Key result
Cardiac rehab adherence models show moderate predictive accuracy but lack external validation and methodological rigor.
Why the study?
The methodological quality and clinical readiness of prediction models for adherence to cardiac rehabilitation programs in patients with CVD required critical evaluation.
Scoping Review
Existing prediction models for cardiac rehabilitation adherence are at an early stage of development with high risk of bias and lack of external validation, meaning none can currently be recommended for clinical use.
Answers are grounded in this paper and cite their sources.
Existing models should not guide clinical decisions; leaves open need for externally validated, low-bias tools.
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. 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). 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. The application of prediction models for adherence to CR programs in patients with cardiovascular disease represents an emerging but methodologically heterogeneous research area. Mapping of the existing evidence indicates that most published models remain at an early stage of development, with limited validation and variable reporting quality. Consequently, no existing prediction model can be confidently recommended for clinical use. These findings highlight the need for future studies to prioritize external validation, model transparency, and adherence to established methodological guidelines to support potential translation into clinical contexts. Registered on the Open Science Framework (OSF) (https://doi.org/10.17605/OSF.IO/8JMDW).
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Xia et al. (2026) conducted a scoping review in Adult patients (≥18 years) with cardiovascular disease participating in or referred to cardiac rehabilitation programs. Prediction models for adherence to cardiac rehabilitation programs was evaluated on Adherence to cardiac rehabilitation programs, measured by session completion rates, validated scales, or wearable device tracking (AUROC 0.62 to 0.893). Prediction models for adherence to cardiac rehabilitation programs in patients with cardiovascular disease demonstrated AUROC values ranging from 0.62 to 0.893, with non-adherence rates varying from 41% to 61.4%, but existing models lack external validation and methodological rigor.
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