A High-Intensity Jump-Based Aquatic Exercise Program Improves Bone Mineral Density and Functional Fitness in Postmenopausal Women
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it