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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

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
  3. Gao X, Wu Y, Zheng R, Kou Y, Xing H, Li K, Zhang M. Neural mechanisms of food preference and reward processing: a review of multifaceted influencing factors and intervention strategies. Frontiers in Nutrition 2026;13 View