Multidomain Analysis of Chronic Pain Intensity in Labor-Intensive Occupations: Insights from Network and Regression Modeling

Construction workers face high exposure to physically demanding and repetitive tasks, making them vulnerable to chronic musculoskeletal pain. While prior studies often evaluate pain risk factors in isolation, this study uses an integrative framework, combining correlation analysis, network modeling, and ordinal regression, to identify key predictors of pain intensity among construction laborers. A validated survey was administered to 90 workers, capturing data on physical function, mental health, sleep disturbance, job characteristics, injury history, and demographics. Survey domains were validated using reliability testing and exploratory factor analysis. Spearman correlations showed strong associations between pain intensity and awkward postures (ρ=0.60), sleep disturbance due to pain (ρ=0.69), fatigue (ρ=0.75), and injury frequency (ρ=0.86). Network analysis revealed that pain interference, sleep disturbance, fatigue, and injury frequency were structurally central variables. Regression modeling identified sleep disturbance (p=0.004), repetitive task difficulty (p=0.003), injury frequency (p<0.001), and age (p=0.001) as significant predictors. The findings highlight the value of a multidimensional approach in understanding chronic pain risks in labor-intensive work. This framework supports targeted safety interventions and offers a foundation for proactive worker health monitoring in the construction industry.