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Published on: February 21, 2025
Development and Internal Validation of an Explainable Machine Learning Model for Physical Activity Adherence Among
Yiming Zhao1, Mo Zhou1, Feng Sun1,2
1Department of Rehabilitation Medicine, The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.
Background:
Physical activity adherence is critical for effective diabetes management, yet non-adherence remains common in community settings. This study sought to construct and internally evaluate an interpretable machine learning model for identifying poor physical activity adherence among patients with Type 2 diabetes mellitus (T2DM) in a community-based setting.
Methods:
This single-center cross-sectional study included 207 patients with T2DM receiving community-based care at the Shiqiao Community Health Service Center, China, between January and September 2025. Candidate predictors were selected using Elastic Net regularization followed by multivariable logistic regression. Multiple machine learning algorithms were developed and compared using a randomly allocated training dataset (70%, n = 145) and an internal validation dataset (30%, n = 62). Model discrimination and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA), respectively. Shapley Additive exPlanations (SHAP) was used to interpret individual predictor contributions to model outputs.
Results:
Among the 207 participants, 113 (54.6%) demonstrated high physical activity adherence. The final model incorporated five key predictors: Summary of Diabetes Self-Care Activities (SDSCA) score, estimated glomerular filtration rate (eGFR_CKD-EPI_2009), family history of diabetes, International Physical Activity Questionnaire (IPAQ) score, and albumin (ALB). The SVM model achieved the highest discrimination ability among the tested algorithms and was subsequently selected as the final model (AUC = 0.787, 95% CI: 0.671-0.903). SHAP analysis provided transparent visualization of the relative contribution of each predictor to model outputs.
Conclusion:
This study provides an explainable machine learning approach for identifying key variables associated with physical activity adherence among individuals with T2DM in community diabetes management. Further validation in independent populations is needed before the model can be considered for risk assessment and personalized behavioral intervention planning in community diabetes care.