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Evaluation of an AI-Supported Nutrition Application (WiseFood) in a Living Lab Context: Protocol for a User Needs
Niamh M Walsh1, Pauline Dunne1, Cathal O'Hara1
1School of Population Health, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland.
Background:
Unhealthy and unsustainable diets remain a major global challenge, contributing significantly to poor health outcomes, environmental degradation, and social inequalities. Despite growing awareness, individuals face persistent barriers to adopting sustainable dietary practices, including cost, availability, cultural norms, and low food literacy. While digital tools and artificial intelligence (AI) offer promising avenues to support dietary behavior change, few interventions target the household as a unit of change. The WiseFood project addresses this gap by developing AI-supported apps to promote healthier and more sustainable food choices at the household level through co-designed interventions in multisite Living Labs (LLs) across Europe.
Objective:
The WiseFood project aims to co-design, develop, and test the feasibility of an AI-supported digital platform to promote sustainable, healthy diets at the household level. This protocol outlines the recruitment of stakeholders, the user needs and requirements phase, the co-design phase, and the feasibility study phase.
Methods:
The WiseFood project follows a 4-phase design across 3 LL sites in Ireland, Hungary, and Slovenia. Phase 1 involves the recruitment of diverse stakeholders, including households and experts for co-design activities. In Phase 2, user needs and requirements are assessed through household surveys and expert focus groups exploring AI in nutrition. Phase 3 consists of co-design workshops and iterative feedback loops to refine the WiseFood digital tools. Phase 4 is an 8-week feasibility study involving 300 households (n=100 per site), evaluating usability, acceptability, and outcomes related to nutrition knowledge, environmental awareness, and dietary behaviors. Data will be collected at baseline and postintervention using validated surveys.
Results:
The 3-year project (January 1, 2025-December 31, 2027) follows a 4‑phase structure to develop, refine, and test a user‑focused app across LL sites in Ireland, Hungary, and Slovenia. Phase 1 was completed in May 2025, while Phase 2 ran from June to July 2025. Phase 3, which commenced in September 2025, is expected to continue until June 2026. Phase 4 will commence in July 2026, and will run through to November 2027. The findings from the co-design and feasibility phases will be published separately and will include insights into usability, acceptability, and changes in nutrition knowledge, environmental awareness, and dietary behaviors. These results will inform further refinement of the WiseFood platform and guide future implementation and evaluation efforts.
Conclusions:
The WiseFood project adopts an evidence-based approach to develop AI-supported digital apps that encourage informed, healthy, and sustainable food practices in the home. By considering the differing needs of household members, WiseFood advances applied approaches that deliver targeted support in everyday household contexts.
International Registered Report Identifier (Irrid):
DERR1-10.2196/88810.
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