EFFECTS OF MAT PILATES TRAINING ON BODY COMPOSITION AND CARDIOMETABOLIC MARKERS IN POSTMENOPAUSAL WOMEN WITH MULTIMORBIDITY
Bibliographic record
Abstract
Objective: The aim of this study was to evaluate the effects of Mat Pilates training on body composition, resting blood pressure, and lipids, glucose, and adiponectin levels in postmenopausal women with cardiometabolic multimorbidities. Design and method: Forty-seven postmenopausal women between 50 and 70 years were allocated into two groups based on the number of comorbidities: no more than 1 (COM n = 23) and at least 2 comorbidities (MULT n = 24). Both groups performed Mat Pilates three times a week for 12 weeks. Before and after the intervention, resting blood pressure, body composition, and blood samples (lipidogram, adiponectin, glucose, glycated hemoglobin, and uric acid) were evaluated. A two-factor Generalized Estimated Equation was used to compare groups, time, and their interaction (groups∗time). Results: Women in MULT presented higher body mass, body mass index, fat mass, and abdominal circumference than COM (p < 0.01). Blood pressure was reduced (p < 0.05) in both groups. Triglycerides decreased (p < 0.05) in COM and increases in MULT (p < 0.05) over time with no changes in total cholesterol, HDL, or LDL. Uric acid increases (p < 0.01) over time in both groups. Adiponectin had higher (p < 0.01) levels in the COM group and glycated hemoglobin decrease (p < 0.05) over time in both groups without changes in blood glucose. No interaction was found in any analyzed variable. Conclusions: These results suggest that 12 weeks of Mat Pilates training can improve blood pressure and glycated hemoglobin independent of the number of morbidities of cardiometabolic disease, but may not change body composition, lipids, glucose, or adiponectin levels 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".