The Space From Heart Disease Intervention for People With Cardiovascular Disease and Distress: A Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Poor self-management of symptoms and psychological distress leads to worse outcomes and excess health service use in cardiovascular disease (CVD). Online-delivered therapy is effective, but generic interventions lack relevance for people with specific long-term conditions, such as cardiovascular disease. OBJECTIVE: To develop a comprehensive online CVD-specific intervention to improve both self-management and well-being, and to test acceptability and feasibility. METHODS: Informed by the Medical Research Council (MRC) guidance for the development of complex interventions, we adapted an existing evidence-based generic intervention for depression and anxiety for people with CVD. Content was informed by a literature review of existing resources and trial evidence, and the findings of a focus group study. Think-aloud usability testing was conducted to identify improvements to design and content. Acceptability and feasibility were tested in a cross-sectional study. RESULTS: Focus group participants (n=10) agreed that no existing resource met all their needs. Improvements such as "collapse and expand" features were added based on findings that participants' information needs varied, and specific information, such as detecting heart attacks and when to seek help, was added. Think-aloud testing (n=2) led to changes in font size and design changes around navigation. All participants of the cross-sectional study (10/10, 100%) were able to access and use the intervention. Reported satisfaction was good, although the intervention was perceived to lack relevance for people without comorbid psychological distress. CONCLUSIONS: We have developed an evidence-based, theory-informed, user-led online intervention for improving self-management and well-being in CVD. The use of multiple evaluation tests informed improvements to content and usability. Preliminary acceptability and feasibility has been demonstrated. The Space from Heart Disease intervention is now ready to be tested for effectiveness. This work has also identified that people with CVD symptoms and comorbid distress would be the most appropriate sample for a future randomized controlled trial to evaluate its effectiveness.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".