The Use of Gamification in the Self-Management of Patients With Chronic Diseases: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Chronic disease self-management is a public health issue of worldwide concern, and gamification is an emerging strategy to improve patients' participation in chronic disease self-management. Some studies have summarized designs for the gamification of chronic disease self-management from the perspective of eHealth technology, but they have not mentioned differences in design methods, functions, and evaluation methods of gamified designs for self-management in different chronic diseases. OBJECTIVE: This scoping review aims to synthesize the characteristics of realization forms, functions, and evaluation methods in chronic disease self-management gamification to improve self-management among the chronic disease population. METHODS: We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. As of January 7, 2023, we systematically searched 9 databases for relevant studies from January 2012 to December 2022. Related data were extracted based on the research questions. We calculated the frequencies, charted the quantitative data, and coded the extracted material for qualitative content analysis. RESULTS: We retrieved 16,221 records, of which 70 (0.43%) met the eligibility criteria. In the included research, the target populations for gamified designs for self-management of chronic diseases included patients with stroke, cancer, diabetes, chronic obstructive pulmonary disease, coronary heart disease, obesity, and hypertension. Almost all studies mentioned technical support for gamification (68/70, 97%), mainly in the form of active video games (58/70, 83%); however, less than half of the studies mentioned the theoretical basis for gamification (31/70, 44%). There were 37 concepts or theories relevant to gamification design, most of which were in the field of psychology or were cross-disciplinary (n=33, 89%). Gamification for the self-management of chronic diseases has been widely recognized, including for promoting physical exercise and rehabilitation training (48/99, 48%), increasing initiative for symptom management (18/99, 18%), providing psychological support (14/99, 14%), improving cognitive function (12/99, 12%), and improving medication adherence (7/99, 7%). A total of 39 studies mentioned the gamification effect; however, we did not find a unified evaluation standard. CONCLUSIONS: This scoping review focuses on gamification designs for chronic disease self-management and summarizes the realization forms and functions of gamification in self-management for different patient populations. With practice in a gamified internet-based environment, patients can not only master the knowledge and skills of self-management in fascinating scenarios but also benefit from gaming experience and make better health-related decisions in real life. It is worth noting that a comprehensive evaluation of the users as well as a personalized and targeted intervention should be developed before gamification.
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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 | 0.026 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".