Exploratory analysis of the use of a secondary prevention application for unhealthy alcohol use
Bibliographic record
Abstract
Background and aims: Unhealthy alcohol use is a leading risk factor for mortality and morbidity worldwide. Smartphone applications are a recently developed approach to secondary prevention of unhealthy alcohol use. As smartphone apps targeting unhealthy alcohol use is an emerging field, a better understanding of its use will provide information that may help future research. We used data from two recent studies on the efficacy of a smartphone app to study how the app is used, by whom and what characteristics are associated with its use. Methods: The data analyzed in this work come from the two randomized controlled trials assessing the effect of the same smartphone intervention. The first study, held in Switzerland, provided access to the smartphone app to university students with unhealthy alcohol use. The second study, held in Canada, assessed the efficacy of the smartphone app in a sample recruited from the general population. First, we studied the app different modules’ frequency of use. Then, we investigated whether there were any specific uses associated with selected participants’ characteristics. Analyses were conducted separately in the Swiss and Canadian samples. Results: The Swiss sample, recruited in a student population, had a mean age (SD) of 22 (2.8) years. The Canadian sample, recruited from the general population, was older, with a mean age of 41.7 (12.5) years. In terms of alcohol consumption, the Canadian sample consumed more alcohol than the Swiss sample, with a mean of 30.5 (19.5) drinks per week against 8.9 (8.6) drinks a week. Regarding the use of the app, the median number of openings per module ranged from 0 to 1 for both samples. In both samples, the “game-type” and “assessment-type” modules were the most often open modules. The “follow-up” modules, which were little used by the Swiss sample, were used more by the Canadian sample. The following participants characteristics were significantly associated with the use of the various modules: gender, AUDIT score (“Alcohol Use Disorder Identification Test”, a score indicative of the severity of alcohol use and its consequences), level of education and age at baseline (Swiss sample); AUDIT score, formal treatment for alcohol use, highest level of education achieved and age at baseline (Canadian sample). Conclusion: Overall, participants made very little use of the app. Nevertheless, while usage was limited it seemed that this level of use was enough to have an effect on drinking, as the two studies showed a significant effect on drinking. The Swiss sample showed a greater interest in the more entertaining, interactive “game-type” and “assessment-type” modules. The Canadian sample showed interest in the interactive “game-type” and “assessment-type” modules. However, unlike the Swiss sample, the Canadian population, also demonstrated an interest in the “follow-up” modules. This study highlighted the fact that preferences for using the various modules differed according to the characteristics of the participants, and that the modules used matched the age and the severity of alcohol use. Thus, to optimize the effectiveness of the app, it seems essential to adjust its content according to the target audience.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".