Beyond “just” fun: The role of exergames in advancing health promotion and disease prevention
Bibliographic record
Abstract
Applying innovations in digital health technologies, such as exergames, has been recommended by official bodies like the World Health Organization for health promotion and disease prevention across various populations and age groups. Given a key advantage of interactive and gamified digital health technologies is promoting user engagement, a substantial proportion of studies have implemented recreational exergames - games primarily designed to make specific activities more fun and entertaining. In this article, we aim to move beyond the benefits of "just" providing a more engaging environment for physical and motor-cognitive activities/exercises by shedding light on serious exergame features that enhance the ecological validity of exercises and offer unique advantages for tailoring interventions beyond conventional approaches. To this end, we review the roles and mechanisms of specific exergame features in supporting adherence to relevant behavior change, neuroscience, and exercise science principles, and integrate our findings into the 'Beyond "Just" Fun of Exergames Framework'. This framework (i) implements a definition and classification approach to harmonize and provide more nuanced terminology for specific application scenarios of exergame technologies, and (ii) delineates best practices for the theoretically grounded selection and implementation of exergame features in health promotion and primary through tertiary disease prevention (including rehabilitation). By introducing this framework, we aim to support a paradigm shift by guiding game designers, researchers, and exercise and therapy practitioners from entertainment-centered recreational solutions towards serious exergames that are purposefully designed with adequate theoretical underpinnings, thereby unlocking the full potential of exergame-enhanced interventions for individuals and public health needs.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".