Couch potatoes to jumping beans: A pilot study of the effect of active video games on physical activity in children
Bibliographic record
Abstract
UNLABELLED: The primary objective of this pilot study was to evaluate the effect of active video games on children's physical activity levels.Twenty children (mean +/- SD age = 12 +/- 1.5 years; 40% female) were randomised to receive either an active video game upgrade package or to a control group (no intervention). Effects on physical activity over the 12-week intervention period were measured using objective (Actigraph accelerometer) and subjective (Physical Activity Questionnaire for Children [PAQ-C]) measures. An activity log was used to estimate time spent playing active and non-active video games.Children in the intervention group spent less mean time over the total 12-week intervention period playing all video games compared to those in the control group (54 versus 98 minutes/day [difference = -44 minutes/day, 95% CI [-92, 2]], p = 0.06). Average time spent in all physical activities measured with an accelerometer was higher in the active video game intervention group compared to the control group (difference at 6 weeks = 194 counts/min, p = 0.04, and at 12 weeks = 48 counts/min, p = 0.06).This preliminary study suggests that playing active video games on a regular basis may have positive effects on children's overall physical activity levels. Further research is needed to confirm if playing these games over a longer period of time could also have positive effects on children's body weight and body mass index. TRIAL REGISTRATION NUMBER: ACTRN012606000018516.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".