The influence of physical activity on lean mass accrual during adolescence: a longitudinal analysis
Bibliographic record
Abstract
During childhood, physical activity is likely the most important modifiable factor for the development of lean mass. However, the effects of normal growth and maturation must be controlled. To distinguish effects of physical activity from normal growth, longitudinal data are required. One hundred nine boys and one hundred thirteen girls, participating in the Saskatchewan Pediatric Bone Mineral Accrual Study, were repeatedly assessed for 6 yr. Age at entry was 8-15 yr. Stature, body mass, and physical activity were assessed biannually. Body composition was assessed annually by dual-energy X-ray absorptiometry. Physical activity was determined using the physical activity questionnaires for children and adolescence. Biological age was defined as years from age of peak height velocity. Data were analyzed using multilevel random-effects models. In boys, it was found that physical activity had a significant time-dependent effect on lean mass accrual of the total body (484.7+/-157.1 g), arms (69.6+/-27.2 g), legs (197.7+/-60.5 g), and trunk (249.1+/-91.4 g) (P<0.05). Although the physical activity effects were similar in the girls (total body: 306.9+/-96.6 g, arms: 31.4+/-15.5 g, legs: 162.9+/-40.0 g, and trunk: 119.6+/-58.2 g; P<0.05), boys for the same level of activity accrued, depending on the site, between 21 and 120% more absolute lean mass (g). In conclusion, habitual physical activity had a significant independent influence on the growth of lean body mass during adolescence, once biological maturity and stature were controlled.
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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 | 0.003 | 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 | 0.001 | 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".