Effects of alertness management training on sleepiness among long-haul truck drivers: A randomized controlled trial

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Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap

Effects of alertness management training on sleepiness among long-haul truck drivers: A randomized controlled trial ⁎

M. Pylkkönena,b, , A. Tolvanenb, C. Hublina, J. Kaartinenb, K. Karhulaa, S. Puttonena,c, M. Sihvolaa, M. Sallinena,b a

Finnish Institute of Occupational Health, Helsinki, Finland Department of Psychology, University of Jyväskylä, Jyväskylä, Finland c Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland b

A R T I C LE I N FO

A B S T R A C T

Keywords: Randomized controlled trial Driver education Sleepiness Sleep Sleepiness countermeasure Shift work

Education is a frequently recommended remedy for driver sleepiness in occupational settings, although not many studies have examined its usefulness. To date, there are no previous on-road randomized controlled trials investigating the benefits of training on sleepiness among employees working in road transport. To examine the effects of an educational intervention on long-haul truck drivers’ sleepiness at the wheel, amount of sleep between work shifts, and use of efficient sleepiness countermeasures (SCM) in association with night and non-night shift, a total of 53 truck drivers operating from southern Finland were allocated into an intervention and a control group using a stratified randomization method (allocation ratio for intervention and control groups 32:21, respectively). The intervention group received a 3.5-hour alertness management training followed by a two-month consultation period and motivational self-evaluation tasks two and 4–5 months after the training, while the control group had an opportunity to utilize their usual statutory occupational health care services. The outcomes were measured under drivers’ natural working and shift conditions over a period of two weeks before and after the intervention using unobtrusive data-collection methods including the Karolinska Sleepiness Scale measuring on-duty sleepiness, a combination of actigraphy and a sleep-log measuring sleep between duty hours, and self-report questionnaire items measuring the use of SCMs while on duty. The data analysis followed a perprotocol analysis. Results of the multilevel regression models showed no significant intervention-related improvements in driver sleepiness, prior sleep, or use of SCMs while working on night and early morning shifts compared to day and/or evening shifts. The current study failed to provide support for a feasible non-recurrent alertness-management training being effective remedy for driver sleepiness in occupational settings. These results cannot, however, be interpreted as evidence against alertness management training in general but propose that driver education is not a sufficient measure as such to alleviate driver sleepiness.

1. Introduction Sleepiness is a notorious issue among professional drivers and remains a popular target of studies and interventions. The phenomenon is particularly prevalent during work shifts that overlap drivers’ natural sleep periods (e.g., Åkerstedt and Wright, 2009; Sandberg et al., 2011; Di Milia and Kecklund et al., 2013). In addition to laws and hours of service regulations restricting professional drivers’ working hours and driving time,1 efforts have been made to prevent and reduce sleepy driving via infrastructure (e.g., rumble strips), technical solutions (e.g.,

sleepiness detectors), and driver training aspiring after enhanced alertness management. The current study seeks to shed light on the latter, since many previous studies have recommended education as a potential remedy for sleepiness (e.g., Brown, 1997; Carter et al., 2003; Bunn et al., 2005; Philip, 2005; Crummy et al., 2008; Armstrong et al., 2010) yet only a few non-randomized and randomized studies have reported promising results of its usefulness in transport and industrial sectors. Most of the studies suggesting educational interventions are beneficial in improving alertness and mitigating sleepiness have either been conducted in aviation (Gander et al. 2005; Rosekind et al. 2006;

⁎

Corresponding author at: Finnish Institute of Occupational Health, Working hours, Sleep, and Recovery -program, P.O. Box 40, FI-00032 TYÖTERVEYSLAITOS, Finland. E-mail address: mia.pylkkonen@ttl.fi (M. Pylkkönen). In Finland, truck drivers’ working times are regulated by the Finnish Working Hour Act (9.8.1996/605), the EU’s Working Time Directive (2003/88/EC), and the Regulation (EC) No 561/2006 of the European Parliament and of the Council on the harmonization of certain social legislation relating to road transport and amending Council Regulations (EEC) No 3821/ 85 and (EC) No 2135/98 and repealing Council Regulation (EEC) No 3820/85. 1

https://doi.org/10.1016/j.aap.2018.05.008 Received 4 May 2017; Received in revised form 6 May 2018; Accepted 7 May 2018 0001-4575/ © 2018 Published by Elsevier Ltd.

Please cite this article as: M., P., Accident Analysis and Prevention (2018), https://doi.org/10.1016/j.aap.2018.05.008

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were enrolled in the study. Fifty-four long-haul truck drivers were recruited from a total of 677 drivers the four companies employed at the time of recruitment. The recruitment proceeded from March till September 2010, and the drivers were self-selected for inclusion through advertisements sent to company representatives. The volunteered drivers were registered for the study in order of enrollment, and the recruitment was ceased when the sample exceeded the size defined sufficient on the basis of power calculations (Subsection 2.9.1). All drivers enrolled in the study (8.0% of the approached) satisfied the entry criteria (see Pylkkönen et al., 2015). Drivers were inquired for their possible participation in co-interventions. None of the drivers reported taking part in similar interventions while participating in the current study. The participating drivers were required an informed consent and were paid a compensation of 150 euros (USD 160) for a completed study phase (300 euros, USD 320) for completing both study phases.

Hauck et al. 2011) or among non-shift working day-time workers (Melamed & Oksenberg, 2002) and day-shift workers (Kakinuma et al. 2010; Nishinoue et al. 2012) outside the transport sector. As far as we know, the only randomized controlled trial (RCT) among shift workers operating in transport sector come from van Drongelen and colleagues (2014). After investigating the effects of a mobile-app intervention among airline pilots, the researchers concluded that compared to a minimal intervention providing basic non-tailored information related to fatigue and health, the mHealth intervention providing individuallytailored advice (van Drongelen et al. 2013, 2014) was beneficial in reducing self-reported fatigue at 6-month follow-up. To date, there are no previous on-road RCT studies investigating the benefits of training on sleepiness among employees working in road transport. Effectiveness of any training depends on the attainment of its pre-set objectives. According to Grossmann and Salas (2011), learning alone is not sufficient for training to be considered effective though. A fundamental goal of on-the-job education is that learning transfers to the job (i.e., is generalized to the job context and maintained over time) and leads to relevant changes in work performance (Baldwin and Ford, 1988). Unless transferred, a training is of little value for individuals taking part, and for organizations investing in it. Transfer problem (Michalak, 1981) is widely recognized in training research (Baldwin and Ford, 1988; Burke and Hutchins, 2007). According to Saks (2002), only 50% of training investments result in desired improvements at work with 40% of trainees failing to transfer immediately and 70% a year after training (Burke and Hutchins, 2007). This study is an extension to a previous one reporting baseline results of a RCT on-road study among Finnish long-haul truck drivers (Pylkkönen et al., 2015). The current study focuses on the follow-up phase of the study. It aims to find out whether a feasible, non-recurrent alertness management training is effective in improving driver alertness, amount of prior sleep, and use of efficient on-duty sleepiness countermeasures (SCM), especially while working night and early morning shifts. The training was considered effective if it led to positive changes in drivers’ on-duty alertness and alertness-related behaviors (sleep habits and use of SCMs) in real life. It was hypothesized that after participating in the training, drivers would be less sleepy at the wheel, obtain more sleep between duty hours, and use more efficient SCMs at work.

2.3. Randomization As one of the volunteers withdrew from the study before the sample was randomized, a total of fifty-three drivers were eventually assigned individually either to receive (intervention group) or not to receive (control group) the intervention. The allocation ratio for the two parallel study groups was 32:21, respectively. The randomization process was carried out by a statistician outside the research group and the random allocation sequence was generated by a computer randomnumber generator (SAS Power Procedure). To control for the possible influence of potentially confounding variables (age and employing logistic company) on outcome variables, the study groups were balanced using the stratified randomization method. Blinding the participants and those administering the intervention to group assignment was not possible in practice, as there were no ways to prevent the participants or the experimenters from knowing which study group the participants belonged to. The outcome assessors were not blinded to group assignment either. 2.4. Attrition and compliance with the intervention An overall attrition rate was 7.5% (4/53). Of those initially randomized to intervention and control groups, 98% (52/53) completed the baseline measurements, and 92.5% (49/53) completed both study phases. Of those assigned to intervention group, 9.4% (3/32) did not receive the treatment as allocated (compliance rate for the intervention 90.6%), and of those in the two study groups who successfully completed the baseline measurements, 5.8% (3/52) were lost to follow-up. None of the randomized participants were excluded by the experimenter. Comparison of the attrition rates in the intervention (6.5%, 2/ 31) and control (4.8%, 1/21) groups revealed no statistically significant difference (p = 0.798) in the drop-out rates between the two groups.

2. Methods 2.1. Study settings and procedures A two-armed RCT study with repeated-measures design investigating the effectiveness of an educational intervention was approved by the ethical committee of the Helsinki-Uusimaa hospital district. Data collection comprised of a set of pre-measurement questionnaires and two-week field measurements before (baseline) and after (follow-up) the intervention. The baseline measurements were conducted 5–6 months before (November 2010–April 2011) and the follow-up measurements 4–5 months after (November 2011–March 2012) the intervention. The mean duration of the time between baseline and follow-up measurements was 362 (range 313–431) days. The data collection took place in Finland, and the baseline and follow-up measurements were carried out at the same time of a year to control for seasonal effects. The educational intervention was implemented (during May–September 2011) separately in the participating companies. Drivers attended the training within their duty hours. The control group was given an opportunity to participate in the training after the study was concluded.

2.5. Interventions 2.5.1. Educational intervention Designing the intervention was influenced by the previous studies of Rosekind et al. (2001) and Gander et al. (2005), and the training was designed by the research group working on the study without the involvement of participating haulage companies. To enable its later exploitability, the central idea guiding the development work was that the training was to be designed feasible – that is, as time-efficient and costeffective as possible and simple enough to be implemented without substantial prior knowledge on the topic (e.g., by occupational health care professionals).

2.2. Participants 2.5.1.1. Objectives and content. The training aimed at promoting safe, economic, and environmentally friendly driving by optimizing driver alertness at the wheel. Training objectives were set on the basis of what was considered, based on the existing research literature, the most

Flow of the participants through the trial is presented in Fig. 1. Altogether five domestic middle-sized logistic companies operating countrywide from southern Finland were initially approached, and four 2

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Fig. 1. Double column (full width: 190 mm). Flowchart of the participants throughout the trial.

and were encouraged to engage in pair and group discussions related to the topics of the training. The trainees were provided with personalized advice on how to efficiently counteract sleepiness during day and night shift spells. In addition to general recommendations, the advice included tailored recommendations regarding optimal sleep, in-vehicle countermeasures (e.g., napping, caffeine), physical activity, and nutrition, based on trainees’ actual working times, sleep habits, and chronotype. The recommendations were complemented with personal, computerized estimations demonstrating how trainees’ alertness would alter during their typical shift spells and working hours. The estimations were produced by computer software which mathematically models the human sleep/wake cycle and recuperating system (Sleep/Wake Predictor, Åkerstedt et al., 2008). After the training session, the trainees were given concise feedback on their learning process and the lecture material in writing for prospective self-education subsequent to the training. The training was followed by a two-month consultation period during which trainees were encouraged to contact the trainers by phone or e-mail, had they any questions or inquiries related to the training.

relevant factors influencing the behaviors and conditions associated with driver sleepiness, and the objectives were defined according to the classification of learning outcomes by Kraiger et al. (1993). The training pursued to increase trainees’ knowledge on alertness, its underlying mechanisms, and efficient ways to improve it as well as to motivate trainees to become more interested in their on-duty alertness. The changes in knowledge were expected to lead to improved skills/ competencies to counteract sleepiness at the wheel and be reflected in improved alertness at the wheel. Training content, designed to match the training objectives, encompassed the basics of circadian rhythms, sleep, sleepiness, and alertness management, all discussed in relation to shift work and driving. Common misbeliefs and misconceptions regarding these topics were covered and discussed to reduce trainees’ motivation to continue using undesired SCMs and to increase their motivation to use desired ones instead. 2.5.1.2. Delivery. The training was delivered as a single training event separately in the participating haulage companies. Training sessions, delivered by two trainers and lasting around 3.5 h, brought together 4–9 trainees each. The training session had two parts, both comprising of a 45-min face-to-face lecture (served as a theoretical introduction) and a one-hour workshop following it. The trainees were given written exercises (requiring solo, pair, and group work) during the workshops

2.5.1.3. Evaluation. The training was designed to be self-contained with its own evaluation methods which were not designed to serve for data collection purposes. Evaluation criteria of training outcomes 3

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measured on road using the Karolinska Sleepiness Scale (KSS, Åkerstedt and Gillberg, 1990), and a shift was defined as “severely sleepy” if at least one of the hourly rated KSS values was 7 or greater. Secondary outcome measures included sleep prior to a work shift and use of SCMs during and outside statutory rest breaks. Daily sleep, measured as time in bed, was assessed using a combination of a sleep log and actigraphy (Actiwatch AW7, CamNtech, Cambridge, UK) and was regarded as “insufficient” if the total daily sleep (daily main and nap sleep combined) within a period of 24 h immediately preceding a shift start was 6 h or less. Use of SCMs was measured by closed-ended self-report questionnaire items incorporated in the sleep log. “Efficient” SCMs included napping during and/or outside statutory rest breaks and caffeine (coffee, tea, energy drinks, and other caffeinated compounds) ingestion outside statutory rest breaks, and efficient SCMs were considered being used during a shift if either napping, caffeine ingestion or both were resorted to at least once. The outcome measures are described in more detail in Pylkkönen et al. (2015).

were consistent with the pre-set learning objectives, and the evaluation followed the taxonomy of Kraiger et al. (1993) in outline. Changes in trainees’ knowledge were assessed using written assignments during the workshops. The exercises required the trainees to apply theoretical material presented in the lectures to practical problems rising from their own work. The trainees were asked to apply newly acquired knowledge into producing their own personal alertness management plan after the training. Changes in trainees’ skills/competencies were assessed using a short questionnaire adapted from the Readiness to Change Questionnaire (Rollnick et al., 1992). The questionnaire is based on the Trans-theoretical Model (Prochaska and DiClemente, 1982; Prochaska and Velicer, 1997) according to which behavior change is a process that unfolds over time and involves progressing through a series of stages, often “recycling” through them or regressing to earlier stages from later ones. The trainees were instructed to evaluate the stage of their behavior change process at three time points: at the beginning of training, two months after training, and at the beginning of the follow-up measurements. Since the training was not considered effective unless it transferred to real life, changes in trainees’ knowledge, skills/competencies, and motivation were also assessed at behavioral level by measuring trainees’ performance in real life/work settings on field (Subsection 2.6). Changes in trainees’ motivation were also evaluated at three time points using a self-evaluation questionnaire adapted from the Personal Projects Analysis by Brian Little (1983). The first self-evaluation included goal-setting whereby the trainees were asked to set personal goals related to improving their on-duty alertness based on what they had learned in the training. Having trainees set personal goals (Brown, 2005) and use action plans (Foxon, 1997) has been found to positively impact learning transfer (Burke and Hutchins, 2007). The trainees were then instructed to choose the one goal they considered the most important and to evaluate it at each three time points according to its significance, achievability, controllability, availability of social support, and stressfulness. At the beginning of the training session, the trainees were given an orienting task in which they had to evaluate how important they considered alertness at the wheel personally and how worried they felt over their alertness at the wheel in general. In addition to serving as an introduction and orientation for the training, this task was to measure trainees’ perceived utility and value of the training as well as their pretraining motivation.

2.7. Predictor variable Drivers’ working hours were categorized into shift types (first night, consecutive night, morning, and day and/or evening shifts, see Pylkkönen et al., 2015) which served as a predictor variable. Information about working hours was collected using driver-reports (sleep logs) and employers’ working time accountings (realized rosters). 2.8. Covariates Potential individual-related covariates were based on the questionnaire data and included the following baseline characteristics: age, Body Mass Index (BMI), subjective sleep need, Epworth Sleepiness Scale (ESS) score (Johns, 1991), diurnal type (Torsvall and Åkerstedt, 1980), sleep complaints (problems falling asleep, sleeping, or waking up 1–5 times a week, daily, or almost daily), self-estimated sleep quality, children under school-age (0–6 years), under-aged children (0–17 years), history of accident involvement, wage basis (payment by the hour or piecework pay), type of contract (employment contract or subcontractor), truck driving experience, shift work experience, selfestimated impact of sleepiness on performance in general, worry over one’s own alertness at the wheel, and subjective importance of alertness at the wheel. Potential shift-related covariates included shift duration, shift start time, time since waking up (measured at the end of a shift), and time off before shift start. The shift-related covariates were based on the realized rosters and sleep-log data of the drivers.

2.5.1.4. Feedback. After completing the alertness management plan and the second self-evaluations two months after the training, the trainees were instructed to return the written assignments to the trainers by mail for evaluation. When both study phases were successfully terminated, the trainees were given personal feedback on their learning process based on the evaluated assignments. The trainees were also enquired for their feedback on the training. The feedback covered, for example, training objectives, content, materials, delivery methods, and trainees’ perceived utility of the training.

2.9. Statistical methods 2.9.1. Sample size The sample size necessary for detecting a statistically significant difference was determined on the basis of power calculations (SAS Power Procedure) using subjective sleepiness (Subsection 2.6) as an outcome. In the power calculations, the mean difference in subjective sleepiness between the baseline and follow-up measurements was expected to be 1.3 rating points for the intervention group and 0.3 rating points for the control group. The standard deviation of the difference between the two measurements was expected to be 0.8 for the intervention and 2.0 for the control group. The target sample size was fiftytwo (32 for intervention and 20 for control group), and drop-out rate was expected to be 30%. Nominal power was set to 0.90 and alpha at 0.05.

2.5.2. Control intervention The control group drivers were given an opportunity to use their statutory preventive occupational health care services (e.g., annual medical examinations, evaluations of driving ability, and recommendations aimed at promoting occupational health) as usual.2 2.6. Outcome measures A primary outcome measure, for which the study groups were compared, was subjective sleepiness at the wheel. Sleepiness was

2.9.2. Primary and additional analysis Due to the early drop-out, the data analysis followed a per-protocol analysis (comparison of treatment groups including only participants who completed the treatment as allocated) instead of an intention-totreat analysis (comparison of the treatment groups including all

2 In Finland employers are obliged (Occupational Health Care Act No. 1383/2001) to organize occupational health care services for their employees.

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changes from Shift Type 4 in the control group, whereas the interaction variables between the dummy variables and the independent variable Trial measured changes from Shift Type 4 in the intervention group. Group difference from pre- to post-intervention phase on Shift Type 4 served as a reference to which group difference from pre- to post-intervention phase on the other shift types (Shift Type 1–3) were compared. The interaction terms were labelled Group * Trial * Shift Type 1–3. In Model 3 the within- and between-level parameters were estimated as in Model 2 but were controlled for the effects of the selected covariates. All three models were estimated with the full information maximum likelihood method using Mplus version 7.3 (Muthén and Muthén, 1998–2012). Standard errors were calculated with a robust method (the robust maximum likelihood estimator in Mplus). Alpha was set at 0.05 for all analyses.

participants as allocated after randomization). All data available were included in the analysis (available-case analysis). Intervention effects were analyzed using multilevel modeling which is a useful method for analyzing data arising from longitudinal designs as it takes into account the dependence of observations at the lower levels of hierarchically structured data (Hox and Maas, 2005). Multilevel logistic regression was used to test intervention effects, separately on each outcome measure (Subsection 2.6), with three successive models. Model 1 tested overall intervention effects, Model 2 tested whether the intervention effects differ between the shift types (predictor variable), and Model 3 contained covariates. Dependent variables in the within individual-level logistic regression analysis were the binary variables Severe sleepiness (KSS ≥7 vs. lower), Insufficient daily sleep (total daily sleep ≤ 6 h vs. more), and Use of efficient SCMs (at least once a shift vs. not once). Independent variables were labelled as Trial (0 = baseline measurement, 1 = follow-up measurement) and Group (0 = control group, 1 = intervention group) and interaction variable as Group * Trial. Of the potential individualrelated covariates (Subsection 2.8), the statistical models were adjusted for age, BMI, subjective sleep need, ESS score (continuous variables); diurnal type, sleep complaints, self-estimated sleep quality (categorical variables), children under school-age, under-aged children, history of accident involvement (dichotomous variables). Of the potential shiftrelated covariates (Subsection 2.8), the data were balanced for shift duration and shift start time (continuous variables). In Model 1 within-level parameters of the independent variable Trial and the interaction variable Group * Trial measured individual differences between intervention and control group drivers at the pre- and post-intervention phase. In Model 2 the predictor variable shift type was added to the model as three dummy coded variables, namely Shift Type 1 (first night shift), Shift Type 2 (consecutive night shift), and Shift Type 3 (morning shift). Shift type 4 (day and/or evening shift) served as a reference. Interaction variables (Trial * Shift Type 1–3) between these dummy variables and the independent variable Trial were also calculated and added to the model. Within- and between-level parameters of the dummy variables measured

3. Results 3.1. Characteristics of drivers and shift types Table 1 presents baseline demographic characteristics of those intervention and control group drivers who completed both study phases (compliers) and for those included in the statistical analysis (available cases). Intervention and control group drivers (both, available cases and compliers) were alike for most of the reported characteristics. Comparison of the compliers (n=49) and those lost to follow-up after the baseline measurements (non-compliers, n=3) revealed no statistically significant differences when compared for the baseline variables that were available for analysis, that is, age (compliers 37.5 ± 10.4 years, non-compliers 47.1 ± 7.5 years) and subjective sleep need (compliers 7:46 ± 0:59 h, non-compliers 7:00 ± 1:25 h). Based on the available case analysis, the study groups differed the most in terms of diurnal type (slightly more morning active drivers in the control group), sleep need (approximately 30 min greater in the intervention group), sleep complaints (the proportion of drivers with sleep complaints at least once a week 9.2% units greater in the control

Table 1 Double column (full width: 190 mm). Available cases (n = 52)

Compliers (n = 49) Intervention group (n = 29)

Mean ± SD

Control group (n = 21) Mean ± SD

Mean ± SD

Control group (n = 20) Mean ± SD

38.2 27.7 98.3 7:56 14.7 11.4

37.9 27.7 97.9 7:27 14.9 12.3

37.3 27.7 98.4 7:56 15.0 11.6

37.8 27.7 97.9 7:32 14.9 12.3

Intervention group (n = 31)

Age (yrs) BMIa (kg/m2) Waist circumference (cm) Subjective sleep need (h:min) Trucking experience (yrs) Shift work experience (yrs) DTIb Morning active Evening active Excessive daytime sleepinessc Sleep complaintsd Children < 7 yrs Children < 18 yrs Contract: Employment contract (vs. subcontractor) Wage basis: Payment by the hour (vs. piecework pay) History of accident involvement

± ± ± ± ± ±

11.3 4.8 10.3 1:05 9.9 8.8

± ± ± ± ± ±

9.4 3.8 9.6 0:49 10.4 8.9

± ± ± ± ± ±

11.1 4.9 10.5 1:06 9.9 8.9

± ± ± ± ± ±

9.6 3.8 9.6 0:45 10.4 8.9

% (n) 20.0 (6) 16.7 (5) 23.1 (6) 19.4 (6) 20.7 (6) 41.4 (12) 90.0 (27)

% (n) 35.0 (7) 15.0 (3) 25.0 (4) 28.6 (6) 15.0 (3) 50.0 (10) 95.0 (19)

% (n) 17.2 (5) 17.2 (5) 23.1 (6) 20.7 (6) 20.7 (6) 41.4 (12) 89.7 (26)

% (n) 35.0 (7) 15.0 (3) 25.0 (4) 30.0 (6) 15.0 (3) 50.0 (10) 95.0 (19)

86.7 (26)

75.0 (15)

89.7 (26)

75.0 (15)

12.9 (4)

4.8 (1)

13.8 (4)

5.0 (1)

Between-group differences (intervention vs. control group) in the baseline demographic characteristics of those included in the analysis (available cases), and of those completing both, pre- and post-intervention phases (compliers). (SD = standard deviation). a Body Mass Index. b Diurnal Type Index: 1.0–2.0 (evening active), 2.1–3.0 (intermediate type), 3.1–4.0 (morning active) (Torsvall and Åkerstedt, 1980). c Epworth Sleepiness Scale (ESS) score ≥ 11 (Johns, 1991). d Problems falling asleep, sleeping, or waking up 1–5 times a week, daily, or almost daily. 5

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Fig. 2. Double column (full width: 190 mm). Timing and duration of working hours (mean ± standard deviation) in different shift types (1st night = first night shift, 2nd→night = consecutive night shift) in the intervention (I) and control (C) group before (pre) and 4–5 months after (post) the intervention.

3.2. Intervention effects

group), wage basis (hourly wages 11.7% units more common in the intervention group), history of accident involvement, and having under-aged or under school-aged children (intervention group drivers had more often small children, whereas control group drivers were more often parents to teenagers). None of the intervention group drivers utilized the two-month consultation period after the training session. The self-evaluations were returned by 58.6% (17/29) of the trainees two months and by 86.2% (25/29) 4–5 months after the training. Majority of the trainees (93.1%, 27/29) finished and returned the alertness management plan as instructed. Fig. 2 demonstrates the timing and duration of working hours in the intervention and control group before and after the intervention. Shift length was the shortest (< 10 h on average) during day/evening shifts in both study groups and phases. In other shift types, working hours averaged invariably over 10 h in both study groups and phases being in the longest 12 h on average. Mean duration of morning and consecutive night shifts were rather equal in the two study groups and phases. The intervention group drivers had on average 9.1 (SD 1.5) work shifts (1.9 first and 2.6 consecutive night shifts, 2.7 morning shifts, 1.9 day/evening shifts) in the pre-measurement phase (mean total working time 97:48 ± 13:18 h) and 8.5 (SD 1.3) shifts (1.7 first and 2.7 consecutive night shifts, 2.3 morning shifts, 1.9 day/evening shifts) in the post-measurement phase (mean total working time 92:05 ± 17:18 h), whereas the control group drivers had on average 9.2 (SD 1.5) work shifts (1.7 first and 2.3 consecutive night shifts, 2.9 morning shifts, 2.3 day/evening shifts) in the pre-measurement phase (mean total working time 100:02 ± 17:42 h) and 8.7 (SD 1.1) shifts (1.6 first and 2.1 consecutive night shifts, 3.2 morning shifts, 1.9 day/evening shifts) in the post-measurement phase (mean total working time 93:48 ± 14:48 h). The timing of working hours was least divergent for early morning shifts (with start times ranging between 5:36–5:49 and end times between 16:12–16:49) in both study groups and phases. Some, although not systematic, differences were observable for other shift types within and between the two study groups during pre- and post-intervention phase. For night shifts, the difference was approximately 0.5–3 h (shift start time ranging between 17:32–20:38 and end time between 5:24–8:28), whereas for day/evening shifts it was more moderate, extending from 1.5 to 2.5 h (shift start time ranging between 9:46–12:13 and end time between 19:32–22:02).

3.2.1. Effects of intervention on driver sleepiness Results of the multilevel models (Model 1–3) presented in Table 2 showed no significant Group * Trial * Shift Type interaction effects, indicating the intervention did not yield improvements in driver sleepiness on night and early morning shifts compared to day/evening shifts (reference condition). Fig. 3 illustrates the proportion of shifts characterized by severe sleepiness at the wheel (KSS ≥ 7 at least once a shift) in the two study groups and phases. Severe sleepiness was most prevalent during first night shifts (30.2–38.6%) and least prevalent during morning shifts (3.2–11.9%), in both study groups and phases.

3.2.2. Effects of intervention on insufficient sleep The multilevel models (Model 1–3) did not reveal significant Group * Trial * Shift Type interaction effects, indicating the intervention did not reduce the occurrence of insufficient daily sleep (≤ 6 h) prior to night and early morning shifts compared to day/evening shifts (reference condition) (Table 3). Fig. 4 demonstrates the proportion of shifts preceded by insufficient daily sleep (total daily sleep ≤ 6 h) in the intervention and control group before and after the intervention. The results are in line with the multilevel models described in Table 3. Fig. 5 illustrates the timing of main sleep and the duration of total daily sleep (daily main and nap sleep combined) in the intervention and control group before and after the intervention. Intervention and control group drivers’ bed and get-up times were fairly similar within the four shift types in both study phases. Overall, the total daily sleep was at its shortest prior to morning and consecutive night shifts, and even then, the average duration of sleep exceeded 6 h. Compared to day/ evening shifts, total daily sleep was approximately 1–2 h shorter prior to morning and consecutive night shifts in both study groups and phases. Timing of main sleep preceding different shift types was very similar (variation in shift start/end times 0.5–1.5 h) in both study groups and phases.

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Table 2 Double column (full width: 190 mm). Model 1 (N = 798/52)

95% CI

Within-level effects

B

SE

P-value

Lower

Upper

OR (95% CI)

Trial Group*Trial

−0.60 0.83

0.34 0.44

0.075 0.058

−1.26 −0.03

0.06 1.69

0.55 (0.29–1.06) 2.29 (0.97–5.39)

Between-level effects

B

SE

P-value

Lower

Upper

R-square

Group

−0.35

0.76

0.643

−1.85

1.14

0.006

Model 2 (N=798/52)

95% CI

Within-level effects

B

SE

P-value

Lower

Upper

OR (95% CI)

Trial Group*Trial Shift Type 1 Shift Type 2 Shift Type 3 Group*Trial*Shift Type 1 Group*Trial*Shift Type 2 Group*Trial*Shift Type 3

−0.62 −0.41 2.46 1.00 −0.36 0.94 1.83 1.94

0.39 1.01 0.73 0.73 0.68 1.20 1.10 1.17

0.112 0.685 0.001 0.174 0.598 0.433 0.098 0.097

−1.38 −2.40 1.03 −0.44 −1.70 −1.41 −0.33 −0.35

0.14 1.58 3.89 2.43 0.98 3.28 3.99 4.23

0.54 (0.25–1.16) 0.66 (0.09–4.84) 11.69 (2.80–48.80) 2.71 (0.64–11.37) 0.70 (0.18–2.66) 2.56 (0.25–26.67) 6.22 (0.72–54.02) 6.95 (0.71–68.38)

Between-level effects

B

SE

P-value

Lower

Upper

R-square

Group

−0.53

0.88

0.546

−2.26

1.20

0.010

Model 3 (N=637/42)

95% CI

Within-level effects

B

SE

P-value

Lower

Upper

OR (95% CI)

Trial Group*Trial Shift Type 1 Shift Type 2 Shift Type 3 Group*Trial*Shift Type 1 Group*Trial*Shift Type 2 Group*Trial*Shift Type 3

−0.76 0.29 0.95 −0.29 0.92 −0.25 0.92 1.78

0.55 1.19 0.69 0.81 1.10 1.30 1.11 1.22

0.164 0.806 0.166 0.719 0.405 0.849 0.407 0.146

−1.83 −2.04 −0.39 −1.87 −1.24 −2.79 −1.25 −0.62

0.31 2.62 2.29 1.29 3.08 2.29 3.09 4.18

0.47 1.34 2.59 0.75 2.50 0.78 2.51 5.92

Between-level effects

B

SE

P-value

Lower

Upper

R-square

Group

−0.88

0.72

0.217

−2.29

0.52

0.530

(0.16–1.36) (0.13–13.76) (0.68–9.92) (0.15–3.64) (0.29–21.66) (0.06–9.91) (0.29–21.95) (0.54–65.04)

The effects of intervention on severe sleepiness (KSS ≥ 7 at least once a shift) in different shift types (day/evening shift as a reference). Shift Type 1 = first night shift. Shift Type 2 = consecutive night shift. Shift Type 3 = morning shift. B = estimate. SE = standard error. OR = Odds Ratio. CI = Confidence Intervals. N = number of shifts/number of drivers. Model 3 adjusted for age, Body Mass Index, subjective sleep need, Epworth Sleepiness Scale score, diurnal type, sleep complaints, self-estimated sleep quality, children under school-age, under-aged children, history of accident involvement, shift duration, and shift start time.

Fig. 3. Double column (full width: 190 mm). The proportion of shifts (1st night = first night shift, 2nd→night = consecutive night shift) characterized by severe sleepiness at the wheel (KSS ≥ 7 at least once a shift) in the intervention (I) and control (C) group before (pre) and 4–5 months after (post) the intervention. 7

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Table 3 Double column (full width: 190 mm). Model 1 (N = 727/52)

95% CI

Within-level effects

B

SE

P-value

Lower

Upper

OR (95% CI)

Trial Group*Trial

−0.52 1.15

0.33 0.50

0.116 0.021

−1.17 0.18

0.13 2.12

0.59 (0.31–1.14) 3.15 (1.19–8.31)

Between-level effects

B

SE

P-value

Lower

Upper

R-square

Group

−0.56

0.36

0.115

−1.26

0.14

0.311

Model 2 (N = 727/52)

95% CI

Within-level effects

B

SE

P-value

Lower

Upper

OR (95% CI)

Trial Group*Trial Shift Type 1 Shift Type 2 Shift Type 3 Group*Trial*Shift Type 1 Group*Trial*Shift Type 2 Group*Trial*Shift Type 3

−0.55 1.16 −0.42 1.15 0.55 −0.79 0.11 0.16

0.35 0.84 0.81 0.67 0.60 1.65 1.03 0.94

0.118 0.167 0.608 0.088 0.365 0.630 0.912 0.862

−1.24 −0.48 −2.00 −0.17 −0.64 −4.02 −1.91 −1.69

0.14 2.79 1.17 2.46 1.73 2.43 2.13 2.01

0.58 3.18 0.66 3.15 1.73 0.45 1.12 1.18

Between-level effects

B

SE

P-value

Lower

Upper

R-square

Group

−0.69

0.42

0.104

−1.51

0.14

0.216

Model 3 (N = 580/42)

(0.29–1.15) (0.62–16.34) (0.14–3.23) (0.84–11.75) (0.53–5.64) (0.02–11.40) (0.15–8.43) (0.19–7.49)

95% CI

Within-level effects

B

SE

P-value

Lower

Upper

OR (95% CI)

Trial Group*Trial Shift Type 1 Shift Type 2 Shift Type 3 Group*Trial*Shift Type 1 Group*Trial*Shift Type 2 Group*Trial*Shift Type 3

−0.52 1.53 −1.74 1.09 2.56 −1.52 −0.30 −0.79

0.51 0.96 1.18 0.66 0.75 1.73 1.10 1.08

0.306 0.109 0.140 0.101 0.001 0.381 0.787 0.463

−1.51 −0.34 −4.04 −0.21 1.09 −4.92 −2.45 −2.91

0.47 3.40 0.57 2.39 4.04 1.88 1.86 1.33

0.60 (0.22–1.61) 4.62 (0.71–30.07) 0.18 (0.02–1.77) 2.97 (0.81–10.89) 12.96 (2.96–56.75) 0.22 (0.01–6.53) 0.74 (0.09–6.42) 0.45 (0.05–3.77)

Between-level effects

B

SE

P-value

Lower

Upper

R-square

Group

−0.88

0.50

0.077

−1.85

0.10

1.000

The effects of intervention on insufficient daily sleep (total daily sleep ≤ 6 h) in different shift types (day/evening shift as a reference). Shift Type 1 = first night shift. Shift Type 2 = consecutive night shift. Shift Type 3 = morning shift. B = estimate. SE = standard error. OR = Odds Ratio. CI = Confidence Intervals. N = number of shifts/number of drivers. Model 3 adjusted for age, Body Mass Index, subjective sleep need, Epworth Sleepiness Scale score, diurnal type, sleep complaints, self-estimated sleep quality, children under school-age, under-aged children, history of accident involvement, shift duration, and shift start time.

Fig. 4. Double column (full width: 190 mm). The proportion of night (1st night = first night shift, 2nd→night = consecutive night shift) and non-night shifts characterized by insufficient daily sleep (total daily sleep ≤ 6 h) in the intervention (I) and control (C) group before (pre) and 4–5 months after (post) the intervention. The total daily sleep comprises a combination of daily main and daily nap sleep measured as time in bed.

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Fig. 5. Double column (full width: 190 mm). The timing of main sleep and the duration of total daily sleep (mean ± standard deviation) in night (1st night = first night shift, 2nd→night = consecutive night shift) and non-night shifts in the intervention (I) and control (C) group before (pre) and 4–5 months after (post) the intervention. The total daily sleep comprises a combination of daily main and daily nap sleep measured as time in bed.

Table 4 Double column (full width: 190 mm). Model 1 (N = 798/52)

95% CI

Within-level effects

B

SE

P-value

Lower

Upper

OR (95% CI)

Trial Group*Trial

−0.29 0.47

0.29 0.42

0.321 0.256

−0.86 −0.34

0.28 1.28

0.75 (0.42–1.33) 1.60 (0.71–3.61)

Between-level effects

B

SE

P-value

Lower

Upper

R-square

Group

0.18

0.38

0.635

−0.56

0.91

0.007

Model 2 (N = 798/52)

95% CI

Within-level effects

B

SE

P-value

Lower

Upper

OR (95% CI)

Trial Group*Trial Shift Type 1 Shift Type 2 Shift Type 3 Group*Trial*Shift Type 1 Group*Trial*Shift Type 2 Group*Trial*Shift Type 3

−0.30 −0.11 2.01 1.72 0.53 0.48 0.55 1.18

0.33 0.62 0.50 0.48 0.47 0.68 0.71 0.67

0.350 0.865 < 0.001 < 0.001 0.264 0.484 0.445 0.077

−0.94 −1.33 1.03 0.79 −0.40 −0.86 −0.86 −0.13

0.33 1.12 2.99 2.66 1.45 1.82 1.95 2.49

0.74 0.90 7.46 5.60 1.69 1.61 1.72 3.25

Between-level effects

B

SE

P-value

Group

0.08

0.42

0.854

Model 3 (N = 635/42)

(0.39–1.40) (0.27–3.05) (2.80–19.87) (2.19–14.28) (0.67–4.25) (0.42–6.16) (0.43–6.99) (0.88–12.02)

Lower

Upper

R-square

−0.75

0.90

0.001

95% CI

Within-level effects

B

SE

P-value

Lower

Upper

OR (95% CI)

Trial Group*Trial Shift Type 1 Shift Type 2 Shift Type 3 Group*Trial*Shift Type 1 Group*Trial*Shift Type 2 Group*Trial*Shift Type 3

−0.23 −0.57 0.68 0.45 0.58 0.96 0.77 1.88

0.43 0.70 0.60 0.53 0.75 0.80 0.85 0.76

0.584 0.421 0.257 0.399 0.440 0.228 0.362 0.013

−1.07 −1.94 −0.50 −0.59 −0.88 −0.60 −0.89 0.40

0.60 0.81 1.86 1.49 2.04 2.53 2.43 3.36

0.79 0.57 1.98 1.57 1.78 2.62 2.16 6.56

Between-level effects

B

SE

P-value

Lower

Upper

R-square

Group

0.15

0.40

0.704

−0.64

0.94

0.514

(0.34–1.83) (0.14–2.25) (0.61–6.44) (0.55–4.43) (0.41–7.65) (0.55–12.53) (0.41–11.35) (1.49–28.85)

The effects of intervention on the use of efficient sleepiness countermeasures (at least once a shift) in different shift types (day/evening shift as a reference). Shift Type 1 = first night shift. Shift Type 2 = consecutive night shift. Shift Type 3 = morning shift. B = estimate. SE = standard error. OR = Odds Ratio. CI = Confidence Intervals. N = number of shifts/number of drivers. Model 3 adjusted for age, Body Mass Index, subjective sleep need, Epworth Sleepiness Scale score, diurnal type, sleep complaints, self-estimated sleep quality, children under school-age, under-aged children, history of accident involvement, shift duration, and shift start time. 9

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Fig. 6. Double column (full width: 190 mm). The proportion of night (1st night = first night shift, 2nd→night = consecutive night shift) and non-night shifts during which intervention (I) and control (C) group drivers reported using efficient sleepiness countermeasures (napping during and/or outside statutory rest breaks and caffeine ingestion outside statutory rest breaks at least once a shift) before (pre) and 4–5 months after (post) the intervention.

4.1. Interpretation of the results

3.2.3. Effects of intervention on the use of efficient sleepiness countermeasures Results of the multilevel models (Model 1–3) in Table 4 showed no significant Group * Trial * Shift Type interaction effects, indicating the intervention did not increase the use of efficient SCMs on night and early morning shifts compared to day/evening shifts (reference condition). Early morning shifts formed an exception though as the adjusted model (Model 3) revealed a significant Group * Trial * Shift Type interaction (p=0.013) and an increased odds ratio (OR 6.56). The results suggest that the intervention increased the use of efficient SCMs on early morning shifts compared to day/evening shifts. Proportion of night and non-night shifts during which the intervention and control group drivers reported using efficient SCMs (at least once a shift) is depicted in Fig. 6. Efficient SCMs were utilized most frequently during night shifts in both study groups and phases (in 59.6% of the first and 47.3% of the consecutive night shifts in the intervention group, and in 51.4% of the first and 48.9% of the consecutive night shifts in the control group before the intervention; in 58.7% of the first and 48% of the consecutive night shifts in the intervention group, and in 41.9% of the first and 34.1% of the consecutive night shifts in the control group after the intervention). During non-night shifts, the average usage of efficient SCMs was equally frequent in both study groups and phases (in 21.3% of the morning and 30.4% of the day/evening shifts in the intervention group, and in 24.6% of the morning and 11.1% of the day/evening shifts in the control group before the intervention; in 32.8% of the morning and 15.1% of the day/ evening shifts in the intervention group, and in 21.9% of the morning and 30.6% of the day/evening shifts in the control group after the intervention).

The findings are likely to stem from sources related to study design and methodology, intervention per se, individuals taking part in the study, and truck drivers’ work environment/conditions in general. 4.1.1. Study-related explanations Perhaps the most focal explanation for the discrepancy lies in the measures used to assess the intervention effects. The training evaluated here was expected to lead to changes in trainees’ knowledge, skills, and motivation. The trainees were expected to apply their newly-acquired knowledge and skills to their daily lives, and the transferred knowledge and skills were expected to become evident in trainees’ improved alertness at the wheel. The effectiveness of the training was assessed prospectively and at behavioral level (under naturalistic shift schedules and working conditions on field) by measuring drivers’ alertness, sleepwake patterns, and use of SCMs, before and after the training. Contrary to the current study, previous studies have assessed the benefits by using evaluation methods relying mainly on retrospective self-reports. For instance, Gander et al. (2005) assessed training effectiveness in a non-RCT design by asking trainees how well they were able to immediately recall the material introduced at the training session, how useful they considered the training, and whether trainees reported changes in their use of fatigue management strategies as a result. Similarly, Kakinuma et al. (2010), Nishinoue et al. (2012), and van Drongelen et al. (2014) evaluated intervention effects by using selfreport questionnaires on sleep and on-duty alertness, before and after intervention, without actual field measurements. The only pertinent study conducting field measurements is provided by Rosekind et al. (2006). However, its comparison with the present study is somewhat problematic because the intervention examined by Rosekind and colleagues did not consist of an educational component alone but included rescheduling of trainees’ flight schedules as well. For this reason, and for the fact that the study was lacking control condition, it is difficult to attribute the found improvements in sleep and on-duty alertness solely to the educational part of the intervention. Moreover, the inconsistent findings of the current study could result from differences in study designs, for example, in follow-up length. According to Kwasnicka et al. (2016), behavior change interventions are effective in supporting individuals to achieve temporary behavior changes but behavior change maintenance is rarely attained. It could be that the follow-up in the current study was either too long to capture potential temporary behavior changes or not enough to bring out

4. Discussion The current study failed to provide clear evidence for a feasible alertness management training being beneficial in improving long-haul truck drivers’ on-duty alertness, amount of prior sleep, and use of efficient on-duty SCMs, while working early morning and night shifts. The findings conflict with the previous randomized and non-randomized studies reporting positive findings regarding the effects of driver training on alertness management (Gander et al., 2005; Rosekind et al., 2006), sleep quality (Nishinoue et al., 2012), and on-duty sleepiness (Kakinuma et al., 2010; van Drongelen et al., 2014).

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4.1.4. Work-related explanations Another compromise that designing the training as feasible meant was that it was designed to be deliverable without the involvement of company management and foremen, and this may have had an impact on the current findings. Perhaps not engaging the companies in designing and delivering of the training decreased their ownership and commitment to the training objectives and thus made them less supportive of their employees’ transfer process. Considering social support is relevant because drivers’ decisions are likely to be influenced by organizations’ attitudes towards, for instance, napping while on duty (e.g., Fallis et al., 2011). Organizational factors that facilitate or hinder the application of learned knowledge to the job are referred to as transfer climate (Rouiller and Goldstein, 1993), and studies have shown that it has a direct as well as an indirect influence on training transfer (see Burke and Hutchins, 2007). One of the features of a positive transfer climate is social support. Organizational support is known to increase employees’ organizational commitment (e.g., Rhoades et al., 2001) which, in turn, has been found to influence employees’ pretraining motivation and their views about the usefulness and value of training (Burke and Hutchins, 2007). Both, pre-training motivation and perceived usefulness/value of training are considered important antecedents to training effectiveness (Facteau et al., 1995). Instead of lacking organizational support or motivation to change behavior, the trained drivers could have also been simply lacking opportunities (e.g., due to tight schedules) to perform accordingly and make the needed changes although they knew what was best for them. Previous studies have indicated that drivers are aware of their responsibility to be well-rested for duty (Johansson, 2012), and although they have the knowledge of how to efficiently combat sleepiness on duty, they nevertheless resort to the inefficient strategies that allow them to continue driving and get to destination on time (Armstrong et al., 2010). According to Burke and Hutchins (2007), training research has consistently shown that lack of opportunities to use new knowledge and skills in work setting limits the training transfer. As behavior change is also known to be more easily maintained in contexts that facilitate rather than hinder behavior change maintenance (Kwasnicka et al., 2016), it could be that the type of short, non-recurrent, and minimally tailored training examined in the current study only proves beneficial if anchored to organization-level measures, such as shift rescheduling, organizational support (e.g., in decision-making if one feels sleepy at the wheel), and clear organizational fatigue risk management policies (e.g., Phillips, 2010; Anund et al., 2015), especially when training employees working in shifts (Sallinen and Hublin, 2015).

potential long-term behavior changes and changes in driver alertness as a result. The fact that the trainees did not utilize the consultation period following the training may reflect a flagging of trainees’ change process. 4.1.2. Intervention-related explanations The lack of significant findings could also stem from inadequate duration and/or intensity of the intervention as designing the intervention as feasible meant some compromises with those. Firstly, to enable its later exploitability, the training was designed to comprise a single training event. Secondly, the current training included only minimal tailoring and was for many parts identical to all participants. The consultation period and post-training assignments were intended to compensate for these shortcomings as they were expected to motivate trainees’ and help maintain the change process initiated in the training. However, none of the trainees utilized the consultation option, and not all trainees returned the alertness management plan and self-evaluations as instructed. Studies on behavior change have found that only a minority of a population is prepared to take action at any given time, and therefore interventions trying to move a person through the stages too quickly, without taking into account his/her stage of change, are more likely to create resistance which will impede behavior change (Prochaska and DiClemente, 1982; Prochaska and Velicer, 1997; Kwasnicka et al., 2016). Norcross et al. (2011) argue that tailoring training to take individuals’ readiness for change into account, and using more stage-specific techniques, would prevent resistance to change and thereby lead to more effective behavior change interventions. Because tailoring and time-/cost-efficiency are often mutually exclusive, different modes of training delivery should perhaps be considered. One way to overcome the dearth of the current study would be to employ modern ICT technologies that enable flexible and more intensive implementation of interventions. This was demonstrated by van Drongelen et al. (2013, 2014) who were able to tailor their training to better fit the needs of individual trainees and deliver it more intensively over a longer period of time by using an online/web-based mobile phone application. Technologies that enable delivering training independent of time and place offer opportunities to design markedly more extended and tailored interventions. 4.1.3. Individual-related explanations The current findings could also be explained by the selection and exclusion of participating companies and drivers. For starters, the participating haulage companies were likely to be generally more interested in the wellbeing of their employees than logistic companies on average, and thereby the drivers enrolled in the study may have had fairly good groundings of the topics the training covered. The drivers may have also been a selective group of truckers in terms of their ability to tolerate the demands of irregular shift work (e.g., Saksvik et al., 2011), or they may have managed their alertness at the wheel reasonably well to start with, leaving only a little room for improvements. These explanations are supported by the previously reported baseline findings of the current study (Pylkkönen et al., 2015). According to those, the drivers obtained relatively high amounts of sleep prior to consecutive night and early morning shifts, and during these shift types, severe sleepiness occurred less frequently than expected. The current findings also suggest the intervention group drivers needed more sleep (c. 30 min) and had less sleep problems (sleep complaints) compared to the control group drivers. The fact that drivers taking part in the current study were all reasonably experienced in trucking may have also affected the results as it is possible that experienced and novice drivers would benefit from slightly different kind of training. Training, as the one examined here, which provides trainees with relevant up-to-date science-based yet very basic-level information could work better in motivating and educating novice drivers, or driver trainees and students whose sleep-wake patterns and preferred strategies for fighting sleepiness on duty are not yet fully established.

4.2. Study strengths and limitations The current study was conducted under a randomized controlled trial, and its prospective data collection was executed employing wellvalidated measures. The field measurements lasted a relatively long period of time, and the data on the primary outcome were collected with a high sampling rate. Further, the data featured sufficient power (nominal power 90%) and were collected at the same time of a year at the baseline and follow-up to eliminate seasonal influence. The followup length was also sufficient to avoid potential” honeymoon effect” (i.e., initially high, declining efficacy) from the intervention. Despite being short, non-recurrent, and minimally tailored, the intervention was pedagogically mature and thoroughly implemented. There are some limitations to the current study worth taking into consideration though. First of all, the study is lacking blinding. This type of deficit is known to increase the risk of performance and detection bias, albeit the current study found no intervention-related improvements that could have been misinterpreted because of the non-blinded trial. Second, the generalizability of the current findings is, in principle, limited, and the results should therefore be considered with caution. The sample of drivers in the current study could be considered 11

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relatively small for a field study where there are many confounding factors likely reduce or dilute intervention effects. Although the randomization should assure that the potentially confounding variables are balanced among the study groups (Suresh, 2011), the current sample may have been simply too small to detect the intervention effects in field settings. Third, for its recruitment process (selection of participants), the current study has a potential for selection bias. As the drivers were recruited in order of enrollment, and the recruitment was ceased when the sample exceeded the size considered sufficient on the basis of power calculations, the sample obtained may not be representative of the truck driver population as a whole. The drivers who volunteered among the first were likely to be more motivated to attend the intervention than those who volunteered late, or did not volunteer to start with, and were thus left outside the study. On the other hand, the non-random sample can also be justified by the nature of the intervention under examination: to work, that is, to result in learning, any education (especially among adult learners) requires a learner intrinsic motivation to learn. A random sample might have comprised of less motivated drivers and thus led to differing findings. Fourth, despite the creditable attrition rate, this study is still subject to attrition bias and confounding as one of the intervention group drivers was lost before data collection. This resulted in the data analysis following a per-protocol analysis instead of the intention-to-treat analysis recommended as the preferred analysis strategy for randomized studies (Schulz et al., 2010). To assess whether the outcomes could be affected by attrition bias, the rates of those lost to follow-up between the two study groups were compared. No statistically significant difference was found at the rates of loss-to-follow-up between the groups, the drop-out being slightly greater in the intervention than in the control group. Fifth, the randomization was carried out using the stratified randomization method which can be criticized for not being the most suitable method for small samples. According to Suresh (2011), the covariate adaptive randomization method (i.e., minimization method) could be a better choice for small to moderate trials (n < 100) with several covariates. Finally, the current study did not take into account sleep disorders, although those are known to be common among truck drivers (e.g., Garbarino et al., 2016; Schwartz et al., 2017). Sleep disorders, especially if untreated, are also likely to inflect the effectiveness of behavioral interventions, as they tend to sustain sleepiness during waking state (e.g., at work) and thus override the effects of interventions.

(grant nos. 109378 and 115510) and the SalWe Research Program for Mind and Body (the Finnish Funding Agency for Technology and Innovation). NordForsk, Nordic Program on Health and Welfare (74809) has provided support for Mikael Sallinen and Kati Karhula. We would like to thank the participating haulage companies, volunteered drivers, and the technical and nursing staff of the Finnish Institute of Occupational Health for their valuable contribution to the study. References Anund, A., Fors, C., Kecklund, G., van Leeuwen, W., Åkerstedt, T., 2015. Countermeasures for Fatigue in Transportation. A Review of Existing Methods for Drivers on Road, Rail, Sea and in Aviation. Swedish national Road and transport Research Institute VTI rapport 852 A. Armstrong, K., Obst, P., Banks, T., Smith, S., 2010. Managing driver fatigue: education or motivation? Road Transp. Res. 19 (3), 14–20. Baldwin, T.T., Ford, J.K., 1988. Transfer of training: a review and directions for future research. Pers. 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5. Conclusions The current findings provide no support for education being an effective remedy for driver sleepiness in road transport. The null findings should not, however, be interpreted as evidence against alertness management training among truck drivers in general. Perhaps utilizing modern technologies, to enable more intensive/lengthy and tailored training, and anchoring the training to relevant organization-level measures would yield more positive findings. The training could also be further developed and the discussed shortcomings eliminated by, for instance, specifying training content more carefully to specific target groups (e.g., novice vs. experienced drivers). Registration and protocol The study has been registered before the baseline assessments were commenced in the ClinicalTrials.gov (https://clinicaltrials.gov/). The trial protocol is accessible in the registration database with a ClinicalTrials.gov identifier NCT01697189. Acknowledgements The study was funded by the Finnish Work Environment Fund 12

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