The Development of Heart Failure Electronic-Message Driven Tips to Support Self-Management: Co-Design Case Study
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is a complex syndrome associated with high morbidity and mortality and increased healthcare utilisation. Patient education is key to improving health outcomes, achieved by promoting self-management to optimise medical management. Newer digital tools like text messaging and smartphone applications provide novel patient education approaches. OBJECTIVE: To partner with clinicians and people with lived experience of HF to identify the priority educational topic areas to inform the development and delivery of a bank of electronic-message driven tips ('e-TIPS') to support HF self-management. METHODS: We conducted three focus groups with cardiovascular clinicians, people with lived experience of HF and their caregivers, which consisted of two stages: Stage 1 - an exploratory qualitative study to identify the unmet educational needs of people living with HF (previously reported) and Stage 2 - a co-design feedback session to identify educational topic areas and inform the delivery of e-TIPS. This paper reports the findings of the co-design feedback session. RESULTS: We identified five key considerations in delivering e-TIPS and five relevant HF educational topics for their content. Key considerations in e-TIP delivery included: (i) Timing of the e-TIPS; (ii) Clear and concise e-TIPS; (iii) Embedding a feedback mechanism; (iv) Distinguishing actionable and non-actionable e-TIPS; and (v) Frequency of e-TIP delivery. Relevant educational topic areas included: (i) cardiovascular risk reduction; (ii) Self-management; (iii) Food and nutrition; (iv) Sleep hygiene; and (v) Mental health. CONCLUSIONS: The findings from this co-design case study have provided a foundation for developing a bank of e-TIPS. These will now be evaluated for usability in the BANDAIDS e-TIPS, a single group, quasi-experimental study of a 24-week e-TIP program (personalised educational messages) delivered via Short Message Service (ACTRN12623000644662).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".