Influencing factors of work-related musculoskeletal disorders among medical staff: a Bayesian network modeling analysis
10.20001/j.issn.2095-2619.20251205
- VernacularTitle:基于贝叶斯网络模型的医务人员工作相关肌肉骨骼疾患影响因素研究
- Author:
Li HU
1
;
Feiruo ZHANG
;
Yongmei ZHAO
;
Ning FANG
;
Guixin YU
;
Dan LIU
;
Dongdong CAO
;
Leihan XU
;
Zihuan WANG
;
Mingxiao GUO
;
Yan YE
Author Information
1. Beijing Center for Diseases Prevention and Control, Beijing 100013, China
- Publication Type:Journal Article
- Keywords:
Medical staff;
Work-related musculoskeletal disorders;
Occupational stress;
Anxiety symptoms;
Depressive symptoms;
Insomnia symptoms;
Bayesian network model;
Influencing factors
- From:
China Occupational Medicine
2025;52(6):631-636
- CountryChina
- Language:Chinese
-
Abstract:
Objective To understand the current situation and influencing factors of work-related musculoskeletal disorders (WMSDs) in medical staff in Beijing City. Methods A total of 2 687 medical staff were selected as the research subjects using the multi-stage sampling method. The current situation of WMSDs and occupational stress, anxiety symptoms, depressive symptoms, and insomnia symptoms were investigated using the Musculoskeletal Disorders Questionnaire, the Core Occupational Stress Scale, the Generalized Anxiety Disorder Scale, the Patient Health Questionnaire Depression Scale, and the Self-Sleep Management Questionnaire. The Max-Min Hill-Climbing algorithm was used to construct a Bayesian network model to analyze the influencing factors and internal relationships of WMSDs and to conduct reasoning and prediction of the model. Results The prevalence of WMSDs among the research subjects was 88.9%. Binary logistic regression analysis was used to identify age, educational level, personal monthly income, anxiety symptoms, depressive symptoms, insomnia symptoms, prolonged forward-head desk work, and prolonged static posture work to construct the Bayesian network model. The model consisted of nine nodes and eleven directed edges. Prolonged static posture work, prolonged forward-head desk work, and anxiety symptoms were directly related to WMSDs. Age and educational level were indirectly related to WMSDs through their influence on prolonged forward-head desk work. Depression symptoms were indirectly associated with WMSDs through their influence on anxiety symptoms. The model's prediction accuracy was 90.5%. Conclusion The prevalence of WMSDs among medical staff in Beijing City is relatively high. Prolonged static posture work, prolonged forward-head desk work, and anxiety symptoms may directly increase the risk of developing WMSDs.
