A Pre–Post, Mixed-Methods Study to Pilot Test a Gamified Heart Failure Self-Care Education Intervention
Bibliographic record
Abstract
Objective : Self-care is essential to improving heart failure patient outcomes. However, the knowledge and behaviours necessary for self-care decision making, such as symptom perception and management, are complex and require patient education. The objective of this study was to test the feasibility, acceptability, and potential effectiveness of a web-based, gamified heart failure patient education solution, Heart Self-Care Patient Education (HeartSCaPE), that used narrative and virtual reward gamification techniques. Materials and Methods: This mixed-methods study used a pre-post-test design with an embedded explanatory qualitative phase. Patients completed the Self-Care of Heart Failure Index, that measured self-care behaviour change and the Dutch Heart Failure Knowledge Scale, used to measure heart failure knowledge. Usability measures of HeartSCaPE were tracked using Google Analytics and the System Usability Scale. Results: Nineteen patients completed the study, with a subset of six participating in semi-structured interviews. We found increases in HF knowledge despite high baseline knowledge scores. Post-intervention self-reported HF self-care behaviours ( maintenance, management and confidence ), as measured by the Self-Care of Heart Failure Index, were also improved. Knowledge and self-care scores were not correlated. Participants also scored HeartSCaPE as highly usable. In interviews, participants described valuing the opportunity to practice self-care decision-making. There were mixed opinions regarding the use of virtual rewards. Conclusion: We found that a gamified web-based solution that uses narrative and reward-based gamification techniques has the potential to improve HF patient knowledge and self-care. Further research is needed to confirm the study's clinical benefits and address technology literacy inequities.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".