Pre-Conception Interventions for Subfertile Couples Undergoing Assisted Reproductive Technology Treatment: Modeling Analysis
Bibliographic record
Abstract
BACKGROUND: Approximately 1 in 7 couples experience subfertility, many of whom have lifestyles that negatively affect fertility, such as poor nutrition, low physical activity, obesity, smoking, or alcohol consumption. Reducing lifestyle risk factors prior to pregnancy or assisted reproductive technology treatment contributes to the improvement of reproductive health, but cost-implications are unknown. OBJECTIVE: The goal of this study was to evaluate reproductive, maternal pregnancy, and birth outcomes, as well as the costs of pre-conception lifestyle intervention programs in subfertile couples and obese women undergoing assisted reproductive technology. METHODS: Using a hypothetical model based on quantitative parameters from published literature and expert opinion, we evaluated the following lifestyle intervention programs: (1) Smarter Pregnancy, an online tool; (2) LIFEstyle, which provides outpatient support for obese women; (3) concurrent use of both Smarter Pregnancy and LIFEstyle for obese women; (4) smoking cessation in men; and (5) a mindfulness mental health support program using group therapy sessions. The model population was based on data from the Netherlands. RESULTS: All model-based analyses of the lifestyle interventions showed a reduction in the number of in vitro fertilization, intracytoplasmic sperm injection, or intrauterine insemination treatments required to achieve pregnancy and successful birth for couples in the Netherlands. Smarter Pregnancy was modeled to have the largest increase in spontaneous pregnancy rate (13.0%) and the largest absolute reduction in potential assisted reproductive technology treatments. Among obese subfertile women, LIFEstyle was modeled to show a reduction in the occurrence of gestational diabetes, maternal hypertensive pregnancy complications, and preterm births by 4.4%, 3.8%, and 3.0%, respectively, per couple. Modeled cost savings per couple per year were €41 (US $48.66), €360 (US $427.23), €513 (US $608.80), €586 (US $695.43), and €1163 (US $1380.18) for smoking cessation, mindfulness, Smarter Pregnancy, combined Smarter Pregnancy AND LIFEstyle, and LIFEstyle interventions, respectively. CONCLUSIONS: Although we modeled the potential impact on reproductive outcomes and costs of fertility treatment rather than collecting real-world data, our model suggests that of the lifestyle interventions for encouraging healthier behaviors, all are likely to be cost effective and appear to have positive effects on reproductive, maternal pregnancy, and birth outcomes. Further real-world data are required to determine the cost-effectiveness of pre-conception lifestyle interventions, including mobile apps and web-based tools that help improve lifestyle, and their effects on reproductive health. We believe that further implementation of the lifestyle app Smarter Pregnancy designed for subfertile couples seeking assistance to become pregnant is likely to be cost-effective and would allow reproductive health outcomes to be collected.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".