Sustained exergaming in adolescents
Bibliographic record
Abstract
Introduction: Exergaming is increasingly popular among youth. However, the extent to which exergaming is sustained over time in a population-based sample of youth is not well documented. The objective of this study was to describe the frequency and correlates of sustained exergaming over 2-3 years in adolescents. Methods: Data were available in a prospective cohort investigation of 1,800 Grade 5 students recruited in a convenience sample of 30 elementary schools in Quebec, Canada in 2005. Data on past week exergaming were collected from 971 participants at age 14 years on average (in 2008–2009), and again when they were age 16 years on average (in 2010–2011), in mailed self-report questionnaires. Potential correlates of sustained exergaming were identified in separate multivariable logistic regression models. Results: Forty-three percent of 185 exergamers at age 14 reported exergaming 2-3 years later. Most sustained exergamers (88%) played up to twice a week at moderate to vigorous intensity. Sex (being female) and weight-related variables (trying to lose weight) were associated with sustained exergaming. Conclusions: Many adolescents who exergame report exergaming 2-3 years later, which suggests that in real life settings, exergaming may be a viable approach to help adolescents increase their physical activity, especially if they are female and actively trying to lose weight.Acknowledgments: EO is supported by the FRSQ
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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) | 0.002 | 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".