Be Healthy in Pregnancy (BHIP): A Randomized Controlled Trial of Nutrition and Exercise Intervention from Early Pregnancy to Achieve Recommended Gestational Weight Gain
Bibliographic record
Abstract
A randomized two-arm prospective superiority trial tested the efficacy of a novel structured and monitored nutrition (bi-weekly counselling for individualized energy and high dairy protein diet) and exercise program (walking goal of 10,000 steps/day) (intervention) compared to usual care (control) in pregnant women to achieve gestational weight gain (GWG) within current recommendations. Women recruited in communities in southern Ontario, Canada were randomized at 12–17 weeks gestation with stratification by site and pre-pregnancy BMI to intervention (n = 119) or control (n = 122). The primary outcome was the proportion of women who achieved GWG within the Institute of Medicine recommendations. Although the intervention compared to control group was more likely to achieve GWG within recommendations (OR = 1.51; 95% CI (0.81, 2.80)) and total GWG was lower by 1.45 kg (95% CI: (−11.9, 8.88)) neither reached statistical significance. The intervention group achieved significantly higher protein intake at 26–28 week (mean difference (MD); 15.0 g/day; 95% CI (8.1, 21.9)) and 36–38 week gestation (MD = 15.2 g/day; 95% CI (9.4, 21.1)) and higher healthy diet scores (22.5 ± 6.9 vs. 18.7 ± 8.5, p < 0.005) but step counts were similar averaging 6335 steps/day. Pregnancy and infant birth outcomes were similar between groups. While the structured and monitored nutrition with counselling improved diet quality and protein intake and may have benefited GWG, the exercise goal of 10,000 steps/day was unachievable. The results can inform future recommendations for diet and physical activity in pregnancy.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".