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A range of adverse outcomes is associated with insufficient and excessive maternal weight gain in pregnancy, but there is no consensus regarding what constitutes optimal gestational weight gain (GWG). Differences in the methodological quality of GWG studies may explain the varying chart recommendations. The goal of this systematic review was to evaluate the methodological quality of studies that aimed to create GWG charts by scoring them against a set of predefined, independently agreed-upon criteria. These criteria were divided into 3 domains: study design (12 criteria), statistical methods (7 criteria), and reporting methods (4 criteria). The criteria were broken down further into items, and studies were assigned a quality score (QS) based on these criteria. For each item, studies were scored as either high (score = 0) or low (score = 1) risk of bias; a high QS correlated with a low risk of bias. The maximum possible QS was 34. The systematic search identified 12 eligible studies involving 2,268,556 women from 9 countries; their QSs ranged from 9 (26%) to 29 (85%) (median, 18; 53%). The most common sources for bias were found in study designs (i.e., not prospective); assessments of prepregnancy weight and gestational age; descriptions of weighing protocols; sample size calculations; and the multiple measurements taken at each visit. There is wide variation in the methodological quality of GWG studies constructing charts. High-quality studies are needed to guide future clinical recommendations. We recommend the following main requirements for future studies: prospective design, reliable evaluation of prepregnancy weight and gestational age, detailed description of measurement procedures and protocols, description of sample-size calculation, and the creation of smooth centile charts or z scores.

Original publication




Journal article


Adv nutr

Publication Date





313 - 322


anthropometry, charts, curves, gestational weight gain, maternal weight, pregnancy weight, systematic literature review, Adult, Bias, Biomedical Research, Data Accuracy, Evidence-Based Medicine, Female, Global Health, Growth Charts, Humans, Maternal Nutritional Physiological Phenomena, Nutrition Policy, Overweight, Pregnancy, Pregnancy Complications, Research Design, Thinness, Weight Gain