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

Web-based and Mobile Health Interventions (4132) Cardiac Risk and Cardiac Risk Calculators (111) Instruments and Questionnaires for Nutrition and Food Intake (219) Innovations and Technology for Healthy Eating Education (502) Research Instruments, Questionnaires, and Tools (764) Cardiovascular Disease Prevention (212)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75106, first published .
Integrating Food Preference Profiling, Behavior Change Strategies, and Machine Learning for Cardiovascular Disease Prevention in a Personalized Nutrition Digital Health Intervention: Conceptual Pipeline Development and Proof-of-Principle Study

Integrating Food Preference Profiling, Behavior Change Strategies, and Machine Learning for Cardiovascular Disease Prevention in a Personalized Nutrition Digital Health Intervention: Conceptual Pipeline Development and Proof-of-Principle Study

Integrating Food Preference Profiling, Behavior Change Strategies, and Machine Learning for Cardiovascular Disease Prevention in a Personalized Nutrition Digital Health Intervention: Conceptual Pipeline Development and Proof-of-Principle Study

Authors of this article:

Hana Fitria Navratilova1, 2 Author Orcid Image ;   Anthony David Whetton1, 3 Author Orcid Image ;   Nophar Geifman3, 4 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Nourazarain A, Vaziri Y. Nutrigenomics meets multi-omics: integrating genetic, metabolic, and microbiome data for personalized nutrition strategies. Genes & Nutrition 2025;20(1) View
  2. Lombardo M, Aulisa G, Muthanna F, Karav S, Baldelli S, Tripodi G, Aiello G. Dietary Behavior Clustering and Cardiovascular Risk Markers in a Large Population Cohort. Nutrients 2026;18(3):533 View