Hormonal contraceptive use, menstrual cycle characteristics and training/nutrition related profiles of elite, sub-elite and amateur athletes and exercisers: One size is unlikely to fit all
Bibliographic record
Abstract
The aim of the present study was to simultaneously audit hormonal contraceptive (HC) use, menstrual cycle characteristics and training/nutrition related profiles in pre-menopausal women from varying athletic and exercise backgrounds. Elite ( n = 51), sub-elite ( n = 118) and amateur ( n = 392) female athletes and exercisers were examined via an anonymous quantitative/qualitative survey tool. All analyses for ratio data were conducted utilising one- and two-way ANOVA/ANCOVA and odds ratio models, with ordinal data analysed via Pearson's Chi-squared tests. HC use was similar across elite, sub-elite and amateurs (34–44%). Menstrual cycle length was not different ( P = 0.08) between competitive levels (28 ± 13 days), but 66% of respondents reported cycle variability (10 ± 11 days). Training profiles were not different based on contraceptive status ( P > 0.05) yet were across competitive groups ( P < 0.05). Daily meal/snack intakes were not different between contraceptive status ( P > 0.05), though elite and sub-elite groups reported a higher daily meal consumption compared to amateurs ( P < 0.01). Forty percent of all respondents skipped meals, attributed to lack of time, schedule and alterations in appetite, with reported changes in taste preferences during pre-menses (14–35%) and menses (15–25%) towards sweet foods (60%), with cravings for chocolate and other confectionary (25%). Prevalence of dietary supplement use was not influenced by contraceptive status ( P = 0.31), though elites (76%) reported higher use ( P = 0.04) than amateurs (63%). Data demonstrates that in female athletes and exercisers, competitive level appears to affect simple markers of training and nutrition practice, yet contraceptive status does not.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".