Impact Exercise Increases BMC During Growth: An 8-Year Longitudinal Study
Bibliographic record
Abstract
Our aim was to assess BMC of the hip over 8 yr in prepubertal children who participated in a 7-mo jumping intervention compared with controls who participated in a stretching program of equal duration. We hypothesized that jumpers would gain more BMC than control subjects. The data reported come from two cohorts of children who participated in separate, but identical, randomized, controlled, school-based impact exercise interventions and reflect those subjects who agreed to long-term follow-up (N = 57; jumpers = 33, controls = 24; 47% of the original participants). BMC was assessed by DXA at baseline, 7 and 19 mo after intervention, and annually thereafter for 5 yr (eight visits over 8 yr). Multilevel random effects models were constructed and used to predict change in BMC from baseline at each measurement occasion. After 7 mo, those children that completed high-impact jumping exercises had 3.6% more BMC at the hip than control subjects whom completed nonimpact stretching activities (p < 0.05) and 1.4% more BMC at the hip after nearly 8 yr (BMC adjusted for change in age, height, weight, and physical activity; p < 0.05). This provides the first evidence of a sustained effect on total hip BMC from short-term high-impact exercise undertaken in early childhood. If the benefits are sustained into young adulthood, effectively increasing peak bone mass, fracture risk in the later years could be reduced.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".