Exercise throughout Pregnancy Prevents Excessive Maternal Weight Gain during the COVID-19 Pandemic: A Randomized Clinical Trial
Bibliographic record
Abstract
The purpose of this study was to examine the effects of a virtual exercise program throughout pregnancy during the COVID-19 pandemic on maternal weight gain. A randomized clinical trial (NCT NCT04563065) was performed. In total, 300 pregnant individuals were assessed for eligibility, and a total of 157 were randomized, of which 79 were in the control group (CG), and 78 were in the intervention group (IG). Those in the intervention group participated in a virtual supervised exercise program throughout pregnancy, 3 days per week. Fewer pregnant participants exceeded the weight gain recommendations in the IG group than in the CG (n = 4/5.9% vs. n = 31/43.1%, p = 0.001). Weight gain during pregnancy was lower in the IG than in the CG (9.96 ± 3.27 kg vs. 12.48 ± 4.87 kg, p = 0.001). Analysis of subgroups based on pre-pregnancy body mass index, showed significant differences in excessive maternal weight gain between study groups in normal-weight (IG, n = 0/0% vs. CG, n = 10/25%, p = 0.001) and those with overweight (IG, n = 2/18% vs. CG, n = 12/60%, p = 0.025). A virtual supervised exercise program throughout pregnancy could be a clinical tool to manage maternal weight gain during the COVID-19 pandemic by controlling excessive gain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".