The Influence of Mat Pilates Training on Cardiometabolic Risk Factors in Postmenopausal Women with Single or Multiple Cardiometabolic Diseases
Bibliographic record
Abstract
This study compared the effects of Mat Pilates training on cardiovascular risk markers in postmenopausal women with single or multiple cardiometabolic conditions. Forty-four women were divided into single-condition (SINGLE; n = 20) and multiple-condition (MULTI; n = 24) groups. Both groups completed Mat Pilates three times per week for 12 weeks. Measurements of resting blood pressure, body composition, dietary intake, and blood markers were taken before and after the intervention. A Generalized Estimating Equation was used for hypothesis testing. MULTI presented higher body mass, BMI, fat mass, and waist circumference. Systolic blood pressure decreased more in SINGLE (−13 ± 15 mmHg) than in MULTI (−3 ± 16 mmHg, p interaction = 0.016 with diastolic reductions in both groups (SINGLE: −9 ± 12 mmHg; MULTI: −2 ± 11 mmHg, p interaction = 0.053). Triglycerides decreased only in SINGLE (−40 ± 98 mg/dL vs. +31 ± 70 mg/dL in MULTI, p interaction = 0.006), while no significant changes were observed in cholesterol levels. Adiponectin levels decreased in both groups (SINGLE: −1.5 ± 16.3; MULTI: −9.3 ± 12.4 vs. µg/dL, p time = 0.015). Glycated hemoglobin levels decreased over time in both groups (−0.3 ± 0.5% in SINGLE, −0.5 ± 0.6% in MULTI, p time < 0.001), with no significant changes in blood glucose. These findings suggest that Mat Pilates may be more effective in reducing cardiometabolic risk factors in women with a single condition compared to those with multiple conditions.
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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) | 0.002 | 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".