Impact of aerobic fitness status, menstrual cycle phase, and oral contraceptive use on exercise substrate oxidation and metabolic flexibility in females
Bibliographic record
Abstract
The influence of menstrual cycle phase and fitness status on metabolism during high-intensity interval exercise (HIIE) was assessed. Twenty-five females (24.4 (3.6) years) were categorized by normal menstrual cycle ( n = 14) vs. oral contraceptive (OC) use ( n = 11) and by aerobic fitness, high-fitness females (HFF; n = 13) vs. low-fitness females (LFF; n = 12). HIIE was four sets of four repetitions with a 3 min rest between intervals on a cycle ergometer at a power output halfway between the ventilatory threshold and V̇O2peak and performed during follicular (FOL: days 2–7 or inactive pills) and luteal phases (LUT: day ∼21 or 3rd week of active pills). Substrate oxidation was assessed via indirect calorimetry, blood lactate via finger stick, and recovery of skeletal muscle oxidative metabolism (mV̇O2) via continuous-wave near-infrared spectroscopy. HFF oxidized more fat (g·kg−1) during the full session (FOL: p = 0.050, LUT: p = 0.001), high intervals (FOL: p = 0.048, LUT: p = 0.001), low intervals (FOL: p = 0.032, LUT: p = 0.024), and LUT recovery ( p = 0.033). Carbohydrate oxidation area under the curve was greater in HFF during FOL (FOL: p = 0.049, LUT: p = 0.124). Blood lactate was lower in LFF in FOL ( p ≤ 0.05) but not in LUT. Metabolic flexibility (Δ fat oxidation g·kg−1·min−1) was greater in HFF than LFF during intervals 2–3 in FOL and 1–4 in LUT ( p ≤ 0.05). Fitness status more positively influences exercise metabolic flexibility during HIIE than cycle phase or OC use.
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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".