Unveiling Gender-Based Musculoskeletal Disorders in the Construction Industry: A Comprehensive Analysis

Without physically intensive building, modern infrastructure development would be impossible. Musculoskeletal diseases (MSDs) and other occupational health issues may arise in such a demanding environment. Construction workers often develop MSDs from repeated actions, uncomfortable postures, and heavy lifting. Musculoskeletal disorders may damage muscles, bones, tendons, ligaments, etc. The effect of MSDs is well known; occupational health studies increasingly include gender-specific aspects. Despite being in the minority, the number of female construction employees is growing. However, physiological variations and occupational activities and environments may provide distinct obstacles. Thus, identifying gender-specific MSDs in construction is essential for worker safety. This research proposes a gender-specific machine learning (ML)-based musculoskeletal disorder detection framework (GS-ML-MSD2F) in the construction industry. A simple random selection procedure chose 250 female and 250 male rebar workers with at least six months of experience for the dataset. In January and June 2023, face-to-face interviews and ergonomic evaluations were undertaken. The data were analyzed using different machine learning methods, and the effectiveness of the methods was studied. The data showed that 60% of female rebar workers had MSD symptoms. The lower back and shoulders accounted for 40% of cases. Multiple machine learning methods revealed two significant factors related to musculoskeletal disorders: lengthy working hours and uncomfortable postures, and long working hours had an adjusted odds ratio of 8.5%, whereas awkward posture had an adjusted odds ratio of 42.5%. These results emphasize the relevance of working hours and posture in MSD prevention for female rebar workers in the construction sector.

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.