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 concentrations and synchronous exogenous profiles. The purpose of this study was to determine if RMR and dual-energy X-ray absorptiometry body composition estimates change across the MC and differ compared with HC users. This was accomplished during a 5-week training camp involving naturally cycling athletes (n = 11) and HC users (n = 7 subdermal progestin implant, n = 4 combined monophasic oral contraceptive pill, n = 1 injection) from the National Rugby League Indigenous Women's Academy. MC phase was retrospectively confirmed via serum estradiol and progesterone concentrations and a positive ovulation test. HC users had serum estradiol and progesterone concentrations assessed at the time point of testing. Results were analyzed using general linear mixed model. There was no effect of MC phase on absolute RMR (p = .877), relative RMR (p = .957), or dual-energy X-ray absorptiometry body composition estimates (p > .05). There was no effect of HC use on absolute RMR (p = .069), relative RMR (p = .679), or fat mass estimates (p = .766), but HC users had a greater fat-free mass and lean body mass than naturally cycling athletes (p = .028). Our findings suggest that RMR and dual-energy X-ray absorptiometry body composition estimates do not significantly differ due to changes in sex hormones in a group of athletes, and measurements can be compared between MC phases or with HC usage without variations in sex hormones causing additional noise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".