Assessing the Posture and predicting the factors affecting musculoskeletal disorders in computer uses by neural networks

Introduction: Working with a computer and workplace conditions expose people to risk factors of musculoskeletal disorders (MSDs). This study aimed to assess posture, examine MSDs, and determine, weigh and prioritize the risk factors among computer users by a neural network algorithm.  Material and Methods: This descriptive-analytical cross-sectional study was conducted in six phases on computer users in 2019. The status of MSDs was determined via Nordic musculoskeletal questionnaire (NMQ). The factors affecting these disorders were determined by the ROSA method, and then these factors were weighed by the neural network algorithm. The data were analyzed in IBM SPSS Modeler. Results: The mean age and work experience of the users were 34 ± 6.9 and 1.5 ± 0.7 years, respectively. Most of years were observed at the lower back, neck, and upper back, respectively. The final mean scores of the chair, telephone-monitor, and mouse-keyboard were 3.7 ± 1, 3.6 ± 1.1, and 3.65 ± 1.2, respectively and the final mean score of ROSA was 4.4 ± 0.9. The greatest correlation with the ROSA score was observed in chair (R2 = 0.46), followed by telephone-monitor (R2 = 0.43), and mouse-keyboard (R2 = 0.42). The highest predictor importance of the effective factors based on the neural network algorithm prioritization belonged to the chair (48%), followed by telephone-monitor (28%) and mouse-keyboard (24%). The accuracy of the neural network algorithm in examining the effect of factors on musculoskeletal disorders was 98% based on the ROSA score. Conclusion: Factors affecting years due to working with computers are the chair, telephone-monitor, and mouse-keyboard, respectively, as prioritized by the neural network algorithm. These disorders can be prevented by ergonomic modification of users’ chairs and correct placement of the monitor and telephone.