Introduction

Work-related stress is defined by the National Institute for Occupational Safety and Health (NIOSH) as the harmful physical and emotional responses that occur when the requirements of a job do not match the capabilities, resources, or needs of the worker (NIOSH). Professional drivers are among the occupational groups most exposed to this risk. Numerous international studies related to the physical and psychological health of drivers have led to consider occupational risks of this category a public health problem (Useche et al. 2021, 2019, 2018a, b).

The main diseases affecting these workers are cardiovascular diseases, especially related to sedentary lifestyle and diet. Especially mentioned are heart attacks, strokes, coronary heart disease and metabolic syndrome (Useche et al. 2018a, b, Tüchsen et al. 2006, Shin et al. 2013, Izadi et al. 2021). This is followed by musculoskeletal diseases, especially vertebral injuries, severe disc herniation and low back pain; posture, generally incorrect and protracted over a long period of time, is also a cause of musculotendinous and skeletal deficits and diseases (Joseph et al. 2020, 2023).

Deafness and noise pollution, especially from hearing impairment developed owing to the constant noise to which drivers are subjected, lead to disability even at a young age (Golbabaei Pasandi et al. 2022, Lopes et al. 2012). Additionally, we must not forget lung cancer due to exposure to exhaust gases (Tsoi and Tse 2012) and infertility problems related to drivers’ genital heat stress (Fraczek et al. 2022).

Digestive system conditions are a serious issue due to a poor diet low in fiber and nutrients, as the driver is often forced to eat junk food, as well as the high psychophysical stress (Tamilarasan et al. 2023, Pourabdian et al. 2020).

At the psychological level, the stress is very intense. Just think of the undisciplined driving of many drivers and all the characteristics associated with the work of professional drivers (traffic congestion, time pressure, social isolation) (Tse 2006); all risk factors that can lead to anxiety and depression to the point of burnout (Tàpia-Caballero et al. 2022a, b, Chalmers and Lal 2022). All this, considering also the fact that drivers are subjected to long working hours and often drive at night, can lead to fatigue and sleepiness (Tàpia-Caballero et al. 2022a,b, Rosso et al. 2018) resulting in performing risky road behaviours and suffering severe crashes involving injured or fatal victims (Ba et al. 2018, Gómez-Ortiz et al. 2018). Thus, evidence-based interventions are needed to reduce hazardous working conditions, occupational stress rates and their negative impact on the health of this occupational group.

It is clear that occupational stress can greatly affect workers’ lives; therefore, it is important to develop instruments that can evaluate it. Nowadays, the Job Content Questionnaire (JCQ) represents one of the most important standardized tools for the assessment of psychosocial risk factors at work, created on Karasek’s assumptions that the relationship between high job demand (job demand) and low decision freedom (decision latitude) may contribute to the emergence of a condition of “job strain” or “perceived job stress” (Karasek et al. 1998). Occupational and psychosocial characteristics can influence the health and the occupational performance of workers. The aim of the present study is to research and quantify working well-being within a transport company, evaluating how work-related stress and positivity are associated with health-related quality of life.

Methods

Study design and setting

A cross-sectional study was carried out in a transport company in Lazio made up of a total of 224 workers, 250 workers in periods of maximum workload, from July 2019 to January 2020. The sample was divided into seven categories: school bus drivers, rental drivers, bus drivers, administratives (including technicians), workshop workers, school bus assistants and parking attendants. STROBE guidelines were followed in this study (Von Elm et al. 2014).

Each employee filled out an anonymous questionnaire which contained four sections: registry and socio-demographic section; Karasek’s Job Content Questionnaire (JCQ) (Karasek et al. 1998) in its Italian version; psychophysical well-being and quality of life section through the Short Form 12 Survey (SF-12) (Jenkinson et al. 1997); and positivity assessment section through the Positivity Scale (Caprara et al. 2012).

The Karasek’s Job Content Questionnaire identifies the condition of job strain or perceived job stress as a result of the combination of high job demand (Job Demand, JD) and low decision-making power (Decision Latitude, DL).

The SF-12 is a self-reported outcome measure assessing the impact of health on an individual’s everyday life. It is often used as a quality of life measure, using two indices: Physical Component Summary (PCS) for the physical state and Mental Component Summary (MCS) for the mental state.

The Positivity Scale consists of eight items and was used to measure positivity, defined as the tendency to view life and experiences with a positive outlook.

In agreement with the company, the questionnaires were left in strategic places: bus depots, garages and administrative offices. Initially, a self-completion of the questionnaire was performed, collected every week starting from July 2019. Later, owing to the low number of answers, a face-to-face interview was opted for.

Statistical analysis

All analyses were performed using SPSS for Windows (Statistical Package for the Social Sciences, version 27; SPSS, Inc., Chicago, USA). Statistical analysis involved the use of mean, standard deviation and correlation coefficient for quantitative variables.

A univariate and bivariate analysis was performed for the physical score (PCS) and mental score (MCS) variables, produced by the results of the SF12 questionnaire psychophysical well-being and quality of life, considered as dependent variables influenced by the independent variables: type of work, age, gender, children, marital status and education status. The dichotomous groups of variables were compared using the ANOVA test for univariate and bivariate analysis.

Three linear regression models were created to study the relationships between the dependent and independent variables.

Pearson’s correlation (r) was calculated to estimate the direct or indirect association between the variables and evaluate its significance.

The significance threshold was set at p < 0.05 for all analysis.

Results

A total of 208 of employees were involved in the study, 155 of which (74.5%) were male, 81 (39.9%) were female, with a mean age of 49.2 years. The majority of the sample consisted of 136 (65.3%) drivers, 140 (67.4%) married or cohabiting, 158 (76.0) with children and 155 (55.3%) of the sample has achieved an education level up to a middle school level (Table 1).

Table 1 Socio-demographic characteristics of the sample
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The frequencies of decision latitude and job demand, resulting from the answers to the Karasek’s Questionnaire, were calculated. The two main dimensions of work, job demand and decision latitude, were considered two independent variables and were placed on orthogonal axes. Job strain is the result of the intersection and interaction of these two dimensions. In this way, it was possible to identify four typical situations (Fig. 1).

  • Active jobs: High levels of both demand and control are the largest share.

    Passive jobs: both the work demands and the worker’s decision-making power are reduced to a minimum, they are a small share.

    Low stress and low stress risk jobs: the portion is lower than the previous one.

  • High stress work: depicted in the lower right quadrant which are unexpectedly the smallest number ever with greater exposure to work-related stress risk.

Fig. 1
figure 1

Graphical representation of Job strain

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Univariate analysis of PCS obtained significant correlation for married/cohabiting (p = 0.026) and having children (p = 0.002). Bivariate analysis for variable PCS detected statistically significant results for MCS (p = 0.001), positivity (p ≤ 0.001) and job demand (p ≤ 0.001). The results of the univariate analysis are shown in Table 2.

Table 2 Results of the univariate bivariate analysis for variable PCS
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The MCS component did not obtain any significance with the independent variables of the univariate analysis.

The situation of the bivariate analysis was different: PCS (p = 0.001), positivity (p ≤ 0.001) and decision latitude (p ≤ 0.001) were significant factors influencing the MCS component. The mental MCS component was positively correlated with the physical PCS component.

The MCS variable instead was inversely correlated with the positivity values in a significant way. The same result was obtained for job demand which was inversely correlated with MCS in a significant way. The results of the univariate analysis are shown in Table 3.

Table 3 Results of the univariate bivariate analysis for variable MCS
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The dependent variables PCS and MCS were related to the qualitative independent variables. The PCS values were significantly correlated with the values of positivity, job demand and having children; the MCS values were significantly correlated with positivity, job demand and education level. Both therefore have in common the fact that they are significantly dependent on two common variables: positivity and job demand.

Positivity and job demand were therefore common variables of statistically significant correlation.

The results of the multivariate analysis are shown in Table 4.

Table 4 Results of the multivariate analyses for variables PCS and MCS
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Discussion

The study aimed to evaluate well-being, positivity towards life and the research of work-related stress risk carried out in a transport company, through three validated questionnaires that study work stress factors (context factors, content and environmental) and health-related quality of life and positivity. The relationship between work stressors and health-related quality of life and positivity was examined. Numerous studies have also examined the conditions of professional drivers and the factors of their activity which can have serious consequences on the psychological and physical health of this category of workers (Kresal et al. 2017, Useche et al. 2018a,b, Arias-Meléndez et al. 2021). The most represented age of drivers in this study is between 45 and 54 years, married or cohabiting and mostly male. In Europe, the average age of a driver is 47 years (IRU 2022); however, in our study, the average age is 2.5 years higher. The advanced age of drivers related to other workers could be explained by the fact that their task requires more qualified and experienced workers (Sampaio et al. 2009, Hernández-Rodríguez et al. 2022).

Navarro’s study reports driving after the age of 40 correlates with a loss of visual acuity, thus resulting in significant losses of visual sensitivity to glare contrast (Navarro et al. 2013). Bellusci and Fischer also find that the 40 to 50-year age has a significant association between ageing and a reduction in the work ability index (WAI) (Bellusci et al. 1999). The data indicates a predominance of the male gender, which is in line with this profession (IRU 2022, Varela-Mato et al. 2018, Cavallari et al. 2021). In the study, the PCS values were significantly correlated with the values of positivity (p < 0.001), job demand (p = 0.003) and the presence of children (p = 0.008). The MCS values were significantly correlated with the values positivity (p < 0.001), job demand (p < 0.001) similar to PCS, and in contrast to PCS, it is significantly correlated with education level (p = 0.054). They are therefore both significantly dependent on two variables: positivity and job demand. The data subjected to analysis revealed results that were both partially expected and also in agreement with the literature (such as the levels of physical health of the drivers in the national standard), and data that proved to be different from what we would have expected. According to the literature, the health of bus drivers is connected to musculoskeletal problems, chronic headache, nervousness and depression, cardiovascular and gastrointestinal affections, eyes irritation and hearing reduction (Sampaio et al. 2009). Drivers have a significantly higher physical demand (p < 0.001) both in terms of having to “lift or move heavy loads”, and both in terms of static (posture maintenance) and dynamic (repeated hand-arm movement) and these results are also confirmed in the study on health and working conditions in road freight transport (Ordaz-Castillo and Maqueda 2014, FSC 2015). Furthermore, again in the Ordaz (Ordaz-Castillo and Maqueda 2014) study, it is underlined how the comfort of the seat is a point of improvement in the design of the workplace; this is because drivers have less space, inadequate lighting and uncomfortable seats. These three indicators are related to the manifestation of health and safety risks, either alone or in combination with other existing risks. In this regard, it must be remembered that the driver’s workspace is the cornerstone of the work because the worker will sit there for at least 8 h, with high levels of attention and concentration, without any possibility of relaxation, rest and physical exercise. Furthermore, the driver’s workplace is not even one square metre and inside also features the driver’s seat, control panel, fire extinguisher and personal belongings. This space should reach dimensions that allow the movement of the seat and the steering wheel so that it can be adapted to the different anthropometric measurements of women and men. The workspace is the seat and constitutes the essential element and defines the position of the driver throughout the working day. The seat must respond to a series of ergonomic characteristics and have a good suspension that absorbs vibrations, supporting stability for the driver’s body and, above all, it must be able to move both vertically and horizontally. Several studies note how the lack of favourable ergonomic conditions, the presence of static and dynamic physical loads and poorly planned work create a direct and negative impact on the driver’s physical health (Navarro et al. 2013, Varela-Mato et al. 2018, Jayatilleke et al. 2009, Chen et al. 2005, Alperovitch-Najenson et al. 2010, Jensen et al. 2008). An unsuitable seat will inevitably lead to musculoskeletal diseases, among other back injuries. For the MCS variant, Hernández-Rodríguez et al. (2022) observe how many positive psychosocial scales are below the bottom tertile (their analysis speaks of development possibilities, sense of work, integration into the company, clarity of role, quality of leadership, social support, sense of group and level of esteem). This fact would indicate that, on average, professional drivers find themselves in a more unfavourable psychosocial environment. The latter aspect can also be found in a recent study that examined the relationship between the psychosocial factors of bus drivers and risky driving behaviours, which demonstrated how stress-related working conditions (work strain, social support and effort/reward imbalance) are relevant predictors of risky driving in drivers and how fatigue is the main driving mechanism (work strain and low social support) (Varela-Mato et al. 2018, Cavallari et al. 2021, Useche et al. 2017). It is important to increase drivers’ perception of major health problems and stimulate changes in their lifestyle. For example, in a study, drivers reported positive outcomes after a health promotion intervention in engaging truck drivers and stimulating changes in their lifestyle behaviour. In fact, they had become more aware of how their daily activities related to food choices and physical activity had an important impact on their health and expressed a desire to improve their health behaviour. Furthemore, they had made concrete changes to their work routines, such as walking or standing more during breaks (Varela-Mato et al. 2018). Increasing the frequency of intensive work shifts (i.e. 6 or more days in a row) was related with a higher prevalence of burnout in the transport maintenance workers. Burnout symptoms for to the psychosocial factors of the job, in particular the psychological demands and social support in the workplace, could reduce the relationship with exposure to long working hours (Varela-Mato et al. 2018, Cavallari et al. 2021). Furthermore, mental health is not isolated but could be associated with increased frequency of unpredictable working hours. In fact, workers with good health habits (e.g. adequate sleep and exercise had a lower prevalence of depressive symptoms after controlling for exposure to long working hours (Cavallari et al. 2021).

Therefore, the data analysed in this study and calculated on the basis of the answers to Karasek’s JCQ unexpectedly revealed a situation of homogeneity of the job strain within the company. This can be interpreted as both positive and negative data. Positive because despite the type of company (of transport, therefore on the basis of the scientific literature one would expect a high level of job strain), it proves to be able to better manage and optimize the workload of personnel in the various types of sectors that compose it. From the data results, it can be thought that this occurs as regards environmental factors (for example, by providing personnel with adequate offices, vehicles in good condition, functioning depots, sufficient number of personnel for each type of task to allow adequate workload, etc.), and when it comes to contextual factors (for example, good communication of decisions, well-defined roles, quick resolution of problems, fair recognition of the work done, attention to the conduct of workers, etc.), as regards content factors (for example, rigid shifts dictated by the lines travelled but adequate for the workload that may be required of people in this type of job, adequate breaks based on the work performed, etc.).

However, one can also think that it is a negative fact. This is primarily because the scientific literature on this issue is not in agreement with our results. In Hernández-Rodríguez et al.’s ((2022) study, job satisfaction is strongly influenced by the psychosocial environment of professional drivers. Job satisfaction should therefore be considered as an emotional response to numerous factors linked to the worker’s perception of their working conditions and work-related stress, and not only to the physical, ergonomic and salary aspects of work activity (Mościcka-Teske et al. 2019, Sureda et al. 2018). Secondly, it is possible that, given the problems related to the administration type of the questionnaire, the respondents were completely affected by the bias of social acquiescence and desirability; or have answered the questionnaires in a way that is not entirely truthful and does not reflect their actual working situation; therefore, wanting to appear in a better situation than they think or really live, out of fear or for any other reason. It is also possible that this situation of job strain level homogeneity, well-being and positivity result, as demonstrated by the univariate and multivariate analysis, is from different factors which are irrelevant to the work performed (such as being married and having children). This is supported by previous research stating: “The result does not appear to be influenced by socio-demographic variables but, in train drivers, by service variables (type of guided train, single driver driving). The results showed a strong relationship between stress and health problems” (Marrucci and Ruggieri 2017).

We can state that since positivity is strongly associated with physical and mental factors and given the homogeneity of the job demand and decision factors latitude, this transport company finds a high level of health-related occupational well-being in each of the activities carried out within it.

Limits and strengths

A notable limit of the study is its sectional study design, for which it is not possible to make causal inferences on the relationships between the variables but only to make hypotheses on the directions. Although the statistical parameters and the model fit coefficients have been adequately verified, some methodological and qualitative biases should be evaluated.

The research was conducted in a single organizational context, but it would be important to evaluate the same relationships in other working contexts belonging to the public and private sectors.

The strengths of the study are the number of participants, the type of survey conducted and the number of items analysed.

Conclusions

In conclusion, we can state that since positivity is strongly associated with physical and mental factors and given the homogeneity of the job demand and decision factors latitude, this transport company finds a high level of health-related work well-being in each of the activities carried out within it. Research and intervention on the presence of occupational stress can constitute a useful and further step to strengthen the road safety results of professional drivers, which are currently a public health problem.