Lifestyle intervention before and during fertility treatment in women with obesity or with overweight and PCOS—a RCT
Bibliographic record
Abstract
CONTEXT: Lifestyle interventions are recommended for women with obesity and infertility, but trial evidence is scarce, and studies have not investigated interventions that continue during fertility treatment. OBJECTIVE: Evaluate effectiveness of a lifestyle intervention compared with usual care on fertility outcomes in women with obesity and infertility. DESIGN: The Obesity-Fertility randomized controlled trial (RCT), at an academic fertility clinic, included 127 women aged 18 to 40 years with infertility and obesity (body mass index ≥30 kg/m2, or ≥27 kg/m2 with polycystic ovary syndrome [PCOS]), excluding those unlikely to conceive naturally. The intervention group (IG) received a 6-month lifestyle intervention alone (individual follow-up with a dietician and kinesiologist, and group sessions) before adding fertility treatments. The control group (CG) received usual fertility care directly. Rate of live birth conceived within 18 months of randomization was analyzed. RESULTS: Compared to CG, the IG lost more weight (-3.21% ± 4.73% vs -0.40% ± 3.66%, P = .003) and waist circumference (-2.62 ± 4.46 cm vs -0.23 ± 3.81 cm, P = .01) at 6 months. The live-birth rate was 44.4% in the IG (n = 63) vs 35.9% in the CG (n = 64) (risk ratio [RR] = 1.24 [95% CI 0.81-1.90]). In IG vs CG, clinical pregnancy rates were 52.4% vs 37.5% (RR = 1.40 [0.94-2.07]) and natural pregnancy rates were 27.0% vs 12.5% (RR = 2.16 [1.01-4.64]). CONCLUSION: Compared with usual care, a lifestyle intervention alone for 6 months and then combined with fertility treatments did not increase the 18-month rate of pregnancies leading to a live birth, but significantly increased natural pregnancy rates, potentially reducing the need for costly assisted reproduction in this population.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".