Exercising women with menstrual disturbances consume low energy dense foods and beverages
Bibliographic record
Abstract
Women with exercise-associated menstrual cycle disturbances (EAMD) restrict energy intake. Reducing energy density (ED; kcals·g(-1) of food or beverage) may be a strategy employed by EAMD women to maintain lower energy intake. The purpose of this study was 3-fold: to determine whether EAMD women consume low ED diets; to identify food groups associated with low ED; and to determine concentrations of total peptide YY (PYY), a satiety factor. Twenty-five active females were divided into 2 groups, according to menstrual status: EAMD (n = 12) and ovulatory controls (OV) (n = 13). Two 3-day diet records were analyzed for ED and other parameters. Body composition, fitness, resting metabolic rate, and PYY were measured. Groups did not differ in age, age of menarche, body mass index, maximal aerobic capacity(), body fat (%), or amount of exercise per week. For fat mass (12.4 ± 1.7 vs. 14.9 ± 3.5 kg; p = 0.046), energy availability (28.8 ± 11.5 vs. 42.1 ± 9.2 kcal·kg(-1) FFM; p = 0.006), and energy intake (29.8 ± 9.2 vs. 36.3 ± 10.6 kcals·kg(-1) BW; p = 0.023), EAMD was lower than OV. ED was lower in EAMD than in OV (0.77 ± 0.06 vs. 1.06 ± 0.09 kcal·g(-1); p = 0.018) when all beverages were included, but not when noncaloric beverages were excluded. Vegetable (p = 0.047) and condiment (p = 0.014) consumption and fasting PYY (pg·mL(-1)) (p = 0.006) were higher in EAMD. EAMD ate a lower ED diet through increased vegetable, condiment, and noncaloric beverage consumption, and exhibited higher PYY concentrations. These behaviors may represent a successful strategy to restrict calories and maximize satiety.
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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 | 0.000 |
| 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".