Digital Games for Type 1 and Type 2 Diabetes: Underpinning Theory With Three Illustrative Examples
Bibliographic record
Abstract
Digital games are an important class of eHealth interventions in diabetes, made possible by the Internet and a good range of affordable mobile devices (eg, mobile phones and tablets) available to consumers these days. Gamifying disease management can help children, adolescents, and adults with diabetes to better cope with their lifelong condition. Gamification and social in-game components are used to motivate players/patients and positively change their behavior and lifestyle. In this paper, we start by presenting the main challenges facing people with diabetes-children/adolescents and adults-from a clinical perspective, followed by three short illustrative examples of mobile and desktop game apps and platforms designed by Ayogo Health, Inc. (Vancouver, BC, Canada) for type 1 diabetes (one example) and type 2 diabetes (two examples). The games target different age groups with different needs-children with type 1 diabetes versus adults with type 2 diabetes. The paper is not meant to be an exhaustive review of all digital game offerings available for people with type 1 and type 2 diabetes, but rather to serve as a taster of a few of the game genres on offer today for both types of diabetes, with a brief discussion of (1) some of the underpinning psychological mechanisms of gamified digital interventions and platforms as self-management adherence tools, and more, in diabetes, and (2) some of the hypothesized potential benefits that might be gained from their routine use by people with diabetes. More research evidence from full-scale evaluation studies is needed and expected in the near future that will quantify, qualify, and establish the evidence base concerning this gamification potential, such as what works in each age group/patient type, what does not, and under which settings and criteria.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".