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Record W4417174801 · doi:10.2196/79306

Exploring the Usability and Acceptability of the FoodMATS-Youth App for Monitoring Food Marketing Exposures: Mixed Methods Study

2025· article· en· W4417174801 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSensory Analysis and Statistical Methods
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsUsabilitySocial mediaSocial marketingFood marketingSocial media marketingDigital marketingMarketing research

Abstract

fetched live from OpenAlex

Background: Unhealthy food and beverage marketing influences children's attitudes, preferences, and behaviors toward food. Most research studies on children's exposure to food marketing focus on single settings, media, or marketing channels, precluding cumulative estimates of food marketing exposure across children's daily lives. Therefore, there is a need for tools to measure food marketing across settings. Objective: This study aimed to test the feasibility of a mobile app to assess food marketing observed by youth aged 13-17 years across settings in their daily life in Newfoundland and Labrador. Methods: Using a digital app, FoodMATS-Youth (Food and Beverage Marketing Assessment Tool for Settings and Youth; developed by MetricWire Inc), 23 participants photographed food marketing they observed over 3 days. Each participant completed a feedback survey on the usability and acceptability of the digital app assessed through a 5-score rating of feasibility outcomes. They also took part in focus groups, sharing their experiences with the app, and these data were thematically analyzed. Descriptive analyses of app-derived feasibility metrics were also conducted. Results: The app had high usability and acceptability based on the feasibility outcomes, app-derived feasibility metrics, and focus group responses. For feasibility outcomes, app navigation had the highest rating at 4.7, similar to ease of use and app responsiveness at 4.48; convenience received the lowest rating at 4.0. App-derived feasibility metrics, such as user compliance, response, and app completion rates were also high at 92%, 85.2%, and 92%, respectively. A total of 146 photos of food marketing were submitted by participants through the app. Focus groups showed great participant satisfaction with the app's interface and functionality. Conclusions: This study found that the FoodMATS-Youth mobile app is highly feasible for monitoring food marketing exposures across multiple settings (eg, social media and grocery stores) and was well received by our participants. The FoodMATS-Youth has the potential to efficiently improve food marketing research in Canada and internationally and generate data that can inform comprehensive food marketing policies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.315
GPT teacher head0.481
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it