The health benefits of interactive video game exercise
Bibliographic record
Abstract
The purpose of this study was to evaluate the effectiveness of interactive video games (combined with stationary cycling) on health-related physical fitness and exercise adherence in comparison with traditional aerobic training (stationary cycling alone). College-aged males were stratified (aerobic fitness and body mass) and then assigned randomly to experimental (n = 7) or control (n = 7) conditions. Program attendance, health-related physical fitness (including maximal aerobic power (VO2 max), body composition, muscular strength, muscular power, and flexibility), and resting blood pressure were measured before and after training (60%-75% heart rate reserve, 3 d/week for 30 min/d for 6 weeks). There was a significant difference in the attendance of the interactive video game and traditional training groups (78% +/- 18% vs. 48% +/- 29%, respectively). VO2 max was significantly increased after interactive video game (11% +/- 5%) but not traditional (3% +/- 6%) training. There was a significantly greater reduction in resting systolic blood pressure after interactive video game (132 +/- 6 vs. 123 +/- 6 mmHg) than traditional (131 +/- 7 vs. 128 +/- 8 mmHg) training. There were no significant changes in body composition after either training program. Attendance mediated the relationships between condition and changes in health outcomes (including VO2 max, vertical jump, and systolic blood pressure). The present investigation indicates that a training program that links interactive video games to cycle exercise results in greater improvements in health-related physical fitness than that seen after traditional cycle exercise training. It appears that greater attendance, and thus a higher volume of physical activity, is the mechanism for the differences in health-related physical fitness.
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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 | 0.001 |
| 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 | 0.000 |
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".