ArticleBMC medical informatics and decision making2026
Prediction models for adherence to cardiac rehabilitation programs in patients with cardiovascular disease: a scoping review.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Strategies to improve adherence to exercise-based phase II cardiac rehabilitation after percutaneous coronary intervention: a best evidence summary.Frontiers in cardiovascular medicine · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
aimsTo 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.
methodsThis 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).
resultsTen 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.
conclusionsThe 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 ).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.