Effect Of Menstrual Cycle Phase And Hormonal Contraceptives On Resting Metabolic Rate And Body Composition
Bibliographic record
Abstract
The cyclical changes in sex hormones across the menstrual cycle (MC) are associated with various biological changes that may alter resting metabolic rate (RMR) and body composition estimates. Hormonal contraceptive (HC) use must also be considered given their impact on endogenous sex hormone levels. Understanding if MC phase or HC use affect RMR may have implications for the energy needs of female athletes, as well as the accurate measurement and interpretation of RMR and body composition. PURPOSE: To determine if RMR and body composition change across the MC and with HC usage. METHODS: During a 5-week training camp involving the National Rugby League Indigenous Women’s Academy, RMR was measured using indirect calorimetry and body composition using dual-energy X-ray absorptiometry. Measurements occurred during Phase 1 (low estrogen and progesterone) and Phase 4 (medium estrogen and high progesterone) of naturally menstruating athletes (n = 8; 21 ± 3 yr). HC users (n = 12; 22 ± 4 yr; n = 7 implants, n = 1 injection, n = 4 oral contraceptive pill) were tested on two occasions, avoiding the withdrawal bleed. Naturally menstruating athletes tracked their MC for 8 weeks prior to the study and MC phase was confirmed retrospectively by assessing serum estrogen and progesterone. Results were analyzed using general linear mixed models. RESULTS: Relative RMR did not differ between MC phase (Phase 1: 33.8 ± 2.6 kcal/kg fat free mass (FFM)/day vs. Phase 4: 33.9 ± 2.8 kcal/kg FFM/day; p = 0.723) or within HC users (32.8 ± 3.0 kcal/kg FFM/day vs. 32.1 ± 2.4 kcal/kg FFM/day; p = 0.185). Similarly, no change in absolute RMR across MC phase (p = 0.577) or between groups was observed (p = 0.075). Neither FFM nor fat mass changed across MC phase (p > 0.05). While both groups had reductions in fat mass during the training camp (p < 0.001), FFM increased in HC users (+0.9 kg; p < 0.001), but not in naturally menstruating athletes (+0.3 kg; p = 0.431). CONCLUSION: Our findings suggest that RMR and body composition do not significantly differ between Phase 1 and Phase 4 of the menstrual cycle, when estrogen and progesterone are significantly higher, or with HC use. This project was co-funded by the Australian Catholic University, Wu Tsai Human Performance Alliance and the Australian Institute of Sport Female Athlete Performance and Health Initiative.
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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".