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.
- 1Department of Endocrinology, Chun'an First People's Hospital, No. 1869, Huanhu North Road, Qiandaohu Town, Chun'an County, Hangzhou, Zhejiang, 311700, China.
- 2Faculty of Nursing, Burapha University, Chon Buri, 20131, Thailand.
- 3Faculty of Nursing, Burapha University, Chon Buri, 20131, Thailand. [email protected].
Abstract
Background
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.Methods
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.Results
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.Conclusion
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.Clinical trial registration
This study is a cross-sectional observational study and was not registered as a clinical trial.
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BibTeX
@article{wang2026analysis,
title = {{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.}},
author = {Meiyan Wang and Yue Ying and Yuan Han and Jiaxiang Zhou},
journal = {BMC endocrine disorders},
year = {2026},
month = aug,
doi = {10.1186/s12902-026-02438-0},
url = {https://europepmc.org/article/MED/42902325},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - 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.
AU - Meiyan Wang
AU - Yue Ying
AU - Yuan Han
AU - Jiaxiang Zhou
PY - 2026
DA - 2026/08/01
JO - BMC endocrine disorders
DO - 10.1186/s12902-026-02438-0
UR - https://europepmc.org/article/MED/42902325
AB - Background 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.Methods 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.Results 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.Conclusion 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.Clinical trial registration This study is a cross-sectional observational study and was not registered as a clinical trial.
SN - 1472-6823
ER -