Deployment and Assessment of a Virtual Coaching Platform to Support Healthy Habits and Whole Health for Vulnerable People Living with HIV
Notice bibliographique
Résumé
Background Office of Health and Human Services of Rhode Island (0HHS-RI) provides care to the most vulnerable people in the state living with HIV. While most of their clients achieve viral load suppression, the majority of these individuals still lack the support they need to tackle mental health issues, manage comorbidities, be physically active, and address other social determinants of Health. Digital health technologies have demonstrated value to provide this support and help people manage whole health. Digital platforms can deliver tailored health interventions and improved outcomes for people with HIV. However, this vulnerable population most in need of that support lacks access to current technology and platforms designed to meet their particular needs. Objective The objective wo design, deploy, and assess an evidence-based digital health platform to support healthy habits and improve outcomes for the target population, TAVIE Red. The platform is the product of two years of patient-centered iterative design and built upon theories of behavioral medicine. The core technology is a clinically-validated virtual nurse app that delivers tailored education to users. On the patient app, clients self-manage health (eg, monitor symptoms and receive treatment reminders), access social services (eg, locate food banks and clinics) and receive evidence-based interventions to promote mental and physical health (eg, decrease stress and increase physical activity). Users receive personalized feedback and rewards that incentivize and engage during each phase of treatment. On the professional portal, case managers can monitor clients remotely, receive actionable insights, and intervene appropriately. Administrators access real-time analytics on health status and delivery of services through a set of customizable dashboards. Methods Eligible clients received an Android phone preloaded with the app. Case managers received access to the professional platform on a desktop and in some cases, tablet devices to use the app in the field. Three generations of the platform were deployed over a two-year period. Participants completed a survey at baseline and follow-up on paper and digitally through the app. Thus far, 200 people living with HIV have participated in this program along with their case managers. Results Two years into the project, users are engaged with the app, enjoying it, and benefiting from it. Seventy-seven percent of users actively engage with the app, earning points and progressing through the coaching sessions with 67% completing self-assessments through the app and most track measures including physical activity, symptoms, and CD4 count and viral load. There was a 5% increase in viral suppression in this population over two years. Eighty-eight percent of users recommend the app to others. Conclusions To date, the TAVIE platform is an engaging appealing platform. Users remain on the app over time and report great benefits of use. Formal evaluation of outcomes ongoing.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».