12606 Gamification In The Management Of Children And Adolescents With Diabetes: A Systematic Review
Bibliographic record
Abstract
Abstract Disclosure: L.C. Hespanhol: None. V. Lins de Menezes: None. P. Souto Maia: None. C.A. Balieiro: None. W. Oliveira Amador: None. R. Lira: None. Background: Educating children and adolescents with diabetes about their own condition presents a significant challenge. Gamification has been proposed as an innovative approach to ease the transition to self-care by engaging and empowering children and adolescents in their diabetes management. Purpose: The purpose of this systematic review is to examine published research on using gamification and technology within diabetes care for young patients. Methods: PubMed, Lilacs and EMBASE were systematically searched in January 2024 to include articles focusing on the use of new virtual technologies to improve the clinical and laboratory conditions of children and adolescents with diabetes. The protocol was registered in PROSPERO (ID: CRD42024499381) adhering to PRISMA recommendations. We assessed the risk of bias using RoB-2 and ROBINS-I. Results: A total of 24 studies were included, of which 7 were randomized trials. Age of participants ranged from 5 to 19 years. We identified five gamification strategies (i.e., applications, video games, minigame, robot, or web game) within seven clinical domains (i.e., psychosocial impairment, glycemic control, autonomy, reduced frequency of error, health lifestyle, nutrition, or knowledge about disease). 246 (27%) patients demonstrated reduced emotional distress and increased enjoyment. Reduction of errors such as hypoglycemia and emergency preparedness was identified in 354 (39%) of patients and improvement in glycemic control measured by HbA1c was described in 277 (30%) of patients. Conclusion: Our systematic review gamification has shown promise in fostering autonomy by providing tools that educate young individuals regarding glycemic control, lifestyle choices, injection practices, carbohydrate counting, and other pertinent aspects of diabetes management. Presentation: 6/1/2024
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".