Peter G. Roma1

Melissa M. Mallis1

Steven R. Hursh1

Andrew M. Mead2

Thomas E. Nesthus2

1

Institutes for Behavior Resources

Baltimore, MD 21218

2

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

December 2010

Final Report

Flight Attendant Fatigue 

Recommendation II: Flight Attendant 

Work/Rest Patterns, Alertness, and 

Performance Assessment

DOT/FAA/AM-10/22

Office of Aerospace Medicine

Washington, DC 20591

OK-11-0024-JAH

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 thereof.

___________

This publication and all Office of Aerospace Medicine 

technical reports are available in full-text from the Civil 

Aerospace Medical Institute’s publications Web site: 

www.faa.gov/library/reports/medical/oamtechreports
i

Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-10/22 

4. Title and Subtitle 5. Report Date 

December 2010 

6. Performing Organization Code 

Flight Attendant Fatigue Recommendation II: Flight Attendant Work/Rest 

Patterns, Alertness, and Performance Assessment 

 7. Author(s) 8. Performing Organization Report No. 

Roma PG,1 Mallis MM,1 Hursh SR,1 Mead AM,2 Nesthus TE2 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

 

11. Contract or Grant No. 

1

Institutes for Behavior Resources, Baltimore, MD 21287 

2

FAA Civil Aerospace Medical Institute, Oklahoma City, OK 73125 12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

 

 

 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

Work was accomplished under approved task AM-AHR521 

16. Abstract 

Impaired performance induced by fatigue may compromise safety in commercial aviation. Given the direct role 

flight attendants play in passenger safety, the U.S. Congress ordered a comprehensive examination of fatigue in 

cabin crew, including a field study of actual flight operations. This report provides an overview of the field study 

results, focusing on objective measures of sleep patterns and neurocognitive performance (Psychomotor Vigilance 

Test, PVT) over a 3-4 week period in 202 U.S.-based flight attendants of all seniority levels working for network, 

low-cost, and regional carriers embarking on domestic and international flight operations. On average, flight 

attendants slept 6.3 hr on days off and 5.7 hr on work days, fell asleep 29 min after going to bed, awoke four 

times per sleep episode, and spent 77% of each episode actually sleeping. After controlling for reserve status, 

gender, and age, junior-level flight attendants had the shortest sleep latencies on their days off. Those working 

international operations slept significantly less (4.9 hr vs. 5.9 hr) and less efficiently (75% vs. 79%), compared 

with their colleagues in domestic operations. All flight attendants exhibited significant impairments during prework PVT tests when compared to their own optimum baseline performance. Across the workday, regional flight 

attendants committed fewer premature PVT responses, junior-level participants produced significantly higher 

post-work reaction times, and those working international flights produced better pre-work reaction times but 

had a greater increase in lapses. These objective data are consistent with other shift work research and echo 

subjective survey findings across the U.S. flight attendant community. Additional planned analyses of this dataset 

may identify the precise operational variables that contribute to fatigue in cabin crew. 

 

17. Key Words 18. Distribution Statement 

 

 

Fatigue, Flight Attendant, Field Study, Actigraphy, PVT, 

Sleep, Performance, Reaction Time, Airlines, Flight 

Operations 

Document is available to the public through the 

Defense Technical Information Center, Ft. 

Belvoir, VA 22060; and the National Technical 

Information Service, Springfield, VA 22161 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 17 

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

iii

Acknowledgments

We thank all of our flight attendant participants for their time, contributions to the project, and dedication to their 

profession. We also thank Dr. John Caldwell for his input during study design, Wendy Krikorian and Dr. Francine 

James for conducting informed consent interviews, and the Archinoetics/Fatigue Science team for their technical 

support. We are especially grateful to Lena Dobbins and Kali Holcomb at CAMI and Carrie Roberts and Suzanne 

Thomas at Xyant Technology for their invaluable support in participant training, and to Marta Genovez for her truly 

heroic manual data processing efforts.

v

Contents

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Participants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Materials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Data Collection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Data Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Sleep/Wake Patterns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

 Neurocognitive Performance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1

Flight Attendant Fatigue Recommendation II: 

Flight Attendant Work/Rest Patterns, 

Alertness, and Performance Assessment

Introduction

Numerous factors can affect safety, performance, and 

quality of life in individuals working in 24-hr operational 

environments, and one issue receiving increased attention 

in commercial aviation is fatigue. Fatigue is generally defined as a state of tiredness due to prolonged wakefulness, 

extended work periods, and/or circadian misalignment, 

and is characterized by decreased alertness, diminished 

cognitive performance, and impaired decision-making 

(Åkerstedt, 1995; Dinges, 1995). Although a considerable amount of scheduling and fatigue research has 

been conducted with pilots in recent years, the issue of 

fatigue in cabin crew has received little systematic attention from the scientific community (Mallis, Banks, & 

Dinges, 2010). Flight attendants are popularly thought 

of as in-flight comfort service providers only; however, 

the fact remains that cabin crew serve on the front lines 

of passenger safety and crisis management. In addition 

to routine safety procedures and negotiating passenger 

welfare during acute emergencies due to weather, mechanical problems, or human error, the heightened threat of 

organized terrorist events and other disruptive passenger 

activities coupled with a generally increasing workload 

requires today’s cabin crew to possess an unprecedented 

level of perceptiveness, interpersonal skill, and sustained 

vigilance.

Recognizing the prominent role played by flight attendants in protecting the traveling public and the increasing demands on this unique segment of the civilian 

workforce, in 2005 and 2008, the U.S. Congress directed 

the FAA’s Civil Aerospace Medical Institute (CAMI) to 

conduct a multi-leveled examination of flight attendant 

fatigue. The comprehensive project included directives 

to review current policies and practices, conduct a 

large-scale survey of active flight attendants, and collect 

objective data in a field study of “real world” flight attendant operations. As expected, the regulatory reviews 

confirmed the potential for fatigue-promoting practices 

(Nesthus, Schroeder, Conners, Rentmeister-Bryant, & 

DeRoshia, 2007), and the survey provided detailed data 

on the ubiquitous perception of fatigue within the flight 

attendant community and the various operational factors thought to produce and exacerbate it (Avers, King, 

Nesthus, Thomas, & Banks, 2009). 

The present report offers an overview of results from 

the flight attendant field study conducted from May 

2009 through June 2010 by the Institutes for Behavior 

Resources (IBR), an independent non-profit research, 

services, and educational organization headquartered in 

Baltimore, MD, USA, in collaboration with researchers at 

CAMI in Oklahoma City, OK, USA. This initial report 

focuses primarily on objective quantitative measures of 

sleep and neurocognitive performance patterns in over 

200 U.S.-based flight attendants of all seniority levels 

working for network, low-cost, and regional carriers 

embarking on domestic and international flight operations. The detailed assessment of sleep and performance 

effectiveness patterns across a broad sample of the flight 

attendant population complements CAMI’s prior work 

by objectively quantifying the presence and extent of 

fatigue and may critically inform the development of 

comprehensive fatigue risk management systems or other 

science-based policy refinements designed to enhance 

cabin safety.

Method

All procedures described in this report were independently reviewed and approved by both the FAA CAMI 

and IBR Institutional Review Boards. The formal letters 

of approval are available upon request from the authors. 

All personal data have been de-identified.

Participants

Recruitment. Study requirements and recruitment 

materials were prepared in cooperation with CAMI officials, several airlines, and several flight attendant labor 

organizations. Once approved, each organization issued 

a customized announcement via Web site, E-mail, and/

or newsletter in February 2009 and February 2010 

encouraging its constituents to participate in the field 

study. The involvement of industry and labor representatives did not extend beyond this phase of the project. 

Individuals initially volunteered through an online 

application form documenting basic eligibility data. A 

total of 6,454 applications were submitted by interested 

volunteers. To capture the diversity inherent to the flight 

attendant population, only a few key exclusion criteria 

were applied. Specifically, applicants were excluded from 
2 

further consideration if they were not actively working as 

a flight attendant (including on furlough), if their base 

of operations was outside the U.S., if a majority of their 

flight legs were greater than or equal to 8 hr (to avoid 

redundancy with ongoing long- and ultra-long-range 

research; this criterion was increased to 10 hr during 

the February 2010 recruitment campaign), if they were 

diagnosed with a sleep disorder, or if they did not provide 

adequate information in the online application form. 

These exclusion criteria reduced the applicant pool to 

approximately 5,000 individuals. These individuals were 

then contacted via E-mail and directed to an online survey to provide more detailed demographic and contact 

information, health information, and answers to several 

work-related questions. The same exclusion criteria were 

then re-applied to these data, which reduced the final 

applicant pool to approximately 3,000 individuals.

Selection. All eligible applicants were categorized according to three broad factors that served as the organizing 

framework for the study’s design and the results presented 

herein. These factors are Carrier Type (Network, LowCost, or Regional), Seniority (self-identified Senior 1/3, 

Mid 1/3, or Junior 1/3), and majority Flight Operations 

(Domestic or International). This framework yielded 12 

distinct flight attendant groups based on the applicable 

combinations of the factors: NSD (Network + Senior 

+ Domestic), NSI (Network + Senior + International), 

NMD (Network + Mid + Domestic), NMI (Network + 

Mid + International), NJD (Network + Junior + Domestic), NJI (Network + Junior + International), LS (LowCost + Senior), LM (Low-Cost + Mid), LJ (Low-Cost + 

Junior), RS (Regional + Senior), RM (Regional + Mid), 

and RJ (Regional + Junior). The study was designed for a 

total of 210 flight attendants according to the schematic 

presented in Figure 1.

Within each of the 12 group types, the total target 

sample size plus 10 individuals was randomly selected. 

This sample was then statistically compared to the larger 

group from which it was drawn to confirm that the two 

did not differ significantly from each other in mean age 

or gender ratio. Once a representative sample was derived 

for each of the 12 groups, 10 individuals within each 

group were randomly assigned to their group’s Backup 

Participant pool, with the remaining assigned as Primary 

Participants, with whom we would conduct the field 

study. All individuals were informed of their assignments via E-mail, which included the Informed Consent 

document and supplementary study reference material. 

The IBR project staff worked with each participant to 

schedule individual 3-4 week study periods and arrange 

for informed consent reviews, equipment delivery, and 

protocol training prior to study launch. 

After accounting for losses due to participant withdrawal, non-compliance, removal by the research team, 

and equipment malfunctions, the field study ultimately 

yielded usable data from a total of 202 flight attendants 

working for 28 different airlines. Participants from Network carriers came from (in alphabetical order) Alaska, 

American, Continental, Delta, Northwest, United, and 

U.S. Airways. Participants from Low-Cost carriers came 

from AirTran, America West, Frontier, JetBlue, Southwest, and Spirit. Participants from Regional carriers came 

from Air Wisconsin, American Eagle, Atlantic Southeast, 

Chautauqua, Colgan, Comair, ExpressJet, Horizon, Mesa, 

Mesaba, Piedmont, PSA, Republic, Shuttle America, 

and SkyWest. All flight attendants were compensated 

for their participation. The final sample sizes and basic 

demographic data for each of the 12 flight attendants 

groups are presented in Table 1.

Figure 1. Stratified field study design and target sample sizes.
3

Materials

As depicted in Figure 2, each participant was issued 

a wristwatch-shaped, waterproof actigraphy device for 

continuous objective recording of sleep/wake patterns 

(ReadiBandTM, Fatigue Science, Honolulu, HI, USA), a 

custom-programmed touchscreen-based personal digital 

assistant (PDA) device for maintaining a daily activity 

log and collecting objective and subjective data (AT&T 

TiltTM), and a consumer-grade pedometer for recording 

steps taken while on duty as a gross objective measure of 

physical workload (Digi-Walker SW-200, Yamax USA, 

Inc., San Antonio, TX, USA). The equipment packages 

also contained peripheral accessories, a detailed equipment user’s guide, and supplementary reference materials 

for use in the field.

Data Collection

Training and Scheduling. Once they received their 

equipment, all participants were trained on equipment 

use and formal data collection procedures via toll-free teleconference according to a standardized script developed 

by IBR scientists. All training sessions were conducted 

in real-time by members of a dedicated training team 

housed at CAMI or by IBR staff when necessary. Most 

training sessions were one-on-one, but no sessions were 

conducted with more than three flight attendants at a 

time. In addition to the formal training sessions, IBR 

scientists were available to assist by phone and E-mail 

24 hr/day throughout data collection. Approximately 20 

participants per month were trained and completed the 

study during two data collection periods: May through 

November 2009 and February through June 2010. Insofar 

as was possible, participants from all 12 flight attendant 

groups were included each month to evenly distribute 

potential seasonal and time-of-year effects.

Actigraphy. Data collection was required every day 

(work days and off days) throughout the duration of 

each participant’s 3-4 week study period. The actigraphy watch was placed on the non-dominant wrist by 

2000 hr the night before the first duty day of the study, 

where it remained for 24 hr/day and was never removed 

except during airport security checks, noted risky events 

such as during contact sports, scuba diving, or special 

occasions with permission from an IBR investigator 

(e.g., weddings). 

Table 1. Final demographic information for each flight attendant group. 

Carrier Age (yr) 

Type Seniority Flight Ops Group ID n %Reserve 

%

Female

Mean Range 

Domestic NSD 14 28.57 64.29 49.81 44.48 – 61.62 Senior International NSI 15 20.00 73.33 55.43 43.56 – 66.67 

Domestic NMD 15 0.00 86.67 46.81 40.48 – 53.21 Mid International NMI 15 6.67 60.00 44.63 39.60 – 61.13 

Domestic NJD 15 46.67 80.00 40.37 30.53 – 62.47 

Network 

Junior International NJI 14 28.57 71.43 39.32 24.48 – 59.30 

Senior LS 20 10.00 70.00 46.37 30.93 – 60.90 

Low-Cost Mid LM 20 0.00 55.00 35.24 24.18 – 61.39 

Junior LJ 16 37.50 50.00 35.60 22.13 – 63.67 

Senior RS 20 0.00 70.00 40.34 28.02 – 58.98 

Regional Mid RM 20 5.00 65.00 42.85 23.51 – 61.93 

Junior

Domestic

RJ 18 55.56 83.33 41.82 25.50 – 57.78 

All 202 18.81 68.81 42.94 22.13 – 66.67 

Figure 2. Actigraphy device (left), PDA device (middle), and pedometer (right) issued to each 

participant.
4 

Pedometer. All participants were told to reset the 

pedometer and place it on the waist or ankle before the 

start of each work day. Once participants determined 

their preference for ankle or waist placement, they were 

requested to keep this position consistent throughout the 

entire data collection period. They manually entered the 

step count into the PDA device when prompted during 

the Post-Work test session (see below).

PDA. The majority of data was collected via the 

PDA device, which thus required the most participant 

interaction. Using a graphical interface, all participants 

had to maintain a daily activity log (including sleep/

wake cycles), noting the location (airport code) and 

start time (local) of the activities described in Table 2. 

A pocket-sized, color-coded copy of columns 1 and 2 

from the table below was provided to all participants as a 

quick-reference guide to assist with event entry whenever 

they were unsure. The logs also included a blank field for 

manually entering notes to provide the researchers with 

additional relevant information (e.g., the real location of 

an event if the airport code was not listed as an option 

on the menu), which was often useful for the research 

team when processing the raw data.

In addition to maintaining the activity log, participants 

were required to complete up to four test sessions per 

day: Pre-Sleep, Post-Sleep, Pre-Work, and Post-Work. 

Participants were instructed to complete the Pre- and 

Post-Sleep sessions within ~15 min of going to bed and 

waking up, respectively, every day throughout the study. 

On work days, participants were instructed to complete 

the Pre- and Post-Work sessions within ~1 hr of check-in 

and check-out (the beginning and end of the entire work 

day), respectively. Each session consisted of several core 

components, starting with a 5-min touchscreen-based 

Psychomotor Vigilance Test (PVT) programmed under 

the same parameters as the Palm-based PDA PVT previously developed at the Walter Reed Army Institute for 

Research (Thorne, Johnson, Redmond, Sing, Belenky, 

& Shapiro, 2005; Lamond, Dawson, & Roach, 2005) 

and effectively utilized for various field studies in 24-hr 

Table 2. Operational definitions of events for the daily activity log. 

Event Type Formal Definition/Instructions Common Practical 

Translations 

Sleep

Log “Sleep” before going to bed for the night (intending to sleep), 

or any time you need to represent a sleep period of 2 or more 

hours.

Unnecessary 

Nap A sleep period of less than 2 hours. Unnecessary 

At Work 

The period beginning at "show time" when you check-in at the 

airport and ending when you “check-out” for the day just before 

leaving the airport to return home, to a crash-pad, or to your hotel. 

“At Work” encompasses all work-required activities except flight 

time and breaks (for example, cabin prep, paperwork, cleaning, 

passenger assistance). Basically, if you checked in at show time 

and have not left the airport to go home/crash-pad/hotel, then 

you’re At Work. 

Actively working but not 

getting paid primary wage; 

most commonly boarding 

and de-planing. 

In Flight Your paid work period when the aircraft you’re attending to is in the 

air.

Primary pay period when 

the doors of the plane are 

closed.

Work Break 

Represents designated in-flight break periods, deadhead flights, 

flight delays, and non-overnight layovers in between legs. 

Basically, times when you don’t have any work-required activities 

to perform but have not left the airport to go home/crash-pad/hotel. 

Authorized breaks during 

long flight legs, but most 

commonly sit time between 

flight legs. 

Commute

We use this term more like a 9-to-5 office worker, so log 

“Commute” to represent any personal transit time from 

home/crash-pad/hotel to show time when you check-in at the 

airport for work. Commuting is also any personal transit time from 

the airport back to home/crash-pad/hotel after your work day has 

ended.

All transit time to and from 

the airport for work. 

No Work: Home 

Represents times when you do not have to work, such as the 

period between waking up and going to work or the period between 

checking out for the day and sleep. “Home” refers to your 

geographical location, as in your home town or home base (not 

necessarily the actual building you call home), "Home" also refers 

to the fact that you're on your own time, including trips or vacations 

outside your actual home town. 

Most commonly time off not 

during a trip or reserve 

period.

No Work: Away 

Represents the same "No Work" periods as above, but your 

geographical location is outside your home town because of work. 

Examples include extended (hotel-worthy) layovers in between 

legs and multi-day layovers prior to a return trip home. "Away" 

refers to the fact that even though you’re not working, your current 

geographical location away from home was determined by your 

work schedule. 

Most commonly time off 

while away on a trip or at 

crash pad on reserve. 
5

operational environments (Lamond, Petrilli, Dawson, 

& Roach, 2006; Ferguson, Lamond, Kandelaars, Jay, 

& Dawson, 2008). Each session also included a simple 

9-item visual analogue scale (VAS) subjective mood assessment and a speech analysis test requiring recitation of 

five randomly ordered fatigue-sensitive phrases designed 

for the study by Dr. Harold P. Greeley (Response Applications, LLC, Hanover, NH, USA). In addition to these 

core components, the Pre-Sleep session included a brief 

daily legal drug/pharmaceutical use questionnaire; the 

Post-Sleep session included a 5-item VAS-based subjective 

sleep quality questionnaire; and the Post-Work session 

included an 11-item Post-Duty Service Questionnaire 

and a VAS-adapted version of the NASA Task Load Index 

(TLX; Hart & Staveland, 1988). Test sessions generally 

took 8-12 min to complete. To maintain consistency 

across days, locations, and conditions, all participants 

were instructed to take their test sessions in a comfortable, 

normally-lit environment with as few sensory distractions as possible. All participants were informed that 

safety and fulfilling their professional duties supersede 

all research requirements and were explicitly instructed 

to never engage in study-related activities (data entry, 

testing, etc.) while actively engaged in or responsible for 

any work-related activities.

Data Analysis

Actigraphy. Although a variety of data were collected 

in the field study, the focus of this report is on the primary 

objective measures of sleep/wake patterns (actigraphy) and 

neurocognitive performance (PVT) during work days. For 

the actigraphy data analysis, the variables calculated for 

each individual were mean total sleep (min), mean sleep 

latency (min to fall asleep), mean number of awakenings, 

and mean sleep efficiency (% time spent sleeping) per 

sleep episode during work days or during a trip vs. offdays at home in between trips. Sleep episodes with 30 

min or less of total sleep time were omitted from these 

calculations but will be accounted for in forthcoming 

fatigue modeling analyses of the field study data.

PVT. Each 5-min PVT session yields a number of output variables, including mean reaction time per trial (RT, 

msec), mean speed per trial (1/RT), total lapses (RTs > 500 

msec), and total false starts (FS, responses prior to stimulus 

onset). For each individual, the top 10% mean RTs of 

all PVT sessions throughout the study were identified, 

then the means of the PVT variables from those sessions 

were used to define that individual’s optimum baseline 

neurocognitive performance. PVT variables were then 

calculated from each Pre-Work and Post-Work session 

as a percent shift (RT and speed) or raw shift (lapses and 

FS) relative to those individualized optimal parameters, 

the means of which were incorporated into the project 

database for final analysis. Preliminary one-way Analyses 

of Variance (ANOVA) revealed no significant effects of 

Carrier Type, Seniority, or Flight Ops on all optimum 

baseline PVT performance measures (Fs < 2.2, ps > 

.10; see Table 3), which were generally consistent with 

PVT performances observed in laboratory settings with 

well-rested healthy volunteers sampled from the general 

population (Lim & Dinges, 2008; Loh, Lamond, Dorrian, Roach, & Dawson, 2004).

Statistics. Actigraphy data were available for 172 participants; PVT data were available for 201 participants. 

All actigraphy measures were analyzed by separate mixed 

Analyses of Covariance (ANCOVA) with respective 

between-groups factors of Carrier Type (Network, LowCost, or Regional), Seniority (Senior, Mid, Junior), and 

Table 3. Individualized optimum baseline PVT performances organized by Carrier Type, Seniority, and Flight Operations. 

Reaction Time 

(msec) 

Speed

(1/RT) 

Lapses

(RTs > 500 msec) False Starts 

Factor Group n

Mean Range Mean Range Mean Range Mean Range 

Network 88 243 191 – 322 5.94 3.38 – 

18.34 0.53 0.00 – 4.67 2.92 0.00 – 17.40

Low-Cost 56 249 193 – 362 6.33 2.82 – 

25.31 0.58 0.00 – 4.33 3.56 0.00 – 22.13

Carrier 

Type 

Regional 57 242 204 – 334 6.01 3.09 – 

20.50 0.44 0.00 – 1.88 3.38 0.00 – 28.40

Senior 68 239 196 – 301 5.98 3.38 – 

25.31 0.40 0.00 – 2.00 3.22 0.00 – 22.13

Mid 70 248 191 – 362 6.22 2.82 – 

20.50 0.60 0.00 – 4.67 3.52 0.00 – 28.40 Seniority 

Junior 63 246 200 – 336 5.99 3.19 – 

19.93 0.55 0.00 – 4.33 2.90 0.00 – 17.43

Domestic 157 244 191 – 362 6.11 2.82 – 

25.31 0.50 0.00 – 4.33 3.31 0.00 – 

Flight 28.40

Ops International 44 244 202 – 322 5.92 3.38 – 

18.34 0.58 0.00 – 4.67 2.94 0.00 – 10.80

All 201 244 191 – 362 6.07 2.82 – 

25.31 0.52 0.00 – 4.67 3.23 0.00 – 28.40
6 

majority Flight Ops (Domestic or International) and a 

repeated-measures factor of Condition (Work and Off). 

All models included covariates of reserve status, gender, 

and age. All PVT measures were evaluated by separate 

mixed ANCOVAs with respective between groups factors 

of Carrier Type, Seniority, and Flight Ops and a repeatedmeasures factor of Time (Pre-Work and Post-Work); as 

with the actigraphy analyses, all models included covariates 

of reserve status, gender, and age. Statistical significance 

was set at α = .05 for all analyses.

Results

Sleep/Wake Patterns

Effects of Carrier Type. After accounting for reserve 

status, gender, and age, the 3 x 2 ANCOVAs revealed no 

significant main or interaction effects involving Carrier 

Type or Condition from off days to work days on sleep 

latency (29.1 to 29.2 min), awakenings (4.3 to 3.6), or 

sleep efficiency (76% to 77%; Fs < 2.3, ps > .13), although 

a Carrier Type x Condition trend was observed in the 

sleep amount variable (F(1,166) = 2.5, p = .086). This 

was presumably due to the Network group losing more 

sleep from off days to work days (from 6.4 to 5.3 hr), 

compared to their Low-Cost (6.0 to 5.8 hr) and Regional 

colleagues (6.4 to 5.9 hr; see Figure 3).

Effects of Seniority. After accounting for reserve 

status, gender, and age, the 3 x 2 ANCOVAs revealed no 

significant main or interaction effects involving Seniority or Condition from off days to work days on sleep 

amount (6.3 to 5.7 hr), awakenings (3.6 to 4.3), or sleep 

efficiency (77% to 76%; Fs < 2.1, ps > .14); however, 

a significant Seniority x Condition interaction emerged 

from the analysis of sleep latency (F(2,166) = 4.6, p < 

.05). Sleep latencies increased from off days to work days 

in the Senior (29 to 31 min) and Junior groups (25 to 

30 min) but decreased in the Mid-level flight attendants 

(32 to 27 min), whose latencies were significantly longer 

than their Junior-level colleagues on off days (Fischer’s 

LSD p < .05; see Figure 4). 

Effects of Flight Operations. After accounting for reserve 

status, gender, and age, the 3 x 2 ANCOVAs revealed no 

significant main or interaction effects involving Flight Ops 

or Condition from off days to work days on sleep latency 

(29.1 to 29.2 min) or awakenings (3.6 to 4.3; Fs < 2.7, ps 

> .10); however, significant Ops x Condition interactions 

emerged from the analyses of sleep amount and sleep efficiency (F(1,167)s > 9.8, ps < .001). Although both groups 

slept less during work trips than on off days at home, the 

International flight attendants slept significantly less than 

their Domestic counterparts while away on work trips (4.9 

vs. 5.9 hr; independent samples t(170) = 3.4, p < .001). 

Figure 3. Actigraphy-derived sleep/wake patterns during off days at home and while away on work trips in 

flight attendants from Network, Low-Cost, and Regional carriers.
7

Interestingly, sleep efficiency shifted significantly in both 

groups from off days to work days (paired-samples ts > 2.4, 

ps < .01) but increased for the Domestic group (76% to 

79%), while decreasing for the International group (78% 

to 75%) such that sleep efficiency during work trips was 

significantly lower for the International flight attendants 

compared to their Domestic colleagues (independent 

samples t(170) = 2.4, p < .05; see Figure 5). 

Neurocognitive Performance

Preliminary paired-samples t-tests revealed that mean 

PVT reaction times were significantly higher (+21.3%), response speeds were significantly slower (-14.1%), and lapses 

were significantly more frequent (+2.8) during Pre-Work 

sessions, compared to individualized optimum baseline 

performances (t(200)s > 7.3, ps < .001; false starts +0.1, 

t(200) = 0.9, p = .38). These data suggest that regardless of 

variations in on-duty activities, all flight attendants manifest 

some degree of fatigue-relevant performance impairment 

even before the start of the workday.

Effects of Carrier Type. After accounting for reserve 

status, gender, and age, the 3 x 2 ANCOVAs revealed no 

significant main or interaction effects involving Carrier 

Type or Time across the workday on PVT reaction times 

(+21% to +28%), speed (-14% to -17%), or lapses (+2.8 to 

+3.7; Fs < 2.2, ps > .14); however, a significant main effect 

of Carrier Type emerged from the analysis of false starts 

(FS) (F(2,195) = 4.4, p < .05). Whereas flight attendants 

from Network and Low-Cost carriers were more likely 

to increase mean FS on work days relative to optimum 

baseline (+0.33 and +0.40, respectively), simple contrasts 

revealed that Regional flight attendants, who committed 

fewer FS on work days relative to baseline (-0.53), did so 

significantly less than their colleagues from Network and 

Low-Cost carriers (ps < .05; see Figure 6).

Effects of Seniority. After accounting for reserve status, 

gender, and age, the 3 x 2 ANCOVAs revealed no significant 

main or interaction effects involving Seniority or Time across 

the workday on PVT speed (-14% to -17%), lapses (+2.8 

to +3.7), or false starts (+0.14 to +0.07; Fs < 2.1, ps > .12); 

however, a significant Seniority x Time interaction emerged 

from the analysis of reaction times (F(2,195) = 3.0, p = .05). 

Paired-samples t tests revealed that mean RTs significantly 

increased from Pre-Work to Post-Work in flight attendants 

of Mid (from +19.5% to +28.3% of optimum baseline) and 

Junior seniority (from +22.6% to +33.1% of baseline; ts 

> 2.9, ps < .01), whereas their Senior colleagues were not 

affected (from +22.0% to +23.5% of baseline; t(67) = 0.96, 

p > .30). Although the groups did not differ from each other 

at Pre-Work (Fischer LSD, ps > .35), Post-Work RTs were 

significantly higher in the Junior group compared to their 

Senior counterparts (p < .05; see Figure 7).

Figure 4. Actigraphy-derived sleep/wake patterns during off days at home and while away on work trips in 

Senior, Mid, and Junior seniority flight attendants (~ indicates Junior significantly different from Mid, p < .05).
8 

Figure 5. Actigraphy-derived sleep/wake patterns during off days at home and while away on work trips in 

flight attendants that work Domestic or International flight operations (significant difference between groups 

indicated by *p < .05 and ***p < .01).

Figure 6. Psychomotor Vigilance Test (PVT) performance during Pre-Work and Post-Work test sessions 

expressed as a shift from individualized optimum baseline (indicated by dashed lines at 0) in flight attendants 

from Network, Low-Cost, and Regional carriers (main effect: Regional vs. Low-Cost ~p < .05, Regional vs. 

Network **p < .01).
9

Effects of Flight Operations. After accounting for 

reserve status, gender, and age, the 3 x 2 ANCOVAs 

revealed no significant main or interaction effects 

involving Flight Ops or Time across the workday on 

PVT speed (-14% to -17%) or false starts (+0.14 to 

+0.07; Fs < 2.9, ps > .09); but significant Flight Ops x 

Time interactions emerged from the analyses of reaction 

times and lapses (Fs(1,196) > 4.1, ps < .05). Mean RTs 

increased from Pre-Work to Post-Work in both the Domestic (from +23% to +28% of optimum baseline) and 

International groups (from +16% to +28% of baseline; 

paired-samples ts > 2.9, ps < .01). However, Pre-Work 

RTs were significantly higher in flight attendants working domestic operations compared to their international 

counterparts (independent samples t(98.6) = 2.5, p < 

.05, adjusted for unequal variance). Similarly, mean 

lapses increased from Pre-Work to Post-Work in both 

the Domestic (from +3.0 to +3.6 relative to baseline) 

and International groups (from +2.1 to +3.9 relative to 

baseline), but because the International group committed 

fewer lapses at Pre-Work, their increase to Post-Work 

was larger than their counterparts working domestic 

operations (independent samples t(199) = 2.2, p < .05; 

see Figure 8).

Discussion

In their recent survey of active flight attendants, Avers 

and colleagues (2009) documented typical nightly sleep 

amounts of 7.7 hr at home and 6.5 hr while away on a 

work trip, with 84% of respondents reportedly experiencing subjective fatigue while on duty during their previous 

bid period. Our data provide evidence supporting this 

perception of widespread fatigue among cabin crew, but 

when reduced to objectively measured sleep and precisely 

defined performance variables, paint an even more striking picture in terms of its prevalence. Specifically, while 

general estimates of sleep latency and awakenings were 

consistent with our actigraphy data, with only 6.3 hr of 

sleep at home and 5.7 hr away, flight attendants’ estimates 

of sleep amounts were approximately 1 hr (-15%) off, 

regardless of the setting, and more closely resemble the 

patterns of workers in industrial shift-work settings (e.g., 

Ohayon, Smolensky, & Roth, 2010; Paech, Jay, Lamond, 

Roach, & Feruson, 2010; Thorne, Hampton, Morgan, 

Skene, & Arendt, 2008). 

In terms of performance, when comparing individualized optimum baseline PVT values to average Pre-Work 

PVT performance, false starts increased in 52% of the field 

study participants, speed decreased in 85%, lapses increased 

in 96%, and reaction times increased in 100% of the 201 

Figure 7. Psychomotor Vigilance Test (PVT) performance during Pre-Work and Post-Work test sessions 

expressed as a shift from individualized optimum baseline (indicated by dashed lines at 0) in flight attendants 

of Senior, Mid, and Junior seniority (Junior vs. Senior *p < .05).
10 

flight attendants that provided PVT data. Similarly when 

compared to optimum baseline, average Post-Work false 

starts increased in 53%, speed decreased in 85%, lapses 

increased in 97%, and reaction times increased in 100%. 

It is worth noting that our data do not reflect performance 

at one specific, potentially anomalous work day or time 

of year, but rather each flight attendant’s average pre- and 

post-work neurocognitive function over 3-4 weeks of 

continuous data collection reflecting multiple workdays 

and multiple trips as they naturally occurred throughout 

a calendar year. As such, if CAMI’s survey respondents and 

our field study participants drawn from across the industry 

are representative of the flight attendant community as 

a whole, and our selection strategy gives us every reason 

to expect they were, then given the sleep/wake data and 

the fact that most individuals began their workday in an 

already compromised neurocognitive state, cabin crew 

may reasonably be considered chronically sleep restricted 

(Mollicone, Van Dongen, Rogers, Banks, & Dinges, 2010) 

while potentially underestimating their own fatigue. 

On average, seemingly few, if any, flight attendants 

begin their workday at their well-rested best. Despite 

the importance of this finding, the significant difference 

between optimum baseline and Pre-Work performance 

variables created the potential for “floor effects;” that is, if 

flight attendants appear fatigued at the start of the workday, 

then there may be little room for performance to decline as 

a function of on-duty activities. However, analysis of the 

sleep/wake and performance data still revealed effects of the 

broad factors of carrier type, seniority, and flight operations. 

Most notably, the actigraphy data revealed significantly less 

sleep and reduced sleep efficiency while away on trips in 

flight attendants working international operations versus 

their domestic colleagues, which is likely a function of 

circadian misalignment as international crew attempt to 

sleep under light/dark schedules that differ radically from 

their own endogenous circadian rhythms. 

In terms of performance, regional flight attendants 

committed fewer lapses than their network and low-cost 

colleagues, which initially suggests superior inhibitory 

control; however, this effect may be a consequence of the 

regional group having the slowest response speed, which 

itself could limit the likelihood of rapid premature responses. 

Among the more noteworthy performance effects were 

seniority-dependent changes in Post-Work reaction times, 

with significantly higher RTs in junior-level crew versus 

their senior-level colleagues (whose RTs did not increase 

significantly across the workday). Junior and senior participants did not differ from each other in total sleep amounts 

or Pre-Work RTs, suggesting some systematically fatiguing 

influence of seniority, specifically during work days. By 

contrast, flight attendants working international routes did 

Figure 8. Psychomotor Vigilance Test (PVT) performance during Pre-Work and Post-Work test sessions 

expressed as a shift from individualized optimum baseline (indicated by dashed lines at 0) in flight attendants 

who work Domestic or International flight operations (significant difference between groups indicated by *p < .05).
11

not differ from their domestic counterparts at the end of 

their respective workdays but began their workdays with 

better reaction times and fewer lapses than the domestic 

group. Since international flight attendants obtained less 

sleep than domestic crew while away on work trips, the 

performance results suggest a superior recovery process in 

between trips for the international group, yet the groups 

did not differ from each other in average sleep amounts 

during off days. Although ultimately an empirical question, informal observations of our field study logs suggest 

that international crew have more days off in between 

trips than domestic crews, which is an intriguing potential 

mechanism given that more recovery time is required to 

restore performance after chronic sleep restriction versus 

acute sleep deprivation (see Balkin, Rupp, Picchioni, & 

Wesensten, 2008 for a review) which may not be accommodated by current hours of service regulations.

The consequences of flight attendant fatigue in terms 

of standardized performance measures are clear based on 

the present study; however, additional data are required 

to elucidate the functional consequences of these effects. 

That is, what does a 20% increase in reaction time or 

doubling of lapse rate mean in terms of routine passenger 

safety, crisis prevention and management, and employee 

health? Fortunately, accidents and other emergencies are 

relatively rare in aviation, but this limits the data suitable 

for quantifying risk, at least within a reasonable timeframe. 

However, validated evidence-based fatigue modeling tools 

are available to predict operational safety risks associated 

with variations in sleep/wake patterns, work schedules, and 

circadian factors. A forthcoming full-scale analysis of all field 

study sleep/wake/work patterns will employ the SAFTE

(Sleep, Activity, Fatigue, and Task Effectiveness) model 

and related tools developed by Hursh (Hursh et al., 2004; 

Van Dongen, 2004) and utilized by the U.S. Department 

of Defense, NASA, and Department of Transportation. 

Such an approach yields quantitative risk metrics across 

individual work days and off days that are not apparent 

in conventional analyses of aggregate data.

In conclusion, the objective sleep/wake and PVT 

performance data echo and extend previous survey work 

suggesting that fatigue is a pervasive condition across the 

flight attendant community. In fact, with sleep/wake patterns similar to those of industrial shift-workers, U.S.-based 

flight attendants appear to share a state of chronic sleep 

restriction and fatigue that is considerably worse than their 

own perceptions. One particular strength of the present 

study’s design that facilitated this insight was comprehensive 

data collection throughout each flight attendant’s individual 

3-4 week study period, including work days and off days, 

thereby allowing standardized measurements across various conditions and states of neurocognitive functioning. 

This approach allowed perhaps the most striking finding 

that regardless of workday activities, virtually all flight 

attendants reported for duty in an already compromised 

state, compared with their own individualized optimal 

performances. Nonetheless, sleep/wake parameters and 

performances across the workday were still systematically 

affected to some extent by the broad factors of carrier type, 

seniority, and flight operations. We urge caution in overgeneralizing, but in terms of fatigue-induced risk, the data 

suggest that (1) regional flight attendants may be more 

vulnerable than network or low-cost, (2) junior-ranking 

flight attendants may be more vulnerable than senior or 

mid-level, and (3) flight attendants working domestic 

operations may be more vulnerable than those working 

international trips. 

Although the pattern of results within each factor suggests some intriguing possibilities, what is ultimately most 

important is identifying the operational variables mediating 

the on-duty and off-duty fatigue effects. That is, carrier type, 

seniority, and flight operations are by no means arbitrary 

categorizations, but what exactly is it about carrier type, 

seniority, and domestic vs. international flight operations 

that matters? Such categorizations may be informative 

for research, but when considering potential regulatory 

changes, the rules at the federal level cannot vary based 

on demographics. Informed by insights from the flight attendant survey results and the current field study findings, 

the stage is now set for in-depth analyses of the predictive 

relationships between specific operational variables and 

sleep/wake patterns and performance effectiveness across 

our entire sample of field study participants regardless of 

carrier type, seniority, or flight destinations. Key variables 

include total length of duty day, number of flight legs/

segments per day, recovery time in the hotel during a trip, 

consecutive duty days/trip length, and number of days off 

in between trips. However, the data also underscore the 

relevance of off-duty time when flight attendants are not 

under direct supervision, so a number of other issues beyond 

regulatory control and corporate management such as distance between home and work base (initial commute) and 

the responsible use of off-duty time for adequate recovery 

sleep, are also worthy of consideration. Ultimately, fatigue 

risk management for an industry as large and complex as 

aviation can only be sustained if a comprehensive approach 

is adopted. Indeed, effectively managing fatigue is a cooperative enterprise between government, management, and 

employees at all levels. The field study presented here joins 

the other CAMI projects by providing carefully collected 

objective data on real world flight attendant operations, 

which may inform discussion and policy development on 

the issue of fatigue in cabin crew. Combined with the use 

of other scientifically valid methods and tools, we support 

the goal of reducing flight attendant fatigue to enhance 

passenger safety and employee health.
12 

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