Accessibility settings

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

Web-based and Mobile Health Interventions (5845) Research Instruments, Questionnaires, and Tools (1203) Instruments and Questionnaires for Nutrition and Food Intake (248) Cardiac Risk and Cardiac Risk Calculators (169) Cardiovascular Disease Prevention (268) Innovations and Technology for Healthy Eating Education (556)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75106, first published .
Elderly couple using laptop and taking notes

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 (7) 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
  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
  4. Wang L, Wang H, Liu T, Zhang N, Zhao L. SPISE index and ensemble machine learning refine cardiovascular risk stratification in stage 0–3 CKM syndrome. The Aging Male 2026;29(1) View
  5. Tao L, Liu Y, Li J, Zhang J, Zhao H, Luo K, Gao Y, Mu J, Yang Q, Yan Z. From nutrient-based to food-based assessment: the evolution of inflammatory indices and their significance for metabolic syndrome and type 3 diabetes mellitus. Frontiers in Nutrition 2026;13 View
  6. Fenni Y, Zaabar S, Kettab K. Artificial Intelligence and Nutrition: A Bibliometric Analysis of Global Research Trends and Thematic Evolution. The North African Journal of Food and Nutrition Research 2026;10(21):251 View

Conference Proceedings

  1. Peng Y, Wang H. 2026 9th International Conference on Artificial Intelligence and Big Data (ICAIBD). GBDT Algorithm-Based Prediction of Life Health Risk View