Evidence map›Paper›PMID 35503652›Full record

ArticleJMIR formative research2022

Testing a Mobile App for Participatory Research to Identify Teen-Targeted Food Marketing: Mixed Methods Study.

Emily Truman, Charlene Elliott

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.7field-weighted citation impact, top 16% of its field
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

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. Article
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

2 authors at 1 institution in 1 country.

Emily TrumanDepartment of Communication, Media and Film, University of Calgary, Calgary, AB, Canada.ORCID https://orcid.org/0000-0003-3377-9869
Charlene ElliottDepartment of Communication, Media and Film, University of Calgary, Calgary, AB, Canada.ORCID https://orcid.org/0000-0003-2615-2144
University of Calgary · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile 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.

objectiveThe 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.

methodsA mixed methods approach, including quantitative user response rates and qualitative focus group discussion feedback, was used in the evaluation process.

resultsA 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.

conclusionsThe 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.

Indexed as

adolescentfeasibilityfood advertisingfood marketingmHealthmobile appmonitoringparticipatory researchteenagerusability

Identifiers

PMID35503652
PMCPMC9115658
OpenAlexW4225311749

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.