A Personalized, Interactive, Cognitive Behavioral Therapy–Based Digital Therapeutic (MODIA) for Adjunctive Treatment of Opioid Use Disorder: Development Study
Bibliographic record
Abstract
BACKGROUND: Opioid use disorder (OUD) is characterized by the inability to control opioid use despite attempts to stop use and negative consequences to oneself and others. The burden of opioid misuse and OUD is a national crisis in the United States with substantial public health, social, and economic implications. Although medication-assisted treatment (MAT) has demonstrated efficacy in the management of OUD, access to effective counseling and psychosocial support is a limiting factor and a significant problem for many patients and physicians. Digital therapeutics are an innovative class of interventions that help prevent, manage, or treat diseases by delivering therapy using software programs. These applications can circumvent barriers to uptake, improve treatment adherence, and enable broad delivery of evidence-based management strategies to meet service gaps. However, few digital therapeutics specifically targeting OUD are available, and additional options are needed. OBJECTIVE: To this end, we describe the development of the novel digital therapeutic MODIA. METHODS: MODIA was developed by an international, multidisciplinary team that aims to provide effective, accessible, and sustainable management for patients with OUD. Although MODIA is aligned with principles of cognitive behavioral therapy, it was not designed to present any 1 specific treatment and uses a broad range of evidence-based behavior change techniques drawn from cognitive behavioral therapy, mindfulness, acceptance and commitment therapy, and motivational interviewing. RESULTS: MODIA uses proprietary software that dynamically tailors content to the users' responses. The MODIA program comprises 24 modules or "chats" that patients are instructed to work through independently. Patient responses dictate subsequent content, creating a "simulated dialogue" experience between the patient and program. MODIA also includes brief motivational text messages that are sent regularly to prompt patients to use the program and help them transfer therapeutic techniques into their daily routines. Thus, MODIA offers individuals with OUD a custom-tailored, interactive digital psychotherapy intervention that maximizes the personal relevance and emotional impact of the interaction. CONCLUSIONS: As part of a clinician-supervised MAT program, MODIA will allow more patients to begin psychotherapy concurrently with opioid maintenance treatment. We expect access to MODIA will improve the OUD management experience and provide sustainable positive outcomes for patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".