Bone Mineral Density and Jumping Height in Pre-Menarcheal and Post-Menarcheal Physically Active Girls
Bibliographic record
Abstract
Research background and hypothesis. Jumping ability correlates well with different bone values. The skeletal benefits of high-impact weight-bearing exercise have been shown to be greater when training is started prior to menarche. We hypothesized that significant differences would be apparent in the relationships between bone values and jumping height in favor of the girls’ prior menarche compared to post-menarcheal group. Research aim. The aim of the study was to investigate the relationships between jumping height and bone mineral density (BMD) in pre-menarcheal and post-menarcheal physically active girls. Research methods. In total, 113 adolescent girls from different competitive extramural athletic programs participated in this study. Femoral neck and lumbar spine BMD were measured. The heights of vertical jumps (i. e. countermovement jump (CMJ) and rebound jumps for 15 (RJ 15 s) and 30 (RJ 30 s) seconds) were obtained.Research results. After adjusting for major confounders (i. e. age, height, and body mass), the height of rebound jumps correlated only with femoral neck BMD and only in pre-menarcheal group (r = 0.37–0.46; p < 0.05). No correlations were found between BMD variables and jumping height in post-menarcheal girls. The height of CMJ did not correlate with measured BMD variables in the studied groups.Discussion and conclusions. Early puberty is an opportune period to increase bone adaptation to mechanical loading due to the velocity of bone growth and endocrine changes at this time. We suggest that powerful repetitive vertical jumping may be more beneficial to bone health compared to single jumping activities in physically active girls prior to menarche rather than after it.Keywords: bone health, vertical jumps, puberty.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".