The Impact of Mobile Apps on Alcohol Use Disorder: A Systematic Review Protocol
Bibliographic record
Abstract
BACKGROUND: Alcohol use disorder (AUD) is among the most prevalent mental disorders worldwide and is associated with a diverse range of physical and psychological comorbidities. Despite various types of treatment, there are many barriers to accessing treatment (ie, stigma, cost, accessibility of service, etc). Mobile apps have the potential to overcome these barriers and provide support to those who need it. OBJECTIVE: The purpose of this systematic review is to assess the effectiveness of mobile apps in reducing alcohol consumption for individuals with AUD and understand the psychological outcomes of using the apps (ie, client empowerment, self-efficacy, etc). METHODS: The search strategy was applied to 7 health sciences and interdisciplinary databases. Two reviewers will independently assess all titles and abstracts for relevance and then full texts of relevant articles for eligibility. To be included, the article must be a quantitative evaluation of clinical outcomes using the intervention and the intervention must be a consumer-facing app focused on supporting individuals with AUD. Two reviewers will independently extract data from all eligible articles using a standardized extraction worksheet and will independently assess the study quality. A meta-analysis will be conducted if appropriate. Depending on outcomes reported, pooled risk ratios or standardized mean differences will be calculated and reported in the review. RESULTS: The search strategy yielded 699 unique citations. Of those, 63 (9.0%, 63/699) articles were assessed as relevant for full-text review. The full-text reviews are currently underway and the final review is projected to be completed in the summer of 2017. CONCLUSIONS: There is potential for mobile apps to support individuals with AUD to reduce their alcohol consumption. This review will be the first to assess the effectiveness of AUD mobile apps and client experiences using the apps.
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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 | 0.062 | 0.064 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.016 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.078 | 0.011 |
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".