RESEARCH ARTICLE

Pilot Fatigue in Short-Haul Operations: Effects of

Number of Sectors, Duty Length, and Time of Day

David M. C. Powell, Mick B. Spencer, David Holland,

Elizabeth Broadbent, and Keith J. Petrie

POWELL DMC, SPENCER MB, HOLLAND D, BROADBENT E, PETRIE KJ.

Pilot fatigue in short-haul operations: effects of number of sectors,

duty length, and time of day. Aviat Space Environ Med 2007; 78:

698 –701.

Introduction: There is little research on what factors are associated

with fatigue in short-haul pilots. The aim was to investigate how length

of duty, number of sectors, time of day, and departure airport affect

fatigue levels in short-haul operations. Methods: Pilots completed SamnPerelli fatigue ratings prior to descent at the end of each rostered

short-haul duty over a 12-wk period. Overall, 1370 usable responses

were collected (67% of rostered duties) and fatigue scores were examined in relation to the departure airport, the number of sectors flown,

time, and the length of duty period. Results: The most important influences on fatigue were the number of sectors and duty length. These were

associated with fatigue in a linear fashion. Time of day had a weaker

influence, with lower levels at midday and increased fatigue later in the

day. Fatigue was also higher during duties originating from an airport

where pilots needed to position the night before and spend the night in

a hotel. Discussion: Data from the study enabled the quantification of

fatigue at this critical phase of flight in duties lasting between 2 and 12 h

and finishing between 08:00 to 24:00. The data obtained may be useful

for identifying factors associated with fatigue, evaluating existing fatigue

models, and identifying or predicting problem duties within an airline

operation. The methodology used in the study may be successfully

applied to gather fatigue data in other airline operations.

Keywords: fatigue, short-haul, work hours, time of day, circadian

rhythm.

FATIGUE IS AN important influence on pilot performance in commercial airline operations. To date,

most research activity has focused on transmeridian

international operations, where tours of duty involve

long sectors and travel across several time zones. There

has been comparatively little research on fatigue in

pilots flying short-haul operations, although the work

pattern of these pilots often has a number of factors

associated with increased reports of fatigue. Pilots flying short-haul operations are often rostered for an irregular pattern of early starts and late finishes, which

can disrupt normal sleep routines and increase fatigue.

In contrast to long-haul flying, the work pattern may

involve multiple takeoffs and landings, resulting in a

more demanding workload across the workday. Furthermore, short haul flying is carried out by two-pilot

crews so there are no in-flight rest opportunities and

this has been shown to result in higher fatigue levels

than three-pilot crews (4).

Surveys have suggested fatigue is a common problem

in short-haul commercial pilots (2,6), with pilots identifying extended duty periods and successive early

starts as the most important causes (2). Short-haul rosters cause pilots to sleep less, wake earlier, and have

less restful sleep over the work period (5). Studies with

UK pilots have identified time of day and the number of

flights per day as important influences on the development of fatigue during the course of a short-haul duty

period (3). It is clear from the small amount of research

conducted with short-haul crew that early starts, late

finishes, and the high workload caused by multiple

sectors are important influencing factors on fatigue levels. However, it is not clear which factors contribute

most, particularly at the most critical period for flight

safety—the final approach and landing phase. This information would be valuable for planning work schedules in order to minimize the risk of fatigue.

In this study we investigated the relative importance

of these factors in predicting fatigue at the end of the

work period. Whereas previous studies have relied on

taking performance and physiological measures on a

small number of pilots, we collected alertness ratings

from a large number of pilots toward the end of their

last duty sector. This enabled us to quantify the relative

importance of time of day, number of sectors flown,

duty start time, and duty length on fatigue levels at this

critical stage.

METHODS

The study was conducted during a 3-mo period from

January 1 to March 31, 2003, on Air New Zealand

Boeing 737 operations. At the time this fleet operated 15

Boeing 737–300 aircraft on sectors within New Zealand

ranging from 30 min to 100 min flight time, but the

great majority of sectors were between 45 and 75 min.

All pilots on the 737 fleet were sent a letter from pilot

From the Medical Unit, Air New Zealand, Auckland, New Zealand

(D. M. C. Powell, D. Holland); Sandhurst, Berks, UK (M. Spencer);

and the Department of Psychological Medicine, University of Auckland, Auckland, New Zealand (K. J. Petrie, E. Broadbent).

This manuscript was received for review in December 2006. It was

accepted for publication in April 2007.

Address reprint requests to: David Powell, Chief Medical Officer,

Air New Zealand, Private Bag 92007, Auckland, New Zealand;

David.Powell@airnz.co.nz.

Reprint & Copyright © by Aerospace Medical Association, Alexandria, VA.

698 Aviation, Space, and Environmental Medicine • Vol. 78, No. 7 • July 2007
management notifying them of the study and asking

them to consider participating in the research. Over the

study period there were a total of 2034 duties with at

least 1 operating sector. During the data collection period pilots were asked to complete a brief anonymous

questionnaire at the top of descent on the last sector of

the duty day. Pilots recorded duty start time, current

time (top of descent), and number of sectors flown

across the day. Pilots also completed the 7-point SamnPerelli fatigue scale (9) where they rated themselves as:

1 “fully alert, wide awake”; 2 “very lively, responsive

but not at peak”; 3 “OK, somewhat fresh”; 4 “a little

tired, less than fresh”; 5 “moderately tired, let down”; 6

“extremely tired, very difficult to concentrate”; or 7

“completely exhausted, unable to function effectively”

as well as a 100-mm visual analog scale rated from

“alert” to “drowsy.” The questionnaires were placed in

a special envelope on the aircraft which was returned to

the Medical Unit.

The times corresponding to duty start and top of

descent were verified, and data were excluded where

these times were missing, inconsistent, or unreliable.

The data were then analyzed by a stepwise least

squares procedure. The factors included in this analysis

were: duty length, defined as the duration of the period

between report time and top of descent; time of day at

the top of descent; the number of sectors; and the departure airport. Interaction terms were only considered

for inclusion if the relevant main effects were significant.

As part of this procedure, contrasts within the main

effects were included as regressor variables in the

form of polynomials (duty length and number of

sectors) and harmonics (time of day). Where the contrast or contrasts accounted for all the significant

differences within a factor, they replaced that factor

in subsequent analyses, including in the interaction

terms. The graphical representation of the main effects in this paper has been derived from a model in

which the terms have been taken from the equation of

best fit. Differences between individual means were

tested using the Newman-Keuls multiple range test.

The analysis was carried out on both the visual analog data and on the Samn-Perelli scores. However,

these two variables were closely related, with a Pearson correlation coefficient of 0.86, and the results of

both analyses were similar. Therefore, only the SamnPerelli data are presented here.

RESULTS

There were 1466 questionnaires returned, giving a

response rate of 72% of total duty periods, and these

were further reduced to 1370 (67%) after the application

Fig. 1. The time-of-day distributions of report times (top) and of times corresponding to the top of descent (bottom).

FATIGUE IN SHORT-HAUL PILOTS—POWELL ET AL.

Aviation, Space, and Environmental Medicine • Vol. 78, No. 7 • July 2007 699
of the exclusion criteria. Most duties originated in

Auckland (39%), followed by Christchurch (32%), Wellington (21%), and Dunedin (8%). The total number of

sectors varied between one and five, with most flights

comprising four (37%) or five (30%) sectors. The distributions of the report times and of times corresponding

to top of descent are shown in Fig. 1. Almost half of all

report times were before 07:00, while a further 30%

were between 11:00 and 15:00. The most frequent time

periods at top of descent were between 11:00 and

16:00 (41%) and between 18:00 and 21:00 (35%). The

average duty time, defined as the time between report and top of descent, was 7.2 h (minimum 1.1;

maximum 11.2).

Reported fatigue at the top of descent was explained

by the length of the duty, the number of sectors, and the

time of day at the top of descent. The main effects of all

four factors—airport of origin, length of duty, time of

day, and number of sectors—were significant

(p  0.001). The mean values are plotted in Fig. 2, after

correcting for the other components of the best fit. The

main effect of airport was demonstrated by the flights

departing from Dunedin and Wellington, which were

associated with higher levels of fatigue than those departing from Auckland (p  0.001) or Christchurch

(p  0.05). The effect due to the number of sectors was

linear, and the increase in fatigue associated with each

additional sector was equivalent to an increase of 0.38

on the 7-point Samn-Perelli scale. The influence of duty

length on fatigue was also best expressed by a positive

linear function of duty time. The influence of time of day

on fatigue was best expressed by the first two harmonics

of time of day. The only interaction that contributed to

the equation of best fit was between duty length and the

first harmonic of time of day (p  0.001). The fitted

trends defined by this term are plotted in Fig. 3 and

show that after midday there is a general increase in

fatigue with time of day and this increase is particularly

strong after 22:00.

Pilots who commenced duty early in the morning

reported higher levels of fatigue. The increase in fatigue

with duty length was larger before and just after midday than later in the evening, when average levels of

fatigue, even at the end of a short duty, were generally

highest. However, it should be noted that the interaction only accounts for a small amount of the overall

variance.

The importance of the individual factors can be

quantified by the size of their contribution to the total

variability. The contribution of each factor was: number of sectors (33.3%); duty length (33.0%); time of

day (12.6%); airport of origin (3.2%); and the interaction between duty length and time of day (1.4%). The

total variance explained by all factors combined was

40.8%. As duty length and time of day are not independent, the combination of duty length and time of

day shown in Fig. 3 gives the best illustration of the

influence of these important factors on overall fatigue

levels.

Fig. 2. The main effect of four factors that have a significant effect on levels of fatigue at the top of descent: the departure airport (top left), the length

of duty (top right), the time of day (bottom left), and the number of sectors (bottom right). Error bars correspond to 1 SEM.

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700 Aviation, Space, and Environmental Medicine • Vol. 78, No. 7 • July 2007
DISCUSSION

We found pilot fatigue in short-haul pilots at the end

of their last duty sector was significantly influenced by

length of duty, time of day, the number of sectors

flown, and airport of departure. The most important

influences on fatigue were the number of sectors and

duty length, which in this particular operation are very

closely related. Duty length and the number of sectors

increased fatigue in a linear fashion. Time of day had a

weaker influence, with lowest levels at midday and an

increased level of fatigue later in the day. The data

showed that the most favorable time of day for the last

landing depended on the duty length. For shorter duties the most favorable time for the last landing was

around midday, whereas for longer duties, the best

time was 20:00. The higher levels of fatigue found in

pilots departing from the Dunedin and Wellington airports were probably due to pilots being required to

position the previous day and stay overnight in a hotel,

as opposed to Auckland and Christchurch, which are

home bases for the pilots.

These results are consistent with three studies carried

out on domestic UK pilots using a different methodology (3). These previous studies also showed the importance of the individual effects of sector and time of day

throughout the course of the duty. In this study we

looked in more detail at the critical phase of the duty

from a safety perspective and we found that fatigue

cannot be represented as a simple summation of the

individual factors. This reflects the complex interaction

between, on the one hand, the timing of duty related to

the circadian rhythm of fatigue and, on the other hand,

the duration of duty and its impact on the timing of

sleep. Typically, a mean value of 5 on the Samn-Perelli

scale gives rise to some concern about the suitability of

that particular roster (9). As illustrated in Fig. 3, this

value is reached after 8 h elapsed time when the top of

descent is around midnight.

This study had several strengths including a large

sample and good response rate, as well as collecting

data at a critical phase of flight rather than retrospectively. However, there are a number of methodological

issues relevant to the interpretation of the results.

Firstly, fatigue was measured using subjective pilot

ratings rather than using objective indices, such as reaction time tasks. However, the Samn-Perelli scores

have been shown to follow similar trends to objective

measures throughout a duty period (8). Secondly, all

the data were collected on domestic sectors of a similar

length with no overnight duties and no time zone

changes, so they may not be directly applicable to different operations. It is also likely that this lack of overnight duties attenuated the time of day effect as highest

levels of fatigue tend to occur in the early morning (3).

The study has a number of implications for research

and fatigue models. There are several mathematical

models designed to predict fatigue levels in pilots operating short-haul operations (1,7). Data from this study

will enable further validation of these models in actual

airline operations. Furthermore, the study demonstrated that collecting fatigue data through a brief questionnaire at a critical time in the workday across an

airline operation is feasible, and produces reliable and

useful data. For example, Fig. 3 can be used to predict

fatigue across duties of various lengths at different

times of the day. This study shows the usefulness of

such data for identifying the factors associated with

fatigue patterns, but also for identifying problem duties

and providing a metric for identifying overall levels of

fatigue across an operation.

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Fig. 3. Fatigue at the top of descent related to time of day and the

length of duty: the trend lines are derived from the equation of best fit.

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