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Aims <p>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.</p> Methods <p>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).</p> Results <p>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.</p> Conclusions <p>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.</p> Registration <p>Registered on the Open Science Framework (OSF) (<a href="https://doi.org/10.17605/OSF.IO/8JMDW">https://doi.org/10.17605/OSF.IO/8JMDW</a>).</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Chengyu Xia,
  • Hao Guo,
  • Liuxia Ji,
  • Yingjun Zheng,
  • Yuheng Du,
  • Hui Liu

摘要

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 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.

Registration

Registered on the Open Science Framework (OSF) (https://doi.org/10.17605/OSF.IO/8JMDW).