OBJECTIVES: To determine the prevalence and severity of HAVS among tyre shop workers in Kelantan, Malaysia.
METHODS: A cross-sectional study involving 200 tyre shop workers from two districts in Kelantan was performed. Part one data were collected at the field using questionnaire, and hand-arm vibration was measured. Part two involved a set of hand clinical examinations. The workers were divided into high (≥5 m s-2 ) and low/moderate (<5 m s-2 ) exposure group according to their 8-hr time weighted average [A(8)] of vibration exposure. The differences between the two exposure group were then compared.
RESULTS: The prevalence of the vascular, neurological, and musculoskeletal symptoms was 12.5% (95% CI 10.16 to 14.84), 37.0% (95% CI 30.31 to 43.69), and 44.5% (95% CI 37.61 to 51.38) respectively. When divided according to their exposure statuses, there was a significant difference in the prevalence of HAVS for all three components of vascular, neurological, and musculoskeletal (22.68% vs 2.91%, 62.89% vs 12.62% and 50.52% and 38.83%) respectively. All the clinical examinations findings also significantly differed between the two groups with the high exposure group having a higher abnormal result.
CONCLUSION: Exposure to high A(8) of vibration exposure was associated with a higher prevalence of all three component of HAVS. There is a need for better control of vibration exposure in Malaysia.
METHODOLOGY: The test was conducted for two different road conditions, tarmac and dirt roads. HAV exposure was measured using a Brüel & Kjær Type 3649 vibration analyzer, which is capable of recording HAV exposures from steering wheels. The data was analyzed using I-kaz Vibro to determine the HAV values in relation to varying speeds of a truck and to determine the degree of data scattering for HAV data signals.
RESULTS: Based on the results obtained, HAV experienced by drivers can be determined using the daily vibration exposure A(8), I-kaz Vibro coefficient (Ƶ(v)(∞)), and the I-kaz Vibro display. The I-kaz Vibro displays also showed greater scatterings, indicating that the values of Ƶ(v)(∞) and A(8) were increasing. Prediction of HAV exposure was done using the developed regression model and graphical representations of Ƶ(v)(∞). The results of the regression model showed that Ƶ(v)(∞) increased when the vehicle speed and HAV exposure increased.
DISCUSSION: For model validation, predicted and measured noise exposures were compared, and high coefficient of correlation (R(2)) values were obtained, indicating that good agreement was obtained between them. By using the developed regression model, we can easily predict HAV exposure from steering wheels for HAV exposure monitoring.