Randomized controlled trial of a smartphone app designed to reduce unhealthy alcohol consumption
Bibliographic record
Abstract
Unhealthy alcohol use is common and causes tremendous harm. Most people with unhealthy alcohol use will never seek formal alcohol treatment. As an alternative, smartphone apps have been developed as one means to provide help to people concerned about their alcohol use. The aim of this study was to test the efficacy of a smartphone app targeting unhealthy alcohol consumption in a general population sample. Participants were recruited from across Canada using online advertisements. Eligible participants who consented to the trial were asked to download a research-specific version of the app and were provided with a code that unlocked it (a different code for each participant to prevent sharing). Those who entered the code were randomized to one of two different versions of the app: 1) the Full app containing all intervention modules; or 2) the Educational only app, containing only the educational content of the app. Participants were followed-up at 6 months. The primary outcome variable was number of standard drinks in a typical week. Secondary outcome variables were frequency of heavy drinking days and experience of alcohol-related problems. A total of 761 participants were randomized to a condition. The follow-up rate was 81 %. A generalized linear mixed model revealed that participants receiving the full app reduced their typical weekly alcohol consumption to a greater extent than participants receiving the educational only app (incidence rate ratio 0.89; 95 % confidence interval 0.80 to 0.98). No significant differences were observed in the secondary outcome variables (p > .05). The results of this trial provide some supportive evidence that smartphone apps can reduce unhealthy alcohol consumption. As this is the second randomized controlled trial demonstrating an impact of this same app (the first one targeted unhealthy alcohol use in university students), increased confidence is placed on the potential effectiveness of the smartphone app employed in the current trial. ClinicalTrials.org number: NCT04745325
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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) | 0.004 | 0.002 |
| 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".