The Heart Game: A New Tool for Digital Patient Education for Patients With Heart Failure
Bibliographic record
Abstract
Background The COVID-19 pandemic has shown the need for new ways to perform remote patient education. Patients with heart failure are associated with a high readmission rate. Rehabilitation can prevent hospital readmissions, but there is poor participation in rehabilitation of patients with heart failure. Based on user-driven innovation in the Future Patient research project, we have developed the prototype of the digital Heart Game. The Heart Game is a new approach to digital patient education, where patients can access a digital board game via an app; through the board game, they can be presented with quizzes, activities, and reflection questions, all related to heart failure. Objective The objective of our study was to assess the usability of the Heart Game prototype app. Methods A total of 6 patients with heart failure were recruited. Think-aloud test, where participants were observed during play and sound and iPad screens were recorded, was conducted, followed by a questionnaire immediately after playing. Finally, an interview (n=6) was carried out. Qualitative data were analyzed in NVivo software (version 12.0). Results In total, 6 patients with heart failure (83% male; mean age 66 years) participated in the think-aloud tests. One participant did not complete the game. The game duration was from 9 minutes and 14 seconds to 16 minutes and 13 seconds. Findings from the think-aloud tests were shown in themes: digital games are a new world; practice makes perfect; the Heart Game is illogical; tasks are not generalizable, in understandable formulations, or entertaining; and the Heart Game is relevant, fun, and entertaining to play. Conclusions The usability of the Heart Game shows opportunities for digital patient education. However, there are some challenges in the prototype with illogical game structures in the board game. Further test and development of the prototype needs to be performed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".