The Association Between Exercise Behavior Regulation and Exergaming in Adolescents
Bibliographic record
Abstract
BACKGROUND: It is not known if or how exercise behavior regulations (EBRs) relate to exergaming in adolescents. The study objectives were 1) to determine if EBRs differ between adolescents who do and do not exergame; and 2) among exergamers, to describe the associations between EBRs and exergame duration, intensity, and achieving physical activity (PA) guidelines. METHODS: This study was a cross-sectional analysis of data collected in mailed self-report questionnaires completed by 1243 students (mean ± SDage = 16.8 ± 0.5 years; 43% boys). RESULTS: In girls, those who exergamed scored higher than nonexergamers on introjected (mean ± SD = 1.9 ± 1.0 vs.1.6 ± 0.9; P = .001) and identified (mean ± SD = 3.1 ± .0 vs.2.9 ± 0.9; P = .049) regulation. Exergame intensity was associated with identified regulation [OR (95% CI) = 2.2 (1.0, 4.5)], minutes exergaming per week was associated with amotivation [β (95% CI) = 0.4 (-0.0, 0.8)], and achieving guidelines was associated with external [OR (95% CI) = 3.7 (1.0, 13.4)] and identified [OR (95% CI) = 5.6 (2.0, 16.0)] regulations. In boys who exergamed, intrinsic regulation was associated with exergame duration [β (95% CI) = -0.3 (-0.6, 0.0)]. CONCLUSIONS: Girls who exergame may have partially internalized exergaming as a PA behavior. Boys may prefer other types of PA such as team sports or other more traditional videogames over exergaming or they may not view exergaming as PA.
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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 | 0.003 |
| 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) | 0.001 | 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".