Patient Feedback on a Mobile Medication Adherence App for Buprenorphine and Naloxone: Closed and Open-Ended Survey on Feasibility and Acceptability
Notice bibliographique
Résumé
BACKGROUND: Opioid use disorders impact the health and well-being of millions of Americans. Buprenorphine and naloxone (BUP and NAL) can reduce opioid overdose deaths, decrease misuse, and improve quality of life. Unfortunately, poor medication adherence is a primary barrier to the long-term efficacy of BUP and NAL. OBJECTIVE: We aimed to examine patient feedback on current and potential features of a Bluetooth-enabled pill bottle cap and associated mobile app for patients prescribed BUP and NAL for an opioid use disorder, and to solicit recommendations for improvement to effectively and appropriately tailor the technology for people in treatment for opioid use disorder. METHODS: A convenience sample of patients at an opioid use disorder outpatient clinic were asked about medication adherence, opioid cravings, experience with technology, motivation for treatment, and their existent support system through a brief e-survey. Patients also provided detailed feedback on current features and features being considered for inclusion in a technology designed to increase medication adherence (eg, inclusion of a personal motivational factor, craving and stress tracking, incentives, and web-based coaching). Participants were asked to provide suggestions for improvement and considerations specifically applicable to people in treatment for opioid use disorder with BUP and NAL. RESULTS: Twenty people with an opioid use disorder who were prescribed BUP and NAL participated (mean age 34, SD 8.67 years; 65% female; 80% White). Participants selected the most useful, second-most useful, and least useful features presented; 42.1% of them indicated that motivational reminders would be most useful, followed by craving and stress tracking (26.3%) and web-based support forums (21.1%). Every participant indicated that they had at least 1 strong motivating factor for staying in treatment, and half (n=10) indicated children as that factor. All participants indicated that they had, at some point in their lives, the most extreme craving a person could have; however, 42.1% indicated that they had no cravings in the last month. Most respondents (73.7%) stated that tracking cravings would be helpful. Most respondents (84.2%) also indicated that they believed reinforcers or prizes would help them achieve their treatment goals. Additionally, 94.7% of respondents approved of adherence tracking to accommodate this feature using smart packaging, and 78.9% of them approved of selfie videos of them taking their medication. CONCLUSIONS: Engaging patients taking treatment for opioid use disorder with BUP and NAL allowed us to identify preferences and considerations that are unique to this treatment area. As the technology developer of the pill cap and associated mobile app is able to take into consideration or integrate these preferences and suggestions, the smart cap and associated mobile app will become tailored to this population and more useful for them, which may encourage patient use of the smart cap and associated mobile app.
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 | 0,013 | 0,033 |
| 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 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| 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 ».