Date of Award

8-2017

Author's School

Graduate School of Arts and Sciences

Author's Department

Mathematics

Degree Name

Master of Arts (AM/MA)

Degree Type

Thesis

Abstract

Regular sleep is required for sensory processing, learning, and brain plasticity. During pregnancy, poor sleep quality and dysregulation of hormones are all associated with increased risk for diseases like postpartum major depression[1]. Seventy-eight percent of pregnant women experience sleep problems at some point during pregnancy according to the National Sleep Foundation's 1998 Women and Sleep poll. Chronodisruption is a frequent sleep disturbance experienced by pregnant women that can be primary or due to co-morbid conditions[2]. For this reason, chronodisruption, which includes insomnia, is currently regarded as one of the most important factors determining pregnancy outcome. Therefore, the goal of this study is to find essential factors to model effects of midpoint time of sleep during different trimesters as a measure of sleep quality. In this study, we are going to focus on sleep changes during pregnancy. Our underlying hypothesis is that circadian rhythms in the mother, fetus, or both regulate timing of parturition and, when disrupted, lead to preterm birth. I used linear regression models to address sleep changes during all three trimesters grouped by weekend and weekdays. Relationships between factors were investigated via correlation analysis. Interactions between melatonin/cortisol peak values and other factors such as sleep medication taken, vitamins taken, whether sleep was achieved within 30 min, and workload factors were explored. Other factors of interest such as race, having a paid job, and whether or not subjects had a night shift were investigated for overall midpoint sleep time as well as interactions with vitamins taken. Graphs were generated for models as well as for group comparisons. Correlation analysis, ANOVA, and linear regression methods were used to identify the most effective variable and to explain as much of the variance as possible.

Factors affecting sleep midpoint and sleep hormones such as workload, sleep medicine taken, and prior pregnancy were successfully selected for non-pregnancy and all three pregnant periods for regression models. Model selection was based on the best adjusted R-squared evaluation metric. More details are discussed.

Language

English (en)

Chair and Committee

Jeff Gill

Committee Members

Edward Spitznagel, Tony Hinrichs