A Brief Cognitive Behavioral Therapy–Based Digital Intervention for Reducing Hazardous Alcohol Use in South Korea: Development and Prospective Pilot Study
Bibliographic record
Abstract
Background: Alcohol consumption is a leading cause of death and disability worldwide, associated with numerous acute and chronic medical conditions. Digital health interventions offer a promising solution to overcome barriers associated with traditional treatment methods, providing accessible, scalable, and cost-effective means to support individuals in reducing hazardous drinking. Objective: This pilot study aims to evaluate the feasibility, acceptability, and preliminary efficacy of the Sober smartphone app in individuals with hazardous alcohol use. Methods: This single-group, pre- and postpilot study included 20 participants with risky alcohol use, identified using the Alcohol Use Disorder Identification Test. Participants used the Sober app for 4 weeks, incorporating cognitive behavioral therapy-based interventions. Feasibility was assessed by study and session completion rates, acceptability by participant satisfaction and perceived usefulness, and preliminary efficacy by changes in alcohol consumption and psychiatric symptoms. Semistructured interviews with participants and clinicians provided qualitative perspectives on the app's usability, efficacy, and areas for improvement. Results: Of the 20 enrolled participants, 17 completed the study. The app demonstrated high feasibility with an 85% (17/20) study completion rate, and 59% (10/17) completed all cognitive behavioral therapy sessions. Participants reported positive acceptability, with average satisfaction and usefulness ratings of 3.8 and 3.7 of 5, respectively. Preliminary efficacy outcomes showed significant improvements: abstinence days increased from 67% to 85% (z=-3.17; P=.002), heavy drinking episodes decreased from 3.3 to 1.9 (t16=-2.97; P=.003), and total alcohol consumption reduced from 456.8 to 195.9 mL (t16=3.16; P=.002). Alcohol Use Disorder Identification Test scores dropped from 17.5 to 10.7 (t16=4.51; P<.001). Additionally, depression (Patient Health Questionnaire-9) scores decreased from 5.8 to 4.4 (t16=2.91; P=.01), and anxiety (Generalized Anxiety Disorder-7) scores from 3.4 to 2.1 (z=-2.80; P=.005). No adverse events were reported. Qualitative analysis found participants valued daily logging but noted usability issues, while clinicians called for tailored goals, enhanced communication features, and age-specific content. Conclusions: The mobile app Sober shows promise as an effective tool for reducing hazardous alcohol consumption and improving related psychiatric symptoms. The study demonstrated high feasibility and positive acceptability, with significant preliminary efficacy in reducing alcohol use. Qualitative findings provided actionable evidence for refining the app's usability and clinical integration. Further research through a randomized controlled trial is warranted to confirm these findings and optimize the app's features and content.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".