A Video Game for Brazilian T1D Children about Knowledge of Disease and Self-care: A Methodological Study
Bibliographic record
Abstract
Background: Video games are interactive technologies able to support children in health promotion, behavior changes, and chronic disease self-management. The use of health behavior change determinants in video game design can increase its effectiveness. This study describes the process of designing a video game for Brazilian children with T1D clarifying the use of health behavior change determinants that may influence self-management behaviors. Methods: This was a methodological study based on health behavior change theories and the user-centered design approach. The results of a qualitative study conducted with children aged 7 to 12 years identified learning needs about knowledge on diabetes and self-care tasks which contribute to inappropriate behaviors. A Behavioral Diagnosis presented health behavior change determinants, capable of influencing children’s learning needs and behaviors, that were considered to design The Heroes of Diabetes—the power of knowledge. Results: The results presented the process of designing 4 mini games with its description and theory foundation to reach children’s lack of understanding about T1D, insulin’s role, SMBG requirements, food groups and physical activity’s role in glycemic control. Knowledge, goal settings, extrinsic and intrinsic motivation determinants were related with video games design features. Conclusions: The findings support the use of health behavior change determinants into video game design as a guide to achieve children learning needs and that might influence self-management behaviors.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".