The Role of Health-related Behaviors in Gestational Weight Gain among Women with Overweight and Obesity: A Cross-sectional Analysis

Healthful Eating Behaviors among Couples Contribute to Lower Gestational Weight Gain

Through longitudinal analysis from the GLOWING cohort study, we examined the independent and joint relationships between couples' eating behaviors and gestational weight gain (GWG). Pregnant persons (n = 218) and their non-pregnant partners (n = 157) completed an Eating Inventory. GWG was calculated as gestation weight at 36 weeks minus that at 10 weeks. General linear models were used to examine the relationships between GWG and the pregnant persons, non-pregnant partners, and couples (n = 137; mean of pregnant persons and non-pregnant partners) cognitive restraint (range 0-21), dietary disinhibition (range 0-18), and perceived hunger (range 0-14), with higher scores reflecting poorer eating behaviors. The adjusted models included race/ethnicity, education, income, marital status, and age. The pregnant persons and their non-pregnant partners' cognitive restraint, dietary disinhibition, and perceived hunger scores were 9.8 ± 4.7, 4.8 ± 3.2, and 4.4 ± 2.5 and 6.6 ± 4.6, 5.4 ± 3.4, and 4.7 ± 3.2, respectively. Higher cognitive restraint scores among the pregnant persons and couples were positively associated with GWG (p ≤ 0.04 for both). Stratified analyses revealed this was significant for the pregnant persons with overweight (p ≤ 0.04). The non-pregnant partners' eating behaviors alone were not significantly associated with GWG (p ≥ 0.31 for all). The other explored relationships between GWG and the couples' eating behaviors were insignificant (p ≥ 0.12 for all). Among the pregnant persons and couples, reduced GWG may be achieved with higher levels of restrained eating. Involving non-pregnant partners in programs to optimize GWG may be beneficial.

The association between multidimensional sleep health and gestational weight gain

BACKGROUND

Although poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep health framework is needed.

OBJECTIVES

This secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n = 745) examined associations between mid-pregnancy sleep health indicators, multidimensional sleep health and gestational weight gain (GWG).

METHODS

Sleep domains (i.e. regularity, nap duration, timing, efficiency and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined 'healthy' sleep in each domain with empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis and composite score defined as the sum of healthy sleep domains. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<-1 SD), moderate (-1 or +1 SD) and high (>+1 SD).

RESULTS

Nearly 50% of the participants had a healthy sleep profile (i.e. healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of unhealthy sleep in each domain. The individual sleep domains were associated with a 20%-30% lower risk of low or high GWG. Each additional healthy sleep indicator was associated with a 10% lower risk of low (vs. moderate), but not high, GWG. Participants with late timing, long duration and low efficiency (vs. healthy) profiles had the strongest risk of low GWG (relative risk 1.5, 95% confidence interval 0.9, 2.4). Probabilistic bias analysis suggested that most associations between individual sleep health indicators, sleep health profiles and GWG were biased towards the null.

CONCLUSIONS

Future research should determine whether sleep health is an intervention target for healthy GWG.

[Do depressive symptoms, anxiety and stressful symptoms during pregnancy affect gestational weight gain?]

The scope of this article is to estimate the effects of symptoms of mental disorders during pregnancy (depressive symptoms, anxiety and stress) on gestational weight gain (kg). It is a longitudinal study, carried out with data from the BRISA Birth Cohort, which was launched in 2010 in São Luís, Maranhão. Gestational weight gain was classified according to the Institute of Medicine. The independent variable was a construct (latent variable) referred to as symptoms of mental disorders, made up of the depressive symptoms, anxiety and stressful symptoms variables (all on an ongoing basis). Structural equation modeling was used to investigate the association between mental health and weight gain. Regarding the association between symptoms of mental disorders and weight gain during pregnancy, no total effect was found (PC=0.043; p=0.377). Regarding indirect effects, no effect was found either through risk behaviors (PC=0.03; p=0.368) or through physical activity (PC=0.00; p=0.974). Finally, the data did not show a direct effect of symptoms of mental disorders during pregnancy such as gestational weight gain (PC=0.050; p=0.404). Gestational weight gain had no direct, indirect or total effect on symptoms of mental disorders in pregnant women.

The association between multidimensional sleep health and gestational weight gain: nuMoM2b Sleep Duration and Continuity Study

Background

Although poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep-health framework is needed. This study examined associations among mid-pregnancy sleep health indicators, multidimensional sleep health, and gestational weight gain (GWG).

Methods

We conducted a secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n=745). Indicators of individual sleep domains (i.e., regularity, nap duration, timing, efficiency, and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined "healthy" sleep in each domain based on empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<-1 SD), moderate (-1 or +1 SD), and high (>+1 SD).

Results

Nearly 50% of the participants had a healthy sleep profile (i.e., healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of poor health in each domain. While indicators of individual sleep domains were not associated with GWG, multidimensional sleep health was related to low and high GWG. Participants with a sleep profile characterized as having low efficiency, late timing, and long sleep duration (vs. healthy sleep profile) had a higher risk (RR 1.7; 95% CI 1.0, 3.1) of low GWG a lower risk of high GWG (RR 0.5 95% CI 0.2, 1.1) (vs. moderate GWG).

Conclusions

Multidimensional sleep health was more strongly associated with GWG than individual sleep domains. Future research should determine whether sleep health is a valuable intervention target for optimizing GWG.

Synopsis

Study question: What is the association between mid-pregnancy multidimensional sleep health and gestational weight gain?What's already known?: Sleep is associated with weight and weight gain outside of pregnancyWhat does this study add?: We identified patterns of sleep behaviors associated with an increased risk of low gestational weight gain.

Prepregnancy overweight and obesity as risk factors for birth defects: a cross-sectional study over a 30-year period

Overweight and obesity are public health problems worldwide despite being modifiable conditions. The association between birth defects and pregestational maternal body mass index is not entirely clear. We aimed to assess the prevalence and estimate the risk of birth defects related to pregestational body mass index and other maternal factors. We explored a 30-year time series database in a cross-section study. We analysed 40,217 cases, among them 2.8% had birth defects. Bivariate analysis showed a higher prevalence of birth defects with increased pre-pregnancy body mass index and in extremes of maternal age, white skin colour, and primiparity. Multivariable logistic regression showed a higher chance of birth defects in women with pre-pregnancy overweight/obesity (OR:1.19 [CI95%:1.01-1.41]), maternal age ≥ 40 years (OR:1.68 [CI95%:1.11-2.54]), and white skin colour (OR:1.44 [CI95%:1.19-1.75]). Maternal weight is a modifiable risk factor that must be considered and addressed in preconception counselling to minimise possible deleterious effects on embryogenesis.IMPACT STATEMENTWhat is already known on this subject? Previous studies have linked some maternal factors with birth defects. However, the association with prepregnancy maternal body mass index is not clear.What do the results of this study add? Our findings provide support for the association of prepregnancy maternal overweight and obesity with birth defects and highlight that BMI is a modified risk factor.What are the implications of these findings for clinical practice and/or further research? Maternal body mass index is a modifiable risk factor, highlighting the importance of preconception counselling for the prevention and possible reduction of factors that increase the risk of birth defects.

Physical Activity, Stress, Depression, Emotional Intelligence, Logical Thinking, and Overall Health in a Large Lithuanian from October 2019 to June 2020: Age and Gender Differences Adult Sample

This study aimed to examine relationships and group differences among adult people’s (aged 18–74) physical activity (PA), expression of stress, depression, emotional intelligence (EI), logical thinking (LT), and overall health assessment. Two hypotheses were formulated before the study. The first hypothesis is that overweight and obesity in young adults (18 to 34 years) females and males, in particular, should increase sharply and this should be associated with decreased PA, abruptly deteriorating subject health, increased stress, depression, and poorer emotion management and EI. Second hypothesis: We further thought that the better people’s reflective thinking, the more they should live a healthier life (e.g., exercise more and eat healthier), their overweight and obesity should be small or none. We aimed to confirm or reject these two hypotheses. We applied a quantitative cross-sectional study design. The study results revealed that during the lifespan of 18–24 and 25–34 years (young adults) there was a sharp increase in overweight and obesity, a decrease in PA (and especially vigorous physical activity (VPA)) (and this was particularly evident in the male), while research participants felt less stress and depression, subjective assessment of health did not change, and EI increased steadily with age (18–24 to 65–74 years). The higher the EI of the research participants from 18–24 to 65–74 years of age the higher their moderate-to-vigorous physical activity (MVPA), the less stress and depression they felt. Based on the results, it can be said that both females and males prefer PA “with a hot heart rather than a cold mind.” We base this conclusion on the fact that females and males who have the highest EI also have the highest MVPA while LT is not associated with MVPA.