Physical fitness improvement in overweight postmenopausal women who do not lose fat mass in response to exercise training
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine if overweight postmenopausal women who do not experience fat mass loss after a 1-year aerobic exercise (AE) and resistance training (RT) program can still improve physical fitness. METHODS: Thirty-one overweight women (body mass index 28-40 kg/m or waist circumference ≥88 cm) participated in three weekly supervised AE and RT sessions for 1 year. All women were categorized according to their fat mass changes after the intervention: 14 women were considered as responders (fat mass loss ≥5% of initial fat mass) and 17 as nonresponders (fat mass loss <5% of initial fat mass). The main outcome measures were absolute and relative body strength, peak aerobic capacity, lower limbs power, flexibility, and body composition (DXA). Total energy intake (3-day dietary record) and physical activity level (physical activity scale for the elderly questionnaire) were also measured before, halfway through and after the intervention. RESULTS: At baseline, nonresponders participants had higher body mass index (P = 0.04). After the intervention, relative and absolute upper and lower body strength, peak aerobic capacity (all P < 0.005), and flexibility increased similarly in the nonresponders and responders groups (P = 0.01). CONCLUSIONS: Although some participants did not respond to exercise by decreasing fat mass, they still experienced important physical fitness benefits from AE and RT. These results suggest that body composition changes should not be the sole indicator of the benefits of exercise in overweight 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) | 0.001 | 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".