DOT/FAA/HFD-13/001

Federal Aviation Administration

Evaluating the Effectiveness of 

Schedule Changes for Air Traffic 

Service (ATS) Providers:

Controller Alertness and Fatigue 

Monitoring Study

Judith Orasanu, Ph.D., NASA Ames Research Center

Bonny Parke, Ph.D., San Jose State University

Norbert Kraft, M.D., San Jose State University

Yuri Tada, Ph.D., San Jose State University

Alan Hobbs, Ph.D., San Jose State University

Barrett Anderson, San Jose State University

Lori McDonnell, San Jose State University

Vicki Dulchinos, San Jose State University

December 2012

Technical Report

This document is available to the public through the National Technical 

Information Service (NTIS), Alexandria, VA 22312. A copy is retained for 

reference at the Federal Aviation Administration Human Factors Division’s

Library.

U.S. Department of Transportation

Federal Aviation Administration
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 the contents or use thereof. The United 

States Government does not endorse products or manufacturers. Trade or 

manufacturers’ names appear herein solely because they are considered essential 

to the objective of this report. This document does not constitute Federal 

Aviation Administration (FAA) certification policy. Consult your local FAA 

aircraft certification office as to its use.
Technical Report Documentation Page

1. Report No.

DOT/FAA/HFD-13/001

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Evaluating the Effectiveness of Schedule Changes for Air Traffic Service (ATS) Providers:

Controller Alertness and Fatigue Monitoring Study

5. Report Date

December 2012

6. Performing Organization Code

NASA Ames Research Center

7. Author(s)

Judith Orasanu, Ph.D., NASA Ames Research Center

Bonny Parke, Ph.D. and Norbert Kraft, M.D., San Jose State University

Yuri Tada, Ph.D., Alan Hobbs, Ph.D., Barrett Anderson, Lori McDonnell, and Vicki 

Dulchinos, San Jose State University

8. Performing Organization Report No.

9. Performing Organization Name and Address

NASA Ames Research Center

Moffett Field, CA 94025-0001

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

12. Sponsoring Agency Name and Address

Federal Aviation Administration 

Human Factors Division 

Mail Stop: ANG-C1 Room 932

800 Independence Avenue, S.W.

Washington, DC 20059

13. Type of Report and Period Covered

Technical Note

14. Sponsoring Agency Code

ANG-C1

15. Supplementary Notes

16. Abstract

The FAA’s Fatigue Risk Management Program is developing a Fatigue Risk Management System that will include science-based shift 

scheduling and other strategies designed to maintain air traffic controllers’ alertness over the 24-hour clock and to reduce negative 

impacts of fatigue on Air Traffic Control (ATC) operations. NASA was tasked with conducting research to provide up-to-date 

knowledge about the state of controller shift work, sleep, alertness and factors contributing to controller fatigue. To that end, the NASA 

research team conducted a two-pronged effort: a web-based survey of fatigue factors that was available to the entire ATC workforce and 

a field study that obtained objective measures of sleep, fatigue and alertness in a sample of controllers from selected facilities, including 

En Route Centers, TRACONs, and ATC Towers. During 2010, 3,268 United States ATC personnel completed the online fatigue survey. 

The field study resulted in complete data being obtained from 211 controllers working at 30 facilities across the country. Data consisted 

of 14 days of continuous sleep and activity monitoring using wrist-worn actigraphs, daily sleep and activity logs, a brief objective measure 

of alertness, and subjective ratings of sleepiness and workload. The purpose of the present study was to establish a quantified baseline for 

evaluating the impact of the FAA’s planned fatigue risk mitigation strategies. Findings also will identify factors affecting fatigue and assist 

in targeting and designing future research areas. Results from this study were compared to those obtained from a 1999 survey (Della 

Rocco et al., 2000a) and several field studies conducted by the FAA between 1995-2005 (Della Rocco & Cruz, 1995; Cruz & Della Rocco, 

1995b; Della Rocco & Cruz, 1996; Della Rocco et al., 2000b; Cruz et al., 2002; Della Rocco & Nesthus, 2005; Broach & Schroeder, 

2005). 

17. Key Words

Air Traffic Control

Fatigue

Human Performance

Shift Work

18. Distribution Statement

This document is available to the public through the 

National Technical Information Service, Alexandria, 

Virginia, 22312. A copy is retained for reference at the 

Federal Aviation Administration Human Factors 

Division’s Library.

19. Security Classification (of this report)

Unclassified 

20. Security Classification (of this page)

Unclassified 

21. No. of Pages

206

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
ACKNOWLEDGEMENTS

The FAA Human Factors Division coordinated the research requirement, and its principal 

representative acquired, funded, and technically managed the execution of the research service under 

FAA/NASA Interagency Agreement #DTFAWA08-X-80020. The FAA sponsor for the work is the FAA 

ATO Safety and Technical Training Fatigue Risk Management Program Office. We are grateful to our 

FAA Program Managers Edmundo A. Sierra, Dino Piccione and Paul Krois, and to our FAA Sponsors, 

Ken Myers, who initiated the study, and Frederick L. Huss, our present sponsor, for their continuing 

advice and support throughout this study. We also express our thanks to members of the FAA/NATCA 

Article 55 Fatigue Risk Management Work Group for their suggestions on design of the project. Tom 

Nesthus of the FAA’s Civil Aerospace Medical Institute (CAMI) generously shared his experience and 

lessons learned from prior Air Traffic Control fatigue investigations. Conduct of this study was truly a 

joint effort involving many people from numerous organizations in the FAA, NASA and the scientific 

community. Initial design of the study benefitted from input from the scientific advisors to the FAA’s 

Fatigue Risk Management Program Office, especially Steve Hursh and Melissa Mallis. Execution of the 

study would have been impossible without the ongoing support and assistance of members of the 

FAA’s Fatigue Risk Management Program Office, led by Rick Huss, including Dave Buczek, Shayne 

Campbell, Annie Glenn, and Bill Yoder. Jeff Richards provided invaluable assistance in working with 

the facilities to conduct the field study. Edward Austrian, Katherine Berry, and Michael Sawyer provided 

technical input to the final version of this report. We especially want to thank the FAA air traffic control 

personnel who gave their time to participate in the survey and the field study. Without their input this 

project would not have been possible. 

The authors gratefully acknowledge the contribution of Dr. Howard Houben, who facilitated data 

analysis.

We also thank Alonso Vera, Chief, Human Systems Integration Division, and Mary M. Connors, Deputy 

Chief, Human Systems Integration Division at NASA Ames Research Center, for their continuing 

support, which enabled completion of this project. Thanks also are extended to Kevin Jordan of San 

Jose State University for his contributions throughout the study. 
PREFACE 

The NASA research team consisted of two coordinated and interleaved work teams that each had 

primary responsibility for one component of the project. Dr. Bonny Parke led the Fatigue Factors 

Survey team, which included Dr. Alan Hobbs, Lori McDonnell, and Vicki Dulchinos. Dr. Parke’s team 

designed, conducted, analyzed, and wrote the survey report. The CAFM Field Study team was led by 

Dr. Norbert Kraft, and included Barrett Anderson and Lori McDonnell. Dr. Kraft’s team designed the 

field study, trained participants at various field locations, managed collection of the objective field study 

data, and contributed to the final report. Our statistical expert, Dr. Yuri Tada, conducted the analyses of 

the field study data and contributed to writing the Results section of the field study report.
FAA FOREWORD

The Federal Aviation Administration (FAA) has long considered the operational impacts of human 

fatigue on Air Traffic Control (ATC) performance and safety. In October 2009, the FAA’s Air Traffic 

Organization (ATO) established the Fatigue Risk Management Team (FRMT), formally committing 

resources to the topic of fatigue risk and the managed improvement of operational fatigue safety. In

July 2011, the FAA and the National Air Traffic Controllers Association (NATCA) signed a 

Memorandum of Understanding that contained agreements on a set of fatigue related mitigations, 

which are on track for completion. In June 2012, the FAA implemented an ATO Fatigue Risk 

Management System (FRMS), with Agency and union membership on the Fatigue Safety Steering 

Committee. The FRMS will institutionalize the goal of effective management of operational fatigue risk, 

executed in alignment with FAA’s Safety Management System (SMS) principles.

The NASA Controller Alertness and Fatigue Monitoring Study (CAFMS) was sponsored and monitored 

by the FRMT, which partnered with NATCA to assist with the design, planning and execution of this 

comprehensive ATC research. The FAA Human Factors Division provided funding and management

for the project. The results of this study will establish a body of objective data from which to identify 

fatigue hazard areas and identify future research areas.

The ATO has recognized that the complex challenges of human fatigue cannot be solved with a single 

remedy. Thus, the ATO is addressing this important safety issue in the ATC workforce with a 

comprehensive, multi-layered approach to fatigue risk mitigation. The elements of this approach 

include: 

• Establishing a scientific understanding of the operational fatigue landscape through: 

o a comprehensive fatigue research agenda, 

o data collection, fusion and analysis, and 

o fatigue modeling, all focusing on the fatigue challenges in the ATC shiftwork environment.

• Providing fatigue education, training and promotion within a broad fatigue safety awareness 

campaign that informs new hires, controllers, managers and all elements of the FAA that influence 

ATC operational fatigue.

• Collaborating with FAA Aerospace Medicine on sleep disorders, with specific attention to 

obstructive sleep apnea.

• Providing science-based recommendations within the joint FRMS to improve fatigue safety through 

policy evolution.

NASA Ames has provided an independent source of data, which will potentially assist the FAA and 

NATCA in implementing risk mitigation initiatives and which will identify the need for further research 

and data collection.
Table of Contents

ACKNOWLEDGEMENTS..........................................................................................................................................II

PREFACE..................................................................................................................................................................III

FAA FOREWORD.................................................................................................................................................... IV

EXECUTIVE SUMMARY............................................................................................................................................1

1. INTRODUCTION ....................................................................................................................................................6

1.1 STUDY PURPOSE...........................................................................................................................................7

1.2 BACKGROUND................................................................................................................................................7

1.2.1 Sleep Science, Fatigue and Alertness......................................................................................................7

1.2.1.1 Circadian Pacemaker.........................................................................................................................................8

1.2.1.2 Homeostatic Pressure ........................................................................................................................................8

1.2.1.3 Chronic Fatigue..................................................................................................................................................9

1.2.1.4 Sleep Inertia.......................................................................................................................................................9

1.2.1.5 Napping and Recovery Sleep ............................................................................................................................9

1.2.2 ATC Shift Schedules.................................................................................................................................9

1.3 PRIOR FAA FATIGUE RESEARCH.............................................................................................................. 12

1.4 GENERAL APPROACH OF THIS STUDY .................................................................................................... 14

2. NASA ATC FATIGUE FACTORS SURVEY....................................................................................................... 15

2.1 METHODOLOGY .......................................................................................................................................... 15

2.1.1 Survey Development.............................................................................................................................. 15

2.1.2 Development and Review of Survey...................................................................................................... 15

2.1.3 Survey Administration............................................................................................................................ 15

2.1.4 2010 Schedule Categories ................................................................................................................... 16

2.1.5 Fatigue Scales ....................................................................................................................................... 16

2.1.5.1 Modified Brief Fatigue Inventory ......................................................................................................... 17

2.1.5.2 Chronic Fatigue Scale......................................................................................................................... 17

2.1.5.3 Epworth Sleepiness Scale .................................................................................................................. 17

2.1.6 Survey Return ........................................................................................................................................ 18

2.2 RESULTS...................................................................................................................................................... 18

2.2.1 Demographic Data ................................................................................................................................. 18

2.2.1.1 Types of Facilities and Number of Respondents..............................................................................................18

2.2.1.2 Positions of Respondents and Response Rates ..............................................................................................19

2.2.1.3 Response Rates by Facility Types...................................................................................................................21

2.2.1.4 Facility Characteristics .....................................................................................................................................21

2.2.1.5 Respondents’ Age and Experience..................................................................................................................23

2.2.1.6 All Respondents' Positions by Age and Experience.........................................................................................24

2.2.2 Schedules .............................................................................................................................................. 25

2.2.2.1 Shift Start Times—3 Week-Bid Schedules and Week Worked ........................................................................25

2.2.2.2 Hours Off Between Shifts................................................................................................................................. 27

2.2.2.3 Rotations..........................................................................................................................................................28

2.2.2.4 Midnight Shifts .................................................................................................................................................30

2.2.2.5 Number of Shifts Worked in a Week ................................................................................................................32

2.2.2.6 Types of Schedules in the 2010 Data ..............................................................................................................33

2.2.2.7 Comparison of the 2010 and 1999 Schedule Data ..........................................................................................35

2.2.2.8 Comparison of Reported Bid and Actual Schedules in the 2010 data..............................................................37

2.2.2.9 Bid and Actual Schedule by Facility .................................................................................................................39

2.2.2.10 Bid and Actual Schedule by Position..............................................................................................................40

2.2.2.11 Satisfaction with Schedule Types ..................................................................................................................41

2.2.2.12 Schedule Type Related to Operational Events ..............................................................................................41

2.2.2.13 Schedule Type Related to Age ......................................................................................................................42

2.2.2.14 Working Over 40 Hours per Week .................................................................................................................43

2.2.2.15 Administrative Schedules...............................................................................................................................46

2.2.2.16 Number of Midnight Shifts by Position ...........................................................................................................47

2.2.3 Workload-related Fatigue....................................................................................................................... 47

2.2.3.1 On-the-Job Training (OJT)...............................................................................................................................47

2.2.3.2 Workload/Staffing.............................................................................................................................................49

2.2.3.3 Supervisor Support, Staffing Support, and Fatigue Safety Culture ..................................................................50

2.2.4 Rotation and Breaks .............................................................................................................................. 52

2.2.4.1 Factors Supporting Alertness (or Not)..............................................................................................................56
2.2.4.2 Respondents' Comments on Rotations and Breaks.........................................................................................58

2.2.5 Stress-Related Fatigue .......................................................................................................................... 59

2.2.5.1 Commute Times and Second Jobs ..................................................................................................................61

2.2.6 Sleep Times and Naps........................................................................................................................... 63

2.2.6.1 Reported Sleep ................................................................................................................................................63

2.2.6.2 Naps................................................................................................................................................................. 65

2.2.7 Sleep Quality and Shifts......................................................................................................................... 66

2.2.7.1 Restfulness of Sleep Between Shifts ...............................................................................................................66

2.2.7.2 Sleep Patterns During the Work Week.............................................................................................................67

2.2.7.3 Causes of Difficulty in Sleeping........................................................................................................................69

2.2.7.4 Hours of Sleep Needed....................................................................................................................................72

2.2.8 Alertness ................................................................................................................................................ 72

2.2.8.1 Alertness at the Beginning and End of Shifts...................................................................................................72

2.2.8.2 Alertness After Periods of Light and Heavy Traffic...........................................................................................73

2.2.8.3 "About to 'Doze Off'" During Work Duties.........................................................................................................74

2.2.8.4 "About to 'Doze Off'" During Breaks After Light and Heavy Traffic...................................................................75

2.2.8.5 Fatigue Scales .................................................................................................................................................76

2.2.8.5.1 Modified Brief Fatigue Inventory ...................................................................................................... 76

2.2.8.5.2 Chronic Fatigue Scale ..................................................................................................................... 77

2.2.8.5.3 Epworth Sleepiness Scale ............................................................................................................... 78

2.2.8.6 Working a Midnight Shift or Not: Effects on Fatigue Scales.............................................................................79

2.2.8.7 Alertness Indicators..........................................................................................................................................79

2.2.8.7.1 Driving Experiences......................................................................................................................... 79

2.2.8.7.2 Operational Events at Work............................................................................................................. 81

2.2.8.7.3 Operational Events and Age........................................................................................................... 83

2.2.9 Extent of Fatigue, Causes and Consequences ..................................................................................... 83

2.2.9.1 Level of Fatigue ...............................................................................................................................................83

2.2.9.2 Perception of Fatigue as a Safety Risk ............................................................................................................85

2.2.9.3 Causes of Fatigue............................................................................................................................................86

2.2.10 Suggestions from Respondents on How to Reduce Fatigue............................................................... 88

2.2.10.1 Analyzing Respondents' Suggestions ............................................................................................................88

2.2.10.2 Suggestions for Immediate Supervisors from All Respondents .....................................................................89

2.2.10.3 Suggestions for Immediate Supervisors by Position ......................................................................................89

2.2.10.4 Suggestions for Immediate Supervisors by Facility Type...............................................................................90

2.2.10.5 Suggestions for Upper-Level FAA Management by All Respondents ............................................................91

2.2.10.6 Suggestions for Upper-Level FAA Management by Position .........................................................................92

2.2.10.7 Suggestions for Upper-Level FAA Management by Facility Types ................................................................93

2.2.10.8 Suggestions for Controllers by All Respondents ............................................................................................94

2.2.10.9 Suggestions for Controllers by Position .........................................................................................................95

2.2.10.10 Suggestions for Controllers by Facility Type ................................................................................................96

2.2.10.11 Training........................................................................................................................................................96

2.2.10.12 Staying Home from Work When Fatigued....................................................................................................98

2.2.11 Job Satisfaction.................................................................................................................................................99

2.2.11.1 Satisfaction with Various Aspects of Job......................................................................................................100

2.3 SURVEY SUMMARY AND DISCUSSION................................................................................................... 102

2.3.1 Work Context for Fatigue Findings ..................................................................................................... 102

2.3.2 Controller Fatigue Levels.................................................................................................................... 102

2.3.3 Fatigue Due to Schedule .................................................................................................................... 103

2.3.4 Reported Sleep Compared with Earlier Studies ................................................................................. 103

2.3.5 Chronic Fatigue Scale Comparison .................................................................................................... 104

2.3.6 The 2-2-1 Schedule: Pros and Cons .................................................................................................. 105

2.3.7 Challenges Associated with Midnight Shifts ....................................................................................... 105

2.3.8 Challenges Associated with Limited Time-Off Intervals Between Shifts ............................................ 106

2.3.9 Challenges Associated with Early Shifts............................................................................................. 106

2.3.10 Six-day Constant Schedules............................................................................................................. 106

2.3.11.10-hour 4-day Week Schedules ........................................................................................................ 106

2.3.12 Staffing Levels Contribute to Difficult Schedules.............................................................................. 106

2.3.13 TRACON Schedules......................................................................................................................... 107

2.3.14 FLM Schedules ................................................................................................................................. 107

2.3.15 Breaks and Rotations........................................................................................................................ 107

2.3.15.1 Breaks During Low Workload Periods..........................................................................................................107

2.3.15.2 Timing of Breaks: Low Workload is Fatiguing ..............................................................................................107

2.3.15.3 Naps during Breaks......................................................................................................................................108
2.3.15.4 Position Rotations ........................................................................................................................................108

2.3.16 Sleep Disorders and Other Sleep Problems..................................................................................... 108

2.3.17 Differences Among Age Groups ....................................................................................................... 108

2.3.18 Training for Fatigue Management..................................................................................................... 108

2.3.19 Fatigue Safety Culture ...................................................................................................................... 109

2.3.20 Limitations and Strengths of the Survey............................................................................................ 109

3. CONTROLLER ALERTNESS AND FATIGUE MONITORING FIELD STUDY................................................ 111

3.1 OVERVIEW ................................................................................................................................................. 111

3.2 METHOD ..................................................................................................................................................... 111

3.2.1 Facility Selection .................................................................................................................................. 111

3.2.2 Participants .......................................................................................................................................... 114

3.2.2.1 Recruitment....................................................................................................................................................114

3.2.2.2 Random Participant Selection........................................................................................................................114

3.2.2.3 Participant Privacy and Human Subjects Protection ......................................................................................115

3.2.2.4 Participant Demographics..............................................................................................................................116

3.2.2.5 Participant Work Schedules ...........................................................................................................................117

3.2.3 Procedure ............................................................................................................................................ 117

3.2.3.1 Activity Monitors.............................................................................................................................................118

3.2.3.2 Sleep/Activity Log...........................................................................................................................................118

3.2.3.3 Psychomotor Vigilance Task..........................................................................................................................119

3.2.3.4 Participant Training ........................................................................................................................................121

3.2.4 Data Review and Pre-processing ........................................................................................................ 122

3.2.4.1 Analysis of Objective Sleep, Fatigue and Alertness Data ..............................................................................124

3.2.4.2 Aligning Field Study Data with Survey Data...................................................................................................126

3.3 RESULTS.................................................................................................................................................... 126

3.3.1 Database Description........................................................................................................................... 126

3.3.1.1 Activity Log Data ............................................................................................................................................126

3.3.1.2 PVT Data .......................................................................................................................................................128

3.3.1.3 Actigraphy Data ................................................................................................................................ 130

3.3.2 Controller Work Schedules .................................................................................................................. 130

3.3.2.1 Five-Day Work Schedules..............................................................................................................................130

3.3.2.2 Six-Day Work Schedules ...............................................................................................................................131

3.3.3 Impact of Work Schedules on Amount, Timing and Efficiency of Sleep.............................................. 132

3.3.3.1 Sleep Duration based on Actigraphy..............................................................................................................132

3.3.3.2 Sleep Duration as a Function of Shift Start Time ...........................................................................................132

3.3.3.3 Time Asleep as a Function of Shift Start Time...............................................................................................133

3.3.3.4 Time Awake as a Function of Shift Start Time ...............................................................................................135

3.3.3.5 Sleep Duration as a Function of Schedule Type ............................................................................................135

3.3.3.6 Time Asleep as a Function of Schedule Type................................................................................................136

3.3.3.7 Time Awake as a Function of Schedule Type................................................................................................136

3.3.3.8 Efficiency of Sleep from Actigraph Data.........................................................................................................137

3.3.4 Impact of Work Schedules on Controller Alertness Measured by PVT Performance ........................ 138

3.3.4.1 Overall performance.......................................................................................................................................138

3.3.4.2 PVT Performance by Shift Start Time ............................................................................................................140

3.3.4.3 PVT Performance: Beginning versus End of Shift..........................................................................................141

3.3.4.4 PVT Performance Across Work Week ...........................................................................................................141

3.3.4.5 PVT Performance by Schedule Type.............................................................................................................142

3.3.4.6 PVT Performance by Time Off between Shifts...............................................................................................143

3.3.4.6.1 Long Duration Time Off ................................................................................................................. 143

3.3.4.6.2 Turn Duration Effects on Controller Sleep and Alertness............................................................. 145

3.3.5 Subjective Ratings of Sleepiness and Workload ................................................................................. 150

3.3.5.1 PVT and Subjective Sleepiness Ratings.......................................................................................................150

3.3.5.2 Relationship Between Subjective Workload and Alertness ...........................................................................150

3.3.6 Moderator Effects on Controller Sleep................................................................................................. 151

3.3.6.1 Controller Demographics as Moderator Variables........................................................................... 151

3.3.6.1.1 Controller Demographics and Sleep Duration .............................................................................. 151

3.3.6.1.2 Controller Demographics and Sleep Efficiency ............................................................................ 151

3.3.6.1.3 Controller Demographics and Time Asleep.................................................................................. 151

3.3.6.1.4 Controller Demographics and Time Awake .................................................................................. 152

3.3.6.2 Facility Features as Moderators of Controller Sleep ....................................................................... 152

3.3.6.3 Facility Features as Moderators of Controller Alertness.................................................................. 152

3.4 FIELD STUDY SUMMARY AND DISCUSSION .......................................................................................... 153
3.4.1 Shift Schedules and Controller Sleep................................................................................................. 153

3.4.1.1 Sleep Duration and Shift Schedule Factors ..................................................................................................153

3.4.1.2 Timing of Sleep .............................................................................................................................................154

3.4.1.3 Sleep Efficiency ............................................................................................................................................154

3.4.1.4 Controller Age and Experience as Moderators of Sleep ...............................................................................154

3.4.1.5 Facility Factors as Moderators of Sleep Duration .........................................................................................154

3.4.2 Shift Schedules and Controller Alertness ........................................................................................... 155

3.4.2.1 Measuring Alertness with the PVT................................................................................................................155

3.4.2.2 Alertness on Midnight Shifts..........................................................................................................................155

3.4.2.3 Shift Schedules and Alertness ......................................................................................................................156

3.4.2.4 Recovery Time Between Shifts .....................................................................................................................157

3.4.2.4.1 Implications for Fatigue Countermeasures................................................................................................. 158

3.4.2.4.2 Individual Differences in Vulnerability to Sleep Loss..................................................................................158

3.4.3 Workload and Alertness...................................................................................................................... 158

3.4.4 Comparison between the Present Field Study and Prior CAMI Studies ............................................ 159

3.4.4.1 Midnight Shifts ..............................................................................................................................................161

3.4.4.2 Early Morning Shifts......................................................................................................................................161

3.4.4.3 Time Off Between Shifts ...............................................................................................................................161

3.4.4.4 Cumulative Fatigue within Work Shifts..........................................................................................................161

3.4.4.5 Cumulative Fatigue across Days of the Work Week .....................................................................................162

3.4.4.6 Sleep Efficiency ............................................................................................................................................162

3.4.4.7 Unique Findings in the Present Field Study ..................................................................................................162

3.4.4.7.1 Relation between Workload and Alertness ................................................................................................. 162

3.4.4.7.2 Day of Shift and Turns Less than 12 Hours ...............................................................................................162

3.4.4.7.3 Influence of Controller Demographics on Sleep and Alertness ..................................................................162

3.4.4.7.4 Influence of Facility Factors on Controller Sleep and Alertness ................................................................. 163

3.4.5 Overall Comments ............................................................................................................................... 163

3.4.6 Field Study Conclusions ...................................................................................................................... 163

4. DISCUSSION AND POTENTIAL FAA FUTURE RESEARCH AREAS........................................................... 164

4.1 INTEGRATION OF FINDINGS FROM THE FATIGUE FACTORS SURVEY AND FIELD STUDY ...................................... 164

4.2 UNANSWERED QUESTIONS ............................................................................................................................ 167

4.3 POTENTIAL FUTURE RESEARCH AREAS.......................................................................................................... 167

5. REFERENCES .................................................................................................................................................. 169

APPENDIX A. NASA ATC FATIGUE FACTORS SURVEY................................................................................. 175

APPENDIX B. FIELD STUDY INFORMED CONSENT........................................................................................ 199

APPENDIX C. C-FAM PARTICIPANT TAKE-HOME GUIDE .............................................................................. 199

APPENDIX D. ACTIWATCH SLEEP PARAMETERS.......................................................................................... 203

APPENDIX E. C-FAM FIELD STUDY WEB SIGNUP PAGE ............................................................................... 205

List of Figures

Figure 1-1. Logical reasoning performance across two nights of sleep deprivation................................................. 9 

Figure 2-1. Distribution of 15,554.8 shift start times in 1 week of 3-week bid schedules compared with 

distribution of 15,557 start times in week actually worked................................................................... 27 

Figure 2-2. Frequency of number of hours off between shifts in a one-week period

for both 3-week bid schedules and actual week worked schedules. .................................................. 29 

Figure 2-3. Number of rotations within the week actually worked and the 3-week 

bid schedule divided by 3..................................................................................................................... 30

Figure 2-4. Number of midnight shifts in the actual week worked and the 3-week 

bid schedule divided by 3..................................................................................................................... 33 

Figure 2-5. Number of shifts in the actual week worked and in the 3-week bid schedule ........................................ 33 

Figure 2-6. Comparison of number of bid schedules and actual schedules reported

worked in a week. ................................................................................................................................ 39 

Figure 2-7. Level of satisfaction or dissatisfaction with schedules by all respondents. ............................................ 42 
Figure 2-8. Respondents in three job categories rate workload and adequacy of staffing on their shifts. ............... 50

Figure 2-9. CPCs from three facility types rate workload and adequacy of staffing on their shifts........................... 51

Figure 2-10. Respondents in three job categories rate supervisor support, staffing, 

and fatigue safety culture..................................................................................................................... 52 

Figure 2-11. CPCs from three facility types rate supervisor support, staffing, 

and fatigue safety culture..................................................................................................................... 53 

Figure 2-12. Respondents in three job categories rate adequacy of position 

rotation to reduce fatigue. .................................................................................................................... 54 

Figure 2-13. CPCs from three facility types rate adequacy of rotation to reduce 

work-related fatigue.............................................................................................................................. 54 

Figure 2-14. Average non-meal break time by position. ........................................................................................... 55 

Figure 2-15. Average CPC non-meal break time by facility. ..................................................................................... 55 

Figure 2-16. Responses to "On average, how frequently do you have breaks when you are on 

position?" by position. .......................................................................................................................... 56 

Figure 2-17. CPC responses to "On average, how frequently do you have breaks 

when you are on position?" by facility type. ......................................................................................... 56 

Figure 2-18. Responses by position to "Please indicate the extent to which you feel the following 

factors are currently at about the right level to support your optimal alertness."................................. 59 

Figure 2-19. CPCs responses by facility to "Please indicate the extent to which you feel the following 

factors are currently at about the right level to support your optimal alertness".................................. 60

Figure 2-20. Free text comments provided after questions on workload-related fatigue, shown as 

percentage of those who answered this question................................................................................ 61

Figure 2-21. Responses to "In general, how stressed do you feel at work?" by position. ........................................ 61

Figure 2-22. CPCs' responses to "In general, how stressed do you feel at work?” by facility.................................. 62 

Figure 2-23. Responses by position to "If stressed, to what extent do you feel that the following 

contribute to your stress?" ................................................................................................................... 62 

Figure 2-24. CPCs responses by facility type to "If stressed, to what extent do you feel that the 

following contribute to your stress?". ................................................................................................... 63 

Figure 2-25. Mean range of minutes of the typical one-way commute time to work. ............................................... 64 

Figure 2-26. Mean estimated cost-of-living for area lived in. .................................................................................... 64 

Figure 2-27. CPCs' mean estimated cost-of-living for area lived in. ......................................................................... 64 

Figure 2-28 Hours of reported sleep by all respondents before various shifts in 2010 survey................................. 65 

Figure 2-29. Reported hours of sleep on Regular Days Off by all respondents. ...................................................... 66 

Figure 2-30. Comparison of number of hours of sleep reported by those with rapidly rotating shifts 

with and without midnights in their bid schedules................................................................................ 67 

Figure 2-31. Responses to "If yes [naps taken on breaks at work would increase your alertness], on 

which shifts(s) would naps be of most benefit?”. .......................................................... ................... 68 

Figure 2-32. Responses of those without and with midnight shifts on their bid schedules to "If yes 

[naps taken on breaks at work would increase your alertness], on which shifts(s) would 

naps be of most benefit?...................................................................................................................... 68 

Figure 2-33. Responses to "How rested do you normally feel after sleep..." between 

various shifts and days off. .................................................................................................................. 69 

Figure 2-34. Frequency of various sleep patterns and habits................................................................................... 69 

Figure 2-35. Mean responses to "Do you awaken in the middle of your sleep period?" 

by schedule type. ................................................................................................................................. 70
Figure 2-36. Mean responses to "Are you unable to sleep when you want to?" by schedule type. ......................... 70

Figure 2-37. Number of caffeine servings typically consumed by respondents in a 

24-hour period...................................................................................................................................... 71

Figure 2-38. Responses to "Do you feel you have trouble sleeping for any of the 

following reasons? ............................................................................................................................... 72 

Figure 2-39. Average responses to "About how many hours of sleep do you feel you need in a 24-

hour period, irrespective of which shift you are on?" ........................................................................... 74 

Figure 2-40. Alertness at beginning of shifts............................................................................................................. 75 

Figure 2-41. Alertness at the end of shifts.. .............................................................................................................. 75 

Figure 2-42. Alertness after light and heavy traffic after varying time periods.......................................................... 76 

Figure 2-43. Shifts on which that respondents were most likely to report "about to doze off'" on . .......................... 77 

Figure 2-44. Mean responses to "In the last year, have you caught yourself "about to doze off'" on a 

break after"........................................................................................................................................... 77 

Figure 2-45. Modified BFI by position. ...................................................................................................................... 78 

Figure 2-46. CPC responses to Brief Fatigue Inventory (Modified) by Facility Type................................................ 78 

Figure 2-47. Mean ratings of those with five schedule types on the Brief Fatigue Inventory. .................................. 79 

Figure 2-48. Chronic Fatigue Scale by position. ....................................................................................................... 79 

Figure 2-49. Chronic Fatigue Scale by CPC's facility type.. ..................................................................................... 79 

Figure 2-50. Mean ratings of those with five schedule types on the Chronic Fatigue Scale. ................................... 80

Figure 2-51. Mean score on Epworth Sleepiness Scale by Position. ....................................................................... 80

Figure 2-52. Mean ratings of respondents those controllers with five schedule types on the Epworth 

Sleepiness Scale.................................................................................................................................. 81

Figure 2-53. Percentage of respondents in three positions who were involved in an operational 

incident and the percentage of these who believed that fatigue had contributed to the 

incident................................................................................................................................................. 84 

Figure 2-54. Percent of respondents at each facility type who were involved in an operational incident, 

and the percentage of these who believed that fatigue had contributed to the incident...................... 84 

Figure 2-55. Sources of fatigue identified by all respondents who had experienced a fatigue-related 

incident................................................................................................................................................. 85 

Figure 2-56. Frequency of perceived Levels of controller fatigue and fatigue consequences as seen 

by all respondents................................................................................................................................ 86 

Figure 2-57. Levels of controller fatigue and consequences as seen by CPCs, Developmentals, and 

FLMs. ................................................................................................................................................... 86 

Figure 2-58. Controller fatigue and consequences as seen by CPCs in three facility types.. .................................. 87 

Figure 2-59. Level of risk of current level of controller fatigue as perceived by those in three positions. ................ 87 

Figure 2-60. Level of risk of current level of controller fatigue as perceived by CPCs according to 

facility type. .......................................................................................................................................... 87 

Figure 2-61. Responses by position to "If you experience fatigue at work, to what extent do you feel 

that the following contribute to your fatigue?"...................................................................................... 89 

Figure 2-62. CPCs' responses by facility type to "If you experience fatigue at work, to what extent do 

you feel that the following contribute to your fatigue?" ........................................................................ 90

Figure 2-63. Responses to the question,” What, if anything, could your immediate 

supervisors or managers do to reduce controller fatigue?” ................................................................. 91

Figure 2-64. Responses to the question “What, if anything, could your immediate supervisors or 

managers do to reduce controller fatigue?” by respondents' position. ................................................ 92 
Figure 2-65. Responses to the question “What, if anything, could your immediate 

supervisors or managers do to reduce controller fatigue?” by facility ................................................. 93 

Figure 2-66. Suggestions in response to the question, “What, if anything, could upper-level FAA 

management do to reduce controller fatigue?” .................................................................................... 94 

Figure 2-67. Responses to the question, “What, if anything, could upper-level FAA management do to 

reduce controller fatigue?” ................................................................................................................... 95 

Figure 2-68. Suggestions in response to the question, “What, if anything, could upper-level FAA 

management do to reduce controller fatigue?” by facility. ................................................................... 96 

Figure 2-69. Suggestions in response to the question “What, if anything, could controllers do to 

reduce their fatigue?” ........................................................................................................................... 97 

Figure 2-70. Responses to the question “What, if anything, could controllers do to reduce their 

fatigue? ................................................................................................................................................ 98 

Figure 2-71. Responses to the question “What, if anything, could controllers do to reduce their 

fatigue?”. .............................................................................................................................................. 99 

Figure 2-72. Ratings by position of comfort levels in taking time off from work due to fatigue, informing 

supervisors of the reason, and the likelihood of supervisors' authorization to do so........................... 101

Figure 2-73. CPC ratings by facility of comfort levels in taking time off from work due to fatigue, 

informing supervisors of the reason, and the likelihood of supervisor's authorization to do 

so.......................................................................................................................................................... 101

Figure 2-74. Position by job satisfaction on a 7-point scale...................................................................................... 102 

Figure 2-75. CPCs' mean ratings of job satisfaction by facility type on a 7-point scale............................................ 102 

Figure 2-76. Satisfaction or dissatisfaction with various aspects of work by position............................................... 103 

Figure 2-77. Facility type by satisfaction or dissatisfaction with various aspects of CPCs' jobs. ............................. 104 

Figure 3-1a. Digital watch (L) and MiniMitter Actiwatch-60 (R)................................................................................. 120

Figure 3-1b. ActiGraph GT3X (L) and digital watch (R)............................................................................................ 120

Figure 3-2. Sleep-Activity Log filled out for 24-hours. ............................................................................................... 121

Figure 3-3. Psychomotor Vigilance Task (PVT) on a Palm Centro PDA. ................................................................. 122

Figure 3-4. Schedule for daily data collection over the 14 days of the study period. ............................................... 124 

Figure 3-5. Sleep/activity log and actigraphy data over 48 hours............................................................................. 126 

Figure 3-6. Hierarchical structure of the field study data. ......................................................................................... 127 

Figure 3-7. Proportion of missing PVT data for each participant across all trials. .................................................... 131

Figure 3-8. Frequency distribution of time asleep, across A, B, D, and E schedules............................................... 136 

Figure 3-9. Frequency distribution of time asleep prior to midnight shifts.. .............................................................. 136 

Figure 3-10. Frequency distribution of average sleep efficiency scores across participants.................................... 140

Figure 3-11. Frequency distribution of mean lapses across all participants. ............................................................ 141

Figure 3-12. Frequency distribution of the standard deviations of lapses across all participants............................. 142 

Figure 3-13. Frequency distribution of mean response speeds as deviation scores from the fastest 

10% of trials across all participants...................................................................................................... 143 

Figure 3-14. Frequency distribution of standard deviations of response speeds as deviation scores 

from the fastest 10% of trials across all participants............................................................................ 143 

Figure 3-15. The model relationship between response speed and time between shifts in various 

illustrative facilities. .............................................................................................................................. 147 

Figure 3-16. The model relationship between lapses and time between shifts in various illustrative 

facilities. ............................................................................................................................................... 148 
Figure 3-17. Frequency distribution of turns less than 12 hours in the analyzed work weeks ................................. 150
List of Tables

Table 1-1. List of shift types................................................................................................................................ 11 

Table 1-2. ‘Typical’ 2-2-1 and 2-1-2 Shift Schedules ......................................................................................... 11 

Table 2-1. Number and Proportion of Respondents from Each Facility Type.................................................... 20

Table 2-2. Proportions of Respondents in Various ATC Positions and Available Response Rates .................. 21 

Table 2-3. Number and Percentage of Respondents in Four Types of Positions.............................................. 21 

Table 2-4. Position Categories of Della Rocco and Comparable Categories in Current Survey ...................... 22 

Table 2-5. Response Rates of CPCs for Each Facility Type.............................................................................. 22 

Table 2-6. Proportion and Number of Respondents at Different Facility Levels ................................................ 23 

Table 2-7. Proportion and Number of Respondents Working at 24-hour Facilities............................................ 24 

Table 2-8. Proportion of CPCs and Developmentals in Different Age Categories............................................. 24 

Table 2-9. Years of Experience in Current Position of CPCs and Developmentals........................................... 25 

Table 2-10. CPCs' and Developmentals' Years of Total Professional Experience with ATC ............................ 25 

Table 2-11. All Respondents' Positions by Age.................................................................................................. 26 

Table 2-12. All Respondents' Position by Years of Affiliation with ATC............................................................. 26 

Table 2-13. Proportion and Number of Bid Shift Start Times During Three Shift Time Periods in 

the Day ..................................................................................................................................... 28 

Table 2-14. Proportion and Number of Morning Shift Start Times for the 3-Week Bid Schedule and 

Actual Week Worked................................................................................................................ 28 

Table 2-15. Proportion and Number of Times-Off Between Shifts Of Less than 11 Hours in One Week 

of Bid Schedules....................................................................................................................... 29 

Table 2-16. Number of Rotations Per Week of 3-Week Bid Schedules and Actual Week Worked Schedules . 31 

Table 2-17. Number and Proportion of Midnights in 3-Week Bid Schedules and 

Actual Week-Worked Schedules.............................................................................................. 32 

Table 2-18. Proportion and Number of Shifts in the 3-Week Bid Schedule and in the Actual Week Worked ... 34 

Table 2-19. Five Categories of Bid Schedules and Frequencies ....................................................................... 35 

Table 2-20. Comparison of 2010 Schedules with 1999 Schedules.................................................................... 36 

Table 2-21. Sample Common Schedules in the 1999 and 2010 Schedule Data............................................... 38 

Table 2-22. Number and Proportion of Reported Schedules Bid and Worked .................................................. 40

Table 2-23. Reported Bid Schedule by Facility Type ......................................................................................... 40 

Table 2-24. Actual Schedule by Facility Type .................................................................................................... 41

Table 2-25. Reported Types of Bid Schedules by Position................................................................................ 41

Table 2-26. Reported Types of Schedule in the Actual Week Worked by Position ........................................... 42 

Table 2-27. Proportion and Number of Operational Errors by those with Different Bid 

Schedule Types........................................................................................................................ 43 

Table 2-28. Proportion of Each Schedule Type Worked by Different Age Groups of Controllers ..................... 43 

Table 2-29. Proportion of Schedule Types Worked by Different Age Groups ................................................... 44 

Table 2-30. Midnight Shift Work of CPCs Under 36 Years of Age Compared to CPCs Age 36 

and Above................................................................................................................................. 44 

Table 2-31. Number and Percentage of Operational Personnel Who Reported Working Over 40 Hours a 

Week by Facility Type .............................................................................................................. 45 

Table 2-32. Number of Hours Worked Per Week by All Respondents .............................................................. 45 

Table 2-33. Number of Hours Reported Working Per Week by CPCs............................................................... 46 

Table 2-34. Positions of Those Reported Working Over 40 Hours per Week.................................................... 46 
Table 2-35. Proportion and Number who Reported Working Administrative Schedules 

by Position ................................................................................................................................ 47 

Table 2-36. Those with Administrative Schedules Compared to Others on Working Over 40 Hours 

a Week...................................................................................................................................... 48 

Table 2-37. Mean Number of Midnight Shifts in 3-week Bid Schedules by Position ......................................... 48 

Table 2-38. Proportion and Number of CPCs Providing Varying Hours of OJT Per Week................................ 49 

Table 2-39. Proportion and Number of Developmentals Who Reported Receiving Varying Hours 

of OJT Per Week ...................................................................................................................... 49 

Table 2-40. Proportion and Number of Developmentals Certified on One or More Positions ........................... 50

Table 2-41. Length of Non-Meal Breaks by Position.......................................................................................... 54 

Table 2-42. Frequency of Breaks by Position .................................................................................................... 55 

Table 2-43. Responses to "Do you feel that more breaks could be safely taken during low workload 

periods?" by Position................................................................................................................ 56 

Table 2-44. CPCs' Responses to "Do you feel that more breaks could be safely taken during 

low workload periods?" by Position .......................................................................................... 57 

Table 2-45. Responses to "If yes, about how often do these low workload periods 

occur during your typical week?" by Position ........................................................................... 57 

Table 2-46. Preferred Break Intervals of CPCs in Three Different Conditions................................................... 58 

Table 2-47. Responses to "How do you usually get to and from work?" ........................................................... 63 

Table 2-48. Number of Hours Worked per Week at a Second Paid Job in Addition to an FAA Job.................. 65 

Table 2-49. Reported Hours of Sleep before Various Shifts in 2010 Survey by all Respondents ..................... 66 

Table 2-50. Responses to "Do you feel that naps taken on breaks at work would 

increase your alertness at work?" ............................................................................................ 67 

Table 2-51. Proportion and Frequency of Caffeine Servings in a 24-hour Period by All Respondent............... 71

Table 2-52. Proportion of Respondents with a Self-Described Sleep Disorder by Age ..................................... 73 

Table 2-53. Proportion of Respondents with a Self-Described Sleep Disorder by 

Schedule Type.......................................................................................................................... 73 

Table 2-54. Proportion and Number of Respondents Who Would Take Over-the-Counter or Prescribed 

Medications for Sleep ............................................................................................................... 74 

Table 2-55. Proportion of Respondents with Various Schedule Types who Reported Catching Themselves 

"About to 'Doze Off'" during Work Duties in the Last Year....................................................... 77

Table 2-56. Responses to "In the last year, how often have you had a momentary lapse of attention while 

you were driving to or from [various types of shifts]?".............................................................. 82 

Table 2-57. Responses to "In the last year, how often did you fall asleep while you 

were driving to or from [the following various types of shifts]?"................................................ 83 

Table 2-58. Proportion of Reported Operational Events by CPCs by Age......................................................... 85 

Table 2-59. Number and Proportion of Controllers Assessing the Current Level of 

Controller Fatigue at Various Risk Levels ................................................................................ 88 

Table 2-60. Proportion and Number of CPCs who Indicate the Safety Risk of the Current Level of Controller 

Fatigue to be High or Extreme.................................................................................................. 88 

Table 2-61. Proportion of Respondents Who Would like Training or Information on 

Ways to Reduce Fatigue .......................................................................................................... 99 

Table 2-62. Proportion of Those With and Without Midnight Schedules Who Would like Training or 

Information on Ways to Reduce Fatigue .................................................................................. 100

Table 2-63. Proportion and Number of CPCs that Called in Sick or Took Annual Leave 

Due to Fatigue .......................................................................................................................... 100

Table 2-64. Hours Slept before Various Shifts from Four Different Data Sets................................................... 106 
Table 3-1. ATC Facility Staffing Levels and Traffic Levels for Participating En Route 

Centers, TRACONS and Air Traffic Control Towers ................................................................ 115 

Table 3-2. Number of Target and Trained Participants from each Participating Facility.................................... 117 

Table 3-3. Proportion and Number of Participants in Different Age Categories................................................. 118 

Table 3-4. Proportion and Number of Participants’ Total Years of Professional

Experience with ATC ............................................................................................................... 118 

Table 3-5. Number and Proportion of Participant Work Shifts during the Study Period .................................... 129 

Table 3-6. Number and Proportion of Participants Providing Complete Data from 

5–8 Work Shifts and 9+ Work Shifts from each Facility Type .................................................. 130

Table 3-7. Number and Proportion of Eliminated Participants from each Facility Type ..................................... 131

Table 3-8. Number of 5-day Schedule Types worked by Controllers from each Facility Type .......................... 133 

Table 3-9. Number of 6-day Schedule Types worked by Controllers from each Facility Type .......................... 134 

Table 3-10. Duration of Sleep in Hours Before Various Shift Start Times ......................................................... 135 

Table 3-11. Time Asleep and Time Awake as a function of Upcoming Shift Start Times.................................. 137 

Table 3-12. Mean Sleep Duration in Minutes and Hours over Days for the 2-2-1 

and 2-x-2 Schedules................................................................................................................. 138 

Table 3-13. Mean Lapses and Standard Deviation at Beginning and End of Shifts .......................................... 144 

Table 3-14. Mean Response Speed and Standard Deviation at Beginning and End of Shifts .......................... 144 

Table 3-15. Mean PVT Lapses and Standard Deviation on Days 1–5 ............................................................... 145 

Table 3-16. Mean PVT Response Speed and Standard Deviation at Days 1–5 ............................................... 145 

Table 3-17. Frequency and Percentage of Hours Off prior to Day 3 Early Shifts ............................................. 150

Table 3-18. Frequency and Percentage of Hours Off prior to Day 4 Early Shifts ............................................. 151

Table 3-19. Mean Total Sleep Duration and Standard Deviation for Each Time Off Duration and Schedule 

Type prior to Early Shifts on Days 3 and 4............................................................................... 152 

Table 3-20. Hours Off between Shifts in a 2-2-1 and a 2-1-2 Schedule ............................................................ 161

Table 3-21. Hours of Sleep prior to Each Shift Type in the Present Survey and Field Study Compared to 

Two Prior CAMI Field Studies .................................................................................................. 164 
EXECUTIVE SUMMARY

The FAA’s Fatigue Risk Management Program is developing a Fatigue Risk Management System 

(FRMS) that will include science-based shift scheduling and other strategies designed to maintain air 

traffic controllers’ alertness over the 24-hour clock and to reduce negative impacts of fatigue on Air 

Traffic Control (ATC) operations. NASA was tasked with conducting research to provide up-to-date 

knowledge about the state of controller shift work, sleep, alertness and factors contributing to controller 

fatigue. To that end, the NASA research team conducted a two-pronged effort: a web-based survey of 

fatigue factors that was available to the entire ATC workforce and a field study that obtained objective 

measures of sleep, fatigue and alertness in a sample of controllers from selected facilities, including En 

Route Centers, TRACONs, and Air Traffic Control Towers. The purpose of the present study was to 

establish a quantified baseline for evaluating the impact of the FAA’s planned fatigue risk mitigation 

strategies. Findings also will identify factors affecting fatigue and assist in targeting and designing 

future research areas. Results from this study were compared to those obtained from a 1999 survey

(Della Rocco et al., 2000a) and several field studies conducted by the FAA between 1995-2005 (Della 

Rocco & Cruz, 1995; Cruz & Della Rocco, 1995b; Della Rocco & Cruz, 1996; Della Rocco et al., 2000b; 

Cruz et al., 2002; Della Rocco & Nesthus, 2005; Broach & Schroeder, 2005). 

Fatigue Factors Survey

During 2010, 3,268 United States ATC personnel completed the online "NASA ATC Fatigue Factors 

Survey." The survey gathered information on factors that could contribute to workplace fatigue, such as 

shift schedules, sleep patterns, and workplace experiences. The survey contained statements that were 

typically rated on a scale of 1 to 5, as well as questions that called for free text responses. Among the 

various personnel completing the survey, the response rate of Certified Professional Controllers (CPCs) 

was 18.9% of the CPC population.

The current sample was found to be significantly1 more fatigued than the comparable sample of 

controllers in the 1999 survey (Della Rocco et al., 2000a). The current ATC personnel sample was 

significantly more fatigued on the Chronic Fatigue Scale than a normative comparison group of nurses 

and industrial shift workers (Barton et al., 1995). Overall 18% of current respondents reported that they 

had an operational event in the last year with 56% of those who had an operational event selfidentifying fatigue as a contributor to the event.

When asked if they had caught themselves "about to 'doze off'" during work duties in the last year, 61% 

of all respondents and 70% of those with regularly scheduled midnight shifts replied "Yes." CPC 

respondents also indicated that "fatigue affects the ability of air traffic controllers to perform their job 

effectively" at an average of 3.7 on a 5 point scale – closer to "Frequently" than "Sometimes." 

Schedule Factors

Of all aspects of their jobs included in the survey, respondents were least satisfied with their schedules 

and felt that their schedules contributed most to their fatigue. Respondents from the current survey had 

a higher proportion of counter-clockwise rapidly rotating schedules, especially with midnight shifts, and 

a lower proportion of straight shifts without midnights than the 1999 survey sample. Although over half 

(54%) of all respondents in this sample did not regularly work midnight shifts, those who did regularly 

work the midnight shift scored significantly higher on the Modified Brief Fatigue Inventory, Chronic 

Fatigue Scale, and the Epworth Sleepiness Scale in comparison to those who do not regularly work the 

midnight shift. 

 

1 All results deemed significant were determined utilizing statistical tests, which are documented in the Results 

section of the report. 
Quick turns. About half (51.8%) of the survey respondents described their regular schedules as having 

a quick turn (8 or 9 hours between shifts) before a morning or midnight shift. The average duration of 

sleep before morning shifts during these quick turns was reported as 5.4 hours. This reported sleep 

duration was consistent with an earlier objective field study, an earlier lab study, and the current field 

study. 

On quick turns before midnight shifts that began the same day, controllers reported 3.1 hours of sleep. 

When respondents’ self-reported ratings of mental sharpness at the end of the shift, the midnight shift 

following a quick-turn ranked as the lowest among all shift types. For midnight shifts following quick 

turns, respondents also indicated that they felt least rested at the beginning of their shift and more likely 

during their shift to catch themselves “about to ‘doze off’" during work duties when compared to all shift 

types. 

Six-day work weeks. The schedule that respondents were least satisfied with was the 6-day schedule. 

Respondents working this schedule reported having had a higher proportion of operational events in 

the previous year than those on any other schedule (32% versus 17%). Although the number of 

respondents that reported having a 6-day constant bid schedule was small (about 4% of the sample), 

the proportion of respondents in this sample who reported they actually worked a 6-day schedule in 

their last full week of work was approximately 14%. 

Those who reported actually working 6-day schedules were not evenly distributed across facilities. 

Over 30% (138/452) of TRACON respondents in this sample reported working a 6-day schedule in the 

last full week they worked. This compares with 11% of En Route controllers and 12% of Tower 

controllers working 6-day schedules. Of the TRACON respondents who worked a 6-day schedule,

approximately two thirds also reported working midnight shifts. Of those in all facilities who reported 

working 6-day schedules in the previous week, over half (53%) also reported working midnight shifts. 

Field Study of Controller Shift Schedules, Sleep and Alertness

The field study was designed to validate findings from the survey by providing objective measures to 

complement the self-reports in the survey. Complete data were obtained from 211 controllers working 

at 30 facilities across the country. Data consisted of 14 days of continuous sleep and activity monitoring 

using wrist-worn actigraphs, daily sleep and activity logs, a brief objective measure of alertness (the 5-

minute Psychomotor Vigilance Task, or PVT) administered three times during each work shift, and 

subjective ratings of sleepiness and workload.

Controller Work Schedules

Almost three-quarters of participants (72.8%) in the field study worked counter-clockwise rapidly 

rotating schedules with or without midnight shifts. 61.4% of participants worked a rapidly rotating 

schedule that included midnight shifts (RRM), a higher level than reported in the survey. This greater 

representation of RRM schedules most likely reflects the participant recruitment strategy that sought 

controllers working midnight shifts. The most common RRM schedule (37%) was the 2-2-1 (AAEEM, or 

two afternoons, two early mornings and one midnight shift, with quick turns before the first morning shift 

and the midnight shift). Almost as frequent (35%) was a variant that substituted a mid-day shift on day 

two in place of the afternoon shift (ABEEM). Ninety percent (38/42) of RR schedules with no midnight 

shifts were characterized as a 2-x-2 (two afternoon shifts, a variable non-midnight shift, and two early 

morning shifts). At most, these schedules had one turn of less than 12 hours. 

Over 21% of field study participants worked one or more 6-day schedules during the study period, a 

slightly higher rate than the 14% reported in the survey. Like the 5-day schedules, 73.4% of these 6-

day schedules were counter-clockwise rapidly rotating schedules with or without midnight shifts; 28% 

were recognizable as regular 5-day schedules plus one overtime day, either at the beginning or at the 

end of the week. Of both 5-day and 6-day schedules, 17.5% were interrupted meaning that a day off 

occurred within a regular work week. 

Shift Effects on Controller Sleep
Based on actigraphy and sleep logs, controllers obtained an average of 5.8 hours of sleep per night 

over the work week, a level similar to that found in prior field studies of controllers (e.g., Cruz & Della 

Rocco, 1995). The amount of sleep obtained prior to any particular shift reflected the start time of the 

upcoming shift, regardless of when that shift occurred within a weekly work schedule. The most sleep 

was obtained prior to afternoon or mid-day shifts: 6.94 and 6.41 hours, respectively. Statistically 

significant less sleep was obtained before early morning (5.4 hours) and midnight shifts (3.25 hours). 

The average wake-up time for a shift start time between 05:30 and 08:00 was 04:20. The restricted 

sleep prior to the midnight shift typically occurred in the afternoon, during a quick turn of 8-9 hours.

Actigraphy data indicated that average total sleep time over a week was greater when participants 

worked an RR schedule without midnight shifts than when a midnight shift was included. For both 

schedule types, sleep duration statistically significantly declined across days of the work week.

Participants working 2-2-1 schedules adopted a sleep pattern not evident in prior studies. These 

participants went to bed earlier prior to early morning shifts as well as arising earlier. In a previous FAA 

Civil Aerospace Medical Institute (CAMI) field study, controllers working the 2-2-1 schedule did not 

adjust the time at which they went to sleep, but did adjust their wake-up times (Cruz & Della Rocco, 

1995a).

Schedule Effects on Controller Alertness

The 5-minute PVT assessment of alertness was sensitive to changes in fatigue. Both response speed 

and lapses reflected cumulative fatigue from the beginning of a shift to the end, regardless of shift type, 

confirming findings from earlier CAMI studies and from the NASA survey.

Alertness also varied with shift start times, regardless of where the shift fell within the work week. 

Responses were slowest and lapses most frequent during midnight shifts compared to day or afternoon 

shifts. Responses were also significantly slower during early morning shifts, though lapses did not differ 

from those in later shifts. 

Both increasing lapses and slowing response speeds were measured across the work week. This 

pattern paralleled the reduction in sleep across the work week, with a significant correlation between 

amount of sleep and alertness measures. As sleep duration decreased, lapses increased and 

responses became slower. 

Turns. Survey data suggested that quick turns prior to early and midnight shifts significantly contributed 

to fatigue. Field study results indirectly confirmed these findings. Individual facility-level models showed 

significant relationships between duration of time off and both response speed and lapses. An analysis 

of turns ranging from 8 hours to 12 hours prior to early morning shifts found no differences in amount of 

sleep or alertness between 8 hours and 9 hours off. However, significantly more sleep was obtained 

when time off increased from 9 to 10 hours and longer, e.g., more sleep was obtained with 13 to 15 

hours off than with 8 to 12 hours off. This increase only held when the turn of less than 12 hours

occurred before early shifts on day 4, not on day 3, suggesting an interaction between day of work 

week and time off, i.e., longer time off appeared to have a greater benefit later in the week. Alertness 

measured by PVT response speed was significantly lower during early shifts following turns of less than 

12 hours than following regular hours off (i.e., 13 to 18 hours off). 

Workload and alertness. Alertness may also be influenced by workplace factors, such as workload. 

Although no objective measure of workload during shifts was available in the present field study, 

subjective perceived workload ratings were significantly related to measured alertness: When workload 

was rated higher, PVT lapses were fewer and responses were faster. This finding held across shift 

types and times of day. 

Facility Factors as Moderators of Sleep and Alertness

Utilizing data from the field study, multilevel models of fatigue were developed to examine the impact of 

various factors on sleep and alertness. Facility factors including facility type (En Route, TRACON, 

Tower), traffic levels, staffing levels relative to FAA target levels, and Certified Professional Controller 

(CPC) /Developmental ratios were entered into the models to determine their impact on sleep and 
alertness. No main or moderator effects were found, except for one: Controllers in TRACONS had a 

longer average sleep duration than those in other facilities. 

Demographic Factors as Moderators of Sleep and Alertness

When examining demographic factors, only one main effect of age was found: Older and more 

experienced controllers responded faster than younger ones, an effect detected when age and 

experience levels were tested as moderators of the within-shift fatigue effect (i.e., from beginning to end 

of a shift). This finding contrasts with the negative effect of age found in several prior FAA field studies 

(e.g., Nesthus, Dattell, & Holcomb, 2005), which found that older controllers generally performed less 

well than younger controllers on cognitive tasks, especially those involving speed and working memory.

Age and experience moderated the cumulative fatigue effects across the week. In contrast to prior to 

FAA fatigue research, older controllers showed less decrement in alertness over the week than 

younger ones. It is unclear whether the differences in age effects between the present study and prior 

FAA CAMI field studies were due to the tasks used to measure alertness (PVT vs. COGSCREEN), to 

the schedules worked by participants in the two studies, to actual changes in the controller population 

across the intervening decade, or to other unknown factors. 

Several policy-relevant issues identified by the 2010 survey were not fully addressed by the field study, 

either because sufficient data were not available for the comparisons or because the survey findings 

were not expected and only emerged after initial field study analysis plan was completed. Most 

important of these were (a) the multiple contributions to fatigue on the midnight shift restricted sleep not 

only during the day prior to the midnight shifts but also in the two prior days, and day of the work week; 

(b) measured effects of working six day weeks, especially with one or more midnight shifts, and (c) 

interactions between turns of less than 12 hours and days of the work week on sleep and alertness.

Summary and Conclusions

Findings from the both the survey and field study largely confirmed the major patterns of sleep, fatigue 

and alertness associated with shift schedules found in prior CAMI studies (summarized in Della Rocco 

& Nesthus, 2005). There were a few notable differences and additions: The present study found (a) 

higher levels of reported fatigue and indicators of fatigue including operational events, (b) a greater 

percentage of survey respondents reported working counter-clockwise rapidly rotating schedules (with 

or without midnights), and (c) significant fatigue associated with the 6-day schedules which were not 

included in the 1999 survey. 

Several issues emerged from the both the survey and field study for future consideration. 

1. Fatigue countermeasures for the midnight shift are necessary to maintain controller performance. 

Sample research topics related to controller performance on the midnight shift may include 

instituting a later start time for the early shift prior to the midnight shift, which could enable 

controllers to accumulate sleep reserve to better cope with the midnight shift. 

2. Field study findings and other sleep research indicates that increasing the minimum numbers of 

hours off (currently 9) between afternoon and morning shifts would allow for longer recovery sleep 

opportunities and potentially improve controller alertness.

3. Investigate the circumstances requiring 6-day work schedules and ways to reduce the frequency of 

6-day work schedules (i.e., those with mandatory overtime).

4. Identify ways to alleviate challenges associated with both scheduling and staffing at TRACONs, and 

assess and manage workload and overtime hours of Front Line Managers at all facilities.

5. Develop guidelines that assist FLMs in determining how many controllers need to be on position for 

various traffic levels. 

6. Investigate and monitor issues related to fatigue safety culture, such as willingness to request and 

give breaks or rotations due to fatigue. 
7. Approximately 8% of ATC respondents in this sample reported fatigue associated with sleep 

disorders. The FAA should encourage affected controllers to seek diagnosis and treatment of sleep 

disorders.

Potential areas for further consideration are included at the end of this report. 
1. INTRODUCTION

Air traffic controllers are among the 15% of the US workforce who are considered ‘shift workers’ 

(Barger, Lockley, Rajaratnam, & Landrigan, 2009). Their schedules involve working outside of normal 

daylight hours, considered to be from about 7 a.m. to 6 p.m. (NIOSH, 1997). Shifts may change within 

or across weeks, and often rotate around a 24-hour clock. Shiftwork frequently requires workers to be 

alert and to function at high levels during hours when they are biologically predisposed to be asleep. 

Additionally, fatigue may be exacerbated when workers have difficulty sleeping during daytime hours, 

when their circadian rhythms are normally set to be alert and awake. 

Many shiftwork domains involve high-risk operations, including the military, police, fire fighting, medical 

care, nuclear power, deep-water operations, transportation, space, and some industrial processing. 

Several accidents with significant consequences have been linked to fatigue among the personnel 

involved, such as Three Mile Island and the NASA Challenger launch. Society is becoming more aware 

of the risks associated with fatigue. In part spurred by research supported by the National Institute for 

Occupational Safety and Health (NIOSH), the medical field is taking steps to limit the number of 

continuous hours that can be worked by interns (now 30 hours) (Blum, Shea, Czeisler, Landrigan & 

Leape, 2011). This step was prompted by the significant number of medical errors committed by 

drowsy personnel, and also because of the high rate of accidents or near accidents while driving home 

after a long shift. 

The National Highway Traffic Safety Administration claims that 100,000 crashes a year are due to 

driver fatigue (NHTSA, 2000). Fatigue also affects the health and well-being of shift-workers, 

manifesting in increased incidence of diabetes, gastrointestinal, cardiovascular or pain problems 

(Caruso, Hitchcock, Dick, Russo & Schmit, 2004). However, our modern 24-hour society requires that 

important services be provided around the clock. A global economy and production processes that 

operate longer than a nominal 8-hour work day demand multiple shifts, including during hours when 

people normally sleep. How to satisfy these productivity and performance demands while maintaining 

the health and well-being of shift workers is a challenge of modern times. 

The role of fatigue in Air Traffic Control (ATC) has long been of concern to the Federal Aviation 

Administration (FAA) (Melton, et al., 1973; Melton et al., 1975). All En Route facilities operate 24 hours 

per day, 7 days per week. Over half of federally operated Terminal facilities operate 24 hours per day, 7 

days per week (GAO, 2008). In 2007 approximately 60% of controllers worked rapidly rotating 8-hour 

shifts with progressively earlier start times across the week (GAO, 2008). Controller fatigue has been 

implicated in several accidents and incidents, notably the Comair 5191 accident in Lexington, KY at 

06:06 as the only controller in the Tower was about to complete his midnight shift (NTSB, 2007a). 

Several runway incursions involved controllers working quick turnaround shifts with 9 hours or less off 

between shifts (Price, 2008). According to a fatigue factors survey conducted by CAMI, 5-6% of 

respondents reported they had been involved in an operational deviation or error in the prior year (Della 

Rocco & Nesthus, 2000); almost half of these felt that fatigue was a factor in their events. 

As a result of these and other findings, the National Transportation Safety Board (NTSB) recommended 

that the FAA and the NATCA work together “to reduce the potential for controller fatigue by revising 

controller work-scheduling policies and practices to provide rest periods that are long enough for 

controllers to obtain sufficient restorative sleep and by modifying shift rotations to minimize disrupted 

sleep patterns, accumulation of sleep debt, and decreased cognitive performance” (NTSB, 2007b). A 

recent analysis of work and scheduling policies in Chicago area air traffic facilities (ZAU, C90 and ORD) 

identified additional factors that may contribute to controller fatigue: high traffic volume and complexity, 

limited position rotation, and a high ratio of trainees to CPCs, which creates higher workload demands 

on the CPCs, including higher on-the-job training (OJT) demands (DOT OIG, 2009).
The FAA plans to reduce the potential for controller fatigue by implementing a number of fatigue risk 

management strategies, such as revising work-scheduling policies to ensure sufficient restorative 

sleep, modifying shift rotations to minimize disrupted sleep and accumulation of sleep debt, and 

exploring the use of napping and educational programs. Exactly what those changes should be has not 

been fully determined. Individual differences between controllers in vulnerability to sleep-related fatigue 

and differences between facilities in traffic volume, staffing levels, or training demands may influence 

both sleep needs and performance capability, as well as risk mitigation practicality. 

The FAA’s ATO Safety and Technical Training Fatigue Risk Management Program Office has two 

requirements that guided the present study. First, the office is tasked with developing a Fatigue Risk 

Management System (FRMS), a task that depends on an accurate picture of the current state of 

controller work schedules and factors contributing to controller fatigue, as well as on recent fatigue 

science. Second, a quantified baseline that reflects the current state of controller sleep, alertness, and 

fatigue related to current work schedules and policies must be established to enable evaluation of the 

efficacy of any FRMS that is put into place. 

Working under the sponsorship of the FAA’s Human Factors Division, the research team at NASA 

Ames Research Center conducted a study to address the FAA’s dual requirements. This study was 

developed in collaboration with the FAA’s ATO Safety and Technical Training, Fatigue Risk 

Management Program Office and the Article 55 Fatigue Risk Management Work Group on Controller 

Fatigue, which was a joint effort of the FAA and NATCA. The present study expanded on the 1999 

survey by Della Rocco and Nesthus (2000a, 2005) and several field studies (summarized in Della 

Rocco & Nesthus, 2005) that identified factors disrupting normal sleep and effective cognitive 

functioning. A new study was deemed necessary due to changes that have occurred during the 

preceding decade, including changes in ATC and aircraft automation, domestic and international traffic 

patterns, and controller workforce demographics. 

1.1 STUDY PURPOSE

The purpose of the present study was two-fold: (1) to establish a quantified baseline for evaluating the 

impact of the FAA’s FRMS in air traffic operations, and (2) to identify fatigue risk areas, clarify fatigue 

risk, and assist in targeting and designing specific mitigation strategies. 

The FAA ATO Safety and Technical Training Fatigue Risk Management Program, in collaboration with 

operational service units and unions, will implement a Fatigue Risk Management System (FRMS). The 

impact of the FRMS will be evaluated following implementation, after sufficient time for the new policies 

and practices to take hold. 

Using similar tools and methods, the present study updates the findings of the earlier research 

conducted by the FAA’s Civil Aerospace Medical Institute (CAMI) (Della Rocco & Nesthus, 2000; 2005) 

to determine what patterns still hold and what patterns have changed. 

1.2 BACKGROUND

A significant amount of literature has accumulated on the nature and sources of fatigue. A parallel body 

of literature has described shiftwork and its relationship to fatigue and human performance. 

1.2.1 Sleep Science, Fatigue and Alertness

Advances in sleep science are important to understanding the impact of shift schedules and fatigue 

management practices on controller alertness and fatigue. These advances can be broadly grouped 

into five major topic areas: circadian pacemaker, homeostatic pressure, chronic fatigue, sleep inertia, 

and napping and recovery sleep (Banks & Dinges, 2007; Barger et al., 2009; Mallis, Mejdal, Nguyen & 

Dinges, 2004). These are reviewed briefly, as they serve as the foundation for understanding the 

results of our study and implications for development of an Air Traffic Organization FRMS. 
1.2.1.1 Circadian Pacemaker

Normal sleep and wake patterns in healthy adults reflect the interaction between two primary factors: 

the circadian pacemaker and the homeostatic drive for sleep (Borbe’ly & Achermann, 1999). Circadian 

factors are endogenous rhythmic patterns that are responsible for fluctuations in wakefulness over the 

course of a 24-hour period. Entrained by changing light over the day-night cycle, they influence major 

biological functions, including metabolic, temperature, neuroendocrine and neurotransmitters. 

Considered our ‘internal clock,’ the circadian pacemaker is responsible for increased alertness when 

the sun comes up—or in environments not exposed to sunlight, such as in a submarine, space vehicle, 

or other artificial environment—and a propensity to sleep after the sun goes down. 

Influence of the circadian pacemaker is evident in patterns of alertness over a 24-hour cycle, regardless 

of the amount of sleep one receives. Major and minor peaks of alertness are seen in the morning 

around 10:00 and again in the evening around 20:00. Alertness and performance decline to a minimum 

at around 04:00, with another dip in the early afternoon, around 13:00-14:00 (Hursh, Balkin, Miller & 

Eddy, 2004; Minors & Waterhouse, 1985).

Even when one is sleep deprived, performance improves during the morning alertness peak.

Conversely, it diminishes during the circadian trough at night. Figure 1-1 illustrates the combined 

effects of circadian rhythms and homeostatic pressure on a test of logical reasoning. The oscillating 

peaks reflect circadian factors, while the overall decline across time reflects increasing homeostatic 

pressure to sleep. 

The role of the circadian pacemaker is especially evident in the case of circadian dysrhythmia, when 

one travels to a new time zone and the day-night cycle becomes de-synchronized with respect to one’s 

internal clock. Circadian factors are believed to be responsible for the high incidence of accidents in the 

early morning hours, at the circadian trough (Dijk, Duffy & Czeisler, 1992), especially if one has been 

up all night. These also may be implicated in some ATC incidents at that hour as well (NTSB, 2007b; 

Pruchnicki, Wu & Belenky, 2011). 

Figure 1-1. Logical reasoning performance across two nights of sleep deprivation. (Angus & 

Heslegrave, 1985) 

1.2.1.2 Homeostatic Pressure

Homeostatic pressure is driven by the number of hours awake. Homeostatic pressure to sleep builds up 

with increasing hours awake. Studies of the effects of sleep restriction show that neurocognitive 

performance on tests of alertness, such as the Psychomotor Vigilance Task (PVT), begins to show 

decrements after 15.84 hours awake (Van Dongen, Maislin, Mulington & Dinges, 2003). Continued lack 

of sleep results in acute fatigue arising from homeostatic pressure. 
The homeostatic pressure combines with circadian processes to determine the timing of sleep resulting 

in a regular sleep-wake cycle. As one sleeps, the homeostatic drive diminishes in an exponential 

fashion meaning that there is a steep drop in the drive to sleep at the beginning of the sleep period 

(Van Dongen, et al., 2003). The rapid satisfaction of homeostatic drive makes naps beneficial. 

However, even when one is sleep deprived and homeostatic pressure is strong, circadian factors may 

be in conflict, especially during the day, making sleep difficult. 

1.2.1.3 Chronic Fatigue

Chronic fatigue is a function of the amount of sleep obtained on an extended basis and occurs when 

the amount of sleep obtained over successive days is less than needed, which is usually 7-9 hours in 

healthy adults. When one obtains less sleep than needed over a period of time, the level of 

neurocognitive functioning diminishes and may remain stable at a less than optimal level (Belenky et 

al., 2003). The need for sleep builds over time and a ‘sleep debt’ accumulates requiring recovery sleep 

to get back to a full level of functioning (Belenky et al., 2003). Recent evidence suggests strong 

individual differences in vulnerability to sleep deprivation with respect to neurocognitive functioning 

(Van Dongen et al., 2004). 

1.2.1.4 Sleep Inertia

Immediately upon awakening, individuals may experience grogginess and lower levels than usual of 

neurocognitive functioning on alertness measures, such as the PVT and other cognitive tests (Barger et 

al., 2009; Bruck & Pisani, 1999). This inertia gradually dissipates, but may last from 30 minutes to two 

hours. Sleep inertia is more severe under conditions of sleep deprivation and during the circadian 

trough at night (Van Dongen & Dinges, 2005).

1.2.1.5 Napping and Recovery Sleep

Overcoming the negative effects of sleep restriction or deprivation can be accomplished by sleeping, 

either as naps (brief sleep periods up to two hours at any time of day) or as recovery sleep (longer 

sleep, usually during the circadian night) (Van Dongen et al., 2003). Naps have been demonstrated to 

release homeostatic pressure resulting in increased alertness and improved neurocognitive functioning 

(Della Rocco et al., 2000). In one study individuals were kept awake 24 hours a day, but permitted 2-

hour naps every 12 hours. These naps served to maintain high levels of cognitive functioning, apart 

from sleep inertia effects observed immediately upon awakening (Banks et al., 2010; Purnell, Feyer, & 

Herbison, 2002). 

The benefit of recovery sleep depends on the nature and extent of prior sleep restriction, as well as the 

recovery sleep dose. The largest impact of recovery sleep is seen in cases of complete sleep 

deprivation compared to partial sleep restriction. For example, recovery sleep almost completely 

eliminated the effect of 14 days of sleep deprivation but only partially reduced the effect of sleep 

restricted to 6 hours per night (Van Dongen & Dinges, 2005). Recovery sleep was more effective when 

sleep was restricted to 4 hours than to 6 hours. Even with 10 hours of time in bed following five nights 

of 4 hour sleep restriction, neurocognitive functioning had not fully recovered to the baseline level 

suggesting that even longer sleep or multiple nights of recovery sleep would be needed. 

The FAA has supported over 40 years of research that addresses fatigue within air traffic control. This 

literature will be reviewed briefly, following a brief description of the actual schedules worked by U.S. air 

traffic controllers. 

1.2.2 ATC Shift Schedules 

Shift schedules may be characterized in terms of the following variables. These features are important

for analyzing the impact of the schedules on workers’ alertness, fatigue and operational performance. 

Schedules are distinguished by:

• Duration of a shift
• Time at which a shift starts

• Number of shifts worked before a day of rest

• Number of opportunities for night-time sleep during the week

• Number of rest days following a work period (week or other block of contiguous work shifts)

• Amount of overtime and how taken (extra hours added to a shift or an extra shift) 

• Amount of time off available between shifts

• Amount of rest taken during the shift

• Whether the work schedule is regular or changes across days

Nominal ATC shift schedules consist of five 8-hour work days followed by two regular days off (RDO). 

How these schedules are configured varies substantially across ATC facilities depending on local 

needs. The schedules are composed from the five different shift types listed below in Table 1-1. The 

most common types of ATC shift schedules are depicted in Table 1-2 below.

Table 1-1. List of Shift Types

(Della Rocco & Nesthus, 2005)

Shift Shift Start Time

Early morning (E): Between 01:00 – 07:59

Day (D): Between 08:00 – 09:59

Mid-Day (B): Between 10:00 – 12:59

Afternoon (A): Between 13:00 – 19:59

Midnight (M): Between 20:00 – 00:59

Table 1-2. ‘Typical’ 2-2-1 and 2-1-2 Shift Schedules 

(from Della Rocco & Nesthus, 2005)

2-2-1 Schedule 2-1-2 Schedule

Shift Schedule

Hours 

between 

Shifts Shift Schedule

Hours 

between 

Shifts

1 (A) 15:00–23:00 15 1 (A) 15:00–23:00 15

2 (A) 14:00–22:00 9 2 (A) 14:00–22:00 13

3 (E) 07:00–15:00 14 3 (B) 11:00–19:00 12

4 (E) 06:00–14:00 8 4 (E) 07:00–15:00 15

5 (M) 22:00–06:00* - 5 (E) 06:00–14:00 - 
* Shift 5 actually begins at 22:00 on Day 4

Counterclockwise Rapid Rotation with a Midnight Shift (RRM): Rapidly rotating shift schedules change 

one or more times during a schedule week. In counterclockwise rotations, each successive shift begins 

earlier than the prior one. A common shift worked by controllers over many years involves two 

afternoon shifts, followed by two early morning shifts, followed by one midnight shift (commonly called 

the 2-2-1 schedule). Typical shift times for a 2-2-1 schedule are shown in the left column of Table 1-2. 

While RRM schedules usually begin with an afternoon shift and end with a midnight shift, the 

intervening shifts may vary, such as AABEM, ABDEM, and other combinations. 

Counterclockwise Rapid Rotation with No Midnight shift (RR): RR schedules are similar to RRM 

schedule, except that they do not include a midnight shift. An example of a common RR schedule in 

ATC, the 2-1-2, is shown in the right column of Table 1-2. 

Straight 5’s or Slow Rotations (SR): This schedule involves working the same shift for five days, then 

rotating to a different shift on succeeding weeks, thus, a slow rotation. For example, week one may 

involve five afternoon shifts, week two may involve five mid-day shifts, and week three may involve five 

early morning shifts.

Straight Shifts: In straight shifts the same shift is worked all week, every week, with no rotation. Shifts 

can be early, afternoon, midnight or other. 

Four-day 10-Hour Shifts: Four-day 10-hour schedules may be straight or rotating; they typically do not 

include midnight shifts. For example, two 10-hour afternoon shifts may be followed by one 10-hour day 

shift, one early shift, and then three days off.

In addition to nominal schedules, controllers may also work overtime, either as extra hours on a

scheduled day or as extra days. This yields a 6-day schedule with potentially only one day off before 

the next week’s schedule begins.

A general overview of the characteristics of each schedule type is described below.

Counterclockwise Rapid Rotation with a Midnight Shift (RRM): The major advantage of the RRM 

schedule is a compressed work week, with only one midnight shift during the week. This schedule 

allows time for recovery sleep following the midnight shift, with approximately 80 hours off between 

work weeks (from completion of the midnight shift between 06:00 and 08:00 on Day 5, followed by 2 full 

days off plus a half day before beginning the next work week on an afternoon shift between 13:00 and 

18:00). 

The main challenge of the RRM schedule, in particular the 2-2-1, is the inclusion of two quick turns 

between shifts that limit the opportunity for restorative sleep during the work week. Quick and very 

quick turns have been defined as time off between shifts of 8 to 9 hours. Turns of less than 12 hours 

were also examined. In a 2-2-1 schedule these quick turns occur between the second afternoon shift 

and the first early morning shift (typically 9 hours) and between the second early morning shift and the 

midnight shift (typically 8 hours), as illustrated in the left column of Table 1-2. The 2-2-1 schedule also 

challenges normal circadian rhythms because of constantly changing shift start and end times, 

sometimes referred to as ‘shift lag’ (Comperatore & Krueger, 1990). This circadian challenge is 

compounded by the fact that it is commonly difficult to sleep during the day before the midnight shift, 

when humans are biologically conditioned to be alert and awake. 

Counterclockwise Rapid Rotation with No Midnight Shift (RR): The major advantage of the RR

schedule (e.g., 2-1-2), similar to the 2-2-1, involves a compressed work week resulting in relatively long 

time off between work weeks. This is a shorter time off between work weeks than the 2-2-1. Since there 

is no midnight shift, this schedule avoids the circadian disruption, stress, and fatigue associated with 

midnight shifts, in particular the difficulty sleeping during the day prior to the midnight shift. This 
schedule may be challenging because shift start and end times also change every day or two, thus 

disrupting circadian rhythms. Time for restorative sleep is limited by the counter-clockwise rotation. 

Straight 5s or Slow Rotation: The major advantage of the Straight-5 schedule is that work shifts start 

and end at the same time over five consecutive days enabling controllers to go to bed and wake up at 

consistent and predictable times during the work week. This schedule enables a more stable work 

routine. 

However, working an early shift or a midnight shift five days in a row may take a toll in terms of reduced 

overall sleep during the week, either because people accommodate to the early shift by awakening very 

early without going to bed sufficiently early or because they have difficulty sleeping during the day when 

working the midnight shift (Folkard, 2008). 

Straight Shifts: The major advantage of the Straight Shift schedule is that it maximizes consistency and 

predictability of sleep and work hours, and permits circadian adjustment to the permanent schedule, 

especially when it begins in the morning, day, or afternoon hours. 

However, straight night shifts pose a major challenge to most workers. Research shows that most 

people have difficulty adapting to working a night shift on a permanent basis (Akerstedt, 1988; Melton

et al., 1973). For controllers, a further disadvantage is that traffic levels vary at most ATC facilities 

across hours of the day, with high levels of traffic during certain daylight hours and low levels of traffic 

during night shifts. 

Four-Day, 10-Hour Shifts: Four-day, 10-hour shifts are desirable because they include an extended 

number of days off after the 4-day work week. The benefits of this schedule may be tempered 

depending on specifics of the shifts, i.e., straight or rotating. Initial concern that problems associated 

with other RR schedules may be exacerbated due to the extra two hours per work day. This concern 

was addressed by research that found equivalent amounts of sleep for RR 10-hour shifts and the first 

four days of the 2-2-1 schedule (i.e., not counting sleep prior to midnight shifts) (Schroeder, Rosa & 

Witt, 1998). Similarly, alertness was maintained in the 10-hour schedules.

Six-Day Schedule: While some facilities require one day of planned overtime each week, 6-day 

schedules are not considered normal schedules. If the sixth day is added to a nominal 5-day schedule 

without maintaining the two regular days off, additional fatigue may accrue. This may be a result of a

longer work week compounded by reduced time off for essential recuperation between weeks. Six-day 

schedules also may involve multiple midnight shifts, which impose further stress.

1.3 PRIOR FAA FATIGUE RESEARCH

The FAA has supported research on controller fatigue for the past 40 years (e.g., Higgins et al., 1976; 

Melton et al., 1973; see Della Rocco and Nesthus, 2005, for a summary of earlier research). This body 

of work involves multiple approaches including surveys, field studies, and laboratory experiments. 

Studies have addressed the impact of work schedules and other fatigue factors on a number of 

measures of importance to controller job performance—namely, the amount and quality of sleep, timing 

of sleep, cognitive performance and alertness, subjective measures of fatigue, alertness, and mood, 

controller health and safety, and operational events. 

Even though some of the CAMI studies go back many years, they are important to consider in relation 

to the present study. The majority of the earlier work was conducted between ten and twenty years ago 

and published between 1995 and 2005. Many aspects of the ATC job and worker demographics have 

changed: ATC technology air traffic volume, routes and complexity, aircraft automation capabilities, air 

traffic management policies. Despite these changes, the job of controlling traffic has maintained a 

constant core over the years. Controllers continue to perform a job around the clock in a highconsequence dynamic environment. The FAA initiated the present study to determine whether findings 

from their prior research still hold or whether fatigue factors have changed due to developments in the 
world and technologies over the past decades. An up-to-date fatigue baseline is needed for assessing 

the efficacy of the FAA’s planned controller fatigue management strategies. 

Several findings from the earlier body of work are relevant because they served as the foundation for 

design of the present study as well as the basis for interpreting current findings.

Finding #1: Shift start times influence the duration, quality, and timing of sleep during time off periods 

between shifts. The greatest amounts of total sleep preceded mid-day and afternoon shifts, significantly 

less sleep preceded early morning shifts (that begin before 07:59), and the least sleep preceded 

midnight shifts (mean total sleep time = 3.3 hours). Sleep adjustments to the early morning start times 

were limited to time of awakening, not time going to sleep (Cruz & Della Rocco, 1995). 

Finding #2: Alertness is significantly challenged during midnight shifts. In addition to combined 

circadian and homeostatic pressures, midnight shifts frequently occur at the end of a work week when

controllers may also suffer from cumulative fatigue. Controllers may find it difficult to sleep during the 

afternoon prior to a midnight shift because of the circadian phase—their bodies are set to be alert in the 

afternoon—and because of personal or family factors (Cruz & Della Rocco, 1995; Della Rocco & Cruz, 

1995). 

Finding #3: The two schedules with the greatest reported fatigue and lowest alertness are the 2-2-1 and 

straight early morning schedules. In some cases, the straight early morning schedule was found to be 

more fatiguing than the 2-2-1. This finding may reflect less total sleep time for those who must get up 

very early (e.g., 04:30) in time to get to work for a shift that starts at 06:00. The 2-1-2 schedule resulted 

in the greatest amount of sleep, most positive mood scores, and highest subjective alertness ratings. 

This schedule permits workers to sleep later in the morning for the first three days of the week and 

involves no midnight shifts (Cruz & Della Rocco, 1995; Nesthus, Dattell & Holcomb, 2005). 

Finding #4. Longer periods of time off between shifts are associated with increased alertness. A 

comparison of numbers of hours off in a 2-2-1 schedule during the quick turn from the second afternoon 

shift to the first early morning shift found that subjective alertness increased significantly from 9 hours 

off compared to 8 hours off (Cruz & Della Rocco, 1995). However, no further improvement was 

associated with 10 hours of time off, although the sample size with 10 hours off may have been too 

small to be reliable. Other comparisons found greater alertness when hours off were greater than 12 

hours (compared to 8-11 hours off) (Nesthus, et al., 2003). 

Finding #5. In contrast to finding #4, a laboratory study that compared clockwise and counter-clockwise 

rapidly rotating schedules found no significant difference in amount of sleep obtained across the work 

week, nor did the groups differ in measured alertness based on the Multiple Task Performance Battery. 

In both schedules the expected differences in sleep duration associated with shift start times were 

observed, e.g. the lowest total sleep times prior to early morning and midnight shifts, the greatest total 

sleep time prior to afternoon shifts (Cruz, Boquet, Detwiler & Nesthus, 2003; Cruz, Detwiler, Nesthus & 

Bouquet, 2003). Clockwise rotating schedules, i.e., EEAAM, were expected to lead to longer sleep and 

greater alertness due to eliminating both quick turns associated with the counter-clockwise 2-2-1 

schedule, but this was not found. 

Finding #6. Both subjective alertness and measured performance deteriorate across the work day. The 

magnitude of this decrement is greatest on a midnight shift, which may begin during a circadian high at 

20:00 and may end between 04:00 and 09:00, typically a circadian low period (Becker, Nesthus, 

Caldararro & Luther, 2006; Nesthus, et al., 2005). 

Finding #7. Fatigue accumulates across consecutive days of the work week, reflected in self-report 

sleepiness ratings and measured performance. In counter-clockwise rapidly rotating schedules, shifts 

later in the week are typically early morning or midnight shifts, both of which are associated with more 

fatigue than daytime shifts. These shifts involve work during circadian lows. In addition, by the end of a 
work week sleep debt accumulates as a result of a compressed week and one or more quick turns that 

limit opportunity for restorative sleep (Cruz & Della Rocco, 1995; Schroeder, Rosa, &Witt, 1998). 

Finding #8. Based on a laboratory study, the total amount of sleep and alertness associated with 10-

hour 4-day shift schedules did not differ significantly from the first four days of a 5-day 8-hour 2-2-1 shift 

schedule when matched for shift types (Schroeder, et al., 1998). 

Finding #9. Age was a factor in controllers’ responses to various shift schedules, primarily by increasing 

vulnerability to the effects of quick turns (Becker, et al., 2006; Della Rocco & Cruz, 1996). Controllers 

greater than 40 years of age also performed less well on tasks associated with higher speed and 

greater working memory. However, when older controllers rated their mental alertness as high, their 

performance also was higher. 

1.4 GENERAL APPROACH OF THIS STUDY

The NASA team conducted a two-part study to address the two FAA requirements. The first part 

consisted of a survey distributed online to the U.S. controller workforce. The survey borrowed from the 

earlier survey by Della Rocco and colleagues (Della Rocco, Ramos, McCloy & Burnfield, 2000a) and 

updated the questions and formats in consultation with the joint FAA and NATCA Article 55 Fatigue 

Risk Management Work Group. The survey was structured to identify both bid and actual schedules 

worked in various facility types by controllers with varying levels of certification and experience. 

Additionally the survey was structured to identify perceived alertness and fatigue and the factors 

contributing to each. Topics addressed in the survey included: bid and actual shift schedules worked, 

work and stress-related fatigue factors, sleep duration, sleep quality and patterns, self-reported 

alertness, observed and self-reported fatigue, strategies for reducing fatigue, and personal 

demographics. The survey also provided fields for respondents to offer their views on what could be 

done at varying levels of management and supervision to mitigate fatigue and ensure controller 

alertness. 

The second component of this effort was a field study involving a sample of approximately 250 

controllers at targeted facilities (En Route, TRACON, and Towers). Over a 14-day study period, 

participants provided data on the duration and efficiency of sleep obtained when working various shift 

schedules along with objectively measured cognitive alertness associated with the schedules and sleep 

practices. Controller demographics and facility features (e.g., traffic levels, staffing levels, facility type) 

were examined as moderators of the relationships.

Together, the two sets of data addressed questions concerning the impact of schedules on sleep 

quantity and quality, perceived fatigue, and perceived and measured alertness. These findings will 

provide the FAA with more accurate and up-to-date estimates of schedule-related fatigue, and identify 

factors contributing to fatigue. The hierarchical structure of the data allow for determining whether these 

effects are consistent across various factors, or vary with the type of facility, levels of traffic or staffing, 

age or level of controller experience, controller job category, or personal rest practices. 

This report is organized in three major sections following this introduction. Section 2 includes the report 

on the Fatigue Factors Survey. Section 3 contains the report on the Controller Alertness and Fatigue 

Monitoring (CAFM) field study. An integrated discussion and recommendations from our findings 

appear in Section 4. 
2. NASA ATC Fatigue Factors Survey

2.1 METHODOLOGY

2.1.1 Survey Development

The survey was developed by researchers from the NASA Ames Human Systems Integration Division, 

with inputs from the FAA, NATCA, and subject matter experts in the fields of air traffic control and 

fatigue. Topics addressed in the survey included shift schedules, sleep patterns and naps, sleep 

quality, alertness and fatigue, workload management, job satisfaction, and workplace stress. The 

survey contained many items from an earlier FAA survey (Della Rocco, et al., 2000). Items retained 

from the previous study demonstrated sensitivity to schedule differences or provided baselines against 

which results could be compared. In addition, the current survey contained questions that did not 

appear in the 1999 survey. These items dealt with such topics as safety culture, workload, stressrelated fatigue, and perceived risk of controller fatigue. Respondents also were asked to suggest how 

controller fatigue could be reduced by supervisors, upper FAA management, and controllers 

themselves. 

The survey consisted of Likert scale ratings (usually from 1 to 5), categorical choice options, and free 

text questions. Free text fields in which optional comments could be added were at the end of each 

section. The survey concluded with demographic questions including age, position, years of experience 

and facility type.

2.1.2 Development and Review of Survey

Once the proposed items were compiled into a draft survey, a panel of Subject Matter Experts (SMEs) 

consisting of recently retired controllers was enlisted to ensure that wording was clear, that correct 

terminology was used, and that response options were appropriate. Each SME completed the draft 

survey, and the time taken to complete the survey was recorded. The SMEs were then debriefed in 

order to identify items that required modification. 

NASA, the FAA, and NATCA jointly reviewed subsequent iterations of the survey to ensure the 

concerns of all parties were addressed. These reviews were conducted via teleconference and the 

parties discussed each item on the survey, made recommendations for additions, deletions, 

modifications, and provided guidance for the study protocol. The FAA and NATCA approved the final 

version of the survey before distribution to ATC personnel. The survey and the associated research 

plan received the necessary approval from the NASA Ames Human Research Institutional Review 

Board (Protocol #HRII-09-10 dated June 30, 2009 and HRII-10-24 dated July 27, 2010). A copy of the 

survey is provided in Appendix A.

2.1.3 Survey Administration

The survey was administered online. This enabled participants to complete it during their work hours at 

computer workstations at their facilities. The survey was available online from April 15, 2010 to 

December 31, 2010. 

The FAA sent a letter describing the goals and the scope of the study to all facilities and NATCA field 

representatives. A poster advertising the survey was also made available. The letter included the 

following points to promote participation:

1. "Facilities should make one or more computer(s) with internet access available for staff to 

use to complete the survey.”

2. “More than one computer may be required to accommodate the number of staff in your 

facility that will need to complete the survey during the survey time frame.”
3. “The survey will be available online from April 15th through June 15th, 2010 

[subsequently extended to December 31, 2010]. “

4. “The survey should take about 35 minutes to complete.”

5. “The survey can be taken on any designated computer during work hours (duty time).” 

6. “Supervisors, Managers and Facility Representatives will need to plan ahead to 

accommodate the schedule flexibility required to allow staff the time needed to complete 

the survey.”

7. “Web-based survey responses will go directly to NASA Ames Research Center for 

analysis.”

8. “All survey responses will be completely anonymous; no personal identifying information 

will be requested.” 

9. “The attached survey poster reflects the information above."

The following personnel were encouraged to take the survey:

• All Controllers, including Developmentals

• Front Line Managers

• Operations Managers

• Supervisor

• Traffic Management Coordinators

Paper surveys were available but were to be used only if the online survey was not easily accessible. A 

copy of the paper survey was included in the package of information provided to facility managers to 

print for use, if needed. Managers were asked to provide employees with postage-paid return 

envelopes to mail the completed surveys directly to NASA. No paper surveys were submitted to NASA

resulting in 100% of the received surveys being completed online. 

In its introduction on the website, the stated goal of the survey read as follows.

"This survey will help NATCA and the FAA understand fatigue in the air traffic controller 

work force. It will gather controller inputs on factors that contribute to workplace fatigue, 

such as shift schedules, sleep patterns and workplace experiences. 

Your input will be analyzed by NASA Ames Research Center and that analysis will be 

used by the joint NATCA and FAA Fatigue Risk Management Work Group to help 

develop ways to reduce fatigue risk to the ATC workforce and the NAS." 

2.1.4 2010 Schedule Categories

The final 2010 schedule categories were based on current research on fatigue and questions 

from the respondents on schedules. The data were later put into the 1999 survey categories for 

purposes of comparison. The 2010 schedule categories were based on their predicted relationship to 

fatigue as well as in response to the suggestions for schedule changes proposed by respondents in 

free text sections of the 2010 survey. The two additional schedule types included in the 2010 schedule 

categories were the 6-day schedule and the 10-hour 4-day schedule. 

2.1.5 Fatigue Scales

There were three fatigue scales in the survey—each measuring a different aspect of fatigue. The 

Modified Brief Fatigue Inventory (MBFI) indicates the extent to which fatigue interferes with recent 

mood and daily activities. The Chronic Fatigue Scale indicates the extent to which one always feels 

fatigued, independent of particular sleep events or activities, and was used in both the 1999 survey and 
2010 survey. The Epworth Sleepiness Scale measures the propensity to fall asleep, e.g., the extent to 

which one tends to fall asleep in various situations in recent months.

2.1.5.1 Modified Brief Fatigue Inventory

The Modified Brief Fatigue Inventory indicates the extent to which fatigue interferes in one's daily 

activities. It was originally designed to assess the level of fatigue in cancer patients (Mendoza, et al., 

1999). It has been shortened and modified to apply to fatigue "in recent months," rather than to fatigue 

occurring currently while taking the test and within the previous 24 hours. It is comprised of the 

following rating items on a scale of 1–5 from "Not at all" to "Very much," which are then added to give a 

MBFI score (see Survey Question 63 in Appendix A).

"Rate the extent to which, in recent months, fatigue has interfered with your:

1. activity level

2. mood

3. work

4. home chores

5. relationships

6. enjoyment of life"

2.1.5.2 Chronic Fatigue Scale

The Chronic Fatigue Scale consists of a series of statements meant to capture chronic fatigue on a 

scale from 1–5, and as stated earlier, was used in both the 1999 and 2010 survey (see Survey 

Question 64 in Appendix A). The even-numbered items are reverse coded2 and added to the oddnumbered items to yield a final score.

"The following items relate to how tired or energetic you generally feel, irrespective of whether you 

have had enough sleep or have been working very hard. Some people appear to "suffer" from 

permanent tiredness, even on rest days and holidays, while others seem to have limitless energy. 

Please indicate the degree to which the following statements apply to your own normal feelings."

01. I generally feel I have plenty of energy.

02. I usually feel drained.

03. I generally feel quite active.

04. I feel tired most of the time.

05. I generally feel full of vigor.

06. I usually feel rather lethargic.

07. I generally feel alert.

08. I often feel exhausted.

09. I usually feel lively.

10. I feel weary much of the time."

2.1.5.3 Epworth Sleepiness Scale

The Epworth Sleepiness Scale (Survey Question #62 in Appendix A) asks the respondents about the 

likelihood of their dozing during a number of activities and provides four categories of responses: No 

chance of dozing, slight chance of dozing, moderate chance of dozing, and high chance of dozing. The 

four rating categories (0–3) were added together to give a final score.

 

2 All items receive a score of 1- 5, but the scores on the even-number items are reversed such that a 5 = 1, 4 = 2, 

3 = 3, 4 =2, and 5 = 1.
"How likely are you to doze off or fall asleep in the following non-work situations in recent months, 

in contrast to just feeling tired?

• Sitting and reading

• Watching TV

• Sitting inactive in a public place (e.g., a theater or a meeting)

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

• Lying down to rest in the afternoon when circumstances permit

• Sitting and talking to someone

• Sitting quietly after a lunch without alcohol

• In a car, while stopped for a few minutes in traffic"

2.1.6 Survey Return

As of December 31, 2010, 3,268 usable surveys had been received. Four surveys were deemed 

unusable due to insufficient responses (less than half of the survey was completed). The relatively low 

number of unusable surveys may reflect the convenience of the online delivery method, which returned 

data only when the respondent chose to click the "finish" button. 

2.2 RESULTS

Results are presented in tables when appropriate and graphically when possible. When error bars are 

used in graphs, they are the 95% Confidence Intervals (CIs) as recommended by the American 

Psychological Association (APA, 2001, p. 22). These error bars can be used to gauge whether the 

means are significantly different from each other. If the confidence intervals around two means do not 

overlap or overlap only slightly, then generally the means are significantly different from each other. 

Note that: 

"...when sample sizes are similar and not small and CI widths are similar, if 95% CIs on 

independent means just touch, the two-tailed p value is about 0.006, not 0.05 as many 

believe." (Belia et al., p. 393 citing Cumming & Finch, 2005; Payton et al., 2003).

2.2.1 Demographic Data

2.2.1.1 Types of Facilities and Number of Respondents

About half (50.5%) of the respondents indicated they were from En Route facilities, one third 

(33.7%) from Towers, and 14.3% from TRACONs, as shown in Table 2-1. As can be seen, nearly all 

(99.4%) respondents identified the type of facility they were from.
Table 2-1. Number and Proportion of 

Respondents from Each Facility Type

Facility Type

Number of 

Respondents

Percent of Total 

Respondents

En Route 1,650 50.5%

Tower* 1,101 33.7%

TRACON 467 14.3%

Other3 29 0.9%

Missing 21 0.6%

Total 3,268 100.0%

*Includes tower/TRACON combinations

2.2.1.2 Positions of Respondents and Response Rates

About 86% of all respondents were operational controllers, i.e. either Certified Professional 

Controllers (CPCs) or Developmentals; the rest were mostly supervisory personnel, as shown in 

Table 2-2. Response rates are shown where possible. The ratio of CPCs to Developmentals (82.5% to 

17.4%) was similar to their ratio in the operational controller population as of December 18, 2010 

(80.3% to 19.7%). 

Three main categories were distinguished for analysis purposes. Several management, 

supervisory and support positions were combined into a single category of “Other,” as shown in Table 

2-2. This yielded three main categories: CPCs, Developmentals, and Front Line Managers (FLMs) who 

supervise CPCs and Developmentals, as shown in Table 2-3. The overall response rate for those 

positions for which there is population data is 17.7% (3224/18204).

 

 

3 Most (18) respondents in the “other” category were from the Document Control Center (DCC), a few (8) were 

from the Air Traffic Control System Command Center (ATCSCC), and several (3) were from FAA Headquarters.
Table 2-2. Proportions of Respondents in Various ATC Positions and 

Available Response Rates

Current Position % # 

Total in 

Population

Response 

Rate

Certified Professional 

Controller (CPC) 70.9% 2,316 12,228 18.9%

Developmental 15.0% 490 3,005 16.3%

Front Line Manager 7.8% 255 1,897 13.4%

Other Positions

Traffic Manager Coordinator 2.5% 83 566 14.7%

Administrative Manager or 

Support 1.0% 33 - - 

Operations Manager 1.0% 32 350 9.1%

Misc. such as Ops Support 

Air Traffic Assistant, Support 

Specialist, etc.

0.7% 22 - - 

Supervisor, Traffic 

Management Coordinator 0.5% 15 158 9.5%

Total Other Positions 5.7 185 - - 

Total 3,246 - - 

Missing 0.7% 22 - - 

Total 100.0% 3,268 - - 

*Note: From FAA Staffing Spreadsheet dated 12/18/2010. 

Table 2-3. Number and Percentage of Respondents in 

Four Types of Positions

Current Position

% of 

Respondents

# of 

Respondents

Certified Professional 

Controller (CPC) 70.9% 2,316

Developmental 15.0% 490

Front Line Manager 7.8% 255

Other 5.7% 185

Total 99.3% 3,246

Missing 0.7% 22

Total 100.0% 3,268
The survey of Air Traffic Controllers conducted in 1999 (Della Rocco et al., 2000a) placed respondents 

in the categories of En Route/Terminal (those who controlled traffic), Flight Service Specialists (FSS)4

, 

and Management/Staff. Since FSS personnel were not included in the current survey, the two 

comparable categories of positions in the current survey is a combination of CPC/Developmentals and 

Front Line Manager/Other, as shown in Table 2-4. When comparing the results with the earlier survey, 

these two categories will be used 

Table 2-4. Position Categories of Della Rocco (2000a) and 

Comparable Categories in Current Survey

Della Rocco (2000a) 

Categories

Current Comparable 

Combined Categories

En Route/Terminal CPC/Developmental

Flight Service Not Included

Management/Staff Front Line Manager/Other

For comparative analysis within the 2010 survey results, it was decided to separate CPCs and 

Developmentals since Developmentals tend not to have schedules that include midnight shifts. 

Hence, combining them with CPCs might yield a misleading view of schedule types. It was also decided 

to separate Front Line Managers from Others since the two groups have different schedules, including 

differing exposure to midnight shifts. 

2.2.1.3 Response Rates by Facility Types

The overall response rates for CPCs in the various facilities are shown in Table 2-5. The 

response rates were 22.2% in En Route, 18.6% in TRACON, and 15.7% in Tower facilities.

Table 2-5. Response Rates of CPCs for Each Facility Type

Facility Type

Response 

Rate

Number of 

Respondents

Total 

Working

% of Respondents 

in Sample

En Route 22.2% 1,167 5,262 50.5%

Towers 15.7% 817 5,201 35.3%

TRACON 18.6% 329 1,765 14.2%

Total 18.9% 2,313 12,228 100.0%

Missing 3 

Average/Total 18.9% 2,316 12,228

2.2.1.4 Facility Characteristics 

Approximately 70% of all respondents worked at level 10 facilities or higher, as shown in Table 

2-6. The FAA classifies ATC facilities into a series of levels, where a high level number indicates a high 

volume of traffic and airspace complexity, the highest level being level 12. The largest proportion of 

 

4 In the 1999 Survey, Traffic Manager Coordinators (TMC) and Traffic Manager Supervisors (TMS) were part of 

the FSS category, along with those working in Flight Service Stations. Since TMCs and TMSs were not in the 

1999 En Route/Terminal category, they were put in the 2010 "Other" category to enable comparison between the 

1999 En Route/Terminal category and the 2010 CPC/developmental category. 
respondents (32.8%) worked at level 12 facilities, followed by level 11 facilities (21.6%), and level 10 

facilities (15.9%).5

 

Table 2-6. Proportion and Number of 

Respondents at Different Facility Levels

Facility Level

Percent of 

Respondents

Number of 

Respondents

4 0.1% 3 

5 2.4% 77

6 4.1% 132

7 7.7% 249

8 6.8% 220

9 7.1% 229

10 15.9% 514

11 21.6% 700

12 32.8% 1,061

Not known 1.6% 52

Total 100.0% 3,237

Missing 31

Total 3,268

A majority of all respondents (89.1%) worked at 24-hour facilities, as shown in Table 2-7. These 

results are consistent with the finding that most respondents work at level 10 facilities or higher and 

suggest that most survey respondents have had some exposure to 24-hour shift patterns.

Table 2-7. Proportion and Number of 

Respondents Working at 24-hour Facilities

Work at 24-hour 

Facilities?

Percent of 

Respondents

Number of 

Respondents

Yes 89.1% 2,879

No 10.9% 352

 

5 Facility levels are those which were in place at the time of the survey prior to the current classifications
Total 100.0% 3,231

Missing 37

Total 3,268

2.2.1.5 Respondents’ Age and Experience

The age ranges of the CPCs and Developmentals have a bimodal distribution that matches the 

pattern in the overall controller workforce. As shown in Table 2-8 the two peaks in respondents' 

ages were in the 46-50 year age group (24.5%) and in the 26-30 year age group (22.1%). This 

distribution is similar to that of the general controller workforce as described in the FAA document, A 

Plan for the Future: 10 Year Strategy for the Air Traffic Control Workforce 2009-2018 (2009). This 

similarity suggests that, in terms of age, the survey respondents constitute a representative sample of 

the larger population of controllers. 

Table 2-8. Proportion of CPCs and 

Developmentals in Different Age Categories

Age Category

Proportion of 

Respondents

Number of 

Respondents

25 or under 6.0% 168

26-30 22.1% 619

31-35 13.8% 386

36-40 7.8% 217

41-45 14.1% 395

46-50 24.5% 686

51-55 10.3% 287

56+ 1.3% 37

Total 100.0% 2,795

Missing - 11

Total 2,806

For CPCs and Developmentals, the distribution of years of experience in their current position 

is also bimodal, as shown in Table 2-9. A large proportion of this group has been in their current 

position for either 1–4 years (30.2%) or 20–29 years (34.0%). The bimodal distribution is most clearly 

visible when adding the first three rows together to show that 48.2% of respondents had 0–9 years of 

experience, 16.7% had 10–19 years of experience, and 34.0% had 20–29 years of experience. 

It is noteworthy that 37% of respondents had four years of experience or less in their current position. 

This possibly reflects their progression through different positions in the course of their careers.

Table 2-9. Years of Experience in Current Position of 

CPCs and Developmentals

Years of 

Experience

Proportion of 

Respondents

Number of 

Respondents

Less than 1 6.8% 190

1-4 30.2% 843
5-9 11.2% 313

10-19 16.7% 466

20-29 34.0% 949

30+ 1.0% 28

Total 100.0% 2,789

Missing - 17

Total 2,806

Yet another bimodal distribution exists in the total years of CPCs' and Developmentals' 

professional experience with ATC, as shown in Table 2-10. Adding the first three rows of Table 2-

10, it can be seen that 36.4% of these respondents had 0-9 years of professional experience; 18.9% 

had 10-19 years of experience; and 40.3% had 20-29 years of experience. On the whole, these 

respondents have been professionally affiliated with ATC for a substantial period of time, with 75.8% 

reporting five or more years of experience. 

Table 2-10. CPCs' and Developmentals' Years of Total Professional 

Experience with ATC (Including Military ATC experience)

Years of Professional 

Experience

Percent of 

Respondents

Number of 

Respondents

Less than 1 3.0% 84

1-4 21.1% 589

5-9 12.3% 344

10-19 18.9% 529

20-29 40.3% 1,126

30+ 4.3% 121

Total 100.0% 2,793

Missing 13

Total 2,806

2.2.1.6 All Respondents' Positions by Age and Experience

Developmentals were the youngest group with 96% under age 35. CPCs were older with 30% 

under age 35; FLMs and Others both had 5% under 35, as shown in Table 2-11.

Table 2-11. All Respondents' Positions by Age

Age

Developmental CPC FLM Other Total

% n % n % n % n % n 

25 or under 22% 110 3% 58 0 0 5% 168

26-30 53% 259 16% 360 2% 6 1% 2 19% 627

31-35 21% 101 12% 285 3% 8 4% 7 12% 401

36-40 2% 9 9% 208 13% 34 4% 7 8% 258

41-45 0% 1 17% 394 19% 48 20% 36 15% 479
46-50 1% 5 30% 681 36% 92 40% 73 26% 851

51-55 0% 2 12% 285 22% 55 27% 50 12% 392

56+ 0% 2 2% 35 5% 12 5% 9 2% 58

Total 100% 489 100% 2,306 100% 255 100% 184 100% 3,234

Missing 34

Total 3,268

Developmentals had the fewest years of affiliation with ATC, with 79% having fewer than five 

years of ATC experience. CPCs are more experienced with 12% having fewer than five years of 

experience. Only 1% of FLMs and Others had fewer than five years of experience, as shown in Table 

2-12. 

Table 2-12. All Respondents' Position by Years of Affiliation with ATC (Includes Military)

Years

Developmental CPC FLM Other Total

% n % n % n % n % n

Less than 1 16% 79 0% 5 - 0 - 0 3% 84

1-4 63% 307 12% 282 1% 2 1% 2 18% 593

5-9 12% 60 12% 284 4% 10 1% 2 11% 356

10-19 7% 33 22% 496 24% 61 15% 27 19% 617

20-29 1% 6 49% 1,120 61% 154 66% 122 43% 1,402

30+ 0% 2 5% 119 10% 26 17% 31 6% 178

Total 100% 487 100% 2,306 100% 253 100% 184 100% 3,230

Missing 38

Total 3,268

2.2.2 Schedules

2.2.2.1 Shift Start Times—3 Week-Bid Schedules and Week Worked

Figure 2-1 shows the start times in a one-week period of the bid schedules (in dashed red) and 

the actual work schedules (in solid black) where D = Day, A = Afternoon, and M = Midnight 

Shifts. All respondents except 106 with administrative schedules (standard business hours between 

the hours of 07:00 and 18:00 5 days a week) were asked to indicate the start times of each day in 

their 3-week bid schedules as well as their start times for each day of the week they actually worked 

in their last full work week. The frequencies of bid schedule start times were divided by 3 to match the 

times of the actual week worked. The close correspondence between the two measures indicates that 

the schedules being bid are being worked and adds validity to the schedule data. Figure 2-1 also 

shows that a relatively low proportion of shifts started between 21:00 and 24:00 (midnight shift) 

compared to earlier shift start times.
Figure 2-1. Distribution of 15,554.8 shift start times in 1 week of 3-week bid schedules (3 week bid 

schedule divided by 3) compared with distribution of 15,557 start times in week actually worked.n = 

3132 bid schedule, 3083 actual schedule (all respondents except 106 with administrative schedules, 

and 30 missing data for bid schedule, 79 missing data for actual schedule). 

Table 2-13 provides further detail on these shift start times and indicates that only 7.6% of the 

bid shifts start between 21:00 and 24:00 (midnight shifts). Of the shift start times, 84.2% fall within 

three 3-hour periods. The greatest proportion of shifts (41.4%) begins between 05:30 and 08:30, 

followed by 35.2% between 13:00 and 16:00, and 7.6% between 21:00 and 24:00. Hence there is only 

a small proportion of start times that lead to shift work during the evening circadian trough. 
Table 2-13. Proportion and Number of Bid Shift Start 

Times During Three Shift Time Periods in the Day

Important 3-hr. Intervals

% of Total Shift 

Start Times

# of Shift Start 

Times*

Between 05:30 & 08:30 41.4% 6,439.33

Between 13:00 & 16:00 35.2% 5,473.33

Between 21:00 & 24:00 7.6% 1,177.83

Sub-total 84.2% 13,090.50

All Other Start Times 15.8% 2,464.33

Total Shift Start Times 100.0% 15,554.83

*Shift start times of the 3-week bid schedule are divided by three to 

compare with those in the actual week worked schedule.

Table 2-14 provides further detail on the morning start times in Figure 2-1 and indicates that 

most of the morning start times are between 06:00 and 07:30. 

Table 2-14. Proportion and Number of Morning Shift Start Times for 

the 3-Week Bid Schedule (Divided by 3) and Actual Week Worked

Shift Start 

Times

% of Start Times 

in 3-Week Bid 

Schedule/3

# of 3-Week Bid 

Schedules/3

% of Start 

Times in 

Week Worked

# of Week 

Worked

01:00-05:29 5.5% 348.3 5.0% 345.0

05:30-05:59 15.1% 961.3 14.4% 993.0

06:00-06:29 20.8% 1,321.7 18.8% 1,304.0

06:30-06:59 28.5% 1,812.8 28.9% 1,999.0

07:00-07:29 21.6% 1,372.2 18.4% 1,272.0

07:30-07:59 6.7% 425.7 6.7% 463.0

08:00-08:29 8.6% 545.7 7.8% 543.0

Total 100.0% 6,362.0 100.0% 6,919.0

2.2.2.2 Hours Off Between Shifts

About 15.5% (1,718/11,101) of the intervals between bid shifts were only 8-9 hours long; higher 

frequency peaks occur at 15-16 hours between shifts. Figure 2-2 shows the frequency of hours off 

between shifts in a one-week period of both bid (in dashed red) and actual week worked schedules (in 

solid black) from all respondents 
Figure 2-2. Frequency of number of hours off between shifts (excluding days off) in a one-week 

period for both 3-week bid schedules (divided by three) and actual week worked schedules.n = 

11,101.5 bid intervals—33,306/3 and 10,769 actual intervals. n = 3238 bid schedules, 3189 

actually worked schedules (30 missing data for bid schedule, 79 missing data for actual 

schedule.)

Table 2-15 gives additional detail on time off between shifts in Figure 2-2 and indicates that 

about 25% of the intervals between shifts were less 11 hours. About half (51.8%, 1708/3238) of 

the survey respondents had at least one quick-turn (8 or 9 hours between shifts) in their 

described 3-week bid schedules. 

Table 2-15. Proportion and Number of Times-Off Between 

Shifts Of Less than 11 Hours in One Week of Bid Schedules

Hours Between 

Shifts

% of Shifts in 3-Week 

Bid Schedule/3

# of 3-Week Bid 

Schedules/3

8 4.7% 527.0

8.5 5.1% 565.7

9 5.6% 625.2

9.5 2.9% 318.7

10 4.8% 529.3

10.5 1.5% 165.5

Total 24.6% 2,731.3

Note: Three-week bid schedules are divided by three to apply 

to a one-week period.

2.2.2.3 Rotations

Figure 2-3 shows that among all respondents, the most frequent number of rotations within 

work weeks (not between weeks) was two per week. This was true for both the actual week 

worked and the 3-week bid schedule (divided by 3). The 2-2-1 has two rotations per week, from two 

afternoon shifts to two day shifts to the midnight shift. Figure 2-3 indicates that the number of rotations 

in the actual schedules is slightly higher than the bid schedules. That is because the bid schedules take 

into consideration schedules over a three-week period divided by three. Hence the bid schedules can 

have an average number of rotations in one week ending in 0.33, 0.5, or 0.66 as well as the integers 

0

500

1,000

1,500

2,000

2,500

3,000

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

Hours Off Between Shifts (Excluding Days Off) Bid Actual

Frequency
1,2,3,4, and 5. It can be seen that on either side of an actual week worked number (integer), there are 

bid schedules at non-integer numbers. During an actual work week, they will fall on the integer.

Figure 2-3. Number of rotations within the week actually worked (black squares) and the 3-week 

bid schedule (dashed red) divided by 3. Note: Rotations are within weeks, not between weeks. n 

= 3238 bid schedule, 3189 actual schedule (30 missing data for bid schedule, 79 missing data for 

actual schedule).

Table 2-16 provides more detail on rotations and indicates that over 90% of all respondents' 

schedules (both 3-week bid schedules and week actually worked) had one or more rotations.

0

200

400

600

800

1,000

1,200

1,400

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

# of Rotations Within Week Bid Actual

# of Schedules
Table 2-16. Number of Rotations Per Week (within Weeks) of 3-Week Bid 

Schedules (Divided by 3) and Actual Week Worked Schedules

# of Rotations 

Per Week

% of 3-Week 

Bid Schedules

# of 3-Week 

Bid Schedules

% of Actual 

Schedules

# of Actual 

Schedules

0.0 7.3% 236 9.2% 292

0.33 0.6% 19 0.0%

0.5 0.5% 15 0.0%

0.66 0.6% 19 0.0%

1.0 22.6% 732 26.5% 845

1.33 1.4% 46 0.0%

1.5 4.5% 146 0.0%

1.66 1.7% 56 0.0%

2.0 40.6% 1,316 41.5% 1,324

2.33 1.0% 31 0.0%

2.5 2.8% 92 0.0%

2.66 0.5% 17 0.0%

3.0 13.8% 446 20.2% 643

3.33 0.1% 2 0.0%

3.5 0.2% 7 0.0%

3.66 0.0% 1 0.0%

4.0 1.6% 51 2.5% 79

4.33 0.0% 0.0%

4.5 0.0% 0 0.0% 0 

4.66 0.0% 0.0%

5.0 0.2% 6 0.2% 6 

Total 100.0% 3,238 100.0% 3,189

Missing 30 79

Total 3,268 3,268

2.2.2.4 Midnight Shifts

Figure 2-4 shows that most respondents’ schedules did not include a midnight shift. Of those 

respondents who worked or bid for midnight shifts, the majority of schedules included only one 

midnight shift per week. Differences between the bid and actual schedules are due to the three-week 

bid schedules being divided by three to get an average, which can end in 0.33, 0.5, or 0.66. During an 

actual work week, the numbers are integer values. 

Table 2-17 shows details of Figure 2-4. The proportion of actual schedules that had no midnights was 

higher than the bid schedule since many bid schedules had midnights only every other week. 
Table 2-17. Number and Proportion of Midnights in 3-Week Bid Schedules

and Actual Week-Worked Schedules

# of Midnights 

Per Week

% of 3-Week 

Bid Schedules

# of 3-Week 

Bid Schedules

% of Actual 

Schedules

# of Actual 

Schedules

0.0 54.1% 1,752 62.9% 2,007

0.33 2.3% 73 

0.5 8.0% 260 

0.66 1.6% 51 

1.0 30.5% 989 31.0% 990

1.33 0.4% 14 

1.5 0.3% 11 

1.66 0.2% 8 

2.0 1.7% 56 4.7% 151

2.33 0.1% 4 

2.5 0.1% 4 

2.66 0 

3.0 0.2% 7 0.8% 26

3.33 1 

3.5 0 0 

3.66 0 

4.0 0.1% 3 0.2% 7 

4.33 0 

4.5 0 0 

4.66 0 

5.0 0.2% 5 0.3% 8 

Total 100.0% 3,238 100.0% 3,189

Missing 30 79

Total 3,268 3,268
Figure 2-4. Number of midnight shifts (start between 20:00 and 01:00) in the actual week worked 

(black squares) and the 3-week bid schedule (dashed red) divided by 3. n = 3238 bid schedule, 

3189 actual schedule (30 missing data for bid schedule, 79 missing data for actual schedule).

2.2.2.5 Number of Shifts Worked in a Week

Figure 2-5 shows that more respondents reported actually working 6 days in the previous full 

week of work than were in their reported bid schedules. There were 452 controllers who worked 6 

days in a row in their actual schedule. It could be argued that the higher number of those working a 6-

day week is a result of respondents working only one or two 6-day weeks in a three-week period. 

However, 452 is about twice the number of bid schedules containing at least one 6-day week out of 

three weeks (179), or more than a 6-day week (49) out of three weeks. Whether this additional staffing 

on the sixth day is accomplished through required and scheduled overtime or short-notice overtime 

remains to be determined. 

Over half (52.9%, 239/452) of those who worked 6-days in a row worked at least one midnight shift, 

and 15% (68/452) worked two or more midnight shifts. 

Figure 2-5. Number of shifts in the actual week worked (black squares) and in the 3-week bid 

schedule (dashed red). n = 3238 bid schedules, 3189 actual schedules (30 missing data for bid 

schedule, 79 missing data for actual schedule). 

Table 2-18 provides more detail on the number of shifts bid versus the actual schedule worked. 

While only 3.7% of respondents reported bidding for 6-day shift schedules, 14.2% of 

respondents reported actually working a 6-day schedule in the previous full week worked.

0

500

1,000

1,500

2,000

2,500

3,000

0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0

# of Shifts Worked in a Week Bid Actual

500

1,000

1,500

2,000

2,500

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

# of Midnight Shifts Within Week Bid Actual

# of Schedules # of Schedules
Table 2-18. Proportion and Number of Shifts in the 3-

Week Bid Schedule and in the Actual Week Worked

# of Shifts in

a Week

Shifts in Bid Schedule / 3 Actual Schedules

% # % # 

1.00 0.2% 8 0.1% 3 

2.00 0.2% 6 0.3% 9 

2.33 0.1% 3 0.0%

3.00 0.2% 8 0.3% 10

3.50 0.1% 4 0.0%

3.67 0.0% 1 0.0%

4.00 5.5% 178 6.6% 209

4.33 0.2% 6 0.0%

4.50 0.2% 7 0.0%

4.67 0.4% 12 0.0%

5.00 87.7% 2,832 78.2% 2,494

5.33 0.8% 25 0.0%

5.50 0.3% 10 0.0%

5.67 0.2% 6 0.0%

6.00 3.7% 119 14.2% 452

6.33 0.0% 1 0.0%

6.50 0.1% 2 0.0%

7.00 0.0% 0 0.4% 12

Total 100.0% 3,228 100.0% 3,189

Other 10

Missing 30 79

Total 3,268 3,268

Note: The number of shifts in a week consists of the number of shifts in the 21-

day bid schedules divided by three to enable comparison with the actual days 

worked. Hence the difference between the 6-day constant schedule defined later 

as 6-days in a row with a day off between. 

Only 7.2% (227/3132) of the respondents had rotating days off. This did not vary by position.

2.2.2.6 Types of Schedules in the 2010 Data

Table 2-19 shows the placement of schedules into five categories. The two largest categories 

are the same as those for the 1999 survey data. They consist of counterclockwise rapidly rotating 

schedules without midnights (RR, n = 1335) and with midnights (RRM, n = 1361). 
The third category, Straights and Slow Rotations (Days), consists of the schedules considered 

least likely to be fatiguing, and was designed to serve as a comparison group. This category 

consists of three types of schedules, none of which contains midnight or rapidly rotating shifts. 

• "Administrative Schedules" (standard business hours between the hours of 07:00 and 18:00, 5 

days a week, n=106)6

, 

• Straight shifts without midnights (n=42)

• Slow rotations without midnights (n=24)

The slow rotations without midnights involve working one week on the same shift and then working a 

different shift on the following week. This schedule, therefore, does not involve rapid rotations within the 

week. Both it and the straight shifts without midnights consist of shifts within a normal period of 

wakefulness. The n for the entire category is 172. 

Category 4 was created to examine the impact of 6-day schedules that were constant. Schedules 

in this "Constant 6-day" category required working 18 days in a 21-day period with a total of 3 days off. 

Those with these schedules worked exactly six days in a row with one day off between each set of six 

days. Respondents reported 40 such 6-day bid shifts without midnights and 74 with midnights. 

Category 5 contains 10-hour 4-days a week work schedules. There were 167 10-hour schedules of 

which 138 were rapidly rotating, 20 were straight and 5 were slowly rotating; only 4 included midnights.

Table 2-19 lists the five schedule categories.

Table 2-19. Five Categories of Bid Schedules and Frequencies

Category Abbreviation Description Percent

Frequency of 

Bid Schedules

1 RR Rapid Rotation without Midnights 41.2% 1,335

2 RRM Rapid Rotation with Midnights 42.0% 1,361

3 Straights & 

SR (Day)

Straights and Slow Rotations (Day); 

Administrative (n=106), 

Other Straights (n=43), & 

Slow Rotations without Midnights (n=23)

5.3% 172

4 Constant 6-

day

RR 6-day without Midnights (n=40) & with 

Midnights (n=74) 3.5% 114

5 10-hr 10-hr 4-day weeks 5.2% 167

Other* 2.7% 89

Total 100.0% 3,238

Missing 30

Total 3,268

* "Other" schedules consist of slowly rotating with midnights (n = 10), straight midnights (n = 5), fewer than 

10 shifts in 3 weeks (n = 24), and 7 days or over in any one week (n = 49). 

 

6 Of those who worked administrative schedules, 31% (33/106) were administrative managers or support 

personnel and were not operational (see section on administrative schedules). Although the 106 who worked 

administrative schedules did not work midnight or rotating schedules, they were more likely to work overtime than 

those with other schedules, somewhat reducing the extent to which this category could serve as an ideal 

comparison group.
2.2.2.7 Comparison of the 2010 and 1999 Schedule Data

The 2010 schedule data were temporarily re-categorized to fit the definitions of the 1999 schedule 

categories so as to enable comparison. The definitions of the Straight Shifts and the Straight 5s used 

for the 1999 data were also used for the 2010 data as follows: 

"The S5 group involved schedules in which individuals worked the same shift for 4-5 

days straight, had 1-2 days off, and returned to work a different straight shift the 

following 4-5 days. The traditional 5-day 2-2-1 was included in the CRM group. The SS 

group included schedules with the same shift for the entire 21-day period. No schedules 

with midnight shifts were included in the SS group" (p. 19, Della Rocco et al., 2000a).

As can be seen in Table 2-20, in the 2010 data there is a lower proportion of straight shifts 

without midnights. There appears to be a higher proportion of counter-clockwise rapidly 

rotating schedules, especially with midnights, than in the 1999 data. In the 2010 data, there is also 

lower proportion of slowly rotating one-week schedules both with and without midnights. 

Table 2-20. Comparison of 2010 Schedules with 1999 Schedules

1999 

Categories 1999 Schedule Description

1999 2010

% # % # 

CR Counterclockwise Rapidly 

Rotating without Midnights 29.5% 1,994 40.9% 1,335

CRM Counterclockwise Rapidly 

Rotating with Midnights 22.0% 1,486 41.5% 1,361

SS Straight Shifts without 

Midnights 10.8% 731 5.2% 169*

S5 Straight 5s Slowly Rotating 

with & without Midnights 4.6% 313 1.0% 33**

Subtotal 67.0% 4,524 88.5% 2,898

Other 33.0% 2,230 11.5% 340

Missing 0.9% 30

Total 100.0% 6,754 100.0% 3,268

Pearson's Chi Square = 146.2, df 3, p <0.0001 calculated using the subtotals of 4524 and 2872 

as column totals (i.e., not including other or missing data).

*20 straight shifts were added to SS from the 10-hour week sample in, since the 1999 

categorization included 10-hour straight shifts in its straight shift category (106 Admin, +43 

other straights +20 10hour straights = 169).

**The following were added to S5 from Table 2-15: 23 slowly rotating without midnights from 

Category 3 (Control) and 10 slowly rotating with midnights from "Other" (10+23 = 33). 

Only 67% of the schedules from the 1999 survey fit into the above categories; 88.5% of the 

schedules from the 2010 survey fit in the above categories. It is unclear what the 2230 (33%) 

"Other" schedules consisted of in the 1999 schedule data. This uncertainty makes precise schedule 

comparisons difficult. In the 2010 data, the 340 (11.5%) that did not fit into the above categories consist 

of 6-day Constants (n = 114), 10-hour 4-day shifts (without straights) (n = 147), and "Other" from Table 

2-19 (n = 89) minus slow rotations with midnights (n = 10). 

Examples of common schedules found in each of the four 1999 categories are presented in 

Table 2-21. The original table is from Della Rocco et al., 2000a (p. 20); the right column has been 
added to present corresponding data from 2010. The totals for each category are bolded in the two 

right columns following Della Rocco et al. 

Table 2-21. Sample Common Schedules in the 1999 and 2010 Schedule Data

Schedule Type Shift Schedule Type Total 1999 Total 2010

1. Straight Shifts (SS) 731 168

OOEEEEEOOEEEEEOOEEEEE 386 16

OODDDDDOODDDDDOODDDDD 84 110

OOOEEEEOOOEEEEOOOEEEE 48 3 
(10 hour schedule)

2. Counterclockwise, Rapidly 

Rotating, without Midnights (CR) 1,994 1,335

OOAAEEEOOAAEEEOOAAEEE 364 355

OOAAAEEOOAAAEEOOAAAEE 206 143

OOAABEEOOAABEEOOAABEE

(2-1-2) 187 132

OOAADEEOOAADEEOOAADEE 131 127

OOAABDEOOAABDEOOAABDE 66 23

OOAAEEEOOAAAEEOOAAAEE 43 1 

3. Counterclockwise, Rapidly 

Rotating, with Midnights (CRM) 1,486 1,342

OOAAEEMOOAAEEMOOAAEEM

(2-2-1)

448 503

OOAADEMOOAADEMOOAADEM 138 102

OOAAEEEOOAAEEMOOAAEEM

(2-3 CR with 2 weeks 2-2-1)

75 5 

OOAABEMOOAABEMOOAABEM 57 47

OOABEEMOOABEEMOOABEEM 46 87

4. Straight 5s (S5) [Slowly Rotating, 

with Midnights] 313 34

OOAAAAAOOEEEEEOOEEEEE 82 7 

OOEEEEEOOAAAAAOOEEEEE 48 5 

OOEEEEEOOAAAAAOOAAAAA 16 5 

OOAAAAAOOMMMMMOOEEEE 10 3 

 Shift codes:

O = Day off B = Midday (1000-1259)

E = Early Morning (<0800) A = Afternoon (1300-1959)

D = Day (0800-0959) M = Midnight (2000-0100

2.2.2.8 Comparison of Reported Bid and Actual Schedules in the 2010 data

Figure 2-6 compares the types of bid schedules with the types of actual worked schedules. 

Again, a higher number of respondents reported that they actually worked the 6-day schedule 

compared to those who bid for the schedule. Those who transferred to the 6-day shift schedule are 

the same respondents who bid the rapidly rotating midnight shifts. The 6-day bid schedules were 

defined as working 6-day schedules (n=114) for the entirety of the three week period (bidding to work 

18 of the 21 possible workdays). This does not include those schedules that have one or two 6-day

shifts in a 3-week period (n=65) or those schedules that have more than a 6-day shift in a 3 week bid 

period (n=49). Combining all bid schedules including at least one week of 6-day work schedules 

accounts for only 50% (228/452) of those who reported actually working a 6-day week indicating that 

the remaining 50% of those who reported actually working a 6-day week did not bid for this schedule. 
Figure 2-6. Comparison of number of bid schedules and actual schedules reported worked in a 

week. n = 3238 bid schedules including 89 other; 3189 schedules reported actually worked 

including 43 other. n = all respondents except for 30 missing on bid schedules and 79 missing on 

actual schedules. 

0

200

400

600

800

1,000

1,200

1,400

1,600

RR RRM Straights & SR (Day) 6-day 10-hr

Types of Schedules Bid Actual

# of Schedules
Table 2-22 provides details on the reported schedules bid and worked.

Table 2-22. Number and Proportion of Reported 

Schedules Bid and Worked

Type of 

Schedule

Bid Schedules Actual Schedule Worked

% # % #

RR 41.2% 1,335 43.7% 1,393

RRM 42.0% 1,361 28.6% 912

Straights & 

SR (Day) 5.3% 172 7.2% 231

Constant 

6-day 3.5% 114 14.2% 452

10hr 5.2% 167 5.0% 158

Other 2.7% 89 1.3% 43

Total 100.0% 3,238 100.0% 3,189

Missing 30 79

Total 3,268 3,268

Six-day constant schedules may be under-represented on the bid schedule since the sixth day 

can be required scheduled overtime.

2.2.2.9 Bid and Actual Schedule by Facility 

Table 2-23 shows that TRACONs differ from En Route and tower facilities by reporting a higher 

proportion of 6-day constant bid schedules and a lower proportion of rapidly rotating without 

midnight schedules. 

Table 2-23. Reported Bid Schedule by Facility Type

Type of 

Schedule

En Route Tower TRACON Total

% # % # % # % #

RR 43.7% 706 43.3% 448 36.1% 163 42.5% 1,317

RRM 45.5% 735 39.1% 404 45.5% 205 43.3% 1,344

Straights & 

SR Days 3.4% 55 7.1% 73 7.3% 33 5.2% 161

6-day 1.8% 0 3.8% 39 10.2% 46 0 114

10-hr 5.6% 91 6.8% 70 0.9% 4 5.3% 165

Total 100.0% 1,616 100.0% 1,034 100.0% 451 100.0% 3,101

Note: Pearson’s Chi-Square = 121.2, df 8, p <0.0001

The actual work schedules reported are shown in Table 2-24 and indicate that about a third of 

TRACON personnel are working a 6-day week in a given week—a higher proportion than 

personnel at the other facilities. Of these, about two thirds (92/138) also reported working midnight 

shifts. The reported number of respondents actually working 6-day schedules was higher than the 

number of respondents bidding 6-day schedules across all facility types. The schedules that show a 

corresponding drop from Table 2-23 to Table 2-24 are the RRM. 
Table 2-24. Actual Schedule by Facility Type

Type of 

Schedule

En Route Tower TRACON Total

% # % # % # % #

RR 43.8% 701 47.1% 494 40.7% 184 44.5% 1,379

RRM 35.2% 563 24.2% 254 19.5% 88 29.2% 905

Straights & 

SR Days

4.6% 74 9.7% 102 8.8% 40 7.0% 216

6-day 11.3% 180 11.9% 125 30.5% 138 14.3% 443

10-hr 5.1% 81 7.0% 73 0.4% 2 5.0% 156

Total 100.0% 1,598 100.0% 1,048 100.0% 452 100.0% 3,098

 Note: Pearson’s Chi-Square = 197.4, df 8, p <0.0001

2.2.2.10 Bid and Actual Schedule by Position

As can be seen in Table 2-25, more than half (54%) of the CPCs reported having bid schedules 

that are rapidly rotating with midnights. The FLMs' reported bid schedules are similar to the 

CPCs', except for a lower proportion of schedules with midnight shifts. A higher proportion of 

CPCs than other personnel have 10 hour 4-day a week schedule. Those in all positions have a similar 

proportion of 6-day constant shifts. Most of the Developmentals have rapidly rotating schedules without 

midnights. 

Table 2-25. Reported Types of Bid Schedules by Position

Type of 

Schedule

CPCs Developmentals FLMs Total

% # % # % # % #

RR 32.4% 722 85.3% 400 46.2% 116 42.0% 1238

RRM 53.9% 1202 5.1% 24 43.0% 108 45.2% 1334

Straights & 

SR Days 3.2% 72 5.8% 24 5.2% 13 3.8% 112

6-day 3.8% 85 3.4% 16 3.6% 9 3.7% 110

10-hr 6.7% 149 0.4% 2 2.0% 5 5.3% 156

Total 100.0% 2230 100.0% 469 100.0% 251 100.0% 2950

 Note: Pearson's Chi Square = 506.31, df 8, p <0.0001

Table 2-26 shows the reported types of schedule in the actual week worked by position. 
Table 2-26. Reported Types of Schedule in the Actual Week Worked by Position

Type of 

Schedule

CPCs Developmentals FLMs Total

% # % # % # % #

RR 36.0% 802 79.5% 376 50.0% 123 44.1% 1,301

RRM 37.0% 824 3.2% 15 24.0% 59 30.5% 898

Straights & 

SR Days

4.6% 103 9.3% 44 8.5% 21 5.7% 168

6-day 15.9% 355 7.6% 36 15.0% 37 14.5% 428

10-hr 6.5% 144 0.4% 2 2.4% 6 5.2% 152

Total 100.0% 2,228 100.0% 473 100.0% 246 100.0% 2,947

 Note: Pearson's Chi-Square = 388.3, df 8, p <0.0001

2.2.2.11 Satisfaction with Schedule Types

Figure 2-7 indicates that respondents with the 6-day constant schedule were least satisfied with 

their schedule—significantly less so than those with the rapidly rotating midnight schedules. 

Respondents were most satisfied with the 10-hour 4-day schedule and the straights and slowly rotating 

schedules. When considering CPCs separately, the 10-hour 4- day schedules were rated significantly 

higher than the Straights and SR (Day) schedules (means 3.44 versus 3.0, p =0.03). CPCs rated the 6-

day Constant schedule similarly to all respondents, significantly lower than the other schedules, as 

portrayed below. 

For all respondents, both the 10-hour 4-day schedule and the Straights and Slowly Rotating 

(days) schedule yielded a higher degree of satisfaction than the rapidly rotating without 

midnight schedule.

Figure 2-7. Level of satisfaction or dissatisfaction with schedules by all respondents. n = 164 for 

10 hour, 165 for Straights & Slowly Rotating (Day), 1316 for Rapidly Rotating, 1347 for Rapidly 

Rotating with Midnights, and 113 for the 6-day Constant Schedules. Error bars = 95% 

Confidence Intervals.

2.2.2.12 Schedule Type Related to Operational Events 

Table 2-27 indicates that 31.6% of those with the 6-day constant schedule reported that they had 

an operational event in the last year — a higher proportion than those on other schedules, 

which averaged 17%. Of those who had rapidly rotating shifts with midnights, 19% reported they had 

an operational event in the last year. Operational events consisted of an operational error, proximity 

event, or an operational deviation.

Dissatisfied Somewhat Dissatisfied Neutral Somewhat Satisfied Satisfied

1 2 3 4 5

10-hr

Straights & SR (Day)

RR

RRM

6-day Constant
Table 2-27. Proportion and Number of Operational 

Events by those with Different Bid Schedule Types

Type of Schedule

Had Operational 

Event

Total # of 

Respondents with 

% # Schedule Type

Rapidly Rotating 16.0% 210 1,316

RRM 19.3% 260 1,346

Control 15.0% 25 167

Constant 6-day 31.6% 36 114

10-hr 4-Day 14.0% 23 164

Average/Total 17.8% 554 3,107

 Note: Pearson’s Chi-Square = 22.44, df 4, p <0.0001

2.2.2.13 Schedule Type Related to Age

Table 2-28 indicates that 27% of those working the 6-day Constant schedule are between the 

ages of 46 and 50 years. 

Table 2-28. Proportion of Each Schedule Type Worked by Different 

Age Groups of Controllers

Age RR RRM

Straights & 

SR Days 6-day 10-hr Totals

% # % # % # % # % # % # 

≤25 9.1% 121 1.8% 25 5.4% 9 4.4% 5 2.4% 4 5% 164

26-30 27.1% 359 14.6% 197 12.5% 21 16.8% 19 9.0% 15 19.6% 611

31-35 14.3% 189 11.5% 155 7.7% 13 12.7% 15 9.0% 15 12.4% 387

36-40 6.3% 84 10.4% 141 5.4% 9 6.2% 7 6.0% 10 8.0% 251

41-45 10.6% 141 17.5% 237 16.7% 28 19.5% 22 21.7% 36 14.9% 464

46-50 20.9% 277 29.9% 404 32.7% 55 27.4% 31 31.9% 53 26.2% 820

51-55 9.7% 129 13.0% 176 14.9% 25 11.5% 13 16.3% 27 11.8% 370

56+ 1.8% 24 1.3% 18 4.8% 8 0.9% 1 3.6% 6 1.8% 57

Total 100% 1,324 100% 1,353 100% 168 100% 113 100% 166 100% 3,124

Note: Pearson’s Chi-Square = 246.114, df 28, p <0.0001

Table 2-29 suggests that in general, younger ATC personnel worked schedules with fewer 

midnights than did older personnel. (Table 2-29 shows the same data as Table 2-28 but with rows 

totaling 100% instead of columns.) 
Table 2-29. Proportion of Schedule Types Worked by Different Age Groups

Age RR RRM

Straights & 

SR Days 6-day 10-hr Totals

% # % # % # % # % # % # 

≤25 73.8% 121 15.2% 25 5.5% 9 3.0% 5 2.4% 4 100% 164

26-30 58.8% 359 32.2% 197 3.4% 21 3.1% 19 2.5% 15 100% 611

31-35 48.8% 189 40.1% 155 3.4% 13 3.9% 15 3.9% 15 100% 387

36-40 33.5% 84 56.2% 141 3.6% 9 2.8% 7 4.0% 10 100% 251

41-45 30.4% 141 51.1% 237 6.0% 28 4.7% 22 7.8% 36 100% 464

46-50 33.8% 277 49.3% 404 6.7% 55 3.8% 31 6.5% 53 100% 820

51-55 34.9% 129 47.6% 176 6.8% 25 3.5% 13 7.3% 27 100% 370

56+ 42.1% 24 31.6% 18 14.0% 8 1.8% 1 10.5% 6 100% 57

Total 42.4% 1,324 43.3% 1,353 5.4% 168 3.6% 113 5.3% 166 100% 3,124

 Note: Pearson’s Chi-Square = 246.114, df 28, p <0.001.

Table 2-30 shows that CPCs under age 36 work significantly fewer midnight shifts than CPCs 

age 36 and above. This analysis focuses solely on CPCs. 

Table 2-30. Midnight Shift Work of CPCs Under 36 

Years of Age Compared to CPCs Age 36 and Above 

Age Have Midnight Shift Total

% # % #

Under 36 52.9% 372 100.0% 703

36 - 55 58.7% 920 100.0% 1,568

Averages/Totals 56.9% 1,292 100.0% 2,271

 Note: Pearson’s Chi Square = 6.6, df 1, p = 0.01.

2.2.2.14 Working Over 40 Hours per Week

In addition to the data calculated from the 3-week bid schedules and actual week worked, a separate 

survey question asked, "In general, about how many hours a week do you work—including overtime?" 

The responses give a more detailed idea of the hours worked by various groups in the sample. 

Half (50.5%) of the CPCs at TRACONs reported working over 40 hours a week compared to 14% 

of En Route and 21% of Tower personnel, as shown in Table 2-31. This difference is significant. It 

confirms the results obtained earlier from analyzing the bid and actual schedules worked.
Table 2-31. Number and Percentage of Operational Personnel Who Reported 

Working Over 40 Hours a Week by Facility Type

Facility Type

% of CPCs Working 

Over 40 Hrs/Wk

# of CPCs Working 

Over 40 Hrs/Wk

Total # of 

CPCs

En Route 14.4% 168 1,164

Tower 21.1% 172 817

TRACON 50.5% 166 329

Average/Totals 21.9% 506 2,310

Note: Pearson’s Chi-Square = 195.1, df 2, p <0.0001

About 25% of all respondents reported worked over 40 hours a week including overtime, as 

shown in Table 2-32. 

Table 2-32. Number of Hours Worked Per Week 

(Including Overtime) by All Respondents

Hours Worked % of Respondents # of Respondents

20 or less 0.7% 22

21-25 0.1% 2 

26-30 0.2% 5 

31-35 0.2% 6 

36-39 0.1% 4 

40 74.0% 2,415

41-45 13.6% 444

46-50 10.0% 326

51-55 0.7% 23

Over 55 0.5% 17

Total 100.0% 3,264

Missing 4

Total 3,268

About 22% (507/2313) of CPCs reported working over 40 hours per week including overtime, as 

shown in Table 2-33. About 12% (275/2313) reported working over 45 hours per week and 1% 

(25/2313) over 50 hours per week.
Table 2-33. Number of Hours Reported Working 

Per Week (Including Overtime) by CPCs

Hours Worked % of CPCs # of CPCs

20 or less 0.6% 13

21-25 0.0% 0 

26-30 0.2% 4

31-35 0.2% 4

36-39 0.2% 4 

40 77.0% 1,781

41-45 10.0% 232

46-50 10.8% 250

51-55 0.6% 15

Over 55 0.4% 10

Total 100.0% 2,313

Missing 3 

Total 2,316

About 68% of FLMs and 84% of operations managers reported working over 40 hours per week

as shown in Table 2-34. Only 8.6% of Developmentals worked over 40 hours per week.

Table 2-34. Positions of Those Reported Working 

Over 40 Hours per Week

Position

% Working 

over 40 

Hours/Week

# Working 

Over 40 

Hours/Week Total

Operations Manager 84.4% 27 32

Front Line Manager 68.2% 174 255

Administrative Manager or 

support 54.5% 18 33

Supervisor, Traffic 

Management Coordinator 53.3% 8 15

Other 36.4% 8 22

Traffic Management 

Coordinator 26.5% 22 83

CPC - Oceanic 25.9% 15 58

CPC - Domestic 21.8% 492 2,258

Developmental - Oceanic 13.3% 2 15

Developmental - Domestic 8.4% 40 475

 Average and Totals 24.8% 806 3,246

 Missing 4 22

 Average and Totals 24.8% 810 3,268
2.2.2.15 Administrative Schedules

Table 2-35 shows that all of the administrative managers and their support personnel worked an 

administrative schedule, but that very few others did.

Table 2-36 indicates that compared to others, those with administrative schedules more 

frequently reported working over 40 hours per week. Since those with administrative schedules 

comprise over half of those in the "Straights and SR Day" schedule, this schedule type was somewhat 

less likely to function as a baseline or ideal schedule type.

Table 2-35. Proportion and Number who Reported Working 

Administrative Schedules (Between the Hours of 0700 and 1800 

Five Days a Week) by Position

Position

% Working 

Admin. 

Schedule

# Working 

Admin. 

Schedule

Total in 

Position

Administrative Manager or support 100.0% 33 33

Other 40.9% 9 22

Operations Manager 18.8% 6 32

Supervisor, Traffic Management 

Coordinator 13.3% 2 15

Developmental - Oceanic 6.7% 1 15

Developmental - Domestic 5.7% 27 475

Front Line Manager 4.3% 11 255

Traffic Management Coordinator 1.2% 1 83

CPC- Domestic 0.4% 10 2,258

CPC- Oceanic 0.0% 0 58

Total 3.1% 100 3,246

Missing 22

Total 3,268
Table 2-36. Those with Administrative Schedules Compared to 

Others on Working Over 40 Hours a Week

Facility Type

% Working 

Over 40 

Hrs/Wk

# Working 

Over 40 

Hrs/Wk Total #

Administrative schedule 46.7% 49 105

Not an administrative schedule 24.1% 761 3,159

 Average/Totals 24.8% 810 3,264

Note: Pearson’s Chi-Square = 27.8, df 1, p <0.0001

2.2.2.16 Number of Midnight Shifts by Position

Table 2-37 shows that oceanic CPCs reported having the highest average number of midnight 

shifts in three weeks, followed by domestic CPCs and FLMs. Midnight shifts are defined as those 

that start between 20:00 and 01:00.

Table 2-37. Mean Number of Midnight Shifts in 3-

week Bid Schedules by Position

Position Mean N 

CPC- Oceanic 2.62 58

CPC- Domestic 1.60 2,243

Front Line Manager 1.20 254

Other 1.00 21

Developmental - Oceanic 0.70 15

Traffic Management Coordinator 0.45 82

Supervisor, Traffic Management 

Coordinator 0.30 15

Developmental - Domestic 0.16 464

Administrative Manager or support 0.00 33

Operations Manager 0.00 31

Total 1.30 3,216

Missing 52

Total 3,268

2.2.3 Workload-related Fatigue

2.2.3.1 On-the-Job Training (OJT)

Two thirds of the CPCs reported providing on-the-job training. The GAO (2009) identified OJT as 

a possible contributor to controller fatigue. About one third (32%) of CPC respondents provided OJT 

between 1 and 9 hours a week, another third (35%) provided OJT 10 hours or more per week, and only 

one third of the CPCs did not provide OJT, as shown in Table 2-38. 

Table 2-38. Proportion and Number of CPCs 
Providing Varying Hours of OJT Per Week

Hours/Week 

Providing OJT % of CPCs # of CPCs

0 33.1% 749

1-4 15.1% 342

5-9 16.7% 379

10-14 15.8% 358

15-19 11.0% 250

20-24 6.0% 135

25 & over 2.3% 51

Total 100.0% 2264

Missing 52

Total 2,316

About 91% of the Developmentals reported typically receiving OJT. About 40% of Developmentals 

received between 10 and 19 hours per week of OJT, about 30% received 20 hours or more per week, 

and about 20% received under 10 hours per week; only about 9% of developmentals received no OJT 

as shown in Table 2-39. 

Table 2-39. Proportion and Number of Developmentals 

Who Reported Receiving Varying Hours of OJT

Per Week

Hrs/Week 

Receive OJT

% of 

Developmentals

# of 

Developmentals

0 9.3% 45

1–4 6.6% 32

5–9 15.3% 74

10-14 19.4% 94

15-19 20.8% 101

20-24 21.0% 102

25 & over 7.6% 37

Total 100.0% 485

Missing 5 

Total 490

Most (84.3%) Developmentals reported being certified on one or more positions, as shown in 

Table 2-40. 
Table 2-40. Proportion and Number of Developmentals 

Certified on One or More Positions

Certification

% of 

Developmentals

# of 

Developmentals

Certified on One or 

More Positions 84.4% 391

Not Certified on One or 

More Positions 15.6% 72

Total 100% 463

Missing 27

Total 490

2.2.3.2 Workload/Staffing

Figure 2-8 indicates that CPCs, Developmentals, and FLMs reported that they could usually

“keep up with their workload”. However, CPCs did not always perceive staffing levels as 

adequate. When CPCs were asked to indicate whether there were enough qualified controllers on their 

shifts to do the work safely, their average rating was 3.5 on a scale of 1-5 (between "Sometimes" and 

"Usually"). 

Figure 2-8. Respondents in three job categories rate workload and adequacy of staffing on their 

shifts. (n = CPCs 2295, Developmentals 473-477, FLMS 221-222.) Error bars = 95% Confidence 

Intervals.

Figure 2-9 indicates that CPCs from TRACONs were less likely to perceive staffing levels as 

adequate compared to CPCs from other facilities. CPCs across all domains indicated that they 

were “usually” able to comfortably keep up with their workload.

Never Rarely Sometimes Usually Always

1 2 3 4 5

I can comfortably keep up with my workload.

There are enough qualified controllers on my

shifts to do the work safely.

CPCs Devel. FLMs

Never Rarely Sometimes Usually Always

1 2 3 4 5

I can comfortably keep up with my workload.

There are enough qualified controllers on my

shifts to do the work safely.

En Route Tower TRACON
Figure 2-9. CPCs from three facility types rate workload and adequacy of staffing on their shifts.

(n = En Route 1161, Tower 806-9, TRACON 324-7.) Error bars = 95% Confidence Intervals.

2.2.3.3 Supervisor Support, Staffing Support, and Fatigue Safety Culture

Figure 2-10 indicates that respondents in all three positions feel that their supervisors support 

them in work-related activities. Work-related activities include providing a support person or 

decombining a position if requested and if staffing permits. 

Respondents in all three positions see less support from their supervisors in providing fatiguerelieving breaks or rotations when requested. These respondents also report that they are less 

comfortable asking for such breaks or rotations than asking for assistance for work-related 

activities. Figure 2-10 also indicates that there might not be enough staffing to allow for fatiguerelieving measures such as providing a support person or decombining a position. The average rating 

for respondents in all three positions on "There is enough staffing to enable a position to be 

decombined" was between "Sometimes" and "Frequently" (3.4). These respondents indicate that it isn't 

often (between "rarely" and "sometimes") that they have to work without a support person when they 

feel they need one (2.8) or in a combined position that they feel should be decombined (2.6). 

 Never Rarely Sometimes Frequently Usually

1 2 3 4 5

My supervisor would provide a support person if I

requested one and staffing permitted.

My supervisor would decombine a position if I

requested it and staffing permitted.

I feel comfortable requesting a support person.

I feel comfortable asking that a position be

decombined.

There is enough staffing to enable a support

person.

When I am fatigued at work, my supervisor

would provide a break or rotation if I requested

one and staffing permitted.

When I am fatigued at work, there is enough

staffing to allow a break or rotation.

There is enough staffing to enable a position to

be decombined that I believe should be

decombined.

I work without a support person when I feel I

need one (e.g. a radar associate or handoff

controller).

When I work a combined position, I believe it

should be decombined. 

When I am fatigued at work, I feel comfortable

asking for a break or rotation.

CPCs Devel. FLMs
Figure 2-10. Respondents in three job categories rate supervisor support, staffing, and fatigue 

safety culture. (n = 2163-2296 CPCs, 301-472 Developmentals, 171-217 FLMs.) Error bars = 

95% Confidence Intervals

Figure 2-11 shows differences in the three facility types regarding perceived supervisor 

support, staffing, and fatigue safety culture. En Route CPCs perceived the most supervisory 

support regarding work activities and felt most comfortable requesting this support. TRACON CPCs 

indicate a higher rate of staffing problems, a higher rate of working without a support person when they 

feel they need one, or working a combined position when they feel it should be decombined. When 

fatigued, CPCs from Towers indicate a higher frequency of supervisors providing breaks if requested 

and more comfort in asking for these breaks.

Figure 2-11. CPCs from three facility types rate supervisor support, staffing, and fatigue safety 

culture. (n = En Route 1131-1163, Tower 702-813, TRACON 309-324.) Error bars = 95% 

Confidence Intervals.

 Never Rarely Sometimes Frequently Usually

1 2 3 4 5

My supervisor would provide a support person if I

requested one and staffing permitted.

I feel comfortable requesting a support person.

My supervisor would decombine a position if I

requested it and staffing permitted.

I feel comfortable asking that it be decombined.

There is enough staffing to enable a support person.

There is enough staffing to enable a position to be

decombined that I believe should be decombined.

When I am fatigued at work, there is enough staffing

to allow a break or rotation.

When I am fatigued at work, my supervisor would

provide a break or rotation if I requested one and

staffing permitted.

I work without a support person when I feel I need

one (e.g. a radar associate or handoff controller).

When I work a combined position, I believe it should

be decombined. 

When I am fatigued at work, I feel comfortable

asking for a break or rotation.

En Route Tower TRACON
2.2.4 Rotation and Breaks

CPCs indicate that position rotations utilized to reduce fatigue are "somewhat adequate"; FLMs 

indicate that they are more adequate, as shown in Figure 2-12. 

Figure 2-12. Respondents in three job categories rate adequacy of position rotation to reduce 

fatigue. (n = 2288 CPCs, 468 Developmentals, 214 FLMs.) Error bars = 95% Confidence 

Intervals.

Of all the CPCs, those from TRACONs rate position rotations for reducing fatigue as least 

adequate; CPCs from Towers rate the rotation as most adequate, as shown in Figure 2-13. 

Figure 2-13. CPCs from three facility types rate adequacy of rotation to reduce work-related 

fatigue. (n = 1152 En Route, 809 Tower, 326 TRACON.) Error bars = 95% Confidence Intervals.

Table 2-41 shows that most non-meal breaks are reported to be between 10 and 50 minutes 

long. Respondents were asked, "On average, about how long are your breaks (non-meal)?" FLMs

reported a higher proportion of non-meal breaks less than 20 minutes in length (about 17% as opposed 

to 4% and 7% for the CPCs and Developmentals respectively). 

Table 2-41. Reported Length of Non-Meal Breaks by Position

CPCs Developmentals FLMs

Break Length % # % # % #

Under 10 minutes 0.1% 3 0.2% 1 8.6% 22

10-19 minutes 4.3% 100 6.9% 34 8.6% 22

20-29 minutes 41.5% 961 39.8% 195 20.0% 51

30-39 minutes 39.3% 910 39.8% 195 31.0% 79

40-49 minutes 10.8% 250 9.6% 47 13.7% 35

50-59 minutes 2.7% 63 1.2% 6 2.0% 5 

Not At All Adequate Somewhat Adequate Extremely Adequate

1 2 3 4 5

In general, are positions adequately rotated

to reduce work-related fatigue where you

work?

CPCs Devel. FLMs

Not At All Adequate Somewhat Adequate Extremely Adequate

1 2 3 4 5

In general, are positions adequately rotated to

reduce work-related fatigue where you work?

En Route Tower TRACON
60+ minutes 0.8% 19 0.8% 4 0.8% 2 

Missing 0.4% 10 1.6% 8 15.3% 39

Total 100.0% 2,316 100.0% 490 100.0% 255

Figure 2-14 shows these data as an average reported length of break time. FLM reported breaks 

are significantly shorter than CPCs, but not than Developmentals.

Figure 2-14. Average reported non-meal break time by position. (n = 2306 CPCs, 482 

Developmentals, 214 FLMs.) Error bars = 95% Confidence Intervals.

Figure 2-15 indicates shorter reported non-meal break times for CPCs from TRACONs than from 

the other facilities.

Figure 2-15. Average CPC reported non-meal break time by facility. (n = 1164 En Route, 813 

Tower, 328 TRACON) Error bars = 95% Confidence Intervals.

As can be seen in Table 2-42, most reported breaks occur between every 1 hour and 15 minutes 

and 1 hour and 30 minutes. Respondents were asked, "On average, how frequently do you have 

breaks when you are on position?" A high proportion of FLM responses are missing (21%), indicating 

that this question may not have been relevant for them. Another 20% of FLMs indicated that their 

breaks occurred every 2.5 hours or longer. 

Table 2-42. Frequency of Reported Breaks by Position

CPCs Developmentals FLMs

Break Frequency % # % # % #

Every 30 min. or less 0.1% 3 0.4% 2 0.0% 0 

Every 45 min. 0.5% 12 1.2% 6 1.6% 4 

Every 1 hr. 7.5% 174 11.6% 57 7.8% 20

Every 1 hr. 15min. 28.8% 666 30.2% 148 16.9% 43

Every 1 hr. 30 min. 39.3% 911 35.3% 173 19.2% 49

Every 1 hr. 45 min. 14.6% 339 9.8% 48 6.7% 17

Every 2 hrs. 6.4% 148 7.6% 37 5.5% 14

Every 2 hrs. 15 min. 0.6% 15 0.2% 1 1.2% 3 

Every 2 hrs. 30 min. or more 0.7% 17 0.2% 1 20.0% 51

Missing 1.3% 31 3.5% 17 21.2% 54

<10min 10-19min 20-29min 30-39min 40-49min 50-59min 60+min 

On average, about how long are your breaks

(non-meal)?

CPCs Devel. FLMs

<10min 10-19min 20-29min 30-39min 40-49min 50-59min 60+min 

On average, about how long are your breaks

(non-meal)?

En Route Tower TRACON
Total 100.0% 2,316 100.0% 490 100.0% 255

Figure 2-16 shows the averages of these responses and indicates that the average reported time 

between breaks is less than 90 minutes for CPCs and Developmentals, but significantly longer 

for FLMs. 

Figure 2-16. Responses to "On average, how frequently do you have breaks when you are on 

position?" by position. (n = 2285 CPCs, 473 Developmentals, 201 FLMs.) Error bars = 95% 

Confidence Intervals.

Figure 2-17 indicates that the average reported time between breaks for CPCs is less than 90 

minutes for En Route and TRACON.

Figure 2-17. CPC responses to "On average, how frequently do you have breaks when you are 

on position?" by facility type. (n = 1157 En Route, 806 Tower, 321 TRACON) Error bars = 95% 

Confidence Intervals.

Table 2-43 indicates that 76% of CPCs reported feeling that more breaks could safely be taken 

during low workload periods compared to 51% of FLMs. Conversely, over 25% of FLMs did not 

feel that more breaks could be taken during low workload periods.

Table 2-43. Responses to "Do you feel that more breaks could be safely taken during 

low workload periods?" by Position

Positions

Yes No N/A Don't know Total

% # % # % # #

CPCs 76.1% 1,751 7.7% 177 16.2% 373 2,301

Developmentals 59.5% 289 5.1% 25 35.4% 172 486

FLMs 50.6% 121 25.5% 61 23.8% 57 239

Total 71.4% 2,161 8.7% 263 19.9% 602 3,026

Note: Pearson’s Chi-Square = 195.1, df 4, p <0.0001.

Table 2-44 below indicates that 85% of CPCs in En Route facilities felt that breaks could be 

safely taken during periods of low workload compared to Towers (66%) and TRACONs (72%). 

 <30min 45min 60min 75 min 90min 105min 120min 135min >150min 

Time between breaks

CPCs Devel. FLMs

Time between breaks

En Route Tower TRACON

 <30min 45min 60min 75 min 90min 105min 120min 135min >150min 
Table 2-44. CPCs' Responses to "Do you feel that more breaks could be 

safely taken during low workload periods?" by Position

Facilities

Yes No N/A Don't know Total

% # % # % # #

En Route 84.7% 982 3.4% 39 11.9% 138 1,159

Tower 65.6% 534 13.1% 107 21.3% 173 814

TRACON 71.6% 234 9.5% 31 19.0% 62 327

Average/Totals 76.1% 1,750 7.7% 177 16.2% 373 2,300

 Note: Pearson’s Chi-Square = 112.5, df 4, p <0.0001.

Of those who responded that more breaks could be safely taken during periods of low 

workload, Table 2-45 indicates that about 36% of CPCs, 31% of FLMs, and 18% of 

Developmentals felt that these conditions could occur 7 or more times a week.

Table 2-45. Responses to "If yes, about how often do these low workload periods (where 

breaks could be safely taken) occur during your typical week?" by Position

Low Workload 

Periods

 CPCs Developmentals FLMs Total

% # % # % # % #

1-2 times a week 8.1% 153 12.5% 46 9.1% 15 8.9% 214

3-4 times a week 23.0% 434 22.6% 83 18.9% 31 22.7% 548

5-6 times a week 22.1% 417 20.7% 76 15.2% 25 21.4% 518

7-8 times a week 10.2% 192 7.4% 27 9.1% 15 9.7% 234

Over 8 times a 

week 26.2% 495 11.4% 42 22.6% 37 23.7% 574

N/A or don't know 10.4% 196 25.3% 93 25.0% 41 13.6% 330

Total 100.0% 1,887 100.0% 367 100.0% 164 100.0% 2,418

Note: Pearson's Chi Square = 109.3, df 10, p <0.0001.

There were no significant differences between CPCs at different facility types on this question. 

CPCs prefer hourly breaks when traffic is busy or workload is high (61.5%) or when providing 

OJT instruction (44.0%). CPCs prefer breaks every 1 1⁄2 hours when traffic is light or workload is 

low (40.1%). It can be seen from Table 2-46 that only 35% of CPCs prefer a break after one hour when 

traffic is light or workload is low, whereas 62% prefer a break after one hour when traffic is busy or 

workload is high. When providing OJT instruction 44% of respondents prefer a break after one hour. 
Table 2-46. Preferred Break Intervals of CPCs in Three Different Conditions

 

When Traffic is 

Light or 

Workload is Low

When Traffic is 

Busy or Workload 

is High

When Providing 

OJT Instruction

Break Interval % # % # % #

After 1/2 hr. 9.6% 221 5.0% 115 1.0% 22

After 1 hr. 34.6% 799 61.5% 1,420 44.0% 1,009

After 1 1/2 hr. 40.1% 927 28.2% 651 35.1% 805

After 2 hrs. 14.7% 339 5.0% 116 6.1% 140

N/A or Don't know 1.0% 24 0.3% 6 13.8% 316

Total 100.0% 2,310 100.0% 2,308 100.0% 2,292

2.2.4.1 Factors Supporting Alertness (or Not)

CPCs and Developmentals said that CPC staffing levels were less than sufficient to support 

optimal alertness. As shown in Figure 2-18, the number of hours worked and the overall workload 

were seen as about right. However, traffic complexity and volume were seen as less than sufficient to 

support optimal alertness, as were the length of time between the breaks, the length of breaks, and 

rotations of positions. Developmentals felt that there were not enough hours of on-the-job training. 
Figure 2-18. Responses by position to "Please indicate the extent to which you feel the following factors 

are currently at about the right level to support your optimal alertness." n = CPCs 1887 (give OJT) - 2299, 

Developmentals 425-476, FLMs 170 (give OJT) - 210. Error bars = 95% Confidence Intervals.

Figure 2-19 shows CPC respondents from all facilities, especially TRACONs, rated low levels of 

staffing as negatively impacting their optimal alertness. CPCs at all facilities rated hours 

providing on-the-job training and overall workload as about right.

 Not nearly enough Not enough About right Too much Way too much

1 2 3 4 5

Number of hours

worked

Hours providing or

receiving on-the-job

training

Overall workload

Length of time between

breaks (non-meal)

Traffic complexity 

Length of breaks (nonmeal)

Traffic volume

Rotation of positions

CPC staffing levels 

CPCs Devel. FLMs
Figure 2-19. CPCs responses by facility to "Please indicate the extent to which you feel the 

following factors are currently at about the right level to support your optimal alertness". n = En 

Route 890 (give OJT) - 1159, Tower 705 (give OJT) - 814, TRACON 291(give OJT) - 327. Error 

bars = 95% Confidence Intervals.

2.2.4.2 Respondents' Comments on Rotations and Breaks

The most frequent comment related to alertness was that positions were staffed unnecessarily 

when traffic levels were low, as shown in Figure 2-20. This comment was made by 14.4% (49/340) 

of the 340 respondents (out of 3268) who chose to enter comments in the free text field at the end of 

this section. These comments support the ratings in Table 2-43, Table 2-44, and Table 2-45, and 

provide a context for understanding these ratings. 

The next most frequent comment, made by 14.1% (48/340) of those who commented, was related to 

the first: low workload creates difficulties for controllers. The third most frequent comment (13.5%, 

46/340) was that air traffic controller workload is highly variable. 

 Not nearly enough Not enough About right Too much Way too much

1 2 3 4 5

Number of hours

worked

Hours providing or

receiving on-the-job

training

Overall workload

Length of time between

breaks (non-meal)

Traffic complexity 

Length of breaks (nonmeal)

Traffic volume

Rotation of positions

CPC staffing levels 

En Route Tower TRACON
Figure 2-20. Free text comments provided after questions on workload-related fatigue, shown as 

percentage of those who answered this question (n = 340). Respondents could make multiple 

comments, hence percentages do not add to 100%. 

2.2.5 Stress-Related Fatigue

Figure 2-21 shows that on average, CPCs, Developmentals, and FLMs felt somewhat stressed at 

work, with CPCs feeling less stressed than the Developmentals or FLMs.

Figure 2-21. Responses to "In general, how stressed do you feel at work?" by position. n = 2297 

CPCs, 481 Developmentals, 254 FLMs. Error bars = 95% Confidence Intervals.

Figure 2-22 indicates that CPCs from TRACONs described themselves as more stressed than 

CPCs from other facilities. This difference was statistically significant.

0% 5% 10% 15%

Would not ask for a break

Single controllers on mid shifts do not get breaks

Too many hours worked per week

High or steady traffic is in demand for training

Supervisors do not get sufficient breaks

OJT is not serving needs of developmentals

Training developmentals is fatiguing

Traffic in NAS is at historically low levels

Complexity caused by weather, airspace design, etc

Breaks, need to be longer or more frequent 

Staffing is too low, or low at certain times

Supervision can be improved

Workload is highly variable 

Low workload is challenging

Positions staffed when not enough traffic 

Not at all Somewhat Very much

1 2 3 4 5

In general, how stressed do you feel at work?

CPCs Devel. FLMs
Figure 2-22.CPCs' responses to "In general, how stressed do you feel at work?” by facility. 

n = 1160 En Route, 809 Tower, 325 TRACON. Error bars = 95% Confidence Intervals.

There were wide discrepancies between sources of stress for CPCs, Developmentals, and 

FLMs. As shown in Figure 2-23, for all three positions, "upper management decisions" was the primary 

source of stress, although CPCs saw this as contributing far more to stress than did Developmentals 

and FLMs. After that, CPCs saw work/life balance, relations with supervisors, and work schedules as 

sometimes contributing to their stress. Work/life balance and work schedule both figured highly for 

FLMs as well, although not relations with supervisors. Finally, for Developmentals, responsibility for 

aviation safety was their second most frequent source of stress, but in general, Developmentals were 

not as stressed by the typical work-place stressors as were CPCs and FLMs. 

Figure 2-23. Responses by position to "If stressed, to what extent do you feel that the following 

contribute to your stress?". n = 2211-2219 (Other = 1199) CPCs, 473-475 (Other 306) 

Developmentals, and 236-9 (117 Other) FLMs. Error bars = 95% Confidence Intervals.

Figure 2-24 shows that workload was seen as a greater source of stress for CPCs from 

TRACONs than from other facilities; work/life balance and work schedule also were greater 

sources of stress for CPCs from TRACONS than from En Route centers. 

Not at all Somewhat Very much

1 2 3 4 5

In general, how stressed do you feel at work?

En Route Tower TRACON

Not at all Sometimes Very much

1 2 3 4 5

Upper management decisions

Work/life balance

Relations with supervisors

Work schedule

Responsibility for aviation safety

Workload

Relations with co-workers

Other

CPCs Devel. FLMs
Figure 2-24. CPCs responses by facility type to "If stressed, to what extent do you feel that the 

following contribute to your stress?". n = 1101-8 (592 Other) En Route, 785-7 (431 Other) Tower, 

and 321-322 (175 Other) TRACON. Error bars = 95% Confidence Intervals.

2.2.5.1 Commute Times and Second Jobs

Table 2-47 shows that 95% of the respondents drive to work alone. About 4% car pool and 0.2% 

take public transportation. 

Table 2-47. Responses to "How do you 

usually get to and from work?"

Method of Transportation Percent Frequency

Drive alone 95.1% 3,084

Car pool 3.8% 122

Take public transportation 0.2% 8 

Other 0.9% 28

Missing 26

Total 100.0% 3,268

Figure 2-25 indicates that the mean one-way commute time range was midway between 16 and 

45 minutes (about 30 minutes) and was shorter during low traffic periods such as midnight 

shifts. This difference was statistically significant.

Not at all Sometimes Very much

1 2 3 4 5

Upper management decisions

Relations with supervisors

Work/life balance

Work schedule

Workload

Responsibility for aviation safety

Relations with co-workers

Other

En Route Tower TRACON
Figure 2-25. Mean range of minutes of the typical one-way commute time to work. n = 2633 

(midnights) to 3212 (early). Error bars = 95% Confidence Intervals.

Figure 2-26 shows that CPCs, Developmentals, and FLMs described the mean cost-of-living for 

the area where they lived as above average.

Figure 2-26. Mean estimated cost-of-living for area lived in. n = 2306 CPCs, 488 Developmentals, 

and 254 FLMs. Error bars = 95% Confidence Intervals.

Figure 2-27 shows that cost of living ratings from TRACON CPCs were higher than cost of living 

ratings from CPCs at Towers and En Route Centers. 

Figure 2-27. CPCs' mean estimated cost-of-living for area lived in. n = 1162 En Route, 814 Tower, 

and 329 TRACON. Error bars = 95% Confidence Intervals.

7.6% (246/3248) of respondents had a second paid job in addition to their FAA job. There were no 

statistical differences between positions or facilities in the response to this question. Of those that did 

have a second paid job, more than half (61.2%) worked under 14 hours per week at their second job as 

shown in Table 2-48.

Extremely low Somewhat low About average Somewhat high Extremely high

1 2 3 4 5

Do you live in a low or

high cost-of-living area?

CPC Develop. FLM

Extremely low Somewhat low About average Somewhat high Extremely high

1 2 3 4 5

Do you live in a low or

high cost-of-living area?

En Route Tower TRACON

1-15 16-30 31-45 46-60 61-75 76-90 90-105 106-120+ 

Day shifts (i.e., start between 0800 & 0959)

Afternoon shifts (i.e., start between 1300 & 1959)

Mid-day shifts (i.e., start between 1000 & 1259)

Early shifts (i.e., start before 0800)

Midnight shifts (i.e., start between 2000 & 0100)
Table 2-48. Number of Hours Worked per Week 

at a Second Paid Job in Addition to an FAA Job

Hours Percent Frequency

Less than 5 15.6% 37

5–9 24.5% 58

10–14 21.1% 50

15–19 16.0% 38

20–24 13.5% 32

25–29 4.2% 10

30+ 5.1% 12

Missing or N/A 3,031

Total 100.0% 3,268

2.2.6 Sleep Times and Naps

2.2.6.1 Reported Sleep

Consistent with previous research, sleep time reported by all respondents before an early shift 

(6.3 hours) was significantly shorter than sleep time before later start times. Figure 2-28 

graphically portrays the average sleep times before the various shifts and Table 2-49 provides further 

details. It can be seen that reported sleep time before an afternoon shift was 7.7 hours, before a midday shift was 7.3 hours, before a day shift was 6.7 hours, and before an early shift was 6.3 hours. 

Reported sleep time was less on an early shift after a very quick turn—5.4 hours—and was 3.1 hours 

on a quick turn before a midnight shift.

Figure 2-28. Hours of reported sleep by all respondents before various shifts in 2010 survey. n = 

1226-3125. Error bars = 95% Confidence Intervals.

2 3 4 5 6 7 8 9 10 11

Before an afternoon shift (starts between 1300 &1959)

Before a mid-day shift (starts between 1000 & 1259) 

Before a day shift (starts between 0800 & 0959) 

Before an early shift (starts before 0800)

Before a midnight shift after another midnight shift 

Before an early shift after a very quick turn-around

Before a midnight shift (starts between 2000 & 0100)

after a very quick turn-around 

Hours of Sleep
Table 2-49. Reported Hours of Sleep before Various Shifts 

in 2010 Survey by all Respondents

Shift Type

Mean 

Hours 

Slept n 

Before an afternoon shift (starts between 

13:00 and19:59) 7.7 2,987

Before a mid-day shift (starts between 10:00 

and 12:59) 7.3 2,431

Before a day shift (starts between 08:00 and 

09:59) 6.7 2,434

Before an early shift (starts before 08:00) 6.3 3,126

Before a midnight shift after another midnight 

shift 5.5 1,228

Before an early shift during a very quick turnaround 5.4 2,819

Before a midnight shift (starts between 2000 

and 0100) during a very quick turn-around 3.1 1,959

Figure 2-29 indicates that on Regular Days Off (RDOs) after a midnight shift respondents 

reported sleeping an average of 10.2 hours. This includes an average of 4.8 hours of sleep 

during the day. On other RDOs, they reported sleeping an average of 8.3 hours. Della Rocco et al. 

(2000a) in the 1999 survey found an average sleep during the day of 4.3 hours after the midnight shift; 

the amount in the associated field study was 4.5 hours (Della Rocco & Nesthus, 2005). 

Figure 2-29. Reported hours of sleep on Regular Days Off (RDOs) by all respondents. n = 2112 

on RDOs after a midnight shift and 3065 on other RDOs. Error bars = 95% Confidence Intervals.

As shown in Figure 2-30, those with rapidly rotating schedules without midnight shifts (RR) in 

their bid schedule reported sleeping slightly but statistically significantly more before all shifts 

than those with midnight shifts (RRM) in their bid schedules. 

2 3 4 5 6 7 8 9 10 11

On RDOs AFTER a 

midnight shift

On other RDOs

Reported Hours of Sleep
Figure 2-30. Comparison of number of hours of sleep reported by those with rapidly rotating shifts 

with and without midnights in their bid schedules. (n = 262-1290 RR, 758-1335 RRM.) Error bars 

= 95% Confidence Intervals.

2.2.6.2 Naps

Most respondents (81.9%) expressed the belief that naps would increase their alertness at work, 

as shown in Table 2-50. However, 4.4% indicated that that naps would not increase their alertness at 

work. 

Table 2-50. Responses to "Do you feel 

that naps taken on breaks at work would 

increase your alertness at work?"

Response Percent Frequency

Yes 81.9% 2,653

No 4.4% 144

N/A Don't know 13.6% 441

Total 100.0% 3,238

Missing 30

Total 3,268

Those who felt that naps taken on breaks at work would increase their alertness had a higher 

mean number of midnight shifts in their 3-week bid schedule than those that who did not. The 

means were 1.4 midnight shifts versus 0.08 midnight shifts, t = 5.3, df 166.8 (equal variances not 

assumed), p <0.0001.

Figure 2-31 shows that the largest proportion of respondents would prefer naps during their 

breaks on the early shifts, followed by the midnight shifts. However, only about half of the 

respondents had midnight shifts. 

2 3 4 5 6 7 8 9 10 11

On other RDO/on days off

Before an afternoon shift

Before a midday shift

Before a day shift

Before an early shift

Before a mid after another mid

Before an early shift after a very quick turn-around

After a midnight shift

Before a mid after a very quick turn-around

Hours of Sleep RR RRM
Figure 2-31. Responses to "If yes [naps taken on breaks at work would increase your alertness], 

on which shifts(s) would naps be of most benefit? (Select all that apply.)”. n = 738-2584. Error 

bars = 95% Confidence Intervals.

Respondents who worked midnight shift schedules felt that naps would provide benefits both 

on early and on midnight shifts as shown in Figure 2-32.

Figure 2-32. Responses of those without and with midnight shifts on their bid schedules to "If yes 

[naps taken on breaks at work would increase your alertness], on which shifts(s) would naps be 

of most benefit? (Select all that apply.)”. n = 416-1355 for No Mids, and 322-1229 for Mids. Error 

bars = 95% Confidence Intervals.

2.2.7 Sleep Quality and Shifts

2.2.7.1 Restfulness of Sleep Between Shifts

Figure 2-33 shows that respondents reported feeling least rested after very quick turn-arounds 

before midnight shifts and before early shifts. This is followed by sleep between successive 

midnight shifts and between successive early shifts. 

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Early shift (shift starts before 0800)

Midnight shift (shift starts between 2000 & 0100)

Day shift (shift starts between 0800 & 0959)

Afternoon shift (shift starts between 1300 & 1959)

Mid-day shift (shift starts between 1000 & 1259)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Early shift (shift starts before 0800)

Midnight shift (shift starts between 2000 & 0100)

Day shift (shift starts between 0800 & 0959)

Afternoon shift (shift starts between 1300 & 1959)

Mid-day shift (shift starts between 1000 & 1259)

No Mids Mids
Figure 2-33. Responses to "How rested do you normally feel after sleep..." between various shifts 

and days off. n = 1813-3186. Error bars = 95% Confidence Intervals.

2.2.7.2 Sleep Patterns During the Work Week

Figure 2-34 indicates that the most frequent sleep problem is awakening in the middle of a sleep 

period, followed by early awakening and being unable to sleep. 

Figure 2-34. Frequency of various sleep patterns and habits. n = 3230-3238. Error bars = 95% 

Confidence Intervals.

Schedule differences exist regarding awakening in the middle of a sleep period. Awakening 

occurred more frequently for those on the 6-day Constant schedule and less frequently for those on the 

10-hour 4-day schedule than for those on many of the other schedules as shown in Figure 2-35.

 Definitely not rested Not very rested Moderately rested Quite rested Extremely rested 

1 2 3 4 5

between successive days off?

on the last night of your RDOs?

between successive afternoon

shifts 

between successive mid-day

shifts 

between successive day shifts 

between successive early shifts 

between successive midnight

shifts 

before early shifts after a very

quick turn-around from an

afternoon shift?

before midnight shifts after a very

quick turn-around from an early

shift?

 Never Rarely Sometimes Frequently Usually

1 2 3 4 5

Do you awaken in the middle of your sleep period?

Do you awaken earlier than you want to?

Are you unable to sleep when you want to?

Do you drink alcohol in the hour before you sleep?
Figure 2-35. Mean responses to "Do you awaken in the middle of your sleep period?" by 

schedule type. MS 3.6, df 4, p =0.01. The 10-hour 4-day schedule was significantly different from 

all but the Rapidly Rotating (RR) schedule. The 6-day Constant schedule was significantly 

different from all but Straights and Slowly Rotating (Day) schedule. Error bars = 95% Confidence 

Intervals.

Schedule differences exist regarding being unable to sleep when wanted. Being unable to sleep 

when wanted occurred more frequently for those on the 6-day Constant schedule and less frequently 

for those on the 10-hour 4-day schedule than for those on many of the other schedules as shown in 

Figure 2-36. Those with midnight shifts in their bid schedule reported being less likely to be able to 

sleep when they wanted to than those without midnights. (Means = 3.1 versus 2.9, t = 3.4, df 3228, p 

<0.001)

Figure 2-36. Mean responses to "Are you unable to sleep when you want to?" by schedule type. 

MS 5.4, df 4, F = 4.8, p <0.001. The 10-hour 4-day schedule was significantly different from all 

but Straights & Slowly Rotating (Day). The 6-day Constant was significantly different from all but 

the Rapidly Rotating with Midnights schedules. Error bars = 95% Confidence Intervals.

Never R arely S ometimes F requently Usually

1 2 3 4 5

6-day Constant

Straights & SR (Day)

RRM

RR

10-hr

Never Rarely Sometimes Frequently Usually

1 2 3 4 5

6-day Constant

RRM

RR

Straights & SR (Day)

10-hr
2.2.7.3 Causes of Difficulty in Sleeping

Average consumption of caffeine seems to be moderate, under 3-4 servings a day, as shown in 

Figure 2-37. It is not known when, relative to a sleep period, the caffeine was consumed. 

Figure 2-37. Number of caffeine servings typically consumed by respondents in a 24-hour period. 

(n = 3244). Error bars = 95% Confidence Intervals.

Table 2-51 indicates that while over half of respondents consumed 1-2 or 0 servings of caffeine 

per 24-hour period, 16% of respondents consume 5 or more servings of caffeine per 24-hour 

period.

Table 2-51. Proportion and Frequency of Caffeine 

Servings in a 24-hour Period by All Respondents

Number of Caffeine 

Servings Per 24 Hour 

Period Percent Frequency

0 13.0% 423

1-2 42.8% 1,389

3-4 28.1% 913

5-6 9.9% 320

7-8 3.7% 121

9-10 1.4% 47

11+ 1.0% 31

Total 100.0% 3,244

Missing 24

Total 3,268

As shown in Figure 2-38, the highest proportion of respondents (68%) identified "shift work" as 

a reason they had trouble sleeping. This was followed by "keyed up, can't relax" by 53% of the 

respondents. Many of the respondents reported non-schedule impediments to sleep. Examples are 

disruptions by partners/children/pets and amount of caffeine ingested. 

0 1-2 3-4 5-6 7-8 9-10 11+ 

About how many servings of caffeine (e.g., coffee,

tea, soda, energy drinks, NoDoz, etc.) do you

typically have in a 24-hour period?
Figure 2-38. Responses to "Do you feel you have trouble sleeping for any of the following 

reasons? (Check all that apply.)." n = 38-2215 out of 3268. Error bars = 95% Confidence Intervals.

"Sleep disorders" was identified by 7.8% (256/3268) of the respondents. Reported sleep disorders 

do not increase markedly with age in this sample. Data in Table 2-52 indicates that the proportion of 

those with self-described sleep disorders rises after age 30 to about 8% and stays fairly constant until 

ages 51–55, where it reaches 9.9%. 

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Shift work

Keyed up, can't relax

Disturbed by partners/children/pets

Worry/anxiety

Other outside noise, light, temperature, etc.

Need to urinate

Amount of caffeine ingested

Gastrointestinal problems

Sleep disorders

Other

Hunger

Other health issues

Desire to smoke
Table 2-52. Proportion of Respondents with a 

Self-Described Sleep Disorder by Age

Age

Proportion in 

Each Age 

Category

Frequency of 

Self-Described 

Sleep Disorder

Total in Each 

Age Category

25 or under 1.8% 3 170

26-30 3.8% 24 627

31-35 7.7% 31 403

36-40 9.3% 24 258

41-45 8.4% 40 479

46-50 9.7% 83 853

51-55 9.9% 39 393

56+ 13.8% 8 58

Total 7.8% 252 3,241

Missing 4 27

Total 256 3,268

Of those who reported a self-described sleep disorder, about 41% have rapidly rotating 

schedules without midnights and a similar proportion have rapidly rotating with midnights, as 

shown in Table 2-53. About 4% have a 6-day constant schedule.

Table 2-53. Proportion of Respondents with a Self-Described 

Sleep Disorder by Schedule Type

Schedule

% of those with 

Self-Described 

Sleep Disorders 

Frequency of 

Self-Described 

Sleep Disorder

RR 41.6% 102

RRM 40.8% 100

Straights & SR (Day) 8.2% 20

6-day Constant 3.7% 9 

10-hr 5.7% 14

Total 100.0% 245

Missing 11

Total 256

If over-the-counter or prescribed medications to aid sleep were allowed, about a quarter of the 

respondents indicated they would take them. Another quarter was undecided and half would not 

take them, as shown in Table 2-54. 

Table 2-54. Proportion and Number of 

Respondents Who Would Take Over-the-Counter 
or Prescribed Medications for Sleep

Response Percent Frequency

Yes 24.4% 796

No 50.0% 1,633

Don't know 24.9% 815

Missing 0.7% 24

Total 100.0% 3,268

2.2.7.4 Hours of Sleep Needed

Figure 2-39 shows that the average amount of sleep respondents believed they needed in a 24-

hour period, irrespective of shifts, was around 7-8 hours. 

Figure 2-39. Average responses to "About how many hours of sleep do you feel you need in a 

24-hour period, irrespective of which shift you are on?" n = 3251. Error bars = 95% Confidence 

Intervals.

2.2.8 Alertness

2.2.8.1 Alertness at the Beginning and End of Shifts

Respondents reported being least mentally sharp at the beginning of early shifts after a very 

quick turn-around. Respondents also reported being less alert at the beginning of a midnight 

shift after a very quick turn-around. Figure 2-40 shows how "mentally sharp (e.g., alertness, 

memory)" Respondents described themselves as being at the beginning of the various shift types. 

At the end of the shifts, respondents were least mentally sharp on midnight shifts, particularly 

midnight shifts after a very quick turn-around, as shown in Figure 2-41. These are the lowest 

alertness ratings received on any of the measures, including time-on-shift without a break under 

varying workloads.

<5 5-6 6-7 7-8 8-9 9-10 11+ 

About how many hours of sleep do you feel you need

in a 24-hour period, irrespective of which shift you

are on?
Figure 2-40. Alterness at the beginning of shifts. ns = 2155-3169. Error bars = 95% Confidence 

Intervals.

Figure 2-41. Alertness at the end of shifts.ns = 2296-3064. Error bars = 95% Confidence Intervals.

2.2.8.2 Alertness After Periods of Light and Heavy Traffic

Figure 2-42 shows that during most time on position periods from 1⁄2 to 11⁄2 hours, respondents 

felt less sharp after light traffic than a heavy push. 

Comparing the results in Figure 2-41 with those in Figure 2-42, it can be seen that midnight 

shifts have more of an adverse impact on alertness (rated sharpness) than time on duty without 

a break, regardless of the traffic level.

 Not at all sharp V ery sharp 

1 2 3 4 5

Afternoon shifts (start between 1300 & 1959)

Mid-day shifts (start between 1000 & 1259)

Day shifts (start between 0800 & 0959)

Early shifts (start before 0800)

Midnight shifts (start between 2000 & 0100)

Midnight shifts after a very quick turn-around

Early shifts after a very quick turn-around

 Not at all sharp V ery sharp 

1 2 3 4 5

Mid-day shifts (start between 1000 & 1259)

Afternoon shifts (start between 1300 & 1959)

Day shifts (start between 0800 & 0959)

Early shifts (start before 0800)

Early shifts after a very quick turn-around

Midnight shifts (start between 2000 & 0100)

Midnight shifts after a very quick turn-around
Figure 2-42. Alertness after light and heavy traffic after varying time periods. ns = 3067-3100 

light traffic, 3079-3085 heavy traffic. Error bars = 95% Confidence Intervals.

2.2.8.3 "About to 'Doze Off'" During Work Duties

When respondents were asked if they had caught themselves "about to 'doze off'" during work 

duties in the last year, 61.2% replied "Yes." (n = 1967 "Yes," 1248 "No," Missing 53, Total 3268.) 

Developmentals were least likely to respond in the affirmative (35%, 169/481). However,

Developmentals typically do not work midnight shifts. FLMs and CPCs responded affirmatively at higher 

rates than Developmentals: 64% (160/250) and 66% (1515/2286), respectively (Pearson’s Chi-Square 

= 163, df 2, p <0.0001). Those with midnight shifts were much more likely to reply “yes” to this question 

(71.1%, 1043/1467) than those without midnight shifts (52.9%, 924/1748) (Pearson’s Chi-Square = 

111.7, df 1, p <0.001).

As found in previous results, CPCs from TRACONs had the highest rate of catching themselves 

"about to 'doze off'" during work duties in the last year at 74.5% (240/322). In Tower facilities, the 

rate was 68.2% (552/809) followed by En Route at 62.6% (722/1154). (Pearson’s Chi-Square = 18.3, df 

2, p <0.0001.)

Table 2-55 shows that 71% of those with 6-day constant and 70% of those with rapid rotating 

with midnight schedules reported catching themselves "about to 'doze off'" during work duties 

in the last year.

 Not at all sharp Very sharp

1 2 3 4 5

For 1⁄2 hr.

For 1 hr.

For 11⁄2 hr.

For 2 hrs.

After Light Traffic After a Heavy Push
Table 2-55. Proportion of Respondents with Various 

Schedule Types who Reported Catching Themselves 

"About to 'Doze Off'" during Work Duties in the Last Year

Schedule % N Total

6-day Constant 71.1% 81 114

RRM 70.4% 946 1,343

Straights & SR (Days) 60.0% 99 165

10-hr. 4-day 57.2% 95 166

RR 51.4% 674 1,312

Average/Total 61.1% 1,895 3,100

Missing 168

Total 3,268

Note: Pearson’s Chi-Square = 107.43, df 4, p <0.0001.

Figure 2-43 indicates that of those who reported catching themselves as "about to 'doze off'" 

during work duties in the past year, the shift where this most frequently occurred was on 

midnight shifts after a very quick turn-around. This was followed second by regular midnight shifts 

and third by early shifts after very quick turn-arounds. 

Figure 2-43. Shifts on which that respondents were most likely to report "about to 'doze off'" on, if 

any. ns = 1499- 2033. Error bars = 95% Confidence Intervals.

2.2.8.4 "About to 'Doze Off'" During Breaks After Light and Heavy Traffic

Respondents rated the frequency of catching themselves "about to 'doze off'" on a break after 

light traffic as higher than after a heavy push as shown in Figure 2-44. 

Figure 2-44. Mean responses to "In the last year, have you caught yourself "about to 'doze off'" 

on a break after..." [light traffic] [a heavy push]. n = 2984 light traffic, 2986 a heavy push. Error 

bars = 95% Confidence Intervals.

 Never R arely S ometimes Frequently Usually

Midnight shifts after a very quick turn-around

Midnight shifts (start between 2000 & 0100)

Early shifts after a very quick turn-around

Early shifts (start before 0800)

Day shifts (start between 0800 & 0959)

Afternoon shifts (start between 1300 & 1959)

Mid-day shifts (start between 1000 & 1259)

 Never R arely S ometimes F requently U sually

light traffic?

a heavy push?
2.2.8.5 Fatigue Scales

2.2.8.5.1 Modified Brief Fatigue Inventory 

The mean score on the Modified Brief Fatigue Inventory was 18.7. (n = 3185, SE = 0.106, SD = 

5.98.) Figure 2-45 shows that Developmentals scored significantly lower on the Modified BFI 

than FLMs and CPCs.

Figure 2-45. Modified BFI by position. n = 2263 CPCs, 249 FLMs, 481 Developmentals. Error 

bars = 95% Confidence Intervals.

Figure 2-46 indicates that CPCs from TRACONs scored significantly higher than those from 

other facilities on the Modified Brief Fatigue Inventory. 

Figure 2-46. CPC responses to Brief Fatigue Inventory (Modified) by Facility Type. TRACON 

significantly different from En Route at p <0.0001 and from Tower at p = 0.045. ns = 319 

TRACON, 800 Tower, 1143 En Route. Error bars = 95% Confidence Intervals.

Figure 2-47 shows that the schedules that contributed the most to fatigue on the MBFI were the 

6-day constant and the rapidly rotating with midnights. The three schedules that contributed the 

least to fatigue were the 10-hour, the rapidly rotating (no midnights), and the straights and slowly 

rotating days.

 

15 16 17 18 19 20 21 22 23 24 25

CPCs

FLMs

Developmentals

 

15 16 17 18 19 20 21 22 23 24 25

TRACON

Tower

En Route
Figure 2-47. Mean ratings of those with five schedule types on the Brief Fatigue Inventory. 

n = 3070. Error bars = 95% Confidence Intervals.

2.2.8.5.2 Chronic Fatigue Scale

The mean Chronic Fatigue Scale score obtained from the respondents was 26.61 (n=3126, 

SD=8.31). Figure 2-48 indicates that developmentals have the lowest scores on the Chronic Fatigue 

Scale.

Figure 2-48. Chronic Fatigue Scale by position. n = 240 FLMs, 2233 CPCs, 459 Developmentals. 

Error bars = 95% Confidence Intervals.

CPC ratings on the Chronic Fatigue Scale in Figure 2-49 show that respondents from TRACONs 

reported the highest chronic fatigue with a mean of over 28.

Figure 2-49. Chronic Fatigue Scale by CPC's facility type. ns = 320 TRACON, 1119 En Route, 

793 Tower. Error bars = 95% Confidence Intervals.

Figure 2-50 shows the schedules that contributed the most to fatigue as measured by the 

Chronic Fatigue Scale. 

 

15 16 17 18 19 20 21 22 23

6-day Constant

RRM

Straights & SR (Day)

RR

10-hr

 

24 25 26 27 28 29 30 31

FLMs

CPCs

Developmentals

 

24 25 26 27 28 29 30 31

TRACON

En Route

Tower
Figure 2-50. Mean ratings of those with five schedule types on the Chronic Fatigue Scale. n = 

3085. Error bars = 95% Confidence Intervals.

2.2.8.5.3 Epworth Sleepiness Scale

The mean score on the Epworth Sleepiness Scale was 8.6 (n = 3129, SE = 0.08, SD = 4.45), which 

would be scored as "high average." The standard ratings are 1–6 = sufficient sleep, 7–8 = average 

sleep, and 9 and up "in need of medical attention." 

As can be seen in Figure 2-51, FLMs were significantly more sleepy than the others on the 

Epworth Sleepiness Scale. There was no significant difference in sleepiness between CPCs by facility 

types.

Figure 2-51. Mean score on Epworth Sleepiness Scale by Position. n = 243 FLMs, 2227 CPCs, 

469 Developmentals. Error bars = 95% Confidence Intervals.

Those working the 6-day schedule experienced the most sleepiness as measured by the 

Epworth Sleepiness Scale and those working 10-hour 4-day schedules experienced the least 

sleepiness as shown in Figure 2-52. 

 

24 25 26 27 28 29 30 31

6-day Constant

RRM

RR

Straights & SR

(Day)

10-hr

 

5 6 7 8 9 10 11 12

FLMs

CPCs

Developmentals
Figure 2-52. Mean ratings of respondents those controllers with five schedule types on the 

Epworth Sleepiness Scale. (All 6-day differences significant at p <0.05; all 10-day differences 

significant at p ≤0.05). n = 3018. Error bars = 95% Confidence Intervals.

2.2.8.6 Working a Midnight Shift or Not: Effects on Fatigue Scales

Working one or more midnight shifts on the bid schedule was significantly associated with 

fatigue on all three fatigue scales used in the survey. The means on the Epworth were 8.1 for no 

midnights and 8.8 with midnights, t = 3.7, df 2149 (equal variances not assumed), p <0.0001; on the 

Brief Fatigue Inventory 18.3 vs. 19.4, t = 2261, p <0.0001; on the Chronic Fatigue Scale 26.0 vs. 27.2, 

df 2139 (equal variances not assumed), p <0.001.

2.2.8.7 Alertness Indicators

2.2.8.7.1 Driving Experiences

Respondents were asked "In the last year, how often have you had a momentary lapse of attention 

while you were driving to or from" various shifts. A second question substituted "did you fall asleep (for 

a few seconds)" for "momentary lapse of attention." A 4-point rating scale of Never, Sometimes, 

Frequently, and Always, was provided along with a not applicable (N/A) option.

In the current survey, momentary lapses of attention while driving to or from work in the past 

year were reported as occurring most frequently with midnight shifts, as shown in Table 2-56. Of 

those responding to this question, 66% reported having had a momentary lapse of attention at least 

"sometimes" while driving to or from midnight shifts in the last year. Following in descending order were 

shifts involving OJT (61.4%), early shifts (60.4%), a busy shift (59.6%), and a light shift (52.6%). 

 

5 6 7 8 9 10 11 12

6-day Constant

RRM

Straights & SR (Day)

RR

10-hr
Table 2-56. Responses to "In the last year, how often have you had a momentary lapse 

of attention while you were driving to or from [various types of shifts]?" 7

Driving To or From

% Who Reported 

Having a Lapse of 

Attention at Least 

Sometimes (of Those 

Who Responded)

# Who Reported 

Having a Lapse 

of Attention at 

Least 

Sometimes

Total n 

Responding

% of Whole 

Sample 

(n = 3268)

Midnight shifts (i.e., shift 

starts between 20:00 & 01:00) 66.0% 1,508 2,285 46.1%

A shift giving or receiving OJT 61.4% 1,493 2,432 45.7%

Early shifts (i.e., shift starts 

before 08:00) 60.4% 1,849 3,061 56.6%

A busy shift 59.6% 1,713 2,875 52.4%

A light shift 52.6% 1,497 2,846 45.8%

Day shifts (i.e., shift starts 

between 08:00 and 09:59) 43.3% 1,172 2,707 35.9%

Afternoon shifts (i.e. shift 

starts between 13:00 and 

19:59)

34.6% 1,033 2,989 31.6%

Mid-day shifts (i.e., shift starts 

between 10:00 and 12:59) 28.9% 784 2,717 24.0%

Falling asleep (for a few seconds) while driving to or from work was also reported as occurring 

most frequently with midnight shifts. Of those responding to this question, 28% (689/2460) reported 

that they had fallen asleep for a few seconds at least “sometimes” in the past year, as shown in Table 

2-57. Following in descending order were a light shift (18.8%), a busy shift (17.9%), a shift involving 

OJT (16.8%), and early shifts (16.6%). Thirty-three percent (489/1486) of those who worked midnight 

shifts on a regular basis reported falling asleep for a few seconds to and from midnight shifts.

 

7 If the respondent’s most recent shift was affecting their driving ability, the effect would occur when driving from 

work rather than to work. However, the wording of the question was left unchanged from that used in the 1999 

survey to enable comparison. 
Table 2-57. Responses to "In the last year, how often did you fall asleep (for a few 

seconds) while you were driving to or from [the following various types of shifts]?"

Driving To or From

% Who Reported 

Falling Asleep at 

Least Sometimes 

(of Those Who 

Responded to 

Question)

# Who 

Reported 

Falling Asleep 

at Least 

Sometimes

Total N 

Responding

% of Whole 

Sample 

(n = 3268)

Midnight shifts (i.e., shift starts 

between 20:00 and 01:00) 28.0% 689 2,460 21.1%

A light shift 18.8% 519 2,761 15.9%

A busy shift 17.9% 496 2,771 15.2%

A shift giving or receiving OJT 16.8% 418 2,494 12.8%

Early shifts (i.e., shift starts 

before 08:00) 16.6% 499 2,999 15.3%

Afternoon shifts (i.e. shift starts 

between 13:00 and 19:59) 10.4% 306 2,947 9.4%

Day shifts (i.e., shift starts 

between 08:00 and 09:59) 10.2% 282 2,768 8.6%

Mid-day shifts (i.e., shift starts 

between 10:00 and 12:59) 5.8% 159 2,738 4.9%

2.2.8.7.2 Operational Events at Work

Overall, 18% (581/3224) of respondents indicated that they had a proximity event (PE), 

Operational Deviation (OD) or Operational Error (OE) in the last year. As can be seen in Figure 2-

53, 20.8% (478/2295) of CPCs reported being involved in an operational event in the last year 

compared to 14.8% (71/481) of Developmentals, and 7.5% (19/253) of the FLMs. 

Of those respondents who had been involved in an operational event, 55.9% (320/572) believed 

that their own fatigue had been a contributing factor. In comparison to those in other positions, 

CPCs who were involved in an operational event were more likely to believe that fatigue was a 

contributing factor, as shown in Figure 2-53. Fatigue was cited as a contributing factor by 58.5% 

(275/470) of the involved CPCs, by 39.4% (28/71) of the involved developmentals, and by 33.3% (6/18) 

of the FLMs.
Figure 2-53. Percentage of respondents in three positions who were involved in an operational 

event and the percentage of these who believed that fatigue had contributed to the operational 

event. ns = 2295 & 470 CPCs, 481 & 71 Developmentals, and 253 & 18 FLMs. Error bars = 95% 

CIs.

Figure 2-54 shows that about 33% (153/463) of respondents at TRACONs had experienced an 

operational event in the previous year, approximately double the proportion at Towers (14.9%, 

101/160) and En Route facilities (16.2%, 129/259). Of those personnel at TRACONs and Towers who 

had experienced an operational event, approximately 60% considered fatigue to have been a factor. 

Fatigue was less likely to have been considered a factor by personnel at En Route centers.

Figure 2-54. Percent of respondents at each facility type who were involved in an operational 

incident, and the percentage of these who believed that fatigue had contributed to the incident. n 

= 1633 & 462 En Route, 1091 & 314 Tower, and 463 & 216 TRACON. Error bars = 95% CIs.

Of all respondents who had experienced a fatigue-related incident, approximately 78% identified 

their work schedule as a cause of their fatigue, as shown in Figure 2-55. Workload was cited by 

approximately 46%, with family, personal problems, health and commute also identified as causes of 

the fatigue that contributed to the operational event. 

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Have you had any proximity events,

operational deviations, or operational errors

in the last year?

If yes, do you believe your own fatigue

contributed to the PE, OD, or OE?

CPCs Devel. FLM

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Have you had any proximity events, operational

deviations, or operational errors in the last year?

If yes, do you believe your own fatigue

contributed to the PE, OD, or OE?

En route Tower TRACON
Figure 2-55. Sources of fatigue identified by all respondents who had experienced a fatiguerelated operational event (n = 329). Respondents could identify more than one contributing cause, 

hence the percentages do not add to 100%. (Error bars = 95% CIs.)

2.2.8.7.3 Operational Events and Age

Table 2-58 indicates that CPCs with the highest proportion of reported operational events were 

between 36 and 40 years. Respondents were asked "Have you had any proximity events (PEs), 

operational deviations (ODs), or operational errors (OEs) in the last year?"

Table 2-58. Proportion of Reported Operational 

Events by CPCs by Age

CPCs Have Had Event Total 

Age % # Number

25 or under 17.2% 10 58

26–30 22.0% 78 354

31–35 21.1% 60 284

36–40 31.4% 65 207

41–45 20.2% 79 392

46–50 18.7% 126 674

51–55 19.5% 55 282

56+ 14.7% 5 34

Averages/Totals 20.9% 478 2,285

Note: Pearson’s Chi-Square is 17.8, df 7, p = 0.01.

2.2.9 Extent of Fatigue, Causes and Consequences

2.2.9.1 Level of Fatigue

Respondents frequently (3.8 on a 5 point scale) observe other air traffic controllers who are 

fatigued at work and believe that fatigue frequently (3.7 on a 5 point scale) impacts other air 

traffic controllers’ ability to perform their job effectively. Respondents indicate that they 

themselves experience fatigue at work significantly more often (3.4) than "Sometimes," and that fatigue 

affects their general health and well-being slightly more than sometimes (3.1). However, they indicate 

that fatigue affects their own ability to perform their job effectively less than "Sometimes" (2.9). 

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Commute

Health

Personal problems

Family

Workload

Work Schedule
Figure 2-56. Frequency of perceived Levels of controller fatigue and fatigue consequences

as seen by all respondents. ns = 3103–3228. Error bars = 95% Confidence Intervals.

Figure 2-57 shows that compared to CPCs and FLMs, Developmentals experience fatigue less 

frequently at work and feel that it less frequently affects their health and performance. 

Figure 2-57. Levels of controller fatigue and consequences as seen by CPCs, 

Developmentals, and FLMs. n = CPCs 2263-2301, Developmentals 463-481, and FLMs 249-

252. Error bars = 95% CIs.

TRACON controllers more frequently report seeing and experiencing fatigue than do controllers 

from other facilities, as shown in Figure 2-58. 

Never Rarely Sometimes Frequently Usually

1 2 3 4 5

I see air traffic controllers who are fatigued at

work.

Fatigue affects the ability of air traffic controllers

to perform their job effectively.

I experience fatigue when at work.

Fatigue affects my general health and wellbeing.

Fatigue affects my ability to perform my job

effectively.

Never Rarely Sometimes Frequently Usually

1 2 3 4 5

I see air traffic controllers who are fatigued at

work.

Fatigue affects the ability of air traffic

controllers to perform their job effectively.

I experience fatigue when at work.

Fatigue affects my general health and wellbeing.

Fatigue affects my ability to perform my job

effectively.

CPCs Devel. FLMs
Figure 2-58. Controller fatigue and consequences as seen by CPCs in three facility types. n = 

En Route 1143-1161, Tower 799-810, and TRACONs 319-326. Error bars = 95% Confidence 

Intervals.

2.2.9.2 Perception of Fatigue as a Safety Risk

CPCs rate the current level of controller fatigue as a moderate safety risk, as shown in Figure 2-

59. There was little difference on perception of risk between those in the three positions, as 

shown in Figure 2-59. 

Figure 2-59. Level of risk of current level of controller fatigue as perceived by those in three 

positions. n = CPCs 2265, Developmentals 459, and FLMs 250. Error bars = 95% Confidence 

Intervals.

The extent to which controller fatigue was seen as a safety risk was significantly higher in 

Terminal facilities than En Route facilities, as shown in Figure 2-60. 

Figure 2-60. Level of risk of current level of controller fatigue as perceived by CPCs according to 

facility type. n = 1144 for En Route, 797 for Tower, and 322 for TRACONs. Error bars = 95% 

confidence intervals.

The most frequently selected risk level was the "moderate" level as shown in Table 2-59. 

Table 2-59. Number and Proportion of 

Controllers Assessing the Current Level of 

Never Rarely Sometimes Frequently Usually

1 2 3 4 5

I see air traffic controllers who are fatigued at

work.

Fatigue affects the ability of air traffic

controllers to perform their job effectively.

I experience fatigue when at work.

Fatigue affects my general health and wellbeing.

Fatigue affects my ability to perform my job

effectively.

En route Tower TRACON

No risk Slight risk Moderate risk High risk Extreme risk

1 2 3 4 5

To what extent do you believe that the current 

level of fatigue experienced by air traffic 

controllers as a whole, represents a safety risk?

CPC Developmental FLM

No risk Slight risk Moderate risk High risk Extreme risk

1 2 3 4 5

To what extent do you believe that the current 

level of fatigue experienced by air traffic controllers 

as a whole, represents a safety risk?

En Route Tower TRACON
Controller Fatigue at Various Risk Levels

Perceived Risk Percent Frequency

1. No risk 2.2% 51

2. Slight risk 29.6% 686

3. Moderate risk 36.0% 833

4. High risk 20.7% 480

5. Extreme risk 9.3% 215

Subtotal 99.2% 2,265

N/A or Don't know 1.4% 33

Missing 0.8% 18

Total 100.0% 2,316

A significantly higher proportion of those in Terminal facilities rated the risk as high or extreme 

compared to those in En Route facilities, as shown in Table 2-60. 

Table 2-60. Proportion and Number of CPCs who Indicate the 

Safety Risk of the Current Level of Controller Fatigue to be 

High or Extreme 

Domain

% Indicating 

High or 

Extreme Risk

# Indicating 

High or 

Extreme Risk

Total CPC 

Respondents

Tower 37.1% 296 797

TRACON 32.3% 104 322

En Route 25.8% 295 1,144

Subtotal 30.7% 695 2,263

Missing 53

Total 2,316

 Note: Pearson's Chi Square = 28.8, df 2, p <0.0001. 

2.2.9.3 Causes of Fatigue

CPCs rated their work schedule as most contributing to their fatigue, as shown in Figure 2-61. 

The controllers were asked, "If you experience fatigue at work, to what extent do you feel the following 

contribute to your fatigue?" The controllers' work schedule, followed by the related work/life balance, 

were seen as contributing more to fatigue than the workload/traffic experienced.

Front Line Managers rated work schedule as contributing to their fatigue more than did CPCs, 

as shown in Figure 2-61. 
Figure 2-61. Responses by position to "If you experience fatigue at work, to what extent do you 

feel that the following contribute to your fatigue?". n = CPCs 2245-2274 (Other = 935), 

Developmentals = 463-468 (Other = 247), and FLMs 242-246 (Other = 98). Error bars = 95% 

Confidence Intervals.

CPCs from all facility types rated work schedule as the main source of fatigue. As can be seen in 

Figure 2-62 the contributions to fatigue did not differ very much by facility type, although work schedule 

was seen as contributing more to fatigue at tower and TRACON facilities than at En Route facilities. 

Note that workplace lighting contributed significantly less to fatigue in the Tower environment, where 

there typically is natural lighting during daylight hours. 

Not at all Slightly Somewhat Very much Extremely

1 2 3 4 5

Work schedule

Work/life balance

Workload/traffic

Workplace

interactions

Personal problems

Commute

Workplace lighting

Sleep disorders

Other health

problems

Other

CPC Develop. FLM
Figure 2-62. CPC responses by facility type to "If you experience fatigue at work, to what extent 

do you feel that the following contribute to your fatigue?" n for En Route 1128-1146 (Other = 438), 

Tower 791-801 (Other = 361), and TRACON 323-326 (Other = 136). Error bars = 95% 

Confidence Intervals.

2.2.10 Suggestions from Respondents on How to Reduce Fatigue

2.2.10.1 Analyzing Respondents' Suggestions

In a free text section of the survey, respondents were asked how controller fatigue could be reduced by:

• Their immediate supervisors or managers, 

• Upper level FAA management, and finally, 

• Controllers themselves. 

All percentages reported in the following sections related to free text inputs are a representation of 

respondents who provided additional comments. Not all survey respondents provided additional 

comments.

Good to excellent levels of inter-rater agreement were obtained when coding the free text 

suggestions provided by respondents. The content of each suggestion was coded; if more than one 

idea was provided by a respondent, each idea was individually coded. The reliability of the coding was 

checked by two analysts independently coding a sample of responses from each of the three questions, 

and the level of inter-rater agreement was calculated using Cohen’s Kappa statistic. For the three 

questions, Kappa was as follows: supervisor actions to reduce fatigue, Kappa = 0.72; management 

actions to reduce fatigue, Kappa = 0.83; controller actions to reduce fatigue, Kappa = 0.84. Using the 

guidelines of Fleiss (1981), these are “good” to “excellent” levels of inter-rater agreement.

Not at all Slightly Somewhat Very much Extremely

1 2 3 4 5

Work schedule

Work/life balance

Workload/traffic

Workplace interactions

Personal problems

Commute

Workplace lighting

Sleep disorders

Other health problems

Other

En Route Tower TRACON
2.2.10.2 Suggestions for Immediate Supervisors from All Respondents 

Respondents were asked, "What, if anything, could your immediate supervisors or managers do to 

reduce controller fatigue?" Over half of the respondents (53%, 1729/3268) provided suggestions to this 

question.

The most frequent suggestion, offered by 20.7% of these respondents, was to staff positions

only when necessary, as shown in Figure 2-63. The second most frequent suggestion, offered 

by 20.2% of respondents, was to allow naps during breaks. The third most frequent suggestion, 

offered by 12.8% of respondents, was to have more or longer breaks. In some cases, respondents 

stated that breaks were too brief to enable them to mentally refresh after time on position, or were not 

long enough for meals. The next most frequent suggestion was made by 11.5% of respondents who 

recommended that quick turnarounds be eliminated or reduced. Furthermore, 11.3% of respondents 

suggested that rapidly rotating shifts should be replaced with straight shifts.

Figure 2-63. Responses to the question,” What, if anything, could your immediate supervisors or 

managers do to reduce controller fatigue?” as a percentage of those who offered a response to this 

question. n = 1729. Respondents could make multiple comments, hence percentages do not add to 100%.

2.2.10.3 Suggestions for Immediate Supervisors by Position 

As shown in Figure 2-64, 23.7% of the CPC respondents made the suggestion that positions be staffed 

only when warranted by traffic, but only 6.4% of FLM respondents made that suggestion. 

0% 5% 10% 15% 20% 25%

Improve work environment-lighting, seating,

etc.

Rotate positions more often or assign other

duties

Supervisor unable to affect change 

Improve management of staff scheduling

Improve culture

Improve break environment

Minimize exposure to low traffic/workload

Ensure equitable rotation of

breaks/OJT/positions

Support CPCs on the floor, monitor workload

Increase staffing 

Tailor breaks to traffic conditions

Have straight shifts in place of rotating shifts

Eliminate or reduce quick turn-arounds 

Have more/longer breaks

Allow naps on breaks

Staff positions only when necessary 
Figure 2-64. Responses to the question “What, if anything, could your immediate supervisors or 

managers do to reduce controller fatigue?” by respondents' position, as a percentage of those 

who offered comments to this question. n = 1365 CPCs, 202 Developmentals, 109 FLMs. 

Respondents could make multiple comments, hence percentages do not add to 100%.

Front Line Managers (33.0%) were more likely to suggest the elimination or reduction of the quick turnarounds. Developmentals (29%) were more likely to suggest more or longer breaks. 

2.2.10.4 Suggestions for Immediate Supervisors by Facility Type 

As shown in Figure 2-65, about a third (30.1%) of respondents from En Route facilities 

suggested that positions should be staffed only when traffic requires it, compared to 8.4% from 

Tower and 17.1% from TRACON facilities. The suggestion that naps be allowed during breaks was 

made more often by Tower respondents (25.5%) than by respondents at other facilities. Staff from 

Tower facilities (16.1%), were also more likely to suggest a reduction in ‘quick turns’ and the 

introduction of straight shifts than respondents from other facility types. 

0% 10% 20% 30%

Staff positions only when necessary

Allow naps on breaks

Have more/longer breaks

Eliminate or reduce quick turn-arounds 

Have straight shifts in place of rotating shifts

Tailor breaks to traffic conditions

Increase staffing 

Support CPC on floor, monitor workload

Ensure equitable rotation of

breaks/OJT/positions

Minimize exposure to low traffic/workload

Improve break environment

Improve management of staff scheduling

Improve culture

Supervisor unable to affect change

Rotate positions more often or assign other

duties

Improve work environment--lighting, seating,

etc.

CPC Devel. FLM
Figure 2-65. Responses to the question “What, if anything, could your immediate supervisors or 

managers do to reduce controller fatigue?” by facility, as a percentage of those who offered 

comments to this question.ns = 880 En Route, 585 Tower, and 251 TRACON. Respondents 

could make multiple comments, hence percentages do not add to 100%.

2.2.10.5 Suggestions for Upper-Level FAA Management by All Respondents

Respondents were asked, "What, if anything, could your immediate supervisors or managers do to 

reduce controller fatigue?" Over half of the respondents (56%,1820/3268) provided suggestions to this 

question.

The most frequent suggestion was that management should allow controllers to take naps 

during breaks at work (28%), as shown in Figure 2-66. About 17% of those responding suggested 

increased staffing. Approximately 11% of those responding suggested the reduction or elimination of 

quick turn-arounds. Approximately 9% of those responding suggested introducing straight shifts, or 

slowly rotating shifts in place of rapidly rotating shifts. 

0% 10% 20% 30%

Staff positions only when necessary

Allow naps on breaks

Have more/longer breaks

Eliminate or reduce quick turn-arounds 

Have straight shifts in place of rotating shifts

Tailor breaks to traffic conditions

Increase staffing 

Minimize exposure to low traffic/workload

Ensure equitable rotation of breaks/OJT/positions

Support CPCs on floor, monitor workload 

Improve break environment

Improve management of staff scheduling

Improve culture

Supervisor unable to affect change

Rotate positions more often or assign other duties

Improve work environment--lighting, seating, etc

En Route Tower TRACON
Figure 2-66. Suggestions in response to the question, “What, if anything, could upper-level FAA 

management do to reduce controller fatigue?” as a percentage of the 1837 who responded to the 

question. Respondents could make multiple comments, hence percentages do not add to 100%.

2.2.10.6 Suggestions for Upper-Level FAA Management by Position

As shown in Figure 2-67, about 25% of Front Line Managers suggested that schedules should 

be revised to eliminate or reduce the need for quick turn-arounds. Another 24% of FLM 

respondents suggested that schedules should be changed to straight or slowly rotating types. 

Approximately 11% of CPCs suggested that the FAA aim to improve their corporate culture. Another 

10% of CPCs suggested that scheduling could be made more flexible to allow for individual needs. 

0% 5% 10% 15% 20% 25% 30%

Provide area for naps during breaks 

Manage influx of trainees

Shorten work week

Don't staff positions when unnecessary

Have more/longer breaks

Improve break areas, exercise equipment,

etc.

Stop micromanaging

Improve schedules (general comment)

Stop applying business model to FAA

More flexibility with scheduling

Improve corporate culture

Introduce straight or slowly rotating shifts

Eliminate or reduce quick turn-arounds

Increase staffing

Allow naps on breaks
Figure 2-67. Responses to the question, “What, if anything, could upper-level FAA management 

do to reduce controller fatigue?” as a percentage of the 1837 who responded to the question, by 

job title. n = 1414 CPCs, 191 Developmentals, 122 FLMs. Respondents could make multiple 

comments, hence percentages do not add to 100%.

2.2.10.7 Suggestions for Upper-Level FAA Management by Facility Types

Figure 2-68 summarizes the comments broken down by facility type. It can be seen that 25% of 

respondents from TRACON facilities suggested that staffing be increased so as to result in 

improvements in schedules. A suggestion offered by 14% of staff from Towers was to change 

schedules to reduce or eliminate quick turns. About 12% of respondents at En Route facilities, more 

than at other facilities, suggested that the FAA not apply a business model to air traffic control. 

0% 10% 20% 30%

Allow naps on breaks

Increase staffing

Eliminate or reduce quick turn-arounds

Introduce straight or slowly rotating shifts

Improve corporate culture

More flexibility with scheduling

Stop applying business model to FAA

Improve schedules (general comment)

Stop micromanaging

Improve break areas, exercise equipment,

etc.

Have more/longer breaks 

Don't staff positions when unnecessary

Shorten work week

Manage influx of trainees

Provide area for naps during breaks 

CPCs Devel. FLM
Figure 2-68. Suggestions in response to the question, “What, if anything, could upper-level FAA 

management do to reduce controller fatigue?” as a percentage of the 1837 who responded to the 

question, by facility. n = 810 En Route, 610 Tower, and 283 TRACON. Respondents could make 

multiple comments, hence percentages do not add to 100%.

2.2.10.8 Suggestions for Controllers by All Respondents

Respondents were asked, "What, if anything, could controllers do to reduce their fatigue?" About 46% 

(1501/3268) of all respondents provided suggestions to this question.

The most frequent (48%) suggestion was that controllers should use sleep strategies and 

discipline, as shown in Figure 2-69. The second most frequent suggestion (38%) involved the need 

to optimize health. Approximately 19% suggested that controllers could take a role in improving their 

schedules. About 17% suggested that controllers take naps. About 8% of the suggestions related to 

fatigue awareness and fatigue management strategies. 

0% 10% 20% 30%

Allow naps on breaks

Increase staffing

Eliminate or reduce quick turn-arounds

Introduce straight or slowly rotating shifts

Improve corporate culture

More flexibility with scheduling

Stop applying business model to FAA

Improve schedules (general comment)

Stop micromanaging

Improve break areas, exercise equipment, etc.

Have more/longer breaks 

Don't staff positions when unnecessary

Shorten work week

Manage influx of trainees

Provide area for naps during breaks 

En Route Tower TRACON
Figure 2-69. Suggestions in response to the question “What, if anything, could controllers do to 

reduce their fatigue?” as a percentage of the 1501 who responded to the question. Respondents 

could make multiple comments, hence percentages do not add to 100%.

2.2.10.9 Suggestions for Controllers by Position

As shown in Figure 2-70, CPCs (12%) were more likely to recommend naps than FLMs (4%). 

CPCs (29%) were less likely than developmentals (38%) and FLMs (37%) to recommend using sleep 

strategies and discipline. FLMs were more likely to state that controllers needed to develop good sleep 

habits and create home conditions conducive to sleep. Some FLMs (16%) suggested that schedules 

should be revised, including eliminate or reduce the need for quick turns. This suggestion was also 

made by CPCs (12%) but less by developmentals (6%). 

0% 5% 10% 15% 20% 25% 30% 35%

Reduce commute

Call in sick when too tired

Give up life outside of work

Quit or retire

Nothing controllers can do

Improve atmosphere

Modify breaks

Use fatigue awareness & strategies

Take naps

Improve schedules

Optimize health

Use sleep strategies & discipline

0% 10% 20% 30% 40%

Use sleep strategies & discipline

Optimize health

Improve schedules

Take naps

Use fatigue awareness & strategies

Modify breaks

Improve atmosphere

Nothing controllers can do

Quit or retire

Give up life outside of work

Call in sick when too tired

Reduce commute

CPC Devel. FLM
Figure 2-70. Responses to the question “What, if anything, could controllers do to reduce their 

fatigue?” as percentage of the 1501 who responded to the question, by position. n = 1775 CPCs, 

290 Developmentals, 185 FLMs. Respondents could make multiple comments, hence 

percentages do not add to 100%.

2.2.10.10 Suggestions for Controllers by Facility Type

Figure 2-71 indicates that the responses on what controllers could do to reduce fatigue were 

very similar across facility types. The top two controller responses on how they can reduce their 

fatigue were “use sleep strategies & discipline” and “optimize health”. 

Figure 2-71. Responses to the question “What, if anything, could controllers do to reduce their 

fatigue?” as percentage of the 1501 who responded to the question, by facility. n = 1172 En 

Route, 812 Tower, and 353 TRACON. Respondents could make multiple comments, hence 

percentages do not add to 100%.

2.2.10.11 Training

Over half of the CPCs answered "Yes" to the question "Would you like training or information 

on ways to reduce fatigue?" This did not vary significantly by facility type. It varied slightly by position, 

with Developmentals being somewhat less interested in training on ways to reduce fatigue, as shown in 

Table 2-61. 

Table 2-61. Proportion of Respondents Who Would like 

Training or Information on Ways to Reduce Fatigue

Respondent Yes % Yes # Total

CPC 53.60% 1,152 2,150

FLM 49.80% 110 221

Developmental 46.10% 201 436

Total 52.21% 1,463 2,807

0% 10% 20% 30%

Use sleep strategies & discipline

Optimize health

Improve schedules

Take naps

Use fatigue awareness & strategies

Modify breaks

Improve atmosphere

Nothing controllers can do

Quit or retire

Give up life outside of work

Call in sick when too tired

Reduce commute

En Route Tower TRACON
 Note: Pearson’s Chi Square = 8.7, df 2, p = 0.01.

Those who had midnight shifts in their schedules were more likely to want training or 

information, as shown in Table 2-62. 
Table 2-62. Proportion of Those With and Without 

Midnight Schedules Who Would like Training or 

Information on Ways to Reduce Fatigue

Schedule

Would Like Training

% # Total

No midnights 49.8% 805 1,617

Have midnights 55.1% 755 1,369

Average/Total 52.2% 1,560 2,986

 Note: Pearson’s Chi Square = 8.6, df 1, p <0.01.

2.2.10.12 Staying Home from Work When Fatigued

About two thirds of the CPCs indicated that they had called in sick due to fatigue at least once, 

and about half used some annual leave for fatigue, as shown in Table 2-63. 

Table 2-63. Proportion and Number of CPCs that Called in Sick 

or Took Annual Leave Due to Fatigue

 

Number of times

How often, in the last year, 

have you called in sick 

because you were fatigued?

How often, in the last year, 

have you taken annual 

leave due to fatigue?

% # % #

0 times 33.20% 768 47.60% 1,102

1-2 times 35.80% 828 25.80% 597

3-5 times 23.00% 532 16.70% 387

6-10 times 5.50% 128 5.30% 122

10+ times 1.60% 36 2.70% 62

Missing 1.00% 24 2.00% 46

Total 100.00% 2,316 100.00% 2,316

CPCs identified their comfort level for taking time off from work due to fatigue between not very 

and somewhat comfortable, as shown in Figure 2-72. CPCs were even less comfortable telling their 

supervisor that they want to take time off due to fatigue. 

The reluctance to take time off from work due to fatigue or tell their supervisor that they are 

doing so is manifested by those in all job categories, as shown in Figure 2-72. However, there are 

some differences between the three groups. FLMs feel less comfortable than CPCs and 

developmentals in taking time off because of fatigue. FLMs see their own supervisors as more willing to 

authorize annual leave to reduce fatigue than do CPCs. 
Figure 2-72. Ratings by position of comfort levels in taking time off from work due to fatigue, 

informing supervisors of the reason, and the likelihood of supervisors' authorization to do so. n = 

2222-70 CPC, 427-465 Developmentals, and 239-40 FLM. Error bars = 95% Confidence Intervals.

Controllers from Towers are slightly more likely to think that their supervisors would authorize 

annual leave to reduce fatigue, as shown in Figure 2-73, but the mean response is between "Not 

at all" and "Not very". 

Figure 2-73. CPC ratings by facility of comfort levels in taking time off from work due to fatigue, 

informing supervisors of the reason, and the likelihood of supervisor's authorization to do so. n = 

1121-44 for En Route, 777-799 for Tower, and 323-6 for TRACON. Error bars = 95% Confidence 

Intervals.

2.2.11 Job Satisfaction

CPCs more than slightly agree with the statement that they are satisfied with their job (5.6 on a 

7-point scale) and disagree with the statement that they frequently think of quitting (2.7 on a 7-

point scale), as shown in Figure 2-74. Developmentals are slightly more satisfied with their job than 

CPCs and do not think of quitting as frequently. 

Not at all Not very Somewhat Very Extremely

1 2 3 4 5

How comfortable do you feel taking time off because of

fatigue?

How likely is it that your supervisor would authorize

annual leave to reduce fatigue?

How comfortable would you feel telling your supervisor

that fatigue is the reason you want to take sick time or

annual leave?

CPC Develop. FLM

Not at all Not very Somewhat Very Extremely

1 2 3 4 5

How comfortable do you feel taking time off because

of fatigue?

How likely is it that your supervisor would authorize

annual leave to reduce fatigue?

How comfortable would you feel telling your supervisor

that fatigue is the reason you want to take sick time or

annual leave?

En Route Tower TRACON

Strongly Disagree Disagree Slightly Disagree Neutral Slightly Agree Agree Strongly Agree

1 2 3 4 5 6 7

Generally speaking, I am

satisfied with my job.

I frequently think of

quitting.

CPC Develop. FLM
Figure 2-74. Position by job satisfaction on a 7-point scale. n = 2293-5 CPC, 475-6 

Developmental, 249 FLM. Error bars = 95% Confidence Intervals.

CPCs in En Route facilities reported level of satisfaction with their job is significantly higher 

than ratings from other facilities as shown in Figure 2-75. CPCs in En Route facilities also reported 

being less likely to think about quitting their jobs than CPCs in other facilities. 

Figure 2-75. CPCs' mean ratings of job satisfaction by facility type on a 7-point scale. 

n = 1157-9 En Route, 810-12 Tower, and 323-5 TRACON. Error bars = 95% Confidence Intervals.

2.2.11.1 Satisfaction with Various Aspects of Job

Schedule was the aspect of their job that controllers were most dissatisfied with, as shown in 

Figure 2-76. They also identified schedule as most contributing to their fatigue (see Figure 2-43).

Controllers were asked to indicate their level of satisfaction or dissatisfaction with various aspects of 

their job. CPCs were most satisfied with the type of work that they were doing and their relations with 

their co-workers. In addition to schedule, the controllers were least satisfied with their job's influence on 

their health and well-being. 

Differences emerged between the CPCs, FLMs, and Developmentals regarding their satisfaction 

with various aspects of their jobs. As shown in Figure 2-76, FLMs were less satisfied than the others 

with both the type of work they do and their schedule, but more satisfied with their relations with 

supervisors. Developmentals were more positive (neutral) than the others regarding their schedule and 

their job's influence on their health and well-being and life-work balance issues. Developmentals were 

also more positive than the CPCs regarding their relations with supervisors. 

Strongly Disagree Disagree Slightly Disagree Neutral Slightly Agree Agree Strongly Agree

1 2 3 4 5 6 7

Generally speaking, I am

satisfied with my job.

I frequently think of

quitting.

En Route Tower TRACON
Figure 2-76. Satisfaction or dissatisfaction with various aspects of work by position. n = 2293-9 

CPCs, 478-80 Developmentals, and 247-8 FLMs. n for "Other" were 370 for CPCs, 106 for 

Developmentals, and 25 for FLMs. Error bars = 95% Confidence Intervals.

Of all controllers, TRACON CPCs were most dissatisfied with their schedules, followed by those 

who work in Towers, as shown in Figure 2-77. Those who work in En Route facilities were neutral 

with regard to their schedules. Similarly TRACON CPCs were more dissatisfied with their job's 

influence on their health and well-being. Those who work in TRACONs were significantly more satisfied 

with their relations with supervisors than were those from other facility types. CPCs from all facilities 

were most satisfied with the type of work they do. Those from Towers were less satisfied than others 

with their relations with co-workers.

Dissatisfied Somewhat Dissatisfied Neutral Somewhat satisfied Satisfied

1 2 3 4 5

Type of work

Relations with co-workers

Commute

Workload

Work/life balance

Relations with supervisors

Influence on health & 

well-being

Schedule

Other

CPC Develop. FLM
Figure 2-77. Facility type by satisfaction or dissatisfaction with various aspects of CPCs' jobs.n = 

1159-1161 En Route, 807-812 Tower, and 325-7 TRACON. "Other" n are 153 En Route,152 

Towers, and 65 TRACON. Error bars = 95% Confidence Intervals.

2.3 SURVEY SUMMARY AND DISCUSSION

2.3.1 Work Context for Fatigue Findings

ATC personnel in general indicated that they were satisfied with their job and the type of work it 

entailed. In general, they reported good relations with their co-workers and did not report a high level of 

stress at work. Personnel in all three positions (CPC, developmental, and FLM) indicated that they 

could usually keep up with their workload, as did CPCs from all facility types (Towers, TRACONs, and 

En Route Centers). Supervisors were perceived as supporting controllers in their work by assigning 

additional staff or by splitting a position when requested, if staffing permitted. In general, the number of 

hours worked and the overall workload were reported to be “about right”. 

2.3.2 Controller Fatigue Levels

Controllers reported they experienced fatigue and also perceived fatigue in their colleagues. 

The current ATC personnel sample was significantly more fatigued, based on their responses to the 

Chronic Fatigue Scale, than a normative comparison group of nurses and industrial shift workers 

(Barton et al., 1995). The same scale and comparison group was used in the previous 1999 ATC 

survey (Della Rocco et al., 2000a). Respondents in the current study were also significantly more 

fatigued than the comparable sample of CPCs and Developmentals in the1999 survey population. 

In comparison to the 1999 study, there is evidence that the 2010 sample had a lower proportion of 

straight shifts without midnights and a higher proportion of counter-clockwise rapidly rotating schedules, 

especially with midnights.8

 

 

8 Beyond these general statements, it is difficult to compare the 1999 and the 2010 schedules since only 67% of 

the schedules from the 1999 survey were categorized into four types (Della Rocco et al., 2000a). In the 2010 

survey data, 88.5% of the schedules could be categorized into these four types and all but 2.7% of the schedules 

could be classified into the five schedule types that were the focus of the 2010 data analysis. 

Dissatisfied Somewhat Dissatisfied Neutral Somewhat Satisfied Satisfied

1 2 3 4 5

Type of work

Relations with co-workers

Commute

Workload

Work/life balance

Relations with supervisors

Influence on health & well-being

Schedule

Other

En Route Tower TRACON
2.3.3 Fatigue Due to Schedule

Of all aspects of their jobs, ATC personnel reported being least satisfied with their schedules 

and felt that their schedules contributed most to their fatigue. When asked to indicate why they 

might have trouble sleeping, the highest proportion of respondents (68%) identified "Shift work" as the 

reason. Of all respondents who had experienced a fatigue-related operational event, approximately 

78% identified their work schedule as a cause of their fatigue. The likelihood of their being "about to 

doze off during work duties" was highest during certain shift types: midnights after a quick turn-around, 

successive midnights, and early morning shifts after a quick turn-around. These shift types were more 

strongly related to reported fatigue than were shifts with either high or low workloads. 

2.3.4 Reported Sleep Compared with Earlier Studies

In contrast to the 1999 survey, the 2010 survey differentiated between sleep obtained on "an 

early shift" with sleep obtained on "an early shift after a very quick turn-around." ("A very quick 

turn-around" referred to a shift rotation with 8 or 9 hours between shifts.) The amount of sleep obtained 

on an early shift in the 1999 survey (6.5 hours), was similar to that obtained in the 2010 survey (6.3 

hours). However, both were far more than the 5.4 hours reported before an early shift after a very quick 

turn-around in the 2010 survey—which was identical to that found in the objective field study associated

with the 1999 study (Della Rocco & Nesthus, 2005) and similar to the 5.2 hours found in the 2010 field 

study (see section 3.3.3.5 of this report). It is likely that the 1999 survey overestimated the number of 

hours slept before early shifts after a very quick turn-around.

Also in contrast to the 1999 survey, instead of only obtaining data on the amount of sleep 

obtained before "a midnight shift," the 2010 survey differentiated between sleep obtained on 

"successive midnight shifts," and "sleep obtained before a midnight shift after a very quick 

turn-around." In the current 2010 survey results, the average reported sleep was 5.5 hours between 

successive midnight shifts —further reduced to 3.1 hours after a very quick turn-around. This compares 

to 3.6 hours before "a midnight shift" in the 1999 survey (Della Rocco, et al., 2000a, p. 25). The field 

study associated with the 1999 survey found a total of 2.3 hours of sleep before a midnight shift 

following a very quick turn-around (Nesthus, et al., 2001), a level almost identical to that found in the 

present field study (2.5 hours, described in section 3.3.3.5).

As can be seen in Table 2-64, the amount of sleep obtained by ATC personnel in the 2010 

survey was generally lower than that found in the 1999 survey before comparable shifts. In 

addition, the amount of objectively measured sleep obtained by controllers in the 2010 field 

study is consistently less than reported in the 2010 survey. 

Respondents indicated they need on average 7 to 8 hours of sleep per night.
Table 2-64. Hours Slept before Various Shifts from Four Different Data Sets

Shift Type

2010 Survey

1999 

Survey a 

(n = 4524)

2010 Field 

Study

2000 Field 

Study

(n =71)

Mean 

Hours n

Mean 

Hours

Mean

Hours n

Mean 

Hours

On RDOs not following a 

midnight shift 8.3 3,065 8.3 - - 8.0 c

Before an afternoon shift (starts 

between 13:00 &19:59) 7.7 2,987 8.0 6.9 327 7.2 b

Before a mid-day shift (starts 

between 10:00 & 12:59) 7.3 2,431 7.9 6.8 111 7.7 b

Before a day shift (starts between 

08:00 & 09:59) 6.7 2,434 7.3 6.4 14 6.5 b

Before an early shift (starts 

before 08:00) 6.3 3,126 6.5 5.4 369 5.8 b

Before an early 

shift following...

8-9 hours off 5.4 2,819

8-10 hours off 5.2 148 5.4 c

8-12 hours off 5.3 164

Before a 

midnight shift 

following... 

8-9 hours off 3.1 1,959

8-10 hours off 2.3 b

8-12 hours off 2.5 52

Before a midnight shift - - 3.6 3.3 d 140 - 

Before a midnight shift after 

another midnight shift 5.5 1,228 - - - - 

After a midnight shift during the 

day 4.8 1,738 4.3 - - 4.5 b

Note a Reported in Della Rocco, et al., 2000a. b

 Reported in Nesthus, et al., 2001. c Reported in Nesthus, et al., 2003. d The mean hours slept prior to midnight shifts in the field study represents all midnight shifts 

regardless of time off before the shift. 

2.3.5 Chronic Fatigue Scale Comparison

The mean score for the survey respondents on the Chronic Fatigue Scale was significantly 

higher than the score used for normative comparison in the 1999 survey. The mean score 

obtained with the current respondents was 26.61 (n = 3126, SD = 8.31), while the normative mean 

score cited for this scale was 25.04 (n = 1864 nurses and industrial shift workers, SD 7.58, Barton et 

al., 1995, see also Della Rocco, 2000, p. 15). The difference was significant (t = 6.67, df 4,988, twotailed p <0.0001). 

The mean score for the subgroup of CPCs and developmentals was significantly higher in the 

current survey respondents than it was for the comparable subgroup in the 1999 survey 

population. This suggests that chronic fatigue has increased in the operational ATC population. The 

mean score for the 1999 ATC sample (5,211 CPCs and developmentals) on the Chronic Fatigue Scale 

was 24.75, SD = 8.32 (Della Rocco, 2000, p. 15). For the comparable respondents in the 2010 sample 
(2,692 CPCs and developmentals) the mean was 26.43, SD = 8.24. This is statistically significant (t = 

8.53, df 7901, two-tailed p <0.0001). 

2.3.6 The 2-2-1 Schedule: Pros and Cons

As discussed in the introduction, the focus of much previous research is the common rapidly rotating 

counterclockwise "2-2-1" schedule. A summary of this research is provided in Della Rocco & Nesthus, 

2005. A typical schedule of this type consists of:

2 Afternoon shifts, e.g. 

15:00 to 23:00 (followed by 15 hrs. off)

14:00 to 22:00 (followed by 9 hrs. off)

2 Day shifts, e.g. 

07:00 to 15:00 (followed by 14 hrs. off)

06:00 to 14:00 (followed by 8 hrs. off)

1 Midnight shift, e.g.

22:00 to 06:00 (followed by 80 hrs. off) 

Two challenges of the 2-2-1 schedule have been documented. The first challenge is that the hours 

between the second afternoon and the first early morning shift limit the opportunity for sleep and 

recovery. This was supported by the average sleep reported between these two shifts as being 5.4 

hours—similar to that found in the field study by Della Rocco and Nesthus (2005) and similar to actual 

objective sleep measured in a laboratory study (Cruz, et al., 2003). In 2011, the FAA increased the 

minimum time between these two shifts from 8 hours to 9 hours. Further increasing this sleep 

opportunity could potentially help to permit adequate sleep time, as will be discussed below (Section 

2.3.6 Challenges Associated with Limited Time-Off Intervals Between Shifts). The second challenge to 

the 2-2-1 is the sleep opportunity obtained prior to the midnight shift. Only 3.1 hours of sleep was 

reported prior to the midnight shift on a 2-2-1 schedule in the current sample. 

2.3.7 Challenges Associated with Midnight Shifts

Respondents reported that between successive midnight shifts, they slept an average of 5.5 

hours. Before midnight shifts during a quick turn-around of 8 or 9 hours, respondents reported 

sleeping only 3.1 hours. These responses indicate that respondents are incurring a sleep debt that 

increases the levels of reported fatigue. 

Respondents with Counter-Clockwise Rapidly Rotating Midnight (RRM) schedules reported that they 

got less sleep before all shifts than those with Rapidly Rotating (RR) schedules without midnights. 

Respondents with one or more midnight shifts on their bid schedules scored significantly higher on all 

three fatigue scales used in the survey when compared to those without midnight shifts. When 

respondents were asked if they had caught themselves "about to 'doze off'" during work duties in the 

previous year, those with midnight shifts were more likely to reply "Yes" (71%) than those without 

midnight shifts (53%). 

Respondents indicated that compared to all other shifts, on midnight shifts after very quick turns they 

felt:

• Least rested at the beginning of their shift

• More likely during their shift to catch themselves about to "doze off" during work duties (closer 

to "frequently" than "sometimes")

• Least sharp at the end of their shift
2.3.8 Challenges Associated with Limited Time-Off Intervals Between Shifts 

About 16% of the time-off intervals between shifts in the bid schedules were only 8-9 hours. 

About 25% of these time-off intervals were shorter than 11 hours. 

2.3.9 Challenges Associated with Early Shifts 

The average reported amount of sleep obtained before an early shift (beginning between 06:00 

and 07:59) was 6.3 hours. This is below the amount of sleep obtained before shifts that started later in 

the day, such as afternoon shifts (7.7 hours) or mid-day shifts (7.3 hours), and is consistent with 

previous research (Della Rocco and Nesthus, 2005; Cruz et al., 2003a). 

The reported time slept before an early shift was further reduced to 5.4 hours following a quick 

turn-around (8-9 hours). Respondents indicated they felt least "sharp" in terms of alertness or 

memory at the beginning of early shifts after a quick turn-around than at the beginning of any other 

shift, including midnight shifts after a quick turn-around.

2.3.10 Six-day Constant Schedules

The schedule that controllers reported being least satisfied with was a 6-day constant schedule.

These schedules involved working 6 days per week, followed by one day off. Almost two-thirds (64%, 

74/114) of these schedules contained midnight shifts. Although the number of personnel that worked a 

6-day constant schedule was less than those who worked RRM or RR schedules, 32% of those with 

these 6-day schedules reported having an operational event in the previous year. This is a higher 

proportion than those with other types of schedules (average 18%). The proportion of personnel in this 

sample who reported actually working a 6-day schedule in their last full week of work was about 14%. 

Of those working a 6-day schedule in their last full work week, over half (53%, 239/452) also worked at 

least one midnight shift; 15% (68/452) reported working two or more midnight shifts. Finally, those who 

actually worked 6-day schedules were not evenly distributed across facility type—over 30% of 

TRACON personnel in this sample reported working 6-day weeks in the last full week they worked; 

again, about half also reported having midnight shifts. 

Those with the 6-day constant schedule in this sample reported the highest proportion of operational 

events. They also had the highest ratings of sleepiness on the Epworth Sleepiness Scale. Also, 

compared to most other schedules, those on the 6-day constant schedules reported awakening more 

frequently and being unable to sleep when wanting to. 

2.3.11.10-hour 4-day Week Schedules

Based on survey responses, the 10-hour 4-day week schedule appeared to be one of the most 

preferred and least fatiguing schedule types. Approximately 5% of respondents actually worked a 

10-hour 4-day week schedule. Respondents reported the most satisfaction with both this schedule and 

the Straights and Slowly Rotating Days (without midnights) schedules. On the Epworth Sleepiness 

Scale, the 10-hour 4-day schedule was associated with the lowest sleepiness ratings of all the 

schedules, including the Straights and Slowly Rotating Days schedule. Respondents reported the 

fewest problems with being unable to sleep when they wanted to (equal to the Straights & Slowly 

Rotating Days schedule) and awakening in the middle of their sleep period (equal to the Rapidly 

Rotating without Midnights schedule). 

2.3.12 Staffing Levels Contribute to Difficult Schedules

CPC staffing levels were reported as “not enough” by respondents, and the presence of 6-day 

schedules may support this issue. When asked how upper level FAA management could reduce 

controller fatigue, "Increase staffing" was the second most frequent suggestion. 
2.3.13 TRACON Schedules

Personnel at TRACONs reported having the least satisfying schedules, most overtime, and 

highest operational events of all the facilities. About one-third (30.5%) of all personnel at TRACON 

facilities reported actually working a 6-day schedule, compared to 11.3% at En Route and 11.9% at 

Tower facilities. Half (50.5%) of the CPCs at TRACONs worked over 40 hours a week compared to 

14% in En Route and 21% in Tower facilities. About 33% of CPCs at TRACONs reported having an 

operational event in the previous year, approximately double the proportion of CPCs at Towers and En 

Route facilities. Of those TRACON personnel who had an operational event, about 60% considered 

fatigue to have been a factor. 

Compared to CPCs in other facilities, CPCs from TRACONs:

• Reported shorter break times. 

• Rated the staffing levels allowing for positions to be rotated for fatigue reduction as least 

adequate. 

• Rated CPC staffing levels as least adequate and were more likely to think that the number of 

hours worked, overall workload, and traffic complexity was above “about right”.

• Reported the highest rate of catching themselves about to “doze off” during work duties in the 

last year.

• Scored statistically significantly higher than those from other facilities on the Modified Brief 

Fatigue Inventory and the Chronic Fatigue Scale. 

• Were least satisfied with their schedules. 

2.3.14 FLM Schedules

Compared to CPCs and Developmentals, survey results indicated that FLM schedules included

a higher number of overtime hours, longer intervals between breaks, and were rated as 

contributing more to fatigue. About 68% of FLMs and 84% of Operations Managers worked over 40

hours per week compared to 22% of CPCs. The FLMs' bid schedules were similar to those of the 

CPCs', except the FLMs had a somewhat lower proportion of rapidly rotating schedules with midnight 

shifts (43% vs. 54%). FLMs rated the time between their breaks as significantly longer than CPCs, and 

some FLMs indicated they received no breaks at all. FLMs reported being significantly more fatigued 

than the others on the Epworth Sleepiness Scale. FLMs rated work schedule as contributing to their 

fatigue more than did CPCs and Developmentals. 

2.3.15 Breaks and Rotations

The decline in rated alertness (sharpness) with each half hour on position indicates the value of 

frequent breaks. About 22% of the respondents reported that their breaks occurred at intervals longer 

than 11⁄2 hours. CPCs prefer hourly breaks when traffic is busy or workload is high (61.5%) or when 

providing OJT instruction (44.0%). CPCs prefer breaks every 1 1⁄2 hours when traffic is light or workload 

is low (40.1%). Having more/longer breaks was the third most frequent suggestion from respondents on 

what immediate supervisors could do to reduce controller fatigue. 

2.3.15.1 Breaks During Low Workload Periods

Most CPCs (76%) felt that more breaks could safely be taken during low workload periods. About 

36% of CPCs indicated that these breaks could occur seven or more times per week. The top three 

suggestions by CPCs on ways that immediate supervisors could reduce fatigue were to staff positions 

only when necessary, allow naps on breaks, and have more/longer breaks. 

2.3.15.2 Timing of Breaks: Low Workload is Fatiguing

Survey respondents indicated that low workload is fatiguing. The most frequent comment regarding 

breaks was that positions were staffed unnecessarily when traffic was low. The next most frequent 

comment made by 14.1% of respondents indicated that low workload creates challenges for controllers. 
Respondents rated their alertness ("sharpness") as being lower after light traffic than after a heavy 

push. The difference between light and heavy traffic effects on controller “sharpness” decreased after 

controllers had been on duty for longer than 1 1⁄2 hours. Respondents also rated their likelihood of 

having caught themselves about to “doze off" during work duties as higher after light traffic than after a 

heavy push. 

2.3.15.3 Naps during Breaks

Most respondents (82%) reported that brief sleep or naps during breaks would increase their 

alertness at work. 

The shifts on which respondents thought naps were to be of most benefit were early morning and

midnight shifts. Those working midnight shifts reported that naps would provide more benefit on 

midnight shifts than those who did not work on midnight shifts—78% versus 43%. 

2.3.15.4 Position Rotations

Position rotations to reduce fatigue were reported as "somewhat adequate" by CPCs. 

2.3.16 Sleep Disorders and Other Sleep Problems 

Approximately 8% (256/3268) of respondents reported that sleep disorders cause difficulty in 

their sleeping. Moderate obstructive sleep apnea (OSA) alone has been estimated to affect 7% of all 

Americans. The proportions for OSA are even higher in a middle-aged American population—9% of 

women and 24% of men (Young et al.,1993; Lee et al., 2008; Young et al., 2002). Of those in this 

sample who have a self-described sleep disorder, about 41.6% work rapidly rotating schedules without 

midnights and a similar proportion work rapidly rotating schedules with midnights (40.8%). About 4% 

work a 6-day constant schedule.

The rate of reported sleep disorders does not increase with age in this sample as it does in the 

general population. The proportion of those with self-described sleep disorders rises after age 30 to 

about 8% and stays fairly constant until ages 51–55, when it reaches 9.9%. The survey also examined 

whether respondents would take certain prescribed or over-the-counter medications to aid sleep if 

permitted. About a quarter of the respondents reported they would take over-the-counter or prescription 

medicine to aid in sleep.

2.3.17 Differences Among Age Groups

Fewer CPCs under the age of 36 worked schedules with midnight shifts than did CPCs age 36 

and above (53% vs. 59%). About 27% of those working the 6-day constant schedule were between the 

ages of 46 and 50. 

In the current sample of survey respondents, older workers do not report an increased 

proportion of operational events even though they work a higher proportion of midnight 

schedules. The age range of CPCs with the highest proportion of operational events is between 36 

and 40 years and decreases thereafter with increasing age. 

2.3.18 Training for Fatigue Management

Over half of the CPCs answered "Yes" to the question "Would you like training or information 

on ways to reduce fatigue?" Those that had midnight shifts in their schedules were more likely to 

want training or information on ways to reduce fatigue. When asked to indicate how their supervisors 

could reduce controller fatigue, many respondents suggested that FLMs could take a more active role 

in monitoring fatigue and could improve their management of controller workload accordingly. 
2.3.19 Fatigue Safety Culture

Overall, 18% of respondents indicated that they had a Proximity Event (PE), Operational 

Deviation (OD), or Operational Error (OE) within the previous year. About 56% of the respondents

self-attributed these operational events to fatigue. This is higher than the 5-6% that Della Rocco et al. 

(2000a) reported in the 1999 survey results. The rise in operational events from the 1999 survey to the 

present study could not be determined from the questions or responses in the survey.

Although respondents reported that their supervisors supported them in work-related activities, 

there was less perceived support from supervisors regarding relief for fatigue, such as 

providing breaks or rotations. Further, respondents reported they were less comfortable asking for 

such breaks or rotations than asking for assistance for work-related activities. 

Even though many controllers did take time off from work due to fatigue at least a few times in 

the past year, they indicated that they did not feel comfortable doing so. They were even less 

comfortable telling their supervisor that they had taken time off due to fatigue. Nor were CPCs 

optimistic that their supervisors would authorize annual leave for controllers to reduce fatigue. An FAA 

policy has been changed to allow the use of sick or annual leave for fatigue (July 1, 2011 Memorandum 

of Agreement). 

2.3.20 Limitations and Strengths of the Survey

The study sample has limitations. Although the response rate was under 20%, there are many

indications that the sample was representative of the overall controller workforce. For example, the 

proportion of respondents by age and experience was consistent with the overall controller and 

developmental population. Further, on important schedule measures, such as the amount of sleep 

before an early shift following a quick turn-around (5.4 hours), the results were consistent with both a 

field study using objective measurements of sleep (Della Rocco & Nesthus, 2005), a laboratory study 

(Cruz et al., 2003), and the field study associated with this study. However, it was not established 

whether there was equal access to computers at different facility types, especially Towers, where the 

response rate was lowest. There were no returns of paper surveys, which were available at all facilities. 

It also would have been scientifically worthwhile to obtain information on gender, but to protect 

respondents' anonymity in smaller facilities, this information was not obtained.

Confirmation of schedule types was challenging. It would have been helpful to confirm that the 

general properties of the schedules (depicted in Section 2.2.2 Schedules) were representative of the 

schedules in the general ATC workforce. Although the FAA has the actual schedule data available, 

classifying the schedule types using the classification scheme from the study was challenging. The vast 

array of schedule types is indeed difficult to classify, and was one of the most challenging aspects of 

this study. The schedules are originally designed at individual facilities to cover the particular staffing 

needs of each facility, which vary greatly. 

A strength of the survey was the input on the questions from NATCA and other controllers and 

the free text input from respondents. Both of these sources conveyed the realities of the work place, 

helping to clarify the meaning of the ratings and helping to provide the rationale for the findings. All of 

the comments from respondents were read and considered.

Other strengths of the survey were the detailed description of the respondents’ schedules, the 

examination of the effects of two new schedule types, and the breakdown of the work force by 

position and facility. New aspects of the respondents' schedules that were described were: the 

distribution of shift start times, number and length of between-shift intervals, number of rotations within 

weeks, number of midnight shifts, and number of shifts worked in a week. The effects of two new 

schedule types were examined: the constant 6-day schedule and the 10-hour 4-day week schedule (in 

addition to the rapidly rotating shifts with and without midnights and the shifts without rapid rotations 
and midnight shifts). Breaking down the work force by position and facility enables focused 

interventions. 

Additional strengths were questions assessing the risk of controller fatigue that respondents' 

perceived, and questions eliciting their suggestions on how to reduce controller fatigue. Many of 

their suggestions are reflected in the findings at the end of this report.
3. CONTROLLER ALERTNESS AND FATIGUE MONITORING FIELD STUDY

3.1 OVERVIEW

A field study assessed individual controllers’ levels and patterns of sleep, subjective fatigue and 

alertness ratings, and behavioral measures of cognitive alertness over 14 days while they were 

engaged in their normal work patterns. Validated measures included wrist activity monitors, 

sleep/activity logs, and the Psychomotor Vigilance Task (PVT). These data were obtained from a 

sample of controllers from a targeted set of facilities from April 2010 through December 2010. Data 

collection ran concurrently with the NASA web-based fatigue survey administered to the entire air traffic 

control population. 

This study was designed to determine whether patterns of work schedule-related sleep and alertness 

were similar to those found in studies conducted by CAMI more than a decade earlier. The present 

study was designed for comparability to these earlier findings, which are described in section 1.3 of this 

report. These findings describe the relationships between a number of variables that define the shifts 

and schedules that controllers work, the amount, quality and patterns of sleep obtained on those 

various schedules, and the associated levels of alertness or fatigue experienced by the controllers. 

3.2 METHOD

3.2.1 Facility Selection

Initially, 14 facilities were selected to participate in the field study, the goal being a sample of 104 

participants from four En Route facilities, 52 from four TRACONs and 52 from six ATC Towers. The 

NASA research team worked with the FAA and the Article 55 Fatigue Risk Management Work Group to 

identify suitable facilities. Several criteria guided this selection:

• 24-hour operations, thus including midnight shifts

• High traffic levels based on the FAA’s facility ratings, (which range from 1 to 12)

• Varying levels of CPC staffing in relation to the FAA’s target staffing levels (FAA, 2009). Note: 

Low levels of CPC staffing were found to be associated with high ratios of developmentals to 

CPCs, a factor that could affect workload and stress of the CPCs (DOT OIG, 2009)

In order to manage time and costs associated with research team travel to facilities for data collection, 

clusters of qualifying facilities in the same geographic area that included a Center, a TRACON and at 

least one Tower were sought. 

At the end of the initial data collection period (which lasted from April 15, 2010, through August 28, 

2010), it became apparent that our recruitment and data collection efforts at the original 14 facilities 

were falling short. Some controllers who signed up to participate were not available to be trained; 

others went through the training, but then either dropped out of the study or provided only partial data. 

By the end of August (the initial completion date), a satisfactory level of participation with complete 

data, with completion defined as 9 work days (out of 14) with associated sleep log, actigraphy and PVT 

data was not achieved. Using this criterion, data completion rates by August 28, 2010 were 77% from 

En Route Centers, 42% from TRACONs and 27% from Towers.9 In order to achieve sufficient power to 

 

9 These differences in participation rates reflected a bias in the data collection process. Because the new En 

Route Automation Modernization (ERAM) technology was scheduled for implementation in the summer of 2010 at 

a number of facilities included in our data collection plan, our initial data collection effort focused on En Route 

Centers. 
detect significant differences between facility types, additional facilities were determined to be needed: 

one En Route Center, six TRACONs, and ten towers. 

In consultation with the FAA and the Article 55 Fatigue Risk Management Work Group, a second wave 

of facilities meeting the original criteria was identified. The full set of participating facilities is listed in 

Table 3-1, along with the number trained from each. Table 3-1 provides information on the facility 

characteristics: staffing levels, ratios of CPCs to developmentals, ratio of CPC staffing to the minimal 

staffing levels, and traffic levels (FAA, 2009). 

It should be noted that it was deemed not practical to go back to facilities that had already participated 

to try to solicit more volunteers after the initial August 28, 2010 date. Some of those facilities had 

adjusted local schedules to permit data collection, and asking them to do so again would have placed a 

significant burden on them. In other cases, enthusiasm for participating in the study was lacking on the 

part of local FAA management or NATCA representative; trying to solicit more volunteers was not 

thought to be productive under those circumstances. In still other cases there simply were not enough 

CPCs who met all of our criteria, including working midnight shifts. 
Table 3-1. ATC Facility Staffing Levels and Traffic Levels for Participating En 

Route Centers, TRACONS and Air Traffic Control Towers

Facility

Number of 

CPCs

Number of 

Developmentals

CPC/ 

Developmental 

Ratio

Minimal 

Defined 

Staffing 

Level-MDSL

Staffing 

Ratio:

CPC/MDSL

Traffic 

Level

En Route Centers

ZAU 338 82 4.12 286 1.18 12

ZDC 262 70 3.74 270 0.97 12

ZDV 243 67 3.63 243 1.00 10

ZNY 232 90 2.58 242 0.96 12

ZOA 176 56 3.14 195 0.90 11

Mean 3.44 1.00

TRACONs

C90 71 16 4.44 85 0.84 12

D01 36 16 2.25 58 0.62 11

D10 56 17 3.29 74 0.76 12

I90 62 5 12.40 68 0.91 12

L30 25 9 2.78 42 0.60 11

N90 174 48 3.63 180 0.97 12

NCT 132 28 4.71 144 0.92 12

PCT 136 35 3.89 147 0.93 12

PHL* 66 21 3.14 73 0.90 12

P50 45 9 5.00 50 0.90 11

SCT 163 44 3.70 196 0.83 12

Mean 4.48 0.83

Air Traffic Control Towers

BWI 27 0 0.00 20 1.35 9 

DEN 31 5 6.20 32 0.97 12

DFW 41 12 3.42 41 1.00 12

IAD 34 6 5.67 28 1.21 11

IAH 30 6 5.00 32 0.94 12

JFK 24 9 2.67 29 0.83 10

LAS 26 7 3.71 34 0.76 11

OAK 22 4 5.50 18 1.22 8 

ORD 48 5 9.60 52 0.92 12

PDX 22 2 11.00 20 1.10 8 

PHX 29 8 3.63 30 0.97 11

SEA 27 0 0.00 23 1.17 9

SFO 26 2 13.00 25 1.04 9 

SLC 30 2 15.00 25 1.20 10

Mean 6.42 0.94 

* PHL is a Combined TRACON and Tower, but all participants were functioning as TRACON controllers during 

the study period.
3.2.2 Participants

The objective data collection effort aimed for a sample of 208 active controllers (104 from En Route 

Centers, 52 from TRACONs and 52 from Air Traffic Control Towers), a number that would yield 

sufficient power for between-subjects statistical comparisons and modeling, based on a power analysis 

(Cohen, 1992). Study volunteers had to meet the following criteria:

• Work a schedule that included at least 1 midnight shift during the 14-day study period

• Work at least 9 shifts during the 14-day study period (not be on leave)

• Actively control traffic (excluded supervisors)

• Be a fully Certified Professional Controller (CPC) and have at least one year of experience as 

a CPC (excluded developmentals)

• Be at least one year from planned or mandatory retirement age

• Not have an untreated sleep disorder 

All participants were asked to participate in the second phase of the study, to be conducted 

approximately two years after initial data collection, in order to evaluate the impact of the controller 

Fatigue Risk Management System (FRMS) the FAA planned to put into place following completion of 

this study.

3.2.2.1 Recruitment

The FAA and NATCA made initial contact with each participating facility to share information about the 

purpose of the study and procedures to be followed. Posters advertising the study were sent to those 

facilities. Prior to visiting each facility, NASA researchers contacted the facility management and 

NATCA representatives by email to enlist their support in recruiting participants and provided them with 

a study recruitment letter to share with their controllers. Participants volunteered using the 

website http://nasasurvey.us (see Appendix E). The data submitted during signup were accessible only 

by the NASA research team.

3.2.2.2 Random Participant Selection 

A procedure was developed to assure random selection of study participants in the event that a greater 

number of controllers from a facility volunteered to participate than could be accommodated. However, 

this procedure was not utilized because no facility yielded excess volunteers.

Target sample sizes and the number of study participants trained in the experimental procedures from 

each participating facility are shown in Table 3-2.

 
Table 3-2. Number of Target and Trained Participants from each Participating Facility

En Route TRACON Tower

Facility 

ID

Target 

n

Trained 

n

Facility 

ID

Target 

n

Trained

n

Facility 

ID

Target 

n

Trained

n

ZAU 26 27 C90 a 8 2 BWI 8 7 

ZDC 26 26 D01 a 9 2 DEN 7 5 

ZDV 26 27 D10 a 9 8 DFW 9 3 

ZNY 26 26 I90 a 9 7 IAD 7 4 

ZOA 26 35 L30 a 7 5 IAH 6 2 

N90 b 15 19 JFK 6 7 

NCT b 19 19 LAS 6 2 

P50 a 1 1 OAK 6 6 

PCT b 14 6 ORD 9 7 

PHLb, c 10 7 PDX 6 6 

SCT b 15 18 PHX 5 5 

SEA 8 8 

SFO 6 6 

SLC 6 6 

Total 130 141 Total 116 94 Total 107 74

Overall Total 353 309

a. TRACON, RAPCON or CERAP

b. Combined TRACON/Tower

c. All participants from PHL were working as TRACON controllers even though it was a 

combined facility. 

3.2.2.3 Participant Privacy and Human Subjects Protection

This study was reviewed and approved by NASA’s Institutional Review Board for the Protection of 

Human Subjects, protocol #HRII-09-10 dated June 30, 2009, and HRII-10-24 dated July 27, 2010.

Each participant was randomly assigned a unique ID number that was associated with all of her/his 

data in order to protect the participant’s privacy. It is not possible to link data (either objective field data 

or survey responses) to an individual’s name. Participation in the study required a signed consent form 

(see Appendix B), which was kept separate from study data and does not include the unique ID 

number. To further ensure anonymity, all data are reported at the aggregate level. 

As another safeguard, participants were informed that in the unlikely event of an operational event

during the data collection period, all relevant data would be purged immediately to ensure that 

identifying information or responses could not be linked to any individual. No such event occurred for 

the duration of the study.

Participants were advised that participation in the study was voluntary and that they had the right to 

withdraw from the study at any time for any reason. They were not compensated for participation in the 

study, as most of the study activities were accomplished during working hours.
3.2.2.4 Participant Demographics

Participant breakdowns by age and years of experience shown for each facility type are in Table 3-3 

and Table 3-4, respectively. Note that some participants chose not to provide their demographic data.

Table 3-3. Proportion and Number of Participants in Different Age Categories

Age 

Overall En Route TRACON Tower

Proportion of 

Participants n

Proportion of 

Participants n 

Proportion of 

Participants n 

Proportion of 

Participants n 

25 or under 3.57% 8 2.59% 3 3.70% 2 5.56% 3 

26-30 14.29% 32 15.52% 18 9.26% 5 16.67% 9 

31-35 8.93% 20 10.34% 12 9.26% 5 5.56% 3 

36-40 12.95% 29 11.21% 13 16.67% 9 12.96% 7 

41-45 21.88% 49 19.83% 23 22.22% 12 25.93% 14

46-50 29.02% 65 31.90% 37 25.93% 14 25.93% 14

51-55 8.93% 20 7.76% 9 12.96% 7 7.41% 4 

56+ 0.45% 1 0.86% 1 0.00% 0 0.00% 0 

Total 100.00% 224 100.00% 116 100.00% 54 100.00% 54

Missing 27 7 16 4 

Total 251 123 70 58

Table 3-4. Proportion and Number of Participants’ Total Years of Professional Experience with 

ATC (Including the Military)

Years of 

Experience

Overall En Route TRACON Tower

Proportion of 

Participants n 

Proportion of 

Participants n 

Proportion of 

Participants n 

Proportion of 

Participants n 

< 1 0.00% 0 0.00% 0 0.00% 0 0.00% 0 

1-4 9.25% 21 10.26% 12 3.57% 2 12.96% 7 

5-9 10.13% 23 11.11% 13 8.93% 5 9.26% 5 

10-19 29.07% 66 26.50% 31 30.36% 17 33.33% 18

20-29 46.70% 106 50.43% 59 48.21% 27 37.04% 20

30+ 4.85% 11 1.71% 2 8.93% 5 7.41% 4 

Total 100.00% 227 100.00% 117 100.00% 56 100.00% 54

Missing 24 6 14 4 

Total 251 123 70 58
3.2.2.5 Participant Work Schedules

Every effort was made to fit the schedules worked by controllers in the present field study to those 

described in our accompanying Fatigue Factors Survey and those defined by Della Rocco, et al. 

(2000a). However, the comparability was limited by the nature of the present study. First, controllers 

with midnight shifts were actively recruited. This automatically eliminated the straight shift without 

midnights (SS) and significantly reduced several other schedule types, such as the counterclockwise, 

rapidly rotating, no-midnights (RR) schedule or the 10-hour 4-day schedule without midnight shifts. 

Second, participants in the present study could have begun data collection on any day during their 

normal work week, not just on the first day, i.e., on days 2 through 5 (or 6). Consequently, it was not 

unusual for the initial sequence of days to consist of a partial week (e.g., two or three work shifts), 

followed by regular days off (RDO), then a full 5-day work week, RDO, and then end with another 

partial week. Thus, 14 days of data collection might yield, for example, three work days (WD), two 

RDO, five work days, two RDO and two work days. This picture was further complicated when an 

unanticipated day off occurred in the middle of a work week, yielding a sequence such as the following:

3 WD, 2 RDO, 2 WD, 1 RDO, 3 WD, 1 RDO, 2 WD = 14 days

Because of variability in start dates and completeness of data (e.g., actigraphy, PVT and sleep/activity 

logs), the nominal 14 days of data collection could yield one full 5-day work week plus two fragments, 

or two full work weeks, or no full work weeks. This mixture of partial and complete work weeks created 

uncertainty in characterizing the participants’ work schedules, which was essential for classifying them 

prior to analysis. Bid schedules were obtained only from participants who signed up to participate on 

the NASA website. A description of their bid schedule was requested on the sign-up page (see 

Appendix E). Many other participants signed up while the NASA experimenters were at their facility to 

conduct training. Volunteers were asked to sign up on the web so that access to their full demographic 

and schedule information was available, but not all complied. In addition, the schedules worked did not 

always conform to the bid schedules. Thus, comparing present schedules with those of Della Rocco, et 

al. (2000) was a challenge. Obtaining the participants’ actual recorded work schedules from the FAA’s 

Business Objects database for the duration of their study period, plus the week prior to and two days 

following their study period, considerably reduced the uncertainty concerning regular bid schedules.

For the analyses relating both sleep patterns and measured alertness to work schedules, only 5-day 

and 6-day work weeks were characterized. Using the FAA’s business objects (BO) data, the schedules 

worked by each participant during the study period were classified into several categories. These 

categories correspond to those used in our analysis of the survey data, as well as those reported in 

Della Rocco, et al. (2000a). 

3.2.3 Procedure

Over the nominal 14-day study period10 at each facility, each participant was required to:

• Wear an actigraphy monitor that recorded active and sleep periods over a 24-hour cycle 

• Keep a daily sleep/activity log 

• Self-administer an objective alertness measure, the Psychomotor Vigilance Task (PVT), three 

times during each work shift

 

10 Each participant’s data collection period was 14 days. However, some participants provided more than 14 days 

of data. Because not all participants at a facility were trained at the same time, the duration of training could 

extend over four days with individual start dates staggered over the four days. The experimenters did not pick up 

the equipment from a facility until after the last-trained participant had completed 14 days. Hence, earlier trained 

participants could, at their discretion, provide up to four extra days of data. 
3.2.3.1 Activity Monitors

Activity monitors, or actigraphs, track sleep and wake periods through an accelerometer that is 

sensitive to movement. Participants continuously wore an actigraph the size and shape of a small 

wristwatch on their non-dominant wrist to record sleep and wake periods for 24 hours per day. These 

activity monitors were removed only during showers, swimming, or other activities likely to damage the 

watch. Figures 3-2a and 3-2b show two actigraph models compared to digital wristwatches. In Figure 3-

2a the activity monitor (to the right of the digital watch) is manufactured by MiniMitter, model Actiwatch64; in Figure 3-2b the monitor (with red cover) is manufactured by ActiGraph, model GT3X Ambulatory 

Monitoring System. 

Figure 3-1a. Digital watch (L) and 

MiniMitter Actiwatch-60 (R) Figure 3-1b. ActiGraph GT3X (L) 

and digital watch (R)

In order to establish the comparability of the two models of watches, prior to the beginning of the study 

five members of the NASA research team wore one or more pairs of actigraph watches, one of each 

model, for a period of a week. Recorded sleep timing, duration and efficiency were compared across 

the two models. No significant differences across models were found. Use of the two models did not 

vary systematically across facility types or schedules. In many cases, both models were used within the 

same facility, especially en route centers, where data were collected simultaneously from a large 

number of participants.

3.2.3.2 Sleep/Activity Log

A daily log was used to record significant events during each day of the study. Based on the logs 

developed initially for studies of fatigue in airline pilots (Gander, Myhre, Graeber, Andersen, & Lauber, 

1989) and for prior CAMI field studies with controllers (for a summary of this work see Della Rocco & 

Nesthus, 2005), the log included a number of separate entries to be filled out across the day. 

Participants recorded the times at which they went to sleep and awakened from their main sleep 

periods and naps, started and ended work, and took the PVT during work hours. They rated their 

alertness/sleepiness levels upon awakening and after taking each PVT using the Stanford Sleepiness 

Scale (Hoddes, Zarcone, Smythe, Phillips & Dement, 1973). They also rated the level of workload they 

experienced during the work period prior to each PVT administration. Following are the codes used in 

the sleep/activity logs:

ACTIVITY Codes

S - Time to Sleep (including main sleep and naps)

A - Time Awake from main sleep or nap

T - PVT Test Time

W - Began Work 
E - Ended Work

O - Actiwatch Off (shower, swimming, etc.)

ALERTNESS/SLEEPINESS Rating 

1 - Feel active and vital; alert; wide awake 

2 - Functioning at a high level, but not at peak; able to concentrate

3 - Relaxed; awake; not at full alertness; responsive 

4 - A little foggy; not at peak; let down

5 - Fogginess; beginning to lose interest in remaining awake; slowed down 

6 - Sleepiness; prefer to be lying down; fighting sleep; woozy

7 - Almost in reverie; sleep onset soon; lost struggle to remain awake 

WORKLOAD Rating (Refers to the work period immediately preceding each PVT)

1-----2-----3-----4-----5-----6-----7 

Very Moderate Very

Low High

Figure 3-2 is an example of a completed Sleep/Activity Log for one 24-hour period. Note that the 

person was awake at midnight (0000) when the log began, went to sleep at 0130, and awoke at 0930. 

Upon awakening, the person reported being fully rested and wide awake (rating of 1 in the Fatigue row 

at 0930). The actiwatch was taken off at 1000 (perhaps for a shower). At the beginning of the work shift 

at 1300, the first PVT was taken; the participant rated sleepiness at that point as 1 (= low, or wide 

awake) and began to work at 1330. The second PVT was taken at 1700, at which point sleepiness was 

rated as 2; workload during the prior work period was rated as 3 (slightly below average). The third PVT 

was taken at 2100 when sleepiness was rated as 4 and prior workload at 5, or slightly above average. 

The work shift ended at 2230.

DAY 1 0000 0100 0200 0300 0400 0500 0600 0700 0800 0900 1000 1100

Activity S A O

Fatigue 1 

Workload

DAY 1 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300

Activity T W T T E 

Fatigue 1 2 4 

Workload 3 5 

Figure 3-2. Sleep-Activity Log filled out for 24-hours.

3.2.3.3 Psychomotor Vigilance Task 

Participants took a 5-minute behavioral test of mental alertness three times during each work shift 

during the study period. The Psychomotor Vigilance Task (PVT) is a simple reaction time task, which 

was administered on a handheld PDA (a Palm Centro with phone capability disabled). Software 

developed by researchers at the Walter Reed Army Institute for Research (WRAIR) for administering 

the PVT on a PDA was used (Thorne, Johnson, Redmond, Sing, Belenky & Shapiro, 2005). 

Participants were instructed to press a designated response button on the device as quickly as possible 

when a stimulus (bull’s-eye) appeared on the screen (See Figure 3-3); either the right or the left 

response button was used, depending on the participant’s handedness. Reaction times in hundredths 

of a second were visually displayed in the target for each trial. Inter-stimulus intervals varied randomly 

from 1 second to 10 seconds. 
The first PVT was taken at the beginning of each work shift. The second PVT was taken after a break 

period as close to the middle of the shift as possible. The third PVT was taken after the final break, 

ideally 1 hour or so before the end of the work shift, in order to assess participants’ alertness going into 

their final segment on duty.

It should be noted that the PVT was originally validated as a 10-minute test (Dinges & Powell, 1985), 

though the sensitivity of the PVT is increased by using a longer task duration (Dinges & Weaver, 2003; 

Dorrian, Rogers & Dinges, 2005). The greater sensitivity is useful when dealing with mild to moderate 

levels of sleepiness. However, given that the PVT was to be administered during working hours, FAA 

managers expressed concern about the cumulative amount of time required for data collection. Hence, 

the 5-minute version of the PVT, which had been validated by several groups in the US and Australia 

(Lamond, Jay, Dorrian, Ferguson, Roach, & Dawson, 2008; Loh, Lamond, Dorrian, Roach & Dawson, 

2004; Thorne, Johnson, Redmond, Sing, Belenky & Shapiro, 2005) was chosen. Most recently, Dinges 

and his colleagues have validated a 3-minute PVT (Basner, Mollicone & Dinges, 2011; Basner & 

Rubenstein, 2011), but this was not available when our study was begun. One concern with using a 

briefer test is that performance degradation may take time to emerge, especially lapses (responses 

slower than 500 msec). However, given prior research reports based on the 5-minute version, it was 

deemed an acceptable compromise between test duration and sensitivity.

Figure 3-3. Psychomotor Vigilance Task (PVT) on a Palm Centro PDA.

In summary, each participant provided 14 days of data that comprised the following measures:

• Actigraphy Data: Continuous 24-hour recordings over 14 days, except when showering or 

engaging in potentially damaging activities. Sleep duration, sleep efficiency and sleep timing 

were recorded.

• Sleep and Activity Logs: Daily records indicating sleep times, wake times, and work hours, as 

well as times at which the PVT was taken.

• Subjective Sleepiness and Workload Ratings: Ratings of how sleepy or alert participants felt 

upon awakening from each sleep period and when taking the PVT. Perceived workload was 

rated for the work period immediately prior to each PVT administration.

• Psychomotor Vigilance Task (PVT): Taken three times during each work shift, at the 

beginning, middle and end, typically over 10 work days within the 14-day data collection 

period. 
3.2.3.4 Participant Training

In preparation for data collection at each facility, two members of the NASA research team were on site 

for up to four days to provide an introduction to the study and training in data collection procedures. 

Study participants met with the NASA team at the beginning of a shift, either individually or in groups of 

up to four controllers. Training sessions occurred any time during the 24-hour day depending on when 

participants’ shifts began. 

The study introduction and training took approximately 30 minutes and covered:

• Actigraph care and use

• How to fill out the sleep/activity log

• Psychomotor Vigilance Task (PVT) administration

• Web-based survey instructions and PIN

• Participant confidentiality and human subjects protection

A graphic representation of the data collection schedule for one participant over the 14-day study 

period is shown in Figure 3-4. 

Throughout the training, the procedures undertaken to assure confidentiality of individual data and the 

use of randomly assigned personal ID numbers on all data, including the sleep/activity logs and the 

self-administered PVT (on the PDA), were emphasized. Actigraph watches were linked to participants’ 

ID codes through their serial numbers.

Figure 3-4. Schedule for daily data collection over the 14 days of the study period. 

Day 1 included a training period. This schedule shows an idealized work week, with five sequential 

workdays followed by two regular days off (RDO). In reality, some participants were trained on the 

second, third, fourth or fifth shift of a work week, which meant that the first and last weeks of data 

collection were broken up. The term ‘day’ refers to shift, acknowledging that midnight shifts in fact often 

began on the same day as the prior shift. 
Participants were provided with a take-home user guide that summarized all of the information 

contained in the training for reference throughout the study; a laminated copy of the procedures was 

also available at the facility for use during on-shift data collection (i.e., when taking the PVT and filling 

out the activity logs). The personal instruction sheet is illustrated in Appendix C. NASA scientists were 

available on site during training or by phone 24/7 to answer questions as needed. 

Participants were instructed where to return their actiwatches and sleep logs at the end of the study 

period. A member of the NASA team picked up all data collection instruments (i.e., actigraphs, Palm 

Centros, logs) after the last participant had completed all days of data collection. 

3.2.4 Data Review and Pre-processing

Each participant’s data were reviewed to determine completeness over the 14 days of data collection. 

By ‘complete’ it is meant that the participant worked at least 9 days, wore the actigraph watch during 

that period, filled out the sleep-activity log, and took three PVTs during each work shift. Participants for 

whom at least 9 days of complete data was collected were included in the overall analyses. Multilevel 

modeling requires sufficient power to assess contributions of variables at various levels; to maximize 

power inherent in the data set, participants who provided at least five days of complete data out of the 

14 days were also included in overall analyses. A second criterion was used to determine inclusion: If a 

participant’s data were complete for at least 5 work days and the five days constituted an entire work 

week unbroken by any regular days off (RDO), their data were included as well in the analyses that 

involved shift schedules. 

Sleep/Activity Logs. Data from each participant’s sleep/activity log were used to determine three critical 

aspects of a person’s day: the times at which the controller went to sleep and woke up, began and 

ended work, and self-administered the PVT (three times during a work shift). Sleep/wake times from the 

logs were essential for aligning the actigraphy data properly. Work shift start and end times were 

needed to establish the schedule each participant was working over the course of the 14 days, the type 

of shift worked on each day, regular days off, and other days off (e.g., sick days). The shift start and 

end times also served to verify the times at which the PVT was taken (i.e., to make sure that the 

reported PVT times aligned with actual work periods).

Participants occasionally neglected to make an entry in their activity logs, which caused difficulties in 

alignment or in establishing their actual worked schedules. Most common was failure to note time 

completing work. The next most common omission was skipping over one or more days off in the log. 

Given that the number of days off varied significantly, this omission increased the uncertainty of 

alignment of the work days with the actiwatch data. 

The FAA provided a solution to this problem.11 Actual shift start and end times were obtained for all 

participants in the study, along with five days prior to the participant’s 14-day data collection period and 

two days after (21 days, or three weeks). This longer span made it possible to establish bid schedules 

and those actually worked, which was impossible to determine from the logs when participation began 

in the middle of a week, i.e., when a participant’s first day of data collection was the third or fourth or 

fifth day of a work week. When participants signed up on the web to participate in the study, they were 

asked to provide the bid schedules they expected to work at the time of data collection at their facility. 

However, not all did so. Even when schedules were provided, they frequently changed due to overtime, 

family issues, or other unknown causes. Hence, incomplete data concerning shift schedules was 

 

11 The NASA research team is grateful for the assistance of the FAA’s Fatigue Risk Management Office, NATCA, 

and the anonymous contractor in developing and executing the procedure to obtain study participants’ actual work 

schedules.
frequently encountered, a problem solved by obtaining actual schedules worked from the FAA’s 

Business Objects (BO) database. 

The following procedure for obtaining this schedule information was reviewed and approved by NATCA. 

Participant confidentiality was maintained by transmitting the request to a contractor who was made a 

member of the NASA research team for this purpose, thereby extending the NASA Human Research 

Institutional Review Board (HRIRB) guidelines and requirements to that individual. The person signed a 

letter of agreement, indicating a commitment to comply with the HRIRB guidelines for the protection of 

human subjects. 

A new ID number was randomly assigned to each individual (different from the one used to identify the 

participant’s data). These new IDs were paired with the participants’ names and the date range for the 

specified work periods and sent to the contractor. The contractor then extracted the daily work schedule 

data for each individual from the FAA’s Business Objects database and returned the data file to NASA 

using only the new participant ID numbers. The new IDs were converted back to the original ID 

numbers and entered the schedules into each participant’s data file. Documents linking the individual 

names and ID numbers were then destroyed. 

Actigraphy Data. The 14 continuous days of actigraphy recordings were downloaded to a computer for 

permanent storage immediately following the data collection period (after equipment pick-up by the 

experimenter at the facility). The downloaded data had to be visually inspected for gaps. Then they 

were aligned with the sleep/activity log information to establish the time at which the participant went to 

sleep and awoke. A full list of all the available sleep parameters provided by the actiwatch is provided in 

Appendix D. Only a subset of these was used to establish the total sleep time (TST) for each major 

sleep period and naps, as well as sleep efficiency.

An example of the preprocessing of the actigraph data, prior to analysis, is shown in the bottom half of 

Figure 3-5. The corresponding sleep and activity log is in the top half, illustrating the correspondence 

between times listed in the log and reflected in the actigraphy. 

Figure 3-5. Sleep/activity log and actigraphy data over 48 hours (n = 1).

The pink blocks represent assumed sleep and the green blocks are actual sleep, defined as “the 

amount of sleep as determined by the algorithm and equivalent to assumed sleep minus wake time.” 

Sleep efficiency is a measure calculated as the ratio of actual sleep time to time in bed, or the 

S 

1:00 

A 

Time 4:00 

Activity

Fatigue

Rating

Workload

Rating

6 

W/T 

5:00 

6 

1 

T

9:00 

4

6

S 

10:30 

A 

11:00

5

T

12:00 

4

1

E 

13:00 

S 

14:00 

A 

15:00 

4 

S 

20:00 

A

3:00 

4

W/T 

4:30 

4

1

T

10:00 

3

6

T

12:00 

5

2

S 

15:00 

A

20:00 

5

W/T

22:00 

3

1

Sleep

Quality 91% 38% 58% 89% 80% 
proportion of sleep in the episode actually filled by sleep. Normal sleep efficiency is at least 85% (i.e., 

asleep 85% of time in bed). Blue lines indicate activity level measured in counts of wrist movements).

PVT Data. Each participant’s PVT data were downloaded to a storage computer from the PDA device 

on which they were collected and then aligned with times of day as noted on the sleep/activity log. The 

PDA automatically recorded the time of day at which each test was taken, thus providing a check on 

the self-reported PVT administration times entered in participants’ log books. 

The PVT software records reaction time to each stimulus in milliseconds, which serves as the 

foundation for two measures of behavioral alertness: response speed and lapses. Response speed is 

the reciprocal of the reaction time, or 1/RT. Lapses were defined as reaction times slower than 500 

msec. The software provides a count of lapses based on specification of the cut-off response time. 

These two measures have been identified as the most appropriate and sensitive to use in studies of 

fatigue (Basner & Dinges, 2011). 

3.2.4.1 Analysis of Objective Sleep, Fatigue and Alertness Data

Data for the field study were analyzed primarily through multilevel modeling because of the nested 

hierarchical structure of the database. The data structure is illustrated in Figure 3-6. 

Figure 3-6. Hierarchical structure of the field study data. 

Level 1 is the day and trial level. PVT, subjective fatigue, and workload measures at three 

time points during each work shift are characterized at this level, along with individual 

work shift types and weekly schedules. Level 2 is the participant level and includes 

demographic variables (e.g., age, gender and years of experience as a controller) 

specific to each participant. Level 3 is the facility level, including features specific to each 

facility used in the analyses: type of ATC facility, traffic level, staffing level, staffing ratio, 

and ratio of CPCs to developmentals.

Often when such nested structures exist, researchers may be tempted to average the data at a lower 

level (e.g., across days) or ignore the nested structure of the data. This leads to either loss of 

information or incorrect partitioning of variability associated with different variables. For example, 

individual differences are considered to account for large variance among sleep durations and fatigue 
associated with sleep deprivation. Such individual differences would be disregarded in analyses that do 

not account for the nested structure. Multilevel modeling is a confirmatory statistical technique where 

researchers create and test a series of hierarchical linear regression models based on theories and 

available evidence. Linear regression in general is an approach to modeling the relationship between 

a dependent variable and one or more explanatory variables or predictors. A relationship is modeled by 

examining the covariation or correlation between the dependent variable and the predictors. Multilevel 

modeling allows for partitioning of variance in the data among different levels so that correct analyses 

can be performed. This is important because a relationship cannot exist between factors where at least 

one is constant (e.g., predicting the work schedule that minimizes fatigue if everyone sampled has the 

same level of fatigue). A simple linear regression assumes that factors at different levels are constant. 

Additionally, multilevel modeling allows for parameters at higher level to vary (e.g., sleep durationresponse speed relationship to vary among participants), thus, enabling researchers to understand 

better the nature of individual differences and other higher level variables. 

The goal of our analyses was to determine the relationships between work schedules, quantitative and 

qualitative measures of sleep, and measured alertness. Specifically, the data was examined to 

determine the following: 

• Whether higher levels of reported fatigue and measured alertness decrements are associated 

with specific work shifts and weekly work schedules (i.e., midnight shifts, rapidly rotating 

shifts). 

• Whether the relationships between work schedules and resulting fatigue and alertness are 

mediated by quantity of sleep. 

• Whether the relationships between schedule types, sleep and alertness are moderated by 

controller demographics or facility features.

Several strategies were used to test these relationships.

Step 1. Individual Level Analyses: The FAA’s Business Objects (BO) data were used to establish the 

schedule each participant worked over the 14-day test period and to determine the timing and duration 

of time off, which created opportunities for restorative sleep. Gaps in a participant’s BO schedule data 

were filled using his or her sleep/activity log. It was especially important to determine accurately the 

time off between shifts (e.g., 8 hrs, 9 hrs, 10 hrs) in a rotating schedule. Second, the duration, timing 

and efficiency of sleep were determined from the actigraph data based on guidelines for identifying 

sleep start and end times provided in the user manuals. Third, subjective reports of 

alertness/sleepiness upon awakening were obtained from self-report ratings on the sleep/activity logs. 

Fourth, PVT data (both response speed and lapses) served as behavioral measures of cognitive 

alertness. These four types of data were input to multilevel modeling to establish causal relationships 

between various schedule factors, sleep patterns and resulting fatigue and alertness.

Step 2. Secondary analyses were conducted to determine:

• Differences in alertness across a work shift (beginning, middle, end of shift), and as a function

of perceived workload during the work period prior to each PVT administration.

• The relationships between amount of time off between shifts, amount and timing of sleep, and 

measured alertness based on the PVT.

Step 3. Moderator Analyses: Moderator analyses were conducted to determine whether the findings 

from the initial analyses held across all participant demographics and facility features. Demographic 

variables included age, gender and years of experience in ATC. Facility variables included facility type 

(En Route Centers, TRACONS and Towers), traffic level, staffing level and ratios of CPCs to 

developmental controllers. 
3.2.4.2 Aligning Field Study Data with Survey Data

Participants in the field study were asked to fill out the web-based fatigue factors survey (if they had not 

already done so) and to include their participant ID number in order to align their survey data with their 

objective data while keeping their identity protected. In the event that they had completed the survey 

prior to the objective data collection, participants were requested to provide the personal ID number 

they had entered on the survey. Some participants had not entered an ID and some had forgotten their 

ID, so it was not possible to align those participants’ two data sources.

After establishing the relationships between work schedules, sleep patterns, and controller alertness, 

the surveys were examined for those controllers who completed both data sets (survey and field study) 

to enrich our understanding of the patterns of field study findings. 

3.3 RESULTS

3.3.1 Database Description

3.3.1.1 Activity Log Data

Altogether, 293 participants completed activity logs. Out of 293, 19 were eliminated because their work 

schedules during the study period did not consist of 5 or more work shifts. However, the FAA’s BO 

database enabled the inclusion of 12 other participants who worked 5 or more shifts and otherwise 

would have been lost to the analyses due to missing log data. The breakdown of number of shifts 

worked during the entire study period for the resulting 286 participants is displayed in Table 3-5. Note 

that although the modal number (29.7%) of participants reported working 11 days within the14-day

participation period, over 21% worked more than 11 days in the 2-week period, presumably due to 

scheduled overtime. The majority of the 286 participants (n = 165, 57.7%) participated for longer than 

14 days. 

Table 3-5. Number and Proportion of Participant Work Shifts 

(not necessarily consecutive) during the Study Period

Number of Work 

Shifts

Number of 

Participants

Percentage of Total 

Participants

5 7 2.4%

6 15 5.2%

7 9 3.1%

8 17 5.9%

9 41 14.3%

10 51 17.8%

11 85 29.7%

12 41 14.3%

13 17 5.9%

14 3 1.0%

Total 286 100.0%

Participants were separated into two major categories based on completeness of their data: those who 

provided data from 5-8 work shifts (n = 48) and those providing data from 9 or more work shifts (n = 

238). Across all facility types, over 83% of participants provided data from 9 or more shifts during the 

study period. The breakdown of complete data work shifts by facility type is shown in Table 3-6. In our 
sample controllers from En Route Centers were significantly more likely to provide complete data from 

9 or more shifts during the study period than those from TRACONs or Towers (93% vs. 74.1% and 

75.0%, respectively), (Pearson’s Chi-Square (1, n = 286) = 17.859, p < 0.001). 
Table 3-6. Number and Proportion of Participants Providing Complete 

Data from 5–8 Work Shifts and 9+ Work Shifts from each Facility Type

Facility Type Number of Shifts n % of Total Participants

En Route

5-8 Work Shifts 9 6.8%

9+ Work Shifts 124 93.2%

Total 133 100.0%

TRACON

5-8 Work Shifts 21 25.9%

9+ Work Shifts 60 74.1%

Total 81 100.0%

Tower

5-8 Work Shifts 18 25.0%

9+ Work Shifts 54 75.0%

Total 72 100.0%

Total

5-8 Work Shifts 48 16.8%

9+ Work Shifts 238 83.2%

Total 286 100.0%

3.3.1.2 PVT Data

Participants were instructed to self-administer the PVT three times each work shift, yielding 

approximately 30 PVT scores per participant in a nominal 14-day period. PVT data was examined for

completeness only for those participants whose activity logs were available and contained at least 5 

work days. The distribution of missing PVT data is shown in Figure 3-7. 

On average, 9% of PVT data were missing per participant. The missing data were not distributed 

evenly across the 3 PVT trials in each work shift: PVT data were most complete on the first trial (taken 

within the first 30 min of the work shift) (missing data M = 7.57%) compared to the second and third 

trials (missing data M’s = 11.42% and 10.69%, respectively), F(2, 504) = 14.978, p < 0.001. A cutoff 

point of 25% missing PVT data was set for eliminating participants from further PVT-related analyses. 

This cutoff point was equivalent to two standard deviations from the mean. Its use resulted in the 

elimination of 16 participants. The breakdown of the eliminations by facility type is displayed in Table 3-

7. 
Figure 3-7. Proportion of missing PVT data for each participant across all trials.

Table 3-7. Number and Proportion of Eliminated 

Participants from each Facility Type

Facility 

Type Number of Shifts n 

% of 

Participants

En Route

5-8 Work Shifts

Eliminated 1 16.7%

Included 5 83.3%

Total 6 100.0%

9+ Work Shifts

Eliminated 3 2.5%

Included 115 97.5%

Total 118 100.0%

TRACON

5-8 Work Shifts

Eliminated 1 6.3%

Included 15 93.8%

Total 16 100.0%

9+ Work Shifts

Eliminated 6 11.1%

Included 48 88.9%

Total 54 100.0%

Tower

5-8 Work Shifts

Eliminated 0 0 

Included 6 100.0%

Total 6 100.0%

9+ Work Shifts

Eliminated 5 9.4%

Included 48 90.6%

Total 53 100.0%
3.3.1.3 Actigraphy Data

Inspection of the actigraphy data revealed that data for 17 participants were at least partly unavailable 

because the actiwatches either stopped recording or otherwise malfunctioned. In those cases, sleep 

data (time to sleep, time awake) from the sleep/activity logs were used to supplement the actiwatch 

data. Participants’ data were retained for analysis providing the majority of the data were available from 

the actiwatch or their logs. Determining completeness of the sleep data was complicated by difficulty 

distinguishing between a legitimate missing datum (i.e., the participant slept, but the actiwatch did not 

detect it) from an anomalous but not necessarily missing datum (i.e., the participant did not sleep at all 

during a time-off period, which the actiwatch recorded correctly). Note that actiwatch data were the 

primary source of sleep data; only when a participant’s actiwatch malfunctioned or the data were 

missing for some undetermined reason were self-reported sleep log information utilized. 

3.3.2 Controller Work Schedules

3.3.2.1 Five-Day Work Schedules

Three primary categories of 5-day schedules emerged:

• RRM: Counter-clockwise rapidly rotating with midnight shifts, including 2-2-1 

• RR: Counter-clockwise rapidly rotating without midnight shits, including 2-1-2, 2-3, and 3-2 

• Straight-5: Slowly rotating weekly schedules (e.g., 5 afternoon shifts followed by 5 day 

shifts, 5 early morning shifts, and 5 midnight shifts)

The remainder of the schedules fell into two other categories:

• Interrupted: The 5-day schedule met the characteristics of RRM or RR, but included a day off 

some time during the week

• Unclassified: The 5-day schedule did not meet the definitions of any of the above schedule 

types

None of the participants in the field study worked straight shifts (i.e., worked the same shift every 

week), and only one worked a forward rotating schedule (i.e., each succeeding shift during a week 

began at a later hour than the previous one. 

The number of 5-day work weeks meeting each of the schedule criteria from each facility type is shown 

in Table 3-8, breaking down the RR and unclassified schedules into those that include midnight shifts 

and those that do not. Across all facilities, the dominant schedule was the counter-clockwise rapidly 

rotating with midnights (RRM), which accounted for 61.4% of the 272 5-day schedules. Within the RRM 

schedules, two patterns dominated: the 2-2-1 (AAEEM) and a variant that provided longer time off prior 

to the first early morning shift: ABEEM. These two schedules accounted for 37% and 35% of the total 

RRM schedules. Other variants included multiple midnight shifts, such as ABEMM. Note that the units 

in these distributions are work weeks, not participants, because an individual controller could contribute 

one, two or no complete work weeks during the study period. 
Table 3-8. Number (and Percentage) of 5-day Schedule Types worked by 

Controllers from each Facility Type

Schedule Category

En Route TRACON Tower All Facilities

n % n % n % n %

RRM 98 69.0% 35 62.5% 34 45.9% 167 61.4%

RR 11 7.7% 8 14.3% 12 16.2% 31 11.4%

Straight 5 2 1.4% 1 1.8% 2 2.7% 5 1.8%

Interrupted 24 16.9% 5 8.9% 19 25.7% 48 17.6%

Unclassified - Mid 5 3.5% 5 8.9% 5 6.8% 15 5.5%

Unclassified - No Mid 2 4.4% 2 3.6% 2 2.7% 6 2.2%

Total 142 100% 56 100% 74 100% 272 100%

Counter-clockwise rapidly rotating schedules with no midnight shifts (RR) accounted for 11% of the 5-

day schedules, a low level not unexpected, given that participants working at least one midnight shift 

were solicited for the study. Ninety percent of the RR schedules were characterized as 2-x-2, that is, all 

began with two afternoon shifts and ended with two early morning shifts, with a variable non-midnight 

third shift. At most, these schedules had one quick turn, for example, prior to day three in AAEEE. 

Somewhat unexpected was the relatively large number of interrupted weeks, whch accounted for 

17.6% of the 5-day weeks. Straight-5 slowly rotating schedules were rare in this sample (1.8% of the 

schedules).

This classification of schedule types permits aggregation into larger categories of those that are 

analyzable versus those that are not. Analyzable schedules inlcuded the two types of RRM schedules, 

those with and those without mids, plus the Straight-5s, for a total of 203 weeks. Those schedules 

considered unanalyzable were the interrupted and unclassifiable schedules, which totaled 69. Thus, 

74.6% of the five day schedules were analyzable. 

A comparison of schedules across facility types shows that the schedule types are not evenly 

distributed in the field study sample. RRM schedules are more prevalent in the Centers (69%) and the 

TRACONS (62.5%) than in the Towers (45.9%). In contrast, RR schedules are somewhat more 

prevalent in Towers (16.2%) and TRACONs (14.3%) than in Centers (7.7%). The greatest frequency of 

interrupted weekly schedules occured in Towers (25.7%), which seems to account for their higher level 

of unanalyzable schedules (35.1%) relative to either TRACONs (21.4%) or Centers (21.8%). 

3.3.2.2 Six-Day Work Schedules

In addition to the 272 5-day schedules characterized above, there also were 75 6-day weeks with 

complete data (i.e., PVT, actigraphy and sleep logs). These 6-day schedules were classified using the 

same categories as for the 5-day schedules shown above in Table 3-8. However, one additional 

category was needed. Some weekly schedules that appeared unclassifiable actually fit a pattern: a 

nominal 5-day schedule, either RRM or RR, with the addition of an extra work shift (scheduled 

overtime) either at the beginning or at the end of the work week. For example, an AAEEM schedule 

might be extended to yield AAEEM(M) or (E)AAEEM. Similarly, an AABEE schedule might be extended 

to AABEE(B) or to (B)AABEE. Examination of prior and partial weeks from the FAA’s BO data 

confirmed that these were normal 5-day schedules plus one day. Thus, they were distinguished from 

the unclassified category which fit no pattern. This 5-day plus OT category yielded 21 normal-plus 

overtime (OT) weeks out of the 75 6-day weeks, leaving only six unclassified weeks. If the OT day fell 

at the end of the week(OT-Final), the first five days were candidates for inclusion in our analysis of 5-

day schedules. This procedure yielded 11 additional weeks, 9 in the RRM category and 2 in the RR

category. The distribution of 6-day work week schedules is shown in Table 3-9. 
Six-day schedules were higher in TRACONs at 38% of all TRACON schedules, compared to 12% for 

Towers and 17% for En Route Centers. Six-day weeks were interrupted at the same rate as 5-day 

weeks (17.3% and 17.6%, respectively). Defining a 6-day week as interrupted was a less certain 

undertaking than for a 5-day, which always had two (or more) days off between work weeks. Six-day 

weeks were considered interrupted if a single day off broke up a sequence that was recognizable as a 

normal schedule pattern. For example, AA-off-EEM-off would have been considered possibly an 

interrupted AAAEEM or an AAEEEM schedule. Certainty was not essential because these interrupted 

weeks were not analyzed due to their fragmentary nature.

Table 3-9. Number (and Percentage) of 6-day Schedule Types worked by 

Controllers from each Facility Type

Schedule Category

En Route TRACONs Tower All Facilities

n % n % n % n %

RRM 13 43.3% 13 37.1% 3 30.0% 29 38.7%

RR 0 0.0% 4 11.4% 1 10.0% 5 6.7%

Straight 6 0 0.0% 1 2.9% 0 0.0% 1 1.3%

OT Final – Mid 7 23.3% 2 5.7% 0 0.0% 9 12.0%

OT Final – No Mid 0 0.0% 2 5.7% 0 0.0% 2 2.7%

OT First – Mid 4 13.3% 4 11.4% 2 20.0% 10 13.3%

Interrupted 4 13.3% 6 17.1% 3 30.0% 13 17.3%

Unclassified – Mid 1 3.3% 2 5.7% 1 10.0% 4 5.3%

Unclassified - No Mid 1 3.3% 1 2.9% 0 0.0% 2 2.7%

Total 30 100% 35 100% 10 100% 75 100%

Repeating the pattern from the 5-day schedules, Centers and TRACONs yielded higher proportions of 

analyzable 6-day schedules than Towers (66.7% and 62.9% versus 40.0%, respectively). 

3.3.3 Impact of Work Schedules on Amount, Timing and Efficiency of Sleep

3.3.3.1 Sleep Duration based on Actigraphy

Three sleep measures were obtained from the wrist activity monitors, which were worn 24 hours per 

day: sleep duration, timing and efficiency of sleep. Sleep duration was measured as total sleep time 

(TST) during each main sleep period plus any naps up to two hours that occurred between shifts during 

a work week. Sleep efficiency was derived from the sleep measures for the same sleep periods. It was 

defined as the ratio of actual sleep time to time in bed (TIB), i.e., the proportion of sleep in the TIB 

episode during which the participant actually slept. The actigraph software automatically generates 

sleep efficiency scores for each sleep episode. Sleep timing referred to the time at which participants 

went to sleep (time asleep) and time at which they awoke (time awake). 

3.3.3.2 Sleep Duration as a Function of Shift Start Time

It was first examined whether the total amount of sleep controllers obtained varied depending on the 

shift type they planned to work following each sleep period, regardless of how that shift fit into a weekly 

schedule. Each work shift was categorized by the time at which the shift began, using the five shift 

categories developed by Della Rocco and colleagues (Della Rocco, et al., 2000): afternoon, mid-day, 

day, early morning and midnight. It was found that the amount of sleep obtained prior to a midnight shift 

was significantly less (M = 3.25 hrs) than the overall average sleep duration, p1 = -228.264, z(25) = -

14.404, p < 0.001. In addition, significantly less sleep (M = 5.39) was obtained by participants before an 

early morning shift, p2 = -85.075, z(25) = -5.606, p < 0.001, whereas the most sleep was obtained 
before either a mid-day (M = 6.76 hrs) or an afternoon (M = 6.94 hrs) shift; these latter two shifts were 

not significantly different from the average sleep duration. Day shifts (those beginning between 08:00 

and 09:59) were infrequent and did not differ from the average sleep duration. These sleep patterns 

replicate prior CAMI findings (Cruz & Della Rocco, 1995). Mean total sleep times for each of these shift 

start times are shown in Table 3-10.

Table 3-10. Duration of Sleep in Hours Before 

Various Shift Start Times

Shift Start Times

Mean Hours 

Slept

Standard 

Deviation n 

Early shift (starts before 08:00) 5.39 1.39 369

Day shift (starts between 08:00 

and 09:59)

6.41 1.21 14

Mid-day shift (starts between 

10:00 and 12:59)

6.76 2.01 111

Afternoon shift (starts between 

13:00 and 19:59)

6.94 1.51 327

Midnight shift (starts between 

20:00 and 01:00) 

3.25 2.13 140

3.3.3.3 Time Asleep as a Function of Shift Start Time

The majority of controllers tended to go to sleep late in the evening (M = 690.2212 or 11:30 pm, except 

for those working a midnight shift). Given the variance around this time, the time asleep data was 

transformed so that 12:00 p.m. received the value of 0.00 to allow for a continuous measurement from 

noon to midnight through early morning and ending at noon, as shown in Figure 3-8 

 

12 For ease of computation, time asleep and time awake were computed in minutes, and then transformed back to 

clock time.
Figure 3-8. Frequency distribution of time asleep, across A, B, D, and E schedules, where 0 = 

12:00 (noon), 720 = 24:00 (midnight), and 1440 = 11:59. 

Figure 3-9. Frequency distribution of time asleep prior to midnight shifts. Note that in contrast to 

Figure 3-8, the origin on the x-axis is 00:00 (midnight), so the midpoint of the scale (720 min) is 

noon (12:00). Mean and modal times asleep are both 15:03 (or 3:03 p.m.). A morning sleep 

period began around 08:00, suggesting that participants were transitioning from a prior midnight 

shift rather than an early morning shift. 

Previous CAMI research found that although the time at which controllers awoke varied depending on 

when their next shift started, time at which they went to sleep did not vary according the upcoming shift 

start time (Cruz & Della Rocco, 1995). However, this was not the case in the present study. As can be 

seen in Table 3-11, controllers who had an upcoming early shift (beginning prior to 08:00) went to sleep 
significantly earlier (M = 638.73 or 22:38) on average than those who faced a mid-day (M = 752.89 or 

00:33) or an afternoon shift (M = 734.16 or 00:14), p1 = 114.123, z(25) = 9.089, p < 0.001, and p2 = 

95.436, z(25) = 9.737, p < 0.001, respectively. In addition, controllers who had an upcoming day shift 

(beginning from 08:00 to 09:59) tended to go to sleep later (M = 675.84 or 11:16 pm) than those with an 

early shift, although the time difference was only marginally significant, p3 = -77.047, z(25) = 1.815, p = 

0.081. Those controllers who had an upcoming midnight shift were not included in this comparison in 

order to reduce the variance while examining comparisons between the other shift start times. 

Participants anticipating a midnight shift went to sleep much earlier than those with any other shifts, 

around 15:00 (15:03, as shown in Figure 3-8), because their time to sleep was constrained by the 

midnight shift start time. 

Table 3-11. Time Asleep and Time Awake as a function of Upcoming Shift Start Times

Shift Start Time

Early

(01:00–

07:59)

Day

(08:00–

09:59)

Mid-Day

(10:00–

12:59)

Afternoon

(13:00–

19:59)

Midnight

(20:00–

00:59)

Weekly 

Mean

Time Asleep 22:38 23:16 00:33 00:14 15:03 23:30

Time Awake 04:20 06:20 07:16 07:29 18:03 07:42

3.3.3.4 Time Awake as a Function of Shift Start Time

The majority of controllers woke up before 08:00 before all shifts except midnight shifts (M = 462.15 

min or 07:42, SD = 288.27), as shown in Table 3-11. However, those who had an upcoming early 

morning shift (M = 259.77 min or 04:20) or a day shift (M = 380.36 min or 06:20) tended to wake up 

significantly earlier in clock time than average, p2 = -192.284, z(25) = -15.952, p < 0.001, and p3 = -

70.397, z(25) = -3.253, p = 0.004. Participants who had an upcoming mid-day or afternoon (i.e., B or A

[see Figure 3-6]) shift did not wake up significantly earlier or later than the average of 07:42. Those who 

were preparing for a midnight shift woke up significantly later (M = 1083.21min or 18:03) than the 

average following their afternoon nap, p1 = 637.920, z(25) = 24.317, p < 0.001. 

Unlike participants in the Cruz and Della Rocco (1995) study, controllers in the present study adjusted 

both their time asleep and time awake as a function of the upcoming shift start time. 

3.3.3.5 Sleep Duration as a Function of Schedule Type 

The prior analyses examined sleep patterns as a function of individual shift start times, but did not 

consider their place in a weekly schedule. The next set of analyses compared weekly work schedules 

with and without midnight shifts to see how these schedules influenced total sleep across a week and 

before specific shift types. These schedules were selected in order to compare findings in the present 

study with those obtained in field studies conducted over a decade ago (see Della Rocco and Nesthus, 

2005). Two weekly schedules were compared: one RRM schedule type, the 2-2-1 (two afternoons, two 

early mornings and one midnight shift, or AAEEM) and one RR schedule called the 2-x-2. The latter 

category included the 2-1-2 studied in several prior CAMI studies (including Cruz & Della Rocco, 1995) 

and involves two afternoons, one mid-day, and two early morning shifts (AABEE). However, due to the 

limited number of current participants working the 2-1-2 schedule (n = 10) for whom complete data 

were available (i.e., log, PVT and actigraph data), this category was expanded to include other 

schedules that began with two afternoon shifts and ended with two early morning shifts, but that 

differed in the mid-week shift: AADEE, AAAEE, and AAEEE. Weekly schedules that did not fall into 

either the 2-2-1 or 2-x-2 categories were not included in the schedule-type analyses. A total of 83 

participants were included, 52 working the 2-2-1 schedule and 36 working the 2-x-2. Five of these 

participants worked both 2-2-1 and 2-x-2 schedules during the study period. Including those five 
participants was not problematic because partitioning of individual variability at the day/trial level was 

performed inherently in the multilevel analyses.

Both schedule type and day progression (Day 1 through Day 5) were used first as predictors of mean 

total sleep time over the entire week. When the schedule type (2-2-1 versus 2-x-2) was entered by itself 

into the model, the effect was significant, p1 = 37.95, z(21) = 2.823, p = 0.011. This suggests that 

participants slept significantly longer over the week when they had no midnight shift in their schedule 

(2-x-2) than when their schedule included a midnight shift (2-2-1). When the day progression 

component within the weekly schedule was included, both effects were significant, p1 = 35.153, z(21) = 

2.589, p = 0.017 for schedule type, and p2 = -50.774, z(20) = -10.503, p < 0.001 for day progression. 

The mean amount of sleep obtained on each night of the week for each schedule type is shown in 

Table 3-12. These results indicate that (a) participants obtained significantly less sleep when they 

worked the 2-2-1 schedule than the 2-x-2 schedule; and (b) both groups slept progressively less over 

their work week, confirming earlier CAMI findings (Cruz, et al., 1995).

The differences between the schedule groups are evident on days 3 and 5. These are days when the 2-

2-1 group experienced a quick turn, typically of 8 to 9 hours, prior to those shifts. The 2-x-2 group had 

the possibility of one quick turn prior to Day 3 (if they worked the AAEEE schedule), but otherwise this 

turn was longer, permitting one hour more sleep before the shift on Day 3 than the 2-2-1 group 

obtained. The 2-x-2 group obtained three hours more sleep before Day 5 prior to an early shift, when 

the 2-2-1 group only slept for 2.5 hours during the day between the Day 4 early morning shift and the 

subsequent Day 5 midnight shift, which began sometime between 21:00 and 24:00 on the same day.

Table 3-12. Mean Sleep Duration in Minutes (Standard Deviation in parentheses) 

and Hours over Days for the 2-2-1 and 2-x-2 Schedules

Schedule Day 1 Day 2 Day 3 Day 4 Day 5

2-2-1 

A

425.33 min

(115.58)

7.1 hr

A 

405.91 min

(98.84)

6.8 hr

E 

314.39 min

(79.17)

5.2 hr

E

329.30 min

(85.76)

5.5 hr

M

152.42 min

(91.28)

2.5 hr

2-x-2 

A

437.78 min

(75.31)

7.3 hr

A

405.80 min

(110.08)

6.8 hr

A/B/D/E

373.09 min

(106.29)

6.2 hr

E

323.38 min

(67.43)

5.4 hr

E

329.29 min

(78.06)

5.5 hr

Notes: 1. Statistical analyses used minutes rather than hours of sleep, but hours are presented for 

ease of understanding. 2. Shifts for each day are indicated by letter in cell: A, B, D, E, and M.

3.3.3.6 Time Asleep as a Function of Schedule Type 

When entered by itself, schedule type (2-2-1 versus 2-x-2) did not affect the time at which participants 

went to sleep, p1 = 20.92, z(21) = 0.273, p = 0.787, nor did the schedule type have an effect when the 

day progression was introduced as well, p1 = 32.261, z(21) = 0.462, p = 0.648. However, day 

progression itself was significant, p2 = 127.693, z(21) = 5.763, p < 0.001, indicating that participants 

went to sleep earlier as the work week progressed, regardless of whether they were working a 2-2-1 or 

a 2-x-2 schedule. 

3.3.3.7 Time Awake as a Function of Schedule Type

The schedule type entered alone influenced the time at which participants awoke, p1 = -103.613, z(21) 

= -3.489, p = 0.003. Participants who worked the 2-x-2 schedule that included no midnight shifts tended 

to wake up significantly earlier before a shift on average than those who worked the 2-2-1 schedule that 

included a midnight shift. With the day progression effect included, this effect remained significant, p1 = 

-103.915, z(21) = -3.591, p = 0.002. The day progression effect suggests that over the course of the 
week, participants tended to awaken later, p2 = 37.784, z(21) = 3.699, p = 0.002. However, when the 

day effect was restricted to Days 1–4 (which excluded sleep prior to the midnight shift on Day 5 in the 

2-2-1 schedule), the results reversed, p1 = 47.837, z(21) = 2.782, p = 0.008, and p2 = -79.976, z(21) = -

13.364, p < 0.001. These results indicate that up to Day 4, controllers who worked the 2-2-1 schedule 

woke up significantly earlier in clock time than those who worked the 2-x-2 schedule; on Day 5 this 

pattern reverses, when participants working the midnight shift typically take a nap during the day and 

awaken sometime in the afternoon in preparation for the midnight shift. This Day 1–4 pattern is 

expected, as the first four shifts in a 2-2-1 schedule involve two afternoon shifts followed by two early 

morning shifts. The first two days of the 2-x-2 schedule also are afternoon shifts, but these are typically 

followed by either a mid-day shift, a day shift or another afternoon shift, allowing a later wake-up time 

than the early morning shift in the 2-2-1 schedule. In addition, all controllers generally appeared to 

awaken progressively earlier up to Day 4, regardless of schedule type, another pattern predicted on the 

basis of the progression of shift types over days.

3.3.3.8 Efficiency of Sleep from Actigraph Data

The efficiency of sleep on average was 82.84, with a fair amount of variability for a percentage measure 

(SD = 10.12). Scores in this study ranged from 0.83 to 100, as shown in Figure 3-13. The norm for 

sleep efficiency is considered to be 85 to 100, so the average in this study was somewhat below the 

norm. The mean efficiency of sleep only differed significantly from average for the sleep period prior to 

a midnight shift, p1 = -4.806, z(25) = -3.400, p = 0.001, when it was significantly lower than average, 

with a mean of 79.15. This finding is in keeping with earlier CAMI results showing that sleep efficiency 

is commensurate with the duration of sleep (Nesthus, Cruz, Boquet, Dobbins & Holcomb, 2003); 

participants slept the least prior to midnight shifts. The lower sleep efficiency during afternoon naps 

prior to midnight shifts suggests that controllers had difficulty falling asleep during the day, when 

circadian pressures encouraged them to be awake. 

Figure 3-10. Frequency distribution of average sleep efficiency scores across participants.

The efficiency of sleep was not affected by the schedule type (i.e., 2-2-1 vs. 2-x-2) when schedule type 

was entered alone, p1 = 0.636, z(21) = 0.321, p = 0.752. It remained non-significant even when the day 

progression effect was entered, p1 = -1.803, z(21) = -0.972, p = 0.343. There was no effect of day 

progression on participants’ sleep efficiency on average, p2 = -1.447, z(21) = -1.156, p = 0.261, despite 
the significant effect of shift start times and significantly lower sleep efficiency prior to midnight shifts 

compared to other shift start times, when weekly schedule was not taken into account. This lack of 

significance may reflect the smaller n in the schedule comparison relative to the prior findings, which 

were based on the entire sample. 

3.3.4 Impact of Work Schedules on Controller Alertness 

Measured by PVT Performance 

Two primary measures of PVT were used in determining participant cognitive alertness and fatigue: 

lapses (responses slower than 500 ms) and response speed (reciprocal of reaction time in ms). Rather 

than using response speed in the analyses, deviation scores from each individual’s baseline were used 

because response speed is known to vary greatly among individuals, which may obscure differences 

associated with variables of interest. The mean of the top 10% of each individual’s performance scores 

served as a baseline proxy, as it was not possible to obtain baseline performance measures when the 

participant was known to be fully rested (all PVT measures were taken at work and began on various 

days of the work week).

For both measures (lapses and response speed), the sample size of 211 was used, due to elimination 

of participants with more than 25% of missing PVT data (n = 16). Additionally, only participants with 9 

or more work shifts were included in the initial analysis by shift type. Subsequent analyses of schedule 

type included participants with 5-8 work shifts.

Because earlier CAMI shiftwork studies had found significant effects of controller age on alertness and 

vulnerability to schedule factors (e.g., Nesthus, et al., 2003, 2005), participant age and years of 

experience as controllers were included in the analyses of alertness, testing both their main effects and 

their role as moderators of relations between schedules and PVT performance. 

3.3.4.1 Overall performance

First, lapses were aggregated across trials (within days) and work shifts for each participant to 

determine the average frequency of lapses and their variability among individuals.

The mean number of lapses presented a positively skewed distribution (as shown in Figure 3-14): the 

majority of participants had very few lapses on average, with a grand mean of 1.9 lapses on each 5-

minute trial. 

Standard deviations of these lapses were also examined, partly as a way to characterize the lapses 

(i.e., descriptive statistics) and partly to justify the appropriateness of multilevel modeling. Multilevel 

modeling can be used whenever individual differences can be detected in the variables of interest. The 

distribution of the standard deviation for lapses (Figure 3-12) indicates that the variability was just as 

wide as the mean. This means that although participants committed few lapses in general, many 

controllers performed significantly worse at some time during the study period, supporting our decision 

to use multilevel modeling. The distribution also suggests that lapses on the 5-min PVT task are 

sufficiently variable to detect fatigue, despite the test’s shorter length than the established 10-min task 

(Basner & Dinges, 2011; Loh, Lamond, Dorrian, Roach & Dawson, 2004).
Figure 3-11. Frequency distribution of mean lapses across all participants.

Figure 3-12. Frequency distribution of the standard deviations of lapses across all participants.

Response speeds (reciprocal of reaction time in ms) were converted to deviations from individual 

‘baselines’ calculated as the mean of the 10% fastest scores for each participant. Therefore, the 

response speed scores are typically negative. They were then aggregated across trials (within days) 

and work days (Day 1 to Day 5) for each participant to determine the average response speed and its 

variability among individuals.

The mean of the mean response speed was -0.00065; the scores formed a negatively skewed 

distribution where the majority of response speeds are clustered near their top speeds (i.e., nominal 
response speed for each individual), as can be seen in Figure 3-13. However, the variability of the 

standard deviations of the response speeds as shown in Figure 3-14 was not as widely distributed as 

the mean response speeds, suggesting that the variability across individuals is not as large as the 

variability within individuals.

Figure 3-13. Frequency distribution of mean response speeds as deviation scores from the 

fastest 10% of trials across all participants.

Figure 3-14. Frequency distribution of standard deviations of response speeds as deviation 

scores from the fastest 10% of trials across all participants.

3.3.4.2 PVT Performance by Shift Start Time

Alertness varied with shift start times, regardless of where the shift fell within a weekly schedule. 

Responses were slowest and lapses most frequent during midnight shifts compared to other shifts, p1 = 
-0.000385, z(25) = -6.573, p < 0.001, and p1 = 1.887, z(25) 4.639, p < 0.001, respectively. Additionally, 

participants tended to respond more slowly on early morning shifts, p2 = -0.000116, z(25) = -2.928, p = 

0.008; however, lapses did not differ from those in later shifts, p2 = 0.288, z(25) = 1.176, p = 0.251. 

This could be due to a highly skewed distribution of the number of lapses, as described in Section 

3.3.4.1.

3.3.4.3 PVT Performance: Beginning versus End of Shift

In order to determine whether individuals’ alertness changed over the course of a work shift, lapses 

from the beginning of a shift (Trial 1) were compared to those from the end of the shift (Trial 3). Lapses 

increased significantly from the beginning to the end of a work shift, as shown in Table 3-13, p1 = 

0.884, z(25) = 4.109, p < 0.001. This held for all shift types and days of the work week. 

Table 3-13. Mean Lapses and Standard 

Deviation at Beginning and End of Shifts

Trial Mean Lapses SD

Trial 1 (Beginning of shift) 1.84 4.54

Trial 3 (End of shift) 2.57 5.35

In addition, participants responses were significantly slower on average at the end of the shift 

compared to the beginning of the shift, p1 = -0.000162, z(25) = -5.853, p < 0.001, as shown in Table 3-

14. These declines in alertness across the shift were not moderated by participant age or years of 

experience. However, older and more experienced controllers performed the PVT task faster overall 

than younger or less experienced ones, b01 = 0.000007, z(25) = 2.610, p = 0.015, and b01 = 0.000006, 

z(25) = 2.329, p = 0.028, respectively. This result is inconsistent with previous CAMI findings that 

showed older controllers (over age 40) performing less well than younger ones on tasks involving 

speed and working memory (Nesthus, et al., 2003, 2005). The earlier CAMI findings are in keeping with 

a significant literature on age and speed of responding, which suggests that the older and more 

experienced controllers in the present study may have adopted a speed-accuracy trade-off strategy, 

though no effects of age or experience were found for lapses. 

Table 3-14. Mean Response Speed (deviation from the baseline) and 

Standard Deviation at Beginning and End of Shifts

Trial Mean Response Speed SD

Trial 1 (Beginning of shift) -0.000549 .000488

Trial 3 (End of shift) -0.000694 .000539

3.3.4.4 PVT Performance Across Work Week

In order to determine whether fatigue accumulated across the work week, the day of week effect on 

PVT performance was examined. The number of PVT lapses was found to increase as the week 

progressed, p1 = 0.458, z(25) = 5.383, p < 0.001, as shown in Table 3-15.
Table 3-15. Mean PVT Lapses and 

Standard Deviation on Days 1–5 

Day Mean Lapses SD

Day 1 1.95 4.81

Day 2 1.62 3.42

Day 3 2.15 4.73

Day 4 2.42 5.07

Day 5 3.13 6.57

Also, response speed slowed significantly across the work week, p1 = -0.000092, z(25) = -7.709, p < 

0.001, as shown in Table 3-16. 

Table 3-16. Mean PVT Response Speed 

(deviation around the baseline) and 

Standard Deviation at Days 1–5 

Day Mean Response Speed SD

Day 1 -0.00055 0.00047

Day 2 -0.00051 0.00046

Day 3 -0.00064 0.00052

Day 4 -0.00070 0.00051

Day 5 -0.00079 0.00059

The day effect on both response speed and lapses was moderated by age and years of experience: 

Both age and experience appeared to protect against increasing fatigue as measured by response 

speed, b01 = 0.000003, z(25) = 2.167, p = 0.040, and b01 = 0.000003, z(25) = 1.955, p = 0.061, 

respectively. The moderating effects of age and years of experience were smaller for the day-lapse 

relationship, b01 = -0.0186, z(25) = -1.935, p = 0.064, and b01 = -0.0188, z(25) = -1.837, p = 0.078, 

respectively. 

3.3.4.5 PVT Performance by Schedule Type

Two specific work schedules, the 2-2-1 (RRM) and the 2-x-2 (RR, previously described in 3.3.3.5), were 

compared to determine whether the presence of a midnight shift and two quick turns during a work 

week influenced controllers’ behavioral alertness. It was found that schedule type alone did not impact 

participants’ response speed, p1 = -0.000002, z(24) = -0.028, p = 0.979, or lapses, p1 = -0.4148, z(24) 

= -0.923, p = 0.366. However, the day progression was statistically significant for both response speed, 

p2 = -0.000097, z(24) = -6.447, p < 0.001, and lapses, p2 = 0.486, z(24) = 4.302, p < 0.001, when 

entered along with the schedule variable, replicating the day effect reported in section 3.3.3.5. This day 

effect reflects cumulative fatigue across the work week. 

 

The decline in measured alertness across the work week parallels the finding that controllers obtained 

less sleep per day as the work week progressed (reported in section 3.3.3.5). The end of the week 

involves a midnight shift for those working the 2-2-1 schedule and back-to-back early morning shifts for 

those working the 2-x-2 schedule. The amount of sleep prior to these shifts was found to be 

significantly less than before other shifts (as reported in section 3.3.3.1), which is likely to have affected 

controllers’ alertness as measured by the PVT, along with circadian factors. To test this hypothesis, the 

relationship between total sleep duration (TSD) and PVT measures was examined. TSD significantly 

predicted lapses, p1 = -0.00785, z(19) = -4.112, p = 0.001 (r = -.752), indicating that the shorter the 
sleep duration prior to a shift, the more lapses the controller experienced during the shift. Similarly, TSD 

significantly predicted response speed, p1 = 0.000002, z(19) = 6.698, p < 0.001: the shorter the sleep 

duration prior to a shift, the slower the response speed during that shift.

3.3.4.6 PVT Performance by Time Off between Shifts

A critical question is whether the significant relationship between amount of sleep and alertness is 

driven by the duration of time off between shifts, because rest and recovery sleep are obtained during 

these off-duty periods. Two sets of analyses were conducted to: (a) determine the impacts of longer 

durations of time off ranging from 8 hours to 72 hours on alertness (PVT response speed and lapses); 

and (b) determine the impact of turns ranging from 8 to 12 hours on amount of sleep and resulting 

alertness. 

3.3.4.6.1 Long Duration Time Off 

Response speeds increased significantly when participants had greater time off between shifts, 

regardless of shift or schedule types, p1 = 0.000003, z(25) = 4.640, p < 0.001. Similarly, lapses 

decreased significantly as the time between shifts increased, p1 = -0.0121, z(25) = -4.309, p < 0.001. 

These findings indicate that alertness improves when time between shifts increases, presumably 

because the longer breaks afford sufficient time for controllers to recuperate. The relations between 

duration of time off between shifts and response speed can be seen in Figure 3-15; the relations 

between time off and lapses is shown in Figure 3-16. 

In Figure 3-15, each line on the graph is a linear regression line representing a single participant’s 

response speed predicted by hours between the previous and current shift. Performance is best (i.e., 

least deviated from the baseline of the mean 10% fastest response speeds) at the 0.000 point (top of 

ordinate). The slope of the line indicates the impact of time off between shifts, beginning with the 

shortest time off. A slope close to zero indicates a relatively small effect of time off, whereas a steeply 

sloped line indicates a significant impact. Variability in time-off effects is seen between individuals and 

in different facilities. In some facilities participant responses are relatively homogeneous and show the 

smallest impact of brief times off, whereas much more variability between participants and greater timeoff effects are seen in other facilities. This variability may reflect differences in schedules worked at 

various facilities or other facility-specific features.
Figure 3-15. The model relationship between response speed (deviated around baseline) and time between shifts 

for individual respondents in illustrative facilities.

In Figure 3-16, the nominal baseline for lapses was 0. As in Figure 3-15, near-zero slopes signify little 

effect of duration of time off between shifts for individual controllers, whereas sharp slopes indicate a 

significant impact of time off duration, with a greater number of lapses seen at the shortest durations (8 

hours). Negative slope reflects the benefits of recovery over longer durations of time off. 

-0.002

-0.001

-0.001

-0.001

-0.000

Deviation Speed

7.00 39.75 72.50105.25138.00

Hours between Shifts

-0.002

-0.001

-0.001

-0.001

-0.000

Deviation Speed

7.00 39.75 72.50105.25138.00

Hours between Shifts

-0.002

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-0.001

-0.001

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Deviation Speed

7.00 39.75 72.50105.25138.00

Hours between Shifts

-0.002

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-0.001

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7.00 39.75 72.50105.25138.00

Hours between Shifts

-0.002

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7.00 39.75 72.50105.25138.00

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-0.002

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-0.001

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7.00 39.75 72.50105.25138.00

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-0.002

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7.00 39.75 72.50105.25138.00

Hours between Shifts

-0.002

-0.001

-0.001

-0.001

-0.000

Deviation Speed

7.00 39.75 72.50105.25138.00

Hours between Shifts
Figure 3-16. The model relationship between lapses and time between shifts for individual respondents in 

illustrative facilities (same participants and facilities as in Figure 3-15).

3.3.4.6.2 Turn Duration Effects on Controller Sleep and Alertness

A preliminary analysis of the impact of turns less than 12 hours was conducted using only a portion of 

the total data set associated with the schedule comparisons (2-2-1 vs. 2-x-2). Similar to prior CAMI 

research, this research considered time off between shifts ranging from 8 to 12 hours (Nesthus, Cruz, 

Boquet, Dobbins, and Holcomb, 2003). Because of their greater variability and potential for 

countermeasures, turns less than 12 hours prior to early morning shifts were the primary focus of this 

analysis. For comparability to Nesthus, et al. (2003), only the following work schedule categories were 

included in the analyses:

1) DAY 3 Early Morning Turns Less than 12 Hours: Time off between work shifts 2 and 3 must be 

equal to 12 hours or less 

a) xxEEE - time off between other shifts was longer than 12 hours (n = 18)

b) xxEEM - time off between work shifts 4 and 5 must also be 12 hours or less; the other turns 

were longer than 12 hours (n = 112)

0.16

3.19

6.22

9.25

12.28

Lapses

7.00 39.75 72.50105.25138.00

Hours between Shifts

0.16

3.19

6.22

9.25

12.28

Lapses

7.00 39.75 72.50105.25138.00

Hours between Shifts

0.16

3.19

6.22

9.25

12.28

Lapses

7.00 39.75 72.50105.25138.00

Hours between Shifts

0.16

3.19

6.22

9.25

12.28

Lapses

7.00 39.75 72.50105.25138.00

Hours between Shifts

0.16

3.19

6.22

9.25

12.28

Lapses

7.00 39.75 72.50105.25138.00

Hours between Shifts

0.16

3.19

6.22

9.25

12.28

Lapses

7.00 39.75 72.50105.25138.00

Hours between Shifts

0.16

3.19

6.22

9.25

12.28

Lapses

7.00 39.75 72.50105.25138.00

Hours between Shifts

0.16

3.19

6.22

9.25

12.28

Lapses

7.00 39.75 72.50105.25138.00

Hours between Shifts
2) DAY 4 Early Morning Turns Less than 12 Hours: Time off between work shifts 3 and 4 must be 

equal to 12 hours or less

a) xxxEE - time off between other shifts was longer than 12 hours (n = 25)

b) xxxEM - the time off between work shifts 4 and 5 must also be 12 hours or less; the other turns 

were longer than 12 hours (n = 13)

In the above schedules, ‘x’ represents other shift types, that is, A, B, D or E. Thus, an xxEEE schedule 

might be AAEEE or ABEEE; likewise, an xxEEM might be AAEEM or ADEEM; xxxEE might be ABDEE; 

xxxEM could be AABEM13. 

The only times off between shifts that were included in the analyses were the following:

• Turns less than 12 hours prior to Day 3 early morning shift from category 1 above

o Comparison of (a) xxEEE and (b) xxEEM examines the potential effect on Day 3 turns of 

anticipating a midnight shift on Day 5

 

• Turns less than 12 hours prior to Day 4 early morning shift from category 2 above

o Comparison of (a) xxxEE and (b) xxxEM examines the potential effect on Day 4 turns of 

less than 12 hours of anticipating a midnight shift on Day 5

o Comparison of Day 3 in category 1 vs. Day 4 in category 2 permits examination of day of 

week on which the turn to early morning shift occurs

Note that time off prior to shift 4 in category 1(a) was not a QT, but these data were included in the 

analysis for comparison of QT with longer durations of time off. 

Turns were defined in two ways, continuous and categorical. First, as a continuous variable, turns for 

this assessment ranged from 7.25 to 12.25 hours.

14 As shown in Figure 3-17, almost 82% of the turns 

less than 12 hours prior to early shifts in the present sample were between 8 and 10 hours, with a 

mean of 9.5 hours. 

 

13 Five of the 112 xxEEM schedules and one of the 25 xxxEE schedules were not counter-clockwise rotations 

(e.g., BAEEM, EBEEM or MABEE).

14 According to FAA rules at the time of data collection, the minimum time off between shifts was 8 hours (FAA, 

2010, sect 2-6-7, Basic Watch Schedule). While a few of the turns were less than 8 hours, these appear to 

represent controllers signing in early when coming on shift or late when going off shift, based on the FAA’s 

Business Objects data.
Figure 3-17. Frequency distribution of turns less than 12 hours in the analyzed work weeks.

Second, turns of less than 12 hours were divided into ordinal categories by rounding the continuous 

variables and then further aggregating some low frequency categories. The final categories depicted for 

each category of data are shown below in Tables 3-17 and 3-18. These categories were created in 

addition to the continuous variable because for turns of less than 12 hours the effect may not be linear 

in nature (e.g., changes in sleep or alertness may be detected between 9-hour turn versus 8-hour turn, 

but not between 10-hour turn versus 9-hour turn). 

Table 3-17. Frequency and Percentage of Hours Off prior to Shift 

3 (before the First Early Morning Shift) in Participant Work Week 

Categories 1(a) and 1(b)

Work Week Category Hours off Frequency Percent

(a) xxEEE

7-8 2 11.1%

9 8 44.4%

10 8 44.4%

11-12 0 0.0%

Total 18 100.0%

(b) xxEEM

7-8 13 11.6%

9 32 28.6%

10 52 46.4%

11-12 15 13.4%

Total 112 100.0%
Table 3-18. Frequency and Percentage of Hours Off prior to Shift 

4 (before the First Early Morning Shift) in Participant Work Week 

Categories 2(a) and 2(b)

Work Week Category Hours off Frequency Percent

(a) xxEEE

8 1 4.0%

9 9 36.0%

10 10 40.0%

11-12 5 20.0%

Total 25 100.0%

(b) xxEEM

8 4 30.8%

9 6 46.2%

10 3 23.1%

11-12 0 0.0%

Total 13 100.0%

The effects of durations of turns less than 12 hours on three dependent measures were assessed: 

amount of sleep, PVT response speed and PVT lapses. The categorical definitions of quick turns were 

used for all of these analyses. For each dependent measure, four tests were conducted:

Test #1. Effects of durations of turns less than 12 hours on Day 3 Early shifts: XXEE(E/M)

Test #2. Effects of durations of turns less than 12 hours on Day 4 Early shifts: XXXE(E/M)

Test #3. Effects on Day 4 Early shifts of durations of turns less than 12 hours versus duration 

of turns greater than 12 hours: XXXE(E/M) versus XXEE(E/M) 

Test #4. Effects of durations of turns less than 12 hours on Day 3 Early shifts versus Day 4 Early 

shifts: XXEE(E/M) versus XXXE(E/M)

Effects of Turns of Less than 12 Hours on Sleep Duration. Turn durations of less than 12 hours had no 

effect on the amount of sleep obtained prior to Day 3 early shifts (Test #1). However, prior to Day 4 

early shifts, sleep increased significantly as time off increased from 9 to 10 hours (p2 = 29.302, z(26) =

2.197, p = 0.037), but did not differ between 8 and 9 hours. Sleep duration decreased from 10 hours to 

11-12 hours (p3 = -39.863, z(26) = -2.231, p = 0.034) (Test #2). This finding may be due to lack of 

reliability associated with an n = 5 in the 11-12 hour turn condition. When turn durations of less than 12 

hours were compared with turn durations of greater than 12 hours, the amount of sleep obtained was 

greater for the longer time off (p6 = 49.680, z(200) = 5.895, p < 0.001) (Test #3). When Day 3 and Day

4 turns of less than 12 hours were compared (Test #4), the effects of turn durations of less than 12 

hours on sleep were no longer significant. The mean total sleep times for each turn duration of less 

than 12 hours are shown in Table 3-19.

Effects of Turns of Less than 12 Hours on Alertness. Alertness measured by PVT response speed or 

lapses was not affected by turn durations of less than 12 hours prior to Day 3 early shifts (Test #1). This 

was not unexpected given that turn duration of less than 12 hours did not affect sleep duration prior to 

Day 3 early shifts. However, on Day 4, response speeds were slower following a turn of less than 12 

hours than a longer duration off (Test #3): (8-12) < (13-15), p6 = 0.000088, z(387) = 2.376, p = .018. 

Response speed continued to increase marginally when time off increased to 16-18 hours off, p5 = 

0.000082, z(387) = 1.679, p < 094. When Day 3 and Day 4 turns of less than 12 hours were compared 

(Test #4), the effect of turn durations of less than 12 hours was not significant for either response speed 

or lapses.
Table 3-19. Mean Total Sleep Duration (TSD) and Standard Deviation 

(S.D.) for each Time Off Duration and Schedule 

Type prior to Day 3 and Day 4 Early Morning Shifts

DAY 3-E Turns of Less than 12 Hours

 

Time Off Duration

XXEEEa XXEEM

TSD (n) S.D. TSD (n) S.D.

7 - 8 239.00 (2) 67.88 322.31 (13) 106.75

9 282.38 (8) 49.32 312.87 (31)

b 90.54

10 303.38 (8) 121.46 325.65 (52) 76.52

11-12 - - 378.50 (14) 55.02

13-15 - - - - 

16-19 - - - - 

DAY 4-E Control

 

Time Off Duration

XXEEE XXEEM 

TSD (n) S.D. TSD (n) S.D.

7 - 8 - - - - 

9 - - - - 

10 - - - - 

11-12 - - - - 

13-15c 327.33 (6) 56.29 343.09 (87) 89.52

16-19 304.45 (11) 80.85 343.33 (24) 74.97

DAY 4-E Turns of Less than 12 Hours

 

Time Off Duration

XXXEE XXXEM 

TSD (n) S.D. TSD (n) S.D.

7 - 8 263.00 (1) - 261.75 (4) 27.12

9 286.56 (9) 72.89 264.40 (5) 62.97

10 364.89 (9) 62.59 290.00 (3) 96.44

11-12 279.60 (5) 68.01 - - 

13-15 - - - - 

16-19 - - - - 

Notes: a Day following turns of less than 12 hours is indicated in red font.

b Italicized (n) indicates that one case is missing TSD. c These time-off durations are provided as comparisons for Day 4 turns of less than 12 hours. 

The only significant and expected effect of turn durations of less than 12 hours on lapses occurred on 

Day 4 in the comparison of turns less than 12 hours and of turns greater than 12 hours (Test #3). 

Lapses diminished when time off increased from 13-15 hours to 16-18 hours, p5 = -0.847, z(388) = -

2.297, p = 0.022, but there was no significant difference between turns less than 12 hours and turns 

greater than 12 hours, p6 = -0.155, z(388) = -0.571, p = .568. However, there were two significant 

unexpected effects: Lapses actually increased with increasing hours off prior to the Day 4 early shifts:

from 9 to 10 hours (p2 = 1.135, z(388) = 2.148, p = 0.032) (Test #3) and from 10 to 11-12 hours (p3 = 

0.849, z(54) = 2.298, p = 0.025) (Test #2). Similarly, response speed slowed from 9 to 10 hours off, p2 

= -0.000193, z(54) = -2.614, p = 0.012 (Test #2). However, these findings are somewhat consistent 

with findings reported by Nesthus, et al. (2003), who found reduced sleepiness at the beginning of the 

early morning shift following a 9-hour turn compared to an 8-hour turn, but no continued reduction in 
sleepiness as the turn increased one more hour to 10 hours off. In addition, negative affect ratings 

paralleled the sleepiness ratings in the Nesthus, et al. (2003) study: negative affect was lower with the 

9-hour turn than with either the 8-hour or 10-hour turn. Nesthus and colleagues interpreted this finding 

in terms of their low n for the group with 10-hour turn (n = 8) versus those with 9-hour turn (n = 36). 

That explanation is not relevant to the present findings, as the frequencies of 9 hours off and 10 hours 

off prior to the early morning shifts were roughly equal (see Tables 3-17 and 3-18).

Some caution is called for in interpreting results from the turn analyses as only a subset of the data 

were included in the reported analyses. Inclusion of the additional data may clarify the anomalous 

findings concerning turn durations of less than 12 hours on alertness, especially given the small 

number of participants contributing to these initial analyses. Also, a parallel analysis is needed to 

assess the impact of hours off prior to midnight shifts on both sleep duration and alertness. Such an 

analysis was not conducted initially because of the limited variability in turns of less than 12 hours prior 

to midnight shifts in the 2-2-1 schedule: 88% were eight or nine hours. This analysis would need to 

examine other schedule types, such as those in which two midnight shifts occurred in sequence or 

where the week began with a midnight shift, in order to identify cases with longer durations off prior to 

the midnight shift.

3.3.5 Subjective Ratings of Sleepiness and Workload 

Participants provided a subjective rating of sleepiness after each PVT trial using the Stanford 

Sleepiness Scale (Hoddes, Zarcone, Smythe, Phillips, & Dement, 1973). Previous research has shown 

that people are not particularly good judges of their actual levels of fatigue, overestimating their 

capacity relative to an objective measure (Dorrian, Lamond & Dawson, 2000). However, subjective 

ratings provide quick and easy indications of perceived capacities and have been used in many prior 

studies of fatigue, including those previously conducted by CAMI, so they were used as an adjunct 

measure in this study. 

In addition, participants were asked to rate their perceived workload level for the period on position

immediately prior to the second and third PVT trials during each shift (the first PVT was taken at the 

beginning of the work day so no workload rating was available).

3.3.5.1 PVT and Subjective Sleepiness Ratings

The subjective sleepiness rating was found to be positively correlated with lapses, p1 = 0.5669, p < 

0.001, indicating that the more sleepiness the participant reported, the more lapses were detected on 

the PVT trial. However, given the sizable number of observations (approximately 4,500) contributing to 

this effect and hence, the associated statistical power, the correlation of 0.57 is low, accounting for only 

about 33% of the variance. This suggests that subjective ratings of sleepiness should not be used as a 

measure of functional alertness without corroboration using other measures.

The relationship between subjective sleepiness ratings and lapses was not moderated by any controller 

demographic variables. 

3.3.5.2 Relationship Between Subjective Workload and Alertness

Participants’ subjective workload ratings at the mid-point and at the end of the shift day were compared 

with lapses during their associated PVT tests. Subjective workload ratings were found to be significantly 

negatively related to number of lapses, p1 = -0.1889, p = 0.012, indicating that the higher levels of 

perceived workload were associated with greater alertness on the associated PVT trial. This result 

remained when the effect was further examined with the inclusion of the trial and shift variables: more 

lapses occurred as a work shift progressed, p2 = 0.4660, p = 0.002; lapses also increased across shifts 

of a work week, p3 = 0.0855, p = 0.010. 

An important question is whether subjective workload varied around the 24-hour clock. In addition to 

reflecting circadian changes, subjective workload would also be expected to reflect differences in actual 
traffic loads at various times of the day. To test this possibility, the time of day of each workload rating 

was entered in the model. Clock time for workload ratings was not significantly correlated with 

subjective workload, p4 = 0.0252, p = 0.493, indicating that workload was not rated lower or higher at 

any particular time of day.

The relationship between subjective workload rating and lapses was moderated by participant age (b01

= -0.0309, p = 0.044) and years of experience (b02 = 0.0323, p = 0.018), but not by gender (b03 = -

0.1935, p = 0.122), when all were entered simultaneously in the model. However, age and years of 

experience were highly correlated with each other (r = 0.762, p < 0.001) and thus should not be 

included in the same model. When entered separately, neither age nor years of experience was a 

significant moderator, thus suggesting that the age/experience moderator effect was spurious.

3.3.6 Moderator Effects on Controller Sleep 

3.3.6.1 Controller Demographics as Moderator Variables

Prior studies have found significant effects of age on tolerance of shiftwork and cognitive alertness 

(Akerstadt, 1990; Della Rocco & Cruz, 1996 ; Nesthus, et al., 2005). These relationships were 

examined by using participants’ age and years of experience as a controller as moderators of the 

relationships between schedule factors and duration, efficiency and timing of sleep.

3.3.6.1.1 Controller Demographics and Sleep Duration

The effect of age was examined after the schedule type (i.e., 2-2-1 vs. 2-x-2) and day progression were

entered into the model of sleep duration. It was found that age did not have a significant moderating 

effect on the relationships between sleep duration and schedule type or day progression, b11 = -2.065, 

z(63) = -1.465, p = 0.148, and b21 = -0.086, z(63) = -0.317, p = 0.752, respectively. Similarly, years of 

experience did not have an effect on the relationship between sleep duration and schedule type, b11 = -

1.046, z(63) = -0.687, p = 0.495, or sleep duration and day progression, b21 = -0.111, z(63) = -0.403, p

= 0.688. These results imply that neither participant age nor years of experience moderated the effect 

of schedule type on sleep duration (i.e., the 2-2-1 schedule led to shorter sleep duration than the 2-x-2), 

nor the progression of work week on sleep duration (i.e., less sleep was obtained as the work week 

progressed). 

3.3.6.1.2 Controller Demographics and Sleep Efficiency

Neither the relationship between sleep efficiency and schedule type nor sleep efficiency and day 

progression was expected to be moderated because neither relationship was statistically significant. 

There was no effect of age on the relationship between schedule type and sleep efficiency, b11 = -

0.342, z(63) = -1.595, p = 0.115. Although no significant relationship between day of week and sleep 

efficiency was found, there was a marginal moderating effect of age on the relationship, b21 = 0.086, 

z(63) = 1.695, p = 0.095. This pattern of results replicated with years of experience as the moderator: 

There was no effect of years of experience on the relationship between schedule type and sleep 

efficiency, b11 = -0.189, z(63) = -0.856, p = .396, but a marginal moderating effect of years of 

experience was measured on the relationship between day of work week and sleep efficiency, b21 = 

0.093, z(63) = 1.843, p = 0.070. The positive moderating effect of age and years of experience 

suggests that older or more experienced participants tended to have more constant sleep efficiency 

(rather than declining) as the work week progressed compared to younger participants, implying that 

age/years of experience provided some resistance to fatigue. This effect cannot be explained by 

differences in schedules worked by participants of varying ages and years of experience, as no 

significant correlation was found between schedule type and participant demographics.

3.3.6.1.3 Controller Demographics and Time Asleep

The relationship between schedule type or day progression and the time at which participants fell 

asleep were not expected to be moderated because neither relationship was found to be statistically 

significant. This was confirmed. There was no effect of age on the relationship between schedule type 
and when participants fell asleep, b11 = 10.380, z(63) = 1.189, p = 0.239. No moderation was found on 

the relationship between day of work week and time to sleep, either, b21 = -1.5618, z(63) = -1.112, p = 

0.271. Similarly, there was no moderating effect of years of experience on the relationship between 

schedule type and time to sleep, b11 = 9.721, z(63) = 1.091, p = 0.280, nor between day of work week 

and time to sleep, b21 = -2.165, z(63) = -1.528, p = 0.131.

3.3.6.1.4 Controller Demographics and Time Awake 

Age did not have an effect on the relationship of time awake with schedule type, b11 = -2.607, z(63) = -

0.705, p = 0.483, or with day of work week, b21 0.321, z(63) = 0.439, p = 0.662. Similarly, years of 

experience had no effect on the relationship between schedule type or day of work week and time 

awake, b11 = -0.404, z(63) = -0.107, p = 0.916, and b21 = 0.352, z(63) = 0.478, p = 0.634, respectively. 

3.3.6.2 Facility Features as Moderators of Controller Sleep

While controllers on average obtained almost six hours of sleep between shifts, those working in 

different types of facilities obtained significantly different amounts of sleep. Controllers working in 

TRACONs slept significantly longer (M = 363.94 min or 6.07 hrs) on average than those in En Route 

Centers (M = 337.76 min or 5.63 hrs), g001 = 19.436, z(24) = 2.671, p = 0.014, and those in Towers (M

= 344.66 min or 5.74 hrs), g001 = 21.501, z(23) = 3.101, p = 0.005.

To determine whether characteristics associated with participating facilities, such as traffic, staffing, and 

CPC/Developmental ratios, played a role in explaining this difference between facility types, several 

models were created to include facility characteristics as predictors of sleep duration. It was found that 

facility traffic level based on the FAA’s classification of each facility did not affect the amount of sleep 

controllers obtained, g001 = -2.564, z(24) = -0.519, p = .608. However, both CPC/ Developmental ratio 

and staffing level affected controller sleep duration when entered individually (due to a moderate 

negative correlation of -.725 between staffing level and CPC/Developmental ratio), g001 = 0.937, z(24) = 

2.770, p = 0.011, and g001 = -100.332, z(24) = -2.419, p = 0.024, respectively. These results suggest 

that controllers in facilities with high CPC/Developmental ratios tended to get significantly more sleep 

than those in facilities with lower CPC/developmental ratios. However, it also was found that controllers 

in facilities with lower staffing levels tended to sleep more than those in facilities with high staffing 

levels. This result may have arisen from the moderate correlation between staffing levels and 

CPC/Developmental ratios. Both models also resulted in non-significant levels of the Level-3 variance 

component, suggesting that no more variance in sleep duration is available to predict sleep duration at 

the facility level.

When the facility type and each of the above facility characteristics were included in models together, 

different patterns of results emerged. First, when CPC/Developmental ratios were considered along 

with facility type, both effects became statistically non-significant, g001 = 0.525, z(22) = 1.221, p = 0.235 

for CPC/Developmental ratios, and g002 = 13.287, z(22) = 1.514, p = 0.144 for facility type, suggesting 

that these two variables are related to each other, hence, reducing their individual unique contributions 

to predicting sleep duration. When staffing level was included along with facility type, staffing was no 

longer statistically significant, g001 = -52.210, z(22) = -0.962, p = 0.347, meaning staffing level did not 

uniquely predict sleep duration. However, the TRACON to En Route Center comparison remained 

significant, g002 = 15.577, z(22) = 1.936, p = 0.001, confirming that TRACON controllers obtained 

significantly more sleep than En Route Center controllers, even after controlling for staffing level 

variations.

3.3.6.3 Facility Features as Moderators of Controller Alertness

No facility level factor uniquely predicted or moderated any of the controller alertness measures or 

relationships.
3.4 FIELD STUDY SUMMARY AND DISCUSSION

This field study was designed to address several specific questions concerning the impact of 

controller’s shift schedules on the amount of sleep they obtain and their associated alertness on the 

job. These questions include the following:

1. How much sleep do controllers obtain on average per night and over the entire work week? 

How do controllers’ work schedules influence the amount of sleep they obtain, its timing and 

efficiency? 

2. How do work schedules affect controller alertness during their shifts?

3. Which shifts and schedules are associated with the greatest challenges to controller 

alertness?

4. Do controller demographics (age, years of experience as a controller, gender) or facility 

factors affect any of the above findings, either directly or as moderators?

5. How do the present findings compare with findings from previous studies conducted by the 

FAA’s CAMI?

6. What are lessons learned and implications for future fatigue research areas? 

Data from 211 participants who participated in the field study and for whom sufficient complete data 

was available were analyzed to determine the impact of shift schedules on their sleep patterns and 

alertness. Sleep was characterized in terms of quantity, efficiency and timing, i.e., time asleep and time 

awake. Alertness was based on objective measures (PVT response speed and lapses, Basner & 

Dinges, 2011) and subjective ratings using the Stanford Sleepiness Scale (Hoddes, et al., 1973), both 

obtained three times during each work shift (beginning, middle and late). Findings based on these data 

were compared to those obtained from our concurrent web-based survey, as well as to earlier studies 

conducted by researchers at the FAA’s CAMI (see Della Rocco & Nesthus, 2005). 

3.4.1 Shift Schedules and Controller Sleep

3.4.1.1 Sleep Duration and Shift Schedule Factors

Overall, controllers in the field study obtained almost 6 hours of sleep per night during the work week. 

This included both their main sleep period plus naps during time off. Variability was quite large because 

it reflected both the brief sleep periods during the quick turn between the early morning shift and the 

midnight shift in the 2-2-1 schedule and the longer sleep duration during the RDO prior to the first 

afternoon shift of the work week. 

Controllers obtained the least amount of sleep prior to midnight shifts (mean of 3.3 hours). Sleep prior 

to early morning shifts also was significantly limited (mean of 5.4 hours), a finding that reflects the start 

time of shifts beginning between 05:30 and 08:00. To meet those early start times, controllers awoke on 

average at 04:20. A second factor that may have restricted sleep prior to early shifts is that people 

entrained to a normal 24-hour clock typically find it difficult to sleep in the evening, when their internal 

circadian clock still supports alertness (Akerstedt, 1988). To obtain 8 hours of sleep and awaken at 

04:20 would mean going to sleep at 20:20 in the evening, before circadian factors typically warrant. 

Controllers actually went to sleep around 22:30 prior to early shifts, yielding an average of 5.4 hours of 

sleep. 

Total sleep time also varied with the type of schedule worked. When schedule type and day of work 

week (order) were both entered into our analysis models, both made significant contributions to total 

sleep time. Controllers slept longer per night on average working the RR schedule with no midnight 

shift (2-x-2) than those working the 2-2-1 schedule with a midnight shift, consistent with Cruz and Della 

Rocco (1995). This effect was due to reduced sleep in the 2-2-1 schedule prior to the first early morning 

shift on Day 3 and the midnight shift on Day 5, both of which were quick turns. Sleep durations on Days 
1 and 2 (afternoon shifts) and Day 4 (early shifts) were almost identical in the two schedules, as can be 

seen in Table 3-12. What this suggests is that the overall mean difference in amount of sleep obtained 

in the two schedules is driven largely by the shift start times rather than by the schedule. 

In addition, both groups slept progressively less per night as they went through their work week. For 

both groups, work weeks began with afternoon shifts and moved toward early morning shifts, with a 

midnight shift ending the week for those working a 2-2-1 schedule. In other words, controllers working 

both schedules began their work weeks with shifts associated with the maximum amount of preparatory 

sleep (afternoons) and ended with shifts associated with the least amounts of preparatory sleep (early 

and midnights).

3.4.1.2 Timing of Sleep

Considering the actual times at which participants went to bed and awoke prior to each shift further 

illuminates the effect of schedule type on total sleep time. The day progression effect was significant for 

both time asleep and time awake, indicating that controllers went to sleep and arose progressively 

earlier as the work week advanced. Controllers went to sleep significantly earlier when facing an early 

morning shift, around 22:30, than they did prior to a mid-day or afternoon shift (asleep between 00:15 

and 00:30). This finding does not confirm Cruz and Della Rocco’s (1995) finding that controllers went to 

sleep at roughly the same time each night regardless of the shift start time the next day. Consistent with 

the Cruz and Della Rocco finding, participants in the present study awoke significantly earlier for the 

early morning shifts (around 04:30 on average) than for the mid-day or afternoon shifts (between 07:15 

and 07:30). 

3.4.1.3 Sleep Efficiency

Sleep efficiency was automatically generated from the activity monitor data. Participants were not 

asked to rate the quality of their sleep, though they did rate how sleepy or alert they felt upon 

awakening using the Stanford Sleepiness Scale. Actigraphy-based sleep efficiency was significantly 

lower for naps prior to midnight shifts than for any other sleep periods. Given that this was the shortest 

sleep period during the work week (average of 3.25 hours), it was expected that sleep efficiency also 

would be lowest based on a prior CAMI finding that the quality of sleep based on self-rating was 

commensurate with sleep duration (Nesthus, Cruz, Boquet, Dobbins & Holcomb, 2003). 

Despite strong homeostatic pressure to sleep between an early shift and a midnight shift due to limited 

sleep preceding that early shift and the early awake time, most people find it difficult to sleep during the 

day, when circadian rhythms are working to keep them alert. Hence, homeostatic and circadian 

pressures are likely to be in conflict during the quick turn between the early morning and midnight 

shifts, from around 14:00 to 22:00, resulting in less than fully restful sleep and low sleep efficiency 

(Torsvall, et al., 1989; Tilley, et al., 1982).

3.4.1.4 Controller Age and Experience as Moderators of Sleep 

Neither controller age nor years of experience had a significant effect on the following relationships: 

amount of sleep and the work week progression, amount of sleep and shift schedule, efficiency of sleep 

and shift schedule, and time asleep and schedule type. However, both demographic variables 

moderated the relationship between sleep efficiency and work week progression. As participants aged 

or there was an increase in the years of experience, they tended to have less efficient sleep as they 

progress through the work week. 

3.4.1.5 Facility Factors as Moderators of Sleep Duration

Because air traffic control facilities differ significantly in factors that were identified as potentially 

contributing to controller fatigue (DOT OIG, 2009), namely, traffic levels, staffing levels, and amount of 

time devoted to training developmentals, facilities that varied in these factors were selected for 

inclusion in the study. Overall, it was found that the overall facility traffic level had no effect on controller 
sleep, suggesting that staffing was commensurate with the traffic demands. When staffing levels and 

CPC/Developmental ratios were entered into the model predicting total sleep time (TST) (ignoring 

facility type), both significantly contributed to TST. However, when entered along with facility type (En 

Route Center, TRACON, Tower), their contributions were no longer statistically significant. 

However, controllers in TRACONS obtained more sleep on average than those in either En Route 

Centers or Towers. TRACONs had the lowest staffing to target ratio of the three facility types in our 

study (0.83 vs. 1.00 for Centers and 0.94 for Towers, based on the FAA’s low target staffing ranges, 

FAA, 2009). This low staffing ratio might be expected to result in a greater demand for overtime hours, 

which could have restricted recovery sleep. Also, TRACONs had a higher CPC to developmental ratio 

than En Route Centers (4.48 vs. 3.44), which should have proven less fatiguing based on findings from 

the OIG report (DOT OIG, 2009). The TRACON versus Center comparison for TST remained 

significant even when staffing levels and CPC/Developmental ratios were also entered in the model. To 

understand this phenomenon, the actual schedules worked by controllers in the various types of 

facilities must be examined, given that facility level factors do not seem to explain the effect. 

3.4.2 Shift Schedules and Controller Alertness

3.4.2.1 Measuring Alertness with the PVT

Two issues were of concern in selecting the 5-minute PVT as an objective measure of controller 

alertness, despite its use in other field studies (e.g., Baulk, Fletcher, Kandelaars, Dawson & Roach, 

2009). First, 10-minutes is the standard PVT duration that has been widely validated and used in many 

environments, both field and lab, to detect changes in fatigue as a result of sleep restriction (Basner & 

Dinges, 2011). There was concern that the 5-minute PVT might not be sufficiently sensitive, given the 

range of sleep restriction expected among controllers. Some studies using the PVT have shown that 

response speed slows and lapses increase in the second five minutes of a 10-minute administration 

compared to the first five minutes (Loh, et al., 2004). 

The second concern was that no baseline was available against which to evaluate individual controller 

performance. In laboratory studies of sleep restriction, measures are typically obtained during a rested 

baseline period when participants are allowed to sleep 8-10 hours for several days prior to introduction 

of sleep restriction. Also, large individual differences in vulnerability to the effects of sleep restriction 

have been reported (Van Dongen, et al., 2004); simply aggregating across subjects could result in high 

variance that might conceal effects associated with shift schedules. 

The solution was to calculate for each 5-minute trial a deviation score from a proxy baseline: the 

average of the top 10% fastest responses for each participant across the entire 14-day data collection 

period. Rather than using raw reaction times, response speed (1/RT) was utilized as recommended by 

Basner and Dinges (2011). In keeping with the literature, lapses were defined as the number of 

responses slower than 500 ms during the 5-minute trial.

The mean number of lapses was found to be 1.9 per trial. However, the variability was large indicating 

that on some trials participants’ alertness waned considerably, even on that brief trial. 

3.4.2.2 Alertness on Midnight Shifts

Midnight shifts in the 2-2-1 schedule posed the greatest challenge to controller alertness, as measured 

by both PVT response speed and lapses. This finding was predicted because midnight shifts in 2-2-1 

schedules suffer from the dual impacts of circadian factors – working when the body normally wants to 

sleep – and homeostatic pressure to sleep due to restricted sleep during the prior 24 hours. Alertness 

decrements during midnight shifts may reflect, but are not limited to, the following factors: (a) the quick 

turn between the prior early morning shift and the midnight shift, typically 8 or 9 hours, during which 

controllers may commute, eat, interact with family, exercise, and take care of hygiene, all of which 

leave little time for sleep; (b) circadian factors which interfere with sleep during the day, reducing sleep 

efficiency and the restorative effects of the sleep; and (c) limited sleep prior to the early morning shift 
preceding the midnight shift, an average of 5.5 hours. Adding those 5.5 hours to the 2.5 hours obtained 

on average prior to the midnight shift in the 2-2-1 schedule means that controllers would have had 8 

hours of sleep in the prior 24 hours when they begin their midnight shift. However, as pointed out in the 

discussion of the survey data (see section 2.3.4), by the end of the midnight shift, controllers would be 

functioning on only 2.5 hours of sleep in the prior 24 hours. 

Note that the mean amount of sleep prior to all midnight shifts, regardless of when they occur in a 

weekly schedule, is 3.25 hours, in contrast to the 2.5 hours of sleep prior to midnight shifts on day 5 of 

the 2-2-1 schedule. This difference reflects longer durations of pre-midnight sleep when a midnight shift 

follows any other shift than an early shift, including another midnight shift. Determining the contributions 

of circadian and homeostatic pressures to the alertness decrements found during the midnight shift in 

the 2-2-1 schedule would require several targeted comparisons with the 2-2-1 midnight shift: (a) 

midnight shifts that occur either at the beginning of the work week (day one) following two days of rest; 

(b) midnight shifts that occur on day five of the work week but do not follow a quick turn, i.e., the prior 

shift is an A, B, D or M shift; and (c) midnight shifts that do not follow a quick turn, as in (b) but occur on 

days two, three or four, in order to determine the contribution of cumulative fatigue over the work week 

on day five midnight shift alertness. 

3.4.2.3 Shift Schedules and Alertness

No main effect of schedule type on controller alertness was found, but the comparison in the present 

study was limited to the 2-2-1 and the 2-x-2 schedules because of the lack of sufficient data associated 

with other schedules, such as the straight 5-slowly rotating, straight shifts, or 10-hour 4-day schedules. 

The absence of significant differences in alertness between the two schedule types is not surprising, 

because both were counter-clockwise rapidly rotating schedules that differed primarily in the presence 

or absence of a midnight shift on the final day of the week. Other field studies have typically failed to 

detect significant differences associated with controller work schedules. One exception was the slightly 

greater increase in choice reaction time (slowing) across days of the week demonstrated by controllers

working the 2-2-1 schedule compared to those working a 10-hour 4-day shift schedule (Schroeder, et 

al., 1995). Despite differences in the mean amount of sleep across the week obtained by controllers 

working 2-2-1 versus 2-x-2 schedules in both the present study and the Cruz and Della Rocco (1995) 

study, neither study found significant commensurate effects on alertness, either self-reported or 

objectively measured. 

Both schedules yielded similar patterns of alertness changes across work shifts and the work week: In 

both alertness declined over the course of a work shift, from trial 1 at the beginning of a shift to trial 3 

near the end of the shift, reflected in both PVT response speed and lapses. In addition, the variability of 

lapses increased over trials, though only from the second trial (around the midpoint of the shift) to the 

third trial. This finding is in keeping with previous findings of increased instability of alertness measures 

with increasing fatigue (Doran, Van Dongen, & Dinges, 2001). 

Similarly, both the 2-2-1 and 2-x-2 schedules were associated with reductions in alertness across days 

of the work week, declining significantly from day one to day five, again reflected in both alertness 

measures. However, the degree to which alertness decreased across the week differed depending on 

schedule: Greater decrements were found for controllers working the 2-2-1 schedule, which included 

two quick turns and one midnight shift, compared to the 2-x-2 schedule with no midnight shifts and only 

one or zero quick turns. Midnight shifts had the greatest impact on alertness, affecting both speed and 

lapses. Performance also was slower on early morning shifts, but there was no effect on lapses. Both 

early and midnight shifts occur at the end of the week, raising the question of the source of the 

apparent “cumulative fatigue“ effect. Further analyses would need to test for changes across days of 

the work week independent of shift type, such as straight shifts. Prior studies of straight or slowly 

rotating shifts have shown relatively constant levels of sleep, 
3.4.2.4 Recovery Time Between Shifts 

The 2-2-1 and 2-x-2 schedules also differ in the frequency and duration of quick turns between 

rotations, as well as in the total amount of time off between shifts during the work week. Both of these 

factors affect the opportunity for restorative sleep between shifts. A comparison of the amount of time 

off between shifts in the two types of schedules is illustrated in Table 3-20. 

The first quick turn in the 2-2-1 schedule (left columns) occurs prior to the first early shift on Day 3 (3-

E), 9 hours in this example; the first quick turn in the 2-1-2 schedule (right columns) occurs prior to 

the first early shift on Day 4 (4-E), and is 13 hours. A second quick turn in the 2-2-1 shift between the 

E and M shifts (5-M) is only 8 hours. There is no corresponding quick turn in the 2-1-2 schedule. 

Overall, controllers working the illustrated 2-2-1 schedule would have 47 hours off between shifts 

across the work week, whereas those working the 2-1-2 schedule would have 55 hours off, a 

difference of 8 hours. In contrast, working a regular 9-to-5 schedule over a 5-day week would provide 

a total of 64 hours off (16 hours off between each shift). These differences would balance out by the 

amount of time off on the subsequent regular days off, with the 2-2-1 schedule offering up to 80 

consecutive hours off.

Table 3-20. Hours Off between Shifts in a 2-2-1 and a 2-1-2 Schedule

2-2-1 Schedule 2-1-2 Schedule

Shift Schedule

Hours 

between 

Shifts Shift Schedule

Hours 

between 

Shifts

1-A 15:00–23:00 15 1-A 15:00–23:00 15

2-A 14:00–22:00 9 2-A 14:00–22:00 13

3-E 07:00–15:00 15 3-B 11:00–19:00 12

4-E 06:00–14:00 8 4-E 07:00–15:00 15

5-M 22:00–06:00* 5-E 06:00–14:00

Total hours off during 

work week 47 Total hours off during work week 55

Note: Shift 5 actually begins at 22:00 on Day 4 and ends on Day 5.

An important question is the extent to which quick turns affect controller alertness. As shown in Table 3-

19, longer durations of time off prior to early morning shifts were associated with increased amounts of 

sleep, but only prior to Day 4, not Day 3. Alertness measured by PVT response speed also improved 

with increased time off on Day 4. 
3.4.2.4.1 Implications for Fatigue Countermeasures

The importance of duration of time off between shifts was documented in our models relating the 

number of hours off between shifts to PVT speed and lapses. Performance suffered the most with eight 

hours time off15 and improved as the time off duration increased permitting greater recovery sleep time. 

Perhaps most important from a countermeasure perspective is the strong relationship between amount 

of sleep and alertness: the correlation between total sleep duration and lapses was -0.752, accounting 

for 57% of the variance. This pattern suggests a complex mediating role of amount of sleep between 

time off and resulting alertness, but this relationship could not be tested using a multilevel modeling 

approach. What could be tested given the present data set was the impact of duration of time off 

between shifts on both sleep duration and alertness. The analysis of turns less than 12 hours prior to 

the early morning shift found that more sleep was obtained as time off increased beyond nine hours, up 

to 16 hours, but only when the turn of less than 12 hours occurred prior to the fourth shift, not the third 

shift (the only comparisons our data permitted).16 This day of week finding suggests an interaction 

between cumulative fatigue across the work week and the benefits of longer time off. Second, across 

all shift types (A, B, D, E, M) and schedules, strong significant relationships were found between 

duration of time off and both measures of alertness (response speed and lapses; see Figures 3-18 and 

3-19) when analyzed at the individual level. 

3.4.2.4.2 Individual Differences in Vulnerability to Sleep Loss

The models in Figures 3-18 and 3-19 illustrate inter-individual differences in vulnerability to time off 

effects: some controllers were essentially unaffected by restriction of time off, while others showed 

large effects. In Figure 3-18 (response speed) the topmost value is 0.0 deviation from the fastest 10% 

of the individual’s responses. Hence, a flat or zero slope regression line would mean that individual 

response speed showed no negative effect of quick turns. Conversely, a sharply sloped line indicates 

significant impairment (slowing of responses) at the shortest turns and recovery with the longest times 

off. The lapses shown in Figure 3-19 mirror the speed patterns, with near zero slope lines reflecting an 

absence of quick turn effects and sharply sloped lines indicating strong effects of quick turn on PVT 

lapses. 

Whether these inter-individual differences in the present study are systematic or random is an open 

question. However, both age and years of experience were found to moderate the day of week effect 

on alertness (PVT speed and lapses). 

3.4.3 Workload and Alertness

Even though controller alertness measures were not significantly affected by facility level factors that 

could affect workload, such as traffic levels, staffing levels, or CPC/Developmental ratio, it was 

important to determine whether workload as perceived by the controllers would have an impact on their 

alertness. Ratings of workload during the period on position immediately prior to taking each PVT were 

significantly related to measured alertness: the higher the perceived workload, the higher the alertness 

measured by PVT performance. This result held even when trial, shift and time of day variables were 

controlled. 

Low workload may be tied to monotony and lead to decreased alertness (Hilburn & Jorna, 2001). This 

finding is in keeping with a study of monotony that found unstimulating shiftwork to be equivalent to 

sleep restricted to 4 hours time in bed (Sallinen, et al., 2004). Studies of driver behavior have found that 

low workload exacerbates fatigue effects, and that drivers perform at higher levels when demands are 

greater, given similar levels of fatigue (Desmond and Matthews, 1997). Desmond and Hoyes (1996) 

 

15 A few quick turns of less than 8 hours reflected early sign in or late sign out during the quick turn. 16 An anomaly in this pattern was that amount of sleep decreased as hours off increased from 10 hours off to 11-

12 hours off, but only five participants contributed to this effect, suggesting it may not be reliable with a larger n.
suggested that participants in an air traffic control simulation failed to mobilize their effort effectively 

when task demands were low versus when they were higher, supporting Hancock and Warm’s (1989) 

adaptive model of stress and performance.

Several authors caution against confusing fatigue with boredom, both of which may result in reductions 

in performance (e.g., Soames-Job & Dalziel, 2001). However, boredom arising from task monotony 

may reflect a drop in motivation rather than a loss of capability, whereas fatigue represents an 

exhaustion of capability.

3.4.4 Comparison between the Present Field Study and Prior CAMI Studies

The present design replicated an earlier CAMI field study conducted by Cruz and Della Rocco (1995) 

that compared the 2-2-1 (AAEEM) and the 2-1-2 (AABEE) schedules. Our design expanded the 2-1-2 

to include other CCR schedules with no midnight shifts to increase data for analysis in this category; 

this schedule was defined as 2-x-2 (or AAXEE) to reflect the third shift as either a mid-day (B, as in the 

2-1-2), another afternoon (A), a day (D), or an early morning (E) shift. The CAMI study also compared 

the straight early morning schedule; however, hardly any controllers worked that schedule in the 

present study sample, so it was not a viable comparison group. (Controllers working midnight shifts 

were specifically recruited in the present study so the low numbers working straight or slow rotation 

schedules are to be expected.)

Overall, findings from the present field study were consistent with those from three field studies 

conducted by CAMI over a decade ago and reported in Cruz and Della Rocco (1995a, 1995b, the 

Miami Field Study), Nesthus, Cruz, Boquet and Dobbins (2003, the AT-SAFE study), Nesthus, Dattell 

and Holcomb (2005, the AT-SAFE study), and Schroeder, Rosa and Witt (1998, the 10-hr. vs. 8-hr. 

Shift Study). Those studies and the present study used similar methods to track sleep and alertness as 

a function of various controller shift schedules: sleep/activity logs to record amount, timing and quality 

of sleep, actigraphy to record actual sleep time, self-ratings of sleep quality and alertness/fatigue, and 

tests of cognitive functioning (Bakan Vigilance test, COGSCREEN or PVT). 

The major difference in methods between the CAMI studies and the present one is that the present 

study used the 5-minute PVT as a simple measure of alertness during each work shift to complement 

the sleepiness/alertness self-ratings using the Stanford Sleepiness Scale. Besides its brevity, the PVT 

is insensitive to practice effects, a singular virtue, given the repeated administrations. Some other 

cognitive tests show significant practice effects, which may distort interpretation of fatigue effects. For 

example, Banks and colleagues (Banks, et al., 2010) found what appeared to be minimal impact of five 

days of sleep restriction (4 hours time in bed) on Digit-Symbol Substitution Test accuracy (DSS-T), 

which could be interpreted as a lack of sensitivity to sleep restriction or absence of sleep restriction 

effect. However, performance of the non-sleep deprived control group significantly improved over the

five days, clearly demonstrating a practice effect on the DSS-T. Also, the PVT and fatigue ratings were 

done at different times: in the present study PVT and ratings were done at the beginning of each work 

shift, following the mid-shift break, and following a break near the end of the shift. The CAMI studies 

typically obtained fatigue ratings at the end of the shift and after the drive home. 

Following are specific comparisons of findings from the present field study and earlier CAMI studies. 

Comparisons of the amount of sleep obtained as a function of shift start times and time off between

shifts among the present survey, the present field study and two CAMI field studies are shown in Table 

3-21.
Table 3-21. Hours of Sleep Prior to Each Shift Type in the 2010 Survey and Field Study Compared to 

Two Prior CAMI Field Studies

Shift Type

2010 Survey

2010 

Field Study

1995 

Field Study a

2000 

Field Study 

(n=71)

Mean 

Hours n 

Mean 

Hours n

Mean

Hours n

Mean 

Hours

Before an afternoon shift (starts between 

13:00 and19:59) 7.7 2,987 6.9 327 7.9 16 7.2 b

Before a mid-day shift (starts between 

10:00 and 12:59) 7.3 2,431 6.8 111 7.6 16 7.7 b

Before a day shift (starts between 08:00 

and 09:59) 6.7 2,434 6.4 14 - - 6.5 b

Before an early shift (starts before 08:00) 6.3 3,126 5.4 369 5.5 16 5.8 b

Before a midnight shift (starts between 

20:00 and 01:00) - - 3.3 d 140 - - - 

Before a midnight shift after another

midnight shift 5.5 1,228 - - - - - 

Before an early 

shift with... 

8-9 hours off 5.4 2,819 5.1 8

8-10 hours off 5.2 148 5.4 c

8-12 hours off 5.3 164

Before a midnight 

shift with... 

8-9 hours off 3.1 1,959 2.4 8

8-10 hours off 2.3 b

8-12 hours off 2.5 52

Note a Calculated from Cruz & Della Rocco, 1995a. 

b

 Reported in Nesthus, et al., 2001. c Reported in Nesthus, et al., 2003. d The mean hours slept prior to midnight shifts in the field study represents all midnight shifts regardless 

of duration of time off before the shift. 
3.4.4.1 Midnight Shifts 

Midnight shifts demonstrated the greatest adverse impact on controller sleep and alertness in our study 

and the CAMI studies. Prior to midnight shifts controllers working all schedule types in the present 

study obtained about 3.25 hours of sleep, almost the same as found for the 2-2-1 participants in the 10-

hour versus 8-hour shift study, 3.57 hours (Schroeder, 1998). However, participants working a 2-2-1 

schedule in the present study only obtained on average 2.35 hours of sleep prior to the midnight shift 

on day five. This compares with the 2.4 hours of sleep in the Miami study (Cruz and Della Rocco, 

1995), the 2.0 hours in the AT-SAFE field study (Nesthus, et al., 2003); and the 2.2 hours reported for 

90% of New Zealand air traffic controllers working a rapidly rotating schedule (Signal & Gander, 2007). 

In the present study measured alertness also was the lowest during the midnight shifts, in agreement 

with the self-ratings of fatigue in the Cruz and Della Rocco (1995) study and cognitive alertness 

measures in the AT-SAFE study (Nesthus et al., 2005). Sleep efficiency and self-reported sleep quality 

also were lowest prior to the midnight shift compared to other shift types in the present study and the 

prior CAMI studies, thereby perhaps reducing the benefit of the daytime sleep prior to the midnight shift. 

3.4.4.2 Early Morning Shifts

Early morning shifts also limited controllers’ sleep and alertness. In both the present study and prior 

CAMI field studies participants arose around 04:20 for early shifts, an hour around most people’s 

circadian lows, and likely before their sleep needs had been completely satisfied. Both studies showed 

that total sleep time was limited prior to the early morning shift in part because the time at which they 

went to sleep was not sufficiently early enough to compensate for the early awake time (Cruz & Della 

Rocco, 1995). However, one difference between the studies was that participants in the present study 

went to sleep significantly earlier prior to early morning shifts (approximately 22:30) than they did for 

other shift start times; in the CAMI study participants went to sleep at approximately the same time 

every night, regardless of shift start time, but then adjusted their awake time to meet the upcoming shift 

start time. The present study did not include a sufficient number of participants who worked straight-5 

early morning shifts to examine their sleep and alertness patterns.

3.4.4.3 Time Off Between Shifts 

Turns of less than 12 hours limited the amount of sleep obtained between shifts in both studies, and 

thereby affected alertness during the subsequent shift. In the present study, turns prior to midnight 

shifts were eight or nine hours between shifts. Turns prior to the first early morning shift were typically 

nine to eleven hours between shifts. Both amount of sleep and response speed in the present study 

were related to duration of time off between shifts, with less sleep and slower responses associate with

the shorter (8-9 hour) turns. An unexpected finding in the present study was that the benefits of 

increasing numbers of hours off prior to an early shift only benefitted sleep and alertness prior to early 

shifts on day four, not on day three, which occur in the 2-2-1 schedule. This pattern suggests an 

interaction between cumulative fatigue across days of a work week and opportunities for sleep, but

requires validation using the full data set for the present study. In the CAMI studies, amount of sleep, 

rated sleepiness, sleep quality and mood varied as a function of duration of the time off between shifts 

(Nesthus et al., 2003; 2005). 

3.4.4.4 Cumulative Fatigue within Work Shifts

Both sets of studies found that fatigue accumulated and alertness waned from the beginning to the end 

of a work shift. This held across all shift start times, but was especially pronounced in midnight shifts. 

The one exception to this pattern was the 10-hour versus 8-hour field study in which choice reaction 

time decreased within each of the first four days (Schroeder, et al., 1998). However, errors increased, 

indicating that performance on this more complex task may reflect a speed-accuracy trade-off. During 

the midnight shift in the 8-hour. 2-2-1 condition, reaction time and errors both reflected increased 

fatigue. The proximity to the circadian low after a midnight shift may further increase risk of driving 

fatigued.
3.4.4.5 Cumulative Fatigue across Days of the Work Week

The CAMI studies and the present study found that fatigue appeared to be cumulative across days in a 

work week, in compressed schedules. Shifts began earlier each day in the compressed counterclockwise rapidly rotating schedules. Also, RR schedules rotate toward early morning shifts and a 

midnight shift in the 2-2-1 and 2-1-2 schedules, which limit sleep and challenge the circadian system. 

The straight-5 early shifts included in the Cruz and Della Rocco (1995) did not produce significant 

increases in fatigue across the work week. A question not answered by these analyses is whether this 

phenomenon is limited to compressed schedules of all types.

3.4.4.6 Sleep Efficiency

Both self-rated sleep quality and the actigraph-generated measure of sleep efficiency (this study) 

reflected the duration of sleep. Higher quality or more efficient sleep was associated with longer sleep 

in both studies, regardless of how sleep quality was assessed (Nesthus, et al., 2003; Schroeder, et al., 

1998). Lowest efficiency of sleep was associated with naps prior to midnight shifts.

3.4.4.7 Unique Findings in the Present Field Study

3.4.4.7.1 Relation between Workload and Alertness

One measure that was included in the present study but not in prior CAMI studies was perceived 

workload. Participants were asked to rate on a 7-point scale the workload for their time on position

immediately prior to taking the second and third PVT trials during each work shift. These ratings were 

then correlated with PVT lapses and response speed for that period. A positive relationship was found 

between workload and alertness: faster responses and fewer lapses were observed when workload 

was higher, suggesting that higher workload was arousing and engaging, whereas low workload 

contributed to lower levels of alertness. Hilburn and Jorna (2001) point out that low workload poses as 

much a threat to controller performance as high workload, based on analysis of controller related 

incidents and accidents (Redding, 1992; Stager, 1991). Findings from the present study are consistent 

with those reported in Melton, et al. (1973): Self-reported sleepiness, fatigue and alertness were lowest 

during the midnight shift, when workload is lowest.

3.4.4.7.2 Day of Shift and Turns Less than 12 Hours

A finding in the present study is the effect of day of the work week on the relation between turn 

durations of less than 12 hours, sleep and controller alertness. The duration of time off (from 8 hours to 

12 hours) had no effect on the amount of sleep controllers obtained or on alertness during the early 

shift that followed it when the early shift occurred on day three of the work week in an RR schedule, 

with or without a midnight shift. However, significant effects of turn durations less than 12 hours were 

found on both amount of sleep and alertness when the turn preceded an early shift on day four of the 

work week. This finding suggests that cumulative fatigue across the work week may have potentiated 

the benefits of extra time off for sleep and associated alertness. No tests were done of turns less than 

12 hours prior to days two or five due to lack of data. This is a finding that deserves further 

investigation. 

3.4.4.7.3 Influence of Controller Demographics on Sleep and Alertness

Controller age seemed to play a somewhat different role in the CAMI studies and the present study. In 

CAMI’s AT-SAFE field study, older controllers (40 years and older) performed significantly less well 

than younger controllers on cognitive measures involving speed and working memory (based on 

COGSCREEN). The adverse effects of quick turns were somewhat greater for older participants, prior 

to both early morning and midnight shifts in those studies. Similarly, in a laboratory study that simulated 

a 2-2-1 schedule (with non-controller participants), Della Rocco and Cruz (1996) compared older (50-55 

years) and younger (30-35 years) participants’ performance on the Multiple Task Performance Battery. 

While both age groups showed significant decrements in performance during the night shifts, older 

participants’ performance also suffered following the afternoon to early morning quick turn. It should be 
noted that the ‘middle-aged’ group (35-50 years) was omitted from this comparison, increasing the 

likelihood of finding age differences.

 

In the present study, both age and years of experience as a controller seemed to protect the controllers 

from cumulative fatigue across the work week, the decline in PVT alertness across the work week was 

moderated by controller age and years of experience, with older controllers showing less cumulative 

fatigue on succeeding work days relative to younger controllers. 

3.4.4.7.4 Influence of Facility Factors on Controller Sleep and Alertness 

Unlike the other field studies conducted by CAMI researchers, the present study solicited participants 

from a variety of facility types and facilities differing in traffic levels, staffing levels, and 

CPC/Developmental ratios. None of these factors had significant effects on controllers’ sleep quantity 

or quality or measured alertness, except for one: controllers working in TRACONs obtained more total 

sleep per day than those in En Route Centers and Towers. No interpretation for this effect is apparent.

3.4.5 Overall Comments

The present study solicited participants who worked midnight shifts, which precluded some 

comparisons with schedules studied previously by CAMI researchers, such as straight-5s (early 

morning). Even though controllers who were working midnight shifts were recruited, some of them did 

not work a midnight shift during the actual data collection period at their facilities. In most cases, those 

controllers worked schedules that were some variant of the 2-1-2. This meant that it was possible to 

compare the 2-2-1 and the 2-1-2 schedules as done in the prior CAMI studies. These comparisons 

involved a reduced n of 72, compared to the full n of 211 used in our other analyses. The dominant 

schedule worked by controllers in the present study was the 2-2-1, with 76% of study participants 

working a 4-day (8%) or 5-day (68%) 2-2-1 schedule. The relatively large n in the present study was 

necessary to assure sufficient power to observe effects—given that participants were from three 

different types of facilities (En Route, TRACON and Tower) which allowed for a number of factors that 

could potentially influence controller sleep or alertness. This hierarchical nested data structure also 

mandated that multilevel modeling be used to analyze the data. 

3.4.6 Field Study Conclusions

Findings from the present field study largely confirmed the major patterns of sleep, fatigue and 

alertness associated with shift schedules found in prior CAMI field studies. The findings confirmed that 

reduced sleep was associated with early morning and midnight shifts, and that sleep decreased across 

the work week with commensurate reductions in alertness both across shifts, across the work week, 

and on midnight shifts. There were a few notable new findings: More sleep was obtained prior to early 

morning shifts when the time off increased from nine hours to ten hours; overall, less sleep was 

obtained during turns of less than 12 hours when time off was over 13 hours. However, this effect was 

only present in turns less than 12 hours prior to early shifts on Day Four compared to Day Three 

suggesting that the benefits of longer time off are significantly greater later in the work week. This effect 

may have reflected the fact that controllers working the 2-2-1 schedule adjusted their time asleep to 

meet the demands of early morning shifts by going to sleep significantly earlier prior to early shifts than 

later shifts. Participants in the present study also obtained somewhat more sleep prior to midnight shifts 

averaged across schedules with turns of less than 12 hours and schedules with turns greater than 12 

hours.

Overall, the amount of sleep obtained between shifts during the work week was on average less than 6 

hours per night. Schedules worked by 76% of controllers in the present study led to what sleep science 

has defined as chronic fatigue, i.e., restricted amounts of sleep over a number of successive nights

(Belenky et al., 2003). Chronic fatigue leads to strong homeostatic pressure to sleep, resulting in very 

short sleep latencies (i.e., ease of falling asleep). 
4. DISCUSSION AND POTENTIAL FAA FUTURE RESEARCH AREAS

The field study and the fatigue survey were developed in close concert to assure that both were 

addressing in the same key issues of concern to the FAA. Now that results from both studies have 

been summarized, it is important to review the extent to which the findings are consistent or conflict 

with each other.

4.1 Integration of Findings from the Fatigue Factors Survey and Field Study

The results from the survey described in Section 2.3 served as the basis for comparison with findings 

from the field study. The following section is organized in terms of topics taken from the survey 

summary, followed by results from the field study with notation indicating whether the survey findings 

were confirmed or not. In many cases, relevant data from the field study are available to address issues 

raised by the survey, but have not been analyzed. Survey topics that were not addressed by the field 

study are not mentioned below. 

1. SURVEY: Challenge with Midnight Shifts 

Midnight shifts pose a challenge for obtaining sufficient pre-shift sleep and for maintaining 

alertness, both after quick turns and between successive midnight shifts.

a. CONFIRMED by Field Study: Mean of only 3.25 hours of sleep was obtained on average 

prior to midnight shifts; sleep during a quick turn of 8 or 9 hours prior to a midnight shift

was only 2.5 hours.

b. CONFIRMED by Field Study: Participants slept less overall during weeks with counterclockwise rapidly rotating schedules that included a midnight shift (RRM) than during RR 

schedules without a midnight shift. 

c. CONFIRMED by Field Study: PVT alertness measures indicated that controllers were 

least alert (‘sharp’ in the survey) at the end of the midnight shift.

d. UNANSWERED by Field Study: Field study did not examine impact of successive 

midnight shifts on either amount or efficiency of sleep prior to midnight shifts or on 

measured alertness. 

2. SURVEY: Challenge with Early Shifts

Controllers working early shifts obtained the second least amount of sleep prior to their shift and

reported obtaining less sleep than the controllers felt they needed. This was especially an issue 

after quick turns. Subjective ratings of alertness were the lowest at the beginning of early shifts

when compared to all other shifts.

a. CONFIRMED Indirectly by Field Study: A mean of 5.4 hours of sleep was measured

during turns of less than 12 hours prior to early shifts in field study, which is consistent 

with the total sleep time as reported in the survey. 

b. PARTIALLY CONFIRMED by Field Study: Participants’ PVT response speed was 

significantly slower during the early morning shift than during other shifts, but there was 

no significant increase in lapses on those shifts. 

c. UNANSWERED by Field Study: Field study did not examine subjective sleepiness at the 

beginning of shifts, as a function of shift start time or amount of sleep prior to the shift.

3. SURVEY: Quick Turns Restrict Sleep

Limited time off between shifts restricts the opportunity for recuperative sleep. Sixteen percent of 

survey respondents reported 8 to 9 hour quick turns; about 25% reported quick turns less than 11 

hours. Less sleep was reported prior to both early and midnight shifts during quick turns compared 

to turns of greater than 11 hours off: 5.4 versus 6.3 hours for early shifts and 3.1 versus 5.5 hours 

for midnight shifts. 
a. CONFIRMED by Field Study: Over 60% of field study participants worked schedules that 

involved one or more quick turns.17 

b. CONFIRMED Indirectly by Field Study: Because of the schedules represented in the 

field study sample, parallel comparisons with survey findings were not possible. No 

difference in sleep duration was found when comparing sleep prior to all early shifts to 

early shifts following turns of 12 hours or less (5.4 versus 5.2 hours). But total sleep 

duration prior to early shifts increased from 9 to 10 hours off, when quick turns of greater 

than 12 hours were compared to 13-15 hours off, and marginally up to 16-18 hours off, 

but only when the turn of less than 12 hours occurred prior to day four of the work week, 

not day three, as occurs in the 2-2-1 schedule. Turns of less than 12 hours prior to 

midnight shifts (mainly 8-9 hours off) were associated with less than prior to all midnight 

shifts across all time off durations (2.5 versus 3.25 hours). 

4. SURVEY: Alertness and Quick Turns

Alertness was found to be degraded by quick turns between shifts.

a. CONFIRMED Indirectly by Field Study: Lowest objective alertness measured by PVT 

was found during midnight shifts following turn durations of 12 hours or less. Participants 

tended to respond more slowly on early morning shifts; however, lapses did not differ 

from those in later shifts. 

b. Partially CONFIRMED by Field Study: Measured alertness confirmed the negative effect 

of duration of turns less than 12 hours on amount of sleep prior to early morning shifts 

on Day 4. Significant decrements in alertness were seen with turns of less than 12 hours 

on both PVT speed and frequency of lapses. Duration of turns less than 12 hours did not 

affect alertness on Day 3 early shifts. PVT performance was directly related to amount 

of time off across all shift types. However, no tests were done on other days of the week 

or for midnight shifts with varying hours off prior to the mid shift. In addition, large 

individual differences were found in response to quick turns. 

5. SURVEY: Schedule Pressure versus Workload

Fatigue is due to schedule pressure, not primarily due to workload.

a. CONFIRMED by Field Study: Higher perceived workload was associated with higher 

levels of alertness in the field study. No significant effects of variables that might 

influence workload were found. Individual facility traffic levels, staffing levels and 

CPC/Developmental ratios had no effect on alertness, most likely due to facility 

management working to prevent overload conditions. It should be noted that the actual 

levels and range of workload during the periods of data collection are unknown. 

6. SURVEY: Six-Day Schedules

Fatigue was associated with working 6-day schedules with only a single day off. Fourteen percent 

of survey respondents reported working a 6-day schedule the week prior to taking the survey. Over 

half of these 6-day schedules included midnight shifts. These schedules were concentrated in 

TRACONs. 

a. CONFIRMED by Field Study: Over 21% of the work weeks analyzed consisted of 6-day 

work schedules during the study period (this trend should not be taken as representative 

of all controller schedules).

 

17 This high rate reflects the participant solicitation criterion of working at least one midnight shift per week, which 

assured at least one quick turn.
b. CONFIRMED by Field Study: Over 68% of the 6-day schedules involved one or more 

midnight shifts (this figure should not be taken as representative of controller schedules

as controllers who worked midnight shifts were actively recruited).

c. UNANSWERED by Field Study: No analyses were conducted on 6-day schedules, with 

or without midnight shifts, to compare sleep duration, timing and efficiency with that 

obtained during normal 5-day or shorter 4-day work weeks. Sufficient data for such 

analyses appear to be available. 

d. UNANSWERED by Field Study: No analyses were conducted to determine whether 

alertness in field study participants was affected by working 6-day schedules, with or 

without midnight shifts. Sufficient data for such analyses appear to be available. 

7. SURVEY: TRACON Schedules and Staffing

Survey responses indicated that 6-day schedules and mandatory overtime were disproportionately 

concentrated in TRACONs relative to En Route Centers or Towers. Respondents also reported that 

they felt staffing was less than adequate in TRACONs. 

a. CONFIRMED by Field Study: Six-day schedules were over-represented in TRACONs at 

38% of all schedules, compared to 12% for Towers and 17% for En Route Centers.

b. CONFIRMED by Field Study: A comparison of current staffing levels to recommended 

target staffing levels based on the FAA’s Ten-Year Staffing Plan (FAA, 2009) shows that 

the 11 TRACONs that participated in the field study were all somewhat below parity, 

based on the lower bound of the target range. The mean actual-to-target ratio for all 

participating TRACONs was 0.83, with a range of 0.60 to 0.97. In contrast, the mean 

staffing ratio for En Route Centers was 1.0 (range from 0.90 to 1.18) and for Towers the 

mean ratio was 0.94 (range from 0.76 to 1.35). See Table 3.1 for detail. While facilities 

were selected based on a range of diverse characteristics, facilities with low staffing 

levels were not deliberately selected.

 

8. SURVEY: Age and Working Midnight Shifts

The survey indicated a slightly greater proportion of older controllers than younger ones working 

midnight shifts, including 6-day weeks containing midnights. In addition, no higher incidence of 

operational events was associated with older age, despite their working more midnights. 

a. PARTIALLY CONFIRMED by Field Study: Since practically all of the field study 

participants worked midnight shifts, there was a disproportionate representation of older 

controllers working midnights. No significant relationship was found between age and 

schedules worked. Age moderated the effect of increasing fatigue across work days, 

with older participants showing a smaller increase in PVT lapses. Years of experience 

working as a controller had a similar effect. 

9. SURVEY: Staffing Levels and Fatigue

Survey respondents reported their view that low workload promotes greater fatigue, and that low 

workload is tied to some positions being overstaffed during low workload periods. They 

recommended not overstaffing positions during low workload periods.

a. Partially CONFIRMED by Field Study: A positive relationship was found between 

perceived workload and measured alertness. Alertness as measured by the PVT was 

greater following on-position periods rated as higher workload by the field study 

participants, but no information was available concerning staffing during those shifts.
4.2 Unanswered Questions

Several unexpected findings from the survey raised questions that the field study design had not 

anticipated; hence, no effort had been made to select participants and schedules to address those 

issues. In some cases, limited data may be available in the present field study dataset to address these 

issues. Given the implications for FAA policy associated with several of the issues, it is important to 

identify them so that future research can address them. 

• Effects of quick turns on alertness during midnight shifts. Analyses are needed to examine the 

impacts of circadian factors associated with working midnight shifts (circadian lows), quick-turn 

restrictions on sleep opportunities prior to midnights, and day of week effects. In the current field 

study these factors are confounded in midnight shifts that occur on Day 5 of 2-2-1 schedules. 

• Impact of amount of sleep in prior 24 hours before a midnight shift. Relating to the first point, 

further analysis is needed to assess the impact of amount of sleep during the 24-hour and 48-

hour periods preceding a midnight shift.

• Impact of working 6-day schedules. Six-day schedules proved to be the least preferred 

schedule type by survey respondents. Controllers in both the survey and field study samples

worked a 6-day schedule during the study period. The impact on controller alertness of one or 

more 6-day weeks in a row is unknown and should be investigated. The field study data may 

begin to address this issue.

• Impact of working multiple midnight shifts. Many of the schedules reported by survey 

respondents involved more than one midnight shift. The field study also found a number of 5- 

and 6-day schedules that included more than one midnight shift, usually back-to-back. Fatigue 

related impacts resulting from working multiple midnight shifts should be investigated. The field 

study data may begin to address this issue. 

• Interactions between shift start times and days of a work week. The finding of cumulative 

fatigue over the course of a work week is widely reported, but these findings are often 

confounded with the nature of the schedule. For example, the counter-clockwise rapid rotating

schedules typically include an early morning shift or midnight shift at the end of the work week.

Both of these shifts are associated with reduced sleep duration and lower alertness levels than 

other shifts. The benefits of longer time off between shifts found in the field study were restricted 

to Day 4 and not evident on Day 3 suggesting a possible interaction between day of the week 

and need for sleep. This issue deserves a more detailed assessment in order to design 

schedules that optimize alertness and performance. 

• Impact of interrupted work weeks on alertness. An unexpected pattern seen in the present field 

study was the ‘interrupted’ work week— 5- or 6-day work weeks that followed known schedule 

patterns (e.g., 2-2-1, 2-1-2, 2-3, etc.) and were interrupted by a day off. The frequency of these 

schedules and impact of these interrupted schedules on sleep and alertness are completely 

unknown. 

4.3 Potential Future Research Areas

The FAA is developing a Fatigue Risk Management System that may address many of the challenges

identified by the present study. In addition, some policies have already been changed to support 

opportunities for increased restorative sleep. The following future research areas, which are based on

findings from the present studies, have been identified as follows:

1. Investigate the scientific effectiveness and operational feasibility of 
a) adjusting shift start times for the shift preceding the midnight shift to build up sleep reserve 

before the midnight shift and

b) increasing the minimum numbers of hours off (currently 9) between afternoon and morning 

shifts to allow for longer recovery sleep opportunities (FAA, 2012). 

2. Continue the development of the Fatigue Risk Management System supported by NATCA and the 

FAA. Important areas of collaboration are to

a) investigate the circumstances requiring 6-day work schedules and ways to reduce the frequency 

of 6-day work schedules (i.e., those with mandatory overtime),

b) consider ways to alleviate challenges associated with both scheduling and staffing at 

TRACONs, 

c) assess and manage workload and overtime hours of Front Line Managers,

d) investigate the availability and the impacts on fatigue of 10-hour 4-day schedules, and 

e) investigate and monitor fatigue safety culture. 

3. Consider developing and/or providing support tools that

a) support standardized classification of workforce schedules, 

b) identify potential schedules that may contribute to fatigue,

c) support day-to-day scheduling that interfaces with predicted weather and traffic levels, and 

d) assist FLMs in staffing to traffic during shifts that minimizes fatigue effects. 

4. Continue to develop and/or clarify existing policy related to encouraging controllers to seek 

diagnosis and treatment of sleep disorders.

5. Enhance training material on fatigue and its management for the controller workforce and for FLMs.

6. Continue periodic surveys and targeted field studies by independent investigators to 

a) assess the effectiveness of air traffic fatigue risk interventions , 

b) assess schedules, fatigue factors and alertness levels, and

c) solicit suggestions from ATC personnel on ways to reduce controller fatigue. 
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Appendix A. NASA ATC Fatigue Factors Survey























Appendix B. Field Study Informed Consent

Appendix C. C-FAM Participant Take-Home Guide

STUDY OVERVIEW
Participants are required to:

• Wear an ‘actigraph’ watch that records sleep and wake periods for the full two weeks 

• Keep a sleep/activity log for the two-week period

• Take a 5-minute alertness test (PVT) three times during each work shift

• Take the NASA on-line Fatigue Factors Survey

Actigraph Watch

• Wear 24 hours per day on your non-dominant wrist throughout the 14 days of the study 

• Take off during showers, swimming, or other activities likely to damage it

• Put back on as soon as you are done with these activities 

• Record the removal of the watch on the sleep/activity log 

Sleep/Activity Log

• Use the Activity Codes to record the beginning of new activities on your sleep/activity log 

• Make a Fatigue Rating each time you wake up from sleep or a nap and before each PVT

• Give a Workload Rating of your immediately prior shift segment before 2nd & 3rd PVT each shift

ACTIVITY Codes 

Use the Activity Codes to record the beginning of each of these activities. After PVT Tests, put 

W if going back to Work or E if Ending work for the day. When putting Actigraph watch back on, 

put code for activity at that time (e.g., A if awake away from work, S if going to sleep).

S – Sleep (begin sleep of any duration, including naps) 

A – Awaken (from sleep of any duration, when not at work) 

W – Start or Resume Work 

E – End Work 

T – PVT Test 

O – Actiwatch Off (e.g., shower, swimming)

FATIGUE Ratings

Complete each time you wake up from sleep or a nap and before each PVT. 

1 - Feel active and vital; alert; wide awake 

2 - Functioning at a high level, but not at peak; able to concentrate 

3 - Relaxed; awake; not at full alertness; responsive 

4 - A little foggy; not at peak; let down

5 - Fogginess; beginning to lose interest in remaining awake; slowed down 

6 - Sleepiness; prefer to be lying down; fighting sleep; woozy 

7 - Almost in reverie; sleep onset soon; lost struggle to remain awake 

WORKLOAD Ratings 

Rate your workload during your immediately prior shift segment before 2nd & 3rd PVT each shift

 1-----2-----3-----4-----5-----6-----7 

Very Average Very

Low High 
Example of a Completed Sleep/Activity Log for One 24-Hour Period 

DAY 1 0000 0100 0200 0300 0400 0500 0600 0700 0800 0900 1000 1100

Activity S A O 

Fatigue 2 

Workload

DAY 1 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300

Activity T T T E O S 

Fatigue 2 4 3

Workload 4 3 

Psychomotor Vigilance Task (PVT) Alertness Measure

• Take on Palm Centro with your ID code on the back three times during each work shift

• Take first PVT at the beginning of each work shift

• Take second PVT as close to the middle of each shift as possible

• Take third PVT after your final break of each work shift

PVT Instructions:

1. Press the red power button (largest button on the right) to turn on the device. Then press the

Palm logo button (in the middle of the device) to unlock the keys. The top of the screen should 

read Psychomotor Vigilance Test.

2. Press the Down button (below the Palm logo) to scroll down and highlight your participant 

ID number. Do NOT press the Up button if you pass your ID number, as this will begin the test. 

Instead continue pressing the Down button until you come to your ID number again.

3. Press the Up button (above the palm logo) to begin the test. 

4. During the test you will watch the screen. As soon as a target appears, press the response 

button with the thumb of your dominant hand. (Hold the Palm in your non-dominant hand.)

If your dominant hand is your right hand, push the ‘HOME’ button (drawing of a house).

If your dominant hand is your left hand, push the ‘PHONE’ button (drawing of a phone). 

When you press the button your response time (in hundredths of a second) will be displayed. 

Do not press any other buttons. 

Try to be as fast as possible, but do NOT press the button before the target appears. The test 

will take 5 minutes. 

5. When the test is complete the data will automatically be saved and the device will turn off.

6. Plug the device back into the power cord to keep it fully charged.

If you accidentally begin a test with the incorrect ID number:

• Press the ‘V’ key to interrupt and cancel the test. 

• When the “Save this session?” dialog appears, select ‘NO’ to cancel the test without saving data.

• If for any reason you are unable to cancel a test taken with the wrong ID number, please call the 

NASA researchers at 650-793-5427 right away, 24/7, and leave a message if there is no 

answer.

If the system freezes and you need to interrupt a test (either the target does not appear for more 

than a minute, or it will not disappear), please press the ‘V’ key and start over. If the freeze was 

caused by a low battery (as indicated by a flashing red light), it may be necessary to complete your 

test while the device is attached to a charger.

Fatigue Survey
Please take the C-FAM survey at http://nasasurvey.us during duty hours during the 14-day data 

collection period. Please enter your ID code, e.g. ZNY12, in the box at the end of the survey.

If the above site is blocked by your firewall, please 

try https://secure.inquisiteasp.com/surveys/XWHZ58/
Appendix D. Actiwatch Sleep Parameters

Sleep Parameter Detailed Explanation

Bed Time Time when lights are switched off. This is set by the operator or set 

automatically by the analysis software reading an event marker.

Get Up Time Time when lights are switched on. This is set by the operator or set 

automatically by the analysis software by reading an event marker.

Time in Bed The difference between the Get Up and Bed times.

Sleep Start / Sleep 

Onset

The start of sleep as set by the operator or determined automatically 

by the sleep algorithm. The first minute that the analysis software 

scores “asleep”.

Sleep End The end of sleep as set by the operator or determined automatically by 

the sleep algorithm. 

Assumed Sleep The difference between sleep end and sleep start. 

Actual Sleep Time/

Total Sleep Time 

(TST)

The amount of sleep as determined by the algorithm and is equivalent 

to assumed sleep minus wake time. The total number of minutes 

scored as “asleep”.

Actual Awake Time /

Wake after Sleep 

Onset (WASO)

The amount of time spent awake as determined by the algorithm. The 

total number of minutes the subject was awake after sleep onset 

occurred.

Actual Sleep and 

Wake Time %

These are displayed to the right of the Actual Sleep and Actual Wake 

boxes.

Sleep Efficiency 

The Actual Sleep Time divided by Time in Bed. Defined as the 

proportion of sleep in the episode potentially filled by sleep (i.e., the 

ratio of total sleep time to time in bed). Normal sleep efficiency is at 

least 85%. It is reduced in a number of situations, such as insomnia or 

lab effect.

Sleep Latency The latency before sleep onset following bed time.

Number of Sleep 

Bouts The actual number of episodes of sleep.

Number of Wake 

Bouts/ Awakenings

The actual number of episodes of wakefulness. The number of 

different awakening episodes as scored by the algorithm. This is 

sometimes referred to as Frequency of Awakenings (shown as the 

number of awakenings per night).

Mean Length of 

Sleep and Wake 

Bouts 

These figures are determined by dividing the total duration of sleep 

and wake by the corresponding number of sleep and wake bouts.

Number of Minutes 

Immobile 

The total number of minutes during the assumed sleep period where 

the counts per minute are below a predetermined “immobility”

threshold. 

Number of Minutes 

Moving 

The converse of the above being the total number of minutes where 

scores greater than the “immobility” threshold were recorded during 

the assumed sleep period.

Percentage of 

Minutes Immobile 

The Number of Minutes Immobile divided by the Assumed Sleep 

period.

Percentage Minutes 

Moving The Number of Minutes Moving divided by the Assumed Sleep period.

The Number of 

Immobile Phases 

The number of periods of continuous periods made up of consecutive 

epochs where the counts are less than the “immobility” threshold.

The Number of 

Immobile Phases of 

1 Minute 

The number of immobile phases where the duration is no more than 1 

minute. 
Sleep Parameter Detailed Explanation

Percentage 

Immobility 

The Number of Immobile Phases of 1 Minute as a proportion of the 

Number of Immobile Phases.

Fragmentation Index The addition of Percentage Minutes Moving and Percentage 

Immobility. This is used as an indicator of restlessness.

Average Awakening The average length in minutes of all awakening episodes.

Total Counts The total actigraphy counts summed together for the entire sleep 

period.
Appendix E. C-FAM Field Study Web Signup Page