The Effectiveness Of Creatine Combined With Protein Supplementation During Resistance Training In Older Men
Bibliographic record
Abstract
PURPOSE To determine the combined effect of creatine and protein supplementation during resistance training (RT) in older men (66y). METHODS Older men were randomized to receive creatine, protein, and sucrose (CP; 0.1 g/kg body mass creatine, 0.3g/kg protein, and 0.8g/kg sucrose; n=10), creatine and sucrose (C; 0.1 g/kg creatine, and 1.1 g/kg sucrose; n=13), or placebo (PLA; 1.2 g/kg sucrose, n=10) on training days. Resistance training consisted of 9 exercises (3x/wk, 10 wks). Prior to and following RT, measurements were made for body composition (air displacement plethysmography), and muscle thickness (ultrasound) of the elbow, knee, and ankle flexors and extensors, and strength (1-RM for leg press and bench press). RESULTS Lean tissue mass increased in all three groups (CP +3.2 kg, 5.1%; C +1.3 kg, 2.2%; PLA +0.6 kg, 1.1%) with the increase for the CP group being greater than PLA (p<0.05). When creatine groups (CP and C) were combined, their increase in total muscle thickness (+2 cm, 8.5%) was greater than PLA (+0.8 cm, 3.3%) (p<0.05). Bench press 1-RM increased with training in all three groups (CP +19 kg, 20%; C +10 kg, 11%, PLA +10 kg, 9%), with a trend for the CP group to have the greatest increase (P=0.09). Leg press 1-RM increased with training (p<0.05) with all three groups increasing by similar amounts (CP +20 kg, 11%; C +20 kg, 10%; PLA +21 kg, 10%). CONCLUSIONS Creatine supplementation is effective for increasing muscle thickness during resistance training. Creatine is most effective for increasing lean tissue mass when combined with protein. Despite the increase in muscle thickness and lean tissue mass with creatine or creatine combined with protein, this did not translate into a significantly greater increase in muscular strength over resistance training alone. Supported by Experimental and Applied Sciences (EAS)
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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) | 0.002 | 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 | 0.001 |
| 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".