Development of a Secondary Prevention Smartphone App for Students With Unhealthy Alcohol Use: Results From a Qualitative Assessment
Bibliographic record
Abstract
BACKGROUND: Despite considerable efforts devoted to the development of prevention interventions aiming at reducing unhealthy alcohol use in tertiary students, their delivery remains often challenging. Interventions including information technology are promising given their potential to reach large parts of the population. OBJECTIVE: This study aims to develop a secondary prevention smartphone app with an iterative qualitative design involving the target population. METHODS: The app development process included testing a first prototype and a second prototype, developed based on the results of 2 consecutive qualitative assessments. Participants (aged ≥18 years, screened positive for unhealthy alcohol use) were students from 4 tertiary education institutions in the French-speaking part of Switzerland. Participants tested prototype 1 or prototype 2 or both and provided feedback in 1-to-1 semistructured interviews after 2-3 weeks of testing. RESULTS: The mean age of the participants was 23.3 years. A total of 9 students (4/9 female) tested prototype 1 and participated in qualitative interviews. A total of 11 students (6/11 female) tested prototype 2 (6 who tested prototype 1 and 5 new) and participated in semistructured interviews. Content analysis identified 6 main themes: "General Acceptance of the App," "Importance of the Targeted and Relevant App Content," "Importance of Credibility," "Importance of the App Usability," "Importance of a Simple and Attractive Design," "Importance of Notifications to Ensure App Use over Time." Besides a general acceptance of the app, these themes reflected participants' recommendations toward increased usability; to improve the design; to include useful and rewarding contents; to make the app look serious and credible; and to add notifications to ensure its use over time. A total of 11 students tested prototype 2 (6 who tested prototype 1 and 5 new) and participated in semistructured interviews. The 6 same themes emerged from the analysis. Participants from phase 1 generally found the design and content of the app improved. CONCLUSIONS: Students recommend prevention smartphone apps to be easy to use, useful, rewarding, serious, and credible. These findings may be important to consider when developing prevention smartphone apps to increase the likelihood of app use over time. TRIAL REGISTRATION: ISRCTN registry 10007691; https://www.isrctn.com/ISRCTN10007691. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-020-4145-2.
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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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".