Developing and Implementing a Web-Based Relapse Prevention Psychotherapy Program for Patients With Alcohol Use Disorder: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Alcohol use disorder (AUD) is characterized by problematic alcohol use accompanied by clinically substantial distress. Patients with AUD frequently experience high relapse rates, and only 1 in 5 remain abstinent 12 months post treatment. Traditional face-to-face relapse prevention therapy (RPT) is a form of cognitive behavioral therapy (CBT) that examines one's situational triggers, maladaptive thought processes, self-efficacy, and motivation. However, access to this treatment is frequently limited due to its high cost, long waitlists, and inaccessibility. A web-based adaptation of RPT (e-RPT) could address these limitations by providing a more cost-effective and accessible delivery method for mental health care in this population. OBJECTIVE: This study protocol aims to establish the first academic e-RPT program to address AUD in the general population. The primary objective of this study is to compare the efficacy of e-RPT to face-to-face RPT in decreasing relapse rates. The secondary objective is to assess the effects of e-RPT on quality of life, self-efficacy, resilience, and depressive symptomatology. The tertiary objective is to evaluate the cost-effectiveness of e-RPT compared to face-to-face RPT. METHODS: Adult participants (n=60) with a confirmed diagnosis of AUD will be randomly assigned to receive 10 sessions of e-RPT or face-to-face RPT. e-RPT will consist of 10 predesigned modules and homework with asynchronous, personalized feedback from a therapist. Face-to-face RPT will comprise 10 one-hour face-to-face sessions with a therapist. The predesigned modules and the face-to-face sessions will present the same content and structure. Self-efficacy, resilience, depressive symptomatology, and alcohol consumption will be measured through various questionnaires at baseline, amid treatment, and at the end of treatment. RESULTS: Participant recruitment is expected to begin in October 2022 through targeted advertisements and physician referrals. Completed data collection and analysis are expected to conclude by October 2023. Outcome data will be assessed using linear and binomial regression (for continuous and categorical outcomes, respectively). Qualitative data will be analyzed using thematic analysis methods. CONCLUSIONS: This study will be the first to examine the effectiveness of e-RPT compared to face-to-face RPT. It is posited that web-based care can present benefits in terms of accessibility and affordability compared to traditional face-to-face psychotherapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT05579210; https://clinicaltrials.gov/ct2/show/NCT05579210. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/44694.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.089 | 0.013 |
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".