Development of a Sham Smartphone App for Opioid Use Disorder: Acceptability and Suitability Study
Bibliographic record
Abstract
Background: Despite having evidence-based medication for opioid use disorder (OUD), dropout is one of the most common issues noted with this treatment. Prescription digital therapeutics, which are app-based interventions prescribed by a health care professional, have the potential to increase adherence to medication for OUD and retention while overcoming treatment barriers, including provider capacity and patient access. Using a sham app as a control condition for a randomized clinical trial is an innovative method to establish the true efficacy of these apps. Objective: This study included the development and testing of a sham smartphone app for OUD. Methods: After the sham app was developed, participants were enrolled in a 4-week trial examining the use and suitability of the sham app as a control condition. Criteria for determining suitability included (1) participants believing the sham app is an active intervention and (2) participants experiencing no clinical improvements in depression severity or quality of life after using the sham app. Self-reported depression severity and quality of life were captured before and after using the sham app. A user satisfaction survey and semistructured interviews were conducted at the end of the study. Quantitative analyses included paired 2-tailed t tests. The semistructured interviews were conducted with 20 of the 21 participants, and these interviews were analyzed using rapid qualitative analysis. Results: Overall, 21 participants (meanage 42.0, SD 6.4 years; female: n=9, 43% and male: n=12, 57%) were enrolled. The average number of app log-ins was 17.8 (SD 10.6; range 1-41). There were 2 participants who only logged in 1 time, and 15 (71%) participants completed the goal of logging in an average of 3 times per week. No significant differences were found in depression severity (P=.50) or quality of life (quality of life: P=.42, physical health: P=.58, psychological health: P=.07, environmental health: P=.44, and social relationships: P=.86) after using the sham app. Of the 20 participants who completed the semistructured interview, 19 (95%) believed that they were using an active intervention. The user satisfaction survey revealed high overall satisfaction with the sham app with a score of 91%. Qualitative analyses revealed several recurring themes, including perceived value and impact, potential for behavior change, use patterns and engagement, perspective and usability, and perceptions of authenticity. Conclusions: Our sham app met our a priori criteria for suitability as a sham app. No clinical improvements from baseline were observed at the end of the study period, and all but 1 participant believed that they were using an active intervention. Demonstrating that this sham app is suitable as a control condition elevates the rigor of randomized clinical trials and ensures the efficacy of prescription digital therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.026 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".