Twelve weeks’ progressive resistance training combined with protein supplementation beyond habitual intakes increases upper leg lean tissue mass, muscle strength and extended gait speed in healthy older women
Bibliographic record
Abstract
Abstract The age-related decline in functional capability is preceded by a reduction in muscle quality. The purpose of this study was to assess the combined effects of progressive resistance training (PRT) and protein supplementation beyond habitual intakes on upper leg lean tissue mass (LTM), muscle quality and functional capability in healthy 50–70 years women. In a single-blinded, randomized, controlled design, 57 healthy older women (age 61.1 ± 5.1 years, 1.61 ± 0.65 m, 65.3 ± 15.3 kg) consumed 0.33 g/kg body mass of a milk-based protein matrix (PRO) for 12 weeks. Of the 57 women, 29 also engaged in a PRT intervention (PRO + PRT). In comparison to the PRO group (n = 28), those in the PRO + PRT group had an increase in upper leg LTM [0.04 (95% CI −0.07 to 0.01) kg vs. 0.13 (95% CI 0.08–0.18) kg, P = 0.027], as measured by Dual-energy X-ray absorptiometry; an increase in knee extensor (KE) torque [−1.6 (95% CI −7.3 to 4.4 N m) vs. 10.2 (95% CI 4.3–15.8 N m), P = 0.007], as measured from a maximal voluntary isometric contraction (Con-Trex MJ; CMV AG); and an increase in extended gait speed [-0.01 (95% CI −0.52–0.04) m s −1 vs. 0.10 (95% CI 0.05–0.22) m s −1 , P = 0.001] as measured from a maximal 900 m effort. There was no difference between groups in the time taken to complete 5 chair rises or the number of chair rises performed in 30 s ( P > 0.05). PRT in healthy older women ingesting a dietary protein supplement is an effective strategy to improve upper leg LTM, KE torque and extended gait speed in healthy older 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".