An <scp>eHealth</scp> Intervention in Pregnancy on Maternal Body Composition and Subsequent Perinatal Outcomes: A Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of a pragmatic multicomponent eHealth intervention in pregnancy on body composition changes and subsequent associations with perinatal outcomes. METHODS: Pregnant individuals (n = 351) enrolled in Louisiana's Women, Infants, and Children program were randomly assigned to a multicomponent eHealth Intervention or Usual Care. Fat percentage, fat mass, and fat-free mass were assessed using bioelectrical impedance at trimester-specific study visits. Mixed models evaluated within- and between-group differences in body composition from early to late pregnancy: overall, by BMI, and by gestational weight gain (GWG) guideline attainment. Effects of body composition changes on perinatal outcomes was evaluated. RESULTS: Compared to Usual Care (n = 172), the Intervention Group (n = 179) had attenuated gains in fat mass, fat mass index, and fat percentage from early to late pregnancy overall, in individuals who had normal weight at enrollment, and in those who exceeded GWG guidelines (p < 0.05). No significant between-group differences in fat-free mass were observed. Fat mass change interacted with intervention effects on neonatal health outcomes (p = 0.01). CONCLUSIONS: Lifestyle interventions during pregnancy may attenuate gestational fat mass gain, particularly among women with normal weight and those who exceed GWG guidelines, with potential implications for neonatal health outcomes. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04028843.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".