Lifestyle intervention on diet and exercise reduced excessive gestational weight gain in pregnant women under a randomised controlled trial
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of an exercise and dietary intervention during pregnancy on excessive gestational weight gain (EGWG), dietary habit and physical activity in pregnant women. DESIGN: Randomised controlled trial. SETTING: Community-based study. POPULATION: Nondiabetic urban-living pregnant women (<26 weeks of gestation). METHODS: Participants in the intervention group were provided with community-based group exercise sessions, instructed home exercise and dietary counselling between 20 and 36 weeks of gestation. Participants in both groups received physical activity and food intake surveys at enrolment and 2 months after the enrolment. MAIN OUTCOME MEASURES: Prevalence of EGWG and measures of physical activity and food intakes between the two groups. RESULTS: A total of 190 pregnant women, 88 in the control group and 102 in the intervention group, completed the study. Decreased daily intakes of calorie, fat, saturated fat and cholesterol were detected in participants in the intervention group at 2 months after enrolment compared with the control group (P<0.01). Participants in the intervention group had higher physical activity 2 months after enrolment compared with the control group (P<0.01). The lifestyle intervention during pregnancy reduced the prevalence of EGWG in the intervention group compared with the control group (P<0.01) according to the guidelines of the Institute of Medicine. CONCLUSION: The findings suggest that lifestyle intervention during pregnancy increased physical activity, improved dietary habits and reduced EGWG in urban-living pregnant women.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".