Evidence-Based Eating Patterns and Behavior Changes to Limit Excessive Gestational Weight Gain: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: International prenatal care guidelines set a standard for clinicians to discuss gestational weight gain with their patients along with the complications associated with prepregnancy obesity and excessive gestational weight gain. Clinicians often lack evidence-based eating, nutrition, and activity strategies to share with patients. METHODS: This systematic review aimed to find eating patterns and behaviors that could be used safely during pregnancy to limit excessive gestational weight gain. PubMed, MEDLINE, and Web of Science were searched for research or systematic reviews performed in the United States or Canada and published in English from 2013 to 2023. Keyword search terms included weight, manage, behavior, strategy, strategies, gestational weight gain, and nutrition. Excluded research used pediatric or adolescent populations, restrictive diets, such as no carbohydrate or no fat diets, fasting, bariatric surgery, weight loss medications, private industry or profit-earning programs using food brands, or specific diet programs. RESULTS: A total of 844 abstracts were retrieved, with 103 full-text studies reviewed. Behaviors had to be useful for maintaining a healthy gestational weight gain and had to be safe for use during pregnancy. Behaviors useful during pregnancy included meal planning, home meal preparation, portion control, using diets such as the Mediterranean diet, the low-glycemic index diet, and the Dietary Approaches to Stop Hypertension diet (DASH), regular physical activity, sleeping 6-7 h a night, mindful eating, intuitive eating, and regular seif-weighing. CONCLUSION: The evidence-based strategies outlined in this review are safe for use during pregnancy and can assist patients in avoiding excessive gestational weight gain while maintaining the nutrition needed for healthy fetal growth.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".