Association of work-life balance with occupational injury and work-related musculoskeletal pain among Korean workers

Exploring the relationship between work-family conflict and sleep disturbance: a study on stratification and interaction

Introduction

Despite several studies on the association between work-family conflict (WFC) and sleep disturbances, a more comprehensive approach considering occupational factors is lacking. We aimed to analyze this association among Korean workers and the combined effects of WFC and job-related factors on sleep disturbance.

Methods

Data on paid workers from the sixth Korean Working Conditions Survey were analyzed. Odds ratios (ORs) with 95% confidence intervals (CIs) for sleep disturbances with WFC were calculated using a multiple logistic regression model among Korean workers. Furthermore, stratification and interaction analyses were conducted between WFC and socioeconomic factors related to sleep disturbance.

Results

Among the 24,923 workers (male: 11,752, female: 13,171) examined, 35.40% of males and 39.95% of females experienced sleep disturbances. In both sexes, the WFC group was significantly associated with sleep disturbance [adjusted OR (95% CI): male, 2.90 [2.67-3.16]; female, 2.54 [2.35-2.74]]. According to the stratification analysis, the association between sleep disturbance and WFC was prominent among younger and highly educated individuals, those engaged in quick returns, and larger companies in both sexes. In the interactions between WFC, quick return, occupation, and company size on sleep disturbance, there were significant additive associations, except between WFC and occupation among female workers.

Conclusion

This study highlights the association between WFC and sleep disturbances in male and female workers and emphasizes the importance of maintaining work-life balance.

Construction and validation of a musculoskeletal disease risk prediction model for underground coal miners

Objective

To understand the prevalence among underground coal miners of musculoskeletal disorders (MSDs), analyze the risk factors affecting MSDs, and develop and validate a risk prediction model for the development of MSDs.

Materials and methods

MSD questionnaires were used to investigate the prevalence of work-related musculoskeletal disorders among 860 underground coal miners in Xinjiang. The Chinese versions of the Effort-Reward Imbalance Questionnaire (ERI), the Burnout Scale (MBI), and the Self-Rating Depression Inventory (SDS) were used to investigate the occupational mental health status of underground coal miners. The R4.1.3 software cart installation package was applied to randomly divide the study subjects into a 1:1 training set and validation set, screen independent predictors using single- and multi-factor regression analysis, and draw personalized nomogram graph prediction models based on regression coefficients. Subject work characteristic (ROC) curves, calibration (Calibrate) curves, and decision curves (DCA) were used to analyze the predictive value of each variable on MSDs and the net benefit.

Results

(1) The prevalence of MSDs was 55.3%, 51.2%, and 41.9% since joining the workforce, in the past year, and in the past week, respectively; the highest prevalence was in the lower back (45.8% vs. 38.8% vs. 33.7%) and the lowest prevalence was in the hips and buttocks (13.3% vs. 11.4% vs. 9.1%) under different periods. (2) Underground coal miners: the mean total scores of occupational stress, burnout, and depression were 1.55 ± 0.64, 51.52 ± 11.53, and 13.83 ± 14.27, respectively. (3) Univariate regression revealed a higher prevalence of MSDs in those older than 45 years (49.5%), length of service > 15 years (56.4%), annual income <$60,000 (79.1%), and moderate burnout (43.2%). (4) Binary logistic regression showed that the prevalence of MSDs was higher for those with 5-20 years of service (OR = 0.295, 95% CI: 0.169-0.513), >20 years of service (OR = 0.845, 95% CI: 0.529-1.350), annual income ≥$60,000 (OR = 1.742, 95% CI: 1.100-2.759), and severe burnout (OR = 0.284, 95% CI: 0.109-0.739), and that these were independent predictors of the occurrence of MSDs among workers in underground coal mine operations (p <  0.05). (5) The areas under the ROC curve for the training and validation sets were 0.665 (95% CI: 0.615-0.716) and 0.630 (95% CI: 0.578-0.682), respectively, indicating that the model has good predictive ability; the calibration plots showed good agreement between the predicted and actual prevalence of the model; and the DCA curves suggested that the predictive value of this nomogram model for MSDs was good.

Conclusion

The prevalence of MSDs among workers working underground in coal mines was high, and the constructed nomogram showed good discriminatory ability and optimal accuracy.