Prediction Model and Assessment of Work-Related Musculoskeletal Disorders Among Nurses in Tertiary Hospitals in China: A Cross-Sectional Study.

Abstract

Aims/Background Work-related musculoskeletal disorders (WMSDs) significantly impact the health and work efficiency of nurses. This study aimed to investigate factors influencing WMSD risk among nurses, including protective measures, physical activity, and biomechanical factors. Methods This cross-sectional study surveyed 3500 nurses from tertiary hospitals to assess musculoskeletal disorder risk factors, yielding 3380 valid responses. The dataset was randomly split into a training set (n = 2350) and a validation set (n = 1030). Univariate analysis and binary logistic regression were applied to develop a predictive model, which was evaluated based on its area under the curve (AUC), sensitivity, and specificity. Results Binary logistic regression analysis identified significant risk factors for WMSDs, including a higher movement and assistance of hospital patients (MAPO) Index (odds ratio (OR) = 1.579, 95% confidence interval (CI): 1.431-1.742, p < 0.001), quick exposure check (QEC) score (OR = 1.028, 95% CI: 1.021-1.034, p < 0.001), work-family interference (OR = 1.016, 95% CI: 1.008-1.023, p < 0.001), lack of protective measures (OR = 1.571, 95% CI: 1.075-2.296, p = 0.020), severe pelvic rotation (OR = 1.746, 95% CI: 1.229-2.480, p = 0.002), and older age (≥50 years, OR = 1.885, 95% CI: 1.171-3.036, p = 0.009). Conversely, higher job satisfaction was associated with a lower risk of WMSDs (OR = 0.807, 95% CI: 0.786-0.828, p < 0.001). Nurses who never exercised (OR = 1.608, 95% CI: 1.127-2.295, p = 0.009) or engaged in ≥16 hours of household labour per week (OR = 1.878, 95% CI: 1.305-2.703, p = 0.001) had an elevated WMSD risk. The nomogram model demonstrated excellent predictive performance, with an AUC of 0.822 (95% CI: 0.803-0.840, p < 0.001) in the training set and 0.810 (95% CI: 0.775-0.845, p < 0.001) in the validation set. Calibration plots and decision curve analyses confirmed its reliability and clinical applicability. Conclusion Implementing comprehensive protective measures, promoting regular physical activity, and ensuring even foot pressure distribution can significantly mitigate WMSD risk. In contrast, a higher MAPO Index, elevated QEC scores, extended household labour, and work-family interference contribute to increased susceptibility. The developed predictive model provides a valuable tool for nurse health risk screening and personalized intervention planning, supporting early identification and prevention of WMSDs among nurses in clinical settings.