Determinants of sleepiness at work among railway control workers

Applied Ergonomics 58 (2017) 293e300

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Applied Ergonomics journal homepage: www.elsevier.com/locate/apergo

Determinants of sleepiness at work among railway control workers
Carvalhais a, b, Catarina Neto a, 1, Júlia Teles c, Paulo Noriega a, b, Teresa Cotrim a, b, *, Jose a, b Francisco Rebelo a

Ergonomics Laboratory, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, 1499-002, Cruz-Quebrada, Portugal
Nogueira, Po
rio do Alto da Ajuda, 1349-055,
lo Universita Centre for Architecture, Urban Planning and Design (CIAUD), Universidade de Lisboa, Rua Sa Portugal c CIPER and Mathematics Unit, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, 1499-002, Cruz-Quebrada, Portugal b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 October 2015 Received in revised form 11 July 2016 Accepted 12 July 2016

In the last two decades the control of the Portuguese railway network has become much more centralized in three centres, there integrating the functions of route flow management, electrical control and signalling. This study aimed to investigate the influence of work and individual determinants in sleepiness among railway control workers, namely socio-demographic factors, work ability, psychosocial factors, shiftwork characteristics, fatigue perception, and sleep. Sleepiness by shift was associated with quality of sleep, job satisfaction, fatigue perception, quantitative demands, and age. The results indicate a high prevalence of sleepiness during the night shift and show the relevance of the quality of sleep as a predictor in the three models of sleepiness for morning, afternoon and night shifts. This study, done at the major Portuguese railway control centre, alerted managers to the importance of schedule planning as well as sleepiness prevention plans and makes these results a reference for future research. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Shiftwork Sleepiness Railway control

1. Introduction In the last two decades the control of the Portuguese railway network has become much more centralized in three centres, there integrating the functions of route flow management, electrical control and signalling. This is also happening in different European countries (Wilson and Norris, 2005). Computerized remote controlling of railway traffic requires constant surveillance of on-going traffic and includes intense periods of information encoding and € et al., 2002). In such situations, the problem-solving (H€ arma increased demand on available resources may cause the worker's overload with consequences to the systems' safety, such as delayed information processing or failures (Fallahi et al., 2016). With the change of the railway control centres organization, it is necessary to preserve safety, efficiency, quality and reliability of the service. In order to make improvements, one needs to understand not only the performance of railway workers, but also the potential effects of

* Corresponding author. Ergonomics Laboratory, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, 1499-002, Cruz-Quebrada, Portugal. E-mail address: [email protected] (T. Cotrim). 1
gico de Automocio
n de Galicia, PontePresent address: CTAG, Centro Tecnolo vedra, Spain. http://dx.doi.org/10.1016/j.apergo.2016.07.006 0003-6870/© 2016 Elsevier Ltd. All rights reserved.

railway systems and jobs on them in terms of health and safety, attitudes and satisfaction, and competency and skills development, as advocated by Wilson and Norris (2005). 1.1. Objectives This study aimed to investigate the influence of work and individual determinants in sleepiness among railway control workers, namely socio-demographic factors, work ability, psychosocial factors, shiftwork characteristics, fatigue perception, and sleep. 1.2. Background 1.2.1. Ageing, work ability and shiftwork Most European countries face the challenge of an ageing population trend. Ageing is typically associated with decreased physical health and, thus, it may increase the adverse impact of shiftwork on health. In particular, ageing is linked to a reduced flexibility of the circadian adjustment, less-efficient sleep and sleep problems (Bonnefond et al., 2006; Costa, 2003; Eurofound, 2000). When considering morning and night shifts, older workers when compared with younger workers, seem to have fewer problems in the morning shift, while the opposite is true for the night shift (Blok

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and de Looze, 2011; Bonnefond et al., 2006). Some studies show that shiftwork, when including nights, predicts poor work ability, poor sleep and job dissatisfaction (Conway et al., 2008). However, several factors other than shiftwork have a negative influence on work ability measured by the Work Ability Index (WAI): overweight, decline in health status, sleep problems, and holding a mostly physical job increase the risk of moderate or poor work ability (Costa and Sartori, 2007; Fassi et al., 2013; Fischer et al., 2006; Yong et al., 2010). On the contrary, holding a mostly mental job function may have a favourable impact on WAI (Fassi et al., 2013).

(Sallinen et al., 2005) and these are often a sleep-deprived group, getting 6 or fewer hours of sleep daily (Gertler and Viale, 2007). 2. Material and methods The study was designed as a cross-sectional survey at the major Portuguese railway control centre. The survey was conducted between November and December of 2011, following a paper and pencil format. Only workers performing railway control tasks were included in the study. 2.1. Participants

1.2.2. Shiftwork, sleep, fatigue and sleepiness in railway workers In general, shiftworkers report a higher number of health complaints when compared to day workers, and sleeping problems are a common symptom (Åkerstedt, 2003; Bambra et al., 2008; Tucker et al., 2011). Shiftworkers with night shifts show a higher frequency of sleep disturbances. The desynchronisation of the sleep/wake rhythm, mainly caused by night shifts, results in disturbances of both the quality and quantity of sleep (Conway et al., 2008; Knauth, 1996; Short et al., 2016); shortened sleep lengths are also associated with morning shifts, especially if they begin too €nen et al., early (Åkerstedt, 2003; Folkard and Barton, 1993; Pylkko 2015; van de Ven et al., 2016). Other factors influencing sleep patterns are the quick changeovers that shorten the rest periods between shifts (Åkerstedt, 2003) and the extended time awake (Åkerstedt et al., 2014). Sleep troubles are also associated with adverse work-related psychosocial risk factors, such as high work demands, high quantitative workload, repetitive work, low decision latitude, role conflicts, or perceived stress (Åkerstedt et al., 2015; Åkerstedt, 2006; Folkard et al., 1995; Knudsen et al., 2007; Rugulies et al., 2009). In general, the length of the main sleep is negatively related €rma € et al., with severe sleepiness at work (Åkerstedt et al., 2014; Ha 2002), but work factors, such as monotony and sedentary restricted postures, may also play a role in the level of sleepiness (Åkerstedt et al., 2014; Sallinen et al., 2004; Zhang and Chan, 2014). Sleepiness is common in transport operations and is regarded as a significant cause of crashes and safety-critical events (Åkerstedt et al., € et al., 2002; Zhang and Chan, 2014). In terms of risk, 2014; H€ arma the time of day and the number of hours slept play an important role, together with other factors such as age, shiftwork, and different types of sleep disorders (Åkerstedt et al., 2014; Anund et al., 2015; Zhang and Chan, 2014). Studies with drivers showed that those who slept less than 6 h had a higher level of excessive daytime sleepiness (Zhang and Chan, 2014). Sleepiness and fatigue are intertwined and some causes are common to both concepts (Anund et al., 2015; Phillips, 2015). Major causes of fatigue are timing and duration of sleep, length of time spent awake, and nature of tasks (Anund et al., 2015; Dawson and McCulloch, 2005; Dorrian et al., 2011; Phillips, 2015; Williamson et al., 2011). Studies with railway controllers showed that workload significantly influences fatigue (Dorrian et al., 2011) but the point at which fatigue is likely to become problematic is more directly related to the duration of wakefulness (Dawson and McCulloch, 2005). Increasing prior sleep loss increases average fatigue levels and the rate at which fatigue accumulates throughout the day (Anund et al., 2015; Dawson and McCulloch, 2005; Dorrian et al., 2011). Shiftwork and associated fatigue have a negative effect on performance, alertness, and safety (Anund et al., 2015; Dorrian et al., 2011; Williamson et al., 2011). In the context of transportation, mental fatigue and sleepiness have the most important effects on the operator's performance (Williamson et al., 2011). Among railway controllers, a high prevalence (25%e58%) of severe sleepiness is found on the night shift

The population of railway controllers consisted of 132 male workers, all of them were included in the study. The response rate was 73.48% (n ¼ 97). The workers were contacted personally, and informed about the study design and its objectives. The questionnaire was applied further to a written informed consent explaining that participation in the study was voluntary and anonymous. 2.2. Questionnaire The questionnaire was developed according to the study objectives, the literature review and the task observation. It included four parts: socio-demographic data; shiftwork, fatigue perception, sleep characteristics and sleepiness; the Work Ability Index; and the Copenhagen Psychosocial Questionnaire. It was applied once, to each railway controller during his work breaks. 2.2.1. Socio-demographic characteristics The first part of the questionnaire included the sociodemographic data considered relevant in studies about shiftwork and sleepiness and related to railway work (Demerouti et al., 2004; €nen et al., 2015; Nigatu et al., 2016; Paterson et al., 2012; Pylkko Ryan et al., 2009a,b), such as age, seniority, height and weight in order to calculate the body mass index (BMI), civil status, dependents (children under 12 years old), smoking habits, coffee intake, regular physical exercise practice, medication intake, and circadian type. The circadian type was characterized by a selfassessment single question adapted from the Portuguese version of the Morningness-Eveningness Questionnaire (Loureiro and Garcia-Marques, 2015), based on its fifth question but using a brief definition of behavioural characteristics of the three circadian types (morning, intermediate and evening type). 2.2.2. Work Ability Index The Portuguese version of the Work Ability Index (Silva et al., 2011) was used to describe the workers assessment regarding their own work ability (Ilmarinen et al., 2005). WAI is made up of seven items, based on the actual work ability, the physical and mental work demands, diagnosed illnesses, work limitations due to illness, absenteeism, work ability prognosis, and psychological resources. Scoring is classified into poor (7e27), moderate (28e36), good (37e43), and excellent (44e49) work ability (Silva et al., 2011). 2.2.3. COPSOQ II The Portuguese medium version of the Copenhagen Psychosocial Questionnaire II (COPSOQ II) (Silva et al., 2012) was used to assess the psychosocial risk factors. The COPSOQ II is a standardized questionnaire covering a broad range of psychosocial factors (Moncada et al., 2014; Pejtersen et al., 2010a,b). According to the work activity analysis, and the team leaders participation, the following scales were selected: work-family conflict, sleeping

T. Cotrim et al. / Applied Ergonomics 58 (2017) 293e300

troubles, burnout, stress, symptoms of depression, cognitive demands, job satisfaction, quantitative demands, work pace, emotional demands, social support from colleagues, and general health. The results of each scale were analysed by using the cutting points 2.33 and 3.66 allowing the classification in three levels: favourable, neutral and critical (Pejtersen et al., 2010; Silva et al., 2012). 2.2.4. Shiftwork, sleep characteristics and sleepiness This part of the questionnaire was developed taking into consideration the specific characteristics of the railway control activity. Sleep duration was measured by a question about the number of hours slept after the different shifts (morning, afternoon, night) based on the concepts used by the Standard Shiftwork Index (SSI) (Barton et al., 1995) and the Survey of Shiftwork (SOS) (Folkard et al., 1995). Quality of sleep was adapted from the SSI and the SOS, based on the question “How well do you normally sleep?”, but asking directly about the quality of sleep after each shift through a 5 points Likert item, from “extremely well” to “extremely badly”. Satisfaction with the shift system and fatigue perception were evaluated adopting the 5 point rating scales used in the Questionnaire REQUEST (Ryan et al., 2009a,b). The satisfaction scale ranged from “very dissatisfied” to “very satisfied”. The fatigue perception was evaluated by shift with a scale ranging from “not at all” to “a great deal”. Sleepiness was characterized, by asking participants to report the frequency of sleepiness at work during each shift from “very seldom or never” to “very often or continuously”. It was also asked whether these episodes of sleepiness impaired work performance on a scale from “not at all” to “very much”. These two items used a €rma € et al. (2002). 5-point scale based on the study of Ha 2.3. Statistical analysis The analysis of the questionnaire was carried out by describing the ratings for the scales and the items. The 5 points (1e5) Likert scales were dichotomized, in respect of the Likert anchors, in order to allow the implementation of the logistic regression model: quality of sleep was classified as good when including the answers “extremely well” and “well”; fatigue was considered present (yes) when the answers were “moderate amount”, “quite a lot”, and “a great deal”; and sleepiness was considered present (yes) for “sometimes”, “often” and “very often or continuously”. Despite the small sample size (n ¼ 97) (Hosmer and Lemeshow, 2005), the sample studied represents 73.48% of the population, which allowed us to consider the use of a multiple logistic regression model. Three logistic regression models were adjusted for each dependent variable, morning, afternoon and night shift sleepiness. The backward stepwise method using the Wald statistic was used for the model variable selection procedure. Linearity in the logit was verified for the continuous predictors. Several goodness-of-fit measures were calculated to assess the fit of the models. In particular, the area under the ROC curves (AUC) showed that the models have good predictive accuracy, i.e., they have the ability to discriminate between participants with sleepiness and those without sleepiness. 3. Results 3.1. Socio-demographics of the study sample All the participants were males, with a mean age of 44.8 years

295

old, a mean work seniority of 21.7 years, and 50% of the sample worked with the railway control system for more than 13 years (Table 1). Almost half of our sample presented pre-obesity (51.5%), and 25.8% were obese (classes I, II and III). Most of them were married (71.1%), 75.3% had children living with them, 69.1% did not practice physical exercise regularly, 40.2% were smokers, 95.9% drunk coffee on a daily basis and 92.8% did not take any sleep medication. With respect to the circadian type, more than half of the sample (57.1%) was from an intermediate type and only 9.9% from the evening type (Table 2). 3.2. Work ability and psychosocial factors at work The WAI mean score was 41.24 (SD ¼ 4.92), indicating a good work ability level. The prevalence of improved work ability (good and excellent WAI) was 86.5%, while only 13.5% showed reduced work ability (poor and moderate WAI) (Table 3). The COPSOQ scales evaluated the psychosocial factors. The workers perceived high cognitive demands [3.92 ± 0.57], and this was the only scale where critical values were found based on the COPSOQ cutting points (Table 4). 3.3. Shiftwork, fatigue perception, sleep characteristics and sleepiness The shifts analysed involved the morning (08:00e16:00 h), the afternoon (16:00e24:00 h) and the night (24:00e08:00 h) shifts. The rotation system had an irregular speed pattern (from 1 to 5 consecutive days on the same shift) and mainly moved backwards. The moments corresponding to rush hours in railway traffic happened from 07H00e09H30 and from 16H30 to 19H30. After the night shift, workers had the lowest sleeping hours' average, compared with the morning and afternoon shifts (Table 5), with 37.9% sleeping 5 h or less (Table 6). After the night shift, the frequency of poor sleep quality was higher (78.4%), when compared with the morning and afternoon shifts. Throughout the night shift, fatigue perception (83.3%) and sleepiness (72.2%) showed high prevalence (Table 6). Almost half of the railway controllers were dissatisfied or very dissatisfied with the shift system (48.9%); over one-third were neutral (35.4%) (Table 7). The sleepiness consequences on the worker's performance were mainly perceived as low (32.0%) or very low (36.1%). Nevertheless, 21.6% considered them as moderate and 10.3% as high/very high, likely to influence distractions (51.5%), and tasks, such as reading graphic information (20.6%), opening traffic signals (20.6%) and train passage priorities (9.3%) (Table 7). 3.4. The effects of socio-demographic characteristics, WAI, psychosocial factors, shiftwork, and sleep characteristics in sleepiness The results of logistic regression are shown in Table 8 for the morning, afternoon and night shifts sleepiness, respectively. Concerning morning shift sleepiness, the significant predictors were age, quantitative demands and morning sleep quality. According to the model, the log of the odds of a worker who has morning sleepiness was positively related to the quantitative demands scores and negatively related to age and morning sleep quality. The odds ratio for morning sleep quality was 0.082, suggesting that those who had poor sleep quality after the morning shift were 12.2 times more likely to have morning sleepiness than those who had good sleep quality. The odds ratio for age and quantitative demands were, respectively, 0.838 and 6.785, which

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Table 1 Age and seniority of the study participants.

Age Seniority Seniority working with the railway control system

Mean ± SD

Median

Minemax

N

44.80 ± 0.53 21.71 ± 6.92 10.65 ± 0.53

45.00 22.00 13.00

34e57 2e41 0.4e20.0

97 95 96

Table 2 Body mass index (BMI according to WHO classification), civil status, children, circadian type, smoking habits, coffee, physical exercise and medication intake of the study participants.

BMI

Underweight Normal weight Pre-Obesity Obesity class I Obesity class II Obesity class III Single Married/Partnership Divorced With Without Morning Intermediate Evening No Yes No Yes No Yes No Yes

Civil status

Children Circadian type

Smoking habits Coffee intake Regular exercise practice Sleep medication intake

N

%

1 21 50 19 5 1 17 69 11 73 24 30 52 9 58 39 4 93 67 30 90 7

1.0 21.7 51.5 19.6 5.2 1.0 17.5 71.1 11.4 75.3 24.7 33.0 57.1 9.9 59.8 40.2 4.1 95.9 69.1 30.9 92.8 7.2

Table 3 Work ability index categories.

WAI

Excellent Good Moderate Poor

Table 4 Characteristics of the COPSOQ scales (1e5 points). Mean ± SD Work-family conflict Sleep troubles Burnout Stress Depressive symptoms Cognitive demands Job satisfaction Quantitative demands Work pace Emotional demands Social support from colleagues General health

2.85 2.43 2.36 2.30 2.20 3.92 3.17 2.85 2.43 2.36 3.32 3.36

± ± ± ± ± ± ± ± ± ± ± ±

1.00 0.95 0.80 0.72 0.74 0.57 0.66 1.00 0.95 0.80 0.66 0.84

% 37.1 49.4 12.4 1.1

Table 6 Sleep quality and sleepiness characteristics by shift. Median

Minemax

N

3.00 2.50 2.50 2.50 2.00 4.00 3.00 3.00 2.50 2.50 3.33 3.00

1.00e4.67 1.00e5.00 1.00e4.50 1.00e4.00 1.00e4.00 2.67e5.00 1.75e5.00 1.00e4.67 1.00e5.00 1.00e4.50 1.33e5.00 1.00e5.00

96 97 97 97 96 96 96 96 97 97 96 97

Table 5 Average sleeping hours after each shift.

Morning (08:00e16:00 h) Afternoon (16:00e24:00 h) Nigth (24:00e08:00 h)

N 33 44 11 1

Mean ± SD

Median

Minemax

N

6.66 ± 1.12 7.37 ± 1.06 6.29 ± 1.96

7.00 8.00 6.00

2.00e08.00 5.00e10.00 3.00e12.00

95 95 95

Morning

Fatigue perception Sleep duration after shift

Sleep quality Sleepiness

Yes No 5 h 6h 7h 8 h Good Poor Yes No

Afternoon

Night

N

%

N

%

N

%

50 45 11 33 25 26 61 36 37 59

52.6 47.4 11.6 34.7 26.3 27.4 62.9 37.1 38.5 61.5

41 54 3 19 24 49 64 33 18 78

43.2 56.8 3.1 20.0 25.3 51.6 66.0 34.0 18.8 81.2

80 16 36 24 13 22 21 76 70 27

83.3 16.7 37.9 25.3 13.7 23.1 21.6 78.4 72.2 27.8

means that for each year of age increase, the odds of having morning sleepiness decreased 1.2 times, and for each unit of quantitative demands increase, the odds of having morning sleepiness increased 6.8 times (Table 8). With respect to afternoon shift sleepiness, the significant predictors were job satisfaction and sleep quality after the afternoon shift, being the log of the odds of a worker having afternoon sleepiness negatively related to both. The odds ratio for sleep

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297

Table 7 Self-perception of satisfaction with the shift system, the level of sleepiness influence on performance, and sleepiness main influence on tasks.

Satisfaction with the shift system

Sleepiness influence

Sleepiness main influence on tasks More distractions More errors in reading graphic information More delays in opening traffic signals More errors in train passage priorities

N

%

Very Satisfied Satisfied Neutral Dissatisfied Very Dissatisfied Very Low Low Moderate High Very High

2 13 34 27 20 35 31 21 4 6

2.0 13.5 35.4 28.1 20.8 36.1 32.0 21.6 4.1 6.2

No Yes No Yes No Yes No Yes

47 50 77 20 77 20 88 9

48.5 51.5 79.4 20.6 79.4 20.6 90.7 9.3

Table 8 Logistic regression model adjusted for the dependent variables morning, afternoon, and night shifts sleepiness (1 ¼ yes, 0 ¼ no). Predictor Morning sleepinessa Constant Age Quantitative demands Sleep quality after morning shift (1 ¼ good, 0 ¼ poor) Afternoon sleepinessb Constant Job satisfaction Sleep quality after afternoon shift (1 ¼ good, 0 ¼ poor) Night sleepinessc Constant Job satisfaction General health Night shift fatigue (1 ¼ no, 0 ¼ yes) Sleep quality after morning shift (1 ¼ good, 0 ¼ poor) Sleep quality after night shift (1 ¼ good, 0 ¼ poor)

b

SE(b)

Wald

d.f.

p

Odds ratio

95% C.I. odds ratio

4.956 0.176 1.915 2.498

2.566 0.059 0.602 0.612

3.729 9.038 10.132 16.640

1 1 1 1

0.053 0.003 0.001 <0.001

0.838 6.785 0.082

(0.747, 0.941) (2.087, 22.060) (0.025, 0.273)

6.356 2.086 3.244

2.143 0.671 0.809

8.801 9.673 16.061

1 1 1

0.003 0.002 <0.001

0.124 0.039

(0.033, 0.462) (0.008, 0.191)

10.766 1.249 1.000 1.906 1.793 2.268

2.950 0.606 0.453 0.819 0.737 0.731

13.316 4.244 4.882 5.419 5.916 9.626

1 1 1 1 1 1

<0.001 0.039 0.027 0.020 0.015 0.002

0.287 0.368 0.149 0.167 0.104

(0.087, (0.151, (0.030, (0.039, (0.025,

a Overall model evaluation (Likelihood ratio test), c2(3) ¼ 39.782, p < Nagelkerke R2 ¼ .466; % correct classification ¼ 73.7% AUC ¼ 0.850 with b Overall model evaluation (Likelihood ratio test), c2(2) ¼ 31.595, p < Nagelkerke R2 ¼ .455; % correct classification ¼ 85.3% AUC ¼ 0.878 with c Overall model evaluation (Likelihood ratio test), c2(5) ¼ 45.369, p < Nagelkerke R2 ¼ .545; % correct classification ¼ 85.3% AUC ¼ 0.885 with

0.941) 0.893) 0.740) 0.706) 0.434)

.001; Goodness-of-fit test (Hosmer & Lemeshow), c2(7) ¼ 5.263, p ¼ .628; Cox & Snell R2 ¼ .342; 95% CI ¼ (0.773, 0.926). .001; Goodness-of-fit test (Hosmer & Lemeshow), c2(8) ¼ 2.848, p ¼ .944; Cox & Snell R2 ¼ .283; 95% CI ¼ (0.800, 0.956). .001; Goodness-of-fit test (Hosmer & Lemeshow), c2(8) ¼ 7.624, p ¼ .471; Cox & Snell R2 ¼ .380; 95% CI ¼ (0.800, 0.971).

quality after the afternoon shift was 0.039, suggesting that those who had poor sleep quality after the afternoon shift were almost 25.6 times more likely to have afternoon sleepiness, when compared to those who had good sleep quality. The odds ratio for job satisfaction was 0.124, which means that for each unit of job satisfaction increase, the odds of having afternoon sleepiness decreased 8.1 times (Table 8). Regarding night shift sleepiness, the significant predictors were job satisfaction, general health, perception of night shift fatigue and sleep quality after morning and night shifts. According to the model, the log of the odds of a worker having night sleepiness was negatively related to all predictors. The odds ratio for sleep quality after morning and night shifts is respectively 0.167 and 0.104, suggesting that those who had poor sleep quality after the morning and the night shifts were respectively 6.0 and 9.6 times more likely to have night sleepiness, when compared to those who had good sleep quality. The odds ratio for nigh shift fatigue was 0.149, suggesting that those who had night shift fatigue were 6.71 times more likely to have night sleepiness than those who without fatigue. The odds ratio for job satisfaction was 0.287, which means that for each

unit of job satisfaction increase, the odds of having night sleepiness decreased 3.5 times. The odds ratio for general health was 0.368, which means that for each unit of general health increase, the odds of having night sleepiness decreased 2.7 times (Table 8). 4. Discussion € According to some authors (Gertler and Viale, 2007; H€ arma et al., 2002), there are few studies researching railway control workers sleepiness and sleep, in connection with different types of work shifts and other individual determinants. Likewise, in Portugal, little research on determinants of sleepiness among railway control workers has been done. In that sense, this research gives a contribution to the data available regarding the influence of work and individual determinants on sleepiness in a sample of Portuguese railway control workers. Our results indicate a high prevalence of sleepiness (72.2%) during the night shift. Earlier studies with railway controllers have also shown that sleepiness is associated mainly with night shifts, and that severe sleepiness is perceived with a high prevalence

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€rma € et al., 2002; Sallinen (25%e61.6%) (Gertler and Viale, 2007; Ha et al., 2005). The main predictors of sleepiness were age, general health, quantitative demands, job satisfaction, night shift fatigue, and sleep quality. The discussion will be focused on these variables. In this study, 50% of the railway workers were aged 45e57 years old. Age appeared in the logistic regression model, with morning shift sleepiness decreasing with age. A similar effect was found €rma € et al., 2002) and aircraft maintenance with train drivers (Ha workers (Bonnefond et al., 2006), while there was no age effect on €rma € et al., 2002; Sallinen sleepiness among traffic controllers (Ha et al., 2005). In the models for afternoon and night sleepiness, age did not appear as a predictive variable. This can be understood because young shift workers have been reported to perceive higher levels of sleepiness during early morning shifts (Bonnefond et al., €rma € et al., 2002) and because they are more sensitive to 2006; Ha an acute sleep loss than middle-aged or older subjects (Harma, 1995). Younger shift workers are more vulnerable to stressors because they are less able to cope with them, and this may represent a risk factor for poor sleep quality (Doi et al., 2003). The interaction between low social support and low age is also associated with a high risk of short sleep duration (Parkes, 2016). On the other hand, our sample was very experienced, with a high mean seniority, in working with the railway control system. A greater length of shiftwork experience may also mean progressive learning and development of better coping strategies (Costa, 2005) among the older groups. Our findings may have been modulated by other variables: 75.3% had young children living with them which is likely to cause work-family conflict (Demerouti et al., 2004; Gertler and Viale, 2007; Sallinen et al., 2005). It also seems plausible to explain these results from the selection processes that may occur in shiftwork (Blok and de Looze, 2011), often called the “healthy shiftworker effect” (Saksvik et al., 2011). With respect to health perception, a high percentage (46.4%) of poor and fair grades were found, which makes the general health perception of this group of controllers lower than what was found in similar studies (Gertler and Viale, 2007), or in other shiftwork professional groups (Demerouti et al., 2004). Gertler and Viale (2007) found that railway controllers on the night shift had a worse health perception when compared to those on dayshifts. Nevertheless, the influence of general health perception revealed to be a predictor in the night shift sleepiness model. As general health perception increased, night shift sleepiness decreased indicating a better tolerance to night shift for those who have a better health perception. The psychosocial factors played an important role in the three sleepiness models; quantitative demands were a predictor of morning sleepiness, and job satisfaction of afternoon and night sleepiness. Morning shift sleepiness was explained by the quantitative demands; as quantitative demands increased, the chances of having morning sleepiness also increased. Some studies showed an association between sleep troubles and adverse psychosocial risk factors, such as high quantitative workload and repetitive work (Åkerstedt, 2006; Akerstedt et al., 2015; Knudsen et al., 2007; Parkes, 2016), which may contribute to explain the observed relationships. Regardless of the associations between greater work overload and more frequent incidence of sleep-related problems (Knudsen et al., 2007), the relationships are not necessarily linear. For high job demands, Parkes (2016) showed that sleep duration initially decreased as age increased, reaching 6.4 h per day at 44e49 years, followed by a rise at higher ages. Another possible explanation has to do with repetitive or monotonous work: rush hour happens from 07H00e09H30 and morning workers start their shift during rush hour and finish it before rail traffic increases again. The

subsequent part of their shift is more monotonous and repetitive, with sedentary restricted postures. They also have lunch, during this period. These factors can contribute to an increase of morning shift sleepiness (Åkerstedt et al., 2014; Knudsen et al., 2007; Parkes, 2016; Sallinen et al., 2004; T. Zhang and Chan, 2014). Another psychosocial factor contributing to explain the variability of sleepiness in our sample was job satisfaction. In both, afternoon and night sleepiness models, job satisfaction increase contributed to a decrease in sleepiness, which is in line with the study of Conway et al. (2008) where night shiftwork was a predictor of job dissatisfaction. Despite not being shown in the sleepiness regression models, dissatisfaction with the shift system was rather prevalent (48.9%) among this sample of railway controllers. The irregularity of the shift pattern present in our context may have had a high impact on satisfaction and subjective health (Jansen et al., 2003). Dissatisfaction with work schedules may have influenced the ability to cope with job demands, and, therefore, contributed to increased stress (Peters et al., 2009). Some studies reported that dissatisfied shift workers had increased sleepiness problems across shifts and the level of satisfaction seemed to reflect on how well the shift workers were coping with the schedule (Axelsson et al., 2004). The perception of fatigue was a significant predictor of night shift sleepiness, which is understandable given the close relationship between the two concepts (Anund et al., 2015). The high prevalence of fatigue during the night shift (83.3%) may explain why it only showed up in the night sleepiness model. In our sample, fatigue perception may be related with sleep quality after the night shift (rS ¼ 0.58; p < 0.001). Similar trends were found in studies with railway workers (Dorrian et al., 2011; Ryan et al., 2009a,b; Williamson et al., 2011). Ryan et al. (2009a,b) found quite high proportions of signallers reporting problems with fatigue, and inadequate sleep between shifts. Dorrian et al. (2011) found that sleep length, shift duration and workload rating were significant predictors of ratings of extreme tiredness, particularly during the night shift. In our sample, high levels of dissatisfaction with the shift system may have influenced fatigue perception (rS ¼ 0.22; p ¼ 0.033) as control over shift scheduling can have significant effects on fatigue (Pisarski and Barbour, 2014). Finally, sleep quality appeared as an explanatory variable in the three models. For each model, it is interesting to note that sleep quality after the morning shift was related to morning sleepiness, sleep quality after the afternoon shift to afternoon sleepiness and sleep quality after the night shift to night sleepiness. This would be easily understandable if the shifts were fixed or had a slow rotation mode but this was not the case because the shift system moved backwards with an irregular pattern. The backwards rotation system may help understand why poor sleep quality after the morning shift determined sleepiness during the night shift. However, there is no solid evidence that sleep should differ depending on the direction of rotation (Åkerstedt, 2003). Some studies showed that further factors (dependents, smoking, work type, workload, recovery opportunities), other than working time, may influence sleep duration and subjective sleep quality of railway workers (Dorrian et al., 2011; Paterson et al., 2012). On the other hand, sleep duration was not found in any of our three models despite its reduction, mainly after the night shift, with 37.9% of the workers with a self-estimated sleep duration of 5 h or less, and also after the morning shift, with 34.7% sleeping 6 h, and 11.6% sleeping 5 h or less. The length of the main sleep period was associated with sleepiness in several studies (Åkerstedt, 2003; Sallinen et al., 2005; Zhang and Chan, 2014), and according to Sallinen et al. (2005), each hour of sleep decreased the odds for the occurrence of severe sleepiness by 35%. The railway control workers' overall lack of sleep is a concern since research has shown that performance decrements occur with less than 7 h sleep (Gertler and Viale, 2007;

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Zhang and Chan, 2014). It may also be associated with attention deficits, such as response failures or lapses, impaired information processing and slower reaction times (Bonnefond et al., 2006). Sleepiness may have the most important effects on the operator's performance (Williamson et al., 2011). This was perceived by our sample: 31.9% of the railway controllers considered that sleepiness influenced performance moderately (21.6%) or highly (10.3%), in tasks such as reading graphic's information, opening traffic signals or giving train passage priorities; they also reported increased distractions (51.5%). The associations between work determinants and sleepiness are complex and its understanding may be conditioned by the limitations of the study; these need to be taken into account when interpreting the present findings. The sample size, the crosssectional design of the study and the self-reported data did not allow for sufficient analysis of the different causal factors linking the effects of shiftwork to sleepiness. Nevertheless, the present findings are consistent with studies of railway control workers addressing similar issues (Dorrian et al., €rma € et al., 2002; Paterson et al., 2011; Gertler and Viale, 2007; Ha 2012; Ryan et al., 2009a,b; Sallinen et al., 2005). Taking that into consideration, this group of railway control workers could benefit from education about vulnerable times of performance impairment, ways to promote wakefulness, how to improve sleep and good health and safety practices (Costa, 2003; Wright et al., 2013). Additionally, managers should consider the need of organizational measures viewing a better adjustment of shift systems to the workers needs. Dissatisfaction with work schedules could be managed by allowing some degree of participation of railway controllers in the decision about the working time schedule as referred by Costa and Sartori (2007). Organizational measures should also consider the enrichment of workers' tasks after rush hour, and reorganization of the breaks model or the schedules pattern. Some studies refer that older shiftworkers show positive changes in terms of health, sleep and scores on well-being, after changing to a very rapid forward rotating shift schedule (Bambra €rma € et al., 2006). et al., 2008; Ha 5. Conclusions Railway control workers perform a safety critical job with many responsibilities. The results indicate a high prevalence of sleepiness during the night shift among this sample of railway control workers and alert to the relevance of the quality of sleep as a predictor in the three models of sleepiness. The existence of sleepiness was associated with the shift type, quality of sleep, job satisfaction, fatigue perception, quantitative demands, and age. Shiftwork has long been recognized as an occupational hazard, and the relevance of the high prevalence of sleepiness during the night shift in our sample is linked to what Gertler and Viale (2007) state in terms of alertness being the key to the workers' ability to carry out their responsibilities safely and effectively. In this perspective, the results reinforce the need to alert railway systems managers to the problem of sleepiness, which may have huge consequences in terms of safety and efficiency. The results observed point out to the need of actions, aiming at the adjustments of shift timing, shift length and time between shifts, in addition to actions aiming at improving psychosocial and organizational work environments. In the control centre observed, managers were alerted to the importance of schedule planning and sleepiness prevention programs, and to the necessity of planning resilient systems and future technological developments in order to create innovative solutions to support railway controllers' actions and make the systems safer. This ergonomic analysis done at the major Portuguese railway control centre included 73.5% of its

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railway controllers' population, which makes these results a reference for future research, while providing some more knowledge about the human factors interactions in railway control real work contexts. When taking into account the ageing population trend, and the fact that the 50-year-olds, or even older workforce participation will increase, it is necessary to conduct additional longitudinal research to provide a basis for future age-related decision-making and for interventions viewing to facilitate workers to be engaged in the workplace until older ages. The prevention of severe sleepiness in railway traffic is of major importance, and, thus there is a continuing need for research on the associations between work, individual characteristics and sleepiness considering that the evolution of rail management systems to various forms of intelligent decision support systems and new communication networks will change the work of railway staff (Wilson and Norris, 2005). 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