Factors Associated with Sustained Exergaming: Longitudinal Investigation
Bibliographic record
Abstract
BACKGROUND: Exergaming is technology-driven physical activity (PA) which, unlike traditional video game play, requires that participants be physically active to play the game. Exergaming may have potential to increase PA and decrease sedentary behavior in youth, but little is known about sustained exergaming. OBJECTIVE: The objectives of this study were to describe the frequency, correlates, and predictors of sustained exergaming. METHODS: Data were available in AdoQuest (2005-11), a longitudinal investigation of 1843 grade 5 students in Montréal, Canada. This analysis used data from grade 9 (2008-09) and 11 (2010-11). Participants at Time 1 (T1; mean age 14 years, SD 0.8 ) who reported past-week exergaming (n=186, 19.1% of AdoQuest sample) completed mailed self-report questionnaires at Time 2 (T2; mean age 16 years, SD 0.8). Independent sociodemographic, psychological, and behavioral correlates (from T2)/predictors (from T1 or earlier) were identified using multivariable logistic regression. RESULTS: Of 186 exergamers at T1, 81 (44%) reported exergaming at T2. Being female and having higher introjected regulation (ie, a type of PA motivation indicative of internalizing PA as a behavior) were independent correlates. None of the predictors investigated were associated with sustained exergaming. CONCLUSIONS: Almost half of grade 9 exergamers sustained exergaming for 2 years. Exergaming may be a viable approach to help adolescents engage in and sustain PA during adolescence. Sex and PA motivation may be important in the sustainability of exergaming.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".