Older Adults’ Physical Activity and Exergames: A Systematic Review
Bibliographic record
Abstract
Exertion games, also referred to as exergames, have become popular because they combine physical activity (PA) with game mechanics, such as actions, challenges, and achievements. Exergames have been also used to encourage PA among older adults, as technological interventions to help achieve the latters’ health and wellness goals and as aids to rehabilitation. To the best of our knowledge, no systematic review of empirical studies on exergaming and older adults’ PA has been reported in the literature. Our review indicates that exergames make a measurable contribution to the improvement of health and wellness goals of older adults. Our systematic review identifies 9 categories and 19 themes of exergame applications in the domain of older adults’ PA. We aggregate these categories and themes into three broader exergaming clusters, of “training,” “rehabilitation,” and “wellness.” Additionally, we outline pathways for future empirical research into applying exergames as health and wellness interventions for older adults through physical activities.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".