Abstract:

Although several pathways for a theoretical relationship between work organizational factors and work-related musculoskeletal disorders (WMSDs) have been identified, little is known about the work organizational factors on the risk of developing WMSDs and the roles of work organization in their prognosis. To better understand these associations, we conducted the analysis in three parts. First, we described the prevalence of WMSDs among sewing machine operators, and evaluate the association between the risk of WMSDs and work organizational factors. The unconditional logistic regression analysis was utilized. We found that several individual factors, including age, gender, ethnicity, marital status, smoking behaviors, and medical history of musculoskeletal disorders or systemic illness, were associated with an upper body disorder. In addition, several ergonomic factors, such as repetitive movements, working on a schedule with a maximum work cycle of more than 2.75 hours, less than two rests in a workday, short total rest periods, a work-rest ratio over 8.7, and psychosocial factors, including high psychological job demands, job dissatisfaction, and perceived high physical exertion, were associated with an elevated prevalence of upper body disorders. Second, we assessed the correlations between work organizational factors and WMSD outcomes including physical signs assigned by nurse practitioners in physical examinations and self reported pain from baseline interview. We assessed the correlations using frequency distribution correlation analysis. We found that self-reported pain and physical signs identified during an examination have different meanings for the classification of subjects as WMSD cases and these two measures can disagree with each other. In addition, ethnicity, medical history of MSDs, and several ergonomic factors and psychosocial factors can be differentially distributed among cases defined by these two measures for upper body WMSDs. Third, we assessed WMSDs recovery over a 4-month follow-up period, using conditions at baseline and four follow-up surveys as predictors. Linear mixed-effects (LME) models were used to analyze longitudinal changes on pain recovery. We found that severity of baseline musculoskeletal pain, age, ethnicity, and work schedule were found to influence musculoskeletal pain recovery. This result suggests that assessments of intervention effectiveness may need to consider effects of both individual and work-related factors as well as severity of baseline musculoskeletal pain.