Digital interventions for reducing alcohol use in general populations: An updated systematic review and meta‐analysis
Bibliographic record
Abstract
This article updates a 2017 review on the effectiveness of digital interventions for reducing alcohol use in the general population. An updated systematic search of the MEDLINE database was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria to identify randomized controlled trials (RCTs) published from January 2017 to June 2022 that evaluated the effectiveness of digital interventions compared with no interventions, minimal interventions, and face-to-face interventions aimed at reducing alcohol use in the general population and, that also reported changes in alcohol use (quantity, frequency, quantity per drinking day, heavy episodic drinking (HED), or alcohol use disorders identification test (AUDIT) scores). A secondary analysis was performed that analyzed data from RCTs conducted in students. The review was not preregistered. The search produced 2224 articles. A total of 80 studies were included in the review, 35 of which were published after the last systematic review. A total of 66, 20, 18, 26, and 9 studies assessed the impact of digital interventions on alcohol quantity, frequency, quantity per drinking day, HED, and AUDIT scores, respectively. Individuals randomized to the digital interventions drank 4.12 (95% confidence interval (CI): 2.88, 5.36) fewer grams of alcohol per day, had 0.17 (95% CI 0.06, 0.29) fewer drinking days per week, drank approximately 3.89 (95% CI: 0.40, 7.38) fewer grams of alcohol per drinking day, had 1.11 (95% CI: 0.32, 1.91) fewer HED occasions per month, and had an AUDIT score 3.04 points lower (95% CI: 2.23, 3.85) than individuals randomized to the control condition. Significant reductions in alcohol quantity, frequency, and HED, but not quantity per drinking day, were observed among students. Digital interventions show potential for reducing alcohol use in general populations and could be used widely at the population level to reduce alcohol-attributable harms.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".