A High-Intensity Jump-Based Aquatic Exercise Program Improves Bone Mineral Density and Functional Fitness in Postmenopausal Women
Bibliographic record
Abstract
The aim of this study was to verify the effects of a high-intensity jump-based aquatic exercise (HIIAE) program on bone mass and functional fitness in postmenopausal women. We randomly assigned 25 women (65 ± 7 years) into two groups: Training group (T, n = 15) and Untrained group (Un, n = 10). The T group was submitted to 24 weeks of HIIAE program, where each session lasted for 30 minutes. The following parameters were assessed before and 6 months following the intervention: bone and physical fitness; lumbar spine (LS), total femur (TF), and whole body (WB) bone mineral density (BMD); agility (time up-and-go, TUG); and leg strength (chair stand test, CS). We observed a significant increase (p < 0.01) in LS, (Un: -0.88 ± 3.55, T: 3.71 ± 3.68; %), TF (Un: -1.38 ± 17.76, T: 6.52 ± 2.71; %), and WB (Un: 2.09 ± 3.17, T: 3.23 ± 4.18) BMD in the T group. Regarding functional fitness, the T group showed improvements in both TUG (before: 6.86 ± 1.24 vs. after: 6.22 ± 1.13 seconds; p < 0.05) and CS (before: 16 ± 4 vs. after: 19 ± 5 repetitions; p > 0.05) tests when compared with the U group's TUG (before: 5 ± 1, after: 6 ± 1 seconds; p < 0.05) and CS (before: 20 ± 2, after: 19 ± 2 repetitions; p > 0.05) scores. Our data suggest that a high-intensity, jump-based interval aquatic exercise program is able to improve BMD and functional fitness parameters in postmenopausal women.
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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 | 0.000 |
| 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".