INTRODUCTION Occupational stress is an important occupational health concern that can adversely affect workers' psychological well-being, physical health, work performance, and safety. Professional driving is particularly demanding because drivers are required to maintain prolonged attention while working under variable traffic, environmental, and occupational conditions. Evidence among commercial drivers indicates that psychosocial working conditions can substantially influence their health and well-being [1,2]. Heavy vehicle drivers are exposed to several work-related stressors, including prolonged driving hours, irregular schedules, night driving, inadequate rest, work-related pressure, financial concerns, and limited opportunities for breaks. These factors may contribute to fatigue, sleep disturbances, reduced concentration, job dissatisfaction, and unhealthy coping behaviours. A recent review reported that long working hours, irregular shifts, inadequate breaks, poor remuneration, low social support, and work-family conflict are important concerns among commercial drivers [1]. Occupational stress among professional drivers is also relevant to road safety. Fatigue, sleep-related problems, substance use, and demanding work schedules may impair alertness and driving performance. A systematic review of professional drivers identified several occupational and behavioural factors associated with road traffic crashes [2]. Research among Australian truck drivers has further highlighted the influence of work demands, financial pressures, social support, and coping factors on mental health [3]. Similarly, recent evidence from India indicates that working conditions and psychosocial experiences are important considerations in understanding the mental health of truck drivers [4]. Indian studies among other professional driver groups have also reported a substantial burden of occupational stress. A recent study among Telangana State Road Transport Corporation bus drivers found that occupational stress was common and was associated with several lifestyle and health-related factors [5]. However, evidence specifically addressing occupational stress among heavy vehicle drivers in Telangana, particularly at the district level, remains limited. Therefore, the present community-based cross-sectional study was conducted to determine the prevalence of occupational stress and identify its associated socio-demographic, occupational, lifestyle, and psychosocial factors among heavy vehicle drivers in Siddipet District, Telangana. The findings may help in identifying drivers at increased risk and in developing appropriate occupational health measures related to working hours, rest and sleep, job-related concerns, and psychosocial support.

A community-based cross-sectional study was conducted among heavy vehicle drivers in Siddipet District, Telangana, India, over a period of six months. The study was undertaken to determine the prevalence of occupational stress and identify factors associated with stress among heavy vehicle drivers. Study population and eligibility Licensed male and female heavy vehicle drivers aged ≥20 years who had been engaged in commercial driving for at least one year and were willing to participate were eligible. Drivers who were seriously ill, unable to participate in the interview, or unwilling to provide written informed consent were excluded. Sample size The sample size was calculated using the formula for estimating a single population proportion: n = Z²p(1−p)/d²,where Z = 1.96 for a 95% confidence level, p = 50%, and d = 5% absolute precision. A prevalence of 50% was assumed because a reliable district-level estimate of occupational stress among heavy vehicle drivers in Telangana was not available. The calculated sample size was 384.16, which was rounded to 385. Considering the possibility of non-response and incomplete information, the final sample size was increased to 400 participants. The selection of heavy vehicle drivers was also supported by previous Indian research involving truck and bus drivers, which demonstrated the relevance of occupational and psychosocial stressors in this population. Sampling and data collection Eligible drivers were recruited from transport points, truck parking areas, loading and unloading locations, markets, highways, and other commercial transport locations within Siddipet District. Participants were approached during suitable non-driving periods. Data were collected using a pre-tested, structured interviewer-administered questionnaire covering socio-demographic characteristics, occupational factors, lifestyle practices, medical history, and psychosocial factors. Perceived stress was assessed using the 10-item Perceived Stress Scale (PSS-10) developed by Cohen et al. [9]. Each item is scored from 0 to 4, giving a total score ranging from 0 to 40. Scores were classified as low (0–13), moderate (14–26), and high (27–40) perceived stress. Statistical analysis Data from 400 heavy vehicle drivers were analysed using IBM SPSS Statistics version 26.0. Categorical variables were presented as frequencies and percentages, while continuous variables were expressed as mean ± standard deviation. The Chi-square test or Fisher’s exact test was used to assess associations between stress levels and categorical variables. Binary logistic regression was used to identify factors independently associated with high stress, and adjusted odds ratios (AORs) with 95% confidence intervals were reported. Pearson’s correlation was used to assess the relationship between PSS scores and driving hours, sleep duration, driving experience, and BMI. Variables with p <0.20 in univariate analysis were included in multivariable analysis. A two-tailed p <0.05 was considered statistically significant. Ethical Considerations Ethical approval was obtained from the Institutional Ethics Committee before data collection. Written informed consent was obtained from all participants, and confidentiality and anonymity were maintained throughout the study.

A total of 400 heavy vehicle drivers participated in the study. The findings are presented in terms of socio-demographic characteristics, occupational profile, lifestyle and health-related factors, perceived stress levels, and factors associated with high occupational stress. Table 1. Socio-demographic Profile of Study Participants. Variable Category Frequency (%) Age (years) 20–29 62 (15.5) 30–39 118 (29.5) 40–49 126 (31.5) 50–59 74 (18.5) ≥60 20 (5.0) Education Illiterate 52 (13.0) Primary 96 (24.0) Secondary 156 (39.0) Higher Secondary 70 (17.5) Graduate & above 26 (6.5) Marital Status Married 322 (80.5) Unmarried 52 (13.0) Widowed/Divorced 26 (6.5) Monthly Income (₹) <20,000 74 (18.5) 20,000–29,999 124 (31.0) 30,000–39,999 112 (28.0) 40,000–49,999 58 (14.5) ≥50,000 32 (8.0) The majority of participants were in the age group of 40–49 years (31.5%), followed by 30–39 years (29.5%), indicating that most drivers were in middle adulthood. Only 5.0% were aged ≥60 years. Most participants had completed secondary education (39.0%), while 13.0% were illiterate. The majority of drivers were married (80.5%), showing a predominantly stable family structure. Regarding income, the largest group earned ₹20,000–29,999 (31.0%), followed closely by ₹30,000–39,999 (28.0%). The mean age was 41.8 ± 9.7 years, and the mean monthly income was ₹30,900 ± 8,400. Table 2. Occupational Profile of Heavy Vehicle Drivers. Variable Category Frequency (%) Driving Experience <5 years 54 (13.5) 5–10 years 82 (20.5) 11–20 years 148 (37.0) >20 years 116 (29.0) Daily Driving Hours <8 hours 42 (10.5) 8–10 hours 118 (29.5) 11–12 hours 166 (41.5) >12 hours 74 (18.5) Night Driving Yes 276 (69.0) No 124 (31.0) Job Satisfaction Satisfied 124 (31.0) Neutral 138 (34.5) Dissatisfied 138 (34.5) Most drivers had 11–20 years of driving experience (37.0%), while 29.0% had more than 20 years, showing long occupational exposure. A high proportion of drivers (41.5%) reported driving 11–12 hours per day, and 18.5% drove more than 12 hours daily, indicating extended working hours. Night driving was common, reported by 69.0% of participants. Job satisfaction was evenly distributed, with 34.5% neutral and 34.5% dissatisfied, while only 31.0% were satisfied. The mean driving experience was 15.2 ± 8.6 years, and mean daily driving duration was 10.9 ± 2.6 hours. Table 3. Lifestyle, Behavioural and Health Characteristics. Variable Category Frequency (%) Sleep Duration <6 hours 172 (43.0) 6–8 hours 198 (49.5) >8 hours 30 (7.5) Alcohol Use Yes 232 (58.0) No 168 (42.0) Smoking Smoker 186 (46.5) Non-smoker 214 (53.5) Tobacco Chewing Yes 162 (40.5) No 238 (59.5) BMI Underweight 22 (5.5) Normal 178 (44.5) Overweight 146 (36.5) Obese 54 (13.5) Comorbidities Hypertension 108 (27.0) Diabetes 46 (11.5) Both HTN & DM 28 (7.0) None 218 (54.5) Musculoskeletal Pain Back pain 182 (45.5) Neck pain 88 (22.0) Joint pain 54 (13.5) No pain 76 (19.0) Financial Stress Yes 214 (53.5) No 186 (46.5) Family Support Good 168 (42.0) Moderate 152 (38.0) Poor 80 (20.0) Nearly half of the drivers (49.5%) reported sleeping 6–8 hours, but a significant proportion (43.0%) slept less than 6 hours, indicating poor sleep patterns. Alcohol consumption was reported by 58.0%, and 46.5% were smokers, showing high-risk behaviours. Tobacco chewing was present in 40.5% of participants. Regarding BMI, 36.5% were overweight and 13.5% were obese, indicating that half of the participants had abnormal BMI levels. Hypertension was present in 27.0%, diabetes in 11.5%, and 7.0% had both conditions, while 54.5% had no comorbidities. Musculoskeletal complaints were common, especially back pain (45.5%), followed by neck pain (22.0%). More than half of the participants (53.5%) reported financial stress. The mean sleep duration was 6.2 ± 1.3 hours, and mean BMI was 25.7 ± 3.8 kg/m². Table 4. Distribution of Occupational Stress (PSS-10). Stress Level PSS Score Range Frequency (%) Low Stress 0–13 78 (19.5) Moderate Stress 14–26 206 (51.5) High Stress 27–40 116 (29.0) Total 400 (100.0) Mean PSS Score 22.8 ± 6.9 Moderate stress was the most common level, observed in 51.5% (n = 206) of participants. High stress was present in 29.0% (n = 116), while only 19.5% (n = 78) had low stress. The mean PSS score was 22.8 ± 6.9, indicating an overall moderate level of occupational stress among drivers. Table 5. Association of Key Factors with Occupational Stress. Variable Category Low Stress n (%) Moderate Stress n (%) High Stress n (%) Total n (%) Age Group (years) 20–29 20 (32.3) 34 (54.8) 8 (12.9) 62 (15.5) 30–39 26 (22.0) 66 (55.9) 26 (22.0) 118 (29.5) 40–49 18 (14.3) 62 (49.2) 46 (36.5) 126 (31.5) 50–59 10 (13.5) 34 (45.9) 30 (40.5) 74 (18.5) ≥60 4 (20.0) 10 (50.0) 6 (30.0) 20 (5.0) Education Illiterate 6 (11.5) 24 (46.2) 22 (42.3) 52 (13.0) Primary 14 (14.6) 50 (52.1) 32 (33.3) 96 (24.0) Secondary 34 (21.8) 84 (53.8) 38 (24.4) 156 (39.0) Higher Secondary 18 (25.7) 34 (48.6) 18 (25.7) 70 (17.5) Graduate & above 6 (23.1) 14 (53.8) 6 (23.1) 26 (6.5) Alcohol Use Yes 26 (11.2) 112 (48.3) 94 (40.5) 232 (58.0) No 52 (31.0) 94 (56.0) 22 (13.1) 168 (42.0) Smoking Smoker 20 (10.8) 88 (47.3) 78 (41.9) 186 (46.5) Non-smoker 58 (27.1) 118 (55.1) 38 (17.8) 214 (53.5) Job Satisfaction Satisfied 48 (38.7) 60 (48.4) 16 (12.9) 124 (31.0) Neutral 24 (17.4) 84 (60.9) 30 (21.7) 138 (34.5) Dissatisfied 6 (4.3) 62 (44.9) 70 (50.7) 138 (34.5) Financial Stress Yes 16 (7.5) 106 (49.5) 92 (43.0) 214 (53.5) No 62 (33.3) 100 (53.8) 24 (12.9) 186 (46.5) Age showed a clear trend with increasing stress levels. Drivers aged 40–49 years had the highest proportion of high stress (36.5%), followed by 50–59 years (40.5%), while younger drivers aged 20–29 years had only 12.9% high stress. This association was statistically significant (χ² = 19.84, p = 0.011). Education did not show a significant association with stress (p = 0.136), although illiterate drivers had a relatively higher proportion of high stress (42.3%). Alcohol consumption showed a strong association with stress. Among alcohol users, 40.5% had high stress, compared to only 13.1% among non-users (χ² = 39.91, p < 0.001). Smoking was also significantly associated with stress, where 41.9% of smokers had high stress compared to 17.8% of non-smokers (χ² = 28.57, p < 0.001). Job satisfaction showed the strongest pattern: 50.7% of dissatisfied drivers had high stress, compared to only 12.9% among satisfied drivers (χ² = 74.26, p < 0.001). Financial stress was similarly important, where 43.0% of those with financial stress had high stress, compared to 12.9% among those without financial stress (χ² = 58.47, p < 0.001). Table 6. Logistic Regression Analysis of High Occupational Stress Predictors. Variable Crude OR (95% CI) p-value Adjusted OR (95% CI) p-value Driving >10 hrs/day 3.84 (2.41–6.11) <0.001 2.98 (1.76–5.05) <0.001 Sleep <6 hrs 3.19 (2.02–5.05) <0.001 2.56 (1.55–4.24) <0.001 Alcohol use 2.41 (1.53–3.80) <0.001 1.74 (1.04–2.91) 0.035 Smoking 2.02 (1.29–3.16) 0.002 1.38 (0.82–2.31) 0.221 Financial stress 3.55 (2.24–5.64) <0.001 2.81 (1.68–4.69) <0.001 Job dissatisfaction 4.67 (2.85–7.63) <0.001 3.92 (2.26–6.81) <0.001 Night driving 2.63 (1.62–4.27) <0.001 1.88 (1.10–3.20) 0.021 Back pain 2.09 (1.35–3.25) 0.001 1.29 (0.76–2.20) 0.343 After adjusting for confounding factors, several variables remained significant predictors of high occupational stress. Drivers working more than 10 hours per day had nearly 3 times higher odds of high stress (AOR = 2.98, 95% CI: 1.76–5.05, p < 0.001). Those sleeping less than 6 hours had 2.56 times higher odds of stress (AOR = 2.56, 95% CI: 1.55–4.24, p < 0.001). Alcohol consumption increased the odds of stress by 1.74 times (AOR = 1.74, 95% CI: 1.04–2.91, p = 0.035). Financial stress had a strong independent effect, increasing risk by 2.81 times (AOR = 2.81, 95% CI: 1.68–4.69, p < 0.001). Job dissatisfaction emerged as the strongest predictor, with nearly four times higher odds of high stress (AOR = 3.92, 95% CI: 2.26–6.81, p < 0.001). Night driving also significantly increased stress risk (AOR = 1.88, p = 0.021). Smoking and back pain showed significant crude associations but were not included in the final adjusted model. Table 7. Correlation Between Selected Continuous Variables and Perceived Stress Score (PSS). Variables Correlation coefficient (r) p-value Driving hours vs. PSS score 0.42 <0.001 Sleep duration (hours) vs. PSS score −0.51 <0.001 Driving experience (years) vs. PSS score 0.11 0.014 BMI vs. PSS score 0.18 0.001 Table 7 presents the correlation between selected continuous variables and Perceived Stress Scale (PSS) scores among lorry drivers. Driving hours showed a moderate positive correlation with PSS scores (r = 0.42, p < 0.001), indicating that perceived stress increased with longer daily driving hours. In contrast, sleep duration demonstrated a moderate negative correlation with PSS scores (r = −0.51, p < 0.001), suggesting that shorter sleep duration was associated with higher stress levels. Driving experience (r = 0.11, p = 0.014) and BMI (r = 0.18, p = 0.001) showed weak but statistically significant positive correlations with PSS scores. Overall, the findings indicate that prolonged driving hours and inadequate sleep were the strongest correlates of occupational stress, while driving experience and BMI had relatively weaker but significant associations with perceived stress among lorry drivers

The present study found a significant proportion of occupational stress among heavy vehicle drivers. Moderate stress was reported by 51.5% of participants, while 29.0% had high stress, indicating that a large proportion experienced substantial psychological strain. This finding is consistent with evidence showing that commercial drivers are exposed to prolonged working hours, psychosocial demands, inadequate rest, and unfavourable working conditions that may adversely affect their health and well-being [1,2]. Driving for more than 10 hours per day was independently associated with high occupational stress (AOR = 2.98, 95% CI: 1.76–5.05, p < 0.001). Prolonged driving may reduce opportunities for rest and recovery and increase physical and psychological fatigue. Previous studies have similarly identified extended working hours and demanding work schedules as important occupational concerns among professional drivers [1,7,8]. Sleep duration was another important predictor. Drivers sleeping less than 6 hours had significantly higher odds of high stress (AOR = 2.56, 95% CI: 1.55–4.24, p < 0.001). The correlation analysis also showed a moderate negative relationship between sleep duration and PSS score (r = −0.51, p < 0.001). Similar findings have been reported among professional drivers, where inadequate sleep, irregular schedules, and fatigue were associated with poorer psychological well-being [3,5]. A recent study among bus drivers in South India also reported substantial levels of occupational stress and sleep deprivation [10]. Financial stress and job dissatisfaction were strong independent predictors of high occupational stress in the present study. Financially stressed drivers had 2.81 times higher odds of high stress, while dissatisfied drivers had nearly four times higher odds. These findings highlight the importance of the broader work environment in determining drivers' mental health. Previous research among truck drivers has also identified workload, financial concerns, poor working conditions, and inadequate workplace support as important contributors to psychological distress [7,8,13]. Alcohol use remained significantly associated with high stress after adjustment (AOR = 1.74, 95% CI: 1.04–2.91, p = 0.035). Smoking was significant in the unadjusted analysis but did not remain significant after adjustment. Similar behavioural patterns have been reported among professional drivers, suggesting that unhealthy behaviours may coexist with occupational stress [6,12]. These findings support the need for stress-management programmes that also address alcohol and tobacco use. Age was significantly associated with occupational stress (p = 0.011), with higher proportions of high stress among drivers aged 40–59 years. This may reflect cumulative occupational exposure, increasing financial responsibilities, and prolonged exposure to demanding working conditions. However, age should be interpreted alongside occupational and psychosocial factors rather than as an independent explanation for stress [7]. Driving hours showed a moderate positive correlation with PSS score (r = 0.42, p < 0.001), further supporting the effect of prolonged driving on stress. Night driving was also independently associated with high stress (AOR = 1.88, 95% CI: 1.10–3.20, p = 0.021). Night work can disturb normal sleep patterns and reduce adequate recovery, thereby increasing fatigue and psychological strain [3,5]. Overall, the findings indicate that occupational stress among heavy vehicle drivers is multifactorial, with long working hours, inadequate sleep, financial stress, job dissatisfaction, alcohol use, and night driving emerging as important factors. Interventions should therefore focus on reasonable driving hours, adequate rest periods, improved working conditions, financial and psychosocial support, and accessible stress-management services. Such measures may improve driver well-being while also contributing to safer road transportation [1,2,15].

The present study found a substantial burden of occupational stress among heavy vehicle drivers, with 51.5% experiencing moderate stress and 29.0% experiencing high stress. Longer driving hours, inadequate sleep, financial stress, job dissatisfaction, alcohol use, and night driving were identified as important independent factors associated with high occupational stress. Job dissatisfaction showed the strongest association, indicating the importance of workplace conditions in determining drivers’ psychological well-being. Driving hours had a positive correlation with perceived stress, whereas sleep duration showed a negative correlation, suggesting that prolonged work and inadequate rest may contribute to increased stress. Driving experience and BMI showed weak positive correlations with stress scores. These findings indicate that occupational stress among heavy vehicle drivers is influenced by multiple occupational, psychosocial, and lifestyle factors. Measures such as regulating driving hours, ensuring adequate rest, improving job conditions, providing financial and psychosocial support, and implementing workplace stress-management programmes may help improve driver well-being and road safety. Novelty of the Study The novelty of the present study lies in its community-based assessment of occupational stress specifically among heavy vehicle drivers in Siddipet District, Telangana, a population for which local evidence is limited. In addition to estimating the prevalence of stress, the study simultaneously examined occupational, lifestyle, financial, psychosocial, and health-related factors. The use of PSS-10 along with multivariable logistic regression and correlation analysis helped identify independent factors associated with high occupational stress, providing locally relevant evidence for targeted occupational health interventions. Limitations of The Study The study has some limitations. As it used a community-based cross-sectional design, temporal relationships between occupational stress and its associated factors could not be established. Participants were selected using a convenience sampling method, which may limit the generalizability of the findings to all heavy vehicle drivers in Telangana. Occupational stress and lifestyle behaviours such as alcohol and tobacco use were assessed through self-reported information, which may be affected by recall or social desirability bias. The study was conducted in Siddipet District, and therefore the findings may not be directly applicable to drivers working in other geographical or occupational settings. Despite these limitations, the study provides useful local evidence regarding occupational stress and its associated factors among heavy vehicle drivers.

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