SIDE-SIDE FORCES AND LOADING RATES IN A JUMPING PROGRAM FOR INCREASING BONE MASS IN POSTMENOPAUSAL WOMEN
Bibliographic record
Abstract
When developing exercise prescriptions for increasing bone mass, it is necessary to quantify variables associated with osteogenesis. In exercise studies specific to increasing hip bone mass, bone mineral density is generally measured on only one side. The one-sided measurement is based on the assumption that both sides of the body are exposed to the same forces. We are currently using 10 sets of 10 jumps-in-place in combination with resistance training exercises to increase bone mass in postmenopausal women. PURPOSE In this pilot work we asked: 1. What are the landing forces and loading rates from jumps-in-place? 2. Are both feet subjected to the same forces and rates? 3. Do the forces change over 10 sets of 10 jumps? METHODS 30 postmenopausal women performed 10 sets of 10 jumps-in-place on 2 force plates. Three variables were quantified: maximum vertical Ground Reaction Forces (vGRF) and loading rates and a left-right difference score, defined for each individual and variable as the absolute value of the mean difference between feet from each set of jumps. One sample t-test were used to compare the mean difference scores to 0. Regression was used to assess changes in vGRF across sets for the group. Statistical significance was p <0.05. RESULTS Maximum vGRF's and loading rates acting at each foot were 2.0 ± 0.6 BW and 24.4 ± 11.2 BW/s, respectively (mean ± SD). Mean left-right differences in vGRF and loading rate were significant, 0.2 ± 0.2 BW and 2.9 ± 3.0 BW/s respectively. vGRF's increased significantly from the first set of 10 jumps to the last set (slope = 0.0093 BW, p = 0.001). CONCLUSIONS Forces and loading rates from jumps-in-place are similar to those reported for running (Munro, 1987). However, we do not know how the forces imposed on the hip during running compare to those during jumping. While significant left-right differences and changes in forces across trials exist, the differences are not likely clinically important. Supported by American Federation of Aging Research, John Erkkila Foundation, BRL Clinical Program.
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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) | 0.001 | 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".