Competitive trampolining influences trabecular bone structure, bone size, and bone strength
Bibliographic record
Abstract
Background Trampolining is a form of gymnastics that has increased in popularity over the last decade and due to its concurrence with the formative years of bone development, it may have an important impact on bone health. However, bone density, microarchitecture, and bone strength of competitive trampolinists have not been explored. Therefore, the purpose of this cross-sectional study was to investigate the relationship between trampolining participation and (1) bone density, area, and microarchitecture; and (2) estimated bone strength and the role of muscle and impact loading in young female adults. Methods We recruited 29 female participants aged 16–29 years for this study ( n = 14 trampolinists; n = 15 controls). Skeletal parameters were assessed using dual X-ray absorptiometry, high-resolution peripheral quantitative computed tomography (HR-pQCT), and finite element analysis (FEA). Muscle strength was measured using dynamometers. Results Trampolinists had higher bone density at the hip and spine, greater trabecular density and thicker trabeculae at the tibia , as well as larger bones at both the tibia and radius than controls ( p < 0.05). Trampolinists also had higher muscle strength than controls at the lower body with no difference between groups in the upper body. Estimates of bone strength using FEA were greater for trampolinists than controls at both the radius and tibia. Conclusion This is the first study to investigate bone density, area, and microarchitecture in female trampolinists using HR-pQCT. Trampolinists had greater bone density, area, microarchitecture, and estimated bone strength than controls.
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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 | 0.000 | 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) | 0.003 | 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".