Accessibility settings

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

Participatory Research Protocols and Proposals (305) Methods and Feasibility Studies (775) mHealth for Wellness, Behavior Change and Prevention (4314) Design and Formative Evaluation of Mobile Apps (1021) Formative Evaluation of Digital Health Interventions (5344)

Published on in Vol 15 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/88810, first published .
Young woman smiling at phone in modern kitchen with fresh produce

Evaluation of an AI-Supported Nutrition Application (WiseFood) in a Living Lab Context: Protocol for a User Needs Assessment, Co-Design, and Feasibility Testing

Evaluation of an AI-Supported Nutrition Application (WiseFood) in a Living Lab Context: Protocol for a User Needs Assessment, Co-Design, and Feasibility Testing

Authors of this article:

Niamh M Walsh1 Author Orcid Image ;   Pauline Dunne1 Author Orcid Image ;   Cathal O'Hara1 Author Orcid Image ;   Saša Štraus2 Author Orcid Image ;   Tamara Kozic2 Author Orcid Image ;   Emese Antal3 Author Orcid Image ;   Vanda Pózner3 Author Orcid Image ;   András Vig4, 5 Author Orcid Image ;   Dávid Szakos4, 5 Author Orcid Image ;   Stylianos Kolidakis6 Author Orcid Image ;   Dimitrios Skoutas6 Author Orcid Image ;   Angela C Flynn1 Author Orcid Image ;   Claire M Timon1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Conference Proceedings

  1. Petrou D, Bakatselos N, Kolidakis S, Arvanitis K, Andrikos K, Kuzmenko E, Sacharidis D, Skoutas D. Proceedings of the 20th ACM Conference on Recommender Systems. WiseFood: A Multi-Task Conversational Recommender for Trustworthy, Integrated, and Personalized Dietary Guidance View