Creatine supplementation and endurance performance: surges and sprints to win the race
Bibliographic record
Abstract
Creatine supplementation is an effective ergogenic aid to augment resistance training and improve intense, short duration, intermittent performance. The effects on endurance performance are less known. The purpose of this brief narrative review is to discuss the potential mechanisms of how creatine can affect endurance performance, defined as large muscle mass activities that are cyclical in nature and are >~3 min in duration, and to highlight specific nuances within the literature. Mechanistically, creatine supplementation elevates skeletal muscle phosphocreatine (PCr) stores facilitating a greater capacity to rapidly resynthesize ATP and buffer hydrogen ion accumulation. When co-ingested with carbohydrates, creatine enhances glycogen resynthesis and content, an important fuel to support high-intensity aerobic exercise. In addition, creatine lowers inflammation and oxidative stress and has the potential to increase mitochondrial biogenesis. In contrast, creatine supplementation increases body mass, which may offset the potential positive effects, particularly in weight-bearing activities. Overall, creatine supplementation increases time to exhaustion during high-intensity endurance activities, likely due to increasing anaerobic work capacity. In terms of time trial performances, results are mixed; however, creatine supplementation appears to be more effective at improving performances that require multiple surges in intensity and/or during end spurts, which are often key race-defining moments. Given creatines ability to enhance anaerobic work capacity and performance through repeated surges in intensity, creatine supplementation may be beneficial for sports, such as cross-country skiing, mountain biking, cycling, triathlon, and for short-duration events where end-spurts are critical for performance, such as rowing, kayaking, and track cycling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".