Effectiveness of digital interventions for people with comorbid heavy drinking and depression: A systematic review and narrative synthesis
Bibliographic record
Abstract
INTRODUCTION: Heavy drinking and depression frequently co-occur and make a substantial contribution to the global non-communicable disease burden. Positive evidence exists for the use of digital interventions with these conditions alone, but there has been limited assessment of combined approaches. OBJECTIVE: A systematic review of the effectiveness of combined digital interventions for comorbid heavy drinking and major depression in community-dwelling populations. METHODS AND ANALYSIS: Electronic databases were searched to October 2021 for randomised controlled trials that evaluated any personalised digital intervention for comorbid heavy drinking and depression. Primary outcomes were changes in quantity of alcohol consumed and depressive symptoms. Two reviewers independently assessed study eligibility, extracted data, and undertook risk of bias assessment. Due to the limited number and heterogeneity of studies identified, meta-analysis was not possible, therefore data were synthesised narratively. RESULTS: Of 898 articles identified, 24 papers were reviewed in full, five of which met the inclusion criteria (N = 1503 participants). Three utilised web-based intervention delivery; two computer programmes delivered in a clinic setting. All involved multi-component interventions; treatment length varied from one to ten sessions. Four studies found no evidence for the superiority of combined digital interventions for comorbid heavy drinking and depression over therapist-delivered approaches, single condition interventions (including online), or assessment-only controls. Positive impacts of integrated online therapy compared to generalist online health advice were reported in a fifth study, but not maintained beyond the 1-month follow-up. LIMITATIONS: Few eligible, heterogeneous studies prevented meta-analysis. CONCLUSION: Limited evidence exists of the effectiveness of combined digital interventions for comorbid heavy drinking and depression in community dwelling populations.
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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 | 0.020 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 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".