From “Step Away” to “Stand Down”: Tailoring a Smartphone App for Self-Management of Hazardous Drinking for Veterans
Notice bibliographique
Résumé
BACKGROUND: US military veterans who screen positive for hazardous drinking during primary care visits may benefit from a mobile app. Step Away is an evidence-based mobile intervention system for the self-management of hazardous drinking. However, Step Away was not designed for veterans, and differences between veterans and civilians could limit the reach and effectiveness of the app with this population. OBJECTIVE: The primary objective of this study was to repurpose Step Away to address the needs and preferences of the veteran primary care population. The Method for Program Adaptation through Community Engagement (M-PACE) model was used to guide the adaptation process. This model can serve as a generalizable approach that other researchers and intervention developers can follow to systematically tailor mobile health tools for a new population. METHODS: Veteran patients who screened positive for hazardous drinking during a primary care visit (n=12) and peer providers employed by the US Veterans Health Administration (n=11) were recruited to systematically review Step Away and provide feedback on its content and presentation via Web-based surveys and a semistructured interview. Participant feedback was reviewed through an iterative process by key stakeholders who adjudicated which suggested modifications to the app could enhance engagement and effectiveness with veterans while maintaining program integrity. RESULTS: Usability ratings of the individual modules of Step Away were uniformly positive across patients and peers, as was the perceived utility of the app overall. Personalized feedback on the health consequences and costs of drinking, options for customization, and the measurement-based care capabilities of the app were viewed as facilitators of engagement. Conversely, lengthy text, small font, and a lack of interactive features were viewed as potential barriers with the older primary care population. Modifications to create a veteran version of the app (Stand Down: Think Before You Drink) included altering the appearance of the app to incorporate more veteran-centric content, adding links and options for resources and activities for veterans, and reducing the amount of text and adding veteran-specific references and common concerns and triggers for drinking in this population. CONCLUSIONS: The M-PACE model provided a systematic approach to repurpose Step Away to fit the needs and preferences of veteran primary care patients who engage in hazardous drinking. Stand Down may serve as an innovative, low-cost means of expanding access to care for veterans who engage in hazardous drinking.
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 | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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) | 0,005 | 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 ».