Effects of slow- <i>v.</i> fast-digested protein supplementation combined with mixed power training on muscle function and functional capacities in older men
Bibliographic record
Abstract
Ageing leads to a progressive loss of muscle function (MF) and quality (MQ: muscle strength (MS)/lean muscle mass (LM)). Power training and protein (PROT) supplementation have been proposed as efficient interventions to improve MF and MQ. Discrepancies between results appear to be mainly related to the type and/or dose of proteins used. The present study aimed at determining whether or not mixed power training (MPT) combined with fast-digested PROT (F-PROT) leads to greater improvements in MF and MQ in elderly men than MPT combined with slow-digested PROT (S-PROT) or MPT alone. Sixty elderly men (age 69 (sd 7) years; BMI 18-30 kg/m2) were randomised into three groups: (1) placebo + MPT (PLA; n 19); (2) F-PROT + MPT (n 21) and (3) S-PROT + MPT (n 20) completed the intervention. LM, handgrip and knee extensor MS and MQ, functional capacity, serum metabolic markers, skeletal muscle characteristics, dietary intake and total energy expenditure were measured. The interventions consisted in 12 weeks of MPT (3 times/week; 1 h/session) combined with a supplement (30 g:10 g per meal) of F-PROT (whey) or S-PROT (casein) or a placebo. No difference was observed among groups for age, BMI, number of steps and dietary intake pre- and post-intervention. All groups improved significantly their LM, lower limb MS/MQ, functional capacity, muscle characteristics and serum parameters following the MPT. Importantly, no difference between groups was observed following the MPT. Altogether, adding 30 g PROT/d to MPT, regardless of the type, does not provide additional benefits to MPT alone in older men ingesting an adequate (i.e. above RDA) amount of protein per d.
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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 | 0.000 |
| 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) | 0.002 | 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".