Testing a Mobile App for Participatory Research to Identify Teen-Targeted Food Marketing: Mixed Methods Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Mobile apps are not only effective tools for promoting health to teenagers but are also useful for engaging teenagers in participatory research on factors that influence their health. Given the impact of food marketing messages on teenagers’ food attitudes and consumption choices, it is important to develop effective methods for capturing the food advertisements targeted at this population to assess their content. OBJECTIVE The aim of this study was to test the feasibility and usability of a mobile app, “GrabFM!” (“Grab Food Marketing!”), designed for teenagers to facilitate monitoring of self-identified targeted food marketing messaging. METHODS A mixed methods approach, including quantitative user response rates and qualitative focus group discussion feedback, was used in the evaluation process. RESULTS A total of 62 teenagers (ages 13-17) completed GrabFM! app pilot testing over a 7-day data collection period. Teenagers submitted a total of 339 examples of food marketing, suggesting high feasibility for the app. Participants also took part in focus group discussions about their experience, providing positive feedback on usability, including ease of use and design aesthetic appeal. CONCLUSIONS The GrabFM! app had high feasibility and usability, suggesting its efficacy in capturing accurate data relevant to the teenage population’s experience with food marketing messaging.
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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 | 0.053 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".