Physical activity during pregnancy and the risk of gestational diabetes mellitus: a systematic review and dose–response meta-analysis
Bibliographic record
Abstract
Abstract Background Previous research has indicated the inverse association between physical activity (PA) and gestational diabetes mellitus (GDM). However, the dose–response relationship currently remains undetermined. This study aims to explore the dose–response relationship between PA during the first and second trimesters of pregnancy and GDM risk. Methods Studies on the relationship between PA during pregnancy and GDM risk published before April 25, 2023, were searched for in six databases. According to the inclusion and exclusion criteria, all literature was screened for eligibility. The Newcastle-Ottawa Scale (NOS) was used to assess risk of bias. Publication bias was examined using funnel plots, Begg’s and Egger’s tests, as well as trim-and-fill analysis. We harmonized exposure estimates of PA during pregnancy to the common unit of the metabolic equivalent of task (MET)-h/week. Restricted cubic splines were used to model the dose–response relationship. The criteria from the World Cancer Research Fund were used to assess the certainty of evidence across outcomes. All analyses were performed using Stata 15.1. Results The results indicated that in contrast with the lowest level of PA, promoting the highest PA level lowers the risk of GDM by 36% (RR = 0.64, 95%CI: 0.53 ~ 0.78). We found a curvilinear dose–response association between PA during the first trimester and incident GDM (Pnonlinearity = 0.012). Compared to inactive pregnant women, for those who achieved the guidelines-suggested minimum level (10 MET-h/week) of PA during the first trimester, the GDM risk was decreased by 13% (RR = 0.87, 95%CI: 0.79 ~ 0.96). A linear relationship was found between PA during the second trimester and the GDM risk (Pnonlinearity = 0.276). The results with a restricted cubic spline model suggested that pregnant women who accumulate 10 MET-h/week have a 1% reduced risk of GDM compared to completely inactive individuals. Twice (20 MET-h/week) or a higher amount of PA (50 MET-h/week) contributed to further reductions in GDM risk. Conclusion There is a dose–response relationship between higher levels of PA in both the first and second trimesters and reduced risk of GDM; the relationship is stronger in the first trimester. Increasing PA during pregnancy can prevent the development of GDM. PROSPERO registration number CRD42023420564.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".