Effect of High Dietary Restraint on Energy Availability and Menstrual Status
Bibliographic record
Abstract
INTRODUCTION: Dietary restraint (DR) is a key eating behavior associated with menstrual disturbances (MD) in exercising women. However, the association between DR and energy availability (EA) has not been examined. PURPOSES: The objective of this study is 1) to compare EA in women when categorized by DR score, to include an evaluation of the frequency of women with low EA, and 2) to compare the distribution of subclinical and clinical MD between DR groups. METHODS: Exercising women (23 ± 4 yr; body mass index, 21.1 ± 1.9 kg·m; and exercise volume, 333 ± 198 min·wk) were retrospectively categorized by DR score into two groups: 1) women with high DR (n = 30) and 2) women with normal DR (n = 56). DR scores were obtained from the Three-Factor Eating Questionnaire. High DR score was defined as ≥13. Body composition was measured using dual-energy x-ray absorptiometry. EA was defined as energy intake - exercise energy expenditure per kilogram lean body mass (LBM). Low EA was defined as <30 kcal·kg LBM. Menstrual status was determined using daily urinary samples assayed for reproductive hormones. RESULTS: EA was lower in the high DR versus the normal DR group (35.0 ± 12.9 vs 42.0 ± 12.9 kcal·kg LBM, P = 0.018). There was no difference (P = 0.866) in frequency of low EA between DR groups. There was a greater frequency of MD (amenorrhea, oligomenorrhea, anovulation, or luteal phase defect) in the high DR group (21/28, 75.0%) versus the normal DR group (24/47, 51.1%) (χ = 4.2, P = 0.041). CONCLUSION: Our findings demonstrate that exercising women with high DR exhibited lower EA and a greater frequency of MD (subclinical and clinical) compared with women with normal DR. However, high DR was not associated with low EA in exercising women.
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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".