Exploring the Usability and Acceptability of the FoodMATS-Youth App for Monitoring Food Marketing Exposures: Mixed Methods Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".