Efficacy of a complex smartphone application for reducing hazardous alcohol consumption: Study protocol for a randomized controlled trial with analysis of in‐app user behavior in relation to outcome
Bibliographic record
Abstract
OBJECTIVE: The efficacy of alcohol reduction applications is variable, and the underlying factors are largely unknown. The aim of this study is threefold: evaluate the relationship between user engagement and intervention efficacy, investigate the efficacy of the different functions applied, and investigate the efficacy of the intervention application compared to control groups. METHODS: A randomized controlled trial will be conducted to determine the efficacy of a newly developed smartphone application compared to the controls in reducing alcohol consumption at a 30, 60, 90, 120, 150, and 180 days follow-up. Hazardous drinkers, aged 18 years or older, will be recruited through web articles and will be randomized (blinded to their allocation), to receive one of the two versions of the application (educational or control application) for 30 days, or will be allocated to a wait-list control group. Function usage times will be recorded on a single-user level to determine the association between application usage and efficacy. RESULTS: Data collection will be completed by July 2020, and follow-up will be completed by January 2021. CONCLUSIONS: The evaluation of intervention efficacy as a function of user behavior will hopefully contribute to the science of developing more efficient alcohol intervention applications in the future.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.062 | 0.012 |
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".