Efficacy of CBT-based digital therapeutic for substance use disorder: study protocol for a randomized controlled trial (Preprint)
Bibliographic record
Abstract
Background: Illicit drug use has been rapidly increasing in South Korea, particularly among individuals in their 20s, contributing to a growing burden of substance use disorder (SUD). However, treatment infrastructure remains limited. Nationwide, only a small number of inpatient treatment hospitals are available, and treatment capacity in the Seoul metropolitan area is insufficient to meet growing demand. In addition, community-based addiction services are scarce, creating barriers to continuous care. Digital therapeutics (DTx) have emerged as a promising approach to improve treatment accessibility and continuity of care. In particular, cognitive behavioral therapy (CBT)-based DTx such as RESET-O, developed in the United States and authorized by the US Food and Drug Administration, have demonstrated clinical benefits in supporting addiction recovery. Building on this concept, our team developed D-STOP, a CBT-based DTx intervention designed to support individuals with SUD in the Korean clinical context. Objective: This study aims to evaluate the efficacy of D-STOP as an adjunctive DTx intervention for patients with SUD. Methods: This study is a randomized controlled clinical trial designed to evaluate the efficacy of D-STOP in individuals diagnosed with SUD. Following an initial screening assessment, 118 participants meeting the diagnostic criteria will be enrolled. Participants will be recruited from psychiatry departments at addiction treatment and clinical care institutions in Chuncheon, Seoul, Daegu, and Changnyeong, South Korea. During the 12-week intervention period, the experimental group will receive D-STOP in addition to treatment as usual, whereas the control group will receive treatment as usual alone. D-STOP delivers structured CBT-based modules and motivational enhancement interventions through a digital platform. During scheduled study visits, participants will also receive therapeutic feedback from psychiatrists or trained study staff. The primary end point is the abstinence success rate during weeks 9 to 12 of treatment. Logistic regression analysis will be used to estimate treatment effects and evaluate superiority compared with the control group. Results: The study protocol was approved by the institutional review board of Hallym University Chuncheon Sacred Heart Hospital on April 14, 2025. Funding began on April 1, 2023. Data collection started on August 4, 2025, and is expected to be completed by November 30, 2026. As of March 6, 2026, a total of 65 participants have been enrolled. An interim analysis of the primary efficacy outcome has been conducted based on the data available at the time of analysis; however, statistical significance has not been established due to the limited sample size. The final analysis is expected to be published in April 2027. Conclusions: DTx have the potential to expand access to evidence-based addiction treatment in resource-constrained settings. This study will provide clinical evidence on the efficacy and feasibility of D-STOP as a digital treatment support tool for patients with SUD.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.113 | 0.018 |
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".