Intention for Liver Cancer Surveillance and Its Influencing Factors Among High-risk Populations Based on the Theory of Planned Behavior
Objective To explore the intention to surveillance and its influencing factors among high-risk populations emplo-ying structural equation modeling based on the theory of planned behavior(TPB).Methods A cross-sectional study was carried out from July to November 2022 on patients with chronic liver diseases at the Affiliated Hospital of North Sichuan Medical College.A self-administered questionnaire was used to collect data on the sociodemographic characteristics and TPB dimensions of the patients and their intention for liver cancer surveillance.The reliability and validity of the question-naire were verified through Cronbach's α coefficients and confirmatory factor analysis.Structural equation modeling was used to analyze the associations between patients'attitudes,subjective norms,perceived behavioral control,and the inten-tion for liver cancer surveillance.Results Among the 375 patients studied,approximately 80%had cirrhosis and hepatitis B.The questionnaire showed good internal consistency,with attitudes(α=0.90,95%CI:0.88～0.91),subjective norms(α=0.87,95%CI 0.85～0.89),perceived behavioral control(α=0.84,95%CI:0.81～0.86),and the intention for sur-veillance(α=0.89,95%CI:0.87～0.91).The structural equation model demonstrated a good fit(χ2/df=2.883,RMSEA=0.078,GFI=0.901,TLI=0.903,PGFI=0.646,PNFI=0.751).Subjective norms(β=0.32,P＜0.01)and perceived behavioral control(β=0.58,P＜0.01)were significantly and positively related to the patients' intention for liver cancer surveillance.Conclusion Intention for liver cancer surveillance among high-risk populations is closely associated with their subjective norms and perceived behavioral control.Patients with stronger perceived control over their actions and positive in-fluences from healthcare professionals,family,and friends had a stronger intention for surveillance.
Liver cancerSurveillance intentionTheory of planned behaviorStructural equation modeling
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