The association of physical activity during pregnancy with birthweight extremes: A scoping review
Bibliographic record
Abstract
OBJECTIVES: Birthweight (BW) indicates newborn health and is a risk factor for development of chronic diseases later in life. The aim was to investigate whether or not prenatal physical activity (PA) influences BW extremes and how PA influences BW extremes in those diagnosed with pregnancy-related diseases. DESIGN: We performed a scoping review. METHODS: Searches were completed on five databases and studies identified were uploaded to Covidence. RESULTS: Across the five databases 3114 studies were identified and after screening, 69 of these studies were used for the final review. Of the 61 studies that considered low BW (LBW)/small for gestational age (SGA) infants, the majority of results (69 %) indicated that PA during pregnancy had no significant impact on LBW or SGA infants. In addition, 11 % of studies reported a significant decrease in the prevalence of LBW infants, however two studies (3 %) reported a significant increase in LBW or SGA infants, likely relating to individuals with high body mass index and poor adherence to PA. Of the 41 studies that did report LGA/macrosomia, 34 % reported that PA significantly reduced the prevalence of higher BW infants. One study reported the association between meeting exercise recommendations and reducing the odds of LGA infants in those with pregnancy-related diseases. CONCLUSIONS: We provide evidence on the association of prenatal PA with BW extremes. It is suggested that prenatal PA does not increase the risk of delivering LBW/SGA infants and may reduce the prevalence of large BW infants. Further research is needed to confirm these relationships and explain their underlying mechanisms.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".