Skip to main navigation Skip to search Skip to main content

FoodCoach: Fully Automated Diet Counseling

  • Jing Wu*
  • , Simon Mayer
  • , Simeon Pilz
  • , Yasmine S. Antille
  • , Jan L. Albert
  • , Melanie Stoll
  • , Kimberly Garcia
  • , Klaus Fuchs
  • , Lia Bally
  • , Lukas Eichelberger
  • , Tanja Schneider
  • , Verena Tiefenbeck
  • , Sybilla Merian
  • , Freya Orban
  • *Corresponding author for this work
  • University of St. Gallen
  • University of Bern
  • Friedrich-Alexander University Erlangen-Nürnberg
  • University of Zurich

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Unhealthy dietary habits are a major preventable risk factor for widespread non-communicable diseases (NCD). Diet counseling is effective in managing diet-related NCDs, but constrained by its manual nature and limited (clinical) resources. To address these challenges, we propose FoodCoach, a fully automated diet counseling system that monitors people’s food purchases using digital receipts from loyalty cards and provides structured dietary recommendations. We introduce the FoodCoach system’s dietary recommender algorithm and architecture, alongside evaluation results from a two-arm randomized controlled trial involving 61 participants. The trial results demonstrate the technical feasibility and potential for scalable, fully automated diet counseling, despite not showing a significant change in participants’ food purchase healthiness. We further show how to deploy and extend the FoodCoach system in new contexts, provide all relevant component implementations, and discuss how to verify and enhance the system efficacy. Our core research contributions are: 1) a novel dietary recommender algorithm designed and implemented with clinical nutritional experts, and 2) a scalable system architecture that employs a knowledge graph for enhanced interoperability and applicability to diverse domains and data sources. From a practical perspective, FoodCoach can augment clinical diet counseling through novel insights about patient food purchases and continuous support between consultations. Its cost-effective automated recommendations can also benefit the general population to help reduce NCD prevalence.

Original languageEnglish
JournalIEEE Journal of Biomedical and Health Informatics
Volume29
Issue number7
Number of pages13
ISSN2168-2194
DOIs
Publication statusPublished - 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Digital health
  • Health informatics
  • Information systems
  • Recommender systems

Fingerprint

Dive into the research topics of 'FoodCoach: Fully Automated Diet Counseling'. Together they form a unique fingerprint.

Cite this