Asian Nurs Res.  2020 Oct;14(4):241-248. 10.1016/j.anr.2020.08.004.

Risk Factors for Work-Related Musculoskeletal Disorders Among Intensive Care Unit Nurses in China: A Structural Equation Model Approach

• Yang S
• Li L
• Wang L
• Zeng J
• Li Y

• KMID: 2508326
• DOI: http://doi.org/10.1016/j.anr.2020.08.004

Abstract

Purpose
This study explored the direct and indirect effects of risk factors of work-related musculoskeletal disorders (WRMDs) in nurses working in intensive care units (ICUs).
Methods
A cross-sectional study design was used. ICU nurses from 28 tertiary hospitals in the Hunan and Guangdong provinces participated in a survey conducted via a self-reported online questionnaire. A structural equation model was used to fit the data and to evaluate associations among WRMDs and risk factors.
Results
Valid questionnaire samples were submitted by 984 ICU nurses. The prevalence of WRMDs within the previous year among ICU nurses was 96.8%. A valid structural equation model was constructed, and a good fit was shown: Chi-square value/degrees of freedom = 2.248; comparative fit index = .931; normal fit index = .905; goodness-of-fit index = .978; adjusted goodness-of-fit index = .966; and root mean square error of approximation = .036. All regression coefficients for direct effect reached significant levels (critical ratio > 1.96 and p < .05). In the structural equation model, the occurrence of WRMDs was directly affected by the following: physical factors, risk perception, and job stress. Physical factors and a safe environment indirectly affected WRMDs through risk perception and job stress. The strongest correlations with WRMDs were physical factors.
Conclusion
The model provided a new perspective for understanding the associations among physical factors, workplace safety environment, risk perception, job stress, and WRMDs. To improve the practice setting of the ICU, efforts should be made to help prevent WRMDs from physical, psychosocial, and environmental factors.

Keyword

intensive care units; musculoskeletal abnormalities; nurses; latent class analysis; risk factors