A mobile app intervention to support nutrition education for heart failure management: co-design, development and user-testing
Bibliographic record
Abstract
BACKGROUND: Mobile apps show promise in supporting patients with heart failure (HF) in adhering to dietary guidelines for sodium and fluid. Though numerous apps to support HF management exist, only a few have dedicated features to support dietary adherence. OBJECTIVES: , a mobile app intervention to engage patients with HF in dietary education and adherence. METHODS: Background research in app development, behaviour change, nutrition and qualitative interviews with patients and healthcare providers informed app content and design. Weekly team meetings were held to establish learning objectives, content, and features of the app until a prototype was developed and approved by the research team. Using a quasi-experimental mixed-methods design, patients with HF (≥ 18 years) evaluated the prototype via one-on-one online user-testing sessions. App engagement, satisfaction, and usability were measured using a 12-question patient-reported Likert-scale questionnaire. Participant feedback on app content and features was gathered using qualitative interviews. RESULTS: Six educational modules (dietary sodium recommendations, contributors of sodium in the diet, nutrition labelling, lowering dietary sodium, fluid restriction and goal setting), ten behaviour change techniques (e.g., feedback on behaviour, social support) and gamified components (i.e., avatar, point-system) were integrated into the app. Participants with HF (n = 10, 56±15 years, 80% women) enjoyed using the app (90%), strongly agreed that the information was meaningful and useful for their general health (80%) and was easy to use (70%). CONCLUSION: to support patients with dietary education and adherence for HF management.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".