What is the optimal level of cardiorespiratory fitness for cognitive outcomes in females: the inverted “U” theory
Bibliographic record
Abstract
Cardiorespiratory fitness (CRF) is related to health outcomes and has been linked to cognitive function improvement through mechanisms such as increased brain activation and hormone release. However, an inverted “U” relationship suggests that extremely high levels of CRF may be associated with reduced cognitive performance, and further studies are needed to clarify this pattern. This study aimed to explore the relationship between CRF and cognitive outcomes in females while controlling for estradiol levels. Thirty-three females aged 18–30 years underwent assessments including CRF measurement using a graded exercise test and an indirect calorimeter, cognitive tests (Corsi Block, Stroop), and blood collection for Brain Derived Neurotrophic Factor (BDNF) and estradiol quantification. Data were analysed using multiple linear and quadratic regressions, controlling for covariates such as fat mass and estradiol. Significant correlations were found between CRF and working memory (Block span: r = 0.415; Total score: r = 0.462), and between estradiol and BDNF (r = 0.39). Linear regression showed that CRF independently predicted variations in working memory (R2 = 21.4%) despite covariates. Quadratic regression indicated an inverted “U” relationship between CRF and inhibitory control accuracy, suggesting optimal cognitive performance at approximately 47 ml.kg−1.min−1 of VO2max. This study underscores the importance of maintaining optimal CRF levels to foster cognitive health. The findings support an association between CRF and working memory, while highlighting the complex relationship with inhibitory control. Future research should focus on intervention studies to establish causal relationships.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".