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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.048 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".