Pre‐pregnancy BMI, physical activity and resting energy expenditure predicts body composition in pregnancy
Bibliographic record
Abstract
A high proportion of women exceed gestational weight gain (GWG) recommendations, but the body composition of women and factors influencing these changes are not explicit. The study objective was to describe the association between pre‐pregnancy body mass index (BMI), lifestyle factors, resting energy expenditure (REE) and fat mass (FM) accretion during pregnancy. Pregnant women (n=600) were measured 2–3 times during pregnancy; data on weight, body composition (skin folds), diet (24 hour recall) and physical activity (questionnaire) were collected. Data was analysed by linear mixed regression. Women with normal and overweight BMI gained similar amounts of total FM while obese women gained less FM and had a slower rate of FM accretion (p<0.01). Sports score was inversely associated with FM (p<0.01), while REE was positively associated with FM (p<0.01). In longitudinal analyses, overweight and obese women had lower sports scores (p<0.05) and higher REE (p<0.01) compared to women with normal BMI. Energy and macronutrient intake did not differ among BMI groups. Energy intakes in overweight and obese women were less than their estimated energy requirements (p<0.01). Effective intervention programs promoting optimal GWG should account for variations in individual woman's energy expenditure. Future studies estimating energy intake requirements during pregnancy according to pre‐pregnancy BMI are warranted. Grant Funding Source : Alberta Innovates‐Health Solutions
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".