Effect of a smartphone intervention as a secondary prevention for use among university students with unhealthy alcohol use: randomised controlled trial
Bibliographic record
Abstract
Objective To estimate the effects of providing access to an alcohol intervention based on a smartphone. Design Randomised controlled trial.. Setting Four higher education institutions in Switzerland. Participants 1770 students (≥18 years) who screened positive for unhealthy alcohol use (ie, a score on the alcohol use disorders identification test-consumption (AUDIT-C) of ≥4 for men and ≥3 for women) were randomly assigned by 1:1 allocation ratio in blocks of 10. Intervention Providing access to a brief, smartphone based alcohol intervention. Outcome measures The primary outcome studied was number of standard drinks per week at six months and the secondary outcome was number of heavy drinking days (past 30 days). Additional outcomes were maximum number of drinks consumed on one occasion, alcohol related consequences, and academic performance. Follow-up assessments occurred at months three, six, and 12. Data were analysed by intention to treat and by using generalised linear mixed models with random intercepts for the recruitment site and participants nested within the recruitment site, and with intervention ( v control), time (three months v six months; 12 months v six months), and baseline outcome values as fixed effects. Results Between 26 April 26 2021 and 30 May 2022, 1770 participants (intervention group (n=884); control group (n=886)) were included. Mean age was 22.4 years (standard deviation 3.07); 958 (54.1%) were women; and 1169 (66.0%) were undergraduate students, 533 (30.1%) were studying for a master’s degree, 43 (2.4%) were studying for a doctorate, and 25 (1.4%) were students of other higher education programme. The baseline mean number of standard drinks per week was 8.59 (standard deviation 8.18); the baseline number of heavy drinking days was 3.53 (4.02). Of 1770 participants, follow-up rates were 1706 (96.4%) at three months, 1697 (95.9%) at six months, and 1660 (93.8%) at 12 months. Of 884 students randomly assigned to the intervention group, 738 (83.5%) downloaded the smartphone application. The intervention had a significant overall effect on the number of standard drinks per week (incidence rate ratio 0.90 (95% confidence interval 0.85 to 0.96)), heavy drinking days (0.89 (0.83 to 0.96)), and the maximum number of drinks consumed on one occasion (0.96 (0.93 to 1.00), P=0.029), indicating significantly lower drinking outcomes in the intervention group than in the control group during the follow-up period. The intervention did not affect alcohol related consequences or academic performance. Conclusions Providing access to the smartphone application throughout the 12 month follow-up was effective at limiting the average drinking volume of university students who had self-reported unhealthy alcohol use at baseline. Trial registration ISRCTN 10007691.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".