The Effect of Resistance Training and Different Sources of Postexercise Protein Supplementation on Muscle Mass and Physical Capacity in Sarcopenic Elderly Men
Bibliographic record
Abstract
The loss of muscle mass (sarcopenia) with aging is related to a progressive loss of muscle strength and physical capacity. Resistance exercise and milk-based protein supplementation have been demonstrated as significant countermeasures for sarcopenia and the loss of muscle strength. However, using high doses of proteins can act as a meal replacement in the elderly. Therefore, we sought to determine whether a standard supplementation (12 g per serving) of protein and resistance training could be an efficient strategy to promote muscle strength and physical capacity in sarcopenic men. Twenty-six participants were randomized in 3 groups in a double-blind control study. All the groups performed exercise and consumed a protein-rich supplement 12 g of protein, 7 g of essential amino acids from milk (n = 8), soy (n = 8), or rice milk (nonprotein control, n = 10). Body composition was measured using dual-energy x-ray absorptiometry. Strength was measured by 1 repetition maximum with different exercises. Different physical capacity measurements were assessed (timed up and go test, chair stand, and walking speed). The results indicated a significant increase in fat-free mass in all groups and changes in muscle strength, with no differences between groups. This study indicates that resistance training is an effective way to increase muscle mass and strength, regardless of protein supplementation. Higher doses of protein-rich foods may have to be recommended to promote muscle mass gains when executing resistance exercise in elderly sarcopenic individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".