Active video games: the mediating effect of aerobic fitness on body composition
Bibliographic record
Abstract
BACKGROUND: Increased understanding of why and how physical activity impacts on health outcomes is needed to increase the effectiveness of physical activity interventions. A recent randomized controlled trial of an active video game (PlayStation EyeToy™) intervention showed a statistically significant treatment effect on the primary outcome, change from baseline in body mass index (BMI), which favored the intervention group at 24 weeks. In this short paper we evaluate the mediating effects of the secondary outcomes. OBJECTIVE: To identify mediators of the effect of an active video games intervention on body composition. METHODS: Data from a two-arm parallel randomized controlled trial of an active video game intervention (n = 322) were analyzed. The primary outcome was change from baseline in BMI. A priori secondary outcomes were considered as potential mediators of the intervention on BMI, including aerobic fitness (VO2Max), time spent in moderate-to-vigorous physical activity (MVPA), and food snacking at 24 weeks. RESULTS: Only aerobic fitness at 24 weeks met the conditions for mediation, and was a significant mediator of BMI. CONCLUSION: Playing active video games can have a positive effect on body composition in overweight or obese children and this effect is most likely mediated through improved aerobic fitness. Future trials should examine other potential mediators related to this type of intervention. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry Website: http://www.anzctr.org.au. Study ID number: ACTRN12607000632493.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".