Analysis of barriers to self-monitoring of blood glucose among community-dwelling elderly patients with diabetes mellitus: a cross-sectional study integrating artificial intelligence-based risk prediction

Abstract

Self-monitoring of blood glucose (SMBG) is essential for diabetes management, yet elderly patients face multiple barriers that compromise adherence and glycaemic control. This study aimed to assess barriers to SMBG among community-dwelling elderly patients with diabetes mellitus, identify associated factors, and develop an artificial intelligence (AI)-based predictive model for high-risk individuals. A cross-sectional study was conducted at Chun’an First People’s Hospital from March to August 2025. Two hundred elderly patients (≥ 60 years) with diabetes were enrolled using convenience sampling. Data were collected using validated instruments: a sociodemographic questionnaire, SMBG Barriers Scale, Flourishing Scale (FS), and Digital Health Literacy Scale (DHLS). Statistical analyses included descriptive statistics, Pearson correlations, and multiple linear regression. For predictive modelling, the dataset was split into training (80%) and testing (20%) sets. Random Forest, XGBoost, and Support Vector Machine algorithms were developed and evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. Model interpretability was assessed with SHAP values. The mean SMBG Barriers score was 47.6 ± 9.8. Significant negative correlations were found between SMBG barriers and FS ( r = − 0.42) and DHLS ( r = − 0.51) scores. Multiple regression showed DHLS (β = −0.38), Flourishing score (β = −0.25), education, and diabetes education as protective factors, while complications and living alone were risk factors (R² = 0.462). The Random Forest model achieved the best performance (accuracy 88.5%, AUC 0.921). SHAP analysis identified digital health literacy, psychological well-being, and AI acceptance as the strongest predictors. This study highlights the importance of psychosocial and digital factors in SMBG barriers and demonstrates the effectiveness of explainable AI for risk prediction in elderly diabetic patients. Targeted interventions addressing modifiable factors could improve self-management. This study is a cross-sectional observational study and was not registered as a clinical trial.