Scaling European Citizen Driven Transferable and Transformative Digital Health: Protocol for an Effectiveness-Implementation Hybrid Trial of a Digital Health Platform to Support Multimorbidity Self-Management
Notice bibliographique
Résumé
BACKGROUND: Multimorbidity, the presence of 2 or more chronic conditions, is becoming increasingly prevalent worldwide, resulting in significant impacts on health care systems. For people with multimorbidity, self-management is challenging, requiring engagement in several tasks. Digital health platforms have been widely acknowledged as having the potential to enhance self-management practices for those with chronic conditions. However, limited longitudinal studies have explored the effectiveness of digital health platforms that support multimorbidity self-management or issues relating to their implementation and scalability in practice. OBJECTIVE: The aim of this study is to determine the effectiveness and implementation of a digital health platform, ProACT, with support services including clinical triage and a care network (consisting of informal and formal caregivers and health care professionals) compared to the use of the platform alone and compared to standard care. METHODS: An effectiveness-implementation type 1 hybrid study will be conducted across 3 European countries. A total of 720 older adults aged 65 years or older with multimorbidity (2 or more of the following: diabetes, a chronic respiratory disease, chronic heart failure, and chronic heart disease) will be recruited and randomized into 1 of 3 trial arms. Those in trial arm 1 will be invited to have up to 5 care network members participate with them, resulting in a maximum of 1500 care network participants. Effectiveness will be assessed through a 3-arm pragmatic randomized controlled trial, while implementation issues will be addressed via a process evaluation. Primary outcomes for participants with multimorbidity are quality of life and health care use, while secondary outcomes focus on the potential of the ProACT platform to support multimorbidity self-management (eg, self-efficacy, usability, engagement, and symptom stabilization). Primary outcomes for informal caregivers in the care network include caring burden, while secondary outcomes for all care network members include usability, engagement, satisfaction, and overall experiences with ProACT. Additional outcomes related to the process evaluation include the reach, uptake, and fidelity of implementation of ProACT and the way organizations implement and deliver ProACT; how they differ in this regard; and the factors underpinning these differences. A range of qualitative and quantitative data will be collected and analyzed to assess these outcomes. RESULTS: Enrollment in the trial began in September 2022, and the trial is anticipated to end by March 2026. Trial outcomes will be submitted for publication in 2026. CONCLUSIONS: The generation of evidence-based support for the routine use of the ProACT platform in applied settings would represent considerable impact. With health care services under increasing strain and traditionally designed to support those with single morbidities, it is more important than ever to develop actionable insights and resources to empower persons with multimorbidity to self-manage their complex care needs at home, with support from their caregivers. TRIAL REGISTRATION: ISRCTN Registry ISRCTN34134007; https://www.isrctn.com/ISRCTN34134007. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/74989.
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 | 0,061 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,007 | 0,006 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,009 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,067 | 0,013 |
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 ».