Evidence map›Paper›PMID 42061864›Full record

ArticleJMIR research protocols2026

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

Niamh M Walsh, Pauline Dunne, Cathal O'Hara, Saša Štraus, Tamara Kozic, Emese Antal, Vanda Pózner, András Vig, Dávid Szakos, Stylianos Kolidakis and 3 more

Abstract read
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Article in JMIR research protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Niamh M WalshSchool of Population Health, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland.ORCID 0000-0002-6320-2043
Pauline DunneSchool of Population Health, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland.ORCID 0000-0002-4913-9682
Cathal O'HaraSchool of Population Health, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland.ORCID 0000-0001-7703-7897
Saša ŠtrausInnovation Technology Cluster Murska Sobota (ITC), Murska Sobota, Slovenia.ORCID 0000-0003-3223-6117
Tamara KozicInnovation Technology Cluster Murska Sobota (ITC), Murska Sobota, Slovenia.ORCID 0009-0005-8905-7058
Emese AntalESSRG Nonprofit Kft, Budapest, Hungary.ORCID 0000-0002-8321-6290
Vanda PóznerESSRG Nonprofit Kft, Budapest, Hungary.ORCID 0009-0007-6822-0332
András VigWasteless Foundation, Budapest, Hungary.ORCID 0009-0001-0295-1710
Dávid SzakosWasteless Foundation, Budapest, Hungary.ORCID 0000-0002-0280-0090
Stylianos KolidakisAthena Research and Innovation Center In Information Communication & Knowledge Technologies, Marousi, Greece.ORCID 0000-0003-0923-8544
Dimitrios SkoutasAthena Research and Innovation Center In Information Communication & Knowledge Technologies, Marousi, Greece.ORCID 0000-0002-6118-5227
Angela C FlynnSchool of Population Health, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland.ORCID 0000-0001-8438-1506
Claire M TimonSchool of Population Health, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland.ORCID 0000-0002-5778-6003

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUnhealthy 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.

objectiveThe 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.

methodsThe 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.

resultsThe 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.

conclusionsThe 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.

Indexed as

Artificial IntelligenceMobile ApplicationsNeeds AssessmentFeasibility StudiesHealth PromotionHumansAIartificial intelligenceco-designliving labsnutritionsustainability

Identifiers

PMID42061864
PMCPMC13161834

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.