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Phthalates, compounds commonly used in plastics and personal care products, have been associated with childhood obesity in cross-sectional and some longitudinal studies. Using advanced statistical methods, we characterized the heterogeneity in body mass development patterns over childhood (ages 2-14 years) and explored associations with maternal prenatal urinary concentrations of phthalates among 335 children in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) cohort study. Height and weight were measured every one to two years in this cohort, which had a high prevalence of obesity and overweight. Building upon a previous analysis that showed a positive association between prenatal phthalate exposure and body mass index (BMI) in CHAMACOS children, we used three advanced statistical methods: generalized additive models, growth mixture models, and functional principal component analysis with tree-based methods to identify patterns of childhood BMI development and allow for non-linear relationships with the environmental exposures. Our results highlight the heterogeneity in childhood BMI development patterns and suggest a sex-specific non-linear association between prenatal monoethyl phthalate urinary concentrations and BMI level in children, confirmed across a variety of statistical methods. There is also evidence to suggest positive associations between DEHP metabolites and BMI stabilization during puberty for girls.

Phthalates, compounds commonly used in plastics and personal care products, have been associated with childhood obesity in cross-sectional and some longitudinal studies. Using advanced statistical methods, we characterized the heterogeneity in body mass development patterns over childhood (ages 2–14 years) and explored associations with maternal prenatal urinary concentrations of phthalates among 335 children in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) cohort study. Height and weight were measured every one to two years in this cohort, which had a high prevalence of obesity and overweight. Building upon a previous analysis that showed a positive association between prenatal phthalate exposure and body mass index (BMI) in CHAMACOS children, we used three advanced statistical methods: generalized additive models, growth mixture models, and functional principal component analysis with tree-based methods to identify patterns of childhood BMI development and allow for non-linear relationships with the environmental exposures. Our results highlight the heterogeneity in childhood BMI development patterns and suggest a sex-specific non-linear association between prenatal monoethyl phthalate urinary concentrations and BMI level in children, confirmed across a variety of statistical methods. There is also evidence to suggest positive associations between DEHP metabolites and BMI stabilization during puberty for girls. • Phthalates as possible obesogens may explain variability in childhood BMI. • Advanced statistical methods allow non-linear relationship with BMI over time. • Prenatal MEP positively associated with BMI level across ages. • Prenatal DEHP positively associated with increasing and then stabilizing BMI. [ABSTRACT FROM AUTHOR]

There is a growing literature that suggests environmental exposure during key developmental periods could have harmful impacts on growth and development of humans. Understanding and estimating the relationship between early-life exposure and human growth is vital to studying the adverse health impacts of environmental exposure. We compare two statistical tools, mixed-effects models with interaction terms and growth mixture models, used to measure the association between exposure and change over time within the context of non-linear growth and non-monotonic relationships between exposure and growth. We illustrate their strengths and weaknesses through a real data example and simulation study. The data example, which focuses on the relationship between phthalates and the body mass index growth of children, indicates that the conclusions from the two models can differ. The simulation study provides a broader understanding of the robustness of these models in detecting the relationships between any exposure and growth that could be observed. Data-driven growth mixture models are more robust to non-monotonic growth and stochastic relationships but at the expense of interpretability. We offer concrete modeling strategies to estimate complex relationships with growth patterns. [ABSTRACT FROM AUTHOR]

Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent, potential metabolic disruptors of concern for infants. Mothers participating in the New Hampshire Birth Cohort Study (NHBCS) provided a plasma sample during pregnancy to measure concentrations of seven PFAS, and infant weight and length were abstracted from well-child visits between birth and 12 months. Sex-specific growth patterns of child body mass index (BMI) were fit using a growth mixture model (GMM) and the relative risk ratios (RRR) and 95% Confidence Intervals (95% CI) for the association of maternal plasma PFAS with BMI growth patterns during infancy were estimated by using multinomial logistic model for the group probabilities in the GMM. Four growth patterns were identified: Group 1) a steep increase in BMI during the first 6 months, then a leveling off; Group 2) a gradual increase in BMI across the year; Group 3) a steep increase in BMI during months 1–3, then stable BMI; and Group 4) a gradual increase in BMI with plateau around 3 months (reference group). For boys, higher maternal pregnancy perfluorooctanoate concentrations were associated with a 60% decreased chance of being in group 3 as compared to group 4, after adjusting for potential confounding variables (RRR = 0.4; 95% CI: 0.1, 0.9). For girls, higher maternal perfluorooctane sulfonate (PFOS) concentrations during pregnancy were associated with a higher likelihood of following the growth pattern of groups 2 (RRR = 2.5; 95% CI: 1.0, 6.1) and 3 (RRR = 2.8; 95% CI: 1.0, 7.6) as compared to group 4, adjusting for potential confounding variables. In this cohort, sex-specific associations of maternal plasma PFAS concentrations during pregnancy with growth patterns during the first year of life were observed, with greater BMI growth observed among infant girls born to mothers with higher pregnancy concentrations of PFOS. • Sex-specific growth patterns in infancy were fit using a growth mixture model. • Associations of PFAS with growth in infancy were sex-specific. • Among girls with higher prenatal PFOS, steeper BMI growth occurred during infancy. [ABSTRACT FROM AUTHOR]

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

As augmented reality (AR) systems proliferate and the technology gets smaller and less intrusive, we imagine a future where many AR users will interact in the same physical locations (e.g., in shared work places and public spaces). While previous research has explored AR collaboration in these spaces, our focus is on co-located but independent work. In this paper, we explore co-located AR user behavior and investigate techniques for promoting awareness of personal workspace boundaries. Specifically, we compare three techniques: showing all virtual content, visualizing bounding box outlines of content, and a self-defined workspace boundary. The findings suggest that a self-defined boundary led to significantly more personal workspace encroachments.