ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2026
Identification of Factors Associated with Glycemic Control Using the PRECEDE- PROCEED Model in Patients with Type 2 Diabetes: A Cross-Sectional Study.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to identify the predisposing, enabling, and reinforcing factors associated with glycemic control in patients with type 2 diabetes mellitus, and to provide preliminary evidence for developing continuity management programs across hospital and community settings. Methods: A cross-sectional survey design was used. Based on the PRECEDE-PROCEED planning model, 220 patients with type 2 diabetes were assessed using a general information questionnaire, the Chronic Disease Management Self-Efficacy Scale, the Type 2 Diabetes Self-Management Behavior Scale, and the Social Support Rating Scale. Data were analyzed using the chi-square test, Spearman correlation analysis, and multiple linear regression analysis. Results: The average age of patients with type 2 diabetes was 57.90 (SD=14.28), and the average course of disease was 8.76 (SD=7.52). Overall, 25% of patients achieved the glycemic control target of HbA1c < 7%. Age, disease duration, smoking history, self-efficacy, self-management behavior, and social support were significantly associated with HbA1c and jointly accounted for 44.4% of its variance. Conclusion: The rate of adequate glycemic control among patients with type 2 diabetes mellitus remains suboptimal. Within the PRECEDE-PROCEED framework, self-efficacy may represent a predisposing factor, self-management behaviors an enabling factor, and social support both an enabling and reinforcing factor affecting glycemic control. These findings indicate that future continuity management programs should integrate tailored interventions targeting self-efficacy enhancement, self-management behavior improvement, and social support reinforcement. The incorporation of intelligent terminal technologies may further support personalized diabetes management and improve long-term glycemic outcomes.
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