Exercise during pregnancy enhances cerebral maturation in the newborn: A randomized controlled trial
Bibliographic record
Abstract
Accumulating research indicates that the regular practice of physical exercise is beneficial to the human brain. From the improvement of academic achievement in children to the prevention of Alzheimer's disease in the elderly, exercise appears beneficial across the developmental spectrum. Recent work from animal studies also indicates that a pregnant mother can transfer the benefits of exercise during gestation to her offspring's brain. Exercising pregnant rats give birth to pups that have better memory and spatial learning as well as increased synaptic density. To investigate whether this transfer from the pregnant mother to her child also occurs in humans, we conducted a randomized controlled trial (n = 18) and measured the impact of exercise during pregnancy on the neuroelectric response of the neonatal brain with electroencephalography (EEG). Here we show that, compared to the newborns of mothers who were inactive during their pregnancy, the children of exercising pregnant women are born with more mature brains. This was measured with the infant slow positive mismatch response (SPMMR), an electroencephalographic potential known to decrease in amplitude with age. The SPMMR reflects processes associated with brain maturation via its response to sound discrimination and auditory memory. In this study, the children of the mothers who exercised throughout their pregnancy have a smaller SPMMR than the children of mothers who remained sedentary (p = .019). Our results demonstrate the impact regular exercise during pregnancy can have on the development of the human fetal brain.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".