An Assessment of 

Commuting Risk Factors 

for Air Traffic Control 

Specialists

T. Nesthus

C. Cruz

C. Hackworth

A. Boquet

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

June 2006

Final Report

DOT/FAA/AM-06/13

Office of Aerospace Medicine

Washington, DC 20591
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assumes no liability for the contents thereof.

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

DOT/FAA/AM-06/13 

4. Title and Subtitle 5. Report Date 

An Assessment of Commuting Risk Factors for Air Traffic Control June 2006 

Specialists 6. Performing Organization Code 

 

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

Nesthus T, Cruz C, Hackworth C, Boquet A 

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

FAA Civil Aerospace Medical Institute 

P.O. Box 25082 11. Contract or Grant No. 

Oklahoma City, OK 73125 

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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-HRR-518 

16. Abstract 

Risk factors for sleepiness-related vehicle accidents have included, among others, time of accident, type of 

roadway, distance traveled, and reduced alertness. This study assessed risk factors for commuting incidents 

reported by air traffic controllers driving to and from work. Analyses were conducted on responses to a modified 

version of the Standard Shiftwork Index survey regarding alertness, commuting variables (i.e., number of miles 

and roadway types), and driving outcomes (i.e., lapses of attention, falling asleep, near misses, and accidents). 

Chi-square tests and odds ratio (OR) risk estimates were computed separately for air traffic controllers in 

Terminal/Enroute and Flight Service Station options. Reduced mental sharpness was associated with elevated 

ORs while driving to or from most shifts, though the greatest risk was found before early mornings and following 

midnight shifts. Elevated ORs for lapses of attention, falling asleep, and near misses were found for those with 

commutes greater than 20 miles and variably, with roadway type. In most cases, too few actual accidents were 

reported to compute chi-square statistics or odds ratios for this outcome.

 

17. Key Words 18. Distribution Statement 

 

 

ATCS, Shiftwork Schedules, Fatigue, Sleepiness, 

Commuting Risk Factors 

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvior, VA 

22060; and the National Technical Information 

Service, Springfield, VA 22161 

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Unclassified Unclassified 12 

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1

An Assessment of Commuting Risk Factors for

Air Traffic Control Specialists

INTRODUCTION

Costa (1999) suggested that critical attention should 

be given to individuals engaged in work requiring schedules that change normal social and biological rhythms. 

Of particular concern is the disruption that occurs in 

sleep-wake cycles as a consequence of the periodic rotations required by typical shiftworking schedules. These 

disruptions are evident in records of both the quantity and 

quality of sleep reported by shiftworkers in subsequent 

expressions of sleepiness and fatigue. 

U.S. Air Traffic Control Specialists (ATCSs) work a 

variety of rapidly rotating schedules. Many rotate in a 

counter-clockwise direction within the week, with, for 

example, afternoon shifts rotating to early morning or 

midday shifts then to an occasional midnight shift (Della 

Rocco, Dobbins, & Nguyen, 1999). Though the internal 

body clock makes little or no adjustment during these 

types of rapidly rotating schedules (Åkerstedt, 1985), 

alertness and sleep are affected by such factors as the 

time of day for the shift and the time of the sleep period 

(Åkerstedt, 1991). 

Folkard (1989) investigated shift start times and found 

that early-morning shifts (starting before 0600) resulted in 

reduced sleep and alertness. Working during the midnight 

shift affects sleep and alertness because it competes with 

the normal time for sleep (i.e., when the biological need 

for sleep is greatest) and reduces sleep that follows the 

shift when attempting to sleep during the day (Klein & 

Wegmann, 1979). Previous research conducted by the 

Civil Aerospace Medical Institute (CAMI) found similar 

results of reduced sleep quantity, quality, and reports of 

elevated sleepiness and fatigue associated with these types 

of schedules (Cruz, Detwiler, Nesthus, & Boquet, 2002; 

Cruz, Della Rocco, & Hackworth, 2000; Cruz & Della 

Rocco, 1995; Nesthus et al., 2001; 2002). 

The combined effects of fatigue and reduced alertness 

with commuting variables have been shown to promote 

conditions that could result in sleepiness-related vehicle 

incidents (Stutts, Wilkins, Osberg, & Vaughn, 2003). 

Conner, Whitlock, Norton, and Jackson (2001) identified 

19 studies concerning fatigue and driving accidents. The 

studies suggested that there are both internal and external 

factors contributing to reduced alertness and driving 

degradation. An expert panel convened by the National 

Center on Sleep Disorders Research and the National 

Highway Traffic Safety Administration on Driver Fatigue 

and Sleepiness (Washington, DC, 1997) identified three 

research needs concerning this topic: 1) the quantification of the prevalence of fatigue-related accidents, 2) 

identification of the risk factors associated with driving 

drowsy and having fatigue-related accidents, and 3) the 

development of effective countermeasures that increase 

the public awareness of fatigue-related accidents. 

Several evaluations of the prevalence of sleepinessrelated vehicle accidents (SRVA) identified a consistent 

temporal pattern, suggesting that higher frequencies of 

accidents/incidents were a function of time-of-day and 

may be influenced by the circadian rhythms of the driver 

(Conner, Whitlock, Norton, & Jackson, 2001; Folkard, 

1997; Horne & Reyner, 1995; Lennè, Triggs, & Redman, 

1997; McCartt, Ribner, Pack, & Hammer, 1996; Pack, 

Pack, Rodgman, Cucchiara, Dinges, & Schwab, 1995). 

Other evaluations have identified a number of common 

risk factors associated with SVRA worldwide. Mondini, 

Cavrini, Roli, Parazzini, Pizza, Contardi, and Cirignotta 

(2003) indicated that the prevalence of sleepiness-related 

driving events vary with factors related to 1) the quantity 

and the quality of sleep the night before driving, 2) the 

length of time awake prior to driving, 3) reported sleepiness and alertness during driving, and 4) general factors 

associated with being a shiftworker. They pointed out that 

other objective variables such as the time of the event (i.e., 

time of day), type of roadway, type of vehicle, location, 

gender, and day of the week were also associated with 

SRVA. Likewise, Horne and Reyner (2001) found that 

roadway-type, time of day, and the duration of the drive 

contributed significantly to SRVA in the UK. A relatively 

high incidence of SRVA occurred during early mornings, 

particularly in nightshift workers returning home. Another UK questionnaire assessed the levels of “tiredness” 

experienced by shiftworkers during their commutes to and 

from work (Rogers, Holmes, & Spencer, 2001), and found 

that both the type of shift and the traveling time (i.e., 

driving duration) contributed significantly to increased 

sleepiness and were associated with driving impairment 

reports, compared with non-shiftworkers. 

Stutts et al. (2003) also examined driver risk factors for 

sleep-related motor vehicle crashes. Their results indicated 

that work-related factors, including working multiple 

jobs, working a night shift or “other” work schedules 

(including rotating shifts), and working long hours 

contributed to negative driving outcomes. Furthermore, 
2

reduced sleep quantity and poor sleep quality were also 

associated with increased risk estimates for these events. 

Nighttime driving (particularly between midnight and 

0600), driving for longer periods of time, and driving 

after sleeping less than 5 hours the night before were also 

prominent predictors of increased risk. 

Background

A Congressional appropriation funded a series of studies conducted by the Federal Aviation Administration 

(FAA) Civil Aerospace Medical Institute (CAMI) on 

Air Traffic Control Specialist (ATCS) shiftwork-related 

issues. Research objectives were to investigate the effects of 

current shiftwork patterns and shift rotation practices on 

the ATCS workforce within the FAA. One study involved 

an agency-wide comprehensive survey of ATCS personnel to determine general health and well being, and the 

extent of reported fatigue among the workforce. 

In a preliminary evaluation of data from this ATCS 

Shiftwork and Fatigue Survey, Ramos, McCloy, and 

Burnfield (2001) found elevated mean scores on items 

pertaining to driving performance, sleep, and fatigue 

relative to scheduled shifts. Increased driving-related 

outcomes (i.e., lapses of attention, falling asleep, and near 

misses while driving) were reported by controllers across 

most of the various shifts, though most were associated 

with the midnight and early-morning shifts. In addition, 

respondents working rapidly rotating schedules with 

midnight shifts fell asleep more often while driving and 

had more near misses than respondents working schedules that rotated slowly or contained no midnight shifts. 

Mental sharpness levels were also reported to be lowest 

when beginning or ending a midnight shift compared 

with the other shifts. 

Because the survey database contained valuable information concerning factors such as sleepiness, alertness, 

and fatigue and their possible influence on driving outcomes, a closer examination of this issue was conducted. 

Also because of differences in the work performed by 

each ATCS option and their different facility locations, 

we categorized the ATCS workforce sample into a 

terminal/enroute (T/E) group that control aircraft traffic 

throughout the National Airspace System (NAS), and 

a flight service station (FSS) group that communicate 

primarily with general aviation pilots providing critical 

weather information and flight planning services. The 

purpose of the focused investigation, therefore, was to 

determine separately for each group if factors associated 

with alertness and commuting variables (i.e., shift, driving 

distance, and type of roadway) represented risk exposure 

for lapses of attention, falling asleep, near misses, and 

accidents while driving to and from work. 

METHOD

Survey Instrument

The survey was based on the Standard Shiftwork 

Index (SSI; Barton et al., 1995), an established, 

comprehensive survey that covered a variety of topics, including: shiftwork history, sleep and fatigue, 

health and well-being, social and domestic situation, 

coping strategies, circadian type, and background 

information. CAMI modified the survey to acquire 

additional information concerning ATC scheduling 

practices and to facilitate a large-scale distribution. 

It was administered to the entire ATC workforce. 

Employees were allowed administrative time to 

complete the survey. 

Procedure

Surveys were mailed to facilities for distribution to approximately 23,958 air traffic control specialists designated 

in the job series 2152 (trainees, certified professional 

controllers, air traffic control specialists, staff specialists, 

supervisors, and managers). Valid surveys were returned 

from a little over 28% (n=6,753) of those surveyed. The 

length and detail of the questionnaire and the mode of 

distribution may have contributed to the low response 

rate. ATCSs, however, have traditionally had lower response rates on surveys when compared with other FAA 

employee groups. Due to the relatively low response rate, 

it was important to assess the extent to which the sample 

represented the ATC population. Cruz, Della Rocco, and 

Hackworth (2000) provided demographic information 

on individuals classified as “2152-ATCS,” obtained from 

the FAA Personnel Management Information System 

(CPMIS). Comparisons of the responders’ demographics (i.e., gender, race, and age) with the CPMIS, overall, 

found the shiftwork sample to be representative of the 

ATC population at that time. For these analyses, data 

from individuals identified as air traffic controllers in 

the T/E options (n = 3,276) and data from controllers 

in the Flight Service Station (FSS) option (n = 876) were 

analyzed separately. 

Measures

For this study, the survey database was parsed relative 

to questions concerning specific commuting variables to 

and from early-morning shifts (starting before 0759), day 

shifts (starting at 0800), mid-day shifts (starting at 1000), 

afternoon shifts (starting at 1300), and midnight shifts 

(starting at 2000). The commuting variables included 

distance traveled to work, in miles; type of commute 

roadway (i.e., country roads, city traffic, and highway); 

and mental sharpness, from “not at all sharp” to “very 
3

sharp” (on a 6-point scale). The outcome variables 

included frequencies of momentary lapses of attention, 

falling asleep, falling asleep and having a near miss, and 

falling asleep and having an accident while driving to or 

from work in the last year. Response options for these 

variables included “never,” “once,” “twice,” and “three 

or more times.” 

Analysis

Chi-square frequency analysis and Odds Ratio (OR) 

risk estimates were computed separately for T/E controllers and for the FSS controllers with regard to each 

driving outcome (i.e., lapses of attention, falling asleep, 

near misses, and accidents). Potential risk factor variables 

such as commute distance and mental sharpness were 

dichotomized for purposes of conducting the analyses. 

Commute miles were dichotomized into 1-20 miles and 

greater than 20 miles. Mental sharpness was dichotomized 

into low and high mental sharpness based on the median 

split-score for the beginning and the end of each shift, 

separately. Each type of roadway (i.e., highway, city traffic, country roads) was assessed separately with regard to 

commuting distance. Because of the exploratory nature 

of the research design and the large sample size, alpha 

was set at 0.01. In addition, to be considered noteworthy, 

OR risk estimates were required to be at least 2.0 and to 

have a confidence interval (CI) with a lower bound of 

no less than 1.5 and a relatively tight range. 

RESULTS

Due to the large sample size, most of the Pearson 

Chi-square analyses were statistically significant, with 

the exception of those for accident outcomes, which had 

much smaller frequencies of occurrence. OR risk estimates 

and CIs are presented and discussed.

Mental Sharpness

Because time of day, sleepiness, and fatigue directly 

affect an individual’s alertness, an evaluation of mental 

sharpness was conducted to determine if this measure 

might also predict the reporting of driving outcomes. 

Average scores of mental sharpness for the beginning 

and end of each shift are presented in Figure 1 for both 

T/E and FSS respondents. It is important to note the 

consistency across the two operational environments 

reflecting stability in this measure. With the exception 

of early-morning shifts where ratings at the beginning 

and end of the shift were similar, respondents generally 

reported greater mental sharpness at the beginning of 

shifts than at the end. Mental sharpness was lowest, as 

expected, at the end of the midnight shifts and markedly 

lower than during any other shift start/end times. 

An examination of the relationship between reported 

mental sharpness and driving outcomes (Table 1) revealed 

that those with lower self-reported mental sharpness were 

at increased risk for lapses in attention while driving to or 

3.9 3.9

4.6

4.3

5.1

4.4

5.3

4.0

3.7

1.9

3.9 4.0

4.8

4.4

5.2

4.4

5.4

4.2

3.9

2.1

0.0

1.0

2.0

3.0

4.0

5.0

6.0

EM Star t EM End Day Star t Day End MD Star t MD End Af t Star t Af t End Mid Star t Mid End

Terminal/Enroute Flight Service Station

Figure 1. Average Ratings of Mental Sharpness by Shift for Terminal/Enroute and Flight 

Service Station Employees (EM – early morning, MD – mid-day, Aft – Afternoon, and Mid – 

midnight). 
4

from most shifts. Among T/E employees, this risk was 

highest for those reporting low mental sharpness at the 

beginning of early-morning shifts (OR = 5.2) and the end 

of midnight shifts (OR = 5.8). Among FSS employees, this 

risk was highest for those reporting low mental sharpness 

at the beginning and end of early-morning shifts (OR = 

6.7 and 5.9, respectively), at the beginning of day shifts 

(OR = 5.6), and at the end of midnight shifts (OR = 

7.5). T/E employees reporting low mental sharpness at 

the end of early morning (OR = 2.0), afternoon (OR = 

2.5), and midnight shifts (OR = 2.5) were at more risk 

of falling asleep while driving to or from these shifts. 

FSS employees reporting low mental sharpness at both 

the beginning and end of early morning shifts (OR = 

2.7 and 2.4, respectively), and at the end of day (OR = 

3.4) and midnight shifts (OR = 2.3) were at more risk 

for falling asleep while driving to or from these shifts. 

T/E employees with low mental sharpness at the end 

of shifts were at risk for experiencing near misses while 

driving. Mental sharpness was not generally a risk factor 

for driving near misses among FSS employees, with the 

exception of low mental sharpness at the end of early 

morning shifts (OR = 2.8). Accidents were too few to 

compute or were not significant, with the exception of 

increased risk for T/E employees with low ratings of 

mental sharpness at the beginning of midnight shifts 

(OR = 3.9, CI = 1.4-10.9).

Commute Distance

An evaluation of commute distance revealed that those 

with commute distances over 20 miles were at least twice 

as likely to report lapses of attention, falling asleep, and 

near misses driving to or from shifts in the previous year 

(Table 2). Generally, T/E employees with longer commutes 

reported increased risk for falling asleep and experiencing 

near misses while driving on all shifts and for lapses of attention on early morning, day, and afternoon shifts. FSS 

employees reported increased risk of lapses of attention

Table 1. Odds Ratio Risk Estimates for Driving Outcomes Based on Self-reported Mental 

Sharpness. 

Lapses of Attention

Terminal/Enroute Flight Service Station

Shift Beginning End Beginning End

Early Morning 5.2 (4.0-6.9) 3.2 (2.5-4.0) 6.7 (3.5-12.5) 5.9 (3.4-10.5) 

Day 3.7 (2.6-5.3) 2.8 (1.9-4.0) 5.6 (2.0-16.0) 11.6 (2.6-52.0) 

Mid-Day 2.7 (1.7-4.3) 3.3 (2.1-5.2) ns 4.0 (1.2-13.4) 

Afternoon 2.3 (1.7-3.0) 3.6 (2.6-5.0) 8.4 (2.4-29.4) 10.9 (2.5-47.7) 

Midnight 3.7 (2.9-4.7) 5.8 (4.5-7.5) 3.3 (2.0-5.2) 7.5 (4.6-12.1) 

Falling Asleep

Terminal/Enroute Flight Service Station

Shift Beginning End Beginning End

Early Morning 1.8 (1.5-2.2) 2.0 (1.7-2.5) 2.7 (1.8-4.2) 2.4 (1.6-3.6) 

Day 1.7 (1.2-2.3) 1.6 (1.1-2.1) 2.7 (1.3-5.5) 3.4 (1.5-7.8) 

Mid-Day 2.0 (1.3-3.0) 1.8 (1.2-2.6) ns ns 

Afternoon 1.6 (1.3-2.0) 2.3 (1.8-3.0) ns 2.3 (1.2-4.5) 

Midnight 1.4 (1.2-1.7) 2.0 (1.7-2.4) ns 2.3 (1.6-3.3) 

Near Misses

Terminal/Enroute Flight Service Station

Shift Beginning End Beginning End

Early Morning 1.8 (1.4-2.4) 2.3 (1.8-3.0) 2.3 (1.3-4.2) 2.8 (1.6-4.9) 

Day 2.1 (1.3-3.3) 2.3 (1.4-3.7) ns ns 

Mid-Day 2.9 (1.4-5.9) 3.4 (1.7-7.0) ns ns 

Afternoon 1.7 (1.2-2.4) 2.5 (1.7-3.7) ns ns 

Midnight 1.6 (1.3-1.9) 2.5 (2.0-3.2) ns 1.9 (1.2-3.0) 

ns – not significant Chi-square statistic 
5

Table 2. Odds Ratio Risk Estimates for Driving Outcomes Based on Commute Distance (> 20 

miles vs.  20 miles). 

Driving Outcomes for T/E Employees

Shift Lapse of 

Attention

Falling Asleep Near Misses Accidents

Early Morning 2.8 (2.2-3.5) 2.9 (2.4-3.5) 3.0 (2.3-3.9) * 

Day 2.7 (2.0-3.8) 3.0 (2.2-4.1) 3.2 (2.0-5.0) * 

Mid-Day 2.0 (1.4-3.0) 2.9 (2.0-4.2) 4.4 (2.4-8.2) * 

Afternoon 2.8 (2.1-3.6) 3.1 (2.5-3.9) 3.6 (2.5-5.1) 4.8 (1.3-17.7) 

Midnight 2.1 (1.6-2.6) 2.2 (1.9-2.6) 2.4 (1.9-2.9) 2.8 (1.1-7.0) 

Driving Outcomes for FSS Employees

Shift Lapse of 

Attention

Falling Asleep Near Misses Accidents

Early Morning 3.5 (2.1-5.9) 5.0 (3.4-7.5) 5.4 (3.0-9.6) * 

Day 2.4 (1.0-5.6) 4.7 (2.4-9.0) 5.6 (2.1-14.9) * 

Mid-Day ns 3.7 (1.5-9.3) 5.6 (1.4-21.8) * 

Afternoon 3.2 (1.3-7.8) 3.2 (1.8-5.7) 6.0 (2.4-14.9) * 

Midnight 2.3 (1.5-3.6) 3.3 (2.3-4.8) 2.7 (1.8-4.3) * 

ns – not significant Chi-square statistic 

* – too few cases to compute Chi-square statistic 

on early morning shifts; falling asleep on early morning, 

day, and midnight shifts; and near misses while driving on 

early-morning, day, and afternoon shifts. Accidents were 

too infrequent to be analyzed, with the exception of the 

afternoon and midnight shifts for T/E employees. While 

these were statistically significant, the lower bound of the 

confidence intervals for the odds ratios were too low to 

consider them valid.

Roadway Type

An evaluation of commute distance by the type of 

roadways traveled was also conducted. The majority of 

T/E employees reported commuting on highways (54%) 

and city traffic roads (33%) compared with 14% traveling country roads. The majority of FSS employees also 

reported highway (42%) and city traffic (35%) road types; 

however, a larger percentage of FSS employees reported 

country roads (23%) than T/E employees. While many 

of the chi-square statistics revealed significant relationships between commute distance and driving outcomes 

for each of the road types, most of them were associated 

with very small ORs or CIs with lower bounds near 1.0. 

In addition, as with the previous analyses, there were 

often too few accidents to compute chi-square statistics. 

Results with meaningful estimates of risk are provided 

for T/E and FSS employees. 

For T/E employees driving country roads, commute 

distance was not a significant factor for driving outcomes 

on most shifts. In city traffic, however, T/E employees 

with commutes of more than 20 miles were at greater 

risk for lapses of attention (OR = 4.4, CI = 2.4-7.9) and 

falling asleep (OR = 2.9, CI = 1.7-4.9) while driving to or 

from afternoon shifts, and for having near misses driving 

to or from early morning shifts (OR = 3.1, CI = 1.7-5.6). 

On the highway, T/E employees with commutes of more 

than 20 miles were at greater risk for a number of driving 

outcomes on a variety of shifts (Table 3).

For FSS employees with long commutes on country 

roads, there was an increased risk of lapses of attention

(OR = 6.3, CI = 1.9-21.1) and falling asleep (OR = 5.0, 

CI = 2.0-12.6) while driving to or from early morning 

shifts. The CI for both outcomes, however, had fairly 

large ranges and should not be over interpreted. Driving 

distance was not a significant factor for lapses of attention

on shifts other than the early morning shift. Although 

driving distance had a statistically significant relationship with falling asleep on day, afternoon, and midnight 

shift commutes, the CIs for the ORs had lower bounds 

that were less than 1.5. Near misses and accidents on 

country roads were not significant outcomes based on 

long commutes.

For FSS employees driving in city traffic, most outcomes for each shift were not significantly related to driving 

distance. Lapses of attention were not at increased risk for 

any shifts based on driving distance. Falling asleep on the 

drive to or from the early morning shift was more likely on 
6

Table 3. Odds Ratio Risk Estimates Based on Commute Distance (> 20 miles vs.  20 miles) for 

Driving Outcomes on Highway Road Types for T/E Employees. 

Driving Outcomes for T/E Employees

Shift Lapse of Attention Falling Asleep Near Misses

Early Morning 2.6 (1.9-3.6) 2.8 (2.2-3.6) 3.1 (2.1-4.5) 

Day 2.3 (1.4-3.6) 2.7 (1.8-4.1) 2.2 (1.2-3.9) 

Mid-Day ns 2.8 (1.7-4.7) 4.8 (1.9-12.5) 

Afternoon 2.4 (1.6-3.4) 2.7 (2.0-3.7) 3.3 (2.0-5.4) 

Midnight 1.9 (1.4-2.6) 2.0 (1.6-2.5) 2.4 (1.8-3.2) 

ns – not significant Chi-square statistic 

Table 4. Odds Ratio Risk Estimates Based on Commute Distance (> 20 miles vs.  20 miles) for 

Driving Outcomes on Highway Road Types for FSS Employees. 

Driving Outcomes for FSS Employees

Shift Lapse of Attention Falling Asleep Near Misses

Early Morning 2.7 (1.2-5.7) 2.9 (1.7-4.9) 4.9 (2.1-11.4) 

Day ns 3.6 (1.4-9.3) ns 

Mid-Day ns 4.6 (1.0-21.8) ns 

Afternoon 6.1 (1.3-28.5) 3.1 (1.3-7.7) 0.9 (0.9-1.0) 

Midnight ns 2.6 (1.5-4.3) 2.6 (1.3-5.1) 

ns – not significant Chi-square statistic 
7

longer commutes (OR = 6.6, CI = 2.2-19.9) but was not 

more likely on other shifts. Near misses were a statistically 

significant outcome on day (OR = 15.3, CI = 2.0-119.0), 

mid-day (OR = 7.1, CI = 1.1-45.9), and afternoon shifts 

(OR = 5.1, CI (0.9-28.4), but the CIs were extremely 

wide or included a lower bound of near 1.0. Accidents

were too few to analyze or not significant.

For highway travel, FSS employees with longer commutes were at increased risk for falling asleep (OR = 2.9, 

CI = 1.7-4.9) and near misses (OR = 4.9, CI = 2.1-11.4) 

while driving to or from early morning shifts. Results for 

a number of other outcomes on a variety of shifts were 

significant; however, most had lower bounds on the CI 

of the OR of less than 1.5 (Table 4). Accidents were too 

few to analyze or were not significant.

DISCUSSION

The results of this evaluation support much of the 

previous research that has been published regarding 

sleepiness-related driving risk factors. Briefly, the study 

indicated that mental sharpness and driving distance 

were significant risk factors associated with driving to 

and from a variety of work-shifts for a large sample of 

ATCSs. Some evidence of a temporal component to driving outcomes was found in these data and may reflect 

the known circadian rhythm for alertness and performance. Lennè et al. (1997), for example, documented 

performance impairment of several simulation driving 

variables in the laboratory that corresponded quite well 

with the actual increased automobile accidents found 

by Langlois, Smolensky, His, and Weir (1985); Lisper, 

Eriksson, Fagerstrom, and Lindholm (1979); Schwing 

(1990); and Summala and Mikkola (1994), regarding 

time of day effects.

Folkard (1997) also found reliable time of day effects 

with a “macro-analysis” of previous research and reported 

“...like accident risk, sleep propensity was clearly highest 

in the early hours of the morning and showed a secondary, minor peak in the early afternoon” (p.422). Horne 

and Reyner (1995) reported three time-peaks for elevated 

SRVA, based on data from the UK, at around 0200-0259, 

0600-0659, and 1600-1659, which coincides with a 

circadian rhythm of reduced alertness. In contrast, low 

incidences of sleep-related accidents were found during 

0900-1059 and 1900-2050, which are times of the day 

when people normally feel more alert. Likewise, Pack et al. 

(1995) reported the highest number of crashes attributed 

to falling asleep occurred between midnight and 0700 

and during mid-afternoon around 1500.

The present evaluation found that low mental sharpness was generally found to be associated with an elevated 

risk for lapses in attention and falling asleep. For the T/E 

employees, greater risk appeared to be associated with 

the beginning of early morning shifts and following the 

midnight shift. The results for FSS employees followed a 

similar trend. However, while low mental sharpness and 

an increased risk of near misses appeared to be associated 

with the end of shifts for T/E employees, the same was 

not true for FSS employees. One should note that the 

commutes following the midnight shifts and before the 

early morning shifts showed lower alertness but less risk 

than expected. One explanation might be in the apparent 

interaction commuters have with traffic density during 

those times, and not controlled for in this study.

While the risk for adverse driving outcomes increased 

for longer commutes (i.e., >20 mi) regardless of workgroup or shift type, there was some evidence that under 

certain circumstances early morning shifts showed an 

even greater risk for FSS employees. Notably, Rogers et 

al. (2001) found that both the type of shift (i.e., time 

of day) and the traveling time (i.e., driving duration), 

much like our driving distance, contributed significantly 

to increased sleepiness driving impairments.

When road type was considered, a complex picture 

appeared in the results. For T/E employees driving country roads, commute distance was not a significant factor 

on most shifts. However, in city traffic T/E employees 

with commutes of more than 20 miles were at greater 

risk for lapses of attention and falling asleep while driving 

to or from afternoon shifts and for having near misses

driving to or from early morning shifts. Likewise, on 

the highway, T/E employees with commutes of more 

than 20 miles had a greater risk for almost every driving 

outcome on all shifts. 

For FSS employees, an increased risk for falling asleep

occurred both on highways and in city traffic during 

longer commutes to or from early morning shifts. Near 

misses also appeared to occur more frequently during long 

commutes on highways to or from early morning shifts. 

However, unlike their T/E counterparts, FSS employees 

with long commutes on country roads showed an increased 

risk of lapses of attention and falling asleep while driving to 

or from early morning shifts. Some caution is warranted 

regarding this latter finding, given the wide confidence 

interval ranges seen with both outcomes. 

Nonetheless, the consistency of our results with previous studies on this topic suggests that the effects of 

commuting to and from work during various times of the 

day, as required by shiftworking schedules, should also 

be addressed. These issues were the focus of the National 

Summit to Prevent Drowsy Driving at the National Academies of Sciences in Washington, D.C., on November 

20-21, 2002 (Drobnich, 2005). Recommendations and 

two products were developed as a result of this Summit, 

including: creation of the Web site dedicated to the 
8

prevention of drowsy driving (www.drowsydriving.org; 

National Sleep Foundation, 2004) and the creation of 

a national action plan. The plan recommended that an 

educational approach for shiftworking employees should 

be developed to raise the awareness of the critical issues 

associated with driving to and from work, particularly 

during the early morning hours. 

Prevention of fatigue is, of course, the best recommendation one could make, but once driving has begun, 

the National Sleep Foundation suggests that the motorist 

attend to the following: Look for the warning signs of 

fatigue, including (a) can’t remember the last few miles 

driven, (b) drift from your lane or hit a rumble strip, (c) 

experience wandering or disconnected thoughts, (d) yawn 

repeatedly, (e) have difficulty focusing or keeping your 

eyes open, (f) tailgate or miss traffic signs, (g) have trouble 

keeping your head up, and (h) keep jerking your vehicle 

back into the lane. If these signs are apparent, recognize 

that you are in danger of falling asleep and cannot predict 

when a micro sleep may occur. Don’t count on the radio, 

opening windows, or other “tricks” to keep you awake. 

Respond to symptoms of fatigue by finding a safe place 

to stop for a break. Pull off into a safe area away from 

traffic, and take a brief nap (15 to 45 minutes) if tired. 

Likewise, drinking coffee or a functional energy drink may 

promote short-term alertness, although it takes about 30 

minutes for caffeine to enter the bloodstream.

SUMMARY

Shiftwork schedules can contribute to conditions that 

promote driving-related incidents during commutes to or 

from the workplace. Prevention of fatigue associated with 

shiftworking schedules is difficult with the 24/7 operational requirements found in many industries today. But, 

“...as public health research continues to give a clearer 

understanding of temporal patterns of driver fatigue or 

sleep, the knowledge generated can be used as an educational tool for public information campaigns directed 

toward drivers of all ages” (Bruno, 2004; p. 55). 

We found evidence that air traffic controllers experience similar responses to work schedules with elevated 

reporting of fatigue-related driving outcomes. Bruno 

(2004) suggested that “...increased driver awareness of 

fatigue related traffic issues would potentially encourage 

positive changes in driving habits and prevent accidents 

or at least reduce the frequency and severity...” (p.55). 

Therefore, our recommendations are consistent with 

this educational suggestion to promote an awareness of 

fatigue-related vehicle driving incidents and the development of effective mitigation strategies. 

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