Development of a Sham Smartphone App for Opioid Use Disorder: Acceptability and Suitability Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».