This study aimed to investigate the characteristics of work-related musculoskeletal disorders (WRMSDs) in occupational disease claims and identify patterns of WRMSDs for each body part by industry and occupation.
This study analyzed the raw data of occupational disease claims for musculoskeletal disorders deliberated by the Occupational Disease Decision Committee of the Korea Workers’ Compensation & Welfare Service in 2020. The data was classified into 6 body parts with the highest numbers of occupational disease cases by using the complete enumeration data on principal diagnoses and 4 types of subdiagnoses in the raw data. The characteristics and approval rates of WRMSDs by body part, industry and occupation were examined and summarized.
A total of 13,015 occupational disease cases for WRMSDs were classified, and lumbar spinal (back) diseases accounted for the largest proportion of claimed diseases, followed by shoulder, elbow, wrist, knee, and neck diseases in a descending order. The occupations with the highest and second highest numbers of occupational disease cases by body part were found to be automobile assemblers and production-related elementary workers for the neck, school meal service workers and cooks for the back, construction frame mold carpenters and school meal service workers for the shoulder, elementary workers in mining and food service workers for the elbow, food service workers and automobile parts assemblers for the wrist, and ship welders and school meal service workers for the knee.
This study examined the characteristics and approval status of WRMSDs by body part and occupation. Based on the study results, management strategies for the prevention of WRMSDs should be established regarding occupations with a high risk of WRMSDs for each body part.
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It has been reported that long working hours are hazardous to the workers’ health. Especially, work-related musculoskeletal disorders (WMSDs) have been considered as one of the significant health issues in workplace. The objective of this study was to identify the association between long working hours and work-related musculoskeletal symptoms.
The analysis was conducted using data from the Fourth Korean Working Conditions Survey (KWCS). Subjects of this study were 24,783 wage workers and divided into three groups according to the weekly working hours, which were ≤ 40, 41–52 and > 52 h. The relationship between long working hours and work-related musculoskeletal symptoms was analyzed by multivariate logistic regression method after adjusting for general, occupational characteristics including specific working motions or postures and psychosocial factors.
Approximately 18.4% of subjects worked more than 52 h per week and 26.4 and 16.4% of male subjects and 33.0 and 23.4% of female subjects experienced work-related upper and lower limb pains, respectively, over the last 12 months. Moreover, the prevalence of upper and lower limb pain was increased in both genders as the weekly working hours increased. The odds ratios (ORs) of upper limb pain for those working 41–52 h and more than 52 h per week when adjusted for general, occupational characteristics including specific motions or postures and psychosocial factors were 1.36 and 1.40 for male workers and 1.26 and 1.66 for female workers compared to the reference group, respectively. Furthermore, ORs of lower limb pain for the same weekly working hour groups were 1.26 and 1.47 for male workers and 1.20 and 1.47 for female workers, respectively.
Long working hours were significantly related to work-related musculoskeletal symptoms in Korean wage workers and appropriate interventions should be implemented to reduce long working hours that can negatively affect workers’ health.
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This study examined the relationships between psychosocial work factors and risk of WRMSDs among public hospital nurses in the Klang Valley, Malaysia.
We conducted a cross-sectional study among 660 public hospital nurses. A self-administered questionnaire was used to collect data on the occurrence of WRMSDs according to body regions, socio-demographic profiles, occupational information and psychosocial risk factors. 468 questionnaires were returned (response rate of 71%), and 376 questionnaires qualified for subsequent analysis. Univariate analyses were applied to test for mean and categorical differences across the WRMSDs; multiple logistic regression was applied to predict WRMSDs based on the Job Strain Model’s psychosocial risk factors.
Over two thirds of the sample of nurses experienced discomfort or pain in at least one site of the musculoskeletal system within the last year. The neck was the most prevalent site (48.94%), followed by the feet (47.20%), the upper back (40.69%) and the lower back (35.28%). More than 50% of the nurses complained of having discomfort in region one (neck, shoulders and upperback) and region four (hips, knees, ankles, and feet). The results also revealed that psychological job demands, job strain and iso-strain ratio demonstrated statistically significant mean differences (p < 0.05) between nurses with and without WRMSDs. According to univariate logistic regression, all psychosocial risk factors illustrated significant association with the occurrence of WRMSDs in various regions of the body (OR: 1.52–2.14). Multiple logistic regression showed all psychosocial risk factors were significantly associated with WRMSDs across body regions (OR: 1.03–1.19) except for region 1 (neck, shoulders and upper back) and region 4 (hips, knees, ankles, and feet). All demographic variables except for years of employment were statistically and significantly associated with WRMSDs (p < 0.05).
The findings indicated the high prevalence of WRMSDs in many body regions, and the risks of developing WRMSDs according to the various body regions were associated with important psychosocial risk factors based on the job strain model. These findings have implications for the management of WRMSDs among public hospital nurses in the Klang Valley, Malaysia.
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Existing methods for practically evaluating musculoskeletal exposures such as posture and repetition in workplace settings have limitations. We aimed to automate the estimation of parameters in the revised United States National Institute for Occupational Safety and Health (NIOSH) lifting equation, a standard manual observational tool used to evaluate back injury risk related to lifting in workplace settings, using depth camera (Microsoft Kinect) and skeleton algorithm technology.
A large dataset (approximately 22,000 frames, derived from six subjects) of simultaneous lifting and other motions recorded in a laboratory setting using the Kinect (Microsoft Corporation, Redmond, Washington, United States) and a standard optical motion capture system (Qualysis, Qualysis Motion Capture Systems, Qualysis AB, Sweden) was assembled. Error-correction regression models were developed to improve the accuracy of NIOSH lifting equation parameters estimated from the Kinect skeleton. Kinect-Qualysis errors were modelled using gradient boosted regression trees with a Huber loss function. Models were trained on data from all but one subject and tested on the excluded subject. Finally, models were tested on three lifting trials performed by subjects not involved in the generation of the model-building dataset.
Error-correction appears to produce estimates for NIOSH lifting equation parameters that are more accurate than those derived from the Microsoft Kinect algorithm alone. Our error-correction models substantially decreased the variance of parameter errors. In general, the Kinect underestimated parameters, and modelling reduced this bias, particularly for more biased estimates. Use of the raw Kinect skeleton model tended to result in falsely high safe recommended weight limits of loads, whereas error-corrected models gave more conservative, protective estimates.
Our results suggest that it may be possible to produce reasonable estimates of posture and temporal elements of tasks such as task frequency in an automated fashion, although these findings should be confirmed in a larger study. Further work is needed to incorporate force assessments and address workplace feasibility challenges. We anticipate that this approach could ultimately be used to perform large-scale musculoskeletal exposure assessment not only for research but also to provide real-time feedback to workers and employers during work method improvement activities and employee training.
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