Effectiveness of Guided and Unguided Low-Intensity Internet Interventions for Adult Alcohol Misuse: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Alcohol misuse ranks within the top ten health conditions with the highest global burden of disease. Low-intensity, Internet interventions for curbing adult alcohol misuse have been shown effective. Few meta-analyses have been carried out, however, and they have involved small numbers of studies, lacked indicators of drinking within low risk guidelines, and examined the effectiveness of unguided self-help only. We therefore conducted a more thorough meta-analysis that included both guided and unguided interventions. METHODS: Systematic literature searches were performed up to September 2013. Primary outcome was the mean level of alcohol consumption and drinking within low risk guidelines for alcohol consumption at post-treatment. FINDINGS: We selected 16 randomised controlled trials (with 23 comparisons and 5,612 participants) for inclusion. Results, showed a small but significant overall effect size in favour of Internet interventions (g = 0.20, 95% CI: 0.13-0.27, p<.001). Participants in Internet interventions drunk on average 22 grams of ethanol less than controls and were significantly more likely to be adhering to low-risk drinking guidelines at post-treatment (RD 0.13, 95% CI: 0.09-0.17, p<.001). Subgroup analyses revealed no significant differences in potential moderators for the outcome of alcohol consumption, although there was a near-significant difference between comparisons with waitlist control and those with assessment-only or alcohol information control conditions (p = .056). CONCLUSIONS: Internet interventions are effective in reducing adult alcohol consumption and inducing alcohol users to adhere to guidelines for low-risk drinking. This effect is small but from a public health point of view this may warrant large scale implementation at low cost of Internet interventions for adult alcohol misuse. Moderator analyses with sufficient power are, however, needed in order to assess the robustness of these overall results and to assess whether these interventions may impact on subgroups with different levels of success.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.048 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".