Evaluating a Latent Measurement Model for Infant Sleep: From Intrinsic and Extrinsic Predictors to Cognitive Outcomes

The development of infant sleep is thought to be jointly guided by the dual processes of sleep consolidation and regulation. However, until now, there have been few empirical studies testing whether there is evidence for these latent processes. The current study uses structural equation modeling to test whether sleep consolidation and regulation can be modeled by two distinct latent variables. Using observed indicators from multiple sleep assessment methods, we found that a two factor model representing the processes of consolidation and regulation fit better than a one factor, undifferentiated model. These two latent factors were predicted by different intrinsic (i.e., infant) and extrinsic (i.e., parenting) factors, as well as interactions between the two classes of predictors. Specifically, we replicated the interaction of infant temperament and maternal emotional availability in predicting both consolidation and regulation. We also found that infant sleep regulation longitudinally predicted infant attention regulation, although this relationship was only true for children whose mothers held a college degree or higher. These findings contribute to the literature by providing a novel measurement model that appropriately accounts for measurement error. Further, our findings suggest that considering the interaction between child characteristics and parental input in promoting high quality sleep is a key avenue for future research. Finally, by partially replicating sleep-cognition linkages previously observed in adolescents and adults, we find support for the notion of hierarchically organized self-regulatory abilities, which motivates areas for future investigation and possible intervention.Doctor of Philosoph

Birth Weight, Birth Length, and Gestational Age as Indicators of Favorable Fetal Growth Conditions in a US Sample

The "fetal origins" hypothesis suggests that fetal conditions affect not only birth characteristics such as birth weight and gestational age, but also have lifelong health implications. Despite widespread interest in this hypothesis, few methodological advances have been proposed to improve the measurement of fetal conditions. A Statistics in Medicine paper by Bollen, Noble, and Adair examined favorable fetal growth conditions (FFGC) as a latent variable. Their study of Filipino children from Cebu provided evidence consistent with treating FFGC as a latent variable that mediates the effects of mother's characteristics on birth weight, birth length, and gestational age. Our study assesses whether the FFGC model of Cebu replicates and generalizes to a population of children from North Carolina and Pennsylvania. Using a series of structural equation models, we find that key features of the Cebu analysis replicate and generalize while we also highlight differences between these studies.Master of Art

Birth Weight, Birth Length, and Gestational Age as Indicators of Favorable Fetal Growth Conditions in a US Sample

<div><p>The “fetal origins” hypothesis suggests that fetal conditions not only affect birth characteristics such as birth weight and gestational age, but also have lifelong health implications. Despite widespread interest in this hypothesis, few methodological advances have been proposed to improve the measurement and modeling of fetal conditions. A <i>Statistics in Medicine</i> paper by Bollen, Noble, and Adair examined favorable fetal growth conditions (FFGC) as a latent variable. Their study of Filipino children from Cebu provided evidence consistent with treating FFGC as a latent variable that largely mediates the effects of mother’s characteristics on birth weight, birth length, and gestational age. This innovative method may have widespread utility, but only if the model applies equally well across diverse settings. Our study assesses whether the FFGC model of Cebu replicates and generalizes to a very different population of children from North Carolina (N = 705) and Pennsylvania (N = 494). Using a series of structural equation models, we find that key features of the Cebu analysis replicate and generalize while we also highlight differences between these studies. Our results support treating fetal conditions as a latent variable when researchers test the fetal origins hypothesis. In addition to contributing to the substantive literature on measuring fetal conditions, we also discuss the meaning and challenges involved in replicating prior research.</p></div

Chi-Squared Difference Testing of Multiple Groups Models.

<p>Chi-Squared Difference Testing of Multiple Groups Models.</p

Descriptive Statistics for Cebu and NC/PA.

<p>Descriptive Statistics for Cebu and NC/PA.</p

Structural Equation Models from Cebu Analyses.

<p>Structural equation model depicting (a) direct-effects only model (Model 1) and (b) favorable fetal growth conditions (FFGC) latent variable model (Model 2) for Cebu. BW = latent newborn weight; BL = latent newborn length, GA = latent gestational age; BW1 = newborn weight measured by birth attendants; BW2 = newborn weight measured by study staff; HTCM = newborn length; LMPGA = gestational age estimated from mother's report of date of her last menstrual period; BALGA = gestational age estimated from Ballard assessment of newborn; NOTPROJ = newborn not weighed on project scale; NOTONE = weight not measured day of birth; WHENBW2 = infant age in days when measured by study staff; WHENBW2SQ = WHEN2BW squared; WHENBAL = age in days when Ballard assessment was done; NOTONE = newborn not weighed on day 1; GIRL = newborn is a girl; AMA = maternal arm muscle area during pregnancy; AFA = maternal arm fat area during pregnancy; MOHT = mother's height; SMOKERS = mother smoked during pregnancy; FIRSTPRG = newborn was firstborn; YOUNGER = mother was <20 years old when pregnant; older = mother was >35 years old when pregnant. (Figures adapted from Bollen et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153800#pone.0153800.ref005" target="_blank">5</a>], p. 13–14).</p

Modified Structural Equation Model from NC/PA Analyses.

<p>Structural equation model relating mother’s traits to birth outcomes through the mediating favorable fetal growth conditions (FFGC) latent variable, following theoretical and empirical modifications (Model 3). BW = birth weight; BL = birth length, GA = gestational age; GIRL = newborn is a girl; MOMWT = mother’s pre-pregnancy weight; MOHT = mother's height; SMOKERS = mother smoked during pregnancy; FIRSTPRG = newborn was firstborn; YOUNGER = mother was <20 years old when pregnant; older = mother was >35 years old when pregnant; AA = mother is African-American.</p

Factor Loadings for FFGC Indicators.

<p>Factor Loadings for FFGC Indicators.</p

Structural Equation Models from NC/PA Analyses.

<p>Structural equation model depicting (a) direct-effects only model (Model 1) and (b) favorable fetal growth conditions (FFGC) latent variable model (Model 2) for NC/PA. BW = birth weight; BL = birth length, GA = gestational age; GIRL = newborn is a girl; MOMWT = mother’s pre-pregnancy weight; MOHT = mother's height; SMOKERS = mother smoked during pregnancy; FIRSTPRG = newborn was firstborn; YOUNGER = mother was <20 years old when pregnant; older = mother was >35 years old when pregnant.</p