European Journal of Cardiovascular Medicine

Workplaces around the globe expose individuals to diverse occupational health hazards. These hazards range from physical injuries to chronic illnesses, all of which can significantly impact the physiological well-being of workers.[1] Despite advancements in workplace safety standards, occupational health hazards remain a leading cause of absenteeism, decreased productivity, and long-term health issues. 2his study aims to delve into the various types of occupational hazards, their physiological implications, and strategies to mitigate their effects. [2]

Driving a bus, whether within a bustling urban environment or in specialized settings like a medical college campus, presents a unique set of challenges for drivers. These challenges not only encompass vehicle operation, maintenance, and passenger safety but also extend to the health risks that drivers face due to the nature of their work. Bus drivers, particularly those at institutions like medical colleges, experience a range of physical, psychological, and environmental stressors that may have lasting physiological implications. [3]

Bus drivers are subject to long hours of sitting, irregular work schedules, exposure to pollutants, and stress from navigating heavy traffic, all of which contribute to a variety of health risks. The repetitive nature of driving, combined with the physiological demands of managing a large vehicle in busy environments, leads to both acute and chronic health issues. These health risks are not just theoretical but are prevalent in bus drivers’ daily routines. [4]

A significant portion of the health risks bus drivers face is physical. Sitting for long hours in a single position places considerable strain on the musculoskeletal system, particularly the spine. Research has consistently shown that prolonged sitting can lead to issues such as lower back pain, neck pain, and even long-term spinal degeneration. Bus drivers, due to the vibrations and jolting from road conditions, are especially susceptible to such problems. [5]

Moreover, the physical strain is exacerbated by the ergonomic challenges of driving a bus. Many buses lack optimal seating arrangements, and the repetitive movements required to steer, accelerate, and brake can contribute to overuse injuries. The lack of ergonomic support in the workplace, coupled with the high demands placed on the body, increases the likelihood of musculoskeletal disorders such as carpal tunnel syndrome, tendonitis, and shoulder injuries. [6]

Occupational health hazards can broadly be classified into physical, chemical, biological, ergonomic, and psychological categories. Each of these hazards has unique physiological impacts, such as respiratory issues from chemical exposure or musculoskeletal disorders from poor ergonomic design. Moreover, industries like construction, healthcare, agriculture, and manufacturing are particularly notorious for exposing workers to multiple hazards simultaneously. Addressing these challenges requires a multidisciplinary approach that encompasses medical research, industrial design, and public health policy. [7]

In recent years, global awareness regarding occupational health has grown. Governments and organizations are increasingly emphasizing workplace safety through regulations and educational programs. However, gaps persist in understanding the specific physiological mechanisms through which these hazards impact human health. [8] This gap is particularly pronounced in low-resource settings, where regulatory oversight is minimal, and workers are often unaware of the risks involved. This study seeks to fill these gaps by providing empirical data and actionable recommendations.

A cross-sectional observational study design will be employed for this research. This type of study allows for the assessment of the health status of bus drivers at a specific point in time, enabling researchers to collect data on a variety of variables such as health status, occupational history, and ergonomics.

Sample Size

A total of 90 bus drivers will be included in the study. The sample size is determined based on the expected prevalence of various health risks (musculoskeletal disorders, cardiovascular issues, etc.) in the bus driver population. Convenience sampling will be used to select participants, meaning that drivers who are available and meet the inclusion criteria will be recruited for the study.

Inclusion of 90 participants ensures a sufficient number of data points to draw meaningful conclusions regarding the health status and potential occupational risks faced by bus drivers. The sample size is designed to be large enough to detect significant differences in health outcomes based on exposure to various risk factors.

Exclusion Criteria

Chronic Illnesses Unrelated to Occupational Exposure: Bus drivers with known chronic illnesses (such as diabetes, cancer, or kidney disease) that are not related to occupational driving will be excluded. These conditions could confound the study results, as they may independently affect the participants' health.

Sampling Technique

Convenience sampling will be used to recruit participants from the pool of bus drivers employed by the medical college. This technique involves selecting individuals who are readily available and willing to participate in the study. While this method may introduce some bias (as it does not ensure random selection), it is practical and appropriate for a study with limited time and resources. Additionally, convenience sampling allows for a quick recruitment process, which is especially useful when working with a specific group such as bus drivers at a medical institution.

Data Collection Tools and Techniques

The data collection for this study will include a combination of questionnaires, clinical measurements, laboratory tests, and ergonomic assessments. These tools will allow researchers to comprehensively assess the health risks associated with bus driving, including physical, mental, and ergonomic factors.

Questionnaires: Participants will complete a self-reported questionnaire that will gather information on:

• Demographic data (age, gender, etc.)
• Occupational history (years of driving, hours per week, etc.)
• Self-reported health issues (musculoskeletal pain, fatigue, sleep disturbances, etc.).

Statistical Methods

Descriptive Statistics: Measures such as mean, median, and standard deviation will be used to summarize the demographic and health data of the participants. T-tests to compare continuous variables (e.g., BMI, blood pressure) between different groups (e.g., male vs. female drivers). Chi-square tests to assess associations between categorical variables (e.g., the presence of musculoskeletal issues in different age groups). ANOVA for comparing health outcomes across multiple groups (e.g., bus drivers with different levels of experience).

Table 1: Demographic and Job Characteristics

This table provides an overview of the study participants. The mean age of the bus drivers is 42.5 years, with an average of 10.3 years of experience. They work 8 hours per day and 6 days per week, which indicates a consistent exposure to occupational stressors.

Table 2: Job Stress Summary

Interpersonal Relationships (3.4 ± 0.8): Moderate stress level, indicating that social interactions at work might be a challenge. Physical Conditions (4.1 ± 0.7): The highest stress dimension, suggesting that environmental factors such as noise, vibration, and work conditions significantly impact drivers. Job Interests (3.8 ± 0.6): Relatively high, indicating that despite some dissatisfaction, drivers still find some level of motivation in their jobs.

Table 3: BP and HR Before and After Driving

Table 4: Environmental Stressors Exposure

Noise exposure (89 dB) exceeds the 85 dB OEL, which means drivers are frequently exposed to harmful noise levels. WBV (1.2 m/s²) exceeds the OEL of 0.9 m/s², suggesting that continuous exposure could lead to musculoskeletal disorders. HAV (3.1 m/s²) is below the OEL of 5 m/s², indicating that the exposure level is within safe limits.

Table 5: Noise Exposure Based on Traffic

As traffic congestion increases, noise exposure rises significantly. The highest noise exposure (94 dB) in heavy traffic exceeds the 85 dB OEL by a wide margin, putting drivers at risk of long-term hearing damage.

Table 6: Vibration Exposure Based on Traffic

WBV increases from 0.8 m/s² in low traffic to 1.4 m/s² in heavy traffic. HAV increases from 2.7 m/s² in low traffic to 3.5 m/s² in heavy traffic. WBV consistently exceeds the safety limit (0.9 m/s²), particularly in heavy traffic, indicating a higher risk for back problems and fatigue.

Table 7: BP and HR Statistical Test Results

Table 8: Correlation between Stressors and Physiology

Noise level correlates highly with BP (r=0.65) and HR (r=0.72) → Strong impact. WBV has a moderate correlation with BP (r=0.58) and HR (r=0.61). HAV has a weaker correlation with BP (r=0.32) and HR (r=0.35).

 Table 9: Regression Model Results

Noise level has the highest impact on BP (β=0.45, p=0.001) and HR (β=0.52, p<0.001). WBV also significantly affects BP (β=0.39, p=0.003) and HR (β=0.42, p=0.002). HAV has a weaker but still significant effect on BP (β=0.21, p=0.048) and HR (β=0.27, p=0.035).

The job stress assessment revealed that physical conditions were the highest stress factor (4.1 ± 0.7), followed by job interests (3.8 ± 0.6) and interpersonal relationships (3.4 ± 0.8). The high stress score related to physical conditions highlights the impact of environmental factors, such as prolonged sitting, noise, and vibration, on drivers' well-being. Job interest scores suggest that while drivers find some motivation in their work, challenges like monotony and limited career progression may affect their job satisfaction. [9]

Interpersonal relationship stress, though moderate, indicates that workplace dynamics and interactions with passengers or colleagues contribute to the overall stress burden. These findings are consistent with previous studies that have linked physical work environments to increased stress and mental health decline (Sauter et al., 1990). [10] Mechanistically, occupational stress activates the sympathetic nervous system, increasing blood pressure and heart rate through catecholamine release. [11] Chronic stress exposure also alters hippocampal and prefrontal cortex function, affecting decision-making and emotional regulation, which could worsen job dissatisfaction. [12]

This table presents clear physiological changes after a driving session, with systolic BP increasing by 14 mmHg, diastolic BP by 7 mmHg, and heart rate by 8 bpm. These findings suggest a direct physiological response to driving stress, likely triggered by prolonged focus, environmental stressors, and physical strain. [13] The increases in BP and HR are significant and indicate heightened sympathetic nervous system activity. [14]

Previous studies have shown that chronic elevation in these markers is linked to long-term cardiovascular risks, including hypertension and arterial stiffness (Munzel et al., 2017). [15] Mechanistically, increased sympathetic activation leads to vascular constriction, increased cardiac output, and ultimately higher blood pressure and heart rate. [16] Repeated episodes of transient hypertension due to work-related stress can contribute to left ventricular hypertrophy and increased risk of myocardial infarction. [17]

The exposure data show that noise levels (89 ± 4 dB) and whole-body vibration (1.2 ± 0.3 m/s²) exceed occupational exposure limits, while hand-arm vibration (3.1 ± 0.6 m/s²) remains within acceptable limits. The excessive noise exposure poses a serious risk of auditory damage and increased stress levels, as continuous exposure above 85 dB has been associated with noise-induced hearing loss and autonomic dysregulation (Basner et al., 2015). [18]

The high WBV levels suggest a risk of musculoskeletal disorders, particularly in the lower back, which is commonly reported among professional drivers. Mechanistically, noise exposure leads to hyperactivation of the amygdala and stress hormone release, which contributes to cardiovascular risks. [19] WBV, on the other hand, induces mechanical stress on spinal structures and can lead to long-term postural and circulatory issues. Chronic exposure to noise and vibration has also been linked to systemic inflammation, oxidative stress, and endothelial dysfunction, increasing cardiovascular disease risk. [20]

Noise exposure varied significantly with traffic load, increasing from 85 dB in low traffic to 94 dB in heavy traffic. This indicates that congestion significantly influences noise pollution levels, potentially exacerbating stress and physiological responses. [21] The highest recorded noise levels exceed occupational safety limits, necessitating interventions such as vehicle soundproofing and improved urban traffic management strategies to mitigate long-term health risks. [22]

Both WBV and HAV levels increased with traffic congestion, with WBV rising from 0.8 m/s² in low traffic to 1.4 m/s² in heavy traffic. Similarly, HAV increased from 2.7 m/s² to 3.5 m/s² under the same conditions. The increase in WBV surpasses the occupational limit in heavy traffic, reinforcing the need for improved ergonomic vehicle designs and periodic health assessments for drivers. [23].

Noise exposure (89 dB) and whole-body vibration (WBV) (1.2 m/s²) consistently exceed occupational exposure limits (OELs), posing risks for hearing loss and musculoskeletal disorders. Hand-arm vibration (HAV) (3.1 m/s²) remains within safe limits but increases with traffic congestion. Stressors like interpersonal relationships (moderate stress) and physical conditions (highest stress) further contribute to the drivers' workload. Driving tasks significantly increase systolic BP (14 mmHg), diastolic BP (7 mmHg), and heart rate (8 bpm), indicating acute physiological stress. Statistical tests confirm these changes are highly significant (p < 0.05), suggesting potential long-term cardiovascular risks. Noise and vibration levels escalate with traffic congestion, with heavy traffic causing the highest exposure (noise: 94 dB, WBV: 1.4 m/s²). These conditions exacerbate stress-related health risks, particularly for hearing and spinal health. Noise shows the strongest correlation with BP (r=0.65) and HR (r=0.72), followed by WBV and HAV. Regression models confirm noise as the most influential predictor of BP (β=0.45) and HR (β=0.52), with WBV also having a significant impact.

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