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BackgroundExpert groups recommend that women set a pregnancy weight gain goal with their care provider to optimise weight gain.ObjectiveOur aim was to describe the concordance between first-trimester personal and provider pregnancy weight gain goals with the Institute of Medicine (IOM) recommendations and to determine the association between these goals and total weight gain.MethodsWe used data from 9353 women in the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be. In the first trimester, women reported their personal pregnancy weight gain goal and their provider weight gain goal, and we categorised personal and provider weight gain goals and total weight gain according to IOM recommendations. We used log-binomial or linear regression models to relate goals to total weight gain, adjusting for confounders including race/ethnicity, maternal age, education, smoking, marital status and planned pregnancy.ResultsApproximately 37% of women reported no weight gain goals, while 24% had personal and provider goals, 31% had only a personal goal, and 8% had only a provider goal. Personal and provider goals were outside the recommended ranges in 12%-23% of normal-weight women, 31%-41% of overweight women and 47%-63% of women with obesity. Women with both personal and provider pregnancy weight gain goals were 6%-14% more likely than their counterparts to have a goal within IOM-recommended ranges. Having any goal or a goal within the IOM-recommended ranges was unrelated to pregnancy weight gain. Excessive weight gain occurred in approximately half of normal-weight or obese women and three-quarters of overweight women, regardless of goal setting group.ConclusionsThese findings do not support the effectiveness of early-pregnancy personal or provider gestational weight gain goal setting alone in optimising weight gain. Multifaceted interventions that address a number of mediators of goal setting success may assist women in achieving weight gain consistent with their goals.

Background: Childhood maltreatment is associated with elevated adult weight. It is unclear whether this association extends to pregnancy, a critical window for the development of obesity. Methods: We examined associations of childhood maltreatment histories with prepregnancy body mass index (BMI) and gestational weight gain among women who had participated for >20 years in a longitudinal cohort. At age 26-35 years, participants reported childhood maltreatment (physical, sexual, and emotional abuse; emotional neglect) and, 5 years later, about prepregnancy weight and gestational weight gain for previous pregnancies (n = 656). Modified Poisson regression models were used to estimate associations of maltreatment history with prepregnancy BMI and gestational weight gain z-scores, adjusting for sociodemographics. We used multivariate imputation by chained equations to adjust outcome measures for misclassification using data from an internal validation study. Results: Before misclassification adjustment, results indicated a higher risk of prepregnancy BMI ≥30 kg/m² in women with certain types of maltreatment (e.g., emotional abuse risk ratio = 2.4; 95% confidence interval: 1.5, 3.7) compared with women without that maltreatment type. After misclassification adjustment, estimates were attenuated but still modestly elevated (e.g., emotional abuse risk ratio = 1.7; 95% confidence interval: 1.1, 2.7). Misclassificationadjusted estimates for maltreatment associations with gestational weight gain z-scores were close to the null and imprecise. Conclusions: Findings suggest an association of maltreatment with prepregnancy BMI ≥30 kg/m² but not with high gestational weight gain. Results suggest a potential need for equitable interventions that can support all women, including those with maltreatment histories, as they enter pregnancy. [ABSTRACT FROM AUTHOR]

Background: The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator. Methods: We used data from 10,038 pregnant women and a 10% subsample (N = 1004) to examine the extent to which the risk difference for the relation between fruit and vegetable consumption and preeclampsia risk changes under different seed values. We fit an augmented inverse probability weighted estimator with two Super Learner algorithms: a simple algorithm including random forests and single-layer neural networks and a more complex algorithm with a mix of tree-based, regression-based, penalized, and simple algorithms. We evaluated the distributions of risk differences, standard errors, and P values that result from 5000 different seed value selections. Results: Our findings suggest important variability in the risk difference estimates, as well as an important effect of the stacking algorithm used. The interquartile range width of the risk differences in the full sample with the simple algorithm was 13 per 1000. However, all other interquartile ranges were roughly an order of magnitude lower. The medians of the distributions of risk differences differed according to the sample size and the algorithm used. Conclusions: Our findings add another dimension of concern regarding the potential for "p-hacking," and further warrant the need to move away from simplistic evidentiary thresholds in empirical research. When empirical results depend on pseudo-random number generator seed values, caution is warranted in interpreting these results. [ABSTRACT FROM AUTHOR]

BackgroundThe significance of periconceptional nutrition for optimizing offspring and maternal health and reducing social inequalities warrants greater understanding of diet quality among US women.ObjectiveOur objective was to evaluate racial or ethnic and education inequalities in periconceptional diet quality and sources of energy and micronutrients.DesignCross-sectional analysis of data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be cohort.Participants and settingNulliparous women (N=7,511) were enrolled across eight US medical centers from 2010 to 2013.Main outcome measuresA semiquantitative food frequency questionnaire assessing usual dietary intake during the 3 months around conception was self-administered during the first trimester. Diet quality, measured using the Healthy Eating Index-2010 (HEI-2010), and sources of energy and micronutrients were the outcomes.Statistical analysesDifferences in diet quality were tested across maternal racial or ethnic and education groups using F tests associated with analysis of variance and χ2 tests.ResultsHEI-2010 score increased with higher education, but the increase among non-Hispanic black women was smaller than among non-Hispanic whites and Hispanics (interaction P value

Pregnancy weight gain standards are charts describing percentiles of weight gain among participants with no risk factors that could adversely affect weight gain. This detailed information is burdensome to collect. We investigated the extent to which exclusion of various pre-pregnancy, pregnancy and postpartum factors impacted the values of pregnancy weight gain percentiles. We examined pregnancy weight gain (kg) among 3178 participants of the US nuMoM2b-Heart Health Study (HHS). We identified five groups of potential exclusion criteria for pregnancy weight gain standards: socio-economic characteristics (group 1), maternal morbidities (group 2), lifestyle/behaviour factors (group 3), adverse neonatal outcomes (group 4) and longer-term adverse outcomes (group 5). We established the impact of different exclusion criteria by comparing the median, 25th and 75th percentiles of weight gain in the full cohort with the values after applying each of the five exclusion criteria groups. Differences > 0·75 kg were considered meaningful. Excluding participants with group 1, 2, 3 or 4 exclusion criteria had no impact on the 25th, median or 75th percentiles of pregnancy weight gain. Percentiles were only meaningfully different after excluding participants in group 5 (longer-term adverse outcomes), which shifted the upper end of the weight gain distribution to lower values (e.g. 75th percentile decreased from 19·6 kg to 17·8 kg). This shift was due to exclusion of participants with excess postpartum weight retention > 5 kg or > 10 kg. Except for excess postpartum weight retention, most potential exclusion criteria for pregnancy weight gain standards did not meaningfully impact chart percentiles. [ABSTRACT FROM AUTHOR]

Background: Prepregnancy body mass index (BMI) and gestational weight gain (GWG) are determinants of maternal and child health. However, many studies of these factors rely on error-prone self-reported measures. Methods: Using data from Life-course Experiences And Pregnancy (LEAP), a US-based cohort, we assessed the validity of prepregnancy BMI and GWG recalled on average 8 years postpartum against medical record data treated as alloyed gold standard (“true”) values. We calculated probabilities of being classified into a self-reported prepregnancy BMI or GWG category conditional on one’s true category (analogous to sensitivities and specificities) and probabilities of truly being in each prepregnancy BMI or GWG category conditional on one’s self-reported category (analogous to positive and negative predictive values). Results: There was a tendency toward under-reporting prepregnancy BMI. Self-report misclassified 32% (95% confidence interval [CI] = 19%, 48%) of those in LEAP with truly overweight and 13% (5%, 27%) with obesity into a lower BMI category. Self-report correctly predicted the truth for 72% (55%, 84%) with self-reported overweight to 100% (90%, 100%) with self-reported obesity. For GWG, both under- and over-reporting were common; self-report misclassified 32% (15%, 55%) with truly low GWG as having moderate GWG and 50% (28%, 72%) with truly high GWG as moderate or low GWG. Self-report correctly predicted the truth for 45% (25%, 67%) with self-reported high GWG to 85% (76%, 91%) with selfreported moderate GWG. Misclassification of BMI and GWG varied across maternal characteristics. Conclusion: Findings can be used in quantitative bias analyses to estimate bias-adjusted associations with prepregnancy BMI and GWG. [ABSTRACT FROM AUTHOR]

BackgroundMaternal weight gain during pregnancy is one of the few potentially modifiable risk factors for many adverse maternal and child health outcomes. Defining the optimal pregnancy weight gain range is difficult because, while lower weight gain may prevent some outcomes, such as maternal and child obesity, it may increase the risk of others such as fetal growth restriction and infant death. These health outcomes vary in their seriousness to mothers and their health care providers, and these differences in seriousness should be taken into account when determining optimal weight gain ranges. However, the relative seriousness that women and their care providers place on different health outcomes is unknown. ObjectiveWe will determine the seriousness of 11 maternal and child health outcomes that have been consistently associated with pregnancy weight gain. We will achieve this by engaging patients and maternal and child health professionals using an online modified Delphi panel process. MethodsWe aim to recruit a racially/ethnically and geographically diverse group of 90 US maternal and child health professionals and 90 women who are pregnant or less than 2 years postpartum. We will conduct 3 concurrent panels using the ExpertLens system, a previously evaluated online modified Delphi system that combines 2 rounds of rating with 1 round of feedback and moderated online discussion. In Round 1, panelists are asked to rate the seriousness of each health outcome on a scale of 0-100 and to provide a rationale for their scores. In Round 2, panelists will review their responses relative to those of other panelists. They will discuss their seriousness ratings anonymously using a moderated online discussion board. In Round 3, participants will revise their Round 1 responses based on group feedback and discussion. Each round will be open for 1-2 weeks. ResultsThe study protocol was reviewed by our ethics boards and did not require approval as human research. A pilot study of 6 professionals and 7 patients was completed in December 2019. ConclusionsOur numeric estimates of the seriousness of maternal and child health outcomes will enable future studies to determine pregnancy weight gain ranges that balance the risks of low and high weight gain for mothers and children. International Registered Report Identifier (IRRID)DERR1-10.2196/16478