Predicting the Determinants of Colorectal Cancer Screening Behaviors Using Protection Motivation Theory: A Cross-Sectional Partial Least Squares Structural Equation Modeling Analysis

利用保护动机理论预测结直肠癌筛查行为的决定因素:一项横断面偏最小二乘结构方程模型分析

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Abstract

BACKGROUND: Colorectal cancer (CRC) screening reduces mortality by 40%-60%, yet participation remains low globally. In this study, we applied protection motivation theory (PMT) to identify determinants of CRC screening behavior in Iran using partial least squares structural equation modeling (PLS-SEM). DESIGN AND METHODS: This cross-sectional study was conducted on 433 adults over 50 years of age using stratified sampling in Neyshabur, Iran in 2024. Participants completed a validated 43-item PMT questionnaire assessing key cognitive factors (perceived sensitivity, severity, self-efficacy, response efficacy, response costs, fear, rewards, and behavioral intention), with self-reported screening behavior as the outcome. RESULTS: Among the PMT constructs, self-efficacy had the highest mean score (69.43 ± 18.97 out of 100), while actual screening behavior had the lowest (26.12 ± 9.93 out of 100). PLS-SEM analysis revealed significant pathways: perceived severity (β = 0.118, p = 0.030) and response efficacy (β = 0.172, p = 0.003) positively influenced behavioral intention, while perceived rewards negatively impacted intention (β = -0.197, p < 0.001). Fear mediated sensitivity/severity effects on intention (β = 0.155, p = 0.003). Notably, self-efficacy and response costs showed nonsignificant relationships with intention. CONCLUSION: Participants demonstrated moderate intention (54.8 of 100) yet low screening behavior (26.1 of 100), highlighting a critical intention-behavior gap. While PMT constructs effectively predicted screening intention, their limited ability to explain behavior underscores the influence of contextual barriers beyond cognitive appraisals in this Iranian cohort. Future interventions should integrate PMT-based education targeting threat appraisal with system-level strategies (e.g., mailed test kits, navigational support) to bridge this implementation gap.

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