Ketone Ester Supplementation Improves Some Aspects of Cognitive Function during a Simulated Soccer Match after Induced Mental Fatigue
Bibliographic record
Abstract
Ketone supplementation has been proposed to enhance cognition during exercise. To assess whether any benefits are due to reduced cognitive fatigue during the latter portions of typical sport game action, we induced cognitive fatigue, provided a ketone monoester supplement (KME) vs. a non-caloric placebo (PLAC), and assessed cognitive performance during a simulated soccer match (SSM). In a double-blind, balanced, crossover design, nine recreationally active men (174.3 ± 4.2 cm, 76.6 ± 7.4 kg, 30 ± 3 y, 14.2 ± 5.5 % body fat, V˙O2 max = 55 ± 5 mL·kg BM−1·min−1; mean ± SD) completed a 45-min SSM (3 blocks of intermittent, variable intensity exercise) consuming either KME (25 g) or PLAC, after a 40-min mental fatiguing task. Cognitive function (Stroop and Choice Reaction Task [CRT]) and blood metabolites were measured throughout the match. KME reduced concentrations of both blood glucose (block 2: 4.6 vs. 5.2 mM, p = 0.02; block 3: 4.7 vs. 5.3 mM, p = 0.01) and blood lactate (block 1: 4.7 vs. 5.4 mM, p = 0.05; block 2: 4.9 vs. 5.9 mM, p = 0.01) during the SSM vs. PLAC, perhaps indicating a CHO sparing effect. Both treatments resulted in impaired CRT performance during the SSM relative to baseline, but KME displayed a reduced (p < 0.05) performance decrease compared to PLAC (1.3 vs. 3.4% reduction in correct answers, p = 0.02). No other differences in cognitive function were seen. These data suggest that KME supplementation attenuated decrements in CRT during repeated, high intensity, intermittent exercise. More study is warranted to assess fully the potential cognitive/physical benefits of KME for athletes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".