Predictors of gestational weight gain in western India: Findings from a longitudinal study across rural and urban cohorts
Bibliographic record
Abstract
BACKGROUND: Gestational weight gain-GWG is an important predictor of neonatal growth. However, there is dearth of literature from rural and urban India depicting longitudinal patterns and determinants of GWG. To address this gap, our objectives were to study longitudinal patterns and predictors of GWG in pregnant women residing in rural and urban areas in and around Pune city, Maharashtra, India and to compare them with pre-existing guidelines provided by IOM, 2009. METHODS: This study enrolled 268(134-rural and 134-urban) healthy singleton pregnant women attending antenatal care centers in and around Pune, India between August 2020-September 2023. Participants were measured for anthropometry and interviewed for socioeconomic status, diet, physical activity, sleep quality, and prenatal distress once in each trimester. Pre-gestational weight status was calculated using WHO, Asian-Pacific, and South Asian BMI cut-points. GWG was estimated using IOM, 2009 guidelines. FINDINGS: The observed mean GWG was 10.9 ± 4.2 kg(rural:9.9 ± 3.7, urban:11.9 ± 4.5). 61.2% of rural and 30% of urban underweight pregnant women did not gain adequate weight. 11.8% of rural and 57.3% of urban pregnant women with overweight or obese BMI exceeded recommended guideliness. Key predictors of inadequate GWG in second and third trimesters were low socio-economic status, parity, underweight pre-gestational BMI, prenatal distress, and poor sleep. The primary predictor of excessive GWG was overweight or obese pre-gestational BMI. These findings were consistent across all BMI classifications. CONCLUSION: Our findings indicate that urban underweight pregnant women gained significantly higher weight. There was health disparity between rural and urban pregnant women that needs to be addressed to improve health of pregnant women. We have identified important modifiable factors such as dietary intake, physical activity, etc. to ensure optimal GWG which can inform public health policies. Further research is needed to assess whether context-specific GWG recommendations would be beneficial as our study is based on single geographical location and timeframe.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".