Clinical Management of Medication-Assisted Treatment for Opioid Use Disorder Using a Mobile Health App Within a Primary Care Clinic: Quasi-Experimental Study
Notice bibliographique
Résumé
Background: Medication-assisted treatment (MAT) is an effective strategy for treating opioid use disorder and reducing opioid-related overdose deaths, yet retention in treatment remains low. Mobile health (mHealth) platforms may be a useful tool for increasing long-term engagement in MAT programs, but evaluation studies of such platforms are limited. Objective: This study aimed to determine whether the use of the Opioid Addiction Recovery Support (OARS) software platform increased MAT engagement for patients with opioid use disorder. Methods: The Technology Improving Success of Medication-Assisted Treatment in Primary Care Study was a quasi-experimental study conducted at a primary care clinic in the United States between January 2021 and February 2022. OARS is a software platform and mobile app (Q2i, LLC) that includes a dashboard of real-time appointment attendance, urine toxicology (UTOX) results, and educational content as well as messaging and journaling features. All patients who were invited to use OARS and had available data across the study were included in the analysis. The primary outcomes were engagement in treatment, defined as no more than a 35-day gap in appointment attendance, and UTOX. Changes in treatment engagement between the treatment as usual (TAU) period and OARS intervention period were assessed using the effect size (Cohen g) and McNemar chi-square test of discordant pairs. Results: Among 205 patients invited to use OARS, 123 had available data and were thus included in the analysis. The median age was 37 (IQR 31-42.5 ) years, 61% (75/123) identified as men, and 95.1% (117/123) identified as non-Hispanic White. There were no statistically significant differences in demographic characteristics for patients who used OARS on more than 1 day compared to patients who used OARS on 0 or 1 day, or patients who did versus did not have available data. Among all patients, 20% (25/123) were engaged in appointment attendance during TAU only compared to 27% (33/123) during OARS only (g=0.07; P=.36), and 13% (16/123) were engaged in UTOX during TAU only and 33% (41/123) during OARS only (g=0.21; P≤.01). Among a subsample of 52 patients who used OARS on more than 1 day, 17% (9/52) were engaged in appointment attendance during TAU only compared to 23% (12/52) during OARS only (g=0.07, P=.67), and 13% (7/52) were engaged in UTOX during TAU only and 35% (18/52) during OARS only (g=0.22, P=.05). Conclusions: Introduction of OARS in a primary care setting may be associated with a moderate change in MAT engagement as measured by UTOX, but not appointment attendance. While barriers to implementation and adoption, including difficulty fully integrating OARS with the clinic's electronic health record, may have attenuated the potential effect of the intervention, this study provides evidence that mHealth interventions, such as OARS, are a promising addition to the MAT treatment landscape.
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 | 0,009 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».