Number of years of participation in some, but not all, types of physical activity during adolescence predicts level of physical activity in adulthood: Results from a 13-year study
Bibliographic record
Abstract
BACKGROUND: Adolescent physical activity (PA) levels track into adulthood. However it is not known if type of PA participated in during adolescence is associated with PA levels later in life. We aimed to identify natural groupings of types of PA and to assess whether number of years participating in these different groupings during adolescence is related to PA level in early adulthood. METHODS: 673 adolescents in Montreal, Canada, age 12-13 years at baseline (54% female), reported participation in 29 physical activities every 3 months over 5 years (1999-2005). They also reported their PA level at age 24 years (2011-12). PA groupings among the 29 physical activities were identified using factor analysis. The association between number of years participating in each grouping during adolescence and PA level at age 24 was estimated using linear regression within a general estimating equation framework. RESULTS: Three PA groupings were identified: "sports", "fitness and dance", and "running". There was a positive linear relationship between number of years participating in sports and running in adolescence and PA level at age 24 years (β (95% confidence interval) = 0.09 (0.04-0.15); 0.08 (0.01-0.15), respectively). There was no relationship between fitness and dance in adolescence and PA level at age 24. CONCLUSIONS: The association between PA participation in adolescence and PA levels in young adulthood may be specific to certain PA types and to consistency of participation during adolescence. Results suggest that efforts to establish the habit of participation in sports and running in adolescence may promote higher PA levels in adulthood.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".