The current landscape of exercise and female fertility research: a narrative review
Bibliographic record
Abstract
In brief: Females with obesity may experience infertility and can improve their fertility through exercise. This review found that most exercise interventions improve fertility outcomes regardless of technique, intensity, or duration. More detailed reporting through the lens of exercise prescription should be included in future studies. Abstract: Female infertility disproportionately affects people with obesity. Exercise often improves fertility outcomes for this population, however, there is limited prescriptive evidence. Specifically, there is a lack of information on the ideal type, frequency, intensity, and setting of exercise to improve fertility outcomes. Using principles of exercise prescription, this review aimed to describe the scope of exercise interventions that have been explored and fertility outcomes measured for people with female infertility and obesity. A search was completed in PubMed, Embase, Cochrane, and CINAHL, identifying 16 relevant published articles. Overall, exercise had a positive impact on female fertility outcomes in people with obesity, though there were large variations in the exercise interventions prescribed and outcomes measured. Cyclic exercise (i.e. walking and cycling) was the most common technique incorporated, though a combination of cyclic, acyclic (i.e. circuit training and boot camp), or individualization was often used. Several fertility outcomes were reported; however, the rate of conception, pregnancy, and live birth rates were the most common, which, we suggest, should always be reported in fertility intervention research. We stress that future studies provide more thorough descriptions of their implemented exercise interventions to facilitate reproducibility and comparisons between studies. Closer attention to the principles of exercise prescription when developing and reporting exercise interventions will help improve fertility outcomes, mainly live birth rates, for those with female infertility and obesity.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".