Physical Activity Mobile App (CareFit) for Informal Carers of People With Dementia: Protocol for a Feasibility and Adaptation Study
Bibliographic record
Abstract
BACKGROUND: Physical activity is a critical component of both well-being and preventative health, reducing the risk of both chronic mental and physical conditions and early death. Yet, there are numerous groups in society who are not able to undertake as much physical activity as they would like to. This includes informal (unpaid) carers, with the United Kingdom national survey data suggesting that 81% would like to do more physical activity on a regular basis. There is a clear need to develop innovations, including digital interventions that hold implementation potential to support regular physical activity in groups such as carers. OBJECTIVE: This study aims to expand and personalize a cross-platform digital health app designed to support regular physical activity in carers of people with dementia for a period of 8 weeks and evaluate the potential for implementation. METHODS: The CareFit for dementia carers study was a mixed methods co-design, development, and evaluation of a novel motivational smartphone app to support home-based regular physical activity for unpaid dementia carers. The study was planned to take place across 16 months in total (September 1, 2022, to December 31, 2023). The first phase included iterative design sprints to redesign an initial prototype for widespread use, supported through a bespoke content management system. The second phase included the release of the "CareFit" app across Scotland through invitations on the Apple and Google stores where we aimed to recruit 50 carers and up to 20 professionals to support the delivery in total. Partnerships for the work included a range of stakeholders across charities, health and social care partnerships, physical activity groups, and carers' organizations. We explored the implementation of CareFit, guided by both Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) and the Complex Intervention Frameworks. RESULTS: Project processes and outcomes were evaluated using mixed methods. The barriers and enablers for professional staff to signpost and use CareFit with clients were assessed through interviews or focus groups and round stakeholder meetings. The usability of CareFit was explored through qualitative interviews with carers and a system usability scale. We examined how CareFit could add value to carers by examining "in-app" data, pre-post questionnaire responses, and qualitative work, including interviews and focus groups. We also explored how CareFit could add value to the landscape of other online resources for dementia carers. CONCLUSIONS: Results from this study will contribute new knowledge including identifying (1) suitable pathways to identify and support carers through digital innovations; (2) future design of definitive studies in carer populations; and (3) an improved understanding of the Reach, Effectiveness, Adoption, Implementation, and Maintenance across a range of key stakeholders. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53727.
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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 | 0.039 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.015 |
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".