Exergames for health and fitness: the roles of GPS and geosocial apps
Bibliographic record
Abstract
Large numbers of children and adolescents in Canada, UK and USA are not getting their recommended daily dose of moderate to vigorous physical activity, and are thus more prone to obesity and its ill health effects. Exergames (video games that require physical activity to play) are rapidly gaining user acceptance, and may have the potential to increase physical activity levels among young people. Mobile exergames for GPS (global positioning system)-enabled smartphones and mini-tablets take players outdoors, in the open air, unlike console exergames, e.g., Xbox 360 Kinect exergames, which limit players to playing indoors in front of a TV set. In this paper and its companion 'Additional file 1', we review different examples of GPS exergames and of gamified geosocial apps and gadgets (mobile, location-aware apps and devices with social and gamification features), and briefly discuss some of the issues surrounding their use. Further research is needed to document best practices in this area, quantify the exact health and fitness benefits of GPS exergames and apps (under different settings and scenarios), and find out what is needed to improve them and the best ways to promote their adoption by the public.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".