Alcohol Use Disorder and Comorbid Depression: A Randomized Controlled Trial Investigating the Effectiveness of Supportive Text Messages in Aiding Recovery
Bibliographic record
Abstract
AIM: The aim of this randomized controlled trial was to examine the impact of daily supportive text messages over a 6-month treatment period on mood and alcohol consumption in individuals with a dual diagnosis of alcohol use disorder (AUD) and depression following completion of an inpatient treatment programme. METHOD: Ninety-five adult participants with AUD and comorbid depression were recruited into this randomized control trial, which took place after completing a 30-day rehabilitation programme. The intervention group (n = 47) received twice-daily supportive text messages over 6-months while control participants (n = 48) had treatment as usual for a 6-month period, with an added 6-month post-treatment follow-up for both groups. Drinking history in the previous 90 days as well as symptoms of depression, anxiety and stress were measured at baseline, 3- and 6-month treatment points and 6-month post treatment follow up. RESULTS: Depression scores (P = 0.02) and perceived stress scores (P < 0.01) were significantly reduced at 3-month treatment point in the intervention group relative to control participants with small to medium effect. The intervention group also showed a significantly greater reduction in units per drinking day from baseline to 6-month treatment point compared to the control group with a medium effect size (P = 0.03). There were no differences in drinking or mood measures at 6-month post treatment follow-up. CONCLUSIONS: Supportive text messages provide an early initial benefit in decreasing symptoms of depression and stress, with a further positive impact on alcohol consumption following a longer treatment period. Benefits did not persist six months after the intervention ended.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".