The Application of a Serious Game Framework to Design and Develop an Exergame for Patients With Heart Failure
Bibliographic record
Abstract
Reducing inactivity in patients with chronic disease is vital since it can decrease the risk of disease progression and mortality. Exergames are an innovative approach to becoming more physically active and positively affecting physical health outcomes. Serious games are designed for purposes beyond entertainment and exergames are serious games for physical activity. However, current commercial exergames might not optimally meet the needs of patients with special needs. Developing tailored exergames is challenging and requires an appropriate process. The primary goal of this viewpoint is to describe significant lessons learned from designing and developing an exergame for patients with chronic heart failure using the player-centered, iterative, interdisciplinary, and integrated (P-III) framework for serious games. Four of the framework's pillars were used in the design and development of a mobile exergame: player-centered design, iterative development of the game, interdisciplinary teamwork, and integration of play and serious content. The mobile exergame was developed iteratively in 7 iterations by an interdisciplinary team involving users and stakeholders in all iterations. Stakeholders played various roles during the development process, making the team stay focused on the needs of the patients and creating an exergame that catered to these needs. Evaluations were conducted during each iteration by both the team and users or patients according to the player-centered design pillar. Since the exergame was created for a smartphone, the assessments were conducted both on the development computer and on the intended platforms. This required continuous deployment of the exergame to the platforms and smartphones that support augmented reality. Our findings show that the serious game P-III framework needs to be modified in order to be used for the design and development of exergames. In this viewpoint, we propose an updated version of the P-III framework for exergame development including (1) a separate and thorough design of the physical activity and physical interaction, and (2) early and continuous deployment of the exergame on the intended platform to enable evaluations and everyday life testing.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".