Social Exergames in Health and Wellness: A Systematic Review of Trends, Effectiveness, Challenges, and Directions for Future Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Exergames are becoming increasingly popular and have shown potential for motivating physical activity. Past research suggests that social (multiplayer) exergames offer players an engaging experience and good aerobic exercises. Our systematic review summarizes existing work and identifies gaps, trends, and patterns on social exergame research in the domain of health and wellness. A search was conducted in the ACM Digital Library, IEEE Xplore, and PubMed. After screening 2272 records, we identified 73 studies from 2013 to 2023 that meet the inclusion criteria. Our results reveal that step tracking is the most commonly implemented measure of physical activity in social exergames, and that competition, rewards, and cooperation are the most common features used for designing the games. Our results also show that the effectiveness of social exergames is intricately linked to a combination of factors, including group size, player matching, and game features. The main contribution of this paper is (1) an analysis of features and group dynamics employed for designing social exergames, and (2) how game features affect the games’ outcome (both positive and negative) uncovering challenges and opportunities to advance future research in this area. Our findings in the current review provides insights for the design and implementation of social exergaming helping users to experience more socially satisfying game experiences thereby increasing the motivation for exercise, as well as gaining social benefits.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it