Influence of maternal physical activity on infant's body composition
Bibliographic record
Abstract
Summary Background Physical activity (PA) during pregnancy might contribute to reduce neonatal adiposity, a predictor of metabolic disturbances. Objective The objective of the study was to evaluate the association between maternal PA intensity and neonatal body composition. Methods Maternal PA measured by accelerometry and nutrition were documented during pregnancy, as well as neonatal body composition by dual‐energy X‐ray absorptiometry following delivery. Associations between PA at 17 and 36 weeks (time spent in moderate PA (MPA), vigorous PA (VPA) status and their interaction) and neonatal body composition were addressed by multivariate regression analyses. Results From 104 women, 50 (48%) and 16 (18%) performed VPA at 17 and 36 weeks of pregnancy. Performing VPA at either time was associated with a decreased birthweight (BW), while only VPA at 17 weeks decreased neonatal adiposity (fat percentage: −2.3 ± 0.8%, p = 0.003). MPA at 36 weeks was associated with an increased lean mass (2.0 ± 0.8 g per min day −1 , p = 0.012). Significant interactions were found for BW and bone mineral content (BMC). MPA at 17 weeks tended to increase BW, but not BMC, in the no VPA strata. By contrast, high levels of MPA (≥112 min d −1 ) combined with VPA at 17 weeks reduced neonatal BMC and BW compared with no VPA (BMC: −5.4 ± 2.0 g, p = 0.008, BW: −302.8 ± 83.7 g, p = 0.0003). Differences were not significant with low MPA levels. Conclusions Exercise intensity modulates neonatal body composition. The long‐term significance of a reduced BW, adiposity and BMC with VPA requires further study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".