IN-DEPTH REVIEW: SHIFT WORK

Shift work, safety and productivity

Simon Folkard and Philip Tucker

Abstract The arguments in favour of introducing shift work clearly depend on productivity

and safety being maintained at an acceptable level. However, the evidence reviewed

in this paper clearly indicates that both productivity and safety may be compromised

at night. More specifically, safety declines over successive night shifts, with increasing

hours on duty and between successive rest breaks. The only known way to minimize

these problems is to improve shift systems with respect to these factors. However,

these factors need to be considered in combination with one another since, for

example, a long night shift that includes frequent rest breaks might well prove safer

than a shorter night shift with less frequent breaks.

Keywords Circadian rhythms; fatigue; performance; productivity; safety; shift work; sleep.

Received 1 December 2002

Revised 1 January 2003

Accepted 1 February 2003

Introduction

Efficiency and safety on shift systems is a topic of major

concern for two main reasons. First, a number of

‘headline’ incidents, such as Bhopal, Three Mile Island,

Chernobyl, the Rhine chemical spillage and the Exxon

Valdez, all occurred at night, and have drawn attention to

both the risk and cost of impaired safety on shift systems.

Secondly, shift work is frequently introduced for purely

economic reasons in order to maximize the use of costly

equipment. While many shift work researchers would

argue that this practice should be discouraged in view of

the health and well-being costs to the individual shift

workers, the economic arguments in favour of introducing shift work clearly depend on productivity and

safety being maintained at an acceptable level. An

impairment of individuals’ performance efficiency on a

shift system may thus seriously undermine any potential

economic benefit derived by introducing it.

‘Real-job’ trends in productivity

Unfortunately, the direct study of productivity and safety

on shift systems is fraught with problems. As regards

productivity, both the number of people at work and the

nature of their work often vary across shifts, with,

for example, ‘long runs’ frequently being saved for the

night. Supervision is normally reduced at night, and there

may be no maintenance personnel available to ensure

that equipment is running efficiently. Despite these

complications, three early published studies managed to

obtain relatively continuous, ‘real-job’ measures of speed

or accuracy over the 24 h day covered by a variety of

continuous shift systems, and these showed similar

trends to one another. The precise measures obtained in

these three studies varied considerably. They were: (i) the

delay in answering calls by switchboard operators [1];

(ii) errors in reading meters [2]; and (iii) the time taken

by ‘spinners’ to tie broken threads in the textile industry

[3]. The averaged results from these studies (based on Z

scores) are shown in Figure 1 in order to give an ‘overall

picture’ of the effect of time of day.

Two major points emerge from inspection of this figure.

First, there was a relatively massive ‘dip’ in these efficiency measures during the course of the night shift, i.e.

from ~22:00 h to 06:00 h, with the trough occurring at

03:00 h. Indeed, these measures had fallen below average

levels (i.e. below a mean Z score of zero) by 23:00 h and

did not climb back up to average until after 06:00 h.

Secondly, there was clear evidence of a secondary ‘dip’ in

the measures shortly after 12:00 h. This secondary dip

has commonly been described as the ‘post-lunch’ dip,

although close inspection of Figure 1 suggests that these

measures started to decline considerably earlier than

normal lunch times, i.e. from ~10:00 h. Indeed, there is

Occupational Medicine, Vol. 53 No. 2

© Society of Occupational Medicine; all rights reserved 95

Body Rhythms and Shiftwork Centre, Department of Psychology, University of

Wales Swansea, Swansea SA2 8PP, UK.

Correspondence to (current address): S. Folkard, Laboratoire d’Anthropologie

Appliquée, UPRES Ergonomie, Université René Descartes-Paris V, 45 rue

Saints-Pères, F-75006 Paris, France. E-mail: S.Folkard@Swansea.ac.uk

Occupational Medicine 2003;53:95–101

DOI: 10.1093/occmed/kqg047
reasonably good evidence that this ‘post-lunch’ dip is only

partially dependent on the ingestion of food [4]. An

alternative way of viewing the results shown in Figure 1

is that these ‘real-job’ speed and accuracy measures are

only above average between 07:00 h and 19:00 h, at all

other times efficiency is likely to be relatively impaired,

especially so during the early hours of the morning.

There appear to be few other studies of productivity on

shift systems that have successfully overcome the problems of differences in the workforce or work practices.

However, an extremely carefully controlled study by

Vidacek et al. [5] examined the number of capacitors

produced by individuals in an electronics component

factory over five successive, 8 h, morning, afternoon and

night shifts. This particular job demanded a very high

level of manual dexterity and was extremely repetitive,

with the workers concerned each producing an average of

>100 capacitors per hour. As might be expected from

Figure 1, overall productivity was highest on the afternoon shift, and was on average ~5% lower at night.

However, there was also clear evidence of an interaction

between the type of shift and successive shifts. Productivity on the morning and afternoon shifts was relative

constant across the five successive shifts, while that on the

night shift rose substantially over the first three nights but

then declined slightly over the subsequent night shifts.

Clearly, there is a need for further studies of productivity

in this area to determine whether the trends obtained in

this study hold good for other work situations.

‘Real-job’ trends in safety

Unfortunately, as indicated above, in many industrial

situations, the a priori risk is not constant across the day

and night. This means that accident or injury rates often

cannot be legitimately compared across the shifts since

fewer ‘incidents’ might be expected on the night shift.

(Note that the term ‘incidents’ is used from here on to

refer to both accidents and injuries.) Indeed, even in

those few industrial situations where the a priori risk of

incidents would appear to be constant across the 24 h

day, there remains the problem that the probability of

actually reporting an injury or accident that occurs may

vary. Thus, for example, in a recent unpublished study of

injury rates in an engineering company, where the a priori

risk of injuries appeared to be constant, we discovered

that substantially fewer injuries were reported on the

night shift than during the day. Further investigation

revealed that when members of the predominantly male

workforce reported an injury during the day, they were

treated by a female nurse at the on-site occupational

health clinic. However, this clinic was closed at night,

when first-aid cover was provided by the male security

guards at the gatehouse situated at the entrance to the

works. It seems highly probable that this dissuaded many

members of the workforce from reporting less serious

injuries on the night shift. Indeed, the nursing sister at

the occupational health clinic also commented that

the number of injuries reported during the day varied

substantially depending on which nurse was on duty!

When these contaminating factors are controlled for,

there appear to be a number of reasonably consistent

trends in incidents associated with aspects of shift

systems.

Risk across the different shifts

The first consistent trend relates to the relative risk of

incidents on the morning, afternoon and night shifts on

8 h shift systems. There are five studies of which the

authors are aware that are based on relatively large

numbers of incidents and that appear to have overcome

the potential confounding factors [6–10]. It should be

noted that while in some of these studies there were equal

numbers of shift workers on each shift, in the others, the

authors had to correct the data to take account of any

inequalities. In addition, three of the studies report two

separate sets of data, for different areas or types of

incident, giving a total of eight sets of data across the

three shifts. For the purpose of this review, the incidents

were summed across the eight data sets and the risk on

the afternoon and night shifts was expressed relative to

that on the morning shift. Risk was found to increase in

an approximately linear fashion across the three shifts,

showing an increased risk of 18.3% on the afternoon

shift and of 30.4% on the night shift, relative to that on

the morning shift, and this is shown in Figure 2. The

conclusion to be drawn from this figure would appear to

be that in situations where the a priori risk would appear

to be constant across the three shifts, there is a consistent

tendency for the relative risk of incidents to be higher on

the afternoon shift than on the morning shift, and for it to

be highest on the night shift.

Figure 1. Industrial performance efficiency over the 24 h day.

96 OCCUPATIONAL MEDICINE
Risk over the course of the night shift

The second reasonably consistent trend in risk is that

over the course of the night shift. In 1923, Vernon [11]

reported one of the earlier studies in this area. He

reported that injury rates declined substantially over the

first few hours of the night shift, and that this trend could

not readily be explained in terms of changes in productivity levels. A number of more recent studies have also

provided hourly incident rates over the course of the

night shift [9,10,12–18]. As before, the incidents were

summed across all 10 studies and the risk expressed

relative to that during the first hour of the night shift.

Using these summed values, risk rose by ~20% from the

first to second hour, but then fell by a total of ~50%, and

in an approximately linear fashion, to reach a minimum

at the end of the shift, and this is shown in Figure 3. It is

notable that there was a slight increase in risk between

03:00 and 04:00 h, when industrial efficiency is at its

lowest ebb (see Figure 1), but this effect was relatively

small compared with the substantial decrease in risk over

most of the night.

Risk over successive shifts

The third consistent trends in risk are those over successive shifts. The authors are aware of a total of seven

studies that have reported incident frequencies separately

for each night over a span of at least four successive night

shifts [7,9,14,17–20]. Note that the study reported by

Monk and Wagner [21] was not included, since the data

reported in that paper was a subset of that reported by

Wagner [14]. As before, the frequency of incidents on

each night was summed across the studies and then

expressed relative to that on the first night shift. On

average, risk was ~6% higher on the second night, 17%

higher on the third night and 36% higher on the fourth

night (see Figure 4).

Two important questions arise regarding this substantial increase in risk over four successive night shifts.

The first is what happens to risk over longer spans of

successive night shifts, but there is a paucity of data

relating to this. While the increase in risk over four

successive nights is difficult to reconcile with the finding

of Vidacek et al. [5], that productivity increased over the

first three nights, it would nevertheless be of great interest

to determine whether risk, like productivity in Vidacek

et al.’s study, reduced over longer spans of successive

night shifts. However, while it is has to be admitted that

this might occur, there is as yet no good evidence to

indicate that this is the case.

Figure 2. The relative risk across the three shifts. Figure 3. The relative risk over the course of the night shift.

Figure 4. The relative risk over four successive night shifts.

S. FOLKARD AND P. TUCKER: SHIFT WORK, SAFETY AND PRODUCTIVITY 97
The second question is whether the increase in risk

over successive shifts is confined to the night shift, or

whether it might be general to all shifts and represent an

accumulation of fatigue over successive workdays. Of the

seven studies that examined risk over successive night

shifts, five also reported the risk over successive morning

or day shifts [7,9,17,18,20]. As before, the frequency of

incidents on each shift was summed across these five

studies and then risk expressed relative to that on the first

morning/day shift. The results are shown in Figure 5; note

that the same scale has been used for this figure as that

used in Figure 4 so that direct comparisons can be made.

On average, risk was ~2% higher on the second morning/

day, 7% higher on the third morning/day, and 17% higher

on the fourth morning/day shift than on the first shift.

Clearly, there was some evidence that risk did increase

over successive morning/day shifts, but this increase was

substantially smaller than that over successive night shifts

(compare Figures 4 and 5).

Risk over hours on duty

There appear to be four studies that have reported the

trend in risk over successive hours on duty and that have

managed to correct for exposure in some manner

[15,22–24] (see also the review by Nachreiner [25]).

However, the study by Folkard [22] was based on a

statistical combining of several relatively small studies,

and made various assumptions in deriving an overall

trend. Since the remaining three studies were all based on

substantial numbers of injuries/accidents and report fairly

similar trends to that derived by Folkard [22], the latter

was omitted from consideration in deriving an averaged

trend. The three studies considered all examined trends

in national accident statistics and corrected for ‘exposure’

in some manner. By setting the mean risk in each study

for the first 8 h at one, it was possible to calculate hourly

relative risk values for each study. The values were then

averaged to derive an averaged trend, and this is shown in

Figure 6. It is clear from this figure that, apart from a

slightly heightened risk from the second to the fifth hour,

risk increased in an approximately exponential fashion

with time on shift such that in the twelfth hour it was

more than double that during the first 8 h. The increased

risk from the second to the fifth hour is considered in

more detail by Folkard [22] and Tucker et al. [26].

Risk as a function of breaks

The trend for hours on duty shown in Figure 6 does not

control for the influence of breaks during a duty period,

and indeed one possible explanation for the decrease in

risk after the fifth hour may be that it reflects the

influence of rest breaks. Although a number of studies on

the effects of breaks have been conducted [27–29], there

appears to be only a single, very recent study that has

examined their impact on the risk of incidents [30]. This

study examined industrial injuries in an engineering plant

in which a 15 min break was given after each period of

2 h of continuous work. The number of injuries within

each of the four 30 min periods between breaks was

calculated, and the risk in each 30 min period was

expressed relative to that in the first 30 min period

immediately following the break. The results are shown in

Figure 7, from which it is clear that risk rose substantially,

and approximately linearly, between successive breaks

such that risk had doubled by the last 30 min period

before the next break. It is also noteworthy that there was

no evidence that this trend differed for the day and night

shifts, or for the three successive periods of 2 h of

continuous work within a shift.

Figure 5. The relative risk over four successive morning/day shifts.

Figure 6. The mean relative risk over hours on duty.

98 OCCUPATIONAL MEDICINE
Underlying factors affecting productivity

and safety

Impaired safety and productivity on shift systems will

almost certainly reflect the combined influence of a large

number of factors; the psychosocial factors and physical

health effects typically associated with abnormal work

hours are discussed in other reviews in this issue [31–33].

It has long been recognized that people’s efficiency at

performing various tasks is not constant, but varies over

the course of the normal waking day. Early theorists

attributed this to either a build-up of ‘mental fatigue’

over a period of wakefulness [34] or an underlying

rhythm in ‘sleepiness’ that was independent of whether

people had actually slept [35]. More recent studies have

confirmed that both these factors contribute to circadian

variations in performance [36]. Indeed, a number of

authors have developed mathematical models based

primarily on these two factors [37–40], and these have

proved relatively successful in predicting variations in

alertness and performance on various laboratory tasks on

both normal and abnormal sleep/wake schedules [41,42].

These models essentially assume that productivity and

safety are low at night because (i) the circadian rhythms

in performance are at a low ebb at this time and (ii) they

remain relatively unadjusted over normal spans of successive night duties.

Unfortunately, while these mathematical models might

be able to account for the trends in productivity described

above, they have great difficulty in accounting for the

trends in risk. Thus, while the models would predict that

safety should be lowest at night, they would also predict

that it should be highest on the afternoon shift, which it is

clearly not (see Figure 2). Likewise, these models would

predict that risk would be highest at ~04:00 h in the

morning, while in fact risk is substantially lower then than

at ~00:00 h (see Figure 3). Finally, the models would

predict that risk should stay constant or reduce slightly

over successive night shifts rather than show the substantial increase illustrated in Figure 4. The reasons for

these large disparities between the predictions made by

current models and the objectively determined trends in

risk are as yet unclear, but may reflect on additional, as yet

unidentified, factors that need to be incorporated into the

models, and/or the possibility that risk is not linearly

related to alertness and performance on laboratory tasks.

Other, mainly laboratory, studies have shown that the

trend in performance over the day varies according to the

nature of the task under consideration. In general, it

would appear that the speed (and in some cases the

accuracy) with which simple perceptual–motor tasks are

performed tends to increase over much of the day and,

with the possible exception of a ‘post-lunch dip’, parallels

changes in body temperature [43,44]. In stark contrast,

short-term memory, and in particular that for the

information presented in prose, has been found to be

at its maximum in the morning (between ~08:00 and

11:00 h) and to decrease over most of the day [43,45]. In

this context, it is noteworthy that Monk and Embrey [46]

found that process controllers made fewer errors at night

in entering codes into a computer, presumably reflecting

on the fact that their task was essentially one of shortterm memory for alphanumeric codes. However, it seems

unlikely that the failure of the mathematical models to

account for the trends in risk reflects on the memory load

involved since most of the studies of risk were based on

highly repetitive perceptual–motor tasks.

Conclusions

The main conclusion to be drawn from the studies

reviewed in this paper is that both safety and productivity

are reduced at night. This reduction probably reflects on a

number of underlying factors, including impaired health,

a disturbed social life, shortened and disturbed sleep, and

disrupted circadian rhythms. Despite the fact that current

mathematical models of alertness and performance have

difficulty in accounting for the precise trends in risk

associated with various features of shift systems, it is clear

that these trends could be used to try to reduce the risks

associated with working at night. More specifically, it

would seem that in order to minimize the overall risk on a

shift system we need to consider the number of successive

night shifts, the length of the night shifts and the provision of breaks within them. Finally, however, it is clear

that these factors need to be considered in combination

with one another since, for example, a 12 h night shift

that included frequent rest breaks might well prove safer

than a shorter 8 h night shift with only a single, mid-shift

break. Likewise, the length of the night shifts and the

number of successive night shifts involved in a shift

system will act in combination to determine the overall

risk on that system.

Figure 7. The trend in relative risk between breaks (from [30]).

S. FOLKARD AND P. TUCKER: SHIFT WORK, SAFETY AND PRODUCTIVITY 99
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