Exercise and Executive Function during Follicular and Luteal Menstrual Cycle Phases
Bibliographic record
Abstract
PURPOSE: A single bout of aerobic or resistance exercise improves executive function. We sought to determine whether menstrual cycle variations in ovarian hormone concentrations differentially influence the expression and/or magnitude of a postexercise executive benefit. METHODS: Eumenorrheic female participants completed 20-min single bouts of aerobic exercise (via cycle ergometer) at a moderate intensity (i.e., 80% of estimated lactate threshold) during the early follicular and midluteal phases of their menstrual cycle. Pre- and postexercise executive function was examined via antisaccades-an executive task requiring a saccade mirror-symmetrical to a visual stimulus. Antisaccades are an ideal tool for examining postexercise executive changes because the task is mediated via the same frontoparietal networks as modified following single-bout and chronic exercise. RESULTS: Antisaccade reaction times decreased from the pre- to postexercise assessments by an average of 22 ms (P = 0.003), and this benefit was independent of changes in directional errors or end point accuracy (P's > 0.26). In other words, participants did not decrease their postexercise reaction times at the cost of increased planning times or execution errors. Most notably, the postexercise antisaccade benefit did not vary in magnitude across follicular or luteal phases (P = 0.33) and a two one-sided test statistic (i.e., equivalence testing) provided support for the null hypothesis (P = 0.008). CONCLUSIONS: A postexercise executive benefit is independent of hormonal variations in the menstrual cycle. Further, our results evince that the phase of a female participant's menstrual cycle should not be a limiting factor in determining their inclusion in exercise neuroscience research.
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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 | 0.000 | 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".