Impact of energy restriction with or without resistance training on energy metabolism in overweight and obese postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: The present study measured the impact of adding resistance training to an energy-restricted diet on the components of energy expenditure in overweight or obese postmenopausal women. METHODS: Participants (n = 137) were randomly divided into two groups: (1) a diet and resistance training (DRT) group and (2) a diet-only (DO) group. Women followed a 6-month energy-restricted diet consisting of 2,100 to 3,360 kJ less than daily needs. The DRT group also followed a resistance training program (three times a week). Resting energy expenditure (REE) was measured by indirect calorimetry. Total energy expenditure was measured with doubly labeled water. Body composition was measured by dual-energy x-ray absorptiometry. RESULTS: Eighty nine women were included in the analyses for this study (DRT, n = 21; DO, n = 68). REE in both groups was significantly lower after the intervention (mean difference ± SD: DO, -0.26 ± 0.4 MJ d; DRT, -0.33 ± 0.4 MJ d; P ≤ 0.05). Relative REE, expressed per kilogram of lean body mass corrected for fat mass change, remained stable in both groups. Physical activity energy expenditure remained stable in both groups (mean difference ± SD: DO, 0.02 ± 1 MJ d, P = 0.91; DRT, -0.14 ± 1 MJ d, P = 0.64). CONCLUSIONS: Adding resistance training to an energy-restricted diet does not significantly alter any compartment of energy expenditure. REE is lower owing to reduction in body composition compartments, but relative REE is not significantly altered.
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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".