Usability Testing of the BRANCH Smartphone App Designed to Reduce Harmful Drinking in Young Adults
Bibliographic record
Abstract
BACKGROUND: Electronic screening and brief intervention (eSBI) apps demonstrate potential to reduce harmful drinking. However, low user engagement rates with eSBI reduce overall effectiveness of interventions. As "Digital Natives," young adults have high expectations of app quality. Ensuring that the design, content, and functionality of an eSBI app are acceptable to young adults is an integral stage to the development process. OBJECTIVE: The objective of this study was to identify usability barriers and enablers for an app, BRANCH, targeting harmful drinking in young adults. METHODS: The BRANCH app contains a drinking diary, alcohol reduction goal setting functions, normative drinking feedback, and information on risks and advice for cutting down. The app includes a social feature personalized to motivate cutting down and to promote engagement with a point-based system for usage. Three focus groups were conducted with 20 users who had tested the app for 1 week. A detailed thematic analysis was undertaken. RESULTS: The first theme, "Functionality" referred to how users wanted an easy-to-use interface, with minimum required user-input. Poor functionality was considered a major usability barrier. The second theme, "Design" described how an aesthetic with minimum text, clearly distinguishable tabs and buttons and appealing infographics was integral to the level of usability. The final theme, "Content" described how participants wanted all aspects of the app to be automatically personalized to them, as well as providing them with opportunities to personalize the app themselves, with increased options for social connectivity. CONCLUSIONS: There are high demands for apps such as BRANCH that target skilled technology users including young adults. Key areas to optimize eSBI app development that emerged from testing BRANCH with representative users include high-quality functionality, appealing aesthetics, and improved personalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".