Adolescent physical activity profiles as determinants of emerging adults’ physical activity
Bibliographic record
Abstract
BACKGROUND: Although adolescent physical activity (PA) tracks into adulthood, it is unclear if the context of PA practiced during adolescence predicts adult PA. We previously identified five adolescent PA profiles and aimed to assess associations between these profiles and levels of PA in emerging adulthood. METHODS: Using data from the first 8 years of the MATCH study, when participants were 11 to 18 years, we identified five adolescent PA profiles: "non-participants" (9% of the sample), "dropouts" (30%), "active in unorganized PA" (19%), "active in organized PA" (27%), and "active through a variety of PA" (15%). The same participants reported their PA level (IPAQ) 2.5, 3.5, 4.5, and 5.5 years later when they were emerging adults (20, 21, 22, and 23 years). The adolescent PA profiles were included in a mixed-distribution two parts model as predictors of i) the probability of reporting any PA during emerging adulthood, and ii) the PA level of emerging adults reporting PA. RESULTS: Being categorized as "non-participant" or "dropout" during adolescence was associated with similar likelihoods of reporting PA and PA level during emerging adulthood. In contrast with "non-participants", those in the "active-unorganized" (OR, 95% CI: 2.8, 2.1-3.8), "active-organized" (2.4, 1.7-3.2), and "active-variety" (3.7, 3.3-5.1) were considerably more likely to report any PA during emerging adulthood. Among emerging adults who reported some PA, those with an adolescent profile of "active-unorganized", "active-organized" and "active-variety" had higher PA levels than "non-participants" (all p < 0.01). CONCLUSION: Profiles of PA participation during adolescence represent strong predictors of PA in emerging adulthood. Promoting participation in various types of PA during adolescence is key to preventing low PA among emerging adults.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".