Energy expenditure during pregnancy: a systematic review
Bibliographic record
Abstract
CONTEXT: Contrary to nutritional guidelines, accumulating evidence shows that pregnant women's energy intakes remain stable throughout trimesters. Although pregnant women may eat below their needs or underreport their energy intakes, it is also relevant to question how energy requirements - estimated through measurements of energy expenditure (EE) - change throughout pregnancy. OBJECTIVE: This review examined prospective studies that measured EE during pregnancy, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. DATA SOURCES: PubMed/MEDLINE, Web of Science, Embase, and CINAHL databases were searched to identify relevant publications up to November 14, 2019. STUDY SELECTION: All studies that measured EE prospectively and objectively during pregnancy were included in this systematic review. Two authors independently screened 4852 references. A total of 32 studies were included in the final analysis. DATA EXTRACTION: One author extracted data and assessed the risk of bias and a second author did so for a random sample of studies (n = 7; 22%). DATA ANALYSIS: Increases in resting EE ranged from 0.5% to 18.3% (8-239 kcal), from 3.0% to 24.1% (45-327 kcal), and from 6.4% to 29.6% (93-416 kcal) between early and mid-, mid- and late, and early and late pregnancy, respectively. Increases in total EE ranged from 4.0% to 17.7% (84-363 kcal), from 0.2% to 30.2% (5-694 kcal), and from 7.9% to 33.2% (179-682 kcal) between early and mid-, mid- and late, and early and late pregnancy, respectively. Participants were mainly of normal weight, although many studies did not report important covariates such as prepregnancy body mass index and gestational weight gain adequacy. CONCLUSIONS: Additional high-quality longitudinal studies (ie, with multiple objective measurements of EE in all periods of pregnancy while considering important confounding variables, like gestational weight gain) are required.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".