ArticlePloS one2022
Improving cardiac rehabilitation patient adherence via personalized interventions.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Position Statement of the Italian Society of Cardiovascular Prevention (SIPREC) and Italian Heart Failure Association (ITAHFA) on Cardiac Rehabilitation and Protection Programs as a Cornerstone of Secondary Prevention after Myocardial Infarction or Revascularization.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2024Guideline
- Added Technology for Weight Management in Cardiac Rehabilitation: Biopsychosocial Factors and Rehabilitees' Talk in a Mixed Method Study.Journal of medical Internet research · 2026Trial
- Evaluating a technologically enhanced rehabilitation programme for wound healing in patients with coronary heart disease.International wound journal · 2024Trial
- Knowledge, attitudes and practices towards cardiac rehabilitation among patients with heart failure.BMC public health · 2026Article
- Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.Patient education and counseling · 2026Article
- Artificial intelligence in cardiology: a narrative review with focus on patient outcomes.Cardiovascular diagnosis and therapy · 2026Review
- Integrating Artificial Intelligence Into Digital Health-Optimized Therapeutics for the Remote Cardiac Rehabilitation.Clinical Medicine Insights. Cardiology · 2026Review
- How AI-Based Digital Rehabilitation Improves End-User Adherence: Rapid Review.JMIR rehabilitation and assistive technologies · 2025Review
- The Intervention of Medical Social Workers: Humanistic Care of Cardiovascular Disease Patients.Journal of multidisciplinary healthcare · 2025Article
- An eHealth intervention for patients with a low socioeconomic position during their waiting period preceding cardiac rehabilitation: a randomized feasibility study.European heart journal. Digital health · 2025Article
- Prospective Human Validation of Artificial Intelligence Interventions in Cardiology: A Scoping Review.JACC. Advances · 2024Article
- Current Role and Future Perspectives of Cardiac Rehabilitation in Heart Disease.Reviews in cardiovascular medicine · 2024Review
- Editorial: Highlights in medical and surgical rehabilitation 2021/22.Frontiers in rehabilitation sciences · 2023Article
- Enhancing Patient Education on Cardiovascular Rehabilitation with Large Language Models.Missouri medicineArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesDespite documented benefits and physicians' recommendations to participate in cardiac rehabilitation (CR) programs, the average dropout rate remains between 12-56%. This study's goal was to demonstrate that using personalized interventions can significantly increase patient adherence.
methodNinety-five patients (ages 18-90) eligible for the CR program were randomly recruited and received personalized interventions using the Well-Beat system. Adherence levels were compared to those of a historical control group. The Well-Beat system provided Sheba CR Health Care Provider (HCP) guidelines for personalized patient-therapist dialogue. The system also generated ongoing personalized text messages for each patient sent twice a week and related each patient's dynamic profile to their daily behavior, creating continuity, and reinforcing the desired behavior.
resultsA significant increase in patient adherence to the CR program: Three months after initiation, 76% remained active compared to the historical average of 24% in the matched control group (log-rank p-value = 0.001).
conclusionsUsing an Artificial Intelligence (AI)-based engine that generated recommendations and messages made it possible to improve patient adherence without increasing HCP load, benefiting all. Presenting customized patient insights to the HCP and generating personalized communications along with action motivating text messages can also be useful for remote care.
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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.