Prediction models for adherence to cardiac rehabilitation programs in patients with cardiovascular disease: a scoping review

作者：Chengyu Xia, Guo Ht, Liuxia Ji, Yingjun Zheng, Yuheng Du, Hui Liu · 发表于：BMC Medical Informatics and Decision Making · 年份：2026 · DOI：10.1186/s12911-026-03391-7 · 被引用次数：2 · 研究领域：Cardiac Health and Mental Health、Stroke Rehabilitation and Recovery、Medication Adherence and Compliance

AIMS: To critically evaluate the methodological quality and clinical readiness of prediction models for adherence to cardiac rehabilitation (CR) programs in patients with cardiovascular disease (CVD), and to propose a strategic roadmap for future research. METHODS: This scoping review was conducted following the Arksey and O’Malley framework. Nine electronic databases were systematically searched from inception to June 2025 for studies published in English or Chinese. The methodological quality of included prediction models was critically appraised using the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: Ten studies were included. CR non-adherence rates varied from 41% to 61.4%, measured via subjective scales, session completion rates, or wearable devices. Studies exhibited wide heterogeneity in sample sizes (50 to 12,003 participants) and predictor selection. Logistic regression was the most used predictive modeling method, followed by decision tree; random forest and artificial neural network were used in one study each. AUROC values ranged from 0.62 to 0.893. Critically, the PROBAST framework highlighted prevalent methodological concerns across all studies, including inadequate sample sizes, a near-total lack of external validation, and reliance on single-center, retrospective data. CONCLUSIONS: The application of prediction models for adherence to CR programs in patients with cardiovascular disease represents an emerging but methodologically heterogeneo...