Smartphone Application for Unhealthy Alcohol Use: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Technology-delivered interventions are useful tools for addressing unhealthy alcohol use. Smartphones in particular offer opportunities to deliver interventions at the user's convenience. A smartphone application with 5 modules (personal feedback, self-monitoring of drinking, designated driver tool, blood alcohol content [BAC] calculator, information) was developed. Its acceptability and associations between use and drinking outcomes were assessed. METHODS: One hundred thirty adults with unhealthy alcohol use (>14 [men]/>7 [women] drinks/week or ≥1 episode/month with 6 or more drinks) recruited in Switzerland (n = 70) and Canada (n = 60) were offered to use the application. Follow-up occurred after 3 months. Appreciation, usefulness, and self-reported frequency of use of the modules, and drinking outcomes (drinks/week, binge drinking) were assessed. Associations between application use and drinking at 3 months were evaluated with negative binomial and logistic regression models, adjusted for baseline values and gender. RESULTS: Of the participants, 48% were women, mean (SD) age: 32.8 (10.0). Follow-up rate: 86.2%. There were changes from baseline (BL) to follow-up (FU) in number of drinks/week, BL: 15.0 (16.5); FU: 10.9 (10.5), P = .01, and binge drinking, BL: 95.4%; FU: 64.3%, P < .0001. All modules had median ratings between 6 and 8 (scale of 1-10). Among the participants, 77% used the application, 76% used the personal feedback module, 41% the self-monitoring of drinking, 22% the designated driver tool, 53% the BAC calculator, and 31% the information module. Participants using the application more than once reported significantly fewer drinks/week at follow-up: Incidence Rate Ratio (IRR), number of drinks per week = 0.70 (0.51; 0.96). CONCLUSIONS: A smartphone application for unhealthy alcohol use appears acceptable and useful (although there is room for improvement). Without prompting, its use is infrequent. Those who used the application more than once reported less weekly drinking than those who did not. Efficacy of the application should be tested in a randomized trial with strategies to increase frequency of its use.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".