Caffeine Does Not Affect Improvements in Cognition During Prolonged High-Intensity Exercise in Alert Well-Trained Individuals
Bibliographic record
Abstract
Background: Recent research has demonstrated improvements in cognitive function during bouts of high-intensity endurance exercise. Caffeine, the most commonly used ergogenic aid in the world, can improve cognition both at rest and during exercise and can also improve exercise performance during simulated cycling races. Purpose: To determine if caffeine ingestion in conjunction with prolonged high-intensity exercise can synergistically improve cognitive function, and whether the type of exercise trial affects the cognitive response. Methods: Seven well-trained cyclists and triathletes (26.9 ± 3.9 years, VO2 peak 67.7 ± 10.3 mL/kg/h) completed two trials to exhaustion (TTE) at 90% VO2 peak and two 50 km time trials (TTD), 1 hour after consuming a carbohydrate–electrolyte beverage (5 mL/kg, 6.3% CHO, 18 mM sodium) and a capsule containing either 6 mg/kg of caffeine or a placebo (double-blind crossover design). A second CHO beverage was consumed at the onset of each trial. Cognitive function was measured before and after exercise using a computerized ANAM® test and Stroop word–color test (also conducted during exercise). Results: Average wakefulness scores at baseline indicated that subjects were at near peak alertness. Caffeine ingestion did not influence exercise performance, ANAM thruput scores, or Stroop word–color test response times in either trial type. However, exercise significantly improved ANAM and Stroop cognitive results, regardless of exercise trial. Conclusion: In alert well-trained athletes, high-intensity endurance exercise can significantly improve tests of simple and complex cognitive function. Neither exercise trial type nor caffeine ingestion impacts the exercise-induced changes in cognitive function.
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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 | 0.001 |
| 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 | 0.001 | 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".