The effects of lifestyle interventions in dynapenic-obese postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate the impact of caloric restriction (CR) and resistance training (RT) on body composition, metabolic profile and physical capacity in dynapenic-obese postmenopausal women. METHODS: Thirty-eight dynapenic-obese postmenopausal (age, 62.6 ± 4.1 y) women were randomly assigned to one of four groups (1, CR; 2, RT; 3, CR + RT; and 4, control) for a 12-week intervention. The independent variables were body weight, fat mass, and lean body mass (using dual-energy x-ray absorptiometry), waist circumference, fasting lipids and glucose, resting systolic and diastolic blood pressure, and physical capacity (6-min walk, chair stand, and one-leg stand tests). RESULTS: Body weight, fat mass, and waist circumference decreased similarly in the CR and CR + RT groups (all P ≤ 0.05). However, only changes in the CR + RT group were significantly different from the control group (all P ≤ 0.05). Total cholesterol, triglycerides, and systolic and diastolic blood pressure significantly decreased in the CR group (all P ≤ 0.05); whereas total cholesterol, low-density lipoprotein cholesterol, and systolic blood pressure decreased in the CR + RT group (P ≤ 0.05). Physical capacity improved significantly in the RT and CR + RT groups (all P ≤ 0.05), with significant greater improvements in the RT group (P ≤ 0.05). CONCLUSIONS: Our results suggest that CR with or without RT is effective in improving metabolic profile, whereas RT is effective in improving physical capacity. The combination of RT and CR may be particularly relevant in maximizing improvements in physical capacity in dynapenic-obese 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 | 0.001 |
| 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".