Analysis of workers' low back musculoskeletal disorders among a furniture manufacture with structural equation model

Objective To explore the current situation of low back work-related musculoskeletal disorders(WMSDs) and their influencing factors among furniture manufacture workers.

Methods The cross-sectional study was designed and structural equation model was constructed to analyze the prevalence of WMSDs and the influencing factors of low back WMSDs among the workers in furniture manufacture.

Results A total of 437 valid questionnaires were collected. The prevalence of low back WMSDs in this furniture manufacture was 11.21% in 2017. The absence rate due to lower back disease was 14.29%. The basic path hypothesis was that posture load, psychological load and working environment load had direct effect on low back WMSDs, and that working environment load could also had indirect effect on WMSDs of low back WMSDs. The average extraction variance (AVE) was 50.0% to 75.7%, indicating that the latent variables could represent observed variables; the composite reliability was 0.715 to 0.851, Tucker Lewis index (TLI) was 0.968, comparative fitting index (CFI) was 0.972, root mean square error (RMSEA) was 0.045, and χ²/df was 2.56, indicating that the model fitted well. The structural equation model showed that three latent variables (posture load, psychological load and work environment load) could explain 34.8% of the causes of low back WMSDs. Postural load and working environmental load had direct effect on low back WMSDs(path coefficients were 0.247 and 0.277 respectively, P < 0. 05), and postural load had an indirect effect on low back WMSDs through working environmental load(path coefficient was 0.225, P < 0.01).

Conclusion Posture load and work environmental load were risk factors of workers'low back WMSDs in furniture manufacture, posture load had indirect effect on low back WMSD through working environment load.It is suggested that furniture manufacturing enterprises should improve the work organization system, speed up the upgrading of production equipment, and strengthen pre job and on-the-job training to reduce the risk of WMSDs.