Perceived Negative Menstrual Cycle Symptoms, But Not Changes in Estrogen or Progesterone, Are Associated with Impaired Cycling Race Performance
Bibliographic record
Abstract
PURPOSE: To examine the relationship between menstrual cycle (MC) phase-dependent fluctuations of estrogen and progesterone and virtual cycling race performance, with a secondary aim of correlating perceived MC-related symptoms with performance. METHODS: In a novel observational study design, 37 female cyclists/triathletes not using any hormonal contraception completed one virtual cycling race (19.5-km time trial (TT)) per week across a 1-month period (totaling four races). Participants completed MC characterization and tracking, including urinary ovulation kits, across two complete MCs. Venous blood samples were collected within 21 h of racing to determine serum 17-β-estradiol and progesterone concentrations, as well as an assessment of self-reported, perceived race-day MC and gastrointestinal (GI) symptoms, which were all then correlated to race performance. RESULTS: There was no relationship between race completion time and individual estradiol ( r = -0.001, P = 0.992) or progesterone ( r = -0.023, P = 0.833) concentrations. There was no difference between race time between MC phases (follicular/luteal, P = 0.238), whether MC bleeding or not bleeding ( P = 0.619), and whether ovulating or not ovulating ( P = 0.423). The total number of perceived MC symptoms recorded on race day was positively correlated to increased race time ( r = 0.268 (95% confidence interval, 0.056-0.457), P = 0.014), as was the number of GI symptoms of at least "moderate" severity before the race ( r = 0.233 (95% confidence interval, 0.021-0.425), P = 0.031), but not post-race ( r = 0.022, P = 0.841). CONCLUSIONS: When implementing a novel, virtual cycling race, fluctuations in ovarian hormone concentrations across the MC do not appear to affect real-world cycling performance among trained cyclists, whereas perceived negative MC and GI symptoms may relate to impaired performance. Therefore, the management of negative MC and GI symptoms appears important for athletic performance enhancement or to mitigate performance decline.
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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 | 0.002 |
| 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) | 0.002 | 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".