Digital Catalysts for Noncommunicable Disease Prevention Serious Games and Gamified Applications: Framework Design Study
Bibliographic record
Abstract
BACKGROUND: Unhealthy behaviors can cause so-called noncommunicable diseases (NCDs), which are on the rise. Notable examples include chronic respiratory diseases, diabetes, cardiovascular diseases, and various types of cancer. They are responsible for approximately 41 million deaths annually, which accounts for a staggering 74% of all global deaths. Major risk factors include physical inactivity, the use of tobacco, unhealthy diets, the harmful use of alcohol, and poor mental health, which can be classified as modifiable behavioral risk factors. Other factors include metabolic and environmental risk factors, such as air pollution. Many individuals struggle to make informed decisions about their health, which contributes to the risk factors mentioned earlier and, ultimately, can lead to the development of one or more NCD. OBJECTIVE: This research presents design and standardization considerations to enable the exchange of medical and game data to maximize their impact and usefulness. Serious games and gamified applications that strategically use behavior change techniques and educational content can help users change their behavior on a lasting basis, thereby reducing the aforementioned NCD risk factors. Still, each of them is currently independently designed and cannot interact with other applications. METHODS: We previously developed serious games and gamified applications to prevent NCDs. These served as the foundation of an interoperable framework for NCD prevention games and applications. On the basis of a comprehensive analysis, 6 key areas were identified, ultimately leading to a framework definition that was then evaluated against the already-developed games and applications. RESULTS: This paper presented a novel interoperable framework to support the design and development of serious games and gamified applications that enable individuals to achieve sustainable behavior change and improve their overall health and well-being by defining 6 key areas, emphasizing interoperability, and exchanging meaningful medical and game data. CONCLUSIONS: The framework presented in this study covers the major design and implementation aspects of NCD prevention games and applications in 6 key areas. Therefore, researchers should consider these guidelines when creating novel serious games and applications in those areas. The framework also intensively encourages the use of standards in the domain of medical informatics to ensure the semantic interoperability of patients' data produced. Thus, it promotes the exchange of meaningful data to improve patient care and anonymous data use for research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".