Building muscle: nutrition to maximize bulk and strength adaptations to resistance exercise training
Bibliographic record
Abstract
Abstract Several nutritional strategies can optimize muscle bulk and strength adaptations and enhance recovery from heavy training sessions. Adequate energy intake to meet the needs of training and carbohydrate intake sufficient to maintain glycogen stores (>7 g carbohydrate·kg −1 ·day −1 for women; >8 g carbohydrate·kg −1 ·day −1 for men) are important. Dietary protein intake for top sport athletes should include some foods with high biological value, with a maximum requirement of approximately 1.7 g·kg −1 ·day −1 being easily met with an energy sufficient diet. The early provision of carbohydrate (>1 g·kg −1 ) and protein (>10 g) early after an exercise session will enhance protein balance and optimize glycogen repletion. Creatine monohydrate supplementation over several days increases body mass through water retention and can increase high‐intensity repetitive ergometer performance. Creatine supplementation can enhance total body and lean fat free mass gains during resistance exercise training; however, strength gains do not appear to be enhanced versus an optimal nutritional strategy (immediate post‐exercise protein and carbohydrate). Some studies have suggested that β‐OH‐methyl butyric acid (β‐HMB) can enhance gains made through resistance exercise training; however, it has not been compared “head to head” with optimal nutritional practices. Overall, the most effective way to increase strength and bulk is to perform sport‐specific resistance exercise training with the provision of adequate energy, carbohydrate, and protein. Creatine monohydrate and β‐HMB supplementation may enhance the strength gains made through training by a small margin but the trade‐off is likely to be greater bulk, which may be ergolytic for any athlete participating in a weight‐supported activity.
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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".