The Effect of the Menstrual Cycle on Energy Intake: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
CONTEXT: Energy intake may differ across the menstrual cycle, with some studies identifying greater energy intake in the luteal phase (LP) compared with the follicular phase (FP) and others finding no clear differences. To date, no study has systematically synthesized the available data to draw more definite conclusions while considering any methodological inconsistencies between studies. OBJECTIVE: The aim was to conduct a systematic review/meta-analysis in an effort to determine if there are differences in energy intake between the FP and LP. DATA SOURCES: A systematic search strategy was developed and the search was conducted in 5 databases for studies that investigated any changes in energy intake across menstrual phases. DATA EXTRACTION: Using Covidence, studies were identified and included if they contained individuals between the ages of 18 and 45 years, maintained an average body mass index (BMI) of 18.5-25 kg/m2, had no history of disordered eating, and included energy intake and menstrual cycle measurements in the FP and LP. DATA ANALYSIS: Effect sizes were calculated for each study and a random-effects model was used to pool the results of each study. RESULTS: Fifteen datasets were included consisting of 330 female participants with a mean age of 26 ± 4 years and mean BMI of 22.4 ± 2.3 kg/m2. Overall, there was a statistically significant difference (standardized mean difference = 0.69; P = .039) with increased energy intake in the LP compared with the FP (crude 168 kcal⋅d-1 average difference between phases). CONCLUSION: Energy intake was found to be greater in the LP compared with the FP, providing insight into the effect of the menstrual cycle on energy intake. However, there were repeated methodological inconsistencies and future work should strive to utilize best practices for both energy intake measurement and menstrual phase specification.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".