Women Empowered to Connect With Addiction Resources and Engage in Evidence-Based Treatment (WE-CARE)—an mHealth Application for the Universal Screening of Alcohol, Substance Use, Depression, and Anxiety: Usability and Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Women of childbearing age (aged 18-44 years) face multiple barriers to receiving screening and treatment for unhealthy alcohol and substance use, depression, and anxiety, including lack of screening in the primary care setting and lack of support in accessing care. The Women Empowered to Connect with Addiction Resources and Engage in Evidence-based Treatment (WE-CARE) mobile app was developed to test universal screening with women of childbearing age and linkage to care after an anonymous assessment. OBJECTIVE: In this study, we aimed to investigate the feasibility and acceptability of providing anonymous screening instruments through mobile phones for alcohol and substance use, as well as depression and anxiety, for women of childbearing age. METHODS: We used agile development principles based on previous formative research to test WE-CARE mobile health app with women of childbearing age (N=30) who resided in 1 of 6 counties in central Florida. WE-CARE included screening instruments (for alcohol, substance use, depression, and anxiety), a moderated discussion forum, educational microlearning videos, a frequently asked questions section, and resources for linkage to treatment. Individuals were recruited using flyers, academic listserves, and a commercial human subject recruiting company (Prolific). Upon completion of the screening instruments, women explored the educational and linkage to care features of the app and filled out a System Usability Scale to evaluate the mobile health app's usability and acceptability. Postpilot semistructured interviews (n=4) were conducted to further explore the women's reactions to the app. RESULTS: A total of 77 women downloaded the application and 30 completed testing. Women of childbearing age gave the WE-CARE app an excellent System Usability Scale score of 86.7 (SD 12.43). Our results indicate elevated risk for substance use in 18 of the 30 (60%) participants, 9/18 (50%) also had an elevated risk for anxiety or depression, and 11/18 (61%) had an elevated risk for substance use, anxiety, or depression. Participants reported that WE-CARE was easy to navigate and use but they would have liked to see more screening questions and more educational content. Linkage to care was an issue; however, as none of the women identified as "at-risk" for substance use disorders contacted the free treatment clinic for further evaluation. CONCLUSIONS: The mobile health app was highly rated for acceptability and usability, but participants were not receptive to seeking help at a treatment center after only a few brief encounters with the app. The linkage to care design features was likely insufficient to encourage them to seek treatment. The next version of WE-CARE will include normative scores for participants to self-evaluate their screening status compared with their age- and gender-matched peers and enhanced linkages to care features. Future development will focus on enhancing engagement to improve change behaviors and assess readiness for change.
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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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 ».