Bouts of Vigorous Physical Activity and Bone Strength Accrual During Adolescence
Bibliographic record
Abstract
PURPOSE: We examined the influence of vigorous physical activity (VPA) bout frequency on bone strength accrual across adolescence, independent of total volume of VPA. METHODS: We measured VPA (6 metabolic equivalents; total volume and bout frequency <5 min in duration) annually using waist-worn accelerometers (ActiGraph GT1M) in 309 adolescents (9-20 y at baseline: 99, <13 y; 126, 13-18 y; 84, >18 y) over a maximum of 4 years. We applied finite element analysis to high-resolution peripheral quantitative computed tomography scans of the distal tibia (8% site) to estimate bone strength (failure load; F.Load, Newtons). We fit a mixed effects model with maturity offset (years from age at peak height velocity) as a random effect and sex, ethnicity, tibia length, lean body mass, and VPA (volume and bout frequency) as fixed effects. RESULTS: VPA volume and bout frequency were positively associated with F.Load across adolescence; however, VPA volume did not predict F.Load once VPA bout frequency was included in the model. Participants in the upper quartile of VPA bout frequency (∼33 bouts per day) had 10% (500 N) greater F.Load across adolescence compared with participants in the lowest quartile (∼9 bouts per day; P = .012). Each additional daily bout of VPA was associated with 21 N greater F.Load, independent of total volume of VPA. CONCLUSION: Frequent VPA should be promoted for optimal bone strength accrual.
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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 | 0.002 |
| 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) | 0.001 | 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".