The Association Between Exergaming and Physical Activity in Young Adults
Bibliographic record
Abstract
BACKGROUND: Compared with traditional nonactive video games, exergaming contributes significantly to overall daily physical activity (PA) in experimental studies, but the association in observational studies is not clear. METHODS: Data were available in the 2011 to 2012 wave of the Nicotine Dependence in Teens (NDIT) study (N = 829). Multivariable sex-stratified models assessed the association between exergaming (1-3 times per month in the past year) and minutes of moderate and vigorous physical activity in the previous week, and the association between exergaming and meeting PA recommendations. RESULTS: Compared with male exergamers, female exergamers were more likely to believe exergames were a good way to integrate PA into their lives (89% vs 62%, P = .0001). After we adjusted for covariates, male exergamers were not significantly different from male nonexergamers in minutes of PA. Female exergamers reported 47 more minutes of moderate PA in the previous week compared with female nonexergamers (P = .03). There was no association between exergaming and meeting PA recommendations. CONCLUSIONS: Exergaming contributes to moderate minutes of PA among women but not among men. Differences in attitudes toward exergaming should be further explored.
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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 | 0.004 |
| 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".