Guidelines for the Gamification of Self-Management of Chronic illnesses: Multimethod Study
Bibliographic record
Abstract
BACKGROUND: Gamification is the use of game elements and techniques in nongaming contexts. The use of gamification in health care is receiving a great deal of attention in both academic research and the industry. However, it can be noticed that many gamification apps in health care do not follow any standardized guidelines. OBJECTIVE: This research aims to (1) present a set of guidelines based on the validated framework the Wheel of Sukr and (2) assess the guidelines through expert interviews and focus group sessions with developers. METHODS: Expert interviews (N=6) were conducted to assess the content of the guidelines and that they reflect the Wheel of Sukr. In addition, the guidelines were assessed by developers (N=15) in 5 focus group sessions, where each group had an average of 3 developers. RESULTS: The guidelines received support from the experts. By the end of the sixth interview, it was determined that a saturation point was reached. Experts agreed that the guidelines accurately reflect the framework the Wheel of Sukr and that developers can potentially use them to create gamified self-management apps for chronic illnesses. Moreover, the guidelines were welcomed by developers who participated in the focus group sessions. They found the guidelines to be clear, useful, and implementable. Also, they were able to suggest many ways of gamifying a nongamified self-management app when they were presented with one. CONCLUSIONS: The findings suggest that the guidelines introduced in this research are clear, useful, and ready to be implemented for the creation of self-management apps that use the notion of gamification as described in the Wheel of Sukr framework. The guidelines are now ready to be practically tested. Further practical studies of the effectiveness of each element in the guidelines are to be carried out.
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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 | 0.065 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".