Lean adolescents achieve higher intensities but not higher energy expenditure while playing active video games compared with obese ones
Bibliographic record
Abstract
BACKGROUND: While decreased physical activity and increased sedentary behaviours are incriminated for their role in the progression of obesity, active video games (AVG) may offer a new alternative to increase energy expenditure in youth. This study is the first to examine the effect of a 1-h AVG play on lean and obese adolescents' energy expenditure. METHODS: Body composition and aerobic fitness were assessed in 19 obese and 12 lean adolescent boys (12-15 years old). Participants performed a 1-h AVG session (Kinect Sports technology) while wearing a portable indirect calorimeter (K4b2) to assess their energy expenditure and heart rate. RESULTS: Body weight (91.0 ± 9.5 vs. 58.5 ± 12.4 kg), body mass index (32.2 ± 3.1 vs. 20.3 ± 1.6 kg m(-2) ) and body fat (38.1 ± 2.7 vs. 13.4 ± 3.9%) were significantly higher in obese adolescents (P < 0.001). Absolute energy expenditure was significantly higher in obese (P < 0.05) but not when corrected for body composition. Maximal heart rate reached during AVG was significantly higher in lean adolescents (190 ± 25 vs. 183 ± 28 bpm, P < 0.05). Time spent between 3 and 6 METs (Metabolic Equivalent Task) was not different between groups but time spent above 6 METs was higher in lean adolescents (P < 0.05). CONCLUSION: Although lean and obese adolescent boys experienced similar energy expenditure relative to their body size during a 1-h Kinect AVG session, lean adolescents spent more time in moderate-to-vigorous physical activity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".