Feasibility, Engagement, and Usability of a Remote, Smartphone-Based Contingency Management Program as a Treatment Add-On for Patients Who Use Methamphetamine: Single-Arm Pilot Study
Notice bibliographique
Résumé
BACKGROUND: In the United States, methamphetamine-related overdoses have tripled from 2015 to 2020 and continue to rise. However, efficacious treatments such as contingency management (CM) are often unavailable in health systems. OBJECTIVE: We conducted a single-arm pilot study to evaluate the feasibility, engagement, and usability of a fully remotely delivered mobile health CM program offered to adult outpatients who used methamphetamine and were receiving health care within a large university health system. METHODS: Participants were referred by primary care or behavioral health clinicians between September 2021 and July 2022. Eligibility criteria screening was conducted by telephone and included self-reported methamphetamine use on ≥5 out of the past 30 days and a goal of reducing or abstaining from methamphetamine use. Eligible participants who agreed to take part then completed an initial welcome phase that included 2 videoconference calls to register for and learn about the CM program and 2 "practice" saliva-based substance tests prompted by a smartphone app. Participants who completed these welcome phase activities could then receive the remotely delivered CM intervention for 12 consecutive weeks. The intervention included approximately 24 randomly scheduled smartphone alerts requesting a video recording of themselves taking a saliva-based substance test to verify recent methamphetamine abstinence, 12 weekly calls with a CM guide, 35 self-paced cognitive behavioral therapy modules, and multiple surveys. Financial incentives were disbursed via reloadable debit cards. An intervention usability questionnaire was completed at the midpoint. RESULTS: Overall, 37 patients completed telephone screenings, with 28 (76%) meeting the eligibility criteria and consenting to participate. Most participants who completed a baseline questionnaire (21/24, 88%) self-reported symptoms consistent with severe methamphetamine use disorder, and most had other co-occurring non-methamphetamine substance use disorders (22/28, 79%) and co-occurring mental health disorders (25/28, 89%) according to existing electronic health records. Overall, 54% (15/28) of participants successfully completed the welcome phase and were able to receive the CM intervention. Among these participants, engagement with substance testing, calls with CM guides, and cognitive behavioral therapy modules varied. Rates of verified methamphetamine abstinence in substance testing were generally low but varied considerably across participants. Participants reported positive opinions about the intervention's ease of use and satisfaction with the intervention. CONCLUSIONS: Fully remote CM can be feasibly delivered within health care settings lacking existing CM programs. Although remote delivery may help reduce barriers to treatment access, many patients who use methamphetamine may struggle to engage with initial onboarding. High rates of co-occurring psychiatric conditions in the patient population may also contribute to uptake and engagement challenges. Future efforts could leverage greater human-to-human connection, more streamlined onboarding procedures, larger incentives, longer durations, and the incentivization of non-abstinence-based recovery goals to increase uptake and engagement with fully remote mobile health-based CM.
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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 | 0,006 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».