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

Viewpoints and Perspectives (619) Food Sciences and Nutrition Engineering (58)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37667, first published .
Design Issues in Personalized Nutrition Advice Systems

Design Issues in Personalized Nutrition Advice Systems

Design Issues in Personalized Nutrition Advice Systems

Authors of this article:

Iris M de Hoogh1, 2 Author Orcid Image ;   Machiel J Reinders3 Author Orcid Image ;   Esmée L Doets4 Author Orcid Image ;   Femke P M Hoevenaars1 Author Orcid Image ;   Jan L Top4 Author Orcid Image
Article Authors Cited by (12) Tweetations (7) Metrics

Journals

  1. Spoladore D, Tosi M, Lorenzini E. Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review. Artificial Intelligence in Medicine 2024;151:102859 View
  2. Papastratis I, Konstantinidis D, Daras P, Dimitropoulos K. AI nutrition recommendation using a deep generative model and ChatGPT. Scientific Reports 2024;14(1) View
  3. Jospe M, Richardson K, Saleh A, Bohlen L, Crawshaw J, Liao Y, Konnyu K, Schembre S. Leveraging continuous glucose monitoring as a catalyst for behaviour change: a scoping review. International Journal of Behavioral Nutrition and Physical Activity 2024;21(1) View
  4. Atampugbire G, Adomako E, Quaye O. Medicinal Plants as Effective Antiviral Agents and Their Potential Benefits. Natural Product Communications 2024;19(9) View
  5. Yang L, Guo Z, Xu X, Kang H, Lai J, Li J. An Online Multimodal Food Data Exploration Platform for Specific Population Health: Development Study. JMIR Formative Research 2024;8:e55088 View
  6. Gimpfl S, Steinle K, Gedrich K. Evaluating Various Methods Used by Direct-to-Consumer Products Promising Personalized Nutrition. Dietetics 2024;3(4):504 View
  7. De Croon R, Segovia-Lizano D, Finglas P, Vanden Abeele V, Verbert K. An Explanation Interface for Healthy Food Recommendations in a Real-Life Workplace Deployment: User-Centered Design Study. JMIR mHealth and uHealth 2025;13:e51271 View
  8. Donovan S, Abrahams M, Anthony J, Bergia R, Blander G, Brisbois T, Keck A, Moore E, Morck T, Nieman K, Ordovas J, Steiber A, Winters B, Wu T. Perspective: Challenges for Personalized Nutrition in the Current U.S. Regulatory Framework and Future Opportunities. Advances in Nutrition 2025:100382 View
  9. De Cock A, Forio M, Hansen H, Jacxsens L, Lachat C, Goethals P. Integrated control of seafood safety and quality beyond farm-to-fork: AI-related opportunities and challenges. Trends in Food Science & Technology 2025;163:105096 View
  10. Chiu C, Le T, Huang C, Chang C, Cheng C. Qualitative and quantitative analysis of the dietary approaches to stop hypertension diet for personalized hypertension management. Computers & Industrial Engineering 2025;207:111286 View
  11. Siragusa N, Baldassari G, Ferrario L, Passera L, Rota B, Pavan F, Santagata F, Capasso M, Londoni C, Manfredi G, Consalvo D, Lasagni G, Pozzi L, Lombardo V, Mascaretti F, Scricciolo A, Roncoroni L, Elli L, Vecchi M, Costantino A. The Ten Dietary Commandments for Patients with Irritable Bowel Syndrome: A Narrative Review with Pragmatic Indications. Nutrients 2025;17(15):2496 View
  12. Aydın S, Ali R, Faiz S, Khan T. An Integrated AI Framework for Personalized Nutrition Using Machine Learning and Natural Language Processing for Dietary Recommendations. Applied Sciences 2025;15(17):9283 View