We conducted this experimental study to estimate a risk of a high-risk group of low back pain (LBP) membership in workers who perform the manual material handling (MMH) tasks in an actual workplace setting.
The subjects include healthy workers who were engaged in 12 MMH tasks at 6 manufacturing companies. We assessed the dynamic motion of trunk or lumbar spine using an industrial lumbar motion monitor (BioDynamics Laboratory of Ohio State University). The subjects were evaluated for the age, gender, years of working and anthropometric measurements (e.g., height, weight, shoulder height, elbow height, iliac height, leg length, trunk length, trunk circumference, iliac width, iliac depth, xiphoid width and xiphoid depth). Moreover, they were also evaluated for a risk of a high-risk group of LBP membership based on lift frequency, average twisting velocity, maximum moment, maximum sagittal flexion and maximum lateral velocity.
The subjects who were engaged in a packaging at a detergent manufacturing company are at the greatest risk of LBP (63.76%). This was followed by packaging at a leather product manufacturing company (57.06%), packaging at a non-metallic casting material manufacturing company (57.03%), manual injection at a non-metallic casting material manufacturing company (52.00%), toggling at a leather product manufacturing company (46.09%), non-metallic casting material manufacturing company (42.88%), rolling at a non-metallic mineral product manufacturing company (42.12%), shooting at a non-metallic casting material manufacturing company (40.99%), vacuum processes at a leather product manufacturing company (35.00%), looping at a general industrial machinery manufacturing company (33.93%), setting at a leather product manufacturing company (30.22%) and packaging at a general metal product manufacturing company (22.02%).
Our approach indicates that there is a risk of a high-risk group of LBP membership in workers who perform the MMH tasks.
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We examined factors related to depressive symptoms in Korean self-employed workers.
This secondary analysis examined data of 14,454 self-employed individuals from the fifth Korean Working Conditions Survey (2017). Multiple logistic regression analysis was used to assess the relationship of depressive symptoms with different variables.
Self-employed workers who had a good work-life balance, a good subjective health, and worked 40 to 48 hours or 48 to 60 hours/week were less likely to report depressive symptoms (all
Several factors known to be related to depressive symptoms do not lead to depression in self-employed individuals. Different interventions may therefore be needed to prevent depression in self-employed workers.
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Previous studies have classified cameramen’s job as physiologically heavy work and identified the risk factors of work-related musculoskeletal disorders (WRMDs) in cameramen. However, those studies limited their research subjects to cameramen. In this study, we compared the frequency and severity of WRMDs between cameramen and office workers.
A total of 293 subjects working in four broadcasting companies in Korea were recruited. A questionnaire survey was conducted for a month, starting in October 2016. The subjects were divided into cameramen and office workers according to their occupation. We compared the frequency and severity of WRMDs and ergonomic risk assessment results between the two groups.
The high-risk WRMD group had a higher proportion of cameramen than office workers. Moreover, the high ergonomic risk group also had a higher proportion of cameramen than office workers for WRMDs in the upper extremities and waist+lower extremities. In the multivariable-adjusted model comparing cameramen and office workers, the odds ratio (OR) with 95% confidence interval (95% CI) for high-risk WRMDs was 3.50 (95% CI: 1.92–7.72) for the upper extremities and 3.18 (95% CI: 1.62–6.21) for the waist and the lower extremities. The ORs by body parts were 3.11 (95% CI: 1.28–7.57) for the neck, 3.90 (95% CI: 1.79–8.47) for the shoulders, and 4.23 (95% CI: 1.04–17.18) for the legs and feet.
Our study suggests that cameramen are at high risk of WRMDs. Workplace improvements and management of the neck, shoulders, and lower extremities, which are susceptible to WRMDs, are necessary to prevent musculoskeletal disorders among cameramen.
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The aim of this study is to determine the risk factors related to upper extremities work-related musculoskeletal disorders (WRMSDs) in cameramen.
A questionnaire survey was performed on 166 cameramen in a broadcasting station. The questionnaire consisted of questions on the general characteristics, the health behavior, work type and duration, physical burden, ergonomic posture, and musculoskeletal symptoms. Definition of musculoskeletal disorders was based on NIOSH criteria.
The positive rate of WRMSDs symptoms by parts of the body was turned out to be the highest in the shoulder (14.5%) and the lowest in arm and elbow (6%). Logistic regression analysis revealed that symptoms in the shoulders increased with BMI (OR = 3.62, 95% CI = 1.03-12.71), physical burden (OR = 9.29, 95% CI = 1.72-61.78 in the very hard group) and ergonomic factors (OR = 4.50, 95% CI = 1.03-19.68). Ergonomic factors were only related to the symptoms of hand and wrist (OR = 10.21, 95% CI = 1.02-102.20). WRMSDs symptoms, in the whole upper extremities, were higher in the 50 or older age group (OR = 5.86, 95% CI = 1.03-33.26), higher BMI group (OR = 3.26, 95% CI = 1.28-13.53), non-exercise group (OR = 2.37, 95% CI = 1.24-12.59), high physical burden group (OR = 7.6, 95% CI = 1.34-52.74), and high grade ergonomic risk group (OR = 4.82, 95% CI = 1.29-16.06).
The most serious musculoskeletal disorders of male cameramen were shoulder pain. Ergonomic factors and physical burden were the most significant factors affecting WRMSDs in cameramen in this study. Cameramen should be educated to be able to improve the ergonomic occupational environment and to set up preventive measures against the risk factors during work.
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The aim of this study was to evaluate musculoskeletal and psychosocial perception and compare these conditions regarding the type of job (white or blue-collar) and the type of management model (private or public).
Forty-seven public white-collar (PuWC), 84 private white-collar (PrWC) and 83 blue-collar workers (PrBC) were evaluated. Job Content Questionnaire (JCQ) and Utrecht Work Engagement Scale (UWES) were applied to evaluate psychosocial factors. Nordic Musculoskeletal Questionnaire (NMQ) was used to assess musculoskeletal symptoms. Pressure Pain Threshold (PPT) was measured to evaluate sensory responses.
According to JCQ, all groups were classified as active profile. There was a significant association between work engagement and workers’ categories (
This study showed differences in psychosocial risk factors and musculoskeletal symptoms in workers engaged in different types of jobs and work organization. Personal and work-related characteristics, psychosocial factors and PPT responses were different across workers’ group. Despite all, there was no significant difference in reported symptoms across the groups, possibly indicating that the physical load is similar among the sectors.
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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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