This paper is in the following e-collection/theme issue:

Non-randomized Protocols and Methods (ehealth) (671) Cardiology (223) Cardiovascular Disease Prevention (206) Artificial Intelligence (2121) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1730) Cardiac Risk and Cardiac Risk Calculators (102) Cardiac Self-Management (146) Web-based and Mobile Health Interventions (4012) Mobile Health (mhealth) (3460) Mobile Apps for Cardiology (189)

Published on in Vol 14 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66068, first published .
Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium

Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium

Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium

Authors of this article:

Rutger van Mierlo1, 2, 3 Author Orcid Image ;   Bart Scheenstra4 Author Orcid Image ;   Joost Verbeek5 Author Orcid Image ;   Anke Bruninx2 Author Orcid Image ;   Petros Kalendralis2 Author Orcid Image ;   Inigo Bermejo2 Author Orcid Image ;   Andre Dekker2 Author Orcid Image ;   Arnoud van 't Hof1, 3, 6 Author Orcid Image ;   Marieke Spreeuwenberg5 Author Orcid Image ;   Laura Hochstenbach5 Author Orcid Image
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