A Novel Just-in-Time Intervention for Promoting Safer Drinking Among College Students: App Testing Across 2 Independent Pre-Post Trials
Bibliographic record
Abstract
Background: Binge drinking, which is linked to various immediate and long-term negative outcomes, is highly prevalent among US college students. Behavioral interventions delivered via mobile phones have a strong potential to help decrease the hazardous effects of binge drinking by promoting safer drinking behaviors. Objective: This study aims to evaluate the preliminary efficacy of bhoos, a novel smartphone app designed to promote safer drinking behaviors among US college students. The app offers on-demand educational content about safer alcohol use, provides dynamic feedback as users log their alcohol consumption, and includes an interactive drink tracker that estimates blood alcohol content in real time. Methods: The bhoos app was tested in 2 independent pre-post studies each lasting 4 weeks, among US college students aged 18-35 years. The primary outcome in both trials was students' self-reported confidence in using protective behavioral strategies related to drinking, with self-reported frequency of alcohol consumption over the past month examined as a secondary outcome. Results: In study 1, bhoos was associated with increased confidence in using protective behavioral strategies. Students also endorsed the high usability of the app and reported acceptable levels of engagement. Study 2 replicated findings of increased confidence in using protective behavioral strategies, and demonstrated a reduction in the self-reported frequency of alcohol consumption. Conclusions: Bhoos is a personalized, accessible, and highly scalable digital intervention with a strong potential to effectively address alcohol-related behaviors on college campuses.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".