Text Messaging Interventions for Reducing Alcohol Consumption Among Harmful and Hazardous Drinkers: Protocol for a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Mobile phone-based interventions have become popular for lifestyle behavior change, particularly the use of text messaging as it is a technology ubiquitous in mobile phones. Reviews and meta-analyses of digital interventions for reducing harmful and hazardous use of alcohol have mainly focused on Web-based interventions; thus, there is a need for a body of evidence to guide health practitioners, policy makers, and researchers with respect to the efficacy of available text messaging interventions. OBJECTIVE: The aim of this systematic review and meta-analysis is to assess the effectiveness of text messaging interventions for reducing the amount of alcohol consumed among harmful and hazardous drinkers; this is compared to receiving no, minimal, or unrelated health information. Specifically, we ask the following questions: (1) Can interventions consisting of only text messages be effective in reducing alcohol consumption compared to no intervention or a minimal or unrelated intervention? (2) Can interventions consisting of only text messages be effective in reducing the prevalence of risky drinking compared to no intervention or a minimal or unrelated intervention? METHODS: Several databases will be searched, including the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, PsycINFO, the Conference Proceedings Citation Index, ClinicalTrials.gov, OpenGrey, among others. Reports of studies that evaluate text messaging interventions for reducing the amount of alcohol consumed will be included. Primary outcomes of interest will be weekly alcohol consumption and frequency of heavy episodic drinking. The Cochrane Collaboration Risk of Bias tool will be used to assess bias in reports, and the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) approach will be used to assess the quality of the body of evidence. A narrative review will be presented, and a meta-analysis will be conducted in case of homogeneity among included studies. RESULTS: The systematic review has not yet begun but is expected to start in May of 2019; publication of the final review and meta-analysis is expected at the end of 2019. CONCLUSIONS: The technology for text messaging is ubiquitous in mobile phones; thus, the potential reach of interventions utilizing this technique is great. However, there are no meta-analyses to date that limit the scope to the use of text messaging interventions for alcohol consumption reduction. Therefore, the proposed systematic review and meta-analysis will help health practitioners, policy decision makers, researchers, and others to better understand the effects of these interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/12898.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.076 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.030 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.071 | 0.007 |
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".