Accepted for/Published in: JMIR Research Protocols
Date Submitted: Dec 2, 2025
Date Accepted: Mar 6, 2026
WiseFood: A Protocol for the User Needs Assessment, Co-Design, and Feasibility testing of an AI-supported nutrition application in a Living Lab context
ABSTRACT
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 AI offer promising avenues to support dietary behaviour change, few interventions target the household as a unit of change. The WiseFood project addresses this gap by developing AI-supported applications to promote healthier and more sustainable food choices at the household level through co-designed interventions in multi-site Living Labs (LL) 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 four-phase design across three LL sites in Ireland, Hungary, and Slovenia, respectively. Phase 1 involves 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 behaviours. Data will be collected at baseline and post-intervention using validated surveys and interviews.
Results:
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 behaviours. 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 applications 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.
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