes for Behavioral Resources). The committee members were provided with copies of the work plan in advance of the meeting (November 21, 2008)

. Task 4: Submit I nstitutional R eview B oard pro posal . The investigators submit ted a protocol to the I nstitutional R eview B oard of Washington State University , which subsequently approved the study. No amendment was needed fol lowing the peer review committee meeting (September 25, 2008)

. Task 5: Perform l aboratory research. A “worst

- case ”

– ” best

- case ” between

- groups comparison of two 5

- day work periods separated by a 34

- hour restart period was conducted, as described in further detail in the Methods section below ( c ompleted August 11, 2009)

. Task 6: Data reduction and analysis. Primary statistical testing involved a within

- subjects comparison of performance in the first 5

- day work period vs. the second 5

- day work period in the “w orst

- case” condition as compared to the “ best

- case” condition, as described in further detail in the Methods section below ( c ompleted September 12, 2009)

. Task 7: Submit draft final report. A draft final project report was written that detailed the methods used and the study results (September 21, 2009)

.4 Task 8: Conduct second peer review committee meeting. The investigators convened another meeting of the peer review committee via teleconference (see Task 3). The peer review committee members were provided with copies of the draft final report in advance of the meeting (October 8, 2009)

. Task 9: Submit final project report and technical brief. To complete the project, this report was submitted. In addition, a technical brief (PowerPoint) was produced and sh ared with the G overnment sponsors (October 26, 2009)

.5 

### 2. METHODS
2.1 EXPERIMENTAL DESIGN Volunteer subjects were run in an in

- residence laboratory research study of sleep and performance. A “ worst

- case ”

– ” best

- case ” between

- groups comparison of two 5

- day work pe riods separated by a 34

- hour restart period was conducted

. Figure 3 illustrates the design of the study for the two conditions. These conditions had equal amounts of scheduled work (simulator driving and performance testing), scheduled wakefulness, and sch eduled sleep. However, they differed in the timing of the sleep/wake/work periods, in that the “worst

- case” condition entailed nighttime wakefulness and work (and daytime sleep) during the two 5

- day work periods, whereas the “ best

- case” condition entailed daytime wakefulness and work (and nighttime sleep) throughout the study. Furthermore, the 34

- hour restart period in the “ worst

- case” condition, in addition to constituting a temporary suspension of work, involved transitioning back to a daytime schedule, w hereas the 34

- hour restart period in the “best

- case” condition involved just a one - day suspension of work. The “ worst

- case” condition began with a day for practicing simulator driving and performance testing procedures, followed by 10 h ours of time in bed (TIB) for baseline sleep (day 1), and then a day which included a 5

- hour scheduled napping period for transitioning to a nighttime work schedule (day 2). Subsequently, subjects were exposed to the first 5

- day nighttime work period, in which there were five 14

- hour duty periods with simulator driving and performance testing, each separated by 10 hours of diurnal TIB for sleep (days 3

– 7). This first nighttime work period was followed, after the accumulation of 70 hours of duty time, by a 34

- hour restart perio d, as permitted by the current HOS regulations. During the restart period, subjects transitioned back to a daytime schedule (days 7 and 8). The restart period began with a 5

- hour scheduled napping period, followed by a 7

- hour off

- duty period of wakefulness and a 10

- hour nocturnal sleep period. This was followed by a second 7

- hour off

- duty period of wakefulness, and another 5

- hour scheduled napping period to transition back to a nighttime schedule for the second work period. Thus, the 34

- hour restart period included a total of 20 hours of TIB for sleep and 14 hours of wakefulness, the same as in the “ best

- case” condition (see below). After the 34

- hour restart period, subjects in the “ worst

- case” condition were exposed to the second 5

- day nighttime work period (days 9

– 13), which was identical to the first nighttime work period. After the accumulation of 70 hours of duty time, the schedule ended with a day that included a 5

- hour scheduled napping period for transitioning back to a daytime schedule (day 13), and finally a recuperation day beginning with 10 hours of TIB for recovery sleep (day 14). The “ best

- case” condition also began with a day for practicing simulator driving and performance testing procedures, followed by 10 hours of ’ TIB for baseline sleep (day 1). The next day was a transitioning day as in the “worst

- case” condition, but since there was no need to transition to a nighttime schedule, this second day was similar to the other days in the “ best

case” condition

— but without scheduled work, and it inc luded a 10

- hour scheduled nocturnal sleep period (day 2). Subsequently, subjects were exposed to the first 5

- day daytime work period, in which there were five 14

- hour duty periods with simulator driving and performance testing, each separated by 10 hours o f nocturnal TIB for sleep (days 3

– 7). This first daytime work period was followed, after the accumulation of 70 hours of duty time, by a 34

- hour restart6 period, as permitted by the current HOS regulations. The restart period included a 10

- hour scheduled no cturnal sleep period, a 14

- hour period of off

- duty wakefulness, and another 10

- hour scheduled nocturnal sleep period (day 8). The 34

- hour restart period included a total of 20 hours of TIB for sleep and 14 hours of wakefulness, the same as in the “worst

- ca se” condition. After the 34

- hour restart period, subjects in the “best

- case” condition were exposed to the second 5

- day daytime work period (days 9

– 13), which was identical to the first daytime work period. After the accumulation of 70 hours of duty time, the schedule ended with a “recuperation” day beginning with 10 hours of TIB for sleep (day 14).

Enter lab, practice testing

Exit lab Baseline 10h Transition day 14h no driving

10h sleep 2a 10h sleep 3a 10h sleep 1a

10h sleep 4a **Day 4 Day 5**  **Best Case** 2 2 2 3 1 8 1 9 2 0 2 1 1 4 1 5 1 6 1 7 1 0 1 1 1 2 8 0 1 2 3

**Day 13 Day 6**  **Day 7 Day 8**  **Day 9**

**Day 14** 1 a 2 a 3 a 4 a Restart

Recov

1 b 2 b 3 b 34h restart: 10h sleep, 14h no driving, 10h sleep Accumulate 14 hours of work Restart 10h Baseline 10h **Day 1 Day 2**  **Day 3** 4 Baseln 5 6 7 1 3 9

4 b Baseln

**Day 12** 10h sleep 4b Accumulate 14 hours of work Accumulate 28 hours of work Accumulate 42 hours of work Accumulate 56 hours of work Accumulate 70 hours of work

Accumulate 28 hours of work Restart 10h
10h sleep 1b Restart

10h sleep 2b **Day 10 Day 11** 

Recovery 10h Accumulate 42 hours of work Accumulate 56 hours of work Accumulate 70 hours of work 10h sleep 3b Enter lab, practice testing

Exit lab Transition nap 5h Recov Recovery 10h Transition day 7h

Transition day 7h Baseline 10h Accum 14h work Accum 28h work Transition nap 5h 10h sleep 1a 23 **Worst Case** 19 20 21 22 15 17 18 11 12 13 14 8 9 10 16 4 5 6 7 0 3 1 2

Accumulate 14 hours of work Accumulate 28 hours of work Accumulate 42 hours of work Accumulate 56 hours of work Accumulate 70 hours of work Restart 10h Baseln

Restart nap 5h Restart 7h no driving Restart 7h no driving Accumulate 14 hours of work Accumulate 28 hours of work Accumulate 42 hours of work Accum 42h work Accum 56h work Accum 70h work Restart nap 5h Restart
Accum 28h work Accum 42h work Accum 56h work Accumulate 56 hours of work Accumulate 70 hours of work Accum 70h work 10h sleep 2a 10h sleep 3a 10h sleep 4a

10h sleep 1b 10h sleep 2b 10h sleep 3b 10h sleep 4b Accum 14h work **Day 1 Day 2**  **Day 3 Day 4**  **Day 5 Day 6** 

**Day 8 Day 9**  **Day 10 Day 11**  **Day 7**

**Day 12 Day 13**  **Day 14**

Figure 3 . Chart. Study design for the “ worst - case” (top schematic) and “ best - case” (bottom schematic) conditions of the study , with h ours of the day (midnight to midnight) progress ing from left to right (see top numbers), days of the study progress ing from top to bottom (see right side) , gray indicating scheduled wakefulness, and black indicat ing scheduled sleep periods
. The experiment was conducted in the Sleep a nd Performance Research Center at Washington State University Spokane . This 10, 000

- square - foot facility includes a state

- of

- the

- art sleep research laboratory and a high

- fidelity job task simulation laboratory connected to form a single contiguous, isolated space with a high degree of laboratory control for the precise and efficient investigation of fatigue and performance responses to prescribed sleep/wake/work schedules. The facility contains a sleep suite (four bedrooms, loung e area, medical exam room, monitoring7 room, and two full baths), a simulation laboratory with two high

- fidelity driving simulators and several other critical job task simulators, and office and work space for investigators, post

doctoral fellows, graduate students, undergraduate research assistants, and technical personnel. The laboratory is fully equipped and staffed for polysomnographic recording of sleep and for computer

- and simulator

- based measurement of performance during long

- term (days to weeks) in

- residence studies. Subjects in both conditions were in the laboratory continuously for a total of 317 hours (14 days, 13 nights). They were monitored continuously by direct observation or, when they were in their room for performance testing or for sleep, through a camera system (with infrared light for observation in darkness during scheduled sleep). No visitors, phone calls, text messaging, e

- mail, live radio and television, or other external, potentially confounding influences were allowed inside the lab oratory; only our trained research assistants interacted with the subjects. During scheduled sleep periods, lights in the bedrooms were off and subjects were not allowed to engage in any activities other than sleeping (or resting if they could not sleep). Ambient temperature was kept at 72 ± 2 oF, and light levels during scheduled wakefulness were fixed at below 50 lux. Meals were served at regular intervals, and breakfast, lunch , and dinner were shifted by 12 hours during nighttime wake periods in the “ wor st

- case” condition.
2.2 MEASUREMENTS During the 5

- day work periods, four times per duty day, subjects drove a 40

- minute route on a PatrolSim IV driving simulator (MPRI, Salt Lake City, UT). This is a high

- fidelity driving simulator widely used to train profess ional drivers. For this project, we developed and installed additional hardware and software to be able to capture driving performance data at high resolution, so as to turn this training device into a research tool

. ( 10 )

The following outcome variables were extracted for each simulator drive: average and variability (standard deviation) of speed in the straightaways; lane deviation (standard deviation of lane position) in the straightaways; reaction time of braking for the pedestrian/dog crossing events; total number of braking errors (i.e., braking unnecessarily in the straightaways, or braking more than once or not at all around the pedestrian/dog crossing events); and fuel use (as calculated by the simulator’s in ternal fuel use model) across the straightaways. Furthermore, we developed a standar dized driving scenario, involving rural highway driving with five to seven randomly located encounters with pedestrians or dogs crossing the road. Braking responses to these unexpected crossing events were recorded to capture any lapses of attention. In ad dition, 10 straight, uneventful road segments in the scenario (“straightaways”) were used to extract unconfounded data on lane deviation and other performance measures potentially indicative of fatigued driving. The speed limit throughout the scenario was 55 mph.

Each 40

- minute driving simulator session was preceded and followed by a 10

- minute psychomotor vigilance test (PVT). This is a simple reaction

- time task with high stimulus density. It is a well

- validated and widely used standard assay of fatigue

. ( 11 ) As in previous studies , ( 2,12,13) the number of performance lapses was extracted, with performance lapses being defined as reaction times greater than 500 ms. Because of high sensitivity to fatigue and favorable st atistical properties , ( 14 ) these PVT lapse counts were used as the primary outcome measure of the study.8 Other assessments of neurobehavioral performance were performed during the study. Each driving simulator session (with preceding and subsequent PVT bout ) was paired with administration of a brief (~12

- minute) neurobehavioral test battery. The battery included computerized versions of the Karolinska Sleepiness Scale (KSS), (15) a visual analog scale of mood (VASM), (12) the Positive Affect Negative Affect Sc hedule (PANAS ), (16) a digit symbol substitution task (DSST), (17) performance and effort rating scales (PERF and EFFR, respectively), (18) and a cardinal direction decision task (CDDT). (19)

Figure 4 shows the timing of the 1

- hour blocks consisting of a 10

- minute PVT, 40

- minute simulator driving, and 10

- minute PVT, as marked by the asterisk triplets. The figure also shows the timing of the brief neurobehavioral performance test bouts, as marked by lower

- case x symbols. On off

- duty days, no driving occurred, but the neurobehavioral performance test battery was administered a few times, augmented with a 10

- minute PVT (as marked with capital X symbol s). This off

- duty performance monitoring served to gauge fatigue levels during the baseline, restart , and recovery periods bracketing the two 5

- day work periods; they were not used for the present analyses. Driving simulator and performance testing practic e occurred on the first day; these practice sessions were also not used for analysis. The KSS, VASM, PANAS, PERF , and EFFR yielded subjective assessments of sleepiness, mood , and effort. For each, an overall score was extracted, except for the PANAS, for which both positive and negative affect scores were determined. The DSST involved matching numbers to symbols. The computer screen showed a key with a set of nine symbols and their corresponding digits (1

– 9). When given a symbol in another, fixed location on the screen, subjects were required to type its corresponding number. After the response, a new symbol was presented immediately. The number of correct r esponses in the 3

- minute task duration was extracted, yielding a measure of cognitive throughput. The CDDT require d participants to make judgments about where a target appear ed . The stimulus consist ed of a first

- person view where a single target wa s highli ghted in a set of eight objects arranged in a circular target field. This view wa s presented adjacent to an allocentric perspective (i.e., a map with the target field at the center), which indicate d the viewing perspective. The task wa s to identify the por tion of the target field containing the target

. A total of 25 trials was presented per test bout, and responses were self

- paced. From the number of attempts needed to complete them the number of error responses was calculated (i.e., the number of attempts minus 25), which served as the outcome measure.

Two driving simulators we re available in the laboratory; up to four subjects could be participating in the study at a given time. Therefore, subjects were randomly assign ed consistently either to do the driving (preceded and followed by the PVT) first and undergo the neurobehavioral performance testing second, or the other way around. There was always a 45

- minute break between the driving/PVT blocks and the neurobehavioral performance test bouts. Figure 4 illustrates the simulator driving and performance testing schedule for the subjects who underwent the driving/PVT block first and the neurobehavioral performance testing second. During scheduled sleep periods, s leep stages were recorded using digital equipment (Nihon Kohden, Foothill Ranch, CA ) for polysomnography (PSG). Scalp and skin electrodes were used to record brain waves (bipolar electroencephalogram, EEG), eye movement (electrooculogram ), muscle activity (submental myogram ), and heart beats (electrocardiogram ). The EEG electrodes were placed at frontal (F3, F4), central (C3, C4) , and occipital (O1, O2) locations, referenced against the mastoids (M1, M2). Sleep stages were scored using standard criteria promulgated by9 the American Academy of Sleep Medicine

. (20)

x x x x X X
x x x x x x x x x x x x x x x x x x x x X X X X
x x x x x x x x x x x x x x x x x x x x X X *** *** ***

*** *** *** ***

PSG **Day 14** *** *** *** *** ***

PSG **Day 13** *** *** *** *** ***

*** *** *** PSG **Day 1**

*** *** ***
*** *** *** *** ***

*** *** PSG **Day 12** PSG PSG **Day 11** *** *** *** *** PSG **Day 10** PSG **Day 9** PSG PSG **Day 8** *** PSG **Day 7** PSG **Day 6** *** *** **Day 5** PSG **Day 4**
*** PSG **Day 3**

*** *** PSG PSG PSG PSG **Day 2** *** *** *** 21 22 23 17 18 19 20 13 14 15 16 9 10 11 12 0 1 2 3 4 5 6 7 8 **Best Case** x x x x X X x x x x x x x x x x x x x x x x x x x x X X X X x x x x x x x x x x x x x x x x x x x x X X ***
*** *** ***

***
***
***
*** ***
***
***

*** ***
*** *** *** *** *** ***

PSG *** ***

*** ***

*** ***

PSG *** *** *** *** PSG

PSG *** ***
*** *** *** PSG ***
*** *** *** PSG PSG PSG PSG PSG ***
*** *** *** PSG PSG PSG PSG *** 20 21 22 23 16 17 18 19 12 13 14 15 8 9 10 11 4 5 6 7 0 1 2 3 **Worst Case**

**Day 7 Day 3**  **Day 4 Day 5**  **Day 6 Day 1**  **Day 2**

**Day 13 Day 9**  **Day 8**

**Day 14 Day 10**  **Day 11 Day 12**  Every third day, electrodes were removed to give subjects an opportunity to take a shower and to heal any skin irritation that might have occurred due to electrode attachment. Figure 4 shows whi ch sleep periods were recorded.

Figure 4 . Chart. Measurement scheme for the “worst - case” (top schematic) and “best case” (bottom sch ematic) conditions of the study, with h ours of the day (midnight to midnight) progress ing from left to right (see top numbers); days of the study progress ing from top to bottom (see right side) ; g ray indicat ing scheduled wakefulness; black indicat ing scheduled sleep periods; triple asterisks marking 40 - minute d riving simulator sessions each preceded and followed by a 10 - minute PVT ; lower - case x marking b rief neurobehavioral performance test bouts; capital X marking brief neurobehavioral performance test bouts augmented with a PVT ( during off - duty days ); and PSG marking polysomnography (i.e., sleep recording s ).
2.3 STATIST ICAL METHODS AND POW ER CALCULATION The primary statistical design involved within

- subject comparison of performance during the first 5

- day work period with performance during the second 5

- day work period (i.e., repeated measures), and between

- groups compar ison of the “worst

- case” condition and the “ best

- case” condition. Therefore, we employed two

- way mixed

- effects analysis of variance (ANOVA) and focused on the interaction of group (condition) by work period (session). This interaction tests10 the null hypoth esis that the 34

- hour restart period is effective at maintaining performance in both conditions (regardless of the presence of any baseline differences). Additional analyses useful for interpretation of the main results involved three

- way mixed

- effects ANO VA of condition by session by day (to look at changes over days within sessions), and three

- way mixed

- effects ANOVA of condition by session by time of day (to examine the effects of circadian timing). The primary outcome measure we considered was PVT lapse s (see above). Additional neurobehavioral outcomes and driving simulator performance outcomes (accounting for subjects’ assignment to either simulator #1 or #2) were used for secondary analyses. For a power calculation performed in advance of the study, it was noted that the literature suggests that daily sleep duration for work days in the “ worst

- case” condition would be expected to be ~5 hours

. ( 8 ) It was therefore useful to consider performance impairment in the 5

- hour sleep restriction condition of a stu dy (1)

Assuming that the “best

- case” condition would show no change in performance across days (i.e., would serve as a control condition) and could therefore be ignored for the purpose of power calculation, the primary comparison for the power calculation would involve a comparison of performance in the first 5

- day work period with performance in the second 5

- day work period in the “ worst

- case” condi tion. It was not known a priori what level of impairment the second 5

- day work period would show, but we would consider it substantively deteriorated if it resembled the 3

- hour sleep restriction condition in the WRAIR study. In order to obtain an expected effect size estimate, we compared performance across the first 5 sleep restrictions days of the 5

- hour and 3

- hour conditions of the WRAIR study ( n = 16 for the 5

- hour group; n = 18 for the 3

- hour group), using PVT lapses as the primary outcome measure. Per forming linear mixed

- effects regression across days and examining the interaction with condition in this subset of the WRAIR study data yielded as the coefficient for the difference in slope between the two conditions 2.06 ± 0.29 (estimate ± standard error ). The effect size was then calculated from the corresponding t statistic: t conducted by the Walter Reed Army Institute of Research (WRAIR) as a basis for effect size estimation. In that study, performance was measured diurnally, and should thus be less impaired th a n would be expected in the nocturnally oriented
“ worst

- case” condition of the present project. In other words, the WRAIR study would yield a conservative effect size estimate.

168 In the present study, the comparison between the first and second 5

- day work periods is within subjects. Based on the above effect size estimate, the power calculation showed that wi th a type I error rate of 0.05 (one

- sided), a substantive deterioration of performance from the first to the second 5

- day work period should be detectable with at least 95 percent statistical power (i.e., a type II error rate of 0.05) if there were 12 subj ects in the “worst

- case” condition. An equal number of subjects would be needed in the “best

- case” (control) condition. We therefore determined that a total sample size of at least N = 24 was needed to test whether or not the 34

- hour restart period provide d enough recovery in the “worst

- case” condition to prevent cumulative performance degradation across work periods, with at least 95 percent statistical power. = 7.12 (p < 0.001). This yielded Cohen’s d = 2 × 7.12 / √168 = 1.10, which is considered to be a large effect size.

Statistical analyses of polysomnographic measures of sleep were driven by which combinations of s leep periods corresponded best with each other between the two study conditions (s ee Figures11 3 and 4 )

. A series of one

- way ANOVAs was performed for comparison of the first (10

- hour) baseline night between the two conditions; comparison of the first (10

- hou r) baseline night plus the second (10

- hour) baseline night in the “best

- case” condition with the first (10

- hour) baseline night plus the first (5

- hour) transition nap in the “ worst

- case” condition; comparison of the sum of the two polysomnographically reco rded (10

- hour) sleep periods in the first 5

- day work period between the two conditions; comparison of the combination of the two (10

- hour) nights in the 34

- hour restart period of the “ best

- case” condition with the combination of the two (5

- hour) transition naps plus the (10

- hour) nocturnal sleep period in the 34

- hour restart period of the “worst

- case” condition; comparison of the sum of the two polysomnographically recorded (10

- hour) sleep periods in the second 5

- day work period between the two conditions; and comparison of the (10

- hour) recovery night at the end of the “best

- case” condition with the (5

- hour) transition nap plus the (10

- hour) recovery night at the end of the “ worst

- case” condition. Furthermore, a two

- way ANOVA was performed focusing on the i nteraction of condition (group) by work period (session).
2.4 SUBJECT RECRUITMENT AND SCREENING Subjects were healthy young individuals in the age range 22 to 40 years. This population was selected because of their relative homogeneity in sleep/wake and circad ian physiology (e.g., minimal aging effects and no sleep disorders). This is beneficial (and necessary) for obtaining sufficient statistical power to demonstrate any effects of the “best

- case” and “worst

- case” conditions on performance. If any performance impairments are found in this population, then even greater deficits would be expected in the more heterogeneous population of CMV drivers. The inclusion/exclusion criteria for the study were as follows:

**•** Physically and psychologically healthy (i.e., no cli nical disorders and/or illnesses), as determined by physical exam, history, and questionnaires.

**•** No current medical or drug treatment (excluding oral contraceptives), as determined by history and questionnaire.

**•** No clinically significant abnormalities in blo od and urine, and free of traces of drugs, as determined by blood chemistry and urinalysis, as well as a urine drug test upon entering the study.

**•** Free of traces of alcohol, as verified with a breathalyzer during screening and upon entering the study.

**•** No hi story of psychiatric illness, as determined by history and questionnaire.

**•** No history of drug or alcohol abuse in the past year, and no history of methamphetamine abuse, as determined by history and questionnaire.

**•** Not a current smoker, as determined by ques tionnaire.

**•** No history of moderate to severe brain injury, as determined by history and questionnaire.

**•** No history of a learning disability, as determined by questionnaire.12 **•** Not susceptible to simulator adaptation syndrome, as determined by supervised test dr iving of the simulator followed by questionnaire and interview

**•** No previous adverse reaction to sleep deprivation, as determined by history and questionnaire

**•** Not vision

- impaired (unless corrected to normal), as determined by questionnaire

**•** Not pregnant, as d etermined by history and questionnaire

**•** No sleep or circadian disorder, as determined by history, suite of questionnaires, and baseline polysomnography

**•** Good habitual sleep, between 6 and 10 hours in duration, as determined by questionnaire and verified with wrist actigraphy and diary in the week before the study

**•** Regular bedtimes, habitually getting up between 06:00 am and 09:00 am, as determined by questionnaire and verified with actigraphy and diary in the week before the study

**•** Neither an extreme morning

- ty pe nor an extreme evening

- type, as determined by questionnaire

**•** No travel across time zones within one month of entering the study, as determined by questionnaire

**•** No shift work within one month of entering the study, as determined by questionnaire

**•** Native En glish speaker, as determined by questionnaire

**•** Proficient driver, as determined by valid driver’s license and supervised test driving of the simulator

**•** Age from 22 to 40 years, as verified by date of birth on driver’s license Prospective subjects were recrui ted with advertisements in local newspapers and on the Internet

. A total of 760 individuals responded to the advertisements and were interviewed by telephone. Those who met key selection criteria such as age were screened during two laboratory

- based screen ing sessions, beginning with an informed consent procedure. Screening procedures included a physical exam, blood and urine samples, supervised test driving of the simulator, and a variety of questionnaires (see inclusion/exclusion criteria above). Subject recruitment continued until the sample size required for the study was reached.13 

### 3. RESULTS
3.1 SUBJECTS Data were available for a total of N = 27 healthy subjects who met the inclusion/exclusion criteria (see above). An additional seven subjects began the study but their data could not be used because of development of flu

- like symptoms ( one subject); polysomnographic evidence of a sleep disorder ( two subjects); suspected non

- compliance ( two subjects); and subject

- initiated withdrawal from the study ( two subject s). Of the sample of 27 subjects, 14 subjects were randomized to the “ best

- case” condition, and 13 subjects were randomized to the “ worst

- case” condition. The subjects in the “ best

- case” condition were seven men and seven women, ages 23

– 38 (mean ± standard deviation: 27.5 ± 5.6). The subjects in the “ worst

- case” condition were six men and seven women, ages 22

– 39 (mean ± standard deviation: 27.0 ± 5.4). There was no significant difference in age distribution between the two groups ( F 1,25
3.2 PERFORMANCE OUTCOMES = 0.055, p = 0.82).

3.2.1 Primary Performance Outcome The primary outcome measure for the study was the number of lapses on the PVT. The primary statistical analysis focused on the interaction of group (“worst

- case” vs. “best

- case” condition) by 5

- day work peri od (pre

- restart vs. post

- restart session), collapsed over days and times of day within sessions. This interaction tests the null hypothesis that the 34

- hour restart period is effective at maintaining performance in both conditions. This interaction was sta tistically significant for PVT lapses ( F 1,2129 = 20.06, p < 0.001). There was also a significant main effect of session ( F 1,2129 = 21.79, p < 0.001), but no significant main effect of group ( F 1,2129 = 1.31, p = 0.25). Figure 5 displays the interaction, sho wing that whereas performance did not vary significantly across the study in the “best

- case” condition (simple effect of session: F 1,2129 = 0.02, p = 0.89), there was significant deterioration of performance from before the 34

- hour restart to after it in t he “ worst

- case” condition (simple effect of session: F 1,2129 = 40.38, p < 0.001).14 0 1 2 3 4 5

1 2 SessionPVT Lapses
Worst Case Best Case

Figure 5 . Graph. Lapses (number of reaction times greater than 500 ms) on the 10 - minute PVT in the pre - restart 5 - day work period (session 1) as com pared to the post restart 5 - day work period (session 2) for the “worst - case” and “best - case” conditions (e rror bars indicate standard error derived from mixed - effects ANOVA )
. In order to look at changes in performance over days within sessions, a further a nalysis of PVT lapses investigated the interaction of group by work period by day, collapsed over time of day. This three

- way interaction was not statistically significant ( F 4,2113 = 0.79, p = 0.53). The main effect of day was also not significant ( F 4,2113 = 0.70, p = 0.59), nor were the two

- way interactions of group by day ( F 4,2113 = 1.50, p = 0.20) and work period by day ( F 4,2113 = 0.69, p = 0.60). Figure 6 below displays the data by day. Although it might seem that performance deteriorated progressively from the first to the third day of the second 5

- day work period in the “worst

- case” condition, this was not borne out by the statistical results. Thus, it may be concluded that the effects seen in Figure 5 above were relatively stable over days within the 5

- day work periods.15 0 1 2 3 4 5

1 2 3 4 5 1 2 3 4 5 Days on ShiftPVT Lapses
Worst Case Best Case34-hour restart

Figure 6 . Graph. Lapses on the 10 - minute PVT as a function of days in the 5 - day work periods before and after the 34 - hour restart period, for the “worst - case” and “best case” conditions (vertical e rror bars in dicate standard error derived from mixed - effects ANOVA )
. In order to study performance in the two conditions as a function of time of day, another secondary analysis of PVT lapses investigated the interaction of group by work period by time of day, collaps ed over days within each 5

- day work period. The three

- way interaction was not statistically significant ( F 7 ,21 01 = 0.44, p = 0.88). Yet, there was a significant main effect of time of day ( F 7 ,21 01 = 16.32, p < 0.001) and a significant interaction of group by time of day ( F 7 ,21 01 = 10.59, p < 0.001), although the interaction of work period by time of day was not significant ( F 7 ,21 01 = 0.22, p = 0.98). Figure 7 displays the data as a function of time of day (collapsed over days within each work period). The f igure shows that performance was relatively stable across time of day in the “best

- case” condition. However, there was a clear time

- of

- day effect in the “worst

- case” condition, with performance degrading progressively across time of night.16 0 1 2 3 4 509:05 pm/am09:55 pm/am12:05 am/pm12:55 am/pm03:05 am/pm03:55 am/pm06:05 am/pm06:55 am/pm09:05 pm/am09:55 pm/am12:05 am/pm12:55 am/pm03:05 am/pm03:55 am/pm06:05 am/pm06:55 am/pm

Time Point within DaysPVT Lapses
Worst Case Best Case First 5-day work period Second 5-day work period

Figure 7 . Graph. Lapses on the 10 - minute PVT as a function of time of day, collapsed over the 5 - day work periods before and after the 34 - hour restart period (e rror bars indicate standard error derived from mixed - effects ANOVA ) . Times of day are through the night (09:05 pm – 06:55 am) for the “ worst - case” condition and through the day (09:05 am – 06:55 pm) for the “ best - case” condition; 1 hour should be added for subjects who were assigned to performance testing first and driving second.
3.2.2 Secondary Pe rformance Outcomes Secondary performance outcomes were derived from a computerized neurobehavioral test battery which included, in order of presentation, the KSS, VASM, PANAS ( both subscales were analyzed), DSST, PERF, EFFR, and CDDT. Results for these out come measures are presented here in that order. For the KSS, the primary analysis focusing on the interaction of group by session (collapsed over days and over times of day within sessions) yielded a significant interaction effect ( F 1, 1050 = 8.34, p = 0.00 4) and a significant effect of group ( F 1, 1050 = 8.11, p = 0.005), but no significant effect of session ( F 1, 1050 < 0.01, p = 0.99). Figure 8 displays these findings, showing that subjects in the “ worst

- case” condition were overall sleepier than those in the “ best

- case” condition. In addition, subjects in the “ worst

- case” condition were slightly less sleepy after the 34

- hour restart than before, whereas subjects in the “ best

- case ” condition were slightly sleepier after the 34

- hour restart than before.17 2 3 4 5 6

1 2 SessionKSS Sleepiness

Worst Case Best Case

Figur e 8 . Graph. Subjective sleepiness score on the KSS in the pre - restart 5 - day work period (session 1) as compared to the post - restart 5 - day work period (session 2) for the
“ worst - case” and “ best - case” conditions (e rror bars indicate standard error derived from mixed - effects ANOVA )
. In order to investigate changes in subjective sleepiness over days within sessions, a further analysis of the KSS examined the interaction of group by work period by day, collapsed over time of day. The thr ee - way interaction was not statistically significant ( F 4, 1034 = 0.92, p = 0.45). However, the main effect of day was significant ( F 4, 1034 = 5.72, p < 0.001), as was the two

- way interaction of group by day ( F 4, 1034 = 4.01, p = 0.003), though not the two

- way interaction of work period (session) by day ( F 4, 1034 = 0.87, p = 0.48). Figure 9 displays the data by day, showing that subjective sleepiness in the “ worst

- case” condition diminished across days in the first 5

- day work period, increased immediately after the 34

- hour restart period, and then diminished again across days in the second 5

- day work period.18 2 3 4 5 6

1 2 3 4 5 1 2 3 4 5 Days on ShiftKSS Sleepiness

Worst Case Best Case34-hour restart

Figure 9 . Graph. Subjective sleepiness on the KSS as a function of days in the 5 - day work periods before and after the 34 - hour res tart period, for the “ worst - case” and
“ best - case” conditions (e rror bars indicate standard error derived from mixed - effects ANOVA )
. In order to investigate subjective sleepiness in the two conditions as a function of time of day, a final analysis of the KS S examined the interaction of group by work period by time of day, collapsed over days within each 5

- day work period. The three

- way interaction was not statistically significant ( F 3 , 1038 = 1.86, p = 0.13). Yet, there was a significant main effect of time o f day ( F 3 , 1038 = 75.87, p < 0.001), a significant interaction of group by time of day ( F 3 , 1038 = 49.28, p < 0.001), and a significant interaction of work period by time of day ( F 3 , 1038 = 5.91, p = 0.005). Figure 10 displays the data as a function of time o f day (collapsed over days within each work period). The figure shows that subjective sleepiness was relatively stable across time of day in the “ best

- case” condition. However, there was a clear time

- of

- day effect in the “worst

- case” condition, with subje ctive sleepiness worsening progressively across time of night, particularly in the 5

- day work period preceding the 34

- hour restart.19 2 3 4 5 609:05 pm/am12:05 am/pm03:05 am/pm06:05 am/pm09:05 pm/am12:05 am/pm03:05 am/pm06:05 am/pm

Time Point within DaysKSS Sleepiness

Worst Case Best Case First 5-day work period Second 5-day work period

Figure 10 . Graph. Subjective sleepiness on the KSS as a function of time of day, collapsed over t he 5 - day work periods before and after the 34 - hour restart period (e rror bars indicate standard error derived from mixed - effects ANOVA ) . Times of day are through the night (09:05 pm – 06:05 am) for the “ worst - case” condition and through the day (09:05 am – 06: 05 pm) for the “ best - case” condition; 1 hour and 50 minutes should be added to each time point for subjects who were assigned to driving first and performance testing second. For the VASM, the primary analysis focusing on the interaction of group by sessio n (collapsed over days and over times of day within sessions) yielded only a statistically significant effect of session ( F 1, 1051 = 13.01, p < 0.001), with no significant interaction effect ( F 1, 1051 = 0.01, p = 0.92) and no significant effect of group ( F 1, 1051 = 0.14, p = 0.71). Figure 11 displays these findings, showing that there was a minor degradation of subjective mood across the study, but no differences between the “ worst

- case” and “ best

- case” conditions.20 2 3 4 5

1 2 SessionVASM Mood
Worst Case Best Case

Figure 11 . Graph. Subjective mood score on the VASM in the pre - restart 5 - day work period (session 1) as compared to the post - restart 5 - day work period (session 2) for the
“ worst - case” and “ best - case” conditions. Further analyses of the VASM to investigate changes in subject ive mood over days within sessions, and as a function of time of day, partially corroborated the absence of statistically significant differences between the two groups. Mixed

- effects ANOVA focusing on the interaction of group by work period by day, collap sed over time of day, yielded no significant three

- way interaction ( F 4, 1035 = 0.98, p = 0.42) and no significant interaction of group by day ( F 4, 1035 = 0.90, p = 0.46), although there was a trend for a main effect of day ( F 4, 1035 = 2.34, p = 0.053) and a s ignificant interaction of work period by day ( F 4, 1035 = 4.64, p = 0.001). However, mixed

- effects ANOVA focusing on the interaction of group by work period by time of day, collapsed over days within each 5

- day work period, yielded a significant interaction of group by time of day ( F 3 , 1039 = 6.02, p < 0.001), as well as a significant interaction of work period by time of day ( F 3 , 1039 = 3.83, p = 0.010) and a main effect of time of day ( F 3 , 1039 = 14.09, p < 0.001), although there was no significant three

- way i nteraction ( F 3 , 1039 = 1.15, p = 0.33). Figure 12 displays the subjective mood data as a function of time of day (collapsed over days within each work period). The figure shows that subjective mood was relatively stable across time of day in the “ best

- case” condition, but gradually declined across time of night in the “ worst

- case” condition. Furthermore, both groups displayed slightly degraded mood at the beginning of the wake period in the 5

- day work period after the 34

- hour restart period as compared to be fore the 34

- hour restart period.21 2 3 4 509:05 pm/am12:05 am/pm03:05 am/pm06:05 am/pm09:05 pm/am12:05 am/pm03:05 am/pm06:05 am/pm

Time Point within DaysVASM Mood
Worst Case Best Case First 5-day work period Second 5-day work period

Figure 12 . Graph. Subjective mood on the VASM as a function of time of day, collapsed over the 5 - day work periods before and after the 34 - hour restart period. Times of day are through the night (0 9:05 pm – 06:05 am) for the “ worst - case” condition and through the day (09:05 am – 06:05 pm) for the “ best - case” condition; 1 hour and 50 minutes should be added to each time point for subjects who were assigned to driving first and performance testing second. The results for the positive affect scale of the PANAS mimicked those of the VASM. The primary analysis focusing on the interaction of group by session (collapsed over days and over times of day within sessions) yielded only a statistically significant ef fect of session ( F 1, 1051 = 20.48, p < 0.001), with no significant interaction effect ( F 1, 1051 = 1.26, p = 0.26) and no significant effect of group ( F 1, 1051 = 0.82, p = 0.37). Figure 13 displays these findings, showing that there was a minor degradation of positive mood across the study. The difference between the
“ worst

- case” and “ best

- case” conditions was not statistically significant.22 18

22

26

30

34 1 2 SessionPANAS Positive Affect

Worst Case Best Case

Figure 13 . Graph. Positive affect score on the PANAS in the pre - restart 5 - day work period (sess ion 1) as compared to the post - restart 5 - day work period (session 2) for the
“ worst - case” and “ best - case” conditions ( the vertical scale is inverted; upward direction corresponds to less positive affect )
. Further analyses of the positive affect scale of th e PANAS partially corroborated the absence of statistically significant differences between the two groups , as was the case also for the VASM. Mixed

- effects ANOVA focusing on the interaction of group by work period by day, collapsed over time of day, yield ed no significant three

- way interaction ( F 4, 1035 = 0.91, p = 0.46), no significant interaction of group by day ( F 4, 1035 = 1.48, p = 0.21), and no main effect of day ( F 4, 1035 = 1.15, p = 0.33), although there was a trend for an interaction of work period by day ( F 4, 1035 = 3.57, p = 0.007). Mixed

- effects ANOVA focusing on the interaction of group by work period by time of day, collapsed over days within each 5

- day work period, yielded a significant interaction of group by time of day ( F 3 , 1039 = 20.37, p < 0.0 01), as well as a significant interaction of work period by time of day ( F 3 , 1039 = 6.12, p < 0.001) and a main effect of time of day ( F 3 , 1039 = 24.00, p < 0.001), although there was no significant three

- way interaction ( F 3 , 1039
For the negative affect scale of the PANAS, the primary analysis focusing on the interaction of grou p by session (collapsed over days and over times of day within sessions) yielded no significant interaction effect ( F = 0.93, p = 0.42). As a func tion of time of day, the positive affect data considered (collapsed over days within each work period) paralleled the subjective mood data (see Figure 12).

1, 1051 = 0.05, p = 0.83), no significant effect of group ( F 1, 1051 = 0.20, p = 0.65), and no significant effect of session ( F 1, 1051 = 0.53, p = 0.46). Overall, the subjects in both conditions showed low negative affect throughout the study.23 Yet, analysis of the interaction of group by work period by day, collapsed over time of day, showed a significant three

- way interaction ( F 4, 1035 = 3.12, p = 0.014) and a significant interaction of group by day ( F 4, 1035 = 2.76, p = 0.027). There was no significant interaction of work period by day ( F 4, 1035 = 1.59, p = 0.018), and no significant main effect of day ( F 4, 1035

9 10 11 12

1 2 3 4 5 1 2 3 4 5 Days on ShiftPANAS Negative Affect

Worst Case Best Case34-hour restart = 0.57, p = 0.69). Figure 14 displays the data by day, showing that in the first days of the study, negative affect was slightly elevated in the “ worst

- case” condition and slightly diminished in the “ best

- case” condition. However, starting on the third day of the first 5

- day work period, the two groups were no longer statistically significantly different.

Figure 14 . Graph. Negative affect score on the PANAS as a function of days in the 5 - day work periods before and after the 34 - hour restart period, for the “ worst - cas e” and
“ best - case” conditions. Analysis of the interaction of group by work period by time of day, collapsed over days within each 5

- day work period, yielded no significant results

— no three

- way interaction ( F 3 , 1039 = 0.17, p = 0.92), no interaction of grou p by time of day ( F 3 , 1039 = 0.21, p = 0.89), no interaction of work period by time of day ( F 3 , 1039 = 0.24, p = 0.87), and no main effect of time of day ( F 3 , 1039 For the number of correct responses on the DSST, the primary analysis focusing on the interaction of group by session (collapsed over days and over times of day within sessions) yielded only a statistically significant effect of session ( F = 0.50, p = 0.68). Thus, negative affect was relative ly stable across time of days in both cond itions.

1, 1050 = 292.92, p < 0.001), with no significant interaction effect ( F 1, 1050 = 0.28, p = 0.60) and no significant effect of group ( F 1, 1050 = 0.78, p = 0.38). Figure 15 displays these findings, showing that performance on the DSST improved significantly across the s tudy in both conditions.24 105

115

125

135 1 2 SessionDSST Number Correct

Worst Case Best Case

Figure 15 . Graph. Number of correct responses on DSST in the pre - restart 5 - day work period (session 1) as compared to the post - restart 5 - day work period (session 2) for the
“ worst - case” and “ best - case” co nditions ( the vertical scale is inverted; downward direction corresponds to performance improvement )
. In order to investigate further the changes in DSST performance across days in the study, an additional analysis examined the interaction of group by work period by day, collapsed over time of day. The three

- way interaction was not statistically significant ( F 4, 1034 = 0.82, p = 0.52), nor was the two

- way interaction of group by day ( F 4, 1034 = 0.51, p = 0.73). However, the main effect of day was significant ( F 4, 1034 = 29.84, p < 0.001), as was the two

- way interaction of work period (session) by day ( F 4, 1034 = 6.13, p < 0.001). Figure 16 displays the data by day, showing that performance on the DSST improved steadily across days in both conditions, in accordan ce with the well

- known practice effect for this task.25 105

115

125

135 1 2 3 4 5 1 2 3 4 5 Days on ShiftDSST Number Correct

Worst Case Best Case34-hour restart

Figure 16 . Graph. Number of correct responses on DSST as a function of days in the 5 - day work periods before and after the 34 - hour restart period, for the “ worst - case” and
“ be st - case” conditions (th e vertical scale is inverted; downward direction corresponds to performance improvement )
. Analysis of the interaction of group by work period by time of day, collapsed over days within each 5

- day work period, yielded a significant tw o - way interaction of group by time of day ( F 3 , 1038 = 2.94, p = 0.032), but no significant three

- way interaction ( F 3 , 1038 = 0.74, p = 0.53), no significant interaction of work period by time of day ( F 3 , 1038 = 1.47, p = 0.22), and no significant main effect of time of day ( F 3 , 1038 = 0.12, p = 0.95). Figure 17 shows the data as a function of time of day (collapsed over days within each work period). The figure shows the combined influences of the practice effect, promoting performance improvement across test b outs, and of time of day, promoting performance degradation across time of night in the “ worst

- c ase” condition (cf. Figure 7).26 105

115

125

13509:05 pm/am12:05 am/pm03:05 am/pm06:05 am/pm09:05 pm/am12:05 am/pm03:05 am/pm06:05 am/pm

Time Point within DaysDSST Number Correct

Worst Case Best Case First 5-day work period Second 5-day work period

Figure 17 . Graph. Number of correct responses on the DSST as a function of time of day, collapsed over the 5 - day work periods before and after the 34 - hour restart period ( the vertical scale is inverted; downward direction corresponds to performance improvement ) . Times of day are through the night (09:05 pm – 06:05 am) for the “ worst case” condition and throu gh the day (09:05 am – 06:05 pm) for the “ best - case” condition; 1 hour and 50 minutes should be added to each time point for subjects who were assigned to driving first and performance testing second. For the PERF administered near the end of each neurobehav ioral test bout, the primary analysis focusing on the interaction of group by session (collapsed over days and over times of day within sessions) yielded a significant effect of group ( F 1, 1051 = 7.29, p = 0.007), but no significant interaction effect ( F 1, 1 051 = 0.14, p = 0.71) and no significant effect of session ( F 1, 1051 = 0.28 , p = 0.60). Figure 18 displays these findings, showing that subjects in the “ worst

- case” condition consistently rated their performance as poorer than did subjects in the “ best

- case ” condition.27 2.0 2.5 3.0 3.5 4.0

1 2 SessionPERF Score
Worst Case Best Case

Figure 18 . Graph. Retrospective self - evaluation score on the PERF in the pre - restart 5 - day work period (session 1) as compared to the post - restart 5 - day work period (session 2) for the “ worst - case” and “ best - case” co nditions (u pward direction corresponds to poorer performance )
. In order to investigate changes in performance ratings over days within sessions, a further analysis of the PERF examined the interaction of group by work period by day, collapsed over time of day. The three

- way interaction was not statistically significant ( F 4, 1035 = 1.60, p = 0.17), and neither was the two

- way interaction of group by day ( F 4, 1035 = 0.73, p = 0.57). However, the main effect of day was significant ( F 4, 1035 = 2.66, p = 0.032), an d there was a trend for a two

way interaction of work period (session) by day ( F 4, 1035 = 2.18, p = 0.069). Figure 19 displays the data by day, revealing primarily that in the 5

- day work period after the 34

- hour restart, both groups rated their performance as slightly improving across the days.28 2.0 2.5 3.0 3.5 4.0

1 2 3 4 5 1 2 3 4 5 Days on ShiftPERF Score
Worst Case Best Case34-hour restart

Figure 19 . Graph. Retrospective self - evaluation score on the PERF as a function of days in the 5 - day work periods before and after the 34 - hour restart period, for the “worst - case” and “best - case” conditions (u pward direction corresponds to poorer performance )
. In order to investigate changes in performance ratings in the two conditions as a function of time of day, an additional analysis of the PERF examined the interaction of group by work p eriod by time of day, collapsed over days within each 5

- day work period. The three

- way interaction was not statistically significant ( F 3 , 1039 = 0.20, p = 0.89), nor were the two

- way interaction of group by time of day ( F 3 , 1039 = 0.68, p = 0.56) and the mai n effect of time of day ( F 3 , 1039 = 1.53 , p = 0.21). However, there was a significant two

- way interaction of work period by time of day ( F 3 , 1039 = 3.27, p = 0.021), which is shown in Figure 20. During the 5

- day work period preceding the 34

- hour restart, sub jects rated their performance as slightly deteriorating during the later hours of the waking period , regardless of condition. This effect was no longer apparent in the 5

- day work period following the 34

- hour restart.29 2.0 2.5 3.0 3.5 4.009:05 pm/am12:05 am/pm03:05 am/pm06:05 am/pm09:05 pm/am12:05 am/pm03:05 am/pm06:05 am/pm

Time Point within DaysPERF Score
Worst Case Best Case First 5-day work period Second 5-day work period

Figure 20 . G raph. Retrospective self - evaluation score on the PERF as a function of time of day, collapsed over the 5 - day work periods before and after the 34 - hour restart period (u pward direction corresponds to poorer perf ormance). Times of day are through the night ( 09:05 pm – 06:05 am) for the “ worst - case” condition and through the day (09:05 am – 06:05 pm) for the “ best - case” condition; 1 hour and 50 minutes should be added to each time point for subjects who were assigned to driving first and performance testing second
. For the EFFR administered near the conclusion of each neurobehavioral test bout, the primary analysis focusing on the interaction of group by session (collapsed over days and over times of day within sessions) yielded no statistically significant effects : no interaction ( F 1, 1051 = 2.19, p = 0.14), and no main effects of group ( F 1, 1051 = 0.96, p = 0.33) and session ( F 1, 1051 = 0.83, p = 0.36). Figure 21 displays these results, showing no differences between conditions , or before and after the 34

- hour rest art , in the amount of effort subjects felt was necessary to complete the neurobehavioral test bouts.30 1.0 1.5 2.0 2.5 3.0

1 2 SessionEFFR Score
Worst Case Best Case

Figure 21 . Graph. Retrospective self - evaluation score on the EFFR in the pre - restart 5 - day work period (session 1) as compared t o the post - restart 5 - day work period (session 2) for the “ worst - case” and “ best - case” conditions (u pward direction corresponds to greater subjective effort needed to complete the neurobehavioral test bouts )
. Further analyses of the EFFR partially corrobora ted the absence of statistically significant differences between the two groups. Mixed

- effects ANOVA focusing on the interaction of group by work period by day, collapsed over time of day, yielded no significant three

- way interaction ( F 4, 1035 = 0.86, p = 0 .49), no significant interaction of group by day ( F 4, 1035 = 0.91, p = 0.45), no significant interaction of work period by day ( F 4, 1035 = 1.32, p = 0.26), and no main effect of day ( F 4, 1035 = 0.37, p = 0.83). Mixed

- effects ANOVA focusing on the interaction of group by work period by time of day, collapsed over days within each 5

- day work period, yielded a significant interaction of group by time of day ( F 3 , 1039 = 4.63, p = 0.003) and a trend for a main effect of time of day ( F 3 , 1039 = 2.43, p = 0.064), altho ugh there was no significant three

- way interaction ( F 3 , 1039 = 0.78, p = 0.50) and no significant interaction of work period by time of day ( F 3 , 1039
For the number of error responses on the CDDT, the primary analysis focusing on the interaction of group by session (collapsed over days and over times of day within sessions) yielded a statistically signif icant effect of session ( F = 0.48, p = 0.70). As a function of time of day, the subjective effort data paralleled the subjective mood d ata (see Figure 12) and the positive affect data.

1, 1051 = 17.78, p < 0.001) and a trend for the interaction effect ( F 1, 1051 = 3.35, p = 0.067), but no significant effect of group ( F 1, 1051 = 1.48, p = 0.22). Figure 22 displays these findings, showing that performance on the CDDT improved across the study in both conditions, but tended to do so more in the “ best

- case” condition.31 0 0.4 0.8 1.2 1.6

1 2 SessionCDDT Errors
Worst Case Best Case

Figure 22 . Graph. Number of error responses on the CDDT in the pre - restart 5 - day work period (session 1) as compared to the post - restart 5 - day work period (session 2) for the “worst - case” and “best - case” conditions. In order to investigate further CDDT performance across days in the study, a secondary analysis examined the interaction of group by work period by day, collapsed over time of day. The three

way interaction was not statistically significant ( F 4, 1035 = 1.09, p = 0.36), nor was the two

- way interaction of group by day ( F 4, 1035 = 1.24, p = 0.29). However, the main effect of day was significant ( F 4, 1035 = 4.00, p = 0.003), as was the two

- way interaction of work period (session) by day ( F 4, 1035 = 2.63, p = 0.033). Figure 23 displays the data by day, showing that in both conditions, performance on the CDDT improved steadily across days in the 5

- day work period preceding the 34

- h our restart, and then leveled off in the 5

- day work period following the 34

- hour restart.32 0 0.4 0.8 1.2 1.6

1 2 3 4 5 1 2 3 4 5 Days on ShiftCDDT Errors
Worst Case Best Case34-hour restart

Figure 23 . Graph. Number of error responses on the CDDT as a function of days in the 5 - day work periods before and after the 34 - hour restar t period, for the “ worst - case” and
“ best - case” conditions. Analysis of the interaction of group by work period by time of day, collapsed over days within each 5

- day work period, yielded only a trend for a two

- way interaction of work period by time of day ( F 3 , 1039 = 2.30, p = 0.076). There was no significant three

- way interaction ( F 3 , 1039 = 0.65 , p = 0.59), no significant two

- way interaction of group by time of day ( F 3 , 1039 = 0.47, p = 0.70), and no significant main effect of time of day ( F 3 , 1039
3.2.3 Simulator Driving Performance = 1.59, p = 0.19). In other words, CDDT performance was relatively stable within days over time of day.
Analyses of driving simulator performance were based on data from 12 subjects randomized to the “ best

- case” condition and 13 subjects randomized to the “ worst

- case” condition

— driving performance data from two subjects were unavailable as a result of data

- capturing problems with the simulators. Driving simulator outcome variables were subjected to the same primary and secondary analyses as were the neurobehavioral performance outcomes described above, but subjects’ assignment to simulator #1 or #2 was added as a covariate to account for any possible simulator hardware differences. Further analyses were run with pre

- driving PVT performance (lapses) as an added covariate, to examine the potential of the PVT for predicting simulator driving performance. For average driving speed in the straightaways, the primary analysis focusing on the interaction of group by session (collapsed over days and over times of day within sessions) yielded a significant effect of session ( F 1, 970 = 47.97, p < 0.001) and a trend for an interaction effect33 ( F 1, 970 = 3.51, p = 0.061). There was no significant effect of group ( F 1, 970 = 0.63, p = 0.43). Figure 24 displa ys these findings, showing that subjects in both groups stayed close to the speed limit of 55 mph, with a very minor increase in average speed from before to after the 34

- hour restart. Average simulator driving speed was not significantly predicted by pre

- driving PVT lapses ( F 1, 968

55.0 55.5 56.0 56.5 57.0 57.5

1 2 SessionAverage Driving Speed (mph)

Worst Case Best Case = 0.07, p = 0.80).

Figure 24 . Graph. Average simulator driving speed in the pre - restart 5 - day work period (session 1) as compared to the post - restart 5 - day work period (session 2) for the “worst - case” a nd “ best - case” conditions. Investigation of average driving speed across days in the study revealed a significant two

- way interaction of session by day ( F 4, 954 = 2.65, p = 0.032) and a main effect of day ( F 4, 954 = 8.90 , p < 0.001), but there was no statist ical significance for the three

- way interaction of group by session by day ( F 4, 954 = 0.77, p = 0.54) and the two

- way interaction of group by day ( F 4, 954 = 0.66, p = 0.62). Figure 25 displays the data by day, showing that in both conditions, average speed increased marginally across the pre

- restart session and leveled off in the post

restart session.34 55.0 55.5 56.0 56.5 57.0 57.5

1 2 3 4 5 1 2 3 4 5 Days on ShiftAverage Driving Speed (mph)

Worst Case Best Case34-hour restart

Figure 25 . Graph. Average simulator driving speed as a function of days in the 5 - day work periods before and after the 34 - hour resta rt period, for the “ worst - case” and “best - case” conditions. Analysis of the interaction of group by work period by time of day, collapsed over days within each 5

- day work period, revealed that there were no statistically significant effects of time of day for average driving speed. There was no significant three

- way interaction ( F 3 , 958 = 0.51 , p = 0.68), no significant two

- way interaction of group by time of day ( F 3 , 958 = 0.18, p = 0.91), no two

- way interaction of work period by time of day ( F 3 , 958 = 1.53, p = 0.21), and no significant main effect of time of day ( F 3 , 958 For variability (standard deviation) of driving speed across the straightaways, the primary analysis of the interaction of group by session (collapsed over days and over ti mes of day within sessions) yielded a significant effect of session ( F = 0.29, p = 0.83).

1, 970 = 43.84, p < 0.001), but no significant interaction effect ( F 1, 970 = 0.16, p = 0.69) and no significant effect of group ( F 1, 970 = 0.29, p = 0.59). Variability of simulator driving speed was significantly predicted by pre

- driving PVT lapses ( F 1, 968 Investigati on of variability in driving speed across days in the study revealed a significant main effect of day ( F = 19.85, p < 0.001)

— on average, an increase of 1 in PVT lapses corresponded to an increase of 0.022 mph (approximately 3.5 percent ) in the standard deviation of driving speed.

4, 954 = 4.05, p = 0.003). There was no significant three

- way interaction of group by session by day ( F 4, 954 = 0.80, p = 0.52), no significant two

- way in teraction of session by day ( F 4, 954 = 1.42, p = 0.22), and no significant two

- way interaction of group by day ( F 4, 954 = 1.55, p = 0.19). Figure 26 shows that in both conditions, speed variability decreases marginally but steadily across the days of the st udy, suggesting a minor practice effect in driving the simulator.35 0.0 0.2 0.4 0.6 0.8 1.0

1 2 3 4 5 1 2 3 4 5 Days on ShiftDriving Speed Variability (S.D.; mph)

Worst Case Best Case34-hour restart

Figure 26 . Graph. Variability (standard deviation) of simulator driving speed as a function of days in the 5 - day work periods before and after the 34 - hour restart period, for the “ worst - case” and “ best - case” conditions (u pward direction corresponds to driving less steadily )
. Analysis of the interaction of group by work period by time of day, collapsed over days within each 5

- day work period, demonstrated a significa nt two

- way interaction of group by time of day ( F 3 , 958 = 4.95, p = 0.002). There were no other significant effects related to time of day: no significant three

- way interaction ( F 3 , 958 = 1.21, p = 0.31), no significant two

- way interaction of work period by time of day ( F 3 , 958 = 0.15, p = 0.93), and no significant main effect of time of day ( F 3 , 958 = 1.03, p = 0.38). Figure 27 shows that variability in driving speed decreased over the hours of the day in the “ best

- case” condition, whereas it increased over th e hours of the night in the “ worst

- case” condition.36 0.0 0.2 0.4 0.6 0.8 1.009:30 pm/am12:30 am/pm03:30 am/pm06:30 am/pm09:30 pm/am12:30 am/pm03:30 am/pm06:30 am/pm

Time Point within DaysDriving Speed Variability (S.D.; mph)

Worst Case Best Case First 5-day work period Second 5-day work period

Figure 27 . Graph. Variability (standard deviation) of simulator driving speed as a function of time of day, collapsed over the 5 - day work periods before and after the 34 - hour re start period (u pwards corre sponds to driving less steadily) . Times of day are through the night (09:30 pm – 06:30 am) for the “ worst - case” condition and through the day (09:30 am – 06:30 pm) for the “ best - case” condition; 1 hour should be added to each time po int for subjects who were assigned to performance testing first and driving second. For lane deviation (standard deviation of lane position) in the straightaways, the primary analysis of the interaction of group by session (collapsed over days and times of day within sessions) yielded a significant interaction effect ( F 1, 970 = 9.15, p = 0.003) and a significant main effect of session ( F 1, 970 = 75.23, p < 0.001), but no significant main effect of group ( F 1, 970 = 0.88, p = 0.35). Figure 28 displays these find ings; subjects in the “ best

- case” condition showed improvement in holding a steady lane position from the pre

- restart session to the post

- restart session (from a standard deviation of 13.6 cm to a standard deviation of 12.0 cm), and subjects in the “ worst

- case” condition showed only half as much improvement (from a standard deviation of 14.5 cm to a standard deviation of 13.8 cm). Simulator lane deviation was significantly predicted by pre

- driving PVT lapses ( F 1, 968 = 69.19, p < 0.001)

— on average, an increa se of 1 in PVT lapses corresponded to an increase of 0.003 m (approximately 2.0 percent ) in the standard deviation of lane position.37 0.10 0.12 0.14 0.16 0.18 0.20

1 2 SessionLane Deviation (S.D.; m)

Worst Case Best Case

Figure 28 . Graph. Lane deviation (standard deviation of lane position) on the driving simulator in the pre - restart 5 - day work period (session 1) as compared to the post restart 5 - day work period (session 2) for the “ worst - case” and “ best - case” conditions. Investigation of lane deviation across days in the study yielded a significant two

- way interacti on of session by day ( F 4, 954 = 2.67, p = 0.031) and a significant main effect of day ( F 4, 954 = 10.47 , p < 0.001), but no significant three

- way interaction of group by session by day ( F 4, 954 = 0.34 , p = 0.85) and no significant two

- way interaction of group by day ( F 4, 954 = 0.40, p = 0.81). Examination of lane deviation by time of day yielded a significant two

- way interaction of group by time of day ( F 3 , 958 = 10.25, p < 0.001) and a significant main effect of time of day ( F 3 , 958 = 5.98, p < 0.001), although t here was no significant three

- way interaction ( F 3 , 958 = 0.63, p = 0.59) and no significant two

- way interaction of session by time of day ( F 3 , 958 = 1.20, p = 0.31). Figure 29 shows that lane deviation gradually increased across time of night in the “ worst

- c ase” condition.38 0.10 0.12 0.14 0.16 0.18 0.2009:30 pm/am12:30 am/pm03:30 am/pm06:30 am/pm09:30 pm/am12:30 am/pm03:30 am/pm06:30 am/pm

Time Point within DaysLane Deviation (S.D.; m)

Worst Case Best Case First 5-day work period Second 5-day work period

Figure 29 . Graph. Lane deviation (standard deviation of lane position) on the driving simulator as a function of time of day, collapsed over the 5 - day work periods before and after the 34 - hour restart period. Time s of day are through the night (09:30 pm – 06:30 am) for the “worst - case” condition and through the day (09:30 am – 06:30 pm) for the
“ best - case” condition; 1 hour should be added to each time point for subjects who were assigned to performance testing first a nd driving second. For the reaction time of emergency braking for the pedestrian/dog crossing events during simulator driving, the primary analysis of the interaction of group by session (collapsed over days and times of day within sessions) yielded no sig nificant effects: no interaction ( F 1, 970 < 0.01, p = 0.98), no effect of session ( F 1, 970 = 1.43, p = 0.23), and no effect of group ( F 1, 970 = 1.06, p = 0.30). Secondary analyses investigating the interactions with day and with time of day did not yield any statistically significant effects , either. However, simulator braking reaction time was significantly predicted by pre

- driving PVT lapses ( F 1, 968

Results were not much different for braking errors (i.e., braking unnecessarily in the straightaways, or failing to brake or braking more than once around the pedestrian/dog crossing events). The primar y analysis of the interaction of group by session yielded a significant (albeit negligible) main effect of session ( F = 15.24, p < 0.001)

— on average, an increase of 1 in PVT lapses corresponded to an increase of 0.011 s (approxi mately 0.9 percent ) in the reaction time for braking.

1, 970 = 6.01, p = 0.014), but no significant interaction of group by session ( F 1, 970 = 0.56, p = 0.45) and no significant main effect of gr oup ( F 1, 970 = 2.43 , p = 0.12). There was a trend for simulator braking errors to be predicted by pre

- driving PVT39 lapses ( F 1, 968 = 2.75, p = 0.098 ). Examination of braking errors across days in the study revealed no further significant effects: no significa nt interaction of group by session by day ( F 4, 954 = 1.19, p = 0.32), no interaction of session by day ( F 4, 954 = 1.36, p = 0.025), no significant interaction of group by day ( F 4, 954 = 0.84, p = 0.50), and no significant main effect of day ( F 4, 954 = 0.57 , p = 0.69). Investigation of braking errors by time of day yielded a trend for a three

- way interaction of group by session by time of day ( F 3 , 958 = 2.42, p = 0.065). There was no significant interaction of group by time of day ( F 3 , 958 = 0.13, p = 0.94), no si gnificant interaction of session by time of day ( F 3 , 958 = 0.97, p = 0.41), and no main effect of time of day ( F 3 , 958 For computed (simulated) fuel use, the primary analysis of the interaction of group by session (collapsed over days and times of day within sessions) yielded a significant main effect of session ( F = 0.11 , p = 0.95).

1, 970 = 35.97, p < 0.001), but no significant interaction effect ( F 1, 970 = 0.09, p = 0.76) and no significant main effect of group ( F 1, 970 = 0.76, p = 0.38). On average, subjects in the “best

- case” condition used 0.248 gallons of fuel to traverse the 10 straightaways before the 34

- hour restart, and 0.247 gallons after the 34

- hour restart. Similarly, subjects in the “ worst

- case” condition used 0.249 gallons of fuel before the 34

- hou r restart, and 0.248 gallons after the 34

- hour restart. Simulator fuel use was significantly predicted by pre

- driving PVT lapses ( F 1, 968 Investigation of fuel use across days in the study revealed a significant interaction of session by day ( F = 7.16, p = 0.008), but on average, an increase of 1 in PVT lapses corresponded to an increase of only 0.0001 gallon o f fuel use (approximately 0.04 percent ).

4, 954 = 4.51, p = 0.001) and a significant main effect of day ( F 4, 954 = 9.94, p < 0.001), but no significant interaction of group by session by day ( F 4, 954 = 0.49, p = 0.75) and no significant interaction of group by day ( F 4, 954 = 0.65, p = 0.63). Investigation of fuel use by time of day, on the other hand, yielded a significant interaction of group by time of da y ( F 3 , 958 = 3.89, p = 0.009). There was no significant three

- way interaction ( F 3 , 958 = 1.05, p = 0.37), no significant interaction of session by time of day ( F 3 , 958 = 0.99, p = 0.39), and no main effect of time of day ( F 3 , 958 = 0.73, p = 0.54). Figure 30 shows that fuel use increased slightly over time of night in the “worst

- case condition.40 0.242 0.246 0.250 0.254 0.25809:30 pm/am12:30 am/pm03:30 am/pm06:30 am/pm09:30 pm/am12:30 am/pm03:30 am/pm06:30 am/pm

Time Point within DaysFuel Use (gallons)

Worst Case Best Case First 5-day work period Second 5-day work period

Figure 30 . Graph. Computed fuel use on the driving simulator as a function of time of day, collapsed over the 5 - day work periods before and a fter the 34 - hour restart period. Times of day are through the night (09:30 pm – 06:30 am) for the “ worst - case” condition and through the day (09:30 am – 06:30 pm) for the “ best - case” condition; 1 hour should be added to each time point for subjects who were as signed to performance testing first and driving second.
3.3 SLEEP AND POLYSOMNOG RAPHY Figure 31 illustrates the primary comparisons being made between the “ best

- case” and “worst

case” conditions for polysomnographic measures of sleep (cf. Figure 4) . Besides co mparison of the first 10

- hour night that the two conditions had in common (performed solely for control purposes), these focused on comparison of the combined first and second 10

- hour nights in the
“ best

- case” condition with the combined first 10

- hour nigh t and first 5

- hour transition nap in the
“ worst

- case” condition (“Baseline”); comparison of the combined two polysomnographically recorded 10

- hour sleep periods in the first 5

- day work period between the two conditions (“Session 1”); comparison of the comb ined two 10

- hour nights in the 34

- hour restart period of the “ best

- case” condition with the combined two 5

- hour transition naps and intervening 10

- hour nocturnal sleep period in the 34

- hour restart period of the “ worst

- case” condition (“Restart”); comparis on of the combined two polysomnographically recorded 10

- hour sleep periods in the second 5

- day work period between the two conditions (“Session 2”); and comparison of the 10

-41 hour recovery night at the end of the “ best

- case” condition with the combined 5

- ho ur transition nap and 10

- hour recovery night at the end of the “ worst

- case” condition (“Recovery”). Total TIB was 20 hours for each comparison set except during the Baseline period, where the
“ worst

- case” condition was shorted by 5 hours as necessary for t he transition to a nocturnal work schedule

— and during the Recovery period, which was only one night of 10 hours of TIB in the
“ best

- case” condition, but involved a total of 15 hours of TIB in the “ worst

- case” condition to make up for the difference during the Baseline period (s ee Figure 32 )

.

**Best Case** 0 1 2 3 4 5 6 7 8 15 16 9 10 11 12 21 22 23 17 18 19 20 13 14

**Day 4**

**Day 7 Day 6** 

**Day 8**

**Day 10 Day 9** 

**Day 12 Day 11**  **Day 5 Day 3**  **Day 1 Day 2** 

**Day 14 Day 13**  Baseline

Restart

××× Session 1 Session 1
×××

Recovery ××× Session 2 Session 2
××× Restart Baseline **Day 14 Day 10**  **Day 11 Day 12**  **Day 2**

**Day 13 Day 9**  **Day 8 Day 7**  **Day 3 Day 4**  **Day 5 Day 6**  **Day 1 Worst Case**  5 6 7 0 1 2 3 21 22 23 16 17 18 19 12 13 14 20 15 8 9 10 11 4

Session 1 Session 1

××× Restart Restart
××× Session 2 Session 2
Recovery ××× ××× Baseline Baseline

Restart

Recovery

Figure 31 . Chart. Comparison scheme for the polysomnographically recorded sleep periods in the “ worst - case” (top schematic) and “ best - case” (bottom schematic) conditions of the study , with h ou rs of the day progress ing from left to right (see top numbers) , days of the study progress ing from top to bottom (see right side) , gray indicating scheduled wakefulness, black indicating scheduled sleep periods, and ××× marking sleep periods that were not recorded polysomnographically. D ays are shown from 22:00 (10:00 pm) to 22:00 ( rather than from midnight to midnight) for easier visual comparison of the scheduled sleep periods between the two conditions . Text labels indicate which sleep periods were combi ned to form the Baseline, Session 1, Restart, Session 2, and Recovery periods that were being compared between the two conditions.42 0.0 5.0 10.0 15.0 20.0

Baseline Session 1 Restart Session 2 RecoveryCombined TIB (h)
Worst Case Best Case

Figure 32 . Graph. Combined TIB during the Baseline, Session 1, Restart, Session 2, and Recovery se ts of sleep periods, comparing the m for “ worst - case” and “best - case” conditions. During the first (10

- hour) baseline night, which the study conditions had in common (see Figure 31), there were no statistically significant differences between the two condit ions in total sleep time ( TST ; F 1 , 25 = 2.72, p = 0.11), non

- rapid eye movement ( non - REM ) stage 1 (N1; F 1 , 25 = 1.55, p = 0.22), non

- REM stage 2 (N2; F 1 , 25 = 1.72, p = 0.20), slow

- wave sleep (SWS or N3; F 1 , 25 = 0.12, p = 0.73), REM ( F 1 , 25 = 0.13, p = 0.72), sleep latency (SL; F 1 , 25 = 0.06, p = 0.81), slow

- wave sleep latency (SWSL; F 1 , 25 = 0.33, p = 0.57), and REM latency (REML; F 1 , 25 Figure 33 shows the comparisons between the “ best

- case” and “ worst

- case” conditions across the study for TST. As expected, the “ worst

- case” condition had significantly less combined TST than the “ best

- case” condition during the Baseline period ( F = 1.33, p = 0.26). Thus, the two subject groups were not significantly different in these indices of baseline sleep architect ure.

1 , 25 = 119.57, p < 0.001). The “worst

- case” condition also had significantly less TST during the Session 1 period ( F 1 , 24 = 11.99 , p = 0.002). There was a trend for less combined TST in the “ worst

- case” condition as compared to the “ best

- case” condition during t he Restart period ( F 1 , 24 = 3.61, p = 0.070). The “ worst

- case” condition again had significantly less TST during the Session 2 period ( F 1 , 24 = 8.76, p = 0.007). However, the “ worst

- case” condition had significantly more combined TST than the “ best

- case” con dition during the Recovery period ( F 1 , 24 = 20.50, p < 0.001), as was also expected.43 0.0 4.0 8.0 12.0 16.0 20.0

Baseline Session 1 Restart Session 2 RecoveryCombined TST (h)
Worst Case Best Case

Figure 33 . Graph. Combined TST during the Baseline, Session 1, Restart, Session 2, and Recovery sets of sleep periods, comparing the m for “ worst - case” and “ best - case” conditions. In a two

- way ANOVA of combined TST focusing on the interaction of condition (“ best

- case” compared to “ worst

- case”) by session (Session 1 compared to Session 2), there was a significant main effect of condition ( F 1 , 48 = 20. 52, p < 0.001), but no significant interaction effect ( F 1 , 48 = 0.05, p = 0.82) and no significant main effect of session ( F 1 , 48 = 0.78, p = 0.38). Thus, the difference in TST between the two conditions (with the “ best

- case” condition exhibiting more TST th an the “ worst

- case” condition) appeared to be stable from Session 1 to Session 2. In the aggregate over all the polysomnographically recorded sleep periods (90 h TIB in total), the
“ worst

- case” condition obtained 66.2 h TST, and the “ best

- case” condition o btained 74.3 h TST (a statistically significant difference of 8.1 h: F 1,21 For slow

- wave sleep (non

- REM sleep stage N3), t he comparisons between the “ best

- case” and
“ worst

- case” conditions across the study are shown in Figure 34

. The “ worst

- case” condition had significantly less combined N3 than did the “ best

- case” condition during the Baseline period ( F = 16.47, p < 0.001).

1 , 25 = 6.75, p = 0.016). There was no statistically significant difference between the conditions during the Session 1 period ( F 1 , 24 = 0.03, p = 0.86), Restart period ( F 1 , 24 = 0.49, p = 0.49), or Session 2 period ( F 1 , 24 = 0.01, p = 0.91). However, there was a trend for the “ worst

- case” condition to have more combined N3 than the “ best

- case” condition during the Recovery period ( F 1 , 24 = 2. 93, p = 0.10). In two

- way ANOVA of combined N3 focusing on the interaction of condition by session, there was no significant interaction effect ( F 1 , 48 = 0.05, p = 0.83), no significant main effect of condition ( F 1 , 48 < 0.01, p = 0.96), and no significant m ain effect of session ( F 1 , 48 = 0.05, p = 0.83). In the aggregate over all the polysomnographically recorded sleep periods, the “ worst

- case” condition accumulated 10.1 h ours of stage N3 sleep, and the44 “ best

- case” condition obtained 10.5 h ours of stage N3 sl eep (a non

- significant difference: F 1,21

0.0 1.0 2.0 3.0 4.0 5.0

Baseline Session 1 Restart Session 2 RecoveryCombined Stage N3 (h)

Worst Case Best Case = 0.03, p = 0.86).

Figure 34 . Graph. Combined slow - wave sleep (non - REM sleep stage N3) during the Baseline, Session 1, Restart, Session 2, and Recovery sets of sleep periods, comparing the m for “ worst - case” and “ best - case” conditions. For REM sleep, t he comparisons between the “best

- case” and “worst

- case” conditions across the study are shown in Figure 35

. The “worst

- case” condition had significantly less combined REM than the “best

- case” c ondition during the Baseline period ( F 1 , 25 = 60.03, p < 0.001) and during the Session 1 period ( F 1 , 24 = 5.69, p = 0.025). There was no statistically significant difference between the conditions during the Restart period ( F 1 , 24 = 2.48, p = 0.13) or during the Session 2 period ( F 1 , 24 = 0.80, p = 0.38). However, the “ worst

- case” condition had significantly more combined REM than the “ best

- case” condition during the Recovery period ( F 1 , 24 = 7.19 , p = 0.013). In two

- way ANOVA of combined REM focusing on the int eraction of condition by session, there was no significant interaction effect ( F 1 , 48 = 1.04, p = 0.31), but there were significant main effects of condition ( F 1 , 48 = 5.31, p = 0.026) and session ( F 1 , 48 = 8.92 , p = 0.004). In the aggregate over all the poly somnographically recorded sleep periods, the “worst

- case” condition accumulated 17.1 h of REM sleep, and the “ best

- case” condition obtained 19.7 h of REM sleep (a significant difference of 2.6 h: F 1,21 = 8.31, p = 0.009).45 0.0 1.0 2.0 3.0 4.0 5.0

Baseline Session 1 Restart Session 2 RecoveryCombined REM (h)

Worst Case Best Case

Figure 35 . Graph. Combined REM sleep during the Baseline, Session 1, Restart, Session 2, and Recovery sets of sleep periods, comparing the m for “worst - case” and “best - case” conditions. For non

- REM sleep stage N2, t he comparisons between the “ best

- case” and “ worst

- case” conditions across the study are shown in Figure 36

. The “ worst

- case” condition had significantly less combined N2 than the “best

- case” condition during the Baseline period ( F 1 , 25 = 46.57 , p < 0.001), during the Session 1 period ( F 1 , 24 = 5.64, p = 0. 026), and during the Session 2 period ( F 1 , 24 = 7.39, p = 0.012). There was no statistically significant difference between the conditions during the Restart period ( F 1 , 24 = 1.23, p = 0.28). However, the “ worst

- case” condition had significantly more combine d N2 sleep than the “ best

- case” condition during the Recovery period ( F 1 , 24 = 5.34, p = 0.030). In two

- way ANOVA of combined N2 focusing on the interaction of condition by session, there was no significant interaction effect ( F 1 , 48 < 0.01, p > 0.99) and no significant main effect of session ( F 1 , 48 < 0.01, p = 0.95), but there was a significant main effect of condition ( F 1 , 48 = 12.80, p < 0.001). In the aggregate over all the polysomnographically recorded sleep periods, the “ worst

- case” condition accumulated 35.3 h of stage N2 sleep, and the
“ best

- case” condition obtained 40.8 h of stage N2 sleep (a significant difference of 5.5 h: F 1,21 = 5.29, p = 0.032).46 0.0 2.0 4.0 6.0 8.0 10.0

Baseline Session 1 Restart Session 2 RecoveryCombined Stage N2 (h)

Worst Case Best Case

Figure 36 . Graph. Combined non - REM sleep stage N2 during the Baseline, Sessi on 1, Restart, Session 2, and Recovery sets of sleep periods, comparing the m for “ worst case” and “ best - case” conditions. For non

- REM sleep stage N1, t he comparisons between the “ best

- case” and “ worst

- case” conditions across the study are shown in Figure 3 7

. There were no statistically significant differences between the conditions during the Baseline period ( F 1 , 25 = 0.06, p = 0.80), Session 1 period ( F 1 , 24 = 0.73, p = 0.40), Restart period ( F 1 , 24 = 0.13, p = 0.72), and Session 2 period ( F 1 , 24 = 0.35, p = 0 .56). However, the “ worst

- case” condition had significantly more combined N1 sleep than the “ best

- case” condition during the Recovery period ( F 1 , 24 = 4.79, p = 0.039). In two

way ANOVA of combined N1 focusing on the interaction of condition by session, the re were no significant effects: no interaction ( F 1 , 48 = 0.07, p = 0.79), no main effect of session ( F 1 , 48 = 0.11, p = 0.74), and no main effect of condition ( F 1 , 48 = 1.07, p = 0.31). In the aggregate over all the polysomnographically recorded sleep periods , the “ worst

- case” condition accumulated 3.6 h of stage N1 sleep, and the “ best

- case” condition obtained 3.3 h of stage N1 sleep (a non

- significant difference: F 1,21 = 0.60, p = 0.45 ).47 0.0 0.4 0.8 1.2 1.6

Baseline Session 1 Restart Session 2 RecoveryCombined Stage N1 (h)

Worst Case Best Case

Figure 37 . Graph. Combined non - REM sleep sta ge N1 during the Baseline, Session 1, Restart, Session 2, and Recovery sets of sleep periods, comparing them for “worst case” and “ best - case” conditions. For sleep latency (SL), slow

- wave sleep latency (SWSL) , and REM latency ( REML ) , analyses focused on av erages within the Baseline, Session 1, Restart, Session 2, and Recovery sets of sleep periods . Averages are more relevant for latency variables than combined totals as used above for the sleep stages. For average SL, t he comparisons between the “ best

- case” and “worst

case” conditions are shown in Figure 38

. There was no statistically significant difference between the conditions during the Baseline period ( F 1 , 25 = 0.04, p = 0.85). However, there were significant differences during the Session 1 period ( F 1 , 2 4 = 23.61, p < 0.001), Session 2 period ( F 1 , 24 = 42.74, p < 0.001) and Recovery period ( F 1 , 24 = 4.60, p = 0.042), where average SL was consistently shorter in the “ worst

- case” condition than in the “ best

- case” condition. However, there was no significant d ifference between conditions in the Restart period ( F 1 , 24 = 0.20, p = 0.66). In two

- way ANOVA of average SL, there was no significant interaction effect of condition by session ( F 1 , 48 = 0.39, p = 0.53) and no main effect of session ( F 1 , 48 = 0.13, p = 0.72) , but there was a significant main effect of condition ( F 1 , 48 = 57.77, p < 0.001).48 0.0 0.4 0.8 1.2 1.6 2.0

Baseline Session 1 Restart Session 2 RecoveryAverage SL (h) Worst Case Best Case

Figure 38 . Graph. Average SL during the Baseline, Session 1, Restart, Session 2, and Recovery sets of sleep periods, comparing them for “ worst - cas e” and “ best - case” conditions. For average SWSL, t he comparisons between the “ best

- case” and “ worst

- case” conditions are shown in Figure 39

. There were no statistically significant differences between the conditions during the Baseline period ( F 1 , 25 = 2.07 , p = 0.16), Session 1 period ( F 1 , 24 = 1.20, p = 0.28), Session 2 period ( F 1 , 24 = 1.60, p = 0.22), and Recovery period ( F 1 , 24 = 0.05, p = 0.82). However, average SWSL was significantly longer in the “ worst

- case” condition than in the “ best

- case” condition during the Restart period ( F 1 , 23 = 10.06, p = 0.004). In two

- way ANOVA of average SWSL, there was no significant interaction effect ( F 1 , 48 = 0.50, p = 0.48), no main effect of session ( F 1 , 48 = 0.50, p = 0.48), and no main effect of condition ( F 1 , 48 = 2.56, p = 0.12).49 0.0 0.4 0.8 1.2 1.6 2.0

Baseline Session 1 Restart Session 2 RecoveryAverage SWSL (h)
Worst Case Best Case

Figure 39 . Graph. Average SWSL during the Baseline, Session 1, Restart, Session 2, and Recovery sets of sleep periods, comparing them for “worst - case” and “best - case” conditions. For average REML, t he comparisons betw een the “best

- case” and “worst

- case” conditions are shown in Figure 40

. There was a trend for a difference between the conditions during the Baseline period ( F 1 , 25 = 3.58, p = 0.070), a significant difference during the Session 1 period ( F 1 , 24 = 9.64, p = 0.005) and during the Session 2 period ( F 1 , 24 = 10.52, p = 0.004), and a significant difference during the Recovery period ( F 1 , 24 = 4.46, p = 0.045), with REML being shorter in the “worst

- case” condition tha n in the “ best

- case” condition during these perio ds. There was no statistically significant difference between the two conditions during the Restart period ( F 1,2 3 = 1.99, p = 0.17). In two

- way ANOVA of average REML, there was no significant interaction of condition by session ( F 1 , 48 = 0.29, p = 0.59) and no main effect of session ( F 1 , 48 = 0.02, p = 0.89), but there was a significant main effect of condition ( F 1 , 48 = 19.64, p < 0.001).50 0.0 0.4 0.8 1.2 1.6 2.0

Baseline Session 1 Restart Session 2 RecoveryAverage REML (h)
Worst Case Best Case

Figure 40 . Graph. REML during the Baseli ne, Session 1, Restart, Session 2, and Recovery sets of sleep periods, comparing them for “ worst - case” and “ best - case” conditions.51 

### 4. CONCLUSIONS
4.1 KEY FINDINGS The objective of the project was to determine the effectiveness of the current 34

- hour restart provision in the HOS regulations governing freight

- carryin g c ommercial m otor v ehicle (CMV) drivers with regard to restoring performance

. An in

- residence laboratory research study was conducted to address this issue. Running the study in the laboratory (as opposed to in the field) helped to eliminate environmental confounds, allowed for the use of sensitive laboratory performance measures, simplif ied the logistics , and moderated the sample size requirement as corroborated by a power calculation performance in advance of the study. A sample of N = 27 healthy subject s was studied in a “ worst

- case ”

– ” best

- case ” between

- groups comparison of two 5

- day (14

- hour/day) work periods separated by a 34

- hour restart period. Half the sample ( n = 14) was randomized to the “ best

- case” condition, which entailed daytime wakefulness an d work (and nighttime sleep) throughout the study. The other half ( n = 13) was randomized to the “ worst

- case” condition, which entailed nighttime wakefulness and work (and daytime sleep) during the two 5

- day work periods, while transitioning back to a dayt ime schedule during the 34

- hour restart period (see Figure 3). Performance on a variety of neurobehavioral tasks and on a high

- fidelity driving simulator was measured throughout the study (see Figure 4). The main goal was to evaluate whether the 34

- hour re start period was effective at maintaining performance in both conditions. The primary performance measure for the study was the number of lapses (reaction times greater than 500 ms) on a 10

- minute PVT, which was administered eight ( 8 ) times per day in the working periods. In the “ best

- case” condition, average PVT performance in the 5

- day work period after the 34

- hour restart was the same as that in the 5

- day work period before the 34

- hour restart, indicating that the restart period was effective at maintain ing performance in this condition. In the “ worst

- case” condition, however, average PVT performance in the 5

- day work period after the 34

- hour restart was impaired relative to average PVT performance in the 5

- day work period before the 34

- hour restart, indi cating that the restart period was not effective at maintaining performance in the “ worst

- case” condition (see Figure 5). That being the case , the null hypothesis that the 34

- hour restart period would be effective at maintaining performance in both conditi ons was rejected. PVT performance deficits in the “ worst

- case” condition were most prominent in the later hours of the night, as is consistent with principles of sleep/wake physiology

. ( 13 ) Two major underlying processes are involved in sleep/wake p hysiolo g y : ( 5 ) a homeostatic process that builds up a pressure for sleep during wakefulness and dissipates that pressure during sleep, and a circadian process that exerts a pressure for wakefulness during the daytime hours and withdraws that pressure during the nig ht. During normal daytime wakefulness and nighttime sleep, the homeostatic pressure for sleep and the circadian pressure for wakefulness act in opposition, which results in stable wakefulness during the day and consolidated sleep during the night

. ( 21 ) Howe ver, when wakefulness is scheduled during the night, as was the case in the “worst

- case” condition, then the homeostatic and circadian processes become desynchronized. This results in a52 progressive deterioration of performance during the nighttime waking p eriod , ( 22 )
In addition, when wakefulness is scheduled during the night, sleep must be obtained during the day, when the circadian pressure for wakefulness is high. This results in sleep curtailment, as observed in the present study (see Figure 33). Indeed, TST was consistently reduced in the
“ worst

- case” condition relative to the “ best

- case” condition during the two 5

- day work periods. Moreover, during the 34

- hour restart period, when subjects in the “ worst

- case” condition transitioned back to a normal nighttime sleep schedule and had transition nap opportunities, they still accumulated less TST than the subjects in the “ best

- case” condition (who maintained a normal nighttime sleep routine throughout the study). T hus, despite equal amounts of sleep opportunity ( TIB ), subjects in the “ worst

- case” condition did not manage to get enough sleep , due to the inopportune circadian placement of the sleep periods, and did not manage to maintain optimal performance during the nighttime waking period , due to the temporal desynchronization of the homeostatic pressure for sleep and the circadian pressure for wakefulness. Perhaps due to the temporary transitioning back to a normal nighttime sleep schedule during the 34

- hour restar t period, this intervention was not effective at mitigating the sleep loss and consequent performance impairment

— and , in fact , might even have contributed to the increased impairment in the post

- restart 5

- day work period. If a nighttime wake schedule were to be maintained during the 34

- hour restart period, it is possible that gradual circadian adjustment would have occurred, potentially making the 34

- hour restart period more effective. The real

- world utility of this possibility is questionable, though, as i t is improbable that many individuals would elect to maintain a permanent night

- shift schedule if given the choice. as was seen in the present study (Figure 7).

The level of PVT performance impairment reached in the “ worst

- case” condition (with group

average lapse counts not exceeding 4 ) was modest w hen compared to the documented effects (1,2) of one night of acute total sleep deprivation or a week of sustained sleep restriction to 6 hours per day ( which are in the range of six to eight lapses ). One reason this may be the case was the restriction of du ty time to 14 hours per day (per the current HOS regulations ), leaving up to 10 hours per day for recuperation. Another reason may be the presence of nap opportunities preceding the transitions to nighttime wakefulness, which effectively served as prophyla ctic naps known to be useful as countermeasures for neurobehavioral performance impairment

. Without strategic napping, performance in the “ worst

- case” condition would probably have shown more considerable deficits. Furthermore, the effects of chroni c sleep restriction are to some extent cumulative , ( 23,24 )

( 1,2 ) with stabilization taking weeks to months , (25) which implies that additional cycles of a 34

- hour restart period followed by a nocturnal 5

- day work period could result in further increases in neurobeha vioral impairment. It should also be noted that our research subjects were healthy young adults with no sleep disorders. Had we studied a sample of patients with sleep apnea

— widely reported to be a common sleep disorder among CMV drivers ( 26)

— or other medic al conditions, the expected performance deficits in the “ worst

- case” condition would have been greater still , strengthening the conclusion that the 34

- hour restart period was not effective at maintaining performance across multiple 5

- day nighttime work per iods.53 4.2 ANCILLARY FINDINGS Subjects in the “ worst

- case” condition were overall subjectively sleepier than subjects in the “best

- case” condition, but did not show an increase in sleepiness after the 34

- hour restart period that would correspond with the PVT fi ndings. In fact, subjects in the “worst

- case” condition showed reduction of sleepiness across days in each of the two 5

- day work periods, suggesting that they subjectively adapted to the nighttime wakefulness schedule but that the 34

- hour restart period un did some of this subjective adaptation (see Figure 9). A dissociation between subjective sleepiness and objective performance impairment under conditions of sustained sleep curtailment has been reported previously , (2)

On the DSST and the CDDT , subjects in both the “best

- case” and the “worst

- case” conditions showed expected practice effects (Figures 16 and 2 3). For the DSST, the practice effect was somewhat counteracted by increasing impairment across time of night in the “ worst

- case” condition (see Figure 17), but otherwise there were no substantive differences between the two conditions for either the DSST or the CDDT. This suggest s that cognitive through p ut (measured by the DSST) and spatial orientation (measured by the CDDT) were not adversely affected much by subjects being on a night shift schedule, nor did the 34

- hour restart period seem to have much of a n impact, positive or negative , on these cognitive functions . There is increasing evidence that the effects of sleep loss and circadian misalignment vary as a function of the components of cognition (e.g., attention, working memory) involved in the neuro behavioral performance task at hand

. although to our knowledge this is the first time it is shown for a night

- shift schedule. Across the hours of each night, however, subjective sleepiness increased in a way similar to the within

- day profile seen for PVT lapses (cf. Figures 7 and 10), suggesting that subjective adaptation took pl ace gradually over days but not acutely within days. Self

- report measures of affect, mood , and effort did not systematically vary much between the two conditions or across the study days, with the exception of subjective mood, which showed the same profile of degradation over time of night in the “worst

- case” condition as seen for subjective sleepiness and PVT lapses.

(12,27

– 30)

There was a modest practice effect for driving performance on the high

- fidelity driving simulator i n terms of speed variability and lane deviation. However, subjects in the “worst

- case” condition displayed only half the reduction in lane deviation across the study that subjects in the “best

case” condition showed (see Figure 28). Furthermore, subjects i n the “ worst

- case” condition displayed an increase in lane deviation over the hours of the night (Figure 29) similar to the increase seen in PVT lapses (Figure 7). This was accompanied by an increase of up to 1 percent in fuel use across the night in the “ worst

- case” condition (see Figure 30). This suggests that the effects of sleep loss and circadian misalignment associated with a night work schedule may impair driving not only in terms of road safety, but also in terms of economic cost. It is therefore not particularly surprising that the results for the DSST and the CDDT did not line up with those for the PVT. However, the results for the PVT, which measures reaction time and sustained atten tion, may ultimately be more significant for CMV drivers. This possibility was in part corroborated by the simulator driving data, which are discussed next.

Indices of driving impairment

— speed variability, lane deviation, emergency braking reaction time, and fuel use

— were significantly predicted by lapses on the PVT administered just before the simulator driving bouts. Fuel use in the simulator was shown to be a function of dri ving impairment; the poorer one drives, the more one tends to use more fuel. Although the54 predictiveness of PVT performance for driving performance was not perfect, this finding suggests that the effects of sleep loss and circadian misalignment on driving performance reflect, at least in part , deficits in sustained attention (as captured by the PVT). This is consistent with an earlier finding that performance on a driving simulator was predicted by sleep latency on a maintenance of wakefulness test

. ( 31 ) T he present finding pertaining to the PVT is a potentially important new contribution, because it indicates that a large volume of literature u sing PVT performance as an index of fatigue

- related impairment (11, 13, 14,32 ) is relevant for driving performance

— incl uding a recently published biomathematical model for the prediction of fatigue and performance that was calibrated on PVT lapses

. Because of the between

- group s design of the research study, the driving simulator scenarios (roads, routes, events, condit ions, etc.) were standardized across the “worst

- case” and “best

case” conditions. The scenarios did not, therefore, control for the increased traffic density typically encountered during daytime driving (for the “best

- case” condition”) or the reduced visib ility associated with nighttime driving (for the “worst

- case” condition)

. That being the case , the driving simulator findings should be interpreted as indicating basal capability for driving rather than actual driving performance in the real world . F urther research is needed to compare the “ worst

- case” and “ best

- case” schedules in terms of real

- world driving performance, safety , and cost

. ( 25 )

Some observations on the structure of sleep are noteworthy. First, despite curtailment of TST in the “ worst

- case” condit ion, slow

- wave sleep (non

- REM sleep stage N3) was conserved relative to the “ best

- case” condition (Figure 34). This phenomenon has been reported in sustained sleep restriction experiments , ( 1,2,33 ) but to our knowledge this is the first time that it is bein g reported for inverted sleep/wake schedules. Second, both REM sleep (Figure 35) and non

- REM sleep stage N2 (Figure 36) varied hand in hand with TST , whereas non

- REM sleep stage N1 (Figure 37) did not show any substantive differences between conditions or across the days of the study. No single sleep stage could be identified as being responsible for the performance impairments in the “worst

- case” condition, making it more parsimonious to assume that overall curtailment of TST was the main effector of the o bserved performance deficits. This is consistent with conclusions drawn from other experimental findings

. ( 2,34 ) Third, the “ worst

- case” condition resulted in reduced REM latencies during the daytime sleep periods (see Figure 40), which is consistent with t he known circadian regulation of REM sleep expression

. ( 35 ) Finally, once sleep loss was incurred in the “ worst

- case” condition, sleep latency was consistently shorter in this condition than it was in the “ best

- case” condition (see Figure 38). Thus, sleep c urtailment in the
“ worst

- case” condition did not result from difficulty initiating sleep, but rather from difficulty maintaining sleep (i.e., increased intermittent wakefulness and early termination of sleep) due to the growing circadian pressure for wakef ulness across daytime sleep periods.55 

### 5. RECOMMENDATIONS On the basis of th e results of the study as des cribed in this report , a number of recommendations can be made.

**•** The key finding of this laboratory research study was that the 34

- hour restart provision i n the HOS regulations governing property

- carrying CMV drivers was sufficiently effective ( even, perhaps , unnecessary) for maintaining optimal performance in subjects randomized to a daytime wake/work schedule, but was insufficiently effective

— and perhaps e ven detrimental

— for maintaining performance in subjects randomized to a nighttime wake/work schedule. The main reason for this is that the 34

- hour restart provision does not take into account the circadian aspect of sleep/wake/work scheduling. HOS regulati ons could be improved by taking circadian timing into account.

**•** PVT performance was shown to be predictive of simulator driving performance, suggesting that mathematical models of fatigue and performance predictive of PVT performance may be predictive of dr iving performance as well. Validation of fatigue and performance models for accurately predicting real

- world driving performance would allow for the development of model

- based fatigue risk management approaches, which would help to make HOS regulations sen sitive to circadian timing, and could ultimately be mandated as a safe alternative for prescriptive HOS regulations.

**•** Objective performance deficits in subjects assigned to a nighttime wake/work schedule were not accurately reflected in s ubject ive sleepines s and mood states

. This discrepancy between actual impairment and introspective awareness is a common finding in sleep and performance research, and presents a safety problem in real

- world settings because individuals cannot be relied upon to accurately se lf

- identify fatigue

- induced impairment. Ongoing work on the development of individualized drowsy

- driver detection technologies may contribute to addressing this issue. The present findings provide support for using lane deviation as an objective performanc e measure in such technologies. CMV drivers should also be educated with regard to the dangers of drowsy driving and the difficulties of self

- evaluating the ability to continue driving safely.

**•** The research subjects in this study were healthy young adults w ith no sleep disorders. However, sleep apnea is widely reported to be a common sleep disorder among CMV drivers and other medical conditions are common among CMV drivers. Although it may be inferred that the results of the present study set a lower limit f or levels of impairment to be expected in the CMV driver population, validation of the study findings in a sample of drivers in a real

- world field study (such as that currently being sponsored by the FMCSA in conjunction with Transport Canada) is important

.
**•** Performance deficits in the nighttime work periods increased as a function of time of night, and in this study, the s leep curtailment underlying these deficits involved difficulty maintaining sleep rather than difficulty initiating sleep. Such findings c ould provide a basis for research on judicious application of fatigue countermeasures, whether pharmacological (including caffeine) or behavioral. Strategic napping is a particularly powerful fatigue countermeasure, and the authors recommend consideration of adapting HOS regulations to allow for greater flexibility in split sleep schedules.56 **•** The finding that the modest increase in indices of driving impairment on the high

- fidelity driving simulators across time of night was associated with an increase of up to 1 percent in computed fuel use suggests that drowsy driving may be associated with direct economic costs. Demonstrati ng the validity of fuel use on the driving simulators as an accurate indicator of fuel use in real trucks on real roads (which could be pursued in closed track settings ) would provide carriers with a bottom

- line incentive for actively manag ing fatigue in CMV drivers. Several of the se recommendations could be explored in follow

- up research in Phase II of this project. The peer review commi ttee for the project underwrote the following specific suggestions for Phase II:

**•** Document the effectiveness of the 34

- h ou r restart if optimal combinations of anchor sleep and naps ( i.e., split sleep) are placed at circadian

- appropriate times during both du ty days and during the 34

- h ou r restart period. There is considerable interest in CMV operations to explore the possibilities of strategic use of split sleep schedules. It would be a natural extension of Phase I to examine split sleep schedules in relation to the restart provision of the HOS regulations in Phase II

.
**•** Investigate whether caffeine would improve performance in the “ worse - case ” condition. There is an outstanding need to investigate the effectiveness of caffeine for counteracting fatigue in CMV op erations. A placebo

- controlled study of caffeine effects in the context of the restart provision would help to address this issue in Phase II

.57 

### ACKNOWLEDGEMENTS We thank the peer review committee of this project for their valuable comments and suggestions : Melissa M. Mallis , PhD ( Chair ) , Siobhan Banks , PhD, and Thomas J. Balkin , PhD

.59 

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