The Rationale Behind the Design Decisions in an Augmented Reality Mobile eHealth Exergame to Increase Physical Activity for Inactive Older People With Heart Failure
Bibliographic record
Abstract
Unlabelled: Physical activity is important for everyone to maintain and improve health, especially for people with chronic diseases. Mobile exergaming has the potential to increase physical activity and to specifically reach people with poor activity levels. However, commercial mobile exergames are not specially designed for older people with chronic illnesses such as heart failure. The primary aim of this viewpoint is to describe the underlying reasoning guiding the design choices made in developing a mobile exergame, Heart Farming, tailored specifically for sedentary older people diagnosed with heart failure. The goal of the exergame is to increase physical activity levels by increasing the daily walking duration of patients with heart failure by at least 10 minutes. The rationale guiding the design decisions of the mobile exergame is grounded in the thoughtful integration of gamification strategies tailored for application in cardiovascular care. This integration is achieved through applying gamification components, gamification elements, and gamification principles. The Heart Farming mobile exergame is about helping a farmer take care of and expand a virtual farm, with these activities taking place while the patient walks in the real world. The exergame can be adapted to individual preferences and physical condition regarding where, how, when, and how much to play and walk. The exergame is developed using augmented reality so it can be played both indoors and outdoors. Augmented reality technology is used to track the patients' movement in the real world and to interpret that movement into events in the exergame rather than to augment the mobile user interface.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".