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Prenatal sleep health, cortisol, and gestational weight gain

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Prenatal sleep health, cortisol, and gestational weight gain

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Content Prenatal Sleep Health, Cortisol, and Gestational Weight Gain
by
Christine Hotaru Naya
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PREVENTIVE MEDICINE)
May 2023
Copyright 2023 Christine Hotaru Naya
ii

Dedication

Dedicated to all mothers of yesterday, today, and tomorrow

iii

Acknowledgements

Thank you to the MADRES staff members, who tirelessly worked to make this research
possible. Thank you to the amazing MADRES participants, without whom this study would not
exist.
Thank you to my amazing dissertation committee for their mentorship and support.
Thank you to my REACH, MADRES, and NEST lab mates, colleagues, and cohort mates for
their camaraderie and friendship. Thank you to my friends for always reminding me that I was
enough. Thank you to my family for always stepping in to be our village and loving us when we
needed it most. ああちゃん、ダダ、勉強しかできない娘だけど納谷博士になるまで私を
支えてくれてありがとう。和英辞典をひきながら宿題を手伝ってくれたああちゃん、
夜遅くまで働いてくれたダダがいるからこそ今日の私がいます。この博士号は私だけで
はなく、ああちゃんとダダの努力の結晶です。本当にありがとう。
Lastly, thank you Ellie for teaching me the profound purpose and joy that is being your
mother. And Andrew, thank you for being my partner through all of this and more. I love you to
infinity and beyond.

iv
Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
Abstract ........................................................................................................................................... x
Chapter One: Introduction .............................................................................................................. 1
Background and Significance ........................................................................................................................... 1
Public Health Implications of Gestational Weight Gain ............................................................................... 1
Sleep Health and Gestational Weight Gain ................................................................................................... 4
The Hypothalamic Pituitary Adrenal Axis and Gestational Weight Gain .................................................... 7
Cortisol: Potential Link Between Poor Prenatal Sleep to Gestational Weight Gain ................................... 12
Sleep: A Putative Intervention Target for Excessive Gestational Weight Gain .......................................... 13
Conclusion .................................................................................................................................................. 14
Limitations in existing literature ..................................................................................................................... 14
Lack of Repeated Assessment across Pregnancy Period ............................................................................ 15
Failure to Examine Daily Effects in Real-World Settings .......................................................................... 15
Lack of Studies with Diverse Mothers ........................................................................................................ 17
Dissertation Goals to Address Current Limitations in Literature ............................................................... 17
Specific Aims and Conceptual Model ............................................................................................................ 19
Chapter Two: Study One
Poor Sleep Quality Increases Gestational Weight Gain Rate in Pregnant People: Findings from
the MADRES Study ...................................................................................................................... 21
Abstract ........................................................................................................................................................... 21
Introduction ..................................................................................................................................................... 23
Methods .......................................................................................................................................................... 26
Sample Population ...................................................................................................................................... 26
Measures ..................................................................................................................................................... 27
Statistical Analysis ...................................................................................................................................... 30
Results............................................................................................................................................................. 32
Sample Population ...................................................................................................................................... 32
Descriptive Characteristics ......................................................................................................................... 33
Bivariate Analyses ...................................................................................................................................... 34
v
Growth Curve Linear Model ....................................................................................................................... 35
Discussion ....................................................................................................................................................... 37
Limitations .................................................................................................................................................. 39
Conclusion .................................................................................................................................................. 39
Chapter Three: Study Two
Prenatal Sleep and Gestational Weight: Examining Mediation by Hypothalamic Pituitary
Adrenal Axis and Moderation by Pre-Pregnancy BMI................................................................. 55
Abstract ........................................................................................................................................................... 55
Introduction ..................................................................................................................................................... 57
Methods .......................................................................................................................................................... 60
Sample ........................................................................................................................................................ 60
Measures ..................................................................................................................................................... 61
Statistical Analysis ...................................................................................................................................... 65
Results............................................................................................................................................................. 66
Sample Population ...................................................................................................................................... 66
Descriptive Characteristics ......................................................................................................................... 67
Non-Compliance and Contamination of Cortisol Samples ......................................................................... 69
Moderated Mediation Model ...................................................................................................................... 70
Discussion ....................................................................................................................................................... 71
Limitations .................................................................................................................................................. 74
Conclusion .................................................................................................................................................. 75
Chapter Four: Study Three
Maternal Sleep Disruption and Morning Cortisol During Pregnancy: An Ecological Momentary
Assessment Study ......................................................................................................................... 92
Abstract ........................................................................................................................................................... 92
Introduction ..................................................................................................................................................... 94
Methods .......................................................................................................................................................... 97
Sample ........................................................................................................................................................ 97
Measures ..................................................................................................................................................... 98
Statistical Analysis ..................................................................................................................................... 102
Results............................................................................................................................................................103
Sample Population .....................................................................................................................................103
Descriptive Characteristics ........................................................................................................................103
vi
Bivariate Analyses .....................................................................................................................................106
Multilevel Model ........................................................................................................................................107
Discussion .......................................................................................................................................................108
Limitations ..................................................................................................................................................110
Conclusion ..................................................................................................................................................111
Chapter Five: Conclusion .............................................................................................................124
Review of Aims and Findings.........................................................................................................................124
Beyond Diurnal Cortisol: Pathways Linking Sleep and Weight Gain ............................................................124
Application of Findings ..................................................................................................................................126
Future Directions ............................................................................................................................................127
Adoption of Ecological Momentary Assessment Study Designs for Pregnancy Research.........................127
Improved Strategies to Aid Salivary Cortisol Sampling in The Naturalistic Setting ..................................129
Future Research in Diverse Populations .....................................................................................................129
Conclusion ......................................................................................................................................................130
Afterword ......................................................................................................................................131
References .....................................................................................................................................134
vii

List of Tables

Introduction
Table 1. Recommendations for Total and Rate of Weight Gain During Pregnancy, by
Pre-Pregnancy BMI….………………………..……………………………………………….2
Study One
Table 1. Descriptive Characteristics of Final Analytic Sample……………………………………….41
Table 2. Early-to-Mid Pregnancy Sleep Quality by Ethnicity by Birthplace, Hypertensive
Disorder, Physical Activity, and Perceived Stress Score N=316)……………………….…...42
Table 3. Growth Curve Model Results for Main Effects, Interactions, and Covariates (N=316)……..43
Table 4. Rates of Mid-to-Late Pregnany Weight Gain by Early-to-Mid Pregnancy Sleep Quality
in Women with Normal Weight Pre-Pregnancy BMI (N=316)………………………………44
Supplemental Table 1. Comparison of All MADRES Participants vs Final Analytical Sample……...45
Study Two
Table 1. Descriptive Characteristics of Final Analytic Sample……………………………….………76
Table 2. Diurnal Cortisol Measures by Protocol Non-Compliance…………………………………...77
Table 3. Diurnal Cortisol Measure by Number of Contaminated Samples………………………..…..78
Table 4. Indirect Effect and Index of Moderated Mediation…………………………….…………….79
Supplemental Table 1. Comparison of All MADRES Participants vs Final Analytical Sample….…..80
Study Three
Table 1. Participant-Level Descriptive Characteristics (N=63)……………………………………...113
Table 2. Day-Level Descriptive Characteristics (n=442)…………………………………………….114
Table 3. Sleep Disruption by Assessment Period…………………………………………………….115
Table 4. Morning Cortisol Values by Protocol Non-Compliance……………………………………116
Table 5. Multilevel Linear Regression of Sleep Disruption Predicting ( →) Cortisol………………..117
Supplemental Table 1. Comparison of All MADRES Participants vs Final Analytical Sample…….119
Supplemental Table 2. Sensitivity Analyses for Multilevel Linear Regression of Sleep
Disruption Predicting Awakening+30min Cortisol………………………………………....120

viii

List of Figures
Introduction
Figure 1. The HPA Axis………………………………………………………………………………...7
Figure 2. Changes in the HPA axis and cortisol levels during pregnancy…………………………….11
Figure 3. Conceptual Model of Prenatal Sleep Health, Diurnal Cortisol Profile, and Gestational
Weight Gain…………………………………………………………………………………20
Study One
Figure 1A. Predicted Values of Maternal Weight by Ethnicity and Birthplace……………………….46
Figure 1B. Predicted Values of Maternal Weight by Hypertensive Disorder…………………………47
Figure 1C. Predicted Values of Maternal Weight by Perceived Stress Scale…………………………48
Figure 1D. Predicted Values of Maternal Weight by Total Activity Score………………………...…49
Figure 2. Early-to-Mid Pregnancy Sleep Quality and Mid-to-Late Pregnancy Weight Gain by
Pre-Pregnancy BMI Status………………………..…………………………………………50
Supplemental Figure 1. Algorithm to Quantify Early-to-Mid Pregnancy Sleep Quality…………...…51
Supplemental Figure 2. Spaghetti Plots Influential Points Excluded from Sample……………….…..52
Supplemental Figure 3. Spaghetti Plots Comparing Influential Points to All Other Participants…..…53
Supplemental Figure 4. Consort Diagram for Data Availability…………………………………..…..54
Study Two
Figure 1. Mediation Model with Moderated b and c’ paths (PROCESS Model 15)…………………..81
Figure 2. Mediation Model with Moderated a, b, and c’ paths (PROCESS Model 59)……………….81
Figure 3. Simplified Model for the Association of Early-to-Mid Pregnancy Sleep Quality (Ref:
Very Good) and Total Weight Gain, with AUC Mediator and Pre-pregnancy BMI
Moderator (Ref: Normal)……………………………………………...………………….…..82
Figure 4. Simplified Model for the Association of Early-to-Mid Pregnancy Sleep Quality (Ref:
Very Good) and Total Weight Gain, with CAR Mediator and Pre-pregnancy BMI
Moderator (Ref: Normal)………...………………………………………………………..….83
Figure 5. Simplified Model for the Association of Early-to-Mid Pregnancy Sleep Quality (Ref:
Very Good) and Total Weight Gain, with DCS Mediator and Pre-pregnancy BMI
Moderator (Ref: Normal)...…………………………………………………………………...84
Figure 6. Schematic of the Main Interactions between Sleep, Stress and Metabolism from
Hirotsu et.al……………………………………………….…………………………………..73
Supplemental Figure 1. Algorithm to Quantify Early-to-Mid Pregnancy Sleep Quality……..……….85
Supplemental Figure 2. Consort Diagram for Data Availability…………………………..…………..86
Supplemental Figure 3. Full Model for the Association of Early-to-Mid Pregnancy Sleep Quality
(Ref: Very Good) and Total Weight Gain, with AUC Mediator and Pre-pregnancy BMI
Moderator (Ref: Normal)..………………………………………………………….……….87
Supplemental Figure 4. Full Model for the Association of Early-to-Mid Pregnancy Sleep Quality
(Ref: Very Good) and Total Weight Gain, with CAR Mediator and Pre-pregnancy BMI
Moderator (Ref: Normal)………………………………...………………………………….88
Supplemental Figure 5. Full Model for the Association of Early-to-Mid Pregnancy Sleep Quality
(Ref: Very Good) and Total Weight Gain, with DCS Mediator and Pre-pregnancy BMI
Moderator (Ref: Normal)……………..……………………………………………………..89
Supplemental Figure 6. Simplified Model for the Association of Early-to-Mid Pregnancy Sleep
Quality (Ref: Very Good) and Total Weight Gain, with CAR Mediator and
Pre-pregnancy BMI Moderator (Ref: Normal) for Positive CAR Observation Only……..…90
Supplemental Figure 7. Full Model for the Association of Early-to-Mid Pregnancy Sleep Quality
(Ref: Very Good) and Total Weight Gain, with CAR Mediator and Pre-pregnancy BMI
Moderator (Ref: Normal) for Positive CAR Observations Only…………………………....91
Study Three
Figure 1. Distribution of Cortisol Profiles by Assessment Period………………………………...…122
Supplemental Figure 1. Consort Diagram for Data Availability……………………………….…….123
Afterword
ix

Figure 1. Socio-Ecological Model of Sleep Disparities from Billings et al………………………...131

x

Abstract

Excessive gestational weight gain (GWG) increases the risk of adverse birth outcomes
and long-term maternal and child health issues. Despite efforts in the last 10 years to curtail
excessive GWG rates, prenatal weight gain counseling and clinical interventions aimed at
increasing physical activity and promoting healthy eating have led to inconsistent results,
especially among low-income minority mothers. Thus, there is a need to identify alternative
modifiable predictors of excessive GWG in minority women to improve prenatal care
recommendations for healthy weight gain.
Sleep health has consistently received little attention in GWG research, even though poor
sleep is a putative risk factor for obesity in non-pregnant populations and is ubiquitous during
pregnancy. A key regulator that has been hypothesized to drive the association between sleep
health and weight gain is the hypothalamic-pituitary-adrenal (HPA) axis and its end-product
cortisol. However, no study to date has examined the relationship between prenatal sleep health,
cortisol, and GWG.
The overall goal of this dissertation is to examine both population-level and day-level
associations among sleep health, diurnal cortisol profiles, and GWG throughout pregnancy. This
project will leverage data from pregnant women enrolled in the Maternal and Developmental
Risks from Environmental and Social Stressors (MADRES) Study and the real-time personal
monitoring sub-study in MADRES.
Chapter One: Introduction
Background and Significance
Public Health Implications of Gestational Weight Gain
Prevalence and Health Consequences of Excessive Gestational Weight Gain
One out of two persons of childbearing age in the United States currently have
overweight or obesity.
1,2
Obesity and overweight status put people at elevated risk for various
long-term adverse health outcomes, such as cardiovascular diseases, diabetes, and obesity-related
cancers.
3,4
People with obesity and overweight are also more likely to gain more weight during
pregnancy.
5
The amount of weight gained during pregnancy—gestational weight gain (GWG)—
significantly impacts the immediate and future health of both the pregnant person and their
baby.
6
Given the long-term health implications of GWG, in 1990 the Institute of Medicine (IOM)
released recommended guidelines for total GWG and rate of weight gain based on the person’s
pre-pregnancy Body Mass Index (BMI). However, given the increasing rate of overweight and
obesity, higher proportion of people from racial/ethnic minorities in the United States, and
increased age at pregnancy, the IOM reexamined these guidelines in 2009 and provided a more
specific, relatively narrow, range of recommended weight gain (Table 1).
7
They also published
future research topics and clinical recommendations to better understand the determinants of
GWG and implement behavioral changes that will allow people to better meet these new
guidelines.
1
Table 1. Recommendations for Total and Rate of Weight Gain During Pregnancy,
by Pre-Pregnancy BMI
7

Total Weight Gain
Rates of Weight Gain*
(2nd and 3rd Trimester)
Pre-Pregnancy BMI
Range
in kg
Range in
lbs
Mean (range)
in kg/week
Mean (range)
in lbs/week
Underweight (<18.5 kg/m
2
) 12.5-18 28-40 0.51 (0.44-0.58) 1 (1-1.3)
Normal weight (18.5 - 24.9 kg/m
2
) 11.5-16 25-35 0.42 (0.35-0.50) 1 (0.8-1)
Overweight (25.0 - 29.9 kg/m
2
) 7-11.5 15-25 0.28 (0.23-0.33) 0.6 (0.5-0.7)
Obese (≥30.0 kg/m
2
) 5-9 11-20 0.22 (0.17-0.27) 0.5 (0.4-0.6)
*Calculations assume a 0.5-2 kg (1.1-4.4 lbs) weight gain in the first trimester.
8–10
More than a decade has passed since these new guidelines and recommendations were
released, and less than one in three people currently gain the recommended amount of weight
during pregnancy.
11
About one in five gain inadequate gestational weight, and almost half gain
excessive weight during pregnancy.
11
This is alarming, given that excessive GWG increases the
risk of various pregnancy complications and long-term chronic health conditions that can
severely impact morbidity and mortality rates for both the mother and child.
12–14

Disparities in Obesity and Excessive Gestational Weight Gain
There are existent health disparities in the prevalence of obesity and excessive GWG
amongst minority populations and people from low-income communities. According to the
National Center for Health Statistics, in 2017-2018 approximately 39.8% of Non-Hispanic White
people of childbearing age have overweight or obesity, while these rates are as high as 43.7% in
Hispanic people and 56.9% in Non-Hispanic Black people.
15
Furthermore, compared to the
average excessive GWG prevalence of 35% in the U.S. population, studies report prevalence
rates as high as 53% in low-income populations, 51% in Hispanic people, and 61% in Black
people.
16,17
While some argue that there are physiological differences in body fat distribution by
2

ethnic background,
18
other have identified social determinants, such as educational attainment,
household income, and health insurance coverage, to contribute to the racial/ethnic disparities in
obesity and excessive GWG.
2
Regardless of the causal factors, it’s indisputable that the
population who are disproportionately burdened with pregnancy complications and adverse
health outcomes are also the ones who are experiencing disparately high rates of obesity and
excessive GWG.
Current Interventions and Lack of Success in Preventing Excessive Gestational Weight Gain
Despite various public health efforts to curtail excessive GWG rates, current prenatal
weight gain counseling and clinical interventions that are aimed to increase physical activity and
promote healthy eating have led to inconsistent results, especially among minority and low-
income mothers.
19–22

Weight gain during pregnancy is viewed as a sensitive and personal topic for many, with
some cultural beliefs and traditions at odds with current physical activity and nutrition guidelines
for pregnant people in the U.S.
23
African-American women often emphasize the importance of
“eating for two” and perceive higher caloric consumption as essential for babies’ health.
24

Pregnant Hispanic and African American mothers also commonly report that their family
members restrict one’s movements by relieving them of household tasks or out of concern that
exercise may harm the fetus.
25
According to a qualitative study on perceptions of GWG amongst
people of color, many viewed pregnancy as a “vacation” from weight gain, physical activity, and
unhealthy food that is normally avoided.
23

Furthermore, various social and environmental factors outside of the individuals’ control
often influence physical activity and eating behaviors in ways that contribute to unhealthy weight
gain. Living in poverty could entail financial constraints, such as limited affordability of healthy
food,
26
and structural constraints, such as living in neighborhoods with limited access to fresh
3

produce, transportation, and safe spaces to exercise.
27
Therefore, many pregnant people view
themselves as incapable of following through with their physicians’ diet and exercise
recommendations because they are at the mercy of broader financial and environmental
circumstances.
23

The discrepancy between people’s cultural beliefs and structural lifestyle factors with
current clinical recommendations may underline one of the reasons why preventive measures for
excessive GWG have not been successful in health disparities populations. There is a need to
identify alternative modifiable predictors of excessive GWG, especially for minority populations,
so healthy weight gain is perceived as a desirable and attainable goal during pregnancy.
Sleep Health and Gestational Weight Gain
Sleep Health during Pregnancy
Sleep is one of the basic behavioral requirements for health and well-being. Until the last
decade, sleep research has primarily focused on health consequences of sleep disorders and
deficiency.
28
More recently, there has been a movement to instead focus on overall sleep health
by examining various dimensions of sleep that are known to be associated with health outcomes,
such as sleep duration, sleep continuity or effectiveness, and alertness or sleepiness.
28

The growing literature on sleep dimensions has helped shed light on a common issue
experienced by expectant mothers: poor sleep. Meta analyses have found that almost half of
pregnant people experience poor sleep quality,
29
and the National Sleep Foundation has found
almost 80% of pregnant people report more sleep disturbances during pregnancy than any other
time in their life.
30
During pregnancy, the majority of people experience poor sleep quality,
insufficient night-time sleep, significant daytime sleepiness, and insomnia.
31,32
These sleep
problems arise from various physiological and psychological shifts that occur during pregnancy,
such as hormonal changes, frequent urination, restless leg syndrome, nausea and other
4

discomforts, and heightened stress and anxiety.
30,33
Furthermore, these symptoms become even
more severe toward the end of pregnancy, as sleep becomes more disturbed and its quality
worsens.
34

Sleep Health and Weight Gain: Evidence in Non-Pregnant Populations
There is growing evidence in non-pregnant populations supporting poor sleep health as a
putative risk factor for weight gain and cardiometabolic outcomes.
35–37
Two population-level
studies using the Behavioral Risk Factor Surveillance System and National Health and Nutrition
Examination Survey data found short sleep duration (<5 or <7 hours) and subjective sleep
insufficiency were associated with increased BMI and risk of obesity.
37,38
A systematic review of
36 publications also found short sleep duration to be consistently associated with more weight
gain.
35

These observational studies add support to the causal pathways between sleep and weight
gain that had previously only been examined in laboratory-based human and animal studies.
Behaviorally, lack of sleep could lead to increased fatigue and psychological stress that can in
turn reduce physical activity and prompt unhealthy eating patterns.
39–41
Physiologically, sleep
loss is associated with decreased glucose tolerance and reduced insulin sensitivity that can lead
to weight gain.
42
Sleep deprivation is also known to affect the circadian cycles of hormones that
influence appetite in ways that increase caloric intake.
43
Given these epidemiological, biological,
and behavioral evidences from large-scale observational and experimental studies, the
association between poor sleep and weight gain is well-established.
Poor sleep’s effect on weight gain is especially of interest in minority populations. A
growing body of research suggests that racial/ethnic and socioeconomic differences in sleep
health may help explain disparities in cardiometabolic outcomes.
44
Various studies have
consistently found Black, Hispanic, and Asian populations are at greater risk for more frequent
5

and severe presentations of poor sleep health, such as shorter sleep duration, worse sleep quality,
and increased sleepiness, compared to White populations.
44–47
Possible mechanisms explaining
these racial and ethnic disparities include neighborhood disadvantage, occupational stressors,
psychosocial stressors, and limited access to treatment.
48
Specifically, in Hispanic populations,
recent evidence suggests acculturation may pose a risk to poor sleep; a nationally representative
study reported that Mexican-Americans are 44% more likely to report short sleep duration than
Mexican immigrants.
49
Furthermore, not only are racial and ethnic minorities more likely to
experience poor sleep health, but their negative health consequences of poor sleep are also more
pronounced. Based on the National Health Interview Survey, among those reporting extremely
short or long sleep duration, Black participants had higher risk of metabolic outcomes, such as
type 2 diabetes mellitus, than White participants.
50
However, underlying mechanisms explaining
this phenomenon and whether this finding applies to other metabolic outcomes, such as weight
gain, remains elusive. Therefore, further research is needed in elucidating poor sleep as a
predictor of health outcomes in racial and ethnic minorities.
Sleep Health and Weight Gain: Evidence in Pregnant Populations
Even with the high rates of sleep problems and excessive GWG in pregnant persons, the
relationship between prenatal sleep health and GWG remains inconclusive. There are only six
studies to date that have examined whether and how sleep health during pregnancy affects GWG,
out of which only one has been conducted in the U.S. These studies have led to inconsistent
results, most likely in part due to the heterogenous populations and varied study methodologies.
For example, a study in Sri Lankan women found short sleep duration during mid to late
pregnancy to increase the odds of inadequate weight gain, while a study in the U.S. found the
opposite.
51,52
A separate study in the Netherlands found those who slept more than 9 hours a
night showed reduced odds of excessive GWG, but a study in Ireland found that those who slept
6

more than 10 hours a night showed increased risk of excessive GWG.
53,54
In addition, of the
three studies that have examined the association between perceived sleep quality and weight
gain, one found poor sleep to increase risk of inadequate weight gain, another found the exact
opposite, and the last found no association.
52,55,56

Given that prevalence and implications of inadequate or excessive GWG significantly
differs by study populations, it is difficult to compare these findings to fully appreciate how sleep
health may be affecting GWG. The significant variation in study design, such as the frequency,
timing, and method of assessment for sleep health and weight during pregnancy, also add to the
complexity of discerning the findings from this small body of literature. Furthermore, there are
no existing studies examining the relationship between sleep and GWG in racial and ethnic
minorities in the U.S., even though pregnant women of color are known to report significantly
shorter sleep duration than pregnant White women.
57
Therefore, the relationship between
prenatal sleep health and GWG remains inconclusive to this day, especially in health disparities
populations.
The Hypothalamic Pituitary Adrenal Axis and Gestational Weight Gain
The Hypothalamic Pituitary Adrenal Axis
An emerging hypothesis posits that neurohormonal pathways
may explain the association between sleep and weight gain. One
such mechanism that is closely linked with both sleep and weight
gain is the Hypothalamic-Pituitary-Adrenal (HPA) axis (Figure 1).
The HPA axis is a major component of the neuroendocrine system
that comprises of a synergistic cascade of enzymes and hormones. It
consists of the production of the corticotropin-releasing hormone
(CRH) from the Hypothalamus, which regulates adrenocorticotropin hormone (ACTH) secretion
Figure 1. The HPA Axis
40

7

from the anterior pituitary gland, which in turn stimulates the adrenal glands to secrete
cortisol.
58–60

Cortisol is the primary glucocorticoid hormone produced by the adrenal cortex when the
HPA axis is triggered under stress.
61
Stress is a multidimensional construct that constitutes any
challenging condition that presents an environmental demand that exceed the body’s resources
and ability to maintain homeostasis.
62
When experiencing stress, cortisol is on one of the main
hormones involved in the “fight or flight” response, which focuses bodily resources and cellular
activity away from long-term metabolic processes to those primarily intended for immediate
survival, such as increasing heart and breathing rate, decreasing appetite, and suppressing the
immune system.
61

In addition to its rapid peaks in reaction to stressors, cortisol levels follow a circadian
pattern, characterized by increasing levels at awakening, followed by a steep peak at 30-45
minutes after awakening, and a slow decline until bedtime.
63,64
Researchers often measure this
diurnal rhythm through repeated sampling of saliva throughout the day, as saliva collection is
non-invasive and simple enough for self-administration outside the laboratory setting.
65
Salivary
cortisol levels can then be examined at each assessment point or circadian profiles can be
summarized using aggregate measures. The most widely used approaches including calculating
the Area Under the Curve (AUC). This captures the circadian change in cortisol levels across a
specific time point and can be calculated with respect to ground/baseline levels (AUCg) or with
respect to increase or reactivity (AUCi).
63
Researchers also often characterize the Cortisol
Awakening Response (CAR), the marked increase in cortisol levels across the first 30-45
minutes after wakening,
66
and the Diurnal Cortisol Slope (DCS), often defined as the rate of
decline from morning to evening.
67
Dysregulated diurnal rhythms include profiles in which
8

cortisol levels are lower in the morning and fail to drop later in the day (e.g., higher AUCg, and
flatter DCS, CAR, and AUCi) during pregnancy.
68–70

In order to maintain the optimal secretion of circadian cortisol at the basal (non-stressed)
and acute activation (stressed) condition, the HPA axis exerts negative feedback at both the
hypothalamic (long feedback loop) and pituitary level (short feedback loop) as a self-regulatory
mechanism.
71
This negative feedback loop is crucial in limiting long-term exposure of
physiological systems to the catabolic and immunosuppressive processes necessary for
survival.
71
However, chronic and repeated exposure to stress can disrupt this negative feedback
mechanism and elicit HPA axis dysregulation, which could present itself as hypercortisolism,
hypocortisolism, or diurnal dysrhythmia.
72,73
While the term chronic stress is often used
synonymously with long-term psychological stress, HPA axis dysregulation can occur under
other demanding physiological conditions such as substance abuse, addiction, and inflammatory
diseases such as asthma, eczema, and rheumatoid arthritis. More recently, there has been a
growing number of literature examining the association of metabolic syndromes, such as obesity,
with HPA axis dysregulation.
74

The Hypothalamic Pituitary Adrenal Axis, Obesity, and Weight Gain: Evidence in Non-Pregnant
Populations
People with obesity often manifest metabolic symptomology, such as hyperglycemia,
hyperlipidemia, insulin resistance, and hypertension, that are similar to those of
hypercortisolism.
75
Though evidence supporting systemic hypercortisolism in individuals with
obesity remains controversial, there are various intracellular mechanisms that may explain the
potential role of obesity on cortisol abnormalities. For example, adipose tissue is known to
secrete adipokines, a type of cytokines known for their vast inflammatory and metabolic effects
that also activates the HPA axis at the hypothalamus, anterior pituitary gland, and adrenal
9

cortex.
76,77
Furthermore, white adipose tissue, specifically visceral adipose tissue, has elevated
levels of 11β-hydroxysteroid-dehydrogenase type 1 (11β-HSD1), which generates cortisol from
cortisone (the inactive precursor molecule of cortisol), and decreased levels of 11β-HSD2, which
converts cortisol to the inactive cortisone.
61
Lastly, individuals with abdominal obesity show
increased responsiveness of the HPA axis to food intake
78
and vasopressin,
79
which has led
researchers to conclude that abdominal obesity is associated with both overactivation of the HPA
axis through attenuation of its negative feedback system and reduced diurnal variation in cortisol
levels.
80

While there is evidence supporting obesity’s association with elevated cortisol levels, the
reciprocal relationship of an overactive HPA axis leading to weight gain has also been
documented. For instance, cortisol is causally linked to accumulation of fat cells and weight
gain, as it plays an essential role in promoting adipogenesis—the differentiation of preadipocytes
to mature adipocytes.
81
Elevated cortisol levels is also known to dysregulate neuropeptides
(Neuropeptide Y) and hormones related to appetite (Leptin) in ways that increases food intake,
which ultimately leads to increased weight and total body fat mass. Given these evidences,
scientists have hypothesized that cortisol dysregulation may play a key role in the development
of obesity.
59
Applying knowledge of this reciprocal relationship between obesity/weight gain and
the HPA axis could help elucidate neurohormonal predictors of excessive GWG.
The Hypothalamic Pituitary Adrenal Axis, Obesity, and Weight Gain: Evidence in Pregnant
Populations
Before we can begin examining this hypothesis in pregnant populations, we must first
consider the significant alterations in HPA functioning that occur during pregnancy. First,
maternal cortisol levels increase two to three fold during the second and third trimester.
82
This
rise in cortisol during pregnancy is due to the increased production of cortisol from the maternal
10

HPA axis, activation of the fetal HPA axis during late pregnancy, and placental production of
corticotropin-releasing hormone (CRH) (Figure 2).
83
Second, typical cortisol reactivity to
physiological and psychological stress is hypothesized to be significantly different in pregnant
mothers compared to the general population.
84
Studies using laboratory-based stress tasks have
identified HPA axis reactivity to be significantly diminished during late pregnancy compared to
early pregnancy.
85
Some have hypothesized that the physiological stress response in pregnant
mothers is blunted as an adaptive process to protect the fetus from the increased glucocorticoid
levels during pregnancy.
86
To add to the complexity, the expected circadian rhythm is sustained
during pregnancy, but some studies have found the CAR to be attenuated in late pregnancy
compared to early pregnancy.
85

Figure 2. Changes in the HPA axis and cortisol levels during pregnancy
83

To date, there has only been a handful of studies that has examined differential levels of
cortisol secretion by pre-pregnancy BMI and the association between HPA axis functioning
during pregnancy and GWG. While some found higher pre-pregnancy BMI to be associated with
lower cortisol levels earlier in the day (i.e. awakening, 30 minutes after awakening, and early
afternoon),
87,88
others only identified higher evening cortisol levels in participants with obesity
11

compared to those without.
89
In addition, some have found no association between cortisol and
GWG, regardless of pre-pregnancy obesity status,
90
while others report increased evening
cortisol levels in pregnant women with obesity who gain excessive gestational weight.
89
There
has also been a study that found total cortisol secretion during the third trimester to be associated
with higher GWG, but only in women with class 1 obesity before pregnancy.
88

Though results in pregnant populations remain mixed, studies in non-pregnant adult
populations have shown the relationship between glucocorticoids and weight gain, specifically
from increased adiposity, may differ by levels of abdominal obesity.
91
Specifically, findings
imply that glucocorticoids, including cortisol, are related to metabolic processes in individuals
with obesity in ways that could further stimulate weight gain and increased adiposity.
92
Given
pre-pregnancy BMI’s crucial role in healthy GWG and its suggested association with HPA axis,
it’s important we consider pre-pregnancy BMI when elucidating the role of HPA axis
functioning on GWG.
Cortisol: Potential Link Between Poor Prenatal Sleep to Gestational Weight Gain
Circadian cortisol profiles have been repeatedly shown to be closely associated with
various sleep dimensions. Experimental studies have found shorter sleep duration to be associated
with reduced morning cortisol levels, higher afternoon/evening cortisol levels, and flatter DCS.
93,94

Poor subjective sleep quality, higher frequency of nightly awakenings, and increased sleepiness
upon awakening are all negatively correlated with cortisol levels at awakening on the next day.
95

Though the literature is more limited, similar relationships between poor sleep health and diurnal
cortisol profiles have been identified in pregnant populations as well. One study found poor
sleepers to show attenuated CAR in late pregnancy.
96
Another more recent study reported greater
evening cortisol concentrations in women experiencing poor sleep, but only during the third
trimester.
97
Although further research is needed in pregnant populations, there is substantial
12

research supporting the relationship between sleep health dimensions and dysregulated circadian
cortisol profiles.
Given this evidence on sleep’s relationship with cortisol secretion and the aforementioned
metabolic effects of cortisol, dysregulation of the HPA axis has been hypothesized as a mechanism
linking poor sleep to weight gain in non-pregnant populations.
98
In pregnant women, there has
been one recent pilot study showing preliminary results supporting circadian cortisol profiles as a
mediator between obstructive sleep apnea during pregnancy and gestational diabetes. However,
no study to date has investigated the role of cortisol as a potential pathway between prenatal sleep
health and excessive GWG.
Sleep: A Putative Intervention Target for Excessive Gestational Weight Gain
Given the potential behavioral and biological association between sleep and weight gain,
promotion of sleep health during pregnancy could be a novel and potentially more effective
strategy against excessive GWG. Regardless of race or socioeconomic status, majority of women
rate sleep as one of the most important health behavior during pregnancy, alongside not smoking
or consuming alcohol.
99
Other studies have found that women perceive healthy sleep as
significantly more important than exercising regularly or having an active lifestyle during
pregnancy.
25,100
Black, African American, and Hispanic women are also more likely to report
pregnancy as time to rest and simply focus on “sleeping and eating”.
24
Therefore, the promotion
of healthy sleep may be more in agreement with cultural and personal beliefs of pregnant people,
especially people of color, compared to current recommendations on diet and exercise during
pregnancy.
Furthermore, while we know that encouraging physical activity and healthy eating during
pregnancy can be challenging, sleep is particularly amenable to intervention even during the
13

perinatal period.
101,102
To improve sleep health during pregnancy, sleep hygiene and education
are first-line treatment options with proven effectiveness.
103
Randomized clinical trials have also
found significant improvement in insomnia symptoms for women who received cognitive
behavioral therapy.
101,104
Furthermore, several studies have reported mindfulness-based
interventions and yoga to significantly improve sleep health dimensions during pregnancy.
102,105

Therefore, compared to interventions aimed to change exercise or eating routines in pregnant
people, promoting healthy prenatal sleep could be more widely accepted and effective in
preventing excessive GWG, especially in minority mothers.
Conclusion
Despite current clinical and public health efforts, the prevalence of excessive GWG
among pregnant people continues to increase, especially among low-income and minority
people. To understand this phenomenon and explore alternative modifiable predictors of
excessive GWG, this dissertation proposes to explore the role of poor prenatal sleep health in
weight gain during pregnancy. We will additionally examine the HPA axis and circadian cortisol
profiles as a potential physiological mechanism that mediates the relationship between sleep
during pregnancy and GWG. Elucidating sleep health as a predictor of excessive GWG could
inform future efforts promoting healthy weight gain in pregnancy that are more widely accepted
by and successful in minority mothers.
Limitations in existing literature

In addition to the general lack of available research on prenatal sleep, circadian cortisol,
and GWG in pregnant mothers, prior studies examining these constructs have several limitations
that restrict their mechanistic and practical impact and applicability to pregnant populations.
14

Lack of Repeated Assessment across Pregnancy Period
The majority of studies examining sleep, HPA axis functioning, or weight during
pregnancy have adopted a cross-sectional study design. These studies typically limit the
participants to those in a specific trimester,
53,54
or recruit mothers from all trimesters, then control
for gestational age in the model.
31,106,107
Longitudinal study designs with repeated assessment
throughout pregnancy allow researchers to model predictors at specific time points during
pregnancy and their change across time. For example, longitudinal examination of weight during
pregnancy allows for quantification of not only total GWG—the most common method of
examining pregnancy weight gain— but also weight gain trajectory and weekly rate of weight gain.
Since the timing and rate of weight gain has implications for risks of long-term chronic disease,
it’s important to study not only the total amount of weight gained but also how people are gaining
this weight throughout the pregnancy period.
5,14

Failure to Examine Daily Effects in Real-World Settings
To date, there have only been two studies examining the relationship between sleep and
diurnal cortisol regulation during pregnancy.
96,108
One these studies implemented a retrospective
questionnaire on subjective sleep quality for the past month then collected salivary cortisol samples
during the three days following the questionnaire.
97
The other was an experimental overnight study
where sleep and cortisol data were collected as participants slept in a laboratory.
96
Both of these
studies pose methodological flaws that limits the understanding of prenatal sleep health and diurnal
cortisol profiles.
A large body of evidence shows that participants often cannot accurately recall past
experiences, especially if they are frequent, mundane, and irregular.
109
Sleep is one such behavior
that is highly sensitive to systematic biases. When participants are asked to recall and summarize
their previous experience, their answers are often more reflective of those that occurred more
15

recently and/or were most memorable (e.g., nights with extreme disturbances). Studies comparing
different methods of sleep measurement have found retrospective measures of sleep to lack
consistent correlations with daily sleep logs.
110
Therefore, while retrospective measures of sleep
are crucial in epidemiological studies, the additional data provided from daily assessments help
reduce recall bias and provide another perspective in understanding participants’ sleep health.
In addition, laboratory-based studies designed for the observation of sleep or diurnal
cortisol provide poor understanding of these constructs in everyday life. While these studies allow
for accurate quantification of sleep health beyond self-reported measures, sleeping in a laboratory
setting have limited generalizability to sleep that is experienced in one’s own home. Similar issues
arise with salivary cortisol measurement, where participants are able to provide cleaner samples
that more precisely follow collection protocols in laboratories, but they do not reflect the usual
circadian rhythms that are reflective of that participant’s regular lived experiences.
111
Therefore,
while laboratory studies provide more accurate measures of sleep and cortisol in a controlled
environment, their generalizability to everyday life is limited.
Studies utilizing ecological momentary assessment (EMA) address these issues by
collecting data that is reflective of both real-world and real-time experiences. EMA is
characterized by the repeated collection of participants’ momentary states in a natural
environment.
109
So rather than having participants recall their average sleep quality over the past
month, they instead report their sleep quality from the previous night first thing in the morning.
And instead of participants coming into a laboratory to measure salivary cortisol, they self-
administer sample collection in their usual everyday setting as they go about their day. Thus,
researchers can quantify measures such as sleep and diurnal cortisol with high ecological validity.
Furthermore, EMA methods allow for the examination of within-subject variability, such as
16

fluctuations in sleep health across days and fluctuations in cortisol levels within days.
109
The
combination of daily sleep and circadian cortisol data allows for the modeling of the day-level
associations between these two constructs, which has yet to be conducted in pregnant populations.
Lack of Studies with Diverse Mothers
Rates of overweight and obesity among people of child-bearing age—those who are most
likely to gain excessive GWG—are disproportionately high in people of lower socioeconomic
status and people of color.
112
Yet, the majority of studies on excessive GWG have been conducted
in non-Hispanic White populations.
53,113
For example, in a recent review of 35 manuscripts
examining the predictors of excess GWG, the study population consisted of predominantly non-
Hispanic White women in 22 studies, while only 7 studies were conducted in Hispanic women and
3 studies in Black women. This lack of literature also applies to the field of prenatal sleep health
and HPA axis functioning, even though minority populations are more likely to experience sleep
disorders and dysregulation of the HPA axis.
44,114–116
The dearth of literature may be, at least in
part, contributing to the lack of success in curtaining excessive GWG in minority populations and
those of low socioeconomic status. Therefore, it’s crucial that we expand our knowledge of
behavioral and physiological mechanisms underlying excessive GWG in groups who
disproportionately bear the highest burden of this disease. This could help inform the next
generation of prenatal programs promoting healthy weight gain that could be more culturally
sensitive and widely endorsed amongst people of color.
Dissertation Goals to Address Current Limitations in Literature
The overall goal of this dissertation is to examine both population-level (between-person)
and day-level (within-person) associations among sleep health, diurnal cortisol profiles, and
GWG throughout pregnancy in a predominantly Hispanic and low-income populations. This
project will leverage data from pregnant women enrolled in the Maternal and Developmental
17

Risks from Environmental and Social Stressors (MADRES) Study and a subsample of pregnant
women from the real-time personal monitoring study in MADRES. Cohort studies, such as
MADRES, have the benefit of longitudinal assessment and increased power to capture between-
subject variability with high external validity. On the other hand, the real-time personal
monitoring study employs EMA methodology that can capture time-intensive data and assess
within-subject variability over multiple days in a naturalistic setting. Through the combined
usage of the MADRES study and real-time personal monitoring study, this dissertation considers
both 1) behavioral and physiological longitudinal-processes across the entire pregnancy and 2)
within-day processes that may contribute to excessive GWG in an accumulated manner over the
pregnancy timeframe. Thus, this dissertation will leverage the strengths of both studies and
examine the association between prenatal sleep health, HPA axis functioning, and GWG from
both a population and behavioral health perspective.
The proposed study design, consisting of three separate research papers, is uniquely
positioned to allow the investigation of both longitudinal changes in sleep, cortisol, and GWG
throughout pregnancy and day-level fluctuations in sleep and cortisol. The longitudinal study
design of MADRES permits us to overcome the limitations posed in a cross-sectional study and
examine how early pregnancy sleep health may influence late pregnancy HPA axis functioning
and weight gain. It also allows for modeling of individual participants’ weight gain rate on a
weekly basis. We will then complement these findings with data on the day-level association of
sleep and diurnal cortisol. The real-time personal monitoring study will allow us to repeatedly
capture behavioral and biological markers with high ecological validity while limiting recall bias.
Lastly, the dissertation will shed light on underlying mechanisms of excessive GWG in the
population who are most vulnerable and least able to rectify the social, behavioral, and
18

environmental causes of this disease. By elucidating prenatal sleep health as an important factor
that informs weight gain trends in minority populations, findings could help improve current
intervention and prenatal care recommendations to be more culturally appropriate and effective in
all expecting mothers.
Specific Aims and Conceptual Model

The overall goal of this dissertation is to examine both population-level and day-level
associations among sleep health, diurnal cortisol profiles, and GWG throughout pregnancy.
AIM 1: Investigate the moderated relationship between early-to-mid pregnancy sleep quality and
mid-to-late pregnancy weekly GWG rate by pre-pregnancy BMI using growth curve modeling.
H1: Compared to pregnant persons with better sleep quality, those with worse sleep quality
during early-to-mid pregnancy will exhibit higher weekly GWG rate during mid-to-late
pregnancy.
AIM 2: Investigate the effect of early-to-mid prenatal sleep quality on total GWG and the
mediating effect of late pregnancy circadian cortisol profiles on this relationship, while
simultaneously considering the moderating effect of pre-pregnancy BMI.
H2: Pregnant persons with poorer early-to-mid pregnancy sleep quality will have higher total
GWG, and this association will be mediated via dysregulated diurnal cortisol profiles (i.e.,
higher AUC and flatter DCS and CAR) in late pregnancy.
AIM 3: Investigate the between and within-subject level associations of sleep disruptions and
morning cortisol profiles during pregnancy using daily EMA.
19

H4: On the between-subject level, people with more frequent sleep disruptions would, on
average, exhibit more dysregulated cortisol profiles, characterized by lower awakening
cortisol, awakening+30min cortisol, and CAR.
H5: On the within-subject level, on any given night, more frequent sleep disruptions would
be followed by more dysregulated cortisol profiles, characterized by lower awakening
cortisol, awakening+30min cortisol, and CAR, on the next day.

Figure 3. Conceptual Model of Prenatal Sleep Health, Diurnal Cortisol Profile, and Gestational Weight Gain

20

Chapter Two: Study One
Poor Sleep Quality Increases Gestational Weight Gain Rate in
Pregnant People: Findings from the MADRES Study

Abstract

Background: The majority of pregnant persons in the U.S. gain excessive weight during
pregnancy. Excessive gestational weight gain (GWG) increases the risk of adverse maternal and
child health outcomes. Poor sleep quality is associated with weight gain in non-pregnant
populations, but evidence in pregnant people is lacking. Our study examined the association
between early-to-mid pregnancy sleep quality and weekly GWG rate during mid-to-late
pregnancy by pre-pregnancy body mass index (BMI).
Method: Participants were 316 pregnant predominantly low-income Hispanic participants from
the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES)
study. During early-to-mid pregnancy (≤20 weeks gestation), participants (mean age=28.7± 6.0
years) reported their sleep quality which was used to construct four categories: very poor, poor,
good, and very good. Linear growth curve models examined the association between early-to-
mid pregnancy sleep quality and weekly rate of GWG (kg/week) during mid-to-late pregnancy
(>20 weeks gestation), with a three-way cross-level interaction between gestational age, sleep
quality, and pre-pregnancy BMI category. Models adjusted for ethnicity by birthplace,
hypertensive disorders, perceived stress score, and physical activity level.
Results: Overall, poorer early-to-mid pregnancy sleep quality was associated with increased
weekly weight gain during mid-to-late pregnancy. For example, amongst normal weight
participants, mid-to-late pregnancy weight gain was, on average, 0.39 kg (95% CI: 0.29, 0.48)
21

per week for those with very good sleep quality, 0.53 kg (95% CI: 0.44, 0.61) per week for those
with poor sleep quality, and 0.54 kg (95% CI: 0.46, 0.62) per week for those with very poor
sleep quality during early-to-mid pregnancy. This difference in GWG rate was statistically
significantly comparing very good to poor sleep (0.14 kg/week, 95% CI: 0.01, 0.26) and very
good to very poor sleep (0.15kg/week, 85% CI: 0.02, 0.27). This association between sleep
quality and GWG rate did not statistically differ by pre-pregnancy BMI.
Conclusion: Our study found very poor early-to-mid pregnancy sleep quality was associated
with higher mid-to-late pregnancy GWG rate. Incorporating pregnancy-specific sleep
recommendations into routine obstetric care may be a critical next step in promoting healthy
GWG.

22

Introduction

Gaining a healthy amount of weight during pregnancy that follows the Institute of
Medicine (IOM) recommendation is important for the immediate and future health of the
expectant mother and her baby.
6,7
And yet, less than one in three pregnant persons currently
gains the recommended amount of weight during pregnancy. About one in five gain inadequate
weight, and almost half gain excessive weight during pregnancy.
11
This high rate of excessive
gestational weight gain (GWG) is problematic, given that excessive GWG increases the risk of
various pregnancy complications and long-term chronic health conditions.
12–14

Alarmingly, rates of excessive GWG are even higher among minority pregnant persons
and those with low socioeconomic status. For example, compared to the average prevalence of
35% in the United States (U.S.) population, rates are as high as 53% in low-income populations,
51% in Hispanic persons, and 61% in Black persons.
16,17
In an attempt to curtail excessive GWG
rates, current public health research and inventions have mostly focused on increasing physical
activity and promoting healthy eating.
7
Unfortunately, these efforts have not consistently led to
success, especially among minority and low-income mothers.
19–22,117,118
Thus, there is a need to
identify alternative modifiable predictors of excessive GWG to improve the current prenatal care
recommendations around healthy weight gain.
Poor sleep health is a putative risk factor for weight gain and other cardiometabolic
outcomes in non-pregnant populations.
35–37
Sleep health is a multidimensional construct that
includes duration, continuity/efficiency, timing, alertness/sleepiness, and satisfaction/quality.
28

Two U.S. based population-level studies using the Behavioral Risk Factor Surveillance System
and National Health and Nutrition Examination Survey data found short sleep duration is both
cross-sectionally and longitudinally associated with increased body mass index (BMI) and risk of
23

obesity.
37,38
Another German national study found poor sleep quality was cross-sectionally
associated with general obesity and high body fat.
119
Furthermore, an Australian survey found
poor sleep health—which consisted of subjective sleep quality, frequency of disturbances, and
duration – to be longitudinally associated with increased BMI and waist circumference.
120

Several systematic reviews and meta analyses have also consistently found poor sleep to be
associated with weight gain.
35,121,122

Given that almost all pregnant persons report poor sleep health, sleep may be a key health
behavior in improving GWG outcomes.
29,123
While poor sleep quality and sleep duration outside
the recommended range are both associated with weight gain, in this study, we focused on a
subjective measure of sleep quality for several reasons. One, almost all (85-100%) of pregnant
persons report poor sleep quality at some point in their pregnancy while only about one in three
report short (<7 hours of sleep) sleep duration.
124
Two, sleep quality scores derived of various
sleep dimensions consistently outperform a single measure of sleep duration in predicting
cardiometabolic outcomes regardless of population.
125
And lastly, despite a small but growing
body of research examining the effect of various indicators of sleep health on weight gain during
pregnancy, there has only been three studies to date that have examined the sleep quality-GWG
association.
126
Gay et al. found poorer subjective sleep quality during the last month of
pregnancy was positively associated with risk of excess GWG.
127
A separate study by Baliero et
al. showed Brazilian pregnant women with poor sleep quality during the 1
st
trimester gained
more weight during the 2
nd
and 3
rd
trimester.
55
Lastly, Lyu et al. reported that poor sleep quality
during mid or late pregnancy were both associated with excessive GWG in Chinese women.
128

While these three studies consistently suggest that poor sleep quality increases the risk of weight
gain during pregnancy, the body of evidence is still limited in this population.
24

In addition to the sheer lack of research on this topic, there remains several limitations
that have left sleep quality’s influence on GWG unanswered. First, only one study has been
conducted in the U.S.
127
Given that prevalence and implications of excessive GWG significantly
differ by country and population, it is difficult to apply the current findings to understand trends
in the U.S. Second, the study population in the U.S. study consisted of majority non-Hispanic
White women with a bachelor’s degree or higher.
127
Thus, we still lack understanding of how
sleep quality affects GWG in U.S. pregnant people from health disparities populations. Third,
most studies only assessed sleep quality during the third trimester, even though studies have
found early-to-mid pregnancy sleep to be more predictive of cardiometabolic outcomes than late
pregnancy sleep.
129–132
Sleep quality assessed at the end of pregnancy can also be influenced by
the amount of weight gained up to that time point, making it difficult to assess the directionality
of the association between sleep and weight.
133
Fourth, the majority of the studies only examine
total weight gain as the outcome of interest and disregards weekly weight gain rate, even though
both are important predictors of various prenatal health.
134
And lastly, most studies did not
examine pre-pregnancy BMI as an effect modifier. This leaves a significant gap in the literature,
as there are some evidence that the relationship between sleep and weight gain differs by pre-
pregnancy BMI,
51,127
and the clinical implications of weight gain is meaningless without
information on the patient’s pre-pregnancy BMI.
To address these gaps in the literature, our study investigated the moderated relationship
between early-to-mid pregnancy sleep quality and mid-to-late pregnancy weekly GWG rate by
pre-pregnancy BMI using growth curve modeling. We hypothesized that compared to pregnant
participants with better sleep quality, those with worse sleep quality during early-to-mid
25

pregnancy will exhibit higher weekly GWG rate during mid-to-late pregnancy. We also explored
pre-pregnancy BMI status as a possible moderator.
Methods
Sample Population
The Maternal and Developmental Risks from Environmental and Social Stressors
(MADRES) study is an ongoing prospective cohort study of primarily Hispanic, low-income
pregnant persons and their children in Los Angeles, California. Details of the MADRES protocol
have been described elsewhere.
135
Participants were recruited from Los Angeles County + USC
(LAC+USC) Medical Center, the Women’s Health Center at Eisner Health, and the South-
Central Family Health Center. Inclusion criteria were: (1) <30 weeks pregnant at the time of
enrollment, (2) ≥18 years of age, (3) singleton pregnancy, and (4) English or Spanish speaking.
Exclusion criteria were (1) HIV positive status; (2) physical, mental, or cognitive disabilities that
prevent participation; (3) current incarceration; or (4) multiple gestation. Maternal consent and
HIPAA authorization for abstracting electronic medical records (EMR) was obtained prior to any
study assessment. The Institutional Review Board at the University of Southern California
approved all aspects of this study.
For this study, we limited the analysis to participants who enrolled at less than 20 weeks
gestation. The data used in this analysis consisted of 1) early-pregnancy in-person visit within 2
weeks of study recruitment, 2) mid-pregnancy telephone interview between 18-27 weeks
gestation, 3) late-pregnancy visit between 30 and 34 weeks and 4) EMR abstraction from
prenatal care clinic visits. All interviews were conducted in English or Spanish, and the in-person
visit also included anthropometric measurements of height and weight.
26

Measures
Prenatal Sleep Quality
Overall sleep quality was assessed using the four-item Jenkin’s Sleep Questionnaire
(JSQ).
136
The JSQ is a commonly used questionnaire in epidemiologic and clinical intervention
studies with pregnant populations.
137–139
The JSQ consists of the following questions: “How
often in the past 30 days did you have the following symptoms?”: (1) Trouble falling asleep, (2)
Waking up several times per night, (3) Trouble staying asleep and (4) Waking up after the usual
amount of sleep, feeling tired and worn out. The six response alternatives are: not at all, 1–3
nights, 4–7 nights, 8–14 nights, 15–21 nights and 22–31 nights. “Not at all” signified no sleep
problems, “1–3 nights” signified rare sleep problems, “4–7 or 8–14 nights” signified occasional
sleep problems, and “15-21 or 22-31 nights” signified frequent sleep problems. The response to
each item is recoded on a 0- to 5-point scale and totaled to create a summary sleep score that
ranges from 0 to 20 with higher scores indicating worse overall sleep quality. We then
categorized the summary score into quantiles. A score of 0-3 represented very good-quality
sleep, 3-6 represented good-quality sleep, 6-9 represented poor-quality sleep, and 9+ represented
very poor-quality sleep.
The JSQ was administered at the early-pregnancy visit (mean=12.9 weeks gestation,
SD=3.7 weeks) and mid-pregnancy telephone interview (mean=20.4 weeks gestation, SD=2.8
weeks).
136
We then applied an algorithm to determine which survey would be used to quantify
the early-to-mid pregnancy (<20 weeks gestation) sleep quality (Supplemental Figure 1). If both
surveys were missing or conducted during mid-to-late pregnancy (≥20 weeks), we excluded the
participant from the study. If either survey was missing, we used the other available survey only
if it was conducted during early-to-mid pregnancy; if it was conducted during mid-to-late
pregnancy or missing, the participant was excluded from the study. If both surveys were
27

conducted during early-to-mid pregnancy, and they were conducted within 8 weeks of each
other, we averaged the two survey answers. If the two surveys were conducted more than 8
weeks apart, we used whichever survey was closest to 15 weeks gestation (the average week
when early-to-mid pregnancy sleep was assessed). This decision was made based on exploratory
analyses showing strong positive correlation between the sleep quality data from early-
pregnancy and mid-pregnancy surveys if they were less than 8 weeks apart (r=0.74, p<0.01 if
less than 4 weeks apart; r=0.69, p<0.01 if 4-8 weeks apart). To examine whether this decision
significantly altered the findings, we ran three separate sensitivity analyses: 1) only using early-
pregnancy sleep quality data, 2) only using mid-pregnancy sleep quality data, and 3) averaging
the sleep quality data from both surveys.
Maternal Height and Weight
Maternal weight and height during pregnancy were abstracted from EMRs and measured
by trained staff during each of the study visits using an electronically calibrated digital scale
(Tanita, Perspective Enterprises, Portage, MI) and a commercial stadiometer (Model PE-AIM-
101, Perspective Enterprises) to the nearest 0.1 kg and 0.1 cm, respectively.
Self-reported pre-pregnancy weight was also ascertained through interviewer-
administered questionnaires during pregnancy. If missing, then the first weight of the index
pregnancy (obtained from the maternal medical records) was used in lieu of self-reported pre-
pregnancy weight. Self-reported pre-pregnancy weight and height were used to calculate the pre-
pregnancy BMI (kg/m
2
) and classified using CDC categories: normal weight (BMI ≥18.5 and <
25), overweight (BMI ≥25 and < 30), and obesity (BMI ≥30).
140
Participants were flagged if the
self-reported pre-pregnancy weight and first measured weight were more than ±10kg discrepant,
28

and sensitivity analyses were conducted to see if exclusion of these participants influenced study
findings.
Covariates
A list of potential covariates was identified a priori based on a literature review on the
correlates of sleep quality and GWG. Then, bivariate analyses of the covariates with sleep
quality were conducted using student’s t-tests, ANOVAs, and chi-square tests. Bivariate analyses
of the covariates with GWG rate were conducted using growth curve models that included
gestational age, covariate, and interaction term between gestational age and covariate. All
statistical significance were examined using two-sided tests with α=0.10. All covariates
significantly associated with both sleep quality and GWG rate were included in the model.
The final list of covariates included were ethnicity by birthplace (ref: foreign-born
Hispanic, US-born Hispanic, and non-Hispanic), hypertensive disorders (ref: normal,
preeclampsia or gestational hypertension, and chronic hypertension), perceived stress score, and
physical activity level. Perceived stress was measured using Cohen’s Perceived Stress Scale
(PSS), and physical activity level was assessed via the Pregnancy Physical Activity
Questionnaire (PPAQ). The PSS, PPAQ, and ethnicity by birthplace were assessed during the
early-pregnancy visit. Hypertensive disorder data were collected from EMR. We ran sensitivity
analyses by excluding those with chronic hypertension and chronic hypertension with
preeclampsia. All continuous covariates (perceived stress and physical activity level) were
centered at the mean to aid interpretation and reduce multicollinearity.
141
While both the PSS and
PPAQ were assessed during both early-pregnancy and late-pregnancy, exploratory analyses
found they were similarly associated with sleep quality and GWG rate. Thus, we only used the
early-pregnancy data due to higher sample size.
29

Statistical Analysis
Descriptive and univariate analyses of participant characteristics were conducted to
examine the distributions of variables, to understand correlation and possible multicollinearity of
exposures, and to identify extreme observations. Additional analyses of residual distributions and
influential points (i.e., examination of leverage, cook’s D, jackknife residuals) were performed to
determine whether modeling assumptions were met before running the final model. All statistical
significance were examined using two-sided tests with α=0.05.
We examined the association between early-to-mid pregnancy sleep health and weekly
GWG rate during mid-to-late pregnancy (≥20 weeks gestation), while simultaneously testing
effect modification by pre-pregnancy BMI. We modeled this relationship using multilevel
growth curve modeling. Growth curve modeling with gestational age (weeks of pregnancy
centered at 20 weeks) as a predictor allows us to model the trajectory of weight gain throughout
the pregnancy.
142
Fitting the growth model within the multilevel modeling framework (level 1:
weight observation, level 2: participant) takes into account the multiple repeated observations of
maternal weight that are nested within each participant.
142
It also accounts for the different
number of weight observations per participant and varying time between each weight
observation and handles missing values in the outcome.
We proposed a model with weight of participant 𝑖𝑖 at each observation 𝑗𝑗 ( 𝑦𝑦 𝑖𝑖 𝑖𝑖 ) as a linear
function of week in pregnancy ( 𝑥𝑥 𝑖𝑖 𝑖𝑖 ) with a correlated random intercept and random slope and
autocorrelated residual structure to account for the nature of the dataset with repeated measures.
Since we also wanted to test whether this relationship was modified by pre-pregnancy BMI, we
created a three-way cross-level interaction term [sleep quality (level 2, ref: very good) × weeks
in pregnancy (level 1; centered at 20 weeks) × pre-pregnancy BMI (level 2, ref: normal)].
30

𝑦𝑦 𝑖𝑖 𝑖𝑖 = ( 𝛽𝛽 0
+ 𝑢𝑢 0 𝑖𝑖 ) + ( 𝛽𝛽 1
+ 𝑢𝑢 1 𝑖𝑖 ) 𝑥𝑥 𝑖𝑖 𝑖𝑖
+ 𝐴𝐴 1
𝑋𝑋 1
+ 𝐺𝐺 1
𝑋𝑋 1
× 𝑥𝑥 𝑖𝑖 𝑖𝑖
+ 𝐴𝐴 2
𝑋𝑋 2
+ 𝐺𝐺 2
𝑋𝑋 2
× 𝑥𝑥 𝑖𝑖 𝑖𝑖
+ 𝐴𝐴 3
𝑋𝑋 3

+ 𝜀𝜀 𝑖𝑖 𝑖𝑖

𝑋𝑋 1
= �
𝑆𝑆𝑆𝑆
1, 1
2
𝑆𝑆𝑆𝑆
1, 1
3
𝑆𝑆𝑆𝑆
1, 1
4
⋮ ⋮ ⋮
𝑆𝑆𝑆𝑆
𝑛𝑛 , 1
2
𝑆𝑆𝑆𝑆
𝑛𝑛 , 1
3
𝑆𝑆𝑆𝑆
𝑛𝑛 , 1
4

𝐵𝐵 𝐵𝐵𝐵𝐵
1, 1
2
𝐵𝐵 𝐵𝐵𝐵𝐵
1, 1
3
⋮ ⋮
𝐵𝐵 𝐵𝐵𝐵𝐵
𝑛𝑛 , 1
2
𝐵𝐵 𝐵𝐵𝐵𝐵
𝑛𝑛 , 1
3
�

�
𝑢𝑢 0 𝑖𝑖 𝑢𝑢 1 𝑖𝑖 � ~ 𝐵𝐵 𝑀𝑀 𝑀𝑀 �
0
0
,
𝜏𝜏 0
2
𝜌𝜌 𝜏𝜏 0
𝜏𝜏 1
𝜌𝜌 𝜏𝜏 0
𝜏𝜏 1
𝜏𝜏 1
2
�
� 𝑒𝑒 𝑖𝑖 𝑖𝑖 � ~ 𝐵𝐵 𝑀𝑀 𝑀𝑀 � 0 ,
1 𝜌𝜌 𝜌𝜌 2
𝜌𝜌 1 𝜌𝜌 𝜌𝜌 2
𝜌𝜌 1
�
SQ=Sleep Quality
BMI=Body Mass Index

In this model, 𝑋𝑋 1
is a n × 5 matrix with three indicator variables of sleep quality ( 𝑆𝑆𝑆𝑆
2
= good
sleep quality, 𝑆𝑆𝑆𝑆
3
= poor sleep quality, 𝑆𝑆𝑆𝑆
4
= very poor sleep quality) and two indicator variables
of BMI ( 𝐵𝐵 𝐵𝐵𝐵𝐵
2
= overweight, 𝐵𝐵 𝐵𝐵𝐵𝐵
3
= obesity), and n equals the total number of observations.
𝐴𝐴 1
𝑋𝑋 1
is the main effects of sleep quality and BMI. 𝐺𝐺 𝑋𝑋 1
× 𝑥𝑥 𝑖𝑖 𝑖𝑖 is the interaction of sleep quality
with gestational age and BMI with gestational age. 𝑋𝑋 2
is a n × 6 matrix of two-way interactions
between the three indicator variables of sleep quality and two indicators of BMI, such that the
first column is 𝑆𝑆𝑆𝑆
2
× 𝐵𝐵 𝐵𝐵𝐵𝐵
2
. 𝐴𝐴 2
𝑋𝑋 2
is the main effects of the two-way interaction between sleep
quality and BMI. 𝐺𝐺 2
𝑋𝑋 1
× 𝑥𝑥 𝑖𝑖 𝑖𝑖 is the three-way interaction between sleep quality, BMI, and
gestational age. 𝐴𝐴 3
𝑋𝑋 3
is the main effects for the covariates in the model.
31

Results

Sample Population
As of January 2023, of the 966 total participants, 720 participants had complete GWG
data (i.e., those who had already given birth) with 10,464 individual observations of weight
measurements. Of those participants, 537 were recruited before 20 weeks gestation, and 390 had
completed at least one sleep health survey during early-to-mid pregnancy (<20 weeks gestation).
We then excluded any weight observations before 20 weeks gestation, as we were only interested
in modeling mid-to-late pregnancy weight gain as the outcome. In addition, because we only had
8 participants who were underweight before pregnancy, we excluded them from the analysis to
aid interpretation of the interaction term with pre-pregnancy BMI. We also excluded 39
participants who had preterm births. This is common practice in GWG literature, as their weight
gain trajectory and behavioral risk factors are significantly different than those with term births,
but we also ran sensitivity analyses by including them.
143
Then, we excluded 19 participants who
only had one weight measurement, since at least two weight measurements are necessary to
examine change in weekly weight gain rate. Finally, based on exploratory data analysis, we
identified and excluded 9 observations that were diagnosed as influential points as these
observations were causing our model to deviate from assumptions and leading to convergence
issues. Individual spaghetti plots for these 9 observations can be found in Supplemental Figure 2.
A spaghetti plot of all influential points in comparison to all other participants can be found in
Supplemental Figure 3. The final analytical sample consisted of 316 participants and 3,009
observations of weight measurements. The consort diagram illustrating data availability can be
found in Supplemental Figure 4. Sensitivity analyses comparing the participants in this dataset to
the overall enrolled sample of 966 participants found no significant differences in participant
characteristics (Supplemental Table 1).
32

Descriptive Characteristics
Descriptive characteristics of the participants, overall and by pre-pregnancy BMI
category, can be found in Table 1. Participants in this study were on average 28.70 years old
(SD=6.00 years), and those with overweight and obesity were older compared to those with
normal pre-pregnancy BMI (F=3.66, p<0.05). The majority of participants were foreign-born
Hispanic (44.98%) or US-born Hispanic (35.92%). A higher proportion of US-born and foreign-
born Hispanic participants had overweight and obesity before pregnancy compared to those who
identified as non-Hispanic (ꭕ2= 23.97 p<0.01). The majority of participants (54.92%) did not
finish high school, and these participants had higher rates of obesity compared to those who did
finish high school (ꭕ2= 6.85, p=0.03). A large majority of participants were married or living
together with their partner (73.60%), but this did not differ by pre-pregnancy BMI. About one in
three (33.66%) participants were nulliparous, while another third had one child and the last third
already had two or more children. Multiparous participants had significantly higher rates of
overweight and obesity compared to those who were nulliparous (ꭕ2= 20.53, p<0.01). About one
in ten (12.66%) had preeclampsia or gestational hypertension, but almost one in three (32.48%)
had glucose intolerance or gestational diabetes. Both hypertensive and glucose intolerance
related pregnancy complications were higher amongst those with obesity compared to those with
normal BMI before pregnancy (hypertensive disorder: ꭕ2= 13.75, p<0.01; glucose tolerance
abnormality: ꭕ2= 22.46, p<0.01).
Participants’ total physical activity score in early pregnancy was 302.80 MET-h/week
(SD=153.35). This value is comparable to other studies that have found average physical activity
scores to range from as low as 126.0 MET-h/week (SD=81.5)
144
to 417.2 MET-h/week
(SD=146.2) during early-to-mid pregnancy.
145
The average score for the PSS was 12.49
33

(SD=6.38) on a range from 0 to 40; this score is comparable to other studies using the 10-item
PSS in pregnant persons.
146,147
The average score for nausea/vomiting based on the Pregnancy-
Unique Quantification of Emesis and nausea (PUQE) survey was 5.85 (SD=2.52); the PUQE
score ranges from 3 (no symptoms) to 15 (maximal symptoms), and a score <6 is considered to
be mild.
148
Lastly, about half of our participants (51.82%) were employed during early-to-mid
pregnancy. None of the time-varying characteristics differed by pre-pregnancy BMI.
Overall, 23.10% of participants reported very good sleep quality (JSQ score of 0-3),
26.90% reported good sleep quality (JSQ 3-6), 22.47% reported poor sleep quality (JSQ 6-9),
and 27.53% reported very poor sleep quality (JSQ > 9). While differences in sleep quality by
pre-pregnancy BMI were not statistically significant, a higher proportion of those with obesity
reported very poor sleep (36.04%) compared to those with overweight (19.44%) or normal BMI
(26.80%).
On average, participants with normal weight before pregnancy weighed 61.5kg
(SD=0.77) at 20 weeks, and they gained 0.49 kg (SD=0.17) per week during late pregnancy.
Overweight participants weighed on average 70.9kg (SD=0.55) at 20 weeks and gained 0.45kg
(SD=0.16) per week during late pregnancy. Participants with obesity weighed on average 87.9kg
(SD=1.53) at 20 weeks and gained 0.36kg (SD=0.04) per week during late pregnancy.
Bivariate Analyses
Results of the bivariate analyses between covariates and sleep quality is shown in Table
2, and the association between covariates and GWG is shown in Figures 1A-1D. Non-Hispanic
participants consistently reported poorer sleep quality (ꭕ2= 22.27, p<0.01) compared to US-born
and foreign-born Hispanic participants. Foreign-born Hispanic participants also had significantly
lower weight at 20 weeks gestation compared to those who identified as non-Hispanic (β=-5.64,
34

p<0.01). Those with hypertensive disorders reported poorer sleep quality (ꭕ2= 12.42, p<0.05),
weighed more at 20 weeks gestation (β=8.14, p<0.001), and gained weight at a steeper rate
(β=0.08, p<0.001) compared to those without hypertensive disorders. Those with very poor sleep
quality had significantly higher average early-to-mid pregnancy PSS scores (F=5.44, p<0.01).
PSS score was also positively associated with higher weekly weight gain rate during late
pregnancy (β=0.01, p<0.01). Lastly, while early-to-mid PPAQ scores did not significantly differ
by sleep quality, higher PPAQ scores were also associated with lower weekly weight gain rate
(β=-0.01, p<0.01).
Growth Curve Linear Model
Findings from the growth curve linear model can be found in Table 3. Overall, we found
those with poorer sleep quality during early-to-mid pregnancy exhibited higher weekly weight
gain rate during mid-to-late pregnancy. In the reference group (participants with normal pre-
pregnancy BMI), we found those who reported poor early-to-mid pregnancy sleep quality gained
on average 0.14 kg (95% CI: 0.01, 0.26) more during mid-to-late pregnancy compared to those
who reported very good sleep quality. Those who reported very poor early-to-mid pregnancy
sleep quality gained on average 0.15kg (95% CI: 0.02, 0.27) more during mid-to-late pregnancy
compared to those who reported very good sleep quality. We did not find significant difference
in weight gain rate when comparing those with very good sleep quality to good sleep quality
(GA × Good sleep quality: 0.12 [95% CI: 0.00, 0.25]).
Furthermore, the relationship between sleep and weekly weight gain did not significantly
differ by pre-pregnancy BMI, as can be seen from the null three-way interaction terms. For
example, amongst those with normal pre-pregnancy BMI, those who reported very poor sleep
quality gained on average 0.15kg (95% CI: 0.02, 0.27) more than those with very good sleep
35

quality. Amongst those with overweight pre-pregnancy BMI, those who reported very poor sleep
quality gained on average 0.10kg (95% CI: 0.07, 0.26) more than those with very good sleep
quality. The interaction term representing the difference between these two slopes (GA × Very
poor sleep quality × Overweight BMI: -0.05 [95% CI: -0.22, 0.12]) is null.
To put this into context, the IOM recommends that pregnant persons with normal pre-
pregnancy BMI gain 0.42 (0.35 – 0.50) kg per week, overweight pre-pregnancy BMI gain 0.28
(0.23 – 0.33) kg per week, and obesity pre-pregnancy BMI gain 0.22 (0.17 – 0.27) kg per week
during the 2
nd
and 3
rd
trimester. Participants with normal pre-pregnancy BMI with very good
quality sleep fell into this range, gaining on average 0.39 kg (95% CI: 0.29, 0.48) per week.
However, all other participants gained above this recommended amount with those with very
poor sleep quality gaining about 0.12kg - 0.26kg above the recommended rate, depending on
pre-pregnancy BMI. The average rates of mid-to-late pregnancy weekly weight gain by early-to-
mid pregnancy sleep quality can be found in Table 4 and Figure 2.
It should be noted that the three sensitivity analyses testing our algorithm to quantify
early-to-mid pregnancy sleep quality (i.e., only using early-pregnancy questionnaire sleep quality
data, only using mid-pregnancy sleep quality data, an averaging the sleep quality data from both
surveys) did not result in significantly different results. In addition, excluding participants with
chronic hypertension did not significantly alter results. Sensitivity analyses including the
participants with preterm births did not significantly change results; given that their weight gain
trajectory and behavioral risk factors are significantly different than those with term births we
implemented what is more common in the GWG literature and excluded these participants.
Excluding participants with ±10kg discrepancies between self-reported pre-pregnancy weight
and first measured weight did not change the results, and thus we did not eliminate these
36

participants from the analysis. Lastly, there was one participant (four weight observations) who
had a clinical diagnosis of sleep disturbances but excluding this participant did not change the
results.
Discussion

This study investigated the effect of early-to-mid pregnancy sleep quality on GWG rate
during mid-to-late pregnancy. We found pregnant persons who reported poorer quality sleep
gained more weight on a weekly basis. This association did not differ across pre-pregnancy BMI
status. The results of our analysis agree with a large body of evidence in non-pregnant
populations that have found poor sleep quality to predict increased weight gain.
121,149
Our
findings are somewhat in line with the previous three studies that found poorer subjective sleep
quality during pregnancy increased weight gain.
55,127,128
However, these studies only examined
total weight gain as the outcome, as opposed to weight gain rate, and assessed sleep quality at
different points during pregnancy compared to our study. This discrepancy makes it difficult to
directly compare these findings to ours.
While the current body of literature supporting the association between sleep quality and
increased GWG is small, there are well-established psychological, behavioral, and biological
mechanisms that explain this relationship. Pregnant persons often cite sleep difficulties as a
source of stress,
150–152
and while the findings are somewhat mixed, various studies have linked
stress exposure to risk of excessive GWG or increased total weight gain in general.
153,153–156
In
both non-pregnant and pregnant populations, sleep and metabolism are intimately linked through
the hypothalamic-pituitary-adrenal (HPA) axis activation.
97,157–159
Poor sleep is associated with
maladaptive changes to the HPA axis, which in turn leads to neuroendocrine dysregulation that
37

are related to fat accumulation, inflammation, insulin sensitivity, and energy metabolism.
160
Poor
sleep can also alter food intake through hormonal (e.g., increased ghrelin and decreased leptin
levels increases appetite)
35,121,126
and psychological pathways (e.g., people are more likely to
consume “comfort foods” as a coping mechanism).
161,162
Furthermore, a recent systematic review
found significant evidence showing that poor prenatal sleep quality was associated with lower
levels/duration of physical activity.
126
Taken together, a pregnant person with poor sleep quality
may experience physiological changes and negative affect in ways that perpetuate obesogenic
behaviors.
It is important to emphasize that this study focused on the effect of early-to-mid
pregnancy sleep on mid-to-late pregnancy weight gain. This choice may lead to questions as to
why this study did not examine mid-to-late pregnancy sleep quality or early-to-mid pregnancy
weight gain. There are three main reasons. One, there is physiological evidence that poor sleep
during early-to-mid pregnancy is more closely associated with obesogenic and metabolic
outcomes (e.g., gestational diabetes and high blood pressure).
106,129,131,132,163
This may be because
poor sleep during late pregnancy is largely due to musculoskeletal discomfort from the growing
fetus, while poor sleep during early-to-mid pregnancy is linked to significant changes in
neuroendocrine system, such as the rising levels of progesterone, which are more intimately
linked to metabolic mechanisms.
164–166
Two, various studies and clinical evidence suggest that
the timing of weight gain is as important as how much weight is gained. The IOM guidelines
only states explicit recommendations for rate of weight gain during the 2
nd
and 3
rd
trimester,
7
and
various studies have found weight gain during this period is more predictive of adverse prenatal
outcomes than weight gain during the 1
st
trimester.
9,134,167–170
Lastly, given that sleep and GWG
have a bidirectional relationship (i.e., those who gain excessive GWG experience poor late-
38

pregnancy sleep), we wanted to establish the proper temporality between exposure (sleep
quality) and outcome (GWG rate) in order to examine causality.
133,171
We did conduct a
sensitivity analysis examining the association of mid-to-late pregnancy sleep quality and GWG,
but we found no significant results even with an increased sample size. Existing bodies of
literature support these results.
106,129,131,132,163

Limitations
One of our study limitations is our lack of objective (i.e., device-derived) sleep data. In
the sleep literature, it is usually recommended to include both objective and subjective measures
of sleep in the same study.
172
It’s widely known that subjective sleep (e.g., self-reported
measurements of sleep duration, quality, disruption via questionnaires) and objective sleep (e.g.,
polysomnography, actigraphy, bed sensors) often do not agree with one another, but both types
of sleep measures are independently associated with health factors.
173–177
Given that the
correlation between subjective and objective measures of sleep varies by perinatal mood
disorders, we cannot conclude how the lack of objective sleep data may have biased our
results.
175,178
Furthermore, pre-pregnancy BMI is based on self-reported weight, and therefore,
may be at risk of recall bias, even with the extra precaution taken to conduct sensitivity analyses
with any participants with a +/- 10kg discrepancy between the self-reported pre-pregnancy
weight and first measured prenatal weight.
Conclusion
Our findings identify sleep health during pregnancy as a promising intervention target to
curtail excessive GWG rates. In non-pregnant populations, improving sleep quality as part of
weight management interventions have led to success amongst those with obesity or overweight
BMI.
179
Sleep is also proven to be extremely amenable via interventions such as sleep hygiene
education, mindfulness-based practices, and cognitive behavioral therapy, even during the
39

perinatal period.
101,180
Furthermore, unlike current GWG interventions aimed at solely
increasing physical activity or improving nutrition, sleep interventions could be more widely
accepted amongst persons of color. Qualitative studies have found Black, Hispanic, and Asian
cultural beliefs view sleep and rest as some of the most important behaviors for pregnancy
health, while many believe physical activity or decreased caloric intake can adversely affect the
health of the baby.
24,181
And yet, current guidelines for perinatal care from the American College
of Obstetricians and Gynecologists includes counseling on physical activity and nutrition but not
sleep hygiene.
182
The development of pregnancy-specific sleep recommendations to be
incorporated into routine obstetric care is a critical next step in promoting healthy GWG.

40
Overall (N=316) Test Statistic (p-value)
Normal (N=97) Overweight (N=108) Obesity (N=111)
Maternal age [years]; mean (SD)
1
28.70 (6.00) 27.35 (6.34) 29.46 (5.92) 29.13 (5.60) F=3.66 (p=0.03)
Ethnicity by birthplace; n(%)
2
ꭕ
2
= 23.97 (p<0.01)
Non-Hispanic 59 (19.09%) 33 (34.74%) 13 (12.50%) 13 (11.82%)
US-Born Hispanic 111 (35.92%) 29 (30.53%) 35 (33.65%) 47 (42.73%)
Foreign-Born Hispanic 139 (44.98%) 33 (34.74%) 56 (53.85%) 50 (45.45%)
Highest educational attainment; n(%)
3
ꭕ
2
= 6.85 (p=0.03)
High School or below 173 (54.92%) 48 (49.48%) 53 (49.53%) 72 (64.86%)
Beyond high school 142 (45.08%) 49 (50.52%) 54 (50.47%) 39 (35.14%)
Marital status; n(%)
4
ꭕ
2
= 3.32 (p=0.19)
Not married or living together 80 (26.40%) 24 (25.81%) 33 (32.35%) 23 (21.30%)
Married or living together 223 (73.60%) 69 (74.19%) 69 (67.65%) 85 (78.70%)
Parity; n(%)
5
ꭕ
2
= 20.53 (p<0.01)
First child 104 (33.66%) 45 (47.37%) 32 (30.77%) 27 (24.55%)
Second child 98 (31.72%) 33 (34.74%) 32 (30.77%) 33 (30.00%)
Third or more child 107 (34.63%) 17 (17.89%) 40 (38.46%) 50 (45.45%)
Hypertensive disorder; n(%)
6
ꭕ
2
= 13.75 (p<0.01)
Normal 260 (82.28%) 86 (88.66%) 93 (86.11%) 81 (72.97%)
Preeclampsia or gestational hypertension 40 (12.66%) 5 (5.15%) 12 (11.11%) 23 (20.72%)
Chronic hypertension 16 (5.06%) 6 (6.19%) 3 (2.78%) 7 (6.31%)
Glucose tolerance abnormality; n(%)
7
ꭕ
2
= 22.46 (p<0.01)
Normal 202 (64.33%) 76 (80.00%) 68 (62.96%) 58 (52.25%)
Glucose intolerance or gestational diabetes 102 (32.48%) 19 (20.00%) 38 (35.19%) 45 (40.54%)
Chronic diabetes 10 (3.18%) 0 2 (1.85%) 8 (7.21%)
Sleep Quality; n(%)
8
ꭕ
2
= 11.00 (p=0.09)
Very good 73 (23.10%) 19 (19.59%) 33 (30.56%) 21 (18.92%)
Good 85 (26.90%) 27 (27.84%) 32 (29.63%) 26 (23.42%)
Poor 71 (22.47%) 25 (25.77%) 22 (20.37%) 24 (21.62%)
Very poor 87 (27.53%) 26 (26.80%) 21 (19.44%) 40 (36.04%)
Physical activity [MET-hr/week]; mean(SD)
9
* 302.80 (153.35) 290.26 (163.92) 288.85 (138.02) 326.89 (156.05) F=2.08 (p=0.13)
Perceived Stress Score; mean(SD)
10 Ⴕ
12.49 (6.38) 13.11 (5.93) 12.09 (6.31) 12.32 (6.83) F=0.68 (p=0.51)
Nausea/vomiting; mean(SD)
11 ǂ
5.85 (2.52) 6.05 (2.70) 5.53 (2.33) 5.97 (2.51) F=1.21 (p=0.29)
Employment status; n(%)
12
ꭕ
2
= 0.98 (p=0.62)
Not employed 146 (48.18%) 41 (44.09%) 50 (49.02%) 55 (50.93%)
Employed 157 (51.82%) 52 (55.91%) 52 (50.98%) 53 (49.07%)
Note: Percentages are column percentages.
*Measured using Pregnancy Physical Activity Questionnaire; mean physical activity level ranges between 126.0 MET-h/week to 417.2 MET-h/week in literature.
ႵMeasured using Cohen's Perceived Stress Scale; ranges from 0 to 40.
ǂMeasured using the Pregnancy-Unique Quantification of Emesis and Nausea survey; ranges from 3 to 15.
Acronyms: BMI: Body Mass Index; US: United States; SD: Standard Deviation; MET: Metabolic Equivalent
Table 1. Descriptive Characteristics of Final Analytic Sample
By Pre-Pregnancy BMI
1
No missing;
2
Missing for 7 participants;
3
Missing for 1 participant;
4
Missing for 14 participants;
5
Missing for 7 participants;
6
No missing;
7
No missing;
8
No missing;
9
Missing for 7 participants;
10
Missing for 7 participants;
11
Missing for 7 participants;
12
Missing for 13
participants
41
Table 2. Early-to-Mid Pregnancy Sleep Quality by Ethnicity by Birthplace, Hypertensive Disorder, Physical Activity, and Perceived Stress Score (N=316)
Test Statistic (p-value)
Very good Good Poor Very poor
Ethnicity by birthplace; n(%) ꭕ
2
= 22.27 (p<0.01)
Non-Hispanic 5 (8.47%) 16 (27.12%) 8 (13.56%) 30 (50.85%)
US-Born Hispanic 32 (28.83%) 29 (26.13%) 23 (20.72%) 27 (24.32%)
Foreign-Born Hispanic 36 (25.9%) 39 (28.06%) 31 (22.3%) 33 (23.74%)
Hypertensive disorder; n(%) ꭕ
2
= 12.42 (p<0.05)
Normal 65 (25%) 78 (30%) 53 (20.38%) 64 (24.62%)
Preeclampsia or gestational hypertension 5 (12.50%) 10 (25.00%) 6 (15.00%) 19 (47.50%)
Chronic hypertension 4 (25.00%) 2 (12.50%) 3 (18.75%) 7 (43.75%)
Physical Activity; mean(SD) 269.06 (146.78) 316.2 (151.74) 287.79 (158.61) 327.08 (152.99) F=2.84 (p<0.05)
Perceived Stress Score; mean(SD) 11.03 (5.97) 11.46 (6.65) 13.34 (5.91) 14.27 (6.04) F=5.44 (p<0.01)
Acronyms: US: United States; SD: Standard Deviation
Sleep Quality
42
Coefficient Estimate 95% Confidence Interval
Intercept 57.6 51.73 , 63.46
GA 0.39 0.29 , 0.48
Good sleep quality 3.01 -3.55 , 9.58
Poor sleep quality 2.9 -3.70 , 9.49
Very poor sleep quality 3.89 -2.77 , 10.56
Overweight BMI 14.2 7.92 , 20.47
Obesity BMI 25.74 18.83 , 32.65
GA × Good sleep quality 0.12 0.00 , 0.25
GA × Poor sleep quality 0.14 0.01 , 0.26
GA × Very poor sleep quality 0.15 0.02 , 0.27
GA × Overweight BMI 0.05 -0.06 , 0.17
GA × Obesity BMI -0.1 -0.23 , 0.03
Good sleep quality × Overweight BMI -3.95 -12.50 , 4.61
Poor sleep quality × Overweight BMI -1.45 -10.33 , 7.44
Very poor sleep quality × Overweight BMI -5.69 -14.54 , 3.17
Good sleep quality × Obesity BMI -4.85 -14.10 , 4.39
Poor sleep quality × Obesity BMI 5.52 -3.77 , 14.80
Very poor sleep quality × Obesity BMI 5.62 -3.13 , 14.38
GA × Good sleep quality × Overweight BMI -0.13 -0.30 , 0.04
GA × Poor sleep quality × Overweight BMI -0.14 -0.32 , 0.03
GA × Very poor sleep quality × Overweight BMI -0.05 -0.22 , 0.12
GA × Good sleep quality × Obesity BMI 0 -0.18 , 0.17
GA × Poor sleep quality × Obesity BMI -0.09 -0.27 , 0.09
GA × Very poor sleep quality × Obesity BMI -0.03 -0.20 , 0.14
Non-Hispanic* 11.16 7.54 , 14.78
US-Born Hispanic* 3.15 0.35 , 5.96
Total Activity Score* 0 -0.01 , 0.00
Perceived Stress Scale* -0.19 -0.39 , 0.02
Hypertensive disorders* 3.41 0.05 , 6.77
*All parameters, except these, should be interpeted for foreign-born Hispanic, non-hypertensive participants with mean physical activity and perceived stress level.
Acronyms: BMI: Pre-Pregnancy Body Mass Index; GA: Gestational age; US: United States
Table 3. Growth Curve Model Results for Main Effects, Interactions, and Covariates (N=316)
43
Pre-Pregnancy BMI Sleep Quality Average Rates of Weight Gain (kg/week)* 95% Confidence Interval
Normal
Very good 0.39 0.29, 0.48
Good 0.51 0.43, 0.59
Poor 0.53 0.44, 0.61
Very poor 0.54 0.46, 0.62
Overweight
Very good 0.44 0.37, 0.52
Good 0.45 0.37, 0.53
Poor 0.42 0.33, 0.51
Very poor 0.54 0.45, 0.63
Obesity
Very good 0.29 0.20, 0.38
Good 0.42 0.34, 0.51
Poor 0.32 0.23, 0.41
Very poor 0.41 0.34, 0.47
* Average rate calculated for a participant that is foreign-born Hispanic, no hypertensive disorder with mean physical activity and perceived stress level.
Acronyms: BMI: Body Mass Index; kg:Kilograms
Table 4. Rates of Mid-to-Late Pregnany Weight Gain by Early-to-Mid Pregnancy Sleep Quality in Women with Normal Weight Pre-Pregnancy BMI (N=316)
44
All MADRES Participants
(N=966)
Final Analytical Sample
(N=316)
Maternal age [years]; mean(SD) 28.37 (6.13) 28.7 (6.0)
Ethnicity by birthplace; n(%)
Non-Hispanic 189 (24.11%) 59 (19.09%)
US-Born Hispanic 275 (35.08%) 111 (35.92%)
Foreign-Born Hispanic 320 (40.82%) 139 (44.98%)
Highest educational attainment; n(%)
High School or below 520 (58.36%) 173 (54.92%)
Beyond high school 371 (41.64%) 142 (45.08%)
Marital status; n(%)
Not married or living together 203 (28.27%) 80 (26.4%)
Married or living together 515 (71.73%) 223 (73.6%)
Parity
First child 287 (38.47%) 104 (33.66%)
Second child 231 (30.97%) 98 (31.72%)
Third or more child 228 (30.56%) 107 (34.63%)
Hypertensive disorder; n(%)
Normal 599 (78.51%) 260 (82.28%)
Preeclampsia or gestational hypertension 121 (15.88%) 40 (12.66%)
Chronic hypertension 42 (5.51%) 16 (5.06%)
Glucose tolerance abnormality; n(%)
Normal 501 (66.53%) 202 (64.33%)
Glucose intolerance or gestational diabetes 216 (28.69%) 102 (32.48%)
Chronic diabetes 36 (4.78%) 10 (3.18%)
Physical activity [MET-hr/week]; mean(SD) 299.37 (148.11) 302.8 (153.4)
Perceived Stress Score; mean(SD) 13.01 (6.51) 12.5 (6.4)
Nausea/vomiting; mean(SD) 5.71 (2.53) 5.9 (2.5)
Employment status; n(%)
Not employed 248 (48.44%) 146 (48.18%)
Employed 264 (51.56%) 157 (51.82%)
Acronyms: MADRES: Maternal and Developmental Risks from Environmental and Social Stressors; SD: Standard Deviation
Supplemental Table 1. Comparison of All MADRES Participants vs Final Analytical Sample
45
Ethnicity by Birthplace
Figure 1A. Predicted Values of Maternal Weight by Ethnicity and Birthplace
Acronyms: US: United States, kg: Kilograms
Week in Pregnancy (centered at 20 weeks)
Maternal Weight (kg)
Ethnicity by Birthplace
Non-Hispanic
US-Born Hispanic
Foreign-Born Hispanic
46
Hypertensive Disorder
Figure 1B. Predicted Values of Maternal Weight by Hypertensive Disorder
Acronyms: HTN: Hypertensive, kg: Kilograms
Week in Pregnancy (centered at 20 weeks)
Maternal Weight (kg)
Normal
HTN
47
Figure 1C. Predicted Values of Maternal Weight by Perceived Stress Scale
(-1SD)
(+1SD)
(Mean)
Acronyms: kg: Kilograms; SD: Standard Deviation
Week in Pregnancy (centered at 20 weeks)
Maternal Weight (kg)
Ethnicity by Birthplace Perceived Stress Score
6.20 (-1SD)
12.72 (Mean)
19.24 (+1SD)
48
Figure 1D. Predicted Values of Maternal Weight by Total Activity Score
Acronyms: kg: Kilograms; SD: Standard Deviation
Week in Pregnancy (centered at 20 weeks)
Maternal Weight (kg)
Total Activity Score
154.57 (-1SD)
304.32 (Mean)
454.07 (+1SD)
49
Sleep Quality
Figure 2. Early-to-Mid Pregnancy Sleep Quality and Mid-to-Late Pregnancy Weight Gain by Pre-Pregnancy BMI Status
Acronyms: BMI: Body Mass Index; kg: Kilograms
Week in Pregnancy (centered at 20 weeks)
Maternal Weight (kg)
Sleep Quality
Very Good
Good
Poor
Very Poor
50
Is EP Survey missing?
Is MP Survey missing?
Is MP Survey ≤ 20 weeks?
Use MP Survey
data
Excluded
Excluded
Yes
Yes No
Yes No
Is EP Survey ≤ 20 weeks?
No
Is MP Survey missing? Excluded
Yes No
Use EP Survey data
Yes No
Is MP Survey ≤ 20 weeks?
How far apart were EP Survey
and MP Survey assessed?
Use EP Survey data
Yes No
Average EP Survey
and MP Survey
Use survey closest
to 15 weeks
Supplemental Figure 1. Algorithm to Quantify Early-to-Mid Pregnancy Sleep Quality
0-8 weeks >8 weeks
Acronyms: EP: Early-Pregnancy; MP: Mid-Pregnancy
51
Supplemental Figure 2. Spaghetti Plots Influential Points Excluded from Sample
52
Supplemental Figure 3. Spaghetti Plots Comparing Influential Points to All Other Participants
Acronyms: kg: Kilograms
53
Not Underweight
N=374
3,304 obs
Recruited <20 weeks gestation
N=537
8,132 obs
Sleep Data
N=537
Sleep Data
(No Missing)
N=390
Merged Data
N=390
6,168 obs
Mid-to-Late Pregnancy
N=381
3, 371 obs
Not outliers or observations with high leverage
N=316
3,009 obs
At least two weight measurements
N=316
3,018 obs
Not preterm
N=335
3,037 obs
Supplemental Figure 4. Consort Diagram for Data Availability
GWG Data
N=720
10,464 obs
Acronyms: GWG: Gestational Weight Gain
54

Chapter Three: Study Two
Prenatal Sleep and Gestational Weight:
Examining Mediation by Hypothalamic Pituitary Adrenal Axis and
Moderation by Pre-Pregnancy BMI

Abstract

Background: Current physical activity and healthy eating interventions aimed to curtail
excessive gestational weight gain (GWG) rates in the U.S. have been largely unsuccessful.
Therefore, there is an urgent need to improve our understanding of risk factors and their
psychobiological mechanisms related to GWG. There is robust evidence supporting dysregulated
sleep as a putative risk factor for obesity, and many have hypothesized that cortisol may be a
probable mediator between poor sleep quality and weight gain. However, evidence in pregnant
people is severely lacking. This study aimed to investigate the effect of early-to-mid pregnancy
sleep quality on total GWG through the mediating effect of late pregnancy circadian cortisol,
while simultaneously testing the moderating effect of pre-pregnancy BMI.
Method: Participants were 136 pregnant predominantly low-income Hispanic women from the
Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) study.
During early-to-mid pregnancy (≤20 weeks gestation), women (mean age=28.9± 6.0 years)
reported their sleep quality which was used to construct four categories: very poor, poor, good,
and very good. Participants collected four salivary cortisol samples at home on one day during
late pregnancy, which were used to quantify diurnal cortisol profiles. Moderated mediation was
tested using the PROCESS procedure in SPSS.
55

Results: We did not find evidence of mediation of early-to-mid pregnancy sleep quality and total
GWG by any of the diurnal cortisol profiles. We also did not find any evidence of moderation by
pre-pregnancy BMI. However, we consistently found those with very poor sleep quality gained
significantly more weight than those with very good sleep quality.
Conclusion: Our study found those who reported poorer quality sleep gained more weight.
However, we found no evidence that diurnal cortisol mediated this relationship. Our findings
identify sleep health during pregnancy as a key behavior for future weight management
interventions. Future research exploring other potential psychological, biological, and behavioral
mediational pathways is needed.

56

Introduction

Unhealthy weight gain during pregnancy significantly increases the risk of adverse
prenatal health and birth outcomes.
6,7
In the United States (U.S.), less than one in three people
currently gain the recommended amount of weight during pregnancy. Excessive gestational
weight gain (GWG), where people gain more than the Institute of Medicine (IOM) recommended
amount based on their pre-pregnancy Body Mass Index (BMI), is especially common, with
almost half of U.S. persons gaining excessive weight during pregnancy.
11
These high rates are
alarming, given excessive GWG’s association with increased risk of pregnancy complications
and long-term chronic health conditions such as asthma, cardiovascular diseases, and metabolic
syndrome for both the mother and child.
12–14
It should also be noted that these rates of excessive
GWG are even higher among minority people and people of low socioeconomic status.
16,17

Current research aimed to elucidate predictors of or curtail excessive GWG have mostly focused
on promoting physical activity and healthy eating behaviors. Unfortunately, these efforts have
led to inconsistent results, especially among health disparities populations.
19–22
Therefore, there
is an urgent need to improve our understanding of risk factors and their psychobiological
mechanisms related to weight gain to better promote healthy GWG in minority people.
There is robust evidence supporting dysregulated sleep health as a putative risk factor for
weight gain.
35–37
Various nation-wide studies and systematic reviews have repeatedly found poor
sleep quality to predict increased weight and risk of obesity in non-pregnant populations.
35,37,38

There is growing evidence supporting similar relationships between poor sleep and increased
weight gain amongst pregnant populations as well.
51–56,183
Given that poor sleep is extremely
common during pregnancy, prenatal sleep may be a key health behavior in uncovering the
mechanisms underlying GWG.
29
While there are several hypothesized psychological, biological,
57

and behavioral processes linking sleep with weight gain, the exact causal pathway still remains
elusive, especially in pregnant populations.
An emerging hypothesis posits that neurohormonal systems may be an important factor in
understanding the association between sleep and weight gain.
184
One such mechanism is the
Hypothalamic-Pituitary-Adrenal (HPA) axis. The HPA axis is a major component of the
neuroendocrine system that comprises of a synergistic cascade of enzymes and hormones that
ultimately leads to the secretion of cortisol from the adrenal cortex.
58–60
Though cortisol is most
commonly known as the “stress hormone” produced in reaction to acute stressors, cortisol is also
secreted in a circadian manner, characterized by increasing levels at awakening, followed by a
steep peak at 30-45 minutes after awakening, and a slow decline until bedtime.
63,64
This circadian
pattern can be quantified by taking repeated samples of saliva throughout the day and calculating
cortisol profiles, such as the Area Under the Curve (AUC), which captures the circadian change
in cortisol levels across a specific time point,
63
Cortisol Awakening Response (CAR), the
marked increase in cortisol levels across the first 30-45 minutes after wakening,
66
and/or the
Diurnal Cortisol Slope (DCS), often defined as the rate of decline from morning to evening.
67

Circadian patterns in which cortisol levels are lower in the morning and fail to drop later in the
day (e.g., higher AUC, and flatter DCS or CAR) during pregnancy are considered markers of
dysregulated HPA axis functioning.
68–70

In non-pregnant populations, sleep quality is intimately associated with diurnal cortisol
profiles,
93,94
and cortisol dysregulation plays a key role in the development of obesity.
59
For
example, poor subjective sleep quality is associated with reduced morning cortisol levels, higher
afternoon/evening cortisol levels, and flatter DCS.
93–95
Though the number of studies is much
more limited, similar relationships between poor sleep health and diurnal cortisol profiles have
58

been identified in pregnant women as well.
96,97
In turn, dysregulation of cortisol secretion causes
metabolic changes that lead to weight gain. Elevated cortisol levels increase glucose, is
associated with insulin resistance, and decreases adiponectin levels.
160
It also stimulates appetite
and food intake, specifically high-caloric palatable food, ultimately leading to increased weight
and total body fat mass.
81
In the presence of insulin, cortisol also promotes visceral adipocyte
accumulation, thus leading to increased central adiposity.
185
Given cortisol’s relationship with
both sleep and weight gain, researchers have hypothesized that cortisol is a probable link
between sleep health and weight gain.
184

However, this relationship remains unexamined in pregnant populations. During
pregnancy, the HPA axis and cortisol processes undergo significant change.
83
For example,
maternal cortisol levels increase two to four-fold during pregnancy due to additional cortisol
production from the maternal HPA axis, in conjunction with the activation of the fetal HPA axis,
and placental production of corticotropin-releasing hormone.
82,83,186
And while the general
circadian cortisol rhythm is still seen in pregnant people, there is some evidence that the CAR
and cortisol reactivity to psychophysiological stress is significantly attenuated during late
pregnancy.
187
These changes, in addition to the alterations in sleep architecture during
pregnancy, makes it difficult to directly apply the current findings about diurnal cortisol, sleep,
and weight gain in non-pregnant subjects to this population.
This study aimed to investigate the effect of early-to-mid pregnancy sleep quality on
GWG and the mediating effect of late pregnancy circadian cortisol profiles, while
simultaneously considering the moderating effect of pre-pregnancy BMI. We hypothesized that
pregnant persons with poorer early-to-mid pregnancy sleep quality will have higher total GWG,
and this association will be mediated via dysregulated diurnal cortisol profiles (i.e., higher AUC
59

and flatter DCS and CAR) in late pregnancy. We also explored pre-pregnancy BMI status as a
possible moderator to help contextualize the study findings for total weight gain.
Methods

Sample
The MADRES study is an ongoing prospective cohort study of primarily Hispanic, low-
income pregnant women and their children in Los Angeles, California. Details of the MADRES
protocol have been described elsewhere.
135
Participants were recruited from Los Angeles County
+ USC (LAC+USC) Medical Center, the Women’s Health Center at Eisner Health, and the
South-Central Family Health Center. Inclusion criteria were: (1) <30 weeks pregnant at the time
of enrollment, (2) ≥18 years of age, (3) singleton pregnancy, and (4) English or Spanish
speaking. Exclusion criteria were (1) HIV positive status; (2) physical, mental, or cognitive
disabilities that prevent participation; (3) current incarceration; or (4) multiple gestation.
Maternal consent and HIPAA authorization for abstracting electronic medical records (EMR)
was obtained prior to any study assessment. The Institutional Review Board at the University of
Southern California approved all aspects of this study.
For women enrolling at less than 20 weeks gestation, the prenatal data collection protocol
consisted of 1) early-pregnancy in-person visit within 2 weeks of recruitment (mean=12.9 weeks
gestation, SD=3.7 weeks); 2) mid-pregnancy telephone interviews between 18-27 weeks
gestation (mean=20.4 weeks gestation, SD=2.8 weeks); 3) late-pregnancy in-person visit
between 30-34 weeks gestation (mean=31.9 weeks gestation, SD=1.9 weeks); and 4) EMR
abstraction from prenatal care clinic visits. Both in-person visits consisted of an interviewer-
administered questionnaire in English or Spanish and anthropometric measurements of height
60

and weight. The late-pregnancy in-person visit additionally included collection of the saliva
samples from the participants for cortisol measurement.
Measures
Prenatal Sleep Quality
Overall sleep quality was assessed using the four-item Jenkin’s Sleep Questionnaire
(JSQ).
136
The JSQ is a commonly used questionnaire in epidemiologic and clinical intervention
studies with pregnant populations.
137–139
The JSQ consists of the following questions: “How
often in the past 30 days did you have the following symptoms?”: (1) Trouble falling asleep, (2)
Waking up several times per night, (3) Trouble staying asleep and (4) Waking up after the usual
amount of sleep, feeling tired and worn out. The six response alternatives are: not at all, 1–3
nights, 4–7 nights, 8–14 nights, 15–21 nights and 22–31 nights. “Not at all” signified no sleep
problems, “1–3 nights” signified rare sleep problems, and “4–7 or 8–14 nights” signified
occasional sleep problems. The response to each item is recoded on a 0- to 5-point scale and
totaled to create a summary sleep score that ranges from 0 to 20 with higher scores indicating
worse overall sleep quality. We then categorized the summary score into quantiles. A score of 0-
3 represented very good-quality sleep, 3-6 represented good-quality sleep, 6-9 represented poor-
quality sleep, and 9+ represented very poor-quality sleep.
The JSQ was administered at all three interviews; however the late-pregnancy in-person
visit was not used to calculated early-to-mid pregnancy sleep quality as none occurred <20
weeks gestation.
136
We applied an algorithm to determine how to quantify the early-to-mid
pregnancy (<20 weeks gestation) sleep quality using the first two surveys (Supplemental Figure
1). If both surveys were missing or conducted during late pregnancy (≥20 weeks), we excluded
the participant from the study. If either survey was missing, we used the other available survey
only if it was conducted during early-to-mid pregnancy; if it was conducted during mid-to-late
61

pregnancy or missing, the participant was excluded from the study. If both surveys were
conducted during early-to-mid pregnancy, and they were conducted within 8 weeks of each
other, we averaged the two survey answers. If the two surveys were conducted more than 8
weeks apart, we used whichever survey was closest to 15 weeks gestation (the average week
when early-to-mid pregnancy sleep was assessed). This decision was made based on exploratory
analyses showing strong positive correlation between the sleep quality data from both surveys if
they were less than 8 weeks apart. To examine whether this decision significantly altered the
findings, we ran three separate sensitivity analyses: 1) only using early-pregnancy sleep quality
data, 2) only using mid-pregnancy sleep quality data, and 3) averaging the sleep quality data
from both surveys.
Maternal Height and Weight
Maternal weight and height during pregnancy were abstracted from EMRs and measured
by trained staff during each of the in-person study visits using an electronically calibrated digital
scale (Tanita, Perspective Enterprises, Portage, MI) and a commercial stadiometer (Model PE-
AIM-101, Perspective Enterprises) to the nearest 0.1 kg and 0.1 cm, respectively.
Total GWG was defined as the difference between a mother’s weight measured within
two weeks before giving birth and her pre-pregnancy weight. Self-reported pre-pregnancy weight
was ascertained through interviewer-administered questionnaires during pregnancy. If missing,
then the first weight of the index pregnancy (obtained from EMR) was used in lieu of self-
reported pre-pregnancy weight. Participants were flagged if the self-reported pre-pregnancy
weight and first measured weight were more than ±10kg discrepant, and sensitivity analyses
were conducted to see if exclusion of these participants influenced study findings.
62

Self-reported pre-pregnancy weight and height were used to calculate the pre-pregnancy
BMI (kg/m2) and classified using CDC categories: underweight (BMI< 18.5), normal weight
(BMI ≥18.5 and < 25), overweight (BMI ≥25 and < 30), obesity (BMI ≥30).
140

Salivary Cortisol
Participants collected saliva samples at home using a Salivette device (Sarstedtf, Inc.
Rommelsdolf, Germany), which is a small cotton dental roll that participants gently chew for two
minutes. A total of four saliva samples per day for each of four days were collected at
awakening, 30 min after awakening, afternoon (around 3:00pm), and bedtime to capture the
diurnal pattern of cortisol secretion.
188
At the time of collection, participants directly noted on the
saliva tube the date and time, and whether any eating, drinking (besides water), tooth brushing,
smoking or exercising occurred in the prior 30 min. Participants stored their samples in their
home refrigerator until the end of the four-day data collection period when the samples were
transferred to a laboratory freezer for storage at -80°C.
Samples were sent to a commercial laboratory in batch and were assayed with
chemiluminescence immunoassay (CLIA; IBL International, Hamburg, Germany), which has a
lower detection limit of .005 ug/dL and intra- and inter-assay coefficients in the range of 3.0 -
4.1%. Samples were flagged if participants had waited more than 15 minutes after awakening to
collect their first sample, waited less than 15 minutes or more than 45 minutes after awakening to
collect their second sample, and if eating, drinking (besides water), tooth brushing, smoking or
exercising occurred in the prior 30 minutes. We examined whether protocol non-compliance and
contamination data affected the summary measure values and conducted sensitivity analyses
where appropriate. Furthermore, given that negative CAR values are regarded as evidence for
non-compliance (i.e., delaying the collection of awakening or awakening+30min samples by
63

more than 30-45 minutes), we also conducted sensitivity analyses by eliminating all negative
CAR observations.
189

We examined all three diurnal cortisol profiles for this study. Daily total cortisol
secretion were quantified by the AUC using the trapezoidal formula ( 𝐴𝐴𝐴𝐴𝐴𝐴 = ∑
� 𝑚𝑚 ( 𝑖𝑖 + 1 )
+ 𝑚𝑚 𝑖𝑖 � ∙ 𝑡𝑡 𝑖𝑖 2
4
𝑖𝑖 = 1
)
with 𝑡𝑡 𝑖𝑖 denoting the time difference between measurements (e.g., 𝑡𝑡 1
is the time between the first
and second measurement), and 𝑚𝑚 𝑖𝑖 denoting the cortisol levels at each measurement. The CAR
was calculated using the trapezoidal formula but only taking into account the increase from the
first measurement ( 𝐴𝐴 𝐴𝐴 𝐶𝐶 =
( 𝑚𝑚 1
+ 𝑚𝑚 2
) ∙ 𝑡𝑡 1
2
− 𝑚𝑚 1
∗ 𝑡𝑡 1
). The DCS was calculated as the change in
cortisol level between the last and first measurement over the time elapsed between last and first
( 𝐷𝐷 𝐴𝐴 𝑆𝑆 =
𝑚𝑚 4
− 𝑚𝑚 1
𝑡𝑡 4
− 𝑡𝑡 1
).
63,190
We examined the distribution of each cortisol profile and windsorized any
outliers that were <3SD or >3SD beyond the mean. As a sensitivity analysis, we also tried
windsorizing each of the four samples, then calculating the cortisol profiles. These are both
common methodologies to deal with extreme measures of salivary cortisol.
191

Covariates
A list of covariates was identified a priori based on literature review and findings from
another MADRES study also examining the effect of prenatal sleep on GWG.
183
All final models
controlled for ethnicity by birthplace (ref: foreign-born Hispanic, US-born Hispanic, and non-
Hispanic), hypertensive disorders (ref: normal, preeclampsia or gestational hypertension, and
chronic hypertension), perceived stress score, and physical activity level. Previous studies have
found that pregnant women of African American or Hispanic race/ethnicity, compared to White
women, have poorer overall sleep quality, even after controlling for socioeconomic status.
192,193

There’s also been evidence that immigration policies, acculturation, and one’s birth country
64

affect sleep health in non-pregnant populations.
194–196
Furthermore, rates of excessive GWG are
disproportionately higher in women of color compared to White or Caucasian women.
112
We
also needed to control for hypertensive disorders because pregnancy complications, such as
preeclampsia, are co-morbid with excessive GWG and associated with higher risk of poor sleep
quality.
197–199
In addition, we included the participants’ early-pregnancy perceived stress score,
assessed via Cohen’s Perceived Stress Scale (PSS), since studies have found positive
associations between daily perceived stress with sleep quality and mixed associations with
GWG.
153,200,201
Lastly, we controlled for participant’s early-pregnancy physical activity level
assessed via the Pregnancy Physical Activity Questionnaire (PPAQ). It was important we
included physical activity in the model, as there is extensive literature supporting the beneficial
role of physical activity during pregnancy in improving sleep quality and efficiency.
202

Furthermore, physical activity is also a known factor to decrease total GWG and risk of
excessive GWG.
203

204
All continuous covariates (perceived stress score and physical activity
level) were centered at the mean to aid interpretation and reduce multicollinearity.
141
Given that
not all participants collected the four saliva samples at the same time, we tried controlling for
awakening time and the time between the first and last measure used to calculate the cortisol
profile (e.g., for AUC and DCS: time between first and fourth sample; for CAR: time between
first and second sample) as a sensitivity analysis.
Statistical Analysis
Descriptive and exploratory analyses of all participant characteristics were conducted to
examine the distributions of all variables, understand correlation and possible multicollinearity of
predictors, and identify influential outliers. Additional analyses of residual distributions were
performed to determine whether modeling assumptions were met for the final models. All tests
of statistical significance were examined using two-sided tests with α=0.05.
65

Moderated mediation analysis was conducted using Andrew Haye’s PROCESS Macro on
SPSS.
205
This allowed us to test how early-to-mid pregnancy sleep quality (X) exerts its effect on
total GWG (Y) through late pregnancy diurnal cortisol profiles (M), while simultaneously testing
effect modification by pre-pregnancy BMI (V). A separate moderated mediation model was run
for each diurnal cortisol profile (AUC, CAR, DCS). In a mediation model, a is the coefficient of
X in a model where X predicts M, b is the coefficient of X in a model where X predicts M, and
c’ is the coefficients in a model predicting Y from both M and X. Based on exploratory analyses,
we tested mediation of early-to-mid pregnancy sleep quality and total GWG by AUC or CAR,
with pre-pregnancy BMI as a hypothesized moderator of the b and c’ paths (PROCESS Model
15; Figure 1). Similarly, based on exploratory analyses, we tested mediation of early-to-mid
pregnancy sleep quality and total GWG by DCS, with pre-pregnancy BMI as a hypothesized
moderator of all three a, b, and c’ paths (PROCESS Model 59; Figure 2). PROCESS uses the
bootstrapping approach with confidence intervals derived from 5000 samples to test for evidence
of moderated mediation.
205

Results

Sample Population
As of February 2023, of the 966 MADRES participants, 772 women had complete GWG
data (i.e., women who had already given birth, and their final gestational weight was measured
within two weeks of birth), and 633 of these participants were recruited before 20 weeks
gestation. A total of 229 women had collected salivary cortisol samples during late pregnancy.
There were 576 women who completed the early-pregnancy or mid-pregnancy survey
questionnaire, and of those women, 405 answered the sleep health survey during early-to-mid
pregnancy (<20 weeks gestation). A total of 150 women had all three data components (i.e., total
66

weight gained, sleep, cortisol). We first excluded 12 participants who had preterm births. This is
common practice in GWG literature, as their weight gain trajectory and behavioral risk factors
are significantly different than those with term births, but we also ran sensitivity analyses by
including them.
143
In addition, because we only had two participants who were underweight
before pregnancy, we excluded them from the analysis to aid interpretation of the interaction
term with pre-pregnancy BMI. Of the 136 women, we were able to model the AUC for 103
participants, CAR for 115 participants, and DCS for 104 participants after excluding any outliers
> or < 3SD for each diurnal cortisol profile. Lastly, we excluded observations missing covariate
information and any outliers or influential points that were identified through exploratory data
analysis of model assumptions. The final analytic sample consisted of 94 participants for the
AUC model, 105 participants for the CAR model, and 95 participants for the DCS model. The
consort diagram illustrating the final sample size for each diurnal cortisol profile can be found in
Supplemental Figure 2. Exploratory analysis comparing the 136 participants in this dataset to the
full MADRES population of 966 participants found no significant differences in descriptive
characteristics (Supplemental Table 1).
Descriptive Characteristics
Descriptive characteristics of the participants, overall and by pre-pregnancy BMI
category, can be found in Table 1. Participants in this study were on average 28.90 years old
(SD=5.96 years), and those with overweight and obesity were older compared to those with
normal pre-pregnancy BMI (F=4.53, p<0.05). Majority of our participants were foreign-born
Hispanic (43.41%) or US-born Hispanic (42.64%) and did not finish high school (63.34%). Our
participants were predominantly married or living together with their partner (80.00%). About
one in three (30.47%) of the participants were nulliparous, while another third had one child and
the last third already had two or more children. Twenty-two participants (16.18%) had
67

preeclampsia or gestational hypertension, and more than one in three (33.09%) had a glucose
intolerance or gestational diabetes. Prevalence of glucose tolerance abnormality was higher
amongst those with overweight or obesity compared to those with normal BMI (glucose
tolerance abnormality: ꭕ2= 6.10, p=0.05). Participants’ total physical activity score was 292.52
MET-h/week (SD=143.62). This is comparable to other studies that have found average physical
activity scores to range from as low as 126.0 MET-h/week to 417.2 MET-h/week during early-
to-mid pregnancy.
144,145
The average score for the PSS was 13.21 (SD=6.38) on a range from 0
to 40; this is comparable to other studies using the 10-item PSS in pregnant women.
146,147
The
average score for nausea/vomiting based on the pregnancy-unique quantification of emesis and
nausea (PUQE) survey was 5.42 (SD=2.26); the PUQE score ranges from 3 (no symptom) to 15
(maximal symptom), and a score <6 is considered to be mild.
148
Lastly, half of our participants
(50.00%) were employed during early-to-mid pregnancy.
Early-to-mid pregnancy sleep quality was measured on average at 15.07 weeks gestation
(SD=2.81). Overall, 27.94% of participants reported very good sleep quality (JSQ score of 0-3),
29.41% reported good sleep quality (JSQ 3-6), 18.38% reported poor sleep quality (JSQ 6-9),
and 24.26% reported very poor sleep quality (JSQ > 9). While differences in sleep quality by
pre-pregnancy BMI were not statistically significant, a higher proportion of those with obesity
reported very poor sleep (31.92%) compared to those with overweight (17.65%) or normal BMI
(23.68%).
Cortisol sampling occurred on average at 30.98 weeks gestation (SD=2.05). The mean
AUC value was 111.03 nmol/L (SD=53.12), mean CAR was 0.50 nmol/L * hr (SD=1.53), and
the mean DCS was -0.59 nmol/L * hr (SD=0.51). The DCS was the only measure that differed
68

across pre-pregnancy BMI categories, with those with overweight and obesity showing flatter
DCS (F=6.04, p<0.01).
Total weight gain was on average calculated at 38.22 weeks gestation (SD=1.77).
Women with normal pre-pregnancy BMI gained on average 14.02kg (SD=6.00), those with
overweight gained on average 11.61kg (SD=5.07), and those with obesity gained on average
7.00kg (SD=6.59); these values were significantly different from each other (F=15.96, p<0.01).
For participants with normal and obesity before pregnancy, these mean values fall in the
recommended total weight gain range set by the IOM. For those with overweight before
pregnancy, the mean is above the recommended range.
Non-Compliance and Contamination of Cortisol Samples
Diurnal cortisol profiles by non-compliance to sampling timing can be found on Table 2.
Self-reported compliance was high, with only 3% ~ 6% of samples not following the timing
protocol for the first two samples in the morning. Non-compliance did not significantly alter the
values of the cortisol profiles except for the mis-timed awawakening+30 sample decreasing the
AUC in three samples. It should be noted that 42 of the 118 CAR samples (35.6%) were
negative.
Diurnal cortisol profiles by contamination categories can be found on Table 3. Majority
of CARs and DCSs, which requires only two samples, did not include contaminated samples.
Contamination was more common for AUCs, given that they require all four samples of the day.
Of the AUC, more than one in four (28 out of 104) included one contaminated sample, about one
in five (21 out of 104) included two contaminated samples, and about one in ten (10 out of 104)
included three contaminated samples. Levels of contamination did not significantly influence
69

AUC values. Given that we did not find any evidence of contamination affecting results, we did
not exclude them from the analysis to preserve sample size.
Moderated Mediation Model
We did not find any evidence of mediation of early-to-mid pregnancy sleep quality and
total GWG by any of the diurnal cortisol profiles; this is indicated by the null indirect effect
parameters. Percent mediated (
𝐼𝐼 𝑛𝑛 𝐼𝐼 𝑖𝑖 𝐼𝐼 𝐼𝐼 𝐼𝐼 𝑡𝑡 𝐸𝐸 𝐸𝐸𝐸𝐸 𝐼𝐼 𝐼𝐼𝑡𝑡 𝑇𝑇 𝑇𝑇 𝑡𝑡 𝑇𝑇𝑇𝑇 𝐸𝐸 𝐸𝐸 𝐸𝐸 𝐼𝐼 𝐼𝐼𝑡𝑡 ) ranged from 4-8% across all models. We also did
not find any evidence of moderation by pre-pregnancy BMI; this is indicated by the null index of
moderated mediation. In other words, early-to-mid pregnancy sleep quality and total GWG is not
mediated by diurnal cortisol profiles, and this relationship does not differ by pre-pregnancy BMI.
Findings for the moderated mediation analysis can be found in Table 4.
Main findings from the three models can be found in simplified versions of the statistical
diagram (Figure 3 for AUC, Figure 4 for CAR, and Figure 5 for DCS). The full version with all
model parameters can be found in Supplemental Figures 3-5. In all three models, we found
significant direct effects (c’ path) of early-to-mid pregnancy sleep quality on total weight gain
when comparing those who reported very good sleep quality and very poor sleep quality. For
example, among people with normal pre-pregnancy BMI, those with very poor sleep quality
during early-to-mid pregnancy gained on average 7.50kg (95% CI: 0.81, 14.19) more than those
with very good sleep quality - independent of the pathway through AUC. To put this into
context, normal pre-pregnancy BMI people are recommended to gain about 11.5-16kg. This
relationship did not differ across pre-pregnancy BMI, as indicated by the null sleep quality × pre-
pregnancy BMI interaction terms.
In the sensitivity analysis conducted in the sub-sample with positive CAR (N=68), the
relationship between sleep quality and weight gain was no longer significant, but the
70

directionality of all model parameters was in line with the findings from the full sample. For
example, among women with normal pre-pregnancy BMI, those with very poor sleep quality
during early-to-mid pregnancy gained on average 5.44 kg (95% CI: -3.20, 14.08) more than
those with very good sleep quality. The simplified model for the positive CAR observations can
be found in Supplemental Figure 6, and the full model can be found in Supplemental Figure 7.
The following sensitivity analyses were conducted, but findings did not significantly differ from
our main analysis: 1) Excluding women with +/- 10kg discrepancy in first measured weight and
self-reported pre-pregnancy weight, 2) Including participants with preterm birth, 3) Only using
early-pregnancy data to define early-to-mid pregnancy sleep quality, 4) Only using mid-
pregnancy data to define early-to-mid pregnancy sleep quality, 5) Averaging both surveys to
define early-to-mid pregnancy sleep quality, 6) Windsorizing individual samples then calculating
cortisol profiles, 7) Controlling for awakening time and time between first and last measure used
to calculate cortisol profile, and 8) Controlling for possible protocol non-compliance
(contamination and incorrect saliva collection timing).
Discussion

This study examined the mediating effect of late pregnancy circadian cortisol on early
prenatal sleep quality and total GWG, while simultaneously testing the moderating effect of pre-
pregnancy BMI. It is the first study to date that has tested the mediating effect of diurnal cortisol
on sleep and weight gain, in non-pregnant and pregnant populations. We found that those who
reported poorer quality sleep gained more weight, and this relationship did not differ by pre-
pregnancy BMI. However, this association was not mediated by any of the diurnal cortisol
profiles.
71

Our results on the obesogenic effect of poor sleep quality are in line with our findings from
study one and a large body of literature in non-pregnant populations that have found poor sleep
quality to predict increased weight gain.
121,149
They are also in agreement with a small but
growing body of literature that found poorer subjective sleep quality during pregnancy increased
GWG.
55,127,128

The relationship between sleep and weight gain was not mediated by circadian cortisol
profiles. This was contrary to our hypothesis, given that there is evidence in non-pregnant
populations that 1) poor sleep is associated with dysregulated diurnal cortisol profiles and 2)
abnormal cortisol levels during pregnancy is a predictor for weight gain.
59,93,94
However, studies
that have found sleep quality to affect cortisol levels during pregnancy measured these constructs
very close in time, and therefore, the causal effect may be confounded. Bublitz et al. used the
Pittsburgh Sleep Quality Index to assess sleep quality over the previous month then collected
ambulatory passive-drool samples over three days after the questionnaire.
206
Suzuki et al.
identified six pregnant women with and without diagnosis of sleep disorders during the third
trimester and collected in-lab blood samples.
96
These suggest that the short timing between sleep
quality and cortisol assessments may be key when examining this association. But, when we
tried re-running all models using late-pregnancy sleep quality, we continued to find no
relationship between sleep and diurnal cortisol profiles. Furthermore, a study published using the
MADRES data found third trimester AUC positively predicted total GWG, but only amongst
women with class 1 obesity before pregnancy.
207
Similarity to this study, we tried categorizing
pre-pregnancy BMI into normal, underweight, class 1 obesity, and class 2/3 obesity but we ran
into small cell issues (4 categories of sleep quality x 4 categories of pre-pregnancy BMI); this is
72

due to the large difference in sample size, where the previous study had 176 participants while
this current study had 94 participants to model AUC.
A closer look at causal relationship between sleep, diurnal cortisol, and weight gain
provides additional possible reasons for our lack of findings. While one’s sleep, HPA axis, and
metabolic outcomes are without doubt intimately and complexly tied with each other, the
psychobiological pathway may not support our mediation hypothesis. In a recent literature
review, Hirotsu et al. poses that stress, specifically prolonged exposure to stress, leads to the
dysregulation of cortisol, which in turns leads to impaired sleep (Figure 8).
184
They posit that
poor sleep, regardless of its cause, leads to psychobiological and behavioral changes that
ultimate lead to increased weight gain and risk of cardiometabolic disorders. Therefore, instead
Figure 6. Schematic of the Main Interactions between Sleep, Stress and Metabolism from Hirotsu et al.
184

of poor sleep→ dysregulated HPA axis→ weight gain, they suggest stress→ dysregulated HPA
axis→ poor sleep→ weight gain. While the relationship between the HPA axis and sleep is most
likely bidirectional, the exact timing and causal relationship are still not completely elucidated.
208

This is especially true amongst pregnant populations, as the HPA axis plays a central role in
prenatal biochemical changes and undergoes significant changes during pregnancy.
209,210
Future
research that incorporates various psychobiological and behavioral factors related to energy
73

balance are needed to elucidate a more holistic understanding of sleep, HPA axis, and
cardiometabolic outcomes.
Limitations
There are several limitations to this study that must be addressed. One major limitation is
the self-reported collection time of the salivary cortisol. Although our self-reported adherence to
the timing protocol was high, the high prevalence of negative CARs and low mean CAR value
compared to that found in the literature (0.5 vs 1.5nmol/L *hr) suggest the likely possibility of
protocol non-adherence during the morning.
211
There are several collection methodologies that
could be implemented in future studies to improve accuracy of saliva sampling such as the
verification of awakening time via objective sensors (e.g., polysomnography, actigraphy) and
saliva sampling time via electronic monitoring systems (e.g., medication event monitoring caps,
time-stamped photographs).
66
Also, this study only collected saliva samples on one day to reduce
participant burden, but expert consensus guidelines on ambulatory assessment of salivary cortisol
recommend at least two or more sampling days (weekend and weekdays) to accurately capture
diurnal patterns.
66
Another limitation is our lack of objective (i.e., device-derived) sleep data. In
the sleep literature, it is usually recommended to include both objective and subjective measures
of sleep in the same study.
172
It’s widely known that subjective sleep (e.g., self-reported
measurements of sleep duration, quality, disruption via questionnaires) and objective sleep (e.g.,
polysomnography, actigraphy, bed sensors) often do not agree with one another, but both types
of sleep measures are independently associated with health factors.
173–177
Given that the
correlation between subjective and objective measures of sleep varies by perinatal mood
disorders, we cannot conclude how the lack of objective sleep data may have biased our
results.
175,178
Lastly, we were underpowered to examine moderated mediation with our limited
sample size. Taking the AUC model as an example, although we were fully powered to detect
74

the b path, we only had 54% power to detect the a path. Similar trends were seen with the CAR
and DCS models. Since 𝑎𝑎 × 𝑏𝑏 quantifies the indirect effect of X on Y (i.e. the effect of X on Y
through X’s effect on M, which in turn affects Y), this may be why we did not find any evidence
of moderated mediation in our study.
212

Conclusion
Our findings suggest that poor sleep quality is a risk factor for increased GWG in this
population, but the underlying mechanism of this relationship may not involve the circadian
profiles of cortisol. Improving sleep quality as a part of weight management interventions has led
to successful results amongst non-pregnant populations.
179
The development of pregnancy-
specific sleep recommendations to be incorporated into routine obstetric care may be a critical
next step in promoting healthy GWG. Future research that examines other psychobiological and
behavioral factors beyond circadian cortisol are needed to elucidate a more holistic
understanding of prenatal sleep and obesity risk during pregnancy.

75
Overall (N=136) Test Statistic (p-value)
Normal (N=38) Overweight (N=51) Obesity (N=47)
Maternal age [years]; mean(SD)
1
28.90 (5.96) 26.56 (5.39) 30.18 (6.40) 29.43 (5.45) F=4.53 (p<0.01)
Ethnicity by birthplace; n(%)
2
ꭕ
2
= 7.30 (p=0.12)
Non-Hispanic 18 (13.95%) 9 (24.32%) 3 (6.38%) 6 (13.33%)
US-Born Hispanic 55 (42.64%) 17 (45.95%) 20 (42.55%) 18 (40.00%)
Foreign-Born Hispanic 56 (43.41%) 11 (29.73%) 24 (51.06%) 21 (46.67%)
Highest educational attainment; n(%)
3
ꭕ
2
= 1.29 (p=0.52)
High School or below 85 (63.34%) 22 (57.89%) 31 (62.00%) 32 (69.57%)
Beyond high school 49 (36.57%) 16 (42.11%) 19 (38.00%) 14 (30.43%)
Marital status; n(%)
4
ꭕ
2
= 3.25 (p=0.20)
Not married or living together 25 (20.00%) 6 (17.14%) 13 (28.26%) 6 (13.64%)
Married or living together 100 (80.00%) 29 (82.86%) 33 (71.74%) 38 (86.36%)
Parity; n(%)
5
ꭕ
2
= 5.37 (p=0.25)
First child 39 (30.47%) 13 (35.14%) 13 (28.26%) 13 (28.89%)
Second child 39 (30.47%) 15 (40.54%) 11 (23.91%) 13 (28.89%)
Third or more child 50 (39.06%) 9 (24.32%) 22 (47.83%) 19 (42.22%)
Hypertensive disorder; n(%)
6
ꭕ
2
= 5.79 (p=0.22)
Normal 107 (78.68%) 32 (84.21%) 42 (82.35%) 33 (70.21%)
Preeclampsia or gestational hypertension 22 (16.18%) 3 (7.89%) 7 (13.73%) 12 (25.53%)
Chronic hypertension 7 (5.15%) 3 (7.89%) 2 (3.92%) 2 (4.26%)
Glucose tolerance abnormality; n(%)
7
ꭕ
2
= 12.09 (p<0.05)
Normal 87 (63.97%) 30 (78.95%) 32 (62.75%) 25 (53.19%)
Glucose intolerance or gestational diabetes 45 (33.09%) 8 (21.05%) 19 (37.25%) 18 (38.30%)
Chronic diabetes 4 (2.94%) 0 0 4 (8.51%)
Physical activity [MET-h/week]; mean(SD)
8
* 292.52 (143.62) 306.95 (162.35) 280.5 (144.37) 292.98 (128.34) F=0.34 (p=0.72)
Perceived Stress Score; mean(SD)
9 Ⴕ
13.21 (6.65) 14.17 (6.71) 12.39 (6.02) 13.29 (7.24) F=0.72 (p=0.49)
Nausea/vomiting; mean(SD)
10 ǂ
5.42 (2.26) 5.89 (2.83) 5.02 (1.61) 5.44 (2.29) F=1.51 (p=0.23)
Employment status; n(%)
11
ꭕ
2
= 0.37 (p=0.83)
Not employed 63 (50.00%) 19 (54.29%) 22 (47.83%) 22 (48.89%)
Employed 63 (50.00%) 16 (45.71%) 24 (52.17%) 23 (51.11%)
Early-pregnancy sleep quality; n(%)
12
ꭕ
2
= 5.79 (p=0.45)
Very good 38 (27.94%) 10 (26.32%) 18 (35.29%) 10 (21.28%)
Good 40 (29.41%) 10 (26.32%) 14 (27.45%) 16 (34.04%)
Poor 25 (18.38%) 9 (23.68%) 10 (19.61%) 6 (12.77%)
Very poor 33 (24.26%) 9 (23.68%) 9 (17.65%) 15 (31.92%)
AUC [nmol/L]; mean(SD)
13
111.03 (53.12) 125.34 (70.23) 111.81 (54.13) 105.75 (53.83) F=0.87 (p=0.42)
CAR [nmol/L * hr]; mean (SD)
14
0.50 (1.53) 0.54 (2.49) 0.78 (1.88) 0.36 (1.23) F=0.51 (p=0.60)
DCS [nmol/L * hr]; mean(SD)
15
-0.59 (0.51) -0.94 (0.88) -0.54 (0.48) -0.46 (0.47) F=6.04 (p<0.01)
Total weight gain [kg]; mean(SD)
16
10.69 (6.51) 14.02 (6.00) 11.61 (5.07) 7.00 (6.59) F=15.96 (p<0.01)
Note: Percentages are column percentages.
*Measured using Pregnancy Physical Activity Questionnaire; mean physical activity level ranges between 126.0 MET-h/week to 417.2 MET-h/week in literature.
ႵMeasured using Cohen's Perceived Stress Scale; ranges from 0 to 40.
ǂMeasured using the Pregnancy-Unique Quantification of Emesis and Nausea survey; ranges from 3 to 15.
Acronyms: BMI: Body Mass Index; US: United States; SD: Standard Deviation; MET: Metabolic Equivalent; AUC: Area Under the Curve; CAR: Cortisol Awakening Response; DCS: Diurnal Cortisol Slope
Table 1. Descriptive Characteristics of Final Analytic Sample
By Pre-Pregnancy BMI
1
No missing;
2
Missing for 7 participants;
3
Missing for 2 participant;
4
Missing for 11 participants;
5
Missing for 7 participants;
6
No missing;
7
No missing;
8
Missing for 10 participants;
9
Missing for 9 participants;
10
Missing for 7
participants;
11
Missing for 10 participants;
12
No missing;
13
Missing for 33 participants;
14
Missing for21 participants;
15
Missing for 32 participants
76
Overall
n
mean (SD)
Awakening Sample < 15min after Awakening
n
mean (SD)
Awakening Sample > 15min after Awakening
n
mean (SD) Test-value, p-value
AUC
111.03
(53.12)
n=130
114.74 (59.18)
n=6
83.26 (38.41) t=1.05, p=0.29
CAR
n=118
0.50 (1.53)
n=114
0.63 (1.92)
n=4
-0.69 (0.97) t=1.36, p=0.18
DCS
n=114
-0.59 (0.51)
n=108
-0.62 (0.65)
n=6
-0.64 (0.50) t=1.36, p=0.18
Overall
n
mean (SD)
Awakening+30min Samples Collected 15-45minutes
after Awakening
n
mean (SD)
Awakening+30min Samples Collected <15minutes or >45minutes
after Awakening
n
mean (SD) Test-value, p-value
AUC
111.03
(53.12)
n=101
115.1 (58.81)
n=3
59.20 (4.30) t=8.80, p<0.01
CAR
n=118
0.50 (1.53)
n=112
0.64 (1.90)
n=6
-0.46 (1.94) t=1.38, p=0.17
DCS
n=106
-0.59 (0.51)
n=103
-0.63 (0.65)
n=3
-0.73 (0.07) t=1.21, p=0.24
Acronyms: SD: Standard Deviation; AUC: Area Under the Curve; CAR: Cortisol Awakening Response; DCS: Diurnal Cortisol Slope
Table 2. Diurnal Cortisol Measures by Protocol Non-Compliance
77
Overall
n
mean (SD)
No Contamination
n
mean (SD)
One Sample Contaminated
n
mean (SD)
Two Samples Contaminated
n
mean (SD)
Three Samples Contaminated
n
mean (SD)
Four Samples Contaminated
n
mean (SD) Test-value, p-value
AUC
n=104
111.03 (53.12)
n=49
122.73 (60.28)
n=28
106.25 (63.2)
n=21
110.89 (48.76)
n=10
95.26 (57.69) NA F=0.86, p=0.47
CAR
n=118
0.50 (1.53)
n=83
0.8 (1.96)
n=35
0.07 (1.69)
n=0
NA NA t=1.93, p=0.06
DCS
n=114
-0.59 (0.51)
n=75
-0.63 (0.71)
n=40
-0.63 (0.48)
n=1
-0.73 NA NA F=0.02, p=0.98
Acronyms: SD: Standard Deviation; AUC: Area Under the Curve; CAR: Cortisol Awakening Response; DCS: Diurnal Cortisol Slope
Table 3. Diurnal Cortisol Measure by Number of Contaminated Samples
78
Diurnal Cortisol Measure
Early-to-Mid Pregnancy
Sleep Quality
(Ref: Very Good) Pre-Pregnancy BMI Indirect Effect
Index of Moderated Mediation (95% CI)
(Ref: Normal Pre-Pregnancy BMI)
Normal 0.14 (-1.09, 1.72) -
Overweight -0.18 (-1.55, 0.69) -0.32 (-2.84, 1.33)
Obesity 0.10 (-1.65, 1.66) -0.04 (-2.39, 1.75)
Normal -0.18 (-1.12, 0.78) -
Overweight 0.23 (-0.44, 1.2) 0.41 (-0.86, 1.93)
Obesity -0.13 (-1.91, 0.92) 0.05 (-1.86, 1.44)
Normal -0.07 (-1.12, 0.95) -
Overweight 0.09 (-0.76, 1.16) 0.16 (-1.36, 1.93)
Obesity -0.05 (-1.74, 1.08)
0.02 (-1.71, 1.52)
Normal 0.31 (-0.59, 1.85) -
Overweight 0.50 (-0.28, 1.61) 0.18 (-1.17, 1.61)
Obesity 1.09 (-0.45, 3.36) 0.77 (-0.77, 3.10)
Normal 0.22 (-0.48, 1.40) -
Overweight 0.35 (-0.31, 1.22) 0.13 (-0.86, 1.22)
Obesity 0.76 (-0.52, 2.9) 0.54 (-0.63, 2.58)
Normal 0.11 (-0.47, 1.20) -
Overweight 0.18 (-0.56, 1.02) 0.07 (-0.82, 0.85)
Obesity 0.39 (-1.02, 2.46)
0.28 (-0.95, 2.00)
Normal 0.09 (-2.13, 2.21) -
Overweight 0.01 (-0.90, 0.60) -0.08 (-2.39, 2.18)
Obesity 0.17 (-2.21, 2.7) 0.08 (-2.88, 3.66)
Normal 0.66 (-1.56, 4.58) -
Overweight -0.04 (-1.29, 0.73) -0.69 (-4.76, 1.58)
Obesity 0.13 (-2.26, 2.06) -0.52 (-5.04, 2.47)
Normal 0.34 (-1.50, 3.40) -
Overweight -0.04 (-1.23, 0.94) -0.39 (-3.66, 1.72)
Obesity -0.19 (-2.72, 1.52)
-0.54 (-4.68, 1.99)
Acronyms: BMI: Body Mass Index; CI: Confidence Interval; AUC: Area Under the Curve; CAR: Cortisol Awakening Response; DCS: Diurnal Cortisol Slope
Table 4. Indirect Effect and Index of Moderated Mediation
Good
AUC (N=94)
CAR (N=105)
DCS (N=95)
Good
Poor
Very poor
Poor
Very poor
Good
Poor
Very poor
79
All MADRES Participants
(N=966)
Final Dataset
(N=136)
Maternal age [years]; mean(SD) 28.37 (6.13) 28.90 (5.96)
Ethnicity by birthplace; n(%)
Non-Hispanic 189 (24.11%) 18 (13.95%)
US-Born Hispanic 275 (35.08%) 55 (42.64%)
Foreign-Born Hispanic 320 (40.82%) 56 (43.41%)
Highest educational attainment; n(%)
High School or below 520 (58.36%) 85 (63.34%)
Beyond high school 371 (41.64%) 49 (36.57%)
Marital status; n(%)
Not married or living together 203 (28.27%) 25 (20.00%)
Married or living together 515 (71.73%) 100 (80.00%)
Parity
First child 287 (38.47%) 39 (30.47%)
Second child 231 (30.97%) 39 (30.47%)
Third or more child 228 (30.56%) 50 (39.06%)
Hypertensive disorder; n(%)
Normal 599 (78.51%) 107 (78.68%)
Preeclampsia or gestational hypertension 121 (15.88%) 22 (16.18%)
Chronic hypertension 42 (5.51%) 7 (5.15%)
Glucose tolerance abnormality; n(%)
Normal 501 (66.53%) 87 (63.97%)
Glucose intolerance or gestational diabetes 216 (28.69%) 45 (33.09%)
Chronic diabetes 36 (4.78%) 4 (2.94%)
Physical activity [MET-hr/week]; mean(SD) 299.37 (148.11) 292.52 (143.62)
Perceived Stress Score; mean(SD) 13.01 (6.51) 13.21 (6.65)
Nausea/vomiting; mean(SD) 5.71 (2.53) 5.42 (2.26)
Employment status; n(%)
Not employed 248 (48.44%) 63 (50.00%)
Employed 264 (51.56%) 63 (50.00%)
Acronyms: MADRES: Maternal and Developmental Risks from Environmental and Social Stressors; SD: Standard Deviation
Supplemental Table 1. Comparison of All MADRES Participants vs Final Analytical Sample
80
Figure 1. Mediation Model with Moderated b and c’ paths
(PROCESS Model 15)
Figure 2. Mediation Model with Moderated a, b, and c’ paths
(PROCESS Model 59)
Acronyms: AUC: Area Under the Curve; CAR: Cortisol Awakening Response; DCS: Diurnal Cortisol Slope; BMI: Body Mass Index
81
Figure 3. Simplified Model for the Association of Early-to-Mid Pregnancy Sleep Quality (Ref: Very Good) and Total Weight Gain, with AUC
Mediator and Pre-pregnancy BMI Moderator (Ref: Normal)
Acronyms: AUC: Area Under the Curve; BMI: Body Mass Index
82
Figure 4. Simplified Model for the Association of Early-to-Mid Pregnancy Sleep Quality (Ref: Very Good) and Total Weight Gain, with CAR
Mediator and Pre-pregnancy BMI Moderator (Ref: Normal)
Acronyms: CAR: Cortisol Awakening Response; BMI: Body Mass Index
83
Figure 5. Simplified Model for the Association of Early-to-Mid Pregnancy Sleep Quality (Ref: Very Good) and Total Weight Gain, with DCS
Mediator and Pre-pregnancy BMI Moderator (Ref: Normal)
Acronyms: DCS: Diurnal Cortisol Slope; BMI: Body Mass Index
84
Is EP Survey missing?
Is MP Survey missing?
Is MP Survey ≤ 20 weeks?
Use MP Survey
data
Excluded
Excluded
Yes
Yes No
Yes No
Is EP Survey ≤ 20 weeks?
No
Is MP Survey missing? Excluded
Yes No
Use EP Survey data
Yes No
Is MP Survey ≤ 20 weeks?
How far apart were EP Survey
and MP Survey assessed?
Use EP Survey data
Yes No
Average EP Survey
and MP Survey
Use survey closest
to 15 weeks
Supplemental Figure 1. Algorithm to Quantify Early-to-Mid Pregnancy Sleep Quality
0-8 weeks >8 weeks
Acronyms: EP: Early-Pregnancy; MP: Mid-Pregnancy
85
Not Underweight
N=136
GWG Data
N=772
Sleep Data
N=576
Complete Sleep Data
N=405
Merged Data
N=150
Supplemental Figure 2. Consort Diagram for Data Availability
Late pregnancy cortisol data
N=229
Recruited <20 weeks gestation
N=633
Not missing AUC
N=104
Not outlier
N=103
Not missing CAR
N=118
Not outlier
N=115
Not missing DCS
N=114
Not outlier
N=104
Not missing covariates
N=95
Final analytic sample
N=94
Final analytic sample
N=105
Final analytic sample
N=95
Term Pregnancy
N=138
Not missing covariates
N=95
Not missing covariates
N=95
86
Supplemental Figure 3. Full Model for the Association of Early-to-Mid
Pregnancy Sleep Quality (Ref: Very Good) and Total Weight Gain, with AUC
Mediator and Pre-pregnancy BMI Moderator (Ref: Normal)
Acronyms: AUC: Area Under the Curve; BMI: Body Mass Index
87
Supplemental Figure 4. Full Model for the Association of Early-to-Mid
Pregnancy Sleep Quality (Ref: Very Good) and Total Weight Gain, with CAR
Mediator and Pre-pregnancy BMI Moderator (Ref: Normal)
Acronyms: CAR: Cortisol Awakening Response; BMI: Body Mass Index
88
Supplemental Figure 5. Full Model for the Association of Early-to-Mid
Pregnancy Sleep Quality (Ref: Very Good) and Total Weight Gain, with DCS
Mediator and Pre-pregnancy BMI Moderator (Ref: Normal)
Acronyms: DCS: Diurnal Cortisol Slope; BMI: Body Mass Index
89
Supplemental Figure 6. Simplified Model for the Association of Early-to-Mid Pregnancy Sleep Quality (Ref: Very Good) and Total Weight Gain,
with CAR Mediator and Pre-pregnancy BMI Moderator (Ref: Normal) for Positive CAR Observations Only
Acronyms: CAR: Cortisol Awakening Response; BMI: Body Mass Index
90
Supplemental Figure 7. Full Model for the Association of Early-to-Mid
Pregnancy Sleep Quality (Ref: Very Good) and Total Weight Gain, with CAR
Mediator and Pre-pregnancy BMI Moderator (Ref: Normal) for Positive CAR
Observations Only
Acronyms: CAR: Cortisol Awakening Response; BMI: Body Mass Index
91

Chapter Four: Study Three
Maternal Sleep Disruption and Morning Cortisol During
Pregnancy: An Ecological Momentary Assessment Study

Abstract

Background: Given the ubiquitous nature and health consequences of prenatal sleep disruption,
understanding how poor sleep alters maternal physiology is critical in pregnancy research. A
proposed factor that may explain the association between frequent sleep disruption and adverse
pregnancy outcomes is the dysregulation of the Hypothalamic-Pituitary-Adrenal (HPA) axis.
This study examined the between and within-subject level associations of sleep and diurnal
cortisol profiles during pregnancy using daily ecological momentary assessment (EMA).
Method: Participants were 63 pregnant low-income Hispanic participants from the Maternal and
Developmental Risks from Environmental and Social Stressors (MADRES) real-time personal
sub-study. During early-to-mid and mid-to-late pregnancy, participants collected salivary cortisol
samples at awakening and awakening +30min for four days. They also reported number of sleep
disruptions from the night before using mobile phone surveys. We examined the day-level
relationships between sleep disruption and morning cortisol levels using multilevel linear
regression with a subject-level random intercept.
Results: We found a significant between-subject level association between sleep disruption and
awakening +30min cortisol, such that people who experienced more sleep disruption had on
average 0.22 nmol/L higher awakening+30min cortisol levels (β=0.22, 95% CI: 0.01, 0.44).
However, sensitivity analyses revealed that these findings are significantly diminished when
92

accounting for possible protocol non-compliance. No significant associations were found with
other morning cortisol variables.
Conclusion: Our results suggest poor sleep quality may affect morning cortisol levels on the
following day, but findings must be interpreted with much caution. Future research that
incorporates improved strategies for ambulatory salivary cortisol sampling is needed to elucidate
the between and within-subject relationship between sleep and cortisol in pregnant populations.

93

Introduction

Poor sleep is one of the most common health issues experienced by pregnant people.
29
In
fact, more than 75% of pregnant people are considered to be poor sleepers, which is significantly
higher than the average prevalence of 35 – 55% in non-pregnant people. While poor sleep can be
differentially defined based on the sleep dimension of interest (i.e., poor subjective sleep quality,
insufficient night-time sleep, significant daytime sleepiness, insomnia),
28
one of the most
commonly reported symptoms in pregnant people is increased sleep disruption.
31,32
Throughout
the perinatal period, but especially toward the end of pregnancy, sleep can become repeatedly
disturbed due to pain and discomfort, frequent urination, and restless leg syndrome.
34
Previous
evidence suggests that increased sleep disruption is positively associated with the risk of adverse
pregnancy outcomes, such as excessive gestational weight gain, preterm birth, low birth weight,
preeclampsia, and gestational diabetes.
198,213–215
In fact, a recent meta-analysis found that the
majority of risk factors related to stillbirth can be attributed to maternal sleep disruption.
216

Given the ubiquitous nature and health consequences of prenatal sleep disruption, understanding
how poor sleep alters maternal physiology is critical in pregnancy research.
A proposed factor that may explain the association between frequent sleep disruption and
adverse pregnancy outcomes is the dysregulation of the Hypothalamic-Pituitary-Adrenal (HPA)
axis. The HPA axis is a complex set of direct influences and feedback interactions between the
hypothalamus, pituitary gland, and adrenal gland that, amongst other roles, ultimately leads to
the secretion of cortisol from the adrenal cortex.
58–60
During pregnancy, cortisol is involved in
the maturation of the lungs, liver, and various other organ systems in ways that facilitates the
neonatal transition from life inside the womb to outside. It also plays a critical role in initiating
parturition.
217
Cortisol secretion follows a circadian pattern, characterized by increasing levels at
94

awakening, followed by a steep peak at 30-45 minutes after awakening (i.e., the Cortisol
Awakening Response [CAR]), and a slow decline until bedtime.
63,64
Researchers can measure
this diurnal rhythm outside the laboratory setting as participants go about their usual lives
through repeated sampling of saliva throughout the day. In this study, we only focus on salivary
cortisol measures after awakening, as salivary cortisol levels in the morning are the most
intimately associated with sleep health, and specifically sleep disruption, according to previous
literature.
95,218,219
Dysregulated morning cortisol measures have been characterized by low
cortisol levels at awakening and lack of steep increase after awakening.
68–70
During pregnancy,
these dysregulated rhythms have been repeatedly shown to be associated with a myriad of
adverse pregnancy outcomes, including but not limited to early labor, respiratory illnesses, and
decreased cognitive development.
68–70
Therefore, morning cortisol levels are of great interest in
pregnancy research.
In non-pregnant populations, a small body of research has supported the potential
association between sleep and HPA axis activity, albeit the findings are somewhat mixed and
mostly focused on cross-sectional between-subject relationship of sleep and cortisol. For example,
studies have found people with elevated cortisol levels are more likely to report night-time
wakefulness and poorer sleep quality,
218,220,221
and patients with sleep disorders, such as insomnia
and obstructive sleep apnea, often exhibit dysregulated HPA axis activity.
222,223
However, within-
subject studies examining the day-level relationships between diurnal cortisol profiles and sleep
are significantly less common. Two studies in non-pregnant populations have found a negative
correlation between frequency of sleep disruption and next-day awakening cortisol levels,
93,95
one
study found a positive association,
224
while others have found no association.
208,221,225–228

95

Therefore, while the intricacies of the daily association are still not entirely understood, there is
some emerging evidence supporting a relationship between circadian cortisol and sleep.
Whether this relationship is also observed in pregnant populations remains largely
unexplored. One reason we cannot simply assume the same relationship between sleep and diurnal
cortisol in pregnant women is due to the significant alterations to HPA axis functioning throughout
pregnancy. Maternal cortisol levels increase two to three fold during the second and third trimester
due to heightened production of cortisol from the maternal HPA axis, activation of the fetal HPA
axis during late pregnancy, and placental production of corticotropin-releasing hormone.
82,83
In
addition, while the expected diurnal circadian rhythm is sustained during pregnancy, but some
studies have found the CAR to be attenuated in late pregnancy compared to early pregnancy.
85
Yet,
there is a dearth of studies examining the associations between sleep and diurnal cortisol during
pregnancy. To date, there have only been two. Bublitz et al. found poor sleep quality to be
associated with greater evening cortisol concentrations but only in late pregnancy (36 weeks
gestation).
108
They also reported those with poor sleep quality exhibited lower awakening and
awakening+30min cortisol levels, but this finding did not reach statistical significance. Suzuki et
al. reported that poor sleepers, compared to good sleepers, exhibited decreased CAR.
96
However,
both of the studies focused on the cross-sectional between-subject effects of sleep on diurnal
cortisol during pregnancy and did not examine within-subject day-to-day variations or associations.
To address these gaps in the literature, this study examined the between and within-subject
level associations of sleep and diurnal cortisol profiles during pregnancy using daily ecological
momentary assessment (EMA). EMA repeatedly collects real-time data in the natural environment
and allows for the examination of both between-subject variability (i.e., comparing participants’
average levels to each other on the person-level) and within-subject variability (i.e., comparing
96

participants’ repeated scores to their own average on the day-level).
109
On the between-subject
level, we hypothesized that people with more frequent sleep disruptions would, on average, exhibit
more dysregulated cortisol profiles, characterized by lower awakening cortisol, awakening+30min
cortisol, and CAR. On the within-subject level, we hypothesized that on any given night, more
frequent sleep disruptions would be followed by lower awakening cortisol, awakening+30min
cortisol, and CAR on the next day. Given the significant changes that occur to both sleep and
cortisol secretion throughout pregnancy, we also examined how the within-subject associations
differed by early-to-mid pregnancy versus mid-to-late pregnancy as an exploratory aim.

Methods
Sample
The Maternal and Developmental Risks from Environmental and Social Stressors
(MADRES) study is an ongoing prospective cohort study of primarily Hispanic, low-income
pregnant women and their children in Los Angeles, California. Further details on the MADRES
protocol have been described elsewhere
135
. MADRES participants were recruited from USC
affiliated hospitals and community health centers serving medically underserved populations or
Medi-Cal patients. Inclusion criteria were: (1) <30 weeks pregnant at the time of enrollment; (2)
≥18 years of age; (3) singleton pregnancy; and (4) English or Spanish speaking. Exclusion
criteria were: (1) HIV positive status; (2) physical, mental, or cognitive disabilities that prevent
participation; (3) current incarceration; or (4) multiple gestation. Maternal consent and HIPAA
authorization for electronic medical records was obtained prior to any study assessment. The
Institutional Review Board at the University of Southern California approved all aspects of this
study.
97

For this analysis, we used data from the real-time personal monitoring sub-study nested
within the MADRES cohort. This MADRES sub-study involved 65 Hispanic cohort participants
who completed additional data collection procedures in an intensive longitudinal, observational,
case-crossover study (see O’Connor et al., 2019 for a description of the full protocol) that
collected various EMA and continuous monitoring data during three assessment periods: early-
to-mid pregnancy (10-24 weeks gestation), mid-to-late pregnancy (25-34 weeks gestation), and
4-6 months post-partum.
229
This study only utilized data from the two pregnancy assessments.
Study coordinators conducted an initial at-home visit, during which written informed consent
was collected, and participants were trained on the use of the EMA smartphone application and
saliva collection kits.
Following the visit, participants completed four days of free-living assessment (i.e., EMA
mobile surveys in English or Spanish, salivary cortisol sampling, hip-worn accelerometers) in
their natural environment as they conducted normal daily activities. Study coordinators called
participants to check on compliance and any issues with completing the study protocol. At the
end of the four-day period, study coordinators returned to participants’ homes to collect the
devices and saliva samples. These protocols were repeated at each study timepoint. Therefore,
the maximum number of observations was eight days (4 days x 2 assessment periods) per
participant.
Measures
Salivary Cortisol
Participants collected saliva samples at home using a Salivette device (Sarstedtf, Inc.
Rommelsdolf, Germany), which is a small cotton dental roll that participants gently chew for two
minutes. A total of four saliva samples per day for each of four days were collected at
awakening, 30 min after awakening, afternoon (around 3:00pm), and bedtime to capture the
98

diurnal pattern of cortisol secretion
188
. For this analysis, we only utilized the cortisol samples
from awakening and 30 min after awakening. At the time of collection, participants directly
noted on the saliva tube the date and time of collection and time of awakening that day. Samples
were flagged if participants had waited more than 15 minutes after awakening to collect their
first sample or waited less than 15 minutes or more than 45 minutes after the first sample to
collect their second sample. They also marked whether any eating, drinking (besides water),
tooth brushing, smoking or exercising occurred in the prior 30 min. Participants stored their
samples in their home refrigerator until the end of the four-day data collection period when the
samples were transferred to a laboratory freezer for storage at -80°C. Samples were sent to a
commercial laboratory in batch and were assayed with chemiluminescence immunoassay (CLIA;
IBL International, Hamburg, Germany), which has a lower detection limit of .005 ug/dL and
intra- and inter-assay coefficients in the range of 3.0 - 4.1%.
We examined three morning cortisol measures for this study. The awakening cortisol
(m1), awakening+30min cortisol (m2), and the CAR. The CAR, which captures the marked
increase in cortisol levels across the first 30 minutes after awakening, is calculated using the
trapezoidal formula, taking into account the increase from the first to second measurement
( 𝐴𝐴 𝐴𝐴 𝐶𝐶 =
( 𝑚𝑚 1
+ 𝑚𝑚 2
) ∙ 𝑡𝑡 1
2
− 𝑚𝑚 1
∗ 𝑡𝑡 1
).
63,190
First, we followed the recommendations set forth by
Scholtz et al and screened the cortisol measures for distributional properties and outlies as the
first step.
230
In addition, we needed to address any issues that may threaten the model
assumptions for linear regressions. Therefore, analyses that modeled awakening or
awakening+30 min cortisol as the outcome used the square root of the respective cortisol
measures to fix the non-normal distribution of the residuals.
99

We winsorized extreme cortisol measures that were more than three standard deviations
above or below the mean. As a sensitivity analysis, we also tried windsorizing each of the four
samples, then calculating the CAR. These are both common methodologies to deal with extreme
measures of salivary cortisol.
191
We also examined the prevalence of contamination and non-
compliance to the sampling timing protocol, in addition to concordance between the self-
reported wake up time on the samples and EMA surveys.
189
We then tested whether cortisol
levels significantly differed by protocol noncompliance, and we ran sensitivity analyses to
examine whether exclusion of/controlling for these observations significantly changed the
results. Furthermore, given that negative CAR values are regarded as evidence for non-
compliance (i.e., delaying the collection of awakening or awakening+30min samples by more
than 30-45 minutes), we also conducted sensitivity analyses while eliminating all negative CAR
observations, as recommended by the CAR expert consensus guide.
189
In addition, given that not
all participants collected the two saliva samples at the same time, we tried controlling for
awakening time and/or the time between the first and second cortisol measure.
EMA Sleep
EMA data were collected through the commercially available MovisensXS© software
application (https://www.movisens.com/en/products/movisensxs/) on the Android smartphones
that were provided for the participants for the duration of each study timepoint (Google, USA,
Inc.). EMA data were wirelessly uploaded and stored on the cloud server after each entry.
Participants were prompted via audio signal to complete one EMA survey at random times
during five pre-specified sampling windows; usual awakening time – 10am; 11am-1pm; 2pm-
4pm; 5pm-7pm; and 8pm – bedtime. The first sampling window was preset based on each
participant’s usual awakening time (5am, 6am, 6:30am, 7am, 8am, 9am, or 10am). They then
completed an electronic survey on the phone’s touch screen. If no entry was made, the
100

application emitted up to two reminder signals at 3-minute intervals to complete the survey. For
this analysis, we will only use data from the first EMA survey of the day (usual awakening time
– 10am), since that was the only timepoint where we asked participants about their sleep from
the previous night. We measured the number of sleep disruptions by asking “How many times
did you wake up during the night”, with answers ranging from 0 to 9+ times.
Covariates
We identified a list of potential person-level and day-level covariates a priori based on
study design and literature review. Then, we conducted bivariate analyses (i.e., Pearson
correlation, student’s t-tests, chi-square tests) of these potential covariates with daily sleep and
cortisol measures. All statistical significance were examined using two-sided tests with α=0.10.
Based on the bivariate analyses, all models controlled for the following person-level
measures: gestational age at time of data collection, pre-pregnancy BMI, and mother’s highest
attained education level. Gestational age at time of data collection was calculated based on
gestational age at birth from the EMR, then centered at the mean to aid interpretability. Self-
reported pre-pregnancy weight was ascertained through interviewer-administered questionnaires.
If missing, then the first weight of the index pregnancy (obtained from the maternal medical
records) was used in lieu of self-reported pre-pregnancy weight. Self-reported pre-pregnancy
weight and height were used to calculate the pre-pregnancy BMI (kg/m
2
) and classified using
CDC categories: normal weight (BMI ≥18.5 and < 25), overweight (BMI ≥25 and < 30), and
obesity (BMI ≥30).
140
Demographic data, including education level, were ascertained at the
initial interviewer-administered questionnaire.
We also controlled for the following day-level measures: fatigue, pain, and positive
affect. We ascertained this data from the EMA surveys by asking “Right before the phone went
101

off, how tired/ much physical pain/ happy/calm were you feeling?” from 1= Not at all to
4=Extremely. Happy and calm were averaged together to create positive affect. Then, day-level
averages were calculated for each day and centered on each participant’s mean. Given that cross-
level confounding from these level 1 covariates is possible, as a sensitivity analysis, we tried
controlling for both between-subject measures (e.g., deviation of the participant’s average from
the grand mean) and within-subject measures (e.g., deviation of the participant’s daily score from
their own mean) in the model.
Statistical Analysis
Descriptive and univariate analyses of both person and day-level characteristics were
conducted to examine the distributions of all variables, the correlation structure between
predictors, and extreme observations. Additional analyses of residual distributions were
performed to determine whether modeling assumptions were met before running the final model.
We examined the day-level relationships between sleep disruption and morning cortisol
levels using multilevel linear regression with a subject-level random intercept. For level-1
predictors ( 𝑥𝑥 𝑖𝑖 𝑖𝑖 ), we included terms for both the group mean-centered values (level-1 predictors
centered using the level-2 subject mean, i.e., 𝑥𝑥 𝑖𝑖 𝑖𝑖 − 𝑥𝑥 ̅ 𝑖𝑖 ∙
) and grand mean-centered predictors
(center the level-2 subject mean on the grand mean, i.e., 𝑥𝑥 ̅ 𝑖𝑖 ∙
− 𝑥𝑥 ̅ ∙ ∙
). This allows the model to
assess both the within-subject (day-level) and between-subject (person-level) effects while
controlling for each other.
231
Model 1 examined the association between sleep disruptions and
awakening cortisol. Model 2 examined the association between sleep disruptions and awakening
+30min cortisol. Model 3 examined the association between sleep disruptions and CAR.
Multilevel linear regressions were conducted using proc MIXED in SAS v9.4.
232,233
Lastly, we
explored the differences in association between morning salivary cortisol and sleep disruption by
102

early vs. late pregnancy by adding a multiplicative interaction term between the sleep disruption
variable and assessment period. All statistical significance were examined using two-sided tests
with α=0.05.
Results
Sample Population
There were 65 pregnant women who participated in the real-time personal monitoring
sub-study, with a total of possible 520 day-level observations. EMA sleep surveys were
completed on 460 days, with at least one day of data from all 65 participants. At least one
measure of salivary cortisol was collected on 456 days from 63 participants. Of the 460 days
with EMA sleep survey data, 18 days were excluded due to the lack of corresponding cortisol
data. Finally, we winsorized extreme cortisol measures that were more than three standard
deviations above or below the mean. Therefore, the final analytical sample consisted of 63
women with 437 day-level observations for awakening cortisol data, 438 day-level observations
for awakening+30min cortisol data, and 434 day-level observations for CAR data. The consort
diagram illustrating data availability can be found in Supplemental Figure 1.
Descriptive Characteristics
Of the 63 participants, about half of the mothers (46.00%) were born in the United States,
and they were on average 28.70 years old (SD=6.00 years). The majority were either married
(36.51%) or living together with their partner (44.44%). A third of our participants (33.33%) did
not finish high school, another third finished high school, and the remaining had some college
education or beyond. For about 25% of the participants, this was their first pregnancy. Less than
a third (26.98%) had normal BMI before pregnancy, 34.92% had overweight BMI, and 30.16%
had obesity. Participant characteristics can be found in Table 1 and comparison with the entire
MADRES study participants can be found in Supplemental Table 1.
103

Of the 442 days of data collection, 243 occurred during early-to-mid pregnancy, 199
during mid to late pregnancy. The early-to-mid pregnancy assessment occurred between 10 to 24
weeks gestation (mean=14.90 weeks, SD=2.90 weeks), and mid to late-pregnancy assessment
occurred between 25 to 34 weeks gestation (mean=29.40 weeks, SD=1.80 weeks). Majority of
participants (74.61%) had all 8 days of data collection. Half of the data collection occurred on
weekdays (50.20%), and 11.30% of data collection fell on a workday. Participants engaged in an
average of 30.24 minutes of MVPA per day (SD=21.18 minutes). On a scale from one to four,
with higher scores representing higher levels of the physical or emotional state, participants’
average daily positive affect was 2.72 (SD=0.64), negative affect was 1.22 (SD=0.29), perceived
stress was 2.08 (SD=0.64), fatigue was 2.35 (SD=0.49), pain was 1.27 (SD=0.38), and nausea
was 1.31 (SD=0.55). Negative affect and nausea were significantly higher during early-to-mid
pregnancy compared to mid-to-late pregnancy (t=2.40, p=0.02 and t=8.31, p<0.01 respectively).
Details on day-level descriptive characteristics by assessment period can be found in Table 2.
Sleep Disruption
Participants reported not waking up during the night on 12.80% of nights, waking up
once on 29.17% of nights, waking up twice on 26.49% of nights, and waking up three or more
times on 31.56% of nights. This number significantly differed between early-to-mid and mid-to-
late pregnancy (ꭕ
2
= 12.6, p<0.01), where participants reported waking up more often during mid-
to-late pregnancy compared to early-to-mid pregnancy. Descriptive information on sleep
disruptions by assessment period can be found in Table 3.
Morning Cortisol Characteristics and Protocol Compliance
Average awakening cortisol levels were 8.30 nmol/L (SD=4.35) and 10.09 nmol/L
(SD=4.42) for early-to-mid and mid-to-late pregnancy, respectively. This is lower than the
average 12~17 nmol/L seen in the literature.
234
Average awakening +30 min cortisol levels were
104

9.27 nmol/L (SD=5.55) and 11.31 nmol/L (SD=5.68) for early-to-mid and mid-to-late
pregnancy, respectively. This is also lower than the average 20~25 nmol/L seen in the
literature.
234
Average CAR was 0.18 nmol/L (SD=1.18) and 0.15 (SD=1.10) for early-to-mid and
mid-to-late pregnancy, respectively. Awakening and awakening+30min cortisol levels
significantly differed between early-to-mid and mid-to-late pregnancy (t=-4.22, p<0.01 and t=-
3.69, p<0.01 respectively), but we did not see significant changes in the CAR by pregnancy
period (t=0.51, p=0.61). Distribution of cortisol levels by assessment period can also be found in
Figure 1.
According to self-reported saliva sample contamination data, 29 of the 432 samples
(6.6%) of the awakening sample and 111 of the 416 samples (26.7%) of the awakening+30min
sample had contamination via eating, drinking (besides water), tooth brushing, smoking or
exercising 30minutes prior to collection. Self-reported compliance to the collection timing
protocol was high, with only 27 of 432 samples (6.1%) for awakening cortisol taken more than
15minutes after wake-up. Of the 416 samples, only 41 samples (9.9%) of awakening+30min
samples were taken <30minutes or >45minutes after awakening. In our study, almost half
(45.5% or 188 of the 413 samples) of our CAR was negative. The effect of contamination and
protocol non-compliance on cortisol values was inconsistent. Contamination significantly
decreased awakening cortisol values (t=-2.61, p<0.01) but did not affect the other cortisol
indicators. When participants waited more than 15 minutes after waking up to take their
awakening sample, their CAR was significantly lower (t=-2.32, p=0.02).
We also examined the concordance between wakeup time reported via EMA surveys and
saliva tubes. The two measures were highly correlated (r=0.67, p<0.01), and the time between
the two on average only differed by 1.7 minutes, but the standard deviation was high (75.0
105

minutes), and the range was significantly wide (-480minutes to 370minutes). When categorized,
202 samples (64.3%) had a 0-15minute difference between the two measures and 112 (35.7%)
had a discordance of more than 15 minutes. A discordance that was more than 15minutes
between awakening time marked on the saliva tubes and EMA survey significantly decreased the
awakening and awakening+30min values, but not the CAR. It should be noted that we attempted
to triangulate the awakening time between the self-reported time on the saliva tube, EMA survey,
and waist-worn actigraphy, but due to data availability, actigraphy data was only used for the
observations with significant discrepancy between the EMA and cortisol awakening times. All
findings regarding the effect of contamination and protocol non-compliance on morning cortisol
levels can be found in Table 4.
Bivariate Analyses
Participants who had obesity compared to those with normal or overweight BMI before
pregnancy (ꭕ
2
=23.92, p<0.01), those who did not finish high school compared to those who
finished high school and/or some college (ꭕ
2
=18.73, p<0.01), and those who were not married or
living together with their partner compared to those who were married or living
together(ꭕ
2
=14.80, p=0.02) were more likely to report higher levels of disrupted sleep. On the
day-level, we found that nights with more sleep disruption were more likely to be followed by
higher levels of pain (F=3.84, p=0.01) and MVPA (F=3.52, p=0.02), but lower levels of positive
affect (F=3.52, p=0.02).
All three cortisol measures were lower in participants who had not finished high school
compared to those who finished high school and/or some college (F=5.50, p<0.01). Awakening
(F=8.92, p<0.01) and awakening + 30min cortisol levels (F=9.35, p<0.01) were consistently
lower in those with obesity before pregnancy compared to those who had normal or overweight
106

BMI. Both awakening (4=0.11, p=0.02) and awakening +30min cortisol levels (r=0.23, p=0.01)
increased as the pregnancy progressed. On the day-level, on days when awakening and
awakening+30min cortisol levels were higher than usual, participants reported more positive
affect, (r=0.20, p<0.01 for both) and less tiredness (r=-0.22, p<0.01 and r=-0.18, p<0.01,
respectively). Participants also reported less pain on days when awakening cortisol was higher
than usual (r=-0.14, p=0.04).
Multilevel Model
We found a significant between-subject level association between sleep disruption and
awakening +30min cortisol, such that people who experienced more sleep disruption had on
average 0.22 nmol/L higher awakening+30min cortisol levels (β=0.22, 95% CI: 0.01, 0.44)
(Model 3a). We did not see a within-subject level association between sleep disruption and
awakening+30min cortisol (β=-0.02, 95% CI: -0.14, 0.10). This relationship between sleep and
awakening+30min cortisol did not differ by assessment period (Model 3b). Neither CAR nor the
awakening cortisol level was significantly associated with sleep disruption on the within-subject
or between-subject level (Models 1a, 2a), and results did not differ by early-to-mid or mid-to-late
pregnancy (Models 1b, 2b). Findings from the multilevel linear regression models of sleep
predicting cortisol can be found in Table 5.
Findings from all sensitivity analyses can be found in Supplemental Table 2. In our
sensitivity analyses, the results did not significantly change when we excluded or controlled for
self-reported protocol non-compliance (contamination [Model 2 and 3] and incorrect saliva
collection timing [Model 4 and 5]). However, exclusion of samples with >15min discordance in
awakening time between the saliva tube and EMA survey (Model 6) or samples with negative
CAR values (Model 8) or did decrease the effect size, and the results were no longer statistically
107

significant. Given that this may mostly be due to the substantial reduction in sample size, we also
tried including indicator variables and control for these issues in the model. Controlling for
saliva tube-EMA discordance (Model 7) or negative CARs (Model 9) both still led to significant
decrease in effect size, and the results were no longer significant. Including the between -subject
measures (e.g., deviation of the participant’s average from the grand mean) and within-subject
measures (e.g., deviation of the participant’s daily score from their own mean) in the model did
not significantly change the result (Model 10). Neither did including the awakening time and the
time between the first and second cortisol measure (Model 11).
Discussion
This study examined associations between sleep disruption and morning cortisol levels
throughout pregnancy on both the between and within-subject level. We found that people who
reported more sleep disruption, on average, exhibited higher awakening+30min cortisol levels.
We did not find any day-to-day association between sleep disruption and morning cortisol on the
next day.
Existing studies in non-pregnant populations have reported controversial findings on the
relationship between sleep and morning salivary cortisol levels, and our findings are in
agreement with only some of the literature. In line with our results, two other studies found those
with higher sleep disruption exhibited elevated awakening+30min levels.
218,220
However, three
studies found the opposite, with higher sleep disruption associated with decreased levels of
morning cortisol levels (awakening and awakening+30min) in general.
95,235,236
Others have found
no association between sleep disruption and morning cortisol.
227,237
As for sleep disruption’s
effect on next day awakening cortisol, most agree with our results and found no
association.
208,221,225–228
However, two studies did report a negative correlation between
108

frequency of sleep disruption and next-day awakening cortisol levels,
93,95
and one study found a
positive association.
224
Therefore, while our findings are in agreement with some of the existing
evidence, the mixed results including many null results complicate comparision.
219

Our results are not in agreement with the two studies conducted in pregnant populations.
One study found no between-subject difference in awakening+30min levels of cortisol based on
sleep disruption.
206
Another found decreased CAR in poor sleepers, but we did not see this
association.
96
No study to date has examined day-to-day associations between sleep and morning
cortisol levels in pregnant populations. Given the small body of literature in pregnant
populations, we cannot make any direct comparisons or conclusions about our findings in
relation to the existing evidence.
It is important to note the findings from the sensitivity analyses that considered self-
reported protocol compliance data. In studies such as MADRES, where saliva collection is
carried out by participants in their naturalistic setting, researchers are heavily reliant upon
participant adherence to the timed sampling protocol. To address this issue and promote best
practices, the International Society of Psychoneuroendocrinology published an expert consensus
guide that discusses the prevalence and impact of inaccurate sampling. The review found that
most participants wait on average 42 minutes (range: 10-135minues) from actual awakening to
collecting their first cortisol sample;
238
other studies have found that about 20% of participants
waited more than 15minutes before taking their first cortisol sample,
239
and almost half did not
take the second sample correctly at 30 minutes after awakening.
240
In comparison, our
participants waited on average 4.4 minutes (range: 0 minutes – 185 minutes) between actual
awakening to collecting their first sample. About 6.1% waited more than 15 minutes from
awakening to first sample collection, and 12.4% did not take the second sample correctly at 30
109

minutes after awakening. Therefore, our prevalence of non-adherence is comparatively lower
based on participants’ self-reported data. Hence, it’s not surprising that when we excluded or
controlled for possible protocol non-compliance (contamination and incorrect saliva collection
timing) based on self-reported data, the results did not significantly change.
However, our other sensitivity analyses accounting for negative CARs and EMA
discrepancy did result in notable changes in our findings. There is significant evidence that
delaying the collection of awakening or awakening+30min cortisol by more than 15minutes
leads to false-low estimates of the CAR, and delaying the collection of these samples by 30-
45minutes leads to negative CAR.
79
While the jury is still out on whether genuine occurrence of
CARs that are negative, flat, or minor do occur (especially amongst health disparities
populations and pregnant women),
44
for now, the consensus is that negative CARs are strong
evidence for severely inaccurate sampling.
76,80,81
Excluding and controlling for the negative CAR
observations diminished the effect size enough that the findings were no longer statistically
significant. The same happened when we excluded/controlled for any observations that had
>15min discrepancies in the awakening time noted on the saliva tube and EMA surveys. Taken
together, these findings suggest that the self-reported awakening and sampling times on the
saliva tube may not be reliable, and our findings must be interpreted with much caution.
Limitations
There are several limitations in this study, regarding both study design and statistical
analysis, that must be addressed. Most importantly, salivary cortisol measures in this sample may
be inaccurate due to possible poor collection protocol compliance. Due to the short time window
(30~45minutes post-awakening) in which this dynamic secretory activity occurs, accurately
quantifying salivary cortisol in the morning has been an ongoing challenge.
241
While our self-
110

reported non-adherence rate is low, the high prevalence of negative CAR and discordance of
awakening time between EMA survey and saliva tube suggest that non-compliance to the
sampling timing was likely. Another limitation with our study design is the lack of objective
(i.e., device-derived) sleep data. In the sleep literature, it is usually recommended to include both
objective and subjective measures of sleep in the same study, as subjective sleep (e.g., self-
reported measurements of sleep duration, quality, disruption via questionnaires) and objective
sleep (e.g., polysomnography, actigraphy, bed sensors) often do not agree with one another, but
both types of sleep measures are independently associated with health factors.
172–177
It’s widely
known that pregnant women often under-report the number of awakenings, especially during
early pregnancy, compared to polysomnography.
242
There’s also been several studies that have
found that the agreement between objective and subjective sleep differs by demographic
characteristics or psychosocial factors. One study found non-Hispanic White participants
significantly overestimated their sleep duration compared to Black participants.
46
Some studies
have found that women with poorer psychosocial health (e.g., overcommitment, lower social
support, diagnosis of mental disorder) are more likely to over-report sleep difficulties and poor
sleep quality,
176,243
while other have found the opposite.
177
While inaccurate reporting of sleep
quality may be biasing our results, without objective data to compare the subjective measures to
we are unable to determine whether over or under-reporting of sleep disruption is present in our
data.
Conclusion
Our study found a between-subject effect for sleep and diurnal cortisol, such that people
who reported more sleep disruption, on average, exhibited higher awakening+30min cortisol
levels. However, these results must be interpreted with significant caution, given the evidence of
high rates of non-compliance with the saliva sample collection protocol and results of our
111

sensitivity analyses. Future research that incorporates improved strategies for ambulatory
salivary cortisol sampling is needed to elucidate the between and within-subject relationship
between sleep and cortisol in pregnant women.

112
Mother's age [years], mean(SD)
1
28.70 (6.00)
Ethnicity by birthplace, n(%)
2
US-Born Hispanic 29 (46.00%)
Foreign-Born Hispanic 34 (54.00%)
Marital status, n(%)
3
Married 23 (36.51%)
Living together 28 (44.44%)
Never married or living together 10 (15.87%)
Declined to answer 2 (3.17%)
Highest education, n(%)
4
Less than 12th grade (did not finish high school) 21 (33.33%)
Completed grade 12 (high school) 21 (33.33%)
Some college or beyond 22 (33.33%)
Parity, n(%)
5
1 (first-born) 16 (25.40%)
2 (second-born) 21 (33.33%)
3 or more (third-born or later) 26 (41.27%)
Pre-pregnancy BMI, n(%)
6
Normal 17 (26.98%)
Overweight 22 (34.92%)
Obese 19 (30.16%)
Hypertensive disorder, n(%)
7
Normal 49 (77.80%)
Preeclampsia or gestational hypertension 8 (12.70%)
Chronic hypertension 2 (3.17%)
Glucose tolerance abnormality, n(%)
8
Normal 36 (57.15%)
Glucose intolerance or gestational diabetes 22 (34.92%)
Chronic diabetes
1 (1.59%)
Note: Percentages are column percentages.
Acronyms: SD: Standard Deviation; BMI: Body Mass Index; US: United States
Table 1. Participant-Level Descriptive Characteristics (N=63)
1
No missing;
2
No missing;
3
No missing;
4
No missing;
5
Missing for 5 participants;
6
Missing for 4
participants;
7
Missing for 4 participants;
8
No missing
113
Table 2. Day-Level Descriptive Characteristics (n=442)
Overall
Early-to-Mid Pregnancy
(10 to 24 weeks gestation)
Mid-to-Late Pregnancy
(25 to 34 weeks
gestation) Test-value, p-value
Day of the week, n (%) ꭕ
2
=0.03, p=0.86
Weekday 222 (50.20%) 123 (50.60%) 99 (49.80%)
Weekend 220 (49.80%) 120 (49.40%) 100 (50.20%)
Workday, n (%) ꭕ
2
=0.83, p=0.66
Workday 50 (11.30%) 26 (10.70%) 24 (12.10%)
Non-workday 388 (87.80%) 214 (88.10%) 174 (87.40%)
Missing 4 (0.90%) 3 (11.20%) 1 (0.50%)
Minutes of moderate-to-vigorous physical activity, mean (SD)
a
30.24 (21.18) 30.43 (22.61) 29.97 (19.00) t=0.18, p=0.86
Positive affect, mean (SD)
b
2.72 (0.64) 2.70 (0.61) 2.74 (0.67) t=-0.73, p=0.47
Negative affect, mean (SD)
b
1.22 (0.29) 1.25 (0.29) 1.18 (0.29) t=2.40, p=0.02
Perceived stress, mean (SD)
b
2.08 (0.64) 2.07 (0.65) 2.09 (0.63) t=-0.28, p=0.78
Fatigue, mean (SD)
b
2.35 (0.49) 2.38 (0.50) 2.31 (0.49) t=1.37, p=0.17
Pain, mean (SD)
b
1.27 (0.38) 1.25 (0.36) 1.3 (0.41) t=-1.31, p=0.19
Nausea, mean (SD)
c
1.31 (0.55) 1.50 (0.64) 1.09 (0.28) t=8.31, p<0.01
Acronyms: SD: Standard Deviation
Note: a=Missing for 145 days, b=Missing for 36 days, c=Missing for 37 days
114
Overall
n=336
Early-to-Mid Pregnancy
(10 to 24 weeks gestation)
n=193
Mid-to-Late Pregnancy
(25 to 34 weeks gestation)
n=143 Test-value, p-value
Number of sleep disruptions, n (%) ꭕ
2
=12.6, p<0.01
0 43 (12.80%) 33 (17.10%) 10 (6.99%)
1 98 (29.17%) 62 (32.12%) 36 (25.17%)
2 89 (26.49%) 42 (21.76%) 47 (32.87%)
3+ 106 (31.56%) 56 (29.02%) 50 (34.97%)
Table 3. Sleep Disruption by Assessment Period
115
Table 4. Morning Cortisol Values by Protocol Non-Compliance
Overall
n=432
Awakening Cortisol:
Not Contaminated
n=403
Awakening Cortisol:
Contaminated
n=29 Test-value, p-value
Cortisol levels, mean (SD)
Awakening 9.12 (4.47) 9.27 (4.44) 7.04 (4.46) t=-2.61, p<0.01
Awakening +30min 10.21 (5.69) NA NA NA
CAR 0.18 (1.18) 0.19 (1.19) -0.06 (0.98) t=-0.96, p=0.34
Overall
n=416
Awakening+30min Cortisol:
Not Contaminated
n=305
Awakening+30min Cortisol:
Contaminated
n=111 Test-value, p-value
Cortisol levels, mean (SD)
Awakening 9.12 (4.47) NA NA NA
Awakening +30min 10.21 (5.69) 10.16 (5.42) 10.36 (6.41) t=-0.31, p=0.76
CAR 0.18 (1.18) 0.19 (1.11) 0.14 (1.36) t=-0.46, p=0.65
Overall
n=432
Awakening Sample Collected < 15min
after Awakening
n=405
Awakening Sample Collected > 15min
after Awakening
n=27 Test-value, p-value
Cortisol levels, mean (SD)
Awakening 9.12 (4.47) 9.03 (4.47) 10.42 (4.37) t=-1.56, p=0.12
Awakening +30min 10.21 (5.69) 10.31 (5.80) 8.87 (3.66) t=1.27, p=0.21
CAR 0.18 (1.18) 0.22 (1.20) -0.34 (0.74) t=-2.32, p=0.02
Overall
n=416
Awakening+30min Samples were
Collected 30-45 min after Awakening
n=375
Awakening+30min Samples were
Collected <30minutes or >45minutes after
Awakening
n=41 Test-value, p-value
Cortisol levels, mean (SD)
Awakening 9.12 (4.47) NA NA NA
Awakening +30min 10.21 (5.69) 10.31 (5.64) 9.34 (5.16) t=1.04, p=0.30
CAR 0.18 (1.18) 0.20 (1.11) -0.07 (1.79) t=1.20, p=0.23
Overall
n=314
0-15min Discordance in Awakening Time
Between Saliva Tube and EMA Survey
n=202
>15min Discordance in Awakening Time
Between Saliva Tube and EMA Survey
n=112 Test-value, p-value
Cortisol levels, mean (SD)
Awakening 9.12 (4.47) 9.74 (4.36) 8.68 (4.32) t=2.03, p=0.04
Awakening +30min 10.21 (5.69) 11.09 (5.54) 9.63 (5.20) t=2.19, p=0.03
CAR 0.18 (1.18) 0.27 (1.12) 0.09 (1.19) t=1.30, p=0.19
Acronyms: SD: Standard Deviation; CAR: Cortisol Awakening Response; EMA: Ecological Momentary Assessment
116
Coeff. [95% CI] Coeff. [95% CI]
BS_Disruption 0.15 [-0.1, 0.4] BS_Disruption 0.16 [-0.09, 0.41]
WS_Disruption -0.09 [-0.28, 0.1] WS_Disruption -0.1 [-0.39, 0.19]
Pre-pregnancy BMI Pre-pregnancy BMI
Normal Ref Normal Ref
Overweight 0.28 [-0.15, 0.71] Overweight 0.3 [-0.13, 0.72]
Obesity 0.15 [-0.3, 0.6] Obesity 0.17 [-0.28, 0.62]
Education Education
Less than high school -0.29 [-0.72, 0.14] Less than high school -0.32 [-0.75, 0.11]
High school Ref High school Ref
Some college or beyond -0.2 [-0.63, 0.23] Some college or beyond -0.2 [-0.63, 0.23]
Gestational age at data collection 0 [-0.02, 0.02] Gestational age at data collection -0.02 [-0.09, 0.04]
Daily fatigue 0.13 [-0.27, 0.53] Daily fatigue 0.11 [-0.29, 0.51]
Daily pain 0.25 [-0.18, 0.68] Daily pain 0.24 [-0.19, 0.68]
Daily positive affect 0.12 [-0.17, 0.4] Daily positive affect 0.1 [-0.18, 0.39]
Assessment period
Early-to-Mid Pregnancy -0.41 [-1.44, 0.62]
Mid-to-Late Pregnancy Ref
WS_Disruption x Early-to-Mid Pregnancy 0 [-0.4, 0.4]
WS_Disruption x Mid-to-Late Pregnancy Ref
Coeff. [95% CI] Coeff. [95% CI]
BS_Disruption 0.11 [-0.1, 0.31] BS_Disruption 0.12 [-0.08, 0.31]
WS_Disruption 0.02 [-0.07, 0.12] WS_Disruption 0.05 [-0.11, 0.2]
Pre-pregnancy BMI Pre-pregnancy BMI
Normal Ref Normal Ref
Overweight 0.18 [-0.18, 0.54] Overweight 0.2 [-0.13, 0.54]
Obesity -0.16 [-0.54, 0.22] Obesity -0.1 [-0.46, 0.25]
Education Education
Less than high school -0.19 [-0.56, 0.18] Less than high school -0.22 [-0.56, 0.12]
High school Ref High school Ref
Some college or beyond -0.07 [-0.44, 0.29] Some college or beyond -0.08 [-0.42, 0.26]
Gestational age at data collection 0.01 [0, 0.02] Gestational age at data collection -0.03 [-0.07, 0.01]
Daily fatigue 0.05 [-0.19, 0.28] Daily fatigue -0.01 [-0.25, 0.23]
Daily pain -0.22 [-0.47, 0.03] Daily pain -0.21 [-0.46, 0.04]
Daily positive affect 0.2 [0.01, 0.39] Daily positive affect 0.18 [-0.01, 0.37]
Assessment period
Early-to-Mid Pregnancy -0.69 [-1.34, -0.04]
Mid-to-Late Pregnancy Ref
WS_Disruption x Early-to-Mid Pregnancy -0.06 [-0.28, 0.16]
WS_Disruption x Mid-to-Late Pregnancy Ref
Model 2b (N=285)
Table 5. Multilevel Linear Regression of Sleep Disruption Predicting ( →) Cortisol
Sleep Disruption → CAR
Model 1b (N=272)
Sleep Disruption → Awakening Cortisol
Model 1a (N=272)
Model 2a (N=285)
117
Coeff. [95% CI] Coeff. [95% CI]
BS_Disruption 0.22 [0.01, 0.44] BS_Disruption 0.24 [0.03, 0.44]
WS_Disruption -0.02 [-0.14, 0.10] WS_Disruption -0.01 [-0.19, 0.18]
Pre-pregnancy BMI Pre-pregnancy BMI
Normal Ref Normal Ref
Overweight 0.25 [-0.12, 0.63] Overweight 0.28 [-0.08, 0.63]
Obesity -0.16 [-0.55, 0.24] Obesity -0.1 [-0.48, 0.27]
Education Education
Less than high school -0.34 [-0.72, 0.04] Less than high school -0.37 [-0.73, -0.02]
High school Ref High school Ref
Some college or beyond -0.2 [-0.58, 0.18] Some college or beyond -0.2 [-0.56, 0.15]
Gestational age at data collection 0.02 [0, 0.03] Gestational age at data collection -0.03 [-0.07, 0.02]
Daily fatigue 0.04 [-0.23, 0.32] Daily fatigue 0 [-0.28, 0.28]
Daily pain -0.17 [-0.47, 0.13] Daily pain -0.15 [-0.44, 0.15]
Daily positive affect 0.22 [0.01, 0.44] Daily positive affect 0.21 [0, 0.42]
Assessment period
Early-to-Mid Pregnancy -0.68 [-1.42, 0.06]
Mid-to-Late Pregnancy Ref
WS_Disruption x Early-to-Mid Pregnancy -0.04 [-0.31, 0.22]
WS_Disruption x Mid-to-Late Pregnancy Ref
Acronyms: CAR: Cortisol Awakening Response; CI: Confidence Interval; BS: Between-Subject; WS: Within-Subject; BMI: Body Mass Index
Sleep Disruption → Awakening + 30min
Model 3b (N=276) Model 3a (N=276)
Table 5. Multilevel Linear Regression of Sleep Disruption Predicting ( →) Cortisol (continued)
118
All MADRES
Participants
(N=966)
Final Dataset
(N=63)
Maternal age [years]; mean(SD) 28.37 (6.13) 28.70 (6.00)
Ethnicity by birthplace; n(%)
Non-Hispanic 189 (24.11%) 0
US-Born Hispanic 275 (35.08%) 29 (46.00%)
Foreign-Born Hispanic 320 (40.82%) 34 (54.00%)
Highest educational attainment; n(%)
High School or below 520 (58.36%) 42 (66.66%)
Beyond high school 371 (41.64%) 22 (33.33%)
Marital status; n(%)
Not married or living together 203 (28.27%) 12 (19.05%)
Married or living together 515 (71.73%) 51 (80.95%)
Parity
First child 287 (38.47%) 16 (25.40%)
Second child 231 (30.97%) 21 (33.33%)
Third or more child 228 (30.56%) 26 (41.27%)
Hypertensive disorder; n(%)
Normal 599 (78.51%) 49 (77.80%)
Preeclampsia or gestational hypertension 121 (15.88%) 8 (12.70%)
Chronic hypertension 42 (5.51%) 2 (3.17%)
Glucose tolerance abnormality; n(%)
Normal 501 (66.53%) 36 (57.15%)
Glucose intolerance or gestational diabetes 216 (28.69%) 22 (34.92%)
Chronic diabetes
36 (4.78%) 1 (1.59%)
Acronyms: SD: Standard Deviation; US: United States
Supplemental Table 1. Comparison of All MADRES Participants vs Final Analytical Sample
119
Coeff. [95% CI]
BS_Disruption 0.22 [0.01, 0.44]*
WS_Disruption -0.02 [-0.14, 0.1]
Pre-pregnancy BMI
Normal Ref
Overweight 0.25 [-0.12, 0.63]
Obesity -0.16 [-0.55, 0.24]
Education
Less than high school -0.34 [-0.72, 0.04]
High school Ref
Some college or beyond -0.2 [-0.58, 0.18]
Gestational age at data collection 0.02 [0, 0.03]*
Daily fatigue 0.04 [-0.23, 0.32]
Daily pain -0.17 [-0.47, 0.13]
Daily positive affect 0.22 [0.01, 0.44]*
Coeff. [95% CI] Coeff. [95% CI]
BS_Disruption 0.22 [0, 0.43] BS_Disruption 0.22 [0, 0.44]
WS_Disruption 0.03 [-0.1, 0.17] WS_Disruption -0.02 [-0.14, 0.1]
Pre-pregnancy BMI Pre-pregnancy BMI
Normal Ref Normal Ref
Overweight 0.22 [-0.15, 0.6] Overweight 0.25 [-0.13, 0.63]
Obesity -0.12 [-0.51, 0.26] Obesity -0.16 [-0.55, 0.24]
Education Education
Less than high school -0.34 [-0.72, 0.04] Less than high school -0.34 [-0.72, 0.04]
High school Ref High school Ref
Some college or beyond -0.34 [-0.71, 0.04] Some college or beyond -0.2 [-0.59, 0.19]
Gestational age at data collection 0.01 [0, 0.03] Gestational age at data collection 0.02 [0, 0.03]
Daily fatigue 0.16 [-0.15, 0.47] Daily fatigue 0.04 [-0.24, 0.32]
Daily pain -0.2 [-0.5, 0.11] Daily pain -0.17 [-0.47, 0.13]
Daily positive affect 0.24 [0, 0.48] Daily positive affect 0.22 [0.01, 0.44]
Contamination
Not contaminated Ref
Contaminated 0.01 [-0.24, 0.25]
Coeff. [95% CI] Coeff. [95% CI]
BS_Disruption 0.22 [-0.02, 0.46] BS_Disruption 0.22 [0, 0.44]
WS_Disruption -0.01 [-0.13, 0.11] WS_Disruption -0.01 [-0.13, 0.11]
Pre-pregnancy BMI Pre-pregnancy BMI
Normal Ref Normal Ref
Overweight 0.22 [-0.19, 0.63] Overweight 0.26 [-0.12, 0.64]
Obesity -0.17 [-0.61, 0.27] Obesity -0.15 [-0.55, 0.25]
Education Education
Less than high school -0.29 [-0.71, 0.13] Less than high school -0.33 [-0.71, 0.06]
High school Ref High school Ref
Some college or beyond -0.12 [-0.55, 0.3] Some college or beyond -0.19 [-0.58, 0.2]
Gestational age at data collection 0.02 [0, 0.03] Gestational age at data collection 0.02 [0, 0.03]
Daily fatigue 0.06 [-0.23, 0.35] Daily fatigue 0.05 [-0.23, 0.33]
Daily pain -0.23 [-0.53, 0.07] Daily pain -0.18 [-0.47, 0.12]
Daily positive affect 0.23 [0, 0.46] Daily positive affect 0.23 [0.02, 0.45]
Sample timing protocol
Compliant Ref
Non-compliant 0.2 [-0.17, 0.56]
Supplemental Table 2. Sensitivity Analyses for Multilevel Linear Regression of Sleep Disruption Predicting Awakening+30min Cortisol
Model 4: Excluding Awakening +30min Samples Collected <30min or >45min min after
Awakening (N=256)
Model 1: Original (N=276)
Model 2: Excluding Contaminated Awakening+30min Samples (N=209) Model 3: Controlling for Contaminated Awakening+30min Samples (N=256)
Model 5: Controlling for Awakening +30min Samples Collected <30min or >45min after
Awakening (N=276)
120
Coeff. [95% CI] Coeff. [95% CI]
BS_Disruption 0.11 [-0.13, 0.43] BS_Disruption 0.15 [-0.13, 0.43]
WS_Disruption -0.03 [-0.17, 0.11] WS_Disruption -0.01 [-0.13, 0.11]
Pre-pregnancy BMI Pre-pregnancy BMI
Normal Ref Normal Ref
Overweight 0.33 [-0.15, 0.81] Overweight 0.19 [-0.21, 0.6]
Obesity -0.14 [-0.65, 0.38] Obesity -0.3 [-0.72, 0.12]
Education Education
Less than high school -0.22 [-0.66, 0.22] Less than high school -0.28 [-0.68, 0.12]
High school Ref High school Ref
Some college or beyond -0.25 [-0.7, 0.2] Some college or beyond -0.24 [-0.64, 0.16]
Gestational age at data collection 0.02 [0.01, 0.04] Gestational age at data collection 0.02 [0, 0.03]
Daily fatigue -0.13 [-0.48, 0.21] Daily fatigue -0.02 [-0.29, 0.26]
Daily pain -0.32 [-0.71, 0.08] Daily pain -0.23 [-0.53, 0.07]
Daily positive affect 0.1 [-0.17, 0.37] Daily positive affect 0.2 [-0.02, 0.42]
Sample timing protocol
Compliant Ref
Non-compliant 0.02 [-0.22, 0.25]
Coeff. [95% CI] Coeff. [95% CI]
BS_Disruption 0.08 [-0.2, 0.36] BS_Disruption 0.16 [-0.05, 0.37]
WS_Disruption 0.03 [-0.1, 0.17] WS_Disruption -0.01 [-0.12, 0.1]
Pre-pregnancy BMI Pre-pregnancy BMI
Normal Ref Normal Ref
Overweight 0.19 [-0.27, 0.65] Overweight 0.18 [-0.18, 0.55]
Obesity -0.08 [-0.58, 0.41] Obesity -0.23 [-0.61, 0.16]
Education Education
Less than high school -0.33 [-0.78, 0.12] Less than high school -0.28 [-0.64, 0.09]
High school Ref High school Ref
Some college or beyond -0.17 [-0.63, 0.29] Some college or beyond -0.14 [-0.51, 0.23]
Gestational age at data collection 0.01 [-0.01, 0.02] Gestational age at data collection 0.01 [0, 0.02]
Daily fatigue 0.3 [-0.06, 0.66] Daily fatigue 0.03 [-0.23, 0.29]
Daily pain 0.01 [-0.35, 0.37] Daily pain -0.16 [-0.44, 0.12]
Daily positive affect 0.33 [0.08, 0.58] Daily positive affect 0.23 [0.03, 0.44]
Negative CAR flag
Non-negative CAR Ref
Negative CAR -0.57 [-0.75, -0.4]
Coeff. [95% CI] Coeff. [95% CI]
BS_Disruption 0.22 [0, 0.44] BS_Disruption 0.21 [0, 0.42]
WS_Disruption -0.02 [-0.14, 0.1] WS_Disruption -0.02 [-0.14, 0.1]
Pre-pregnancy BMI Pre-pregnancy BMI
Normal Ref Normal Ref
Overweight 0.34 [-0.04, 0.72] Overweight 0.2 [-0.18, 0.57]
Obesity -0.13 [-0.52, 0.27] Obesity -0.27 [-0.67, 0.13]
Education Education
Less than high school -0.33 [-0.71, 0.05] Less than high school -0.24 [-0.62, 0.13]
High school Ref High school Ref
Some college or beyond -0.12 [-0.5, 0.27] Some college or beyond -0.21 [-0.58, 0.17]
Gestational age at data collection 0.02 [0.01, 0.03] Gestational age at data collection 0.02 [0, 0.03]
Daily fatigue (within-subject) 0.1 [-0.22, 0.42] Daily fatigue 0.06 [-0.22, 0.33]
Daily pain (within-subject) -0.33 [-0.67, 0] Daily pain -0.18 [-0.48, 0.11]
Daily positive affect (within-subject) 0.16 [-0.13, 0.45] Daily positive affect 0.19 [-0.02, 0.41]
Daily fatigue (between-subject) -0.31 [-0.9, 0.29] Awakening time -0.04 [-0.11, 0.04]
Daily pain (between-subject) 0.51 [-0.17, 1.2]
Minutes between awakening and
awakening+30min sample
0 [0, 0.01]
Daily positive affect (between-subject) 0.21 [-0.12, 0.55]
Acronyms: CI: Confidence Interval; BS: Between-Subject; WS: Within-Subject; BMI: Body Mass Index; EMA: Ecological Momentary Assessment; CAR: Cortisol Awakening Response
Model 10: Both Within and Between-Subject Measures of Day-Level Covariates (N=276)
Model 11: Control for Awakening Time and Time Between Awakening and Awakening
+30min Samples (N=276)
Model 9: Controlling for Negative CARs (N=274) Model 8: Excluding Negative CARs (N=154)
Model 6: Excluding Samples with >15min Discordance in Awakening Time Between
Saliva Tube and EMA Survey (N=172)
Model 7: Controlling for Samples with >15min Discordance in Awakening Time between
Saliva Tube and EMA Survey (N=257)
Supplemental Table 2. Sensitivity Analyses for Multilevel Linear Regression of Sleep Disruption Predicting Awakening+30min Cortisol (continued)
121
Figure 1. Distribution of Cortisol Profiles by Assessment Period
(N=217) (N=188) (N=235) (N=197) (N=224) (N=192)
Acronyms: CAR: Cortisol Awakening Response
122
Missing Cortisol Data
18 days
Cortisol Data
n=63
456 days
EMA Sleep Data
n=65
460 days
Overlapping data
n=63
442 days
Supplemental Figure 1. Consort Diagram for Data Availability
Awakening cortisol data
(no outliers)
n=63
437 days
Awakening +30 cortisol data
(no outliers)
n=63
438 days
CAR data
(no outliers)
n=63
434 days
Acronyms: EMA: Ecological Momentary Assessment; CAR: Cortisol Awakening Response
123

Chapter Five: Conclusion

Review of Aims and Findings
The overall goal of this dissertation was to elucidate both population-level and day-level
associations among prenatal sleep, diurnal cortisol profiles, and gestational weight gain (GWG).
Through the combined usage of data from the Maternal And Developmental Risks from
Environmental and Social stressors (MADRES) study and the real-time personal monitoring sub-
study, this dissertation examined: (Study 1) the longitudinal effect of early-to-mid pregnancy
sleep quality on mid-to-late GWG rate; (Study 2) the mediating effect of diurnal cortisol on
early-to-mid pregnancy sleep quality and total GWG; and (Study 3) the day-level association of
sleep disruption on diurnal cortisol. With the unique methodological strengths of both studies,
we applied an epidemiological and behavioral health perspective to explore the relationship
between prenatal sleep, diurnal cortisol profiles, and weight gain during pregnancy.
Across Study 1 and Study 2, we consistently found poorer early-to-mid pregnancy sleep
quality to predict higher mid-to-late pregnancy GWG rate and total GWG. However, in Study 2
we found no evidence of mediation of sleep quality and total GWG by diurnal cortisol, and in
Study 3 we also did not find any day-level relationships between sleep disruption and diurnal
cortisol. Taken together, our findings suggest that poor sleep quality is a risk factor for increased
GWG in this population, but the underlying mechanism of this relationship may not involve the
circadian secretion patterns of salivary cortisol. Below, we discuss multiple other pathways that
could instead be linking poor sleep to weight in pregnant populations.
Beyond Diurnal Cortisol: Pathways Linking Sleep and Weight Gain
One possible mechanism underlying the relationship between sleep and weight gain is
Hypothalamic-Pituitary-Adrenal (HPA) axis reactivity to psychosocial stress exposure. Several
124

studies utilizing laboratory stress tests have found poor sleep to predict increased stress reactivity
in non-pregnant populations.
244–246
And though the findings are mixed, there are many studies
that have found higher cortisol reactivity to increase risk of weight gain.
247–249
Therefore, it’s
possible that sleep may not be significantly affecting circadian cortisol secretion, but instead,
heightening one’s cortisol response to acute psychosocial stressors. Although there haven’t been
any studies on this topic conducted in pregnant populations, further research could shed light on
whether and how HPA reactivity may be an important mediator between poor sleep and GWG.
While the relationship between sleep health and weight gain is well established, there has
also been growing interest around the variability of these sleep health constructs as a potential
predictor of increased weight gain. Although the literature on sleep variability and cortisol is still
limited, one study found greater sleep duration variability were associated with lower levels of
awakening cortisol levels and flatter diurnal slope.
221
There is also consistent evidence that
greater sleep variability is associated with obesity, weight gain, and metabolic syndrome.
250,251

Though there is some evidence that pregnant women with poor sleep health report greater
variability of sleep duration, sleep disruption, and sleep onset latency, there has been no study
examining the adverse effect of sleep variability on prenatal health outcomes.
252
Therefore, the
examination of sleep variability during pregnancy, and in relation to diurnal cortisol and/or
GWG, is a major gap in the literature.
There are several other psychological pathways that link poor sleep with increased weight
gain. Many pregnant people report sleep difficulties as a source of stress,
150–152
and while the
evidence is still mixed, many studies have linked stress exposure to risk of increased
GWG.
153,153–156
Poor sleep is also a known risk factor of perinatal mood disorders, such as
prenatal or postpartum depression and anxiety disorder, which are also associated with
125

obesogenic behavior and cardiometabolic outcomes.
253,254
Not surprisingly, poor sleep quality
increases fatigue and decreases impulse control, which in turn leads to higher caloric intake (e.g.,
people are more likely to consume “comfort foods” as a coping mechanism) and lower
levels/duration of physical activity.
126,161,162
Taken together, a pregnant person with poor sleep
quality may experience negative affect in ways that perpetuate obesogenic behaviors.
Lastly, there are several neurohormonal pathways that could also explain the relationship
between poor sleep and GWG. Poor sleep heightens one’s appetite through increased ghrelin and
decreased leptin levels.
35,121,126
Various metabolic mechanisms, such as insulin resistance,
adiponectin levels, and glucose effectiveness can also be dysregulated by poor sleep and
ultimately lead to increased adipose tissue and weight gain.
35,184,255
There is also substantial
evidence from studies on sleep and inflammatory biomarkers (e.g., C-reactive protein,
interleukin-6) that have found these biomarkers to be associated with increased oxidative stress,
endothelial dysfunction and inflammation that are associated with obesity.
130,256–260
Therefore,
while diurnal cortisol may not explain the relationship between sleep and weight gain during
pregnancy, the obesogenic consequences of poor prenatal sleep is strongly probable due to a
myriad of psychobiological and behavioral pathways.
Application of Findings
Our study findings suggest early-to-mid pregnancy sleep health as a key intervention
target for behavioral weight management. In non-pregnant populations, there is rapidly growing
recognition and evidence supporting the importance of sleep improvement for obesity
prevention.
261,262
Various weight management interventions have successfully found improving
sleep significantly leads to weight loss, percentile decrease in Body Mass Index (BMI), and
improved glycemic control.
263–265

126

In pregnant populations, the findings have been more controversial. Until 2016, there was
no lifestyle interventions for GWG that included sleep improvement. Skouteris et al. was the first
to conduct a health coaching intervention that aimed to prevent excessive GWG and integrated
sleep education lessons.
266
While they were able to successfully improve sleep quality and
educate their participants on the importance of healthy GWG, they did not find actual
intervention results on weight gain during pregnancy. It should be noted that while this
intervention began holistic coaching sessions covering various health behaviors (e.g., sleep,
mood, stress, body image issues) as early as 16 weeks gestation, they didn’t assess sleep quality
until 33-week pregnancy. Thus, they do not have any data on intervention results for sleep
quality during early-to-mid pregnancy. Another pilot intervention for GWG management
reported that eight of their 11 participants gained the appropriate weight based on Institute of
Medicine (IOM) guidelines, but they found no change in the participants’ sleep quality; however,
the sleep hygiene educational sessions occurred at 30 weeks pregnancy.
118
Future weight
management programs for pregnancy could see more benefits if sleep interventions (e.g., sleep
hygiene education, mindfulness, yoga, cognitive behavioral therapy) were initiated as early as
possible to improve sleep quality during the first half of pregnancy.
267

Future Directions
Based on our studies’ findings and methodological considerations, there are several
future directions for study design and research topics.
Adoption of Ecological Momentary Assessment Study Designs for Pregnancy Research
Most self-reported sleep surveys, such as the Jenkin’s Summary Questionnaire (JSQ)
utilized in Study 1 and 2, ask participants to recall their average sleep health over a period of
time.
136
However, there is evidence that people are unable to accurately recall sleep due to its
frequency and significant day-to-day variability.
268,269
This is especially true during pregnancy,
127

when women not only report poorer sleep quality overall, but also higher intra-individual
variability of sleep onset latency, total sleep duration, and sleep disturbances.
270
Given these
sleep trends during pregnancy and the known disagreement between device-derived (e.g.,
polysomnography, actigraphy, bed sensors) and self-reported sleep in pregnant populations, it’s
crucial that future research incorporate more EMA of both subjective and objective sleep
measures.
Furthermore, while there is a growing movement to conduct more ambulatory cortisol
assessment in naturalistic settings, most cortisol studies to this day are conducted within the
laboratory in a controlled setting designed to elicit an acute stress response.
111
The standardized
nature of these laboratory settings hinder the relevance of their findings to stressors and contexts
naturally encountered in daily life.
230
This is especially an issue during pregnancy, as it is a
unique period in a woman’s life when they experience significant physiological, mental, and
emotional change from day-to-day, and a one-time measurement in a laboratory setting will have
limited generalizability and external validity. Therefore, ambulatory assessment of cortisol via
saliva samples allows researchers to study the dynamic brain-body interaction with high
ecological validity outside restricted laboratory settings.
188
Integration of EMA surveys with
salivary collection also allows for the examination of real-time contextual information about the
cortisol samples (e.g., time stamp, recent stress exposure, physical activity). To our knowledge,
Study 3 was the first study to date to examine day-to-day associations of sleep and salivary
cortisol during pregnancy, which significantly hindered the interpretation of our findings. Future
research on the association between prenatal sleep and diurnal cortisol could greatly benefit from
adopting EMA study designs.
128

Improved Strategies to Aid Salivary Cortisol Sampling in The Naturalistic Setting
The self-reported collection time for saliva samples and high rates of non-adherence to
the sampling protocol posed major challenges for both Study 2 and 3. There are several
collection methodologies that could be implemented in future studies to improve accuracy of
saliva sampling, such as the verification of awakening time via objective sensors (e.g.,
polysomnography, actigraphy) and saliva sampling time via electronic monitoring systems (e.g.,
medication event monitoring caps, time-stamped photographs).
66,271
It is also crucial to clearly
communicate research goals for saliva sampling, the importance of adherence to the protocol,
and definition of awakening to the participants at the initial instruction visit and throughout the
sampling period.
191
Collection kits can be designed to be more user-friendly and organized, such
as pre-labeling and color coding of material, providing timers, and recommending placing the
collection kit by their bedside.
191,272
We also recommend the continued use of automated
electronic reminders, such as app-based notifications utilized in Study 3. Scheduling sufficient
sampling occasions is also crucial, with at least 3-6 samples per day collected over at least 2 days
(weekend and weekday) and ensuring that sampling occasions are appropriately spaced (e.g.,
every 15 minutes over the first two hours after waking when quantifying the Cortisol Awakening
Response, and every four hours for Diurnal Cortisol Slope or Area Under the Curve).
66,111

Future Research in Diverse Populations
There are existent racial and socioeconomic disparities in the prevalence of obesity and
excessive GWG in the United States.
15–17
In addition, large bodies of literature have consistently
found evidence of “sleep disparities”; across various studies and populations, SES and race are
social determinants of sleep health during pregnancy.
114,193,273–276
And yet, almost all studies on
prenatal sleep health and GWG have been conducted in White women with higher
129

socioeconomic status (SES).
52,53,277,278
Thus, there is a dire need for more sleep and GWG
research amongst diverse pregnant women.
Conclusion
The current edition of Guidelines for Perinatal Care from the American Academy of
Pediatrics and American College of Obstetricians and Gynecologists does not include a single
recommendation on the assessment, monitoring, or treatment of sleep issues in pregnant
patients.
182
Good sleep is not only imperative for the well-being of the pregnant mother but
predictive of clinical outcomes including GWG. By improving our understanding of the socio-
ecological, behavioral, and physiological mechanisms that promote weight gain during
pregnancy, we can inform future interventions that are more effective in promoting health and
well-being amongst pregnant mothers.

130

Afterword

This dissertation focuses on the biobehavioral mechanism linking prenatal sleep health,
diurnal cortisol, and gestational weight gain (GWG). Although the socio-cultural implications of
and future direction from this study are beyond the scope of this dissertation, it would be remiss
to not acknowledge the larger conversations that currently surround sleep and weight gain
amongst minority pregnant populations.
In applying our findings, we suggested that improvement of prenatal sleep health is key
intervention target for behavioral weight management. However, it must be noted that a one-
dimensional intervention simply focused on improving sleep would most likely prove
unsuccessful, especially in health disparities populations. Sleep health disparities is an
intersectional issue that is a product of racism, sexism, discrimination, neighborhood
segregation, built environment, cultural norms, and access to health care (Figure 1).
279
To

Figure 1. Socio-Ecological Model of Sleep Disparities from Billings et al.
279

131

design effective, evidence-based, and clinically relevant sleep interventions, public health
professionals must also ensure these interventions are accessible and tailored to individuals’
cultural beliefs around sleep and weight gain during pregnancy. Thus, it’s imperative that we
continue advocating for policies that will promote healthy sleep, including, but not limited to,
sleep-friendly urban planning, safe and cohesive neighborhoods, accessible childcare services,
and healthcare for sleep-related services during perinatal care.
279

Furthermore, it’s important to acknowledge the recent shift in narrative around pregnancy
and weight. The intense focus on the obesity epidemic in the public health field has increased
exponentially within the last few decades and with it an increase in weight stigma and negative
attitudes towards people in larger bodies.
280
Weight stigma, the prejudiced attitudes and
discriminatory actions towards individuals based on weight and body size, is pervasive during
pregnancy.
281
More than two thirds of women report experiences of weight stigma during
pregnancy across multiple sectors ranging from employment, media, close relationships, and
especially in healthcare settings.
281–284
Pregnant women who experience weight stigma report
more depressive symptoms, maladaptive dieting behavior, perceived stress, and even heightened
cortisol reactivity.
285–287
Thus, not surprisingly, pregnancy-related weight stigma predicts the risk
of gestational diabetes and increased weight gain, above and beyond BMI.
288

To combat weight stigma in prenatal health care settings, there is a growing movement
urging healthcare providers to receive sensitivity trainings in weight bias and the importance of
patient-centered approach to discussing lifestyle behaviors around GWG.
289,290
But medical
providers are not the only ones who are continuing to promote weight bias amongst pregnant
women. As public health researchers, we must also challenge ourselves to actively consider the
intersecting biological, psychological and social factors in weight stigma, and perhaps step away
132

from the catastrophizing, critical, and at times discriminatory language too often used to frame
maternal obesity.
291

If the goal is to find the most ethical and effective strategies to achieve optimal public
health, there needs to be an alternative to behavioral interventions solely focused on the
individual and emphasis on maternal health as a social justice issue. We hope the findings from
this dissertation can inform the next generation of research that approaches maternal health from
a progressive and inclusive perspective.

133

References

1. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among
US adults, 1999-2008. JAMA - J Am Med Assoc. 2010. doi:10.1001/jama.2009.2014
2. Vahratian A. Prevalence of overweight and obesity among women of childbearing age:
Results from the 2002 national survey of family growth. Matern Child Health J.
2009;13(2):268-273. doi:10.1007/s10995-008-0340-6
3. Flegal KM, Graubard BI, Williamson DF, Gail MH. Excess deaths associated with
underweight, overweight, and obesity. J Am Med Assoc. 2005.
doi:10.1001/jama.293.15.1861
4. Hillemeier MM, Weisman CS, Chuang C, Downs DS, McCall-Hosenfeld J, Camacho F.
Transition to overweight or obesity among women of reproductive age. J Women’s Heal.
2011;20(5):703-710. doi:10.1089/jwh.2010.2397
5. Kominiarek MA, Peaceman AM. Gestational weight gain. Am J Obstet Gynecol.
2017;217(6):642-651. doi:10.1016/j.ajog.2017.05.040
6. Weight Gain During Pregnancy. Committee Opinion No.548. Vol 121.; 2013.
7. Rasmussen KM, Yaktine AL. Weight Gain During Pregnancy : Reexamining the
Guidelines.; 2009. doi:10.1097/00006250-196901000-00025
8. Siega-Riz AM, Adair LS, Hobel CJ. Institute of medicine maternal weight gain
recommendations and pregnancy outcome in a predominantly hispanic population. Obstet
Gynecol. 1994.
9. Abrams B, Selvin S. Maternal weight gain pattern and birth weight. Obstet Gynecol. 1995.
doi:10.1016/0029-7844(95)00118-B
10. Carmichael S, Abrams B, Selvin S. The pattern of maternal weight gain in women with
good pregnancy outcomes. Am J Public Health. 1997. doi:10.2105/AJPH.87.12.1984
11. Deputy N, Sharma A, Kim S, Hinkle S. Prevalence and Characteristics Associated With
Gestational Weight Gain Adequacy. Obs Gynecol. 2016.
12. Ali Z, Nilas L, Ulrik CS. Excessive gestational weight gain in first trimester is a risk
factor for exacerbation of asthma during pregnancy: A prospective study of 1283
pregnancies. J Allergy Clin Immunol. 2018. doi:10.1016/j.jaci.2017.03.040
13. Jensen DM, Ovesen P, Beck-Nielsen H, et al. Gestational weight gain and pregnancy
outcomes in 481 obese glucose-tolerant women. Diabetes Care. 2005.
doi:10.2337/diacare.28.9.2118
14. Van Lieshout RJ, Taylor VH, Boyle MH. Pre-pregnancy and pregnancy obesity and
neurodevelopmental outcomes in offspring: A systematic review. Obes Rev.
2011;12(501):548-559. doi:10.1111/j.1467-789X.2010.00850.x
15. Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of Obesity and Severe Obesity
134

Among Adults: United States, 2017-2018. NCHS Data Brief. 2020.
16. Walker LO, Hoke MM, Brown A. Risk factors for excessive or inadequate gestational
weight gain among Hispanic women in a U.S.-Mexico border state. JOGNN - J Obstet
Gynecol Neonatal Nurs. 2009;38(4):418-429. doi:10.1111/j.1552-6909.2009.01036.x
17. Brawarsky P, Stotland NE, Jackson RA, et al. Pre-pregnancy and pregnancy-related
factors and the risk of excessive or inadequate gestational weight gain. Int J Gynecol
Obstet. 2005;91(2):125-131. doi:10.1016/j.ijgo.2005.08.008
18. Gutteling BM, De Weerth C, Zandbelt N, Mulder EJH, Visser GHA, Buitelaar JK. Does
maternal prenatal stress adversely affect the child’s learning and memory at age six? J
Abnorm Child Psychol. 2006;34(6):789-798. doi:10.1007/s10802-006-9054-7
19. Guelinckx I, Devlieger R, Mullie P, Vansant G. Effect of lifestyle intervention on dietary
habits, physical activity, and gestational weight gain in obese pregnant women: A
randomized controlled trial. Am J Clin Nutr. 2010. doi:10.3945/ajcn.2009.28166
20. Muktabhant B, Lawrie TA, Lumbiganon P, Laopaiboon M. Diet or exercise, or both, for
preventing excessive weight gain in pregnancy. Cochrane Database Syst Rev. 2015.
doi:10.1002/14651858.CD007145.pub3
21. Polley BA, Wing RR, Sims CJ. Randomized controlled trial to prevent excessive weight
gain in pregnant women. Int J Obes. 2002. doi:10.1038/sj.ijo.0802130
22. Hawkins M, Hosker M, Marcus BH, et al. A pregnancy lifestyle intervention to prevent
gestational diabetes risk factors in overweight Hispanic women: A feasibility randomized
controlled trial. Diabet Med. 2015. doi:10.1111/dme.12601
23. Vanstone M, Kandasamy S, Giacomini M, DeJean D, McDonald SD. Pregnant women’s
perceptions of gestational weight gain: A systematic review and meta-synthesis of
qualitative research. Matern Child Nutr. 2017;13(4):1-18. doi:10.1111/mcn.12374
24. Herring SJ, Henry TQ, Klotz AA, Foster GD, Whitaker RC. Perceptions of low-income
African-American mothers about excessive gestational weight gain. Matern Child Health
J. 2012;16(9):1837-1843. doi:10.1007/s10995-011-0930-6
25. Evenson KR, Moos MK, Carrier K, Siega-Riz AM. Perceived barriers to physical activity
among pregnant women. Matern Child Health J. 2009;13(3):364-375.
doi:10.1007/s10995-008-0359-8
26. Bennett GG, Wolin K. ., Duncan DT. Social determinants of obesity. Obes Epidemiol.
2008;Methods an:342-376.
27. Thomas M, Vieten C, Adler N, et al. Potential for a stress reduction intervention to
promote healthy gestational weight gain: Focus groups with low-income pregnant women.
Women’s Heal Issues. 2014. doi:10.1016/j.whi.2014.02.004
28. Buysse DJ. Sleep Health: Can We Define It? Does It Matter? Sleep. 2014;37(1):9-17.
doi:10.5665/sleep.3298
29. Sedov ID, Cameron EE, Madigan S, Tomfohr-Madsen LM. Sleep quality during
135

pregnancy: A meta-analysis. Sleep Med Rev. 2018;38:168-176.
doi:10.1016/j.smrv.2017.06.005
30. National Sleep Foundation. Pregnancy and Sleep.
https://www.sleepfoundation.org/pregnancy. Published 2015.
31. Mindell JA, Cook RA, Nikolovski J. Sleep patterns and sleep disturbances across
pregnancy. Sleep Med. 2015;16(4):483-488. doi:10.1016/j.sleep.2014.12.006
32. Reichner CA. Insomnia and sleep deficiency in pregnancy. Obstet Med. 2015;8(4):168-
171. doi:10.1177/1753495X15600572
33. Hutchison BL, Stone PR, McCowan LME, Stewart AW, Thompson JMD, Mitchell EA. A
postal survey of maternal sleep in late pregnancy. BMC Pregnancy Childbirth. 2012.
doi:10.1186/1471-2393-12-144
34. Hedman C, Pohjasvaara T, Tolonen U, Suhonen-Malm AS, Myllylä V V. Effects of
pregnancy on mothers’ sleep. Sleep Med. 2002;3(1):37-42. doi:10.1016/S1389-
9457(01)00130-7
35. Patel SR, Hu FB. Short sleep duration and weight gain: A systematic review. Obesity.
2008;16(3):643-653. doi:10.1038/oby.2007.118
36. Gangwisch JE, Malaspina D, Boden-Albala B, Heymsfield SB. Inadequate sleep as a risk
factor for obesity: Analyses of the NHANES I. Sleep. 2005. doi:10.1093/sleep/28.10.1289
37. Altman NG, Izci-Balserak B, Schopfer E, et al. Sleep duration versus sleep insufficiency
as predictors of cardiometabolic health outcomes. Sleep Med. 2012.
doi:10.1016/j.sleep.2012.08.005
38. Gangwisch JE, Malaspina D, Boden-Albala B, Heymsfield SB. Inadequate sleep as a risk
factor for obesity: Analyses of the NHANES I. Sleep. 2005;28(10):1289-1296.
doi:10.1093/sleep/28.10.1289
39. Dinges DF, Pack F, Williams K, et al. Cumulative sleepiness, mood disturbance, and
psychomotor vigilance performance decrements during a week of sleep restricted to 4-5
hours per night. Sleep. 1997. doi:10.1093/sleep/20.4.267
40. Patel SR, Malhotra A, White DP, Gottlieb DJ, Hu FB. Association between reduced sleep
and weight gain in women. Am J Epidemiol. 2006. doi:10.1093/aje/kwj280
41. Liu Y, Song Y, Koopmann J, Wang M, Chang CHD, Shi J. Eating your feelings? Testing
a model of employees’ work-related stressors, sleep quality, and unhealthy eating. J Appl
Psychol. 2017. doi:10.1037/apl0000209
42. VanHelder T, Symons JD, Radomski MW. Effects of sleep deprivation and exercise on
glucose tolerance. Aviat Sp Environ Med. 1993.
43. Spiegel K, Tasali E, Penev P, Van Cauter E. Brief communication: Sleep curtailment in
healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and
increased hunger and appetite. Ann Intern Med. 2004. doi:10.7326/0003-4819-141-11-
200412070-00008
136

44. Laposky AD, Van Cauter E, Diez-Roux A V. Reducing health disparities: The role of
sleep deficiency and sleep disorders. Sleep Med. 2016;18:3-6.
doi:10.1016/j.sleep.2015.01.007
45. Carnethon MR, John P, Chavez D, et al. Disparities in sleep characteristics by race /
ethnicity in a population-based sample : Chicago Area Sleep Study. 2016;18:50-55.
46. Feinstein L, Mcwhorter KL, Gaston SA, Sharkey KM, Jackson CL, Troxel WM. Racial /
ethnic disparities in sleep duration and sleep disturbances among pregnant and non-
pregnant women in the United States. 2020;(September 2019):1-12. doi:10.1111/jsr.13000
47. Williams NJ, Grandner MA, Snipes SA, et al. Racial / ethnic disparities in sleep health
and health care : importance of the sociocultural context. SLEH. 2015;1(1):28-35.
doi:10.1016/j.sleh.2014.12.004
48. Kingsbury JH, Buxton OM, Emmons KM, Redline S. Sleep and its Relationship to Racial
and Ethnic Disparities in Cardiovascular Disease. 2013:387-394. doi:10.1007/s12170-013-
0330-0
49. Hale L, Rivero-Fuentes E. Negative acculturation in sleep duration among Mexican
immigrants and Mexican Americans. J Immigr Minor Heal. 2011. doi:10.1007/s10903-
009-9284-1
50. Zizi F, Pandey A, Murrray-Bachmann R, et al. Race/ethnicity, sleep duration, and diabetes
mellitus: analysis of the National Health Interview Survey. Am J Med. 2012;125(2):162-
167.
51. Abeysena C, Jayawardana P. Sleep deprivation, physical activity and low income are risk
factors for inadequate weight gain during pregnancy: A cohort study. J Obstet Gynaecol
Res. 2011;37(7):734-740. doi:10.1111/j.1447-0756.2010.01421.x
52. Hill C, Lipsky LM, Betts GM, Siega-Riz AM, Nansel TR. A Prospective Study of the
Relationship of Sleep Quality and Duration with Gestational Weight Gain and Fat Gain. J
Women’s Heal. 2020;00(00). doi:10.1089/jwh.2020.8306
53. Althuizen E, van Poppel MNM, Seidell JC, Mechelen W Van. Correlates of Absolute and
Excessive Weight Gain During Pregnancy. J women’s Heal. 2009;18(10).
54. Restall A, Taylor RS, Thompson JMD, et al. Risk factors for excessive gestational weight
gain in a healthy, nulliparous cohort. J Obes. 2014;2014(Table 1).
doi:10.1155/2014/148391
55. Tibiletti Balieiro LC, Gontijo CA, Fahmy WM, Paiva Maia YC, Crispim CA. Does sleep
influence weight gain during pregnancy? A prospective study. Sleep Sci. 2019;12(3):156-
164. doi:10.5935/1984-0063.20190087
56. Merkx A, Ausems M, Budé L, de Vries R, Nieuwenhuijze MJ. Weight gain in healthy
pregnant women in relation to pre-pregnancy BMI, diet and physical activity. Midwifery.
2015;31(7):693-701. doi:10.1016/j.midw.2015.04.008
57. Feinstein L, McWhorter KL, Gaston SA, Troxel WM, Sharkey KM, Jackson CL.
Racial/ethnic disparities in sleep duration and sleep disturbances among pregnant and non-
137

pregnant women in the United States. J Sleep Res. 2020;29(5):1-12. doi:10.1111/jsr.13000
58. Cheney BDA. Examining the Relationship between Exercise , Cortisol Awakening
Response , Diurnal Cortisol , and Mood A Dissertation Submitted to The Faculty of the
College of Arts and Sciences In Candidacy for the Degree of Doctor of Clinical
Psychology Chicago , Illi. 2017.
59. Incollingo Rodriguez AC, Epel ES, White ML, Standen EC, Seckl JR, Tomiyama AJ.
Hypothalamic-pituitary-adrenal axis dysregulation and cortisol activity in obesity: A
systematic review. Psychoneuroendocrinology. 2015;62:301-318.
doi:10.1016/j.psyneuen.2015.08.014
60. Alschuler L. The HPA Axis. Integrative Therapeutics.
https://www.integrativepro.com/Resources/Integrative-Blog/2016/The-HPA-Axis.
Published 2016.
61. Papadimitriou A, Priftis KN. Regulation of the hypothalamic-pituitary-adrenal axis.
Neuroimmunomodulation. 2009;16(5):265-271. doi:10.1159/000216184
62. Folkman S, Lazarus RS. Stress, Appraisal, and Coping. New York: Springer Publishing
Company; 1984.
63. Pruessner JC, Kirschbaum C, Meinlschmid G, Hellhammer DH. Two formulas for
computation of the area under the curve represent measures of total hormone
concentration versus time-dependent change. Psychoneuroendocrinology.
2003;28(7):916-931. doi:10.1016/S0306-4530(02)00108-7
64. Rotenberg S, Mcgrath JJ, Roy-gagnon M. Stability of the diurnal cortisol profile in
children and adolescents. Psychoneuroendocrinology. 2012;37(12):1981-1989.
doi:10.1016/j.psyneuen.2012.04.014
65. Kirschbaum C, Hellhammer DH. Noise and Stress - Salivary Cortisol as a Non-Invasive
Measure of Allostatic Load. Noise Health. 1999.
66. Stalder T, Kirschbaum C, Kudielka BM, et al. Assessment of the cortisol awakening
response: Expert consensus guidelines. Psychoneuroendocrinology. 2016;63:414-432.
doi:10.1016/j.psyneuen.2015.10.010
67. Kuras YI, Rohleder N, Fiksdal A, et al. Blunted Diurnal Cortisol Activity in Healthy
Adults with Childhood Adversity. Front Hum Neurosci. 2017;11(November):1-8.
doi:10.3389/fnhum.2017.00574
68. Zijlmans MAC, Riksen-Walraven JM, de Weerth C. Associations between maternal
prenatal cortisol concentrations and child outcomes: A systematic review. Neurosci
Biobehav Rev. 2015;53:1-24. doi:10.1016/j.neubiorev.2015.02.015
69. Entringer S, Buss C, Andersen J, Chicz-DeMet A, Wadhwa PD. Ecological momentary
assessment of maternal cortisol profiles over a multiple-day period predict the length of
human gestation. Psychosom Med. 2011;73(6):469-474. doi:10.1021/nl061786n.Core-
Shell
70. Cherak SJ, Giesbrecht GF, Metcalfe A, Ronksley PE, Malebranche ME. The effect of
138

gestational period on the association between maternal prenatal salivary cortisol and birth
weight: A systematic review and meta-analysis. Psychoneuroendocrinology. 2018.
doi:10.1016/j.psyneuen.2018.04.023
71. Guilliams TG, Edwards L. Chronic stress and the HPA axis: Clinical assessment and
therapeutic considerations. Stand. 2010;9(2):1-12.
72. Fries E, Hesse J, Hellhammer J, Hellhammer DH. A new view on hypocortisolism.
Psychoneuroendocrinology. 2005. doi:10.1016/j.psyneuen.2005.04.006
73. McEwen BS. Physiology and neurobiology of stress and adaptation: Central role of the
brain. Physiol Rev. 2007. doi:10.1152/physrev.00041.2006
74. Aschbacher K, Rodriguez-Fernandez M, van Wietmarschen H, et al. The hypothalamic-
pituitary -adrenal -leptin axis and metabolic health: A systems approach to resilience,
robustness and control. Interface Focus. 2014;4(5). doi:10.1098/rsfs.2014.0020
75. Alfonso B, Araki T, Zumoff B. Is there visceral adipose tissue (VAT) intracellular
hypercortisolism in human obesity? Horm Metab Res. 2013. doi:10.1055/s-0033-1337986
76. Conde J, Scotece M, Gómez R, et al. Adipokines: Biofactors from white adipose tissue. A
complex hub among inflammation, metabolism, and immunity. BioFactors. 2011.
doi:10.1002/biof.185
77. Liuzzo G, Biasucci LM, Gallimore JR, et al. Enhanced inflammatory response in patients
with preinfarction unstable angina. J Am Coll Cardiol. 1999. doi:10.1016/S0735-
1097(99)00432-5
78. Vicennati V. Response of the Hypothalamic-Pituitary-Adrenocortical Axis to High-
Protein/Fat and High-Carbohydrate Meals in Women with Different Obesity Phenotypes.
J Clin Endocrinol Metab. 2002. doi:10.1210/jc.87.8.3984
79. Pasquali R, Anconetani B, Chattat R, et al. Hypothalamic-pituitary-adrenal axis activity
and its relationship to the autonomic nervous system in women with visceral and
subcutaneous obesity: Effects of the corticotropin-releasing factor/arginine-vasopressin
test and of stress. Metabolism. 1996. doi:10.1016/S0026-0495(96)90290-5
80. Pasquali R, Vicennati V. The abdominal obesity phenotype and insulin resistance are
associated with abnormalities of the hypothalamic-pituitaryadrenal axis in humans. Horm
Metab Res. 2000;32:521-525.
81. Björntorp P, Rosmond R. Obesity and cortisol. In: Nutrition. ; 2000. doi:10.1016/S0899-
9007(00)00422-6
82. Jung C, Ho JT, Torpy DJ, et al. A longitudinal study of plasma and urinary cortisol in
pregnancy and postpartum. J Clin Endocrinol Metab. 2011;96(5):1533-1540.
doi:10.1210/jc.2010-2395
83. Mustonen P, Karlsson L, Scheinin NM, et al. Hair cortisol concentration (HCC) as a
measure for prenatal psychological distress — A systematic review.
Psychoneuroendocrinology. 2018;92(March 2017):21-28.
doi:10.1016/j.psyneuen.2018.03.019
139

84. Fink NS, Urech C, Berger CT, et al. Maternal laboratory stress influences fetal
neurobehavior: Cortisol does not provide all answers. J Matern Neonatal Med.
2010;23(6):488-500. doi:10.3109/14767050903300985
85. Entringer S, Buss C, Shirtcliff EA, et al. Attenuation of maternal psychophysiological
stress responses and the maternal cortisol awakening response over the course of human
pregnancy. Stress. 2010;13(3):258-268. doi:10.3109/10253890903349501
86. De Weerth C, Buitelaar JK. Physiological stress reactivity in human pregnancy - A
review. Neurosci Biobehav Rev. 2005;29(2):295-312.
doi:10.1016/j.neubiorev.2004.10.005
87. Wright RJ, Fisher K, Chiu YHM, et al. Disrupted prenatal maternal cortisol, maternal
obesity, and childhood wheeze: Insights into prenatal programming. Am J Respir Crit
Care Med. 2013;187(11):1186-1193. doi:10.1164/rccm.201208-1530OC
88. Naya C, Toledo-Corral C, Chavez T, et al. Third Trimester Cortisol is Positively
Associated with Gestational Weight Gain in Pregnant Women with Class One Obesity. Int
J Obes. 2020.
89. Aubuchon-Endsley NL, Bublitz MH, Stroud LR. Pre-pregnancy obesity and maternal
circadian cortisol regulation: Moderation by gestational weight gain. Biol Psychol.
2014;102(1):38-43. doi:10.1016/j.biopsycho.2014.07.006
90. Mina TH, Denison FC, Forbes S, Stirrat LI, Norman JE, Reynolds RM. Associations of
Mood Symptoms with Ante-and Postnatal Weight Change in Obese Pregnancy Are Not
Mediated by Cortisol. Vol 45.; 2015. doi:10.1017/S0033291715001087
91. Peckett AJ, Wright DC, Riddell MC. The effects of glucocorticoids on adipose tissue lipid
metabolism. Metabolism. 2011;60(11):1500-1510. doi:10.1016/j.metabol.2011.06.012
92. Vicennati V, Garelli S, Rinaldi E, Di Dalmazi G, Pagotto U, Pasquali R. Cross-talk
between adipose tissue and the HPA axis in obesity and overt hypercortisolemic states.
Horm Mol Biol Clin Investig. 2014;17(2):63-77. doi:10.1515/hmbci-2013-0068
93. Omisade A, Buxton OM, Rusak B. Impact of acute sleep restriction on cortisol and leptin
levels in young women. Physiol Behav. 2010;99(5):651-656.
doi:10.1016/j.physbeh.2010.01.028
94. Leproult R, Buxton O, Cauter E Van. Sleep Loss Results in an Elevation of Cortisol
Levels the Next Evening. Sleep. 1997;20(10):865-870. doi:10.1093/sleep/20.10.865
95. Backhaus J, Junghanns K, Hohagen F. Sleep disturbances are correlated with decreased
morning awakening salivary cortisol. Psychoneuroendocrinology. 2004;29(9):1184-1191.
doi:10.1016/j.psyneuen.2004.01.010
96. Suzuki S, Dennerstein L, Greenwood KM, Armstrong SM, Sano T, Satohisa E. Melatonin
and hormonal changes in disturbed sleep during late pregnancy. J Pineal Res. 1993.
doi:10.1111/j.1600-079X.1993.tb00904.x
97. Bublitz MH, Bourjeily G, D’Angelo C, Stroud LR. Maternal Sleep Quality and Diurnal
Cortisol Regulation Over Pregnancy. Behav Sleep Med. 2018;16(3):282-293.
140

doi:10.1080/15402002.2016.1210147
98. Chaput J-P, Tremblay A. Insufficient Sleep as a Contributor to Weight Gain: An Update.
Curr Obes Rep. 2012;1(4):245-256. doi:10.1007/s13679-012-0026-7
99. Clarke PE, Gross H. Women’s behaviour, beliefs and information sources about physical
exercise in pregnancy. Midwifery. 2004;20(2):133-141. doi:10.1016/j.midw.2003.11.003
100. Clarke PE, Gross H. Women’s behaviour, beliefs and information sources about physical
exercise in pregnancy. Midwifery. 2004;20(2):133-141. doi:10.1016/j.midw.2003.11.003
101. Tomfohr-Madsen LM, Clayborne ZM, Rouleau CR, Campbell TS. Sleeping for Two: An
Open-Pilot Study of Cognitive Behavioral Therapy for Insomnia in Pregnancy. Behav
Sleep Med. 2017. doi:10.1080/15402002.2016.1141769
102. Beddoe AE, Paul Yang C-P, Kennedy HP, Weiss SJ, Lee KA. The effects of mindfulness-
based yoga during pregnancy on maternal psychological and physical distress. J Obstet
Gynecol neonatal Nurs JOGNN. 2009;38(3):310-319. doi:10.1111/j.1552-
6909.2009.01023.x
103. Kay-Stacey M, Attarian HP. Managing Sleep Disorders during Pregnancy. Gend Genome.
2017;1(1):34-45. doi:10.1089/gg.2016.0006
104. Felder JN, Epel ES, Neuhaus J, Krystal AD, Prather AA. Efficacy of Digital Cognitive
Behavioral Therapy for the Treatment of Insomnia Symptoms among Pregnant Women: A
Randomized Clinical Trial. JAMA Psychiatry. 2020;77(5):484-492.
doi:10.1001/jamapsychiatry.2019.4491
105. Felder JN, Laraia B, Coleman-Phox K, et al. Poor Sleep Quality, Psychological Distress,
and the Buffering Effect of Mindfulness Training During Pregnancy. Behav Sleep Med.
2018;16(6):611-624. doi:10.1080/15402002.2016.1266488
106. Haney A, Buysse DJ, Rosario BL, Chen YF, Okun ML. Sleep disturbance and
cardiometabolic risk factors in early pregnancy: A preliminary study. Sleep Med.
2014;15(4):444-450. doi:10.1016/j.sleep.2014.01.003
107. Carla L van der W. A Longitudinal Cohort Study on Maternal Weight Change Preceding
Pregnancy in Relation to Gestational Weight Gain, Pregnancy Outcome and Weight
Retention. Obes Open Access ( ISSN 2380-5528 ). 2015;1(3). doi:10.16966/2380-
5528.110
108. Bublitz MH, Bourjeily G, D’Angelo C, Stroud LR. Maternal Sleep Quality and Diurnal
Cortisol Regulation Over Pregnancy. Behav Sleep Med. 2018;16(3):282-293.
doi:10.1080/15402002.2016.1210147
109. Stone AA, Shiffman S, Atienza AA, Nebeling A. Historical roots and rationale of
ecological momentary assessment (EMA). Sci real-time data capture Self-reports Heal
Res. 2007:3-10.
110. Lauderdale DS, Knutson KL, Yan LL, Liu K, Rathouz PJ. Self-reported and measured
sleep duration: How similar are they? Epidemiology. 2008;19(6):838-845.
doi:10.1097/EDE.0b013e318187a7b0
141

111. Saxbe DE. A field (researcher’s) guide to cortisol: tracking HPA axis functioning in
everyday life. Health Psychol Rev. 2008;2(2):163-190. doi:10.1080/17437190802530812
112. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of
overweight and obesity in the United States, 1999-2004. J Am Med Assoc. 2006.
doi:10.1001/jama.295.13.1549
113. Tovar A, Guthrie LB, Platek D, Stuebe A, Herring SJ, Oken E. Modifiable predictors
associated with having a gestational weight gain goal. Matern Child Health J.
2011;15(7):1119-1126. doi:10.1007/s10995-010-0659-7
114. Amyx M, Xiong X, Xie Y, Buekens P. Racial/Ethnic Differences in Sleep Disorders and
Reporting of Trouble Sleeping Among Women of Childbearing Age in the United States.
Matern Child Health J. 2017;21(2):306-314. doi:10.1007/s10995-016-2115-9
115. Suglia SF, Staudenmayer J, Cohen S, Enlow MB, Rich-Edwards JW, Wright RJ.
Cumulative stress and cortisol disruption among black and hispanic pregnant women in an
urban cohort. Psychol Trauma Theory, Res Pract Policy. 2010. doi:10.1037/a0018953
116. Dowd JB, Goldman N. Do biomarkers of stress mediate the relation between
socioeconomic status and health? J Epidemiol Community Health. 2006.
doi:10.1136/jech.2005.040816
117. Cahill AG, Haire-Joshu D, Cade WT, et al. Weight Control Program and Gestational
Weight Gain in Disadvantaged Women with Overweight or Obesity: A Randomized
Clinical Trial. Obesity. 2018;26(3):485-491. doi:10.1002/oby.22070
118. Sheynblyum M, Conlon RPK, Donofry SD, Levine MD. Incorporating Skills for
Managing Mood, Stress, and Sleep into a Gestational Weight Gain Intervention. J
Contemp Psychother. 2022;(0123456789). doi:10.1007/s10879-022-09577-0
119. Rahe C, Czira ME, Teismann H, Berger K. Associations between poor sleep quality and
different measures of obesity. Sleep Med. 2015;16(10):1225-1228.
120. Magee CA, Reddy P, Robinson L, McGregor A. Sleep quality subtypes and obesity. Heal
Psychol. 2016;35(12):1289.
121. Koren D, Dumin M, Gozal D. Role of sleep quality in the metabolic syndrome. Diabetes,
Metab Syndr Obes Targets Ther. 2016;9:281-310. doi:10.2147/DMSO.S95120
122. Wu Y, Zhai L, Zhang D. Sleep duration and obesity among adults: A meta-analysis of
prospective studies. Sleep Med. 2014;15(12):1456-1462. doi:10.1016/j.sleep.2014.07.018
123. Wang G, Deng Y, Jiang Y, et al. Trajectories of sleep quality from late pregnancy to 36
months postpartum and association with maternal mood disturbances: A longitudinal and
prospective cohort study. Sleep. 2018;41(12):1-10. doi:10.1093/sleep/zsy179
124. Sweet L, Arjyal S. A Review of Sleep Architecture and Sleep Changes During Pregnancy.
Pediatr Emerg Care. 2017;33(12):792-793. doi:10.1097/01.pec.0000526609.89886.37
125. Brindle RC, Yu L, Buysse DJ, Hall MH. Empirical derivation of cutoff values for the
sleep health metric and its relationship to cardiometabolic morbidity: Results from the
142

Midlife in the United States (MIDUS) study. Sleep. 2019;42(9):1-9.
doi:10.1093/sleep/zsz116
126. Pauley AM, Moore GA, Mama SK, Molenaar P, Downs DS. Systematic review of the
associations between prenatal sleep behaviours and components of energy balance for
regulating weight gain. J Sleep Res. 2022;(November 2021). doi:10.1111/jsr.13619
127. Gay CL, Richoux SE, Beebe KR, Lee KA. Sleep disruption and duration in late pregnancy
is associated with excess gestational weight gain among overweight and obese women.
Birth. 2017;44(2):173-180. doi:10.1111/birt.12277
128. Lyu J, Zhu Q, Tong S, Su X, Li S, Hua J. Trajectories of sleep quality and associations
with excessive gestational weight gain during pregnancy. Sleep Biol Rhythms.
2020;18(3):249-257. doi:10.1007/s41105-020-00266-w
129. Zhong C, Chen R, Zhou X, et al. Poor sleep during early pregnancy increases subsequent
risk of gestational diabetes mellitus. Sleep Med. 2018;46:20-25.
doi:10.1016/j.sleep.2018.02.014
130. Haney A, Buysse DJ, Rosario BL, Chen YF, Okun ML. Sleep disturbance and
cardiometabolic risk factors in early pregnancy: A preliminary study. Sleep Med.
2014;15(4):444-450. doi:10.1016/j.sleep.2014.01.003
131. Du M, Liu J, Han N, Zhao Z, Luo S, Wang H. Association between sleep duration in early
pregnancy and risk of gestational diabetes mellitus: a prospective cohort study. Diabetes
Metab. 2021;47(5):101217. doi:10.1016/j.diabet.2020.101217
132. Facco FL, Grobman WA, Reid KJ, et al. Objectively measured short sleep duration and
later sleep midpoint in pregnancy are associated with a higher risk of gestational diabetes.
Am J Obstet Gynecol. 2017;217(4):447.e1-447.e13. doi:10.1016/j.ajog.2017.05.066
133. Guinhouya BC, Bisson M, Dubois L, et al. Body weight status and sleep disturbances
during pregnancy: Does adherence to gestational weight gain guidelines matter? J
Women’s Heal. 2019;28(4):535-543. doi:10.1089/jwh.2017.6892
134. Yong HY, Mohd Shariff Z, Appannah G, et al. Rate of gestational weight gain trajectory
is associated with adverse pregnancy outcomes. Public Health Nutr. 2020;23(18):3304-
3314. doi:10.1017/S1368980020002372
135. Bastain TM, Chavez T, Habre R, et al. Study Design, Protocol and Profile of the Maternal
and Developmental Risks from Environmental and Social Stressors (MADRES)
Pregnancy Cohort: A Prospective Cohort Study in Predominantly Low-Income Hispanic
Women in Urban Los Angeles. BMC Pregnancy Childbirth. 2019. doi:10.1186/s12884-
019-2330-7
136. Jenkins CD, Stanton BA, Niemcryk SJ, Rose RM. A scale for the estimation of sleep
problems in clinical research. J Clin Epidemiol. 1988. doi:10.1016/0895-4356(88)90138-2
137. Vahtera J, Pentti J, Helenius H, Kivimäki M. Sleep disturbances as a predictor of long-
term increase in sickness absence among employees after family death or illness. Sleep.
2006. doi:10.1093/sleep/29.5.673
143

138. Jerlock M, Gaston-Johansson F, Kjellgren KI, Welin C. Coping strategies, stress, physical
activity and sleep in patients with unexplained chest pain. BMC Nurs. 2006.
doi:10.1186/1472-6955-5-7
139. Corno G, Espinoza M, Maria Baños R. A narrative review of positive psychology
interventions for women during the perinatal period. J Obstet Gynaecol (Lahore). 2019.
doi:10.1080/01443615.2019.1581735
140. National Institute of Health. Clinical Guidelines on the Identification, Evaluation, and
Treatment of Overweight and Obesity in Adults.
141. Kleinbaum DG, Kupper LL, Nizam A, Rosenberg ES. Applied Regression Analysis and
Other Multivariable Methods. Cengage Learning; 2013.
142. Singer JD, Willett JB, Willett JB. Applied Longitudinal Data Analysis: Modeling Change
and Event Occurrence. Oxford university press; 2003.
143. Carmichael SL, Abrams B. A critical review of the relationship between gestational
weight gain and preterm delivery. Obstet Gynecol. 1997;89(5):865-873.
144. Suliga E. Validation of the Pregnancy Physical Activity Questionnaire ( PPAQ )
Walidacja kwestionariusza Pregnancy Physical Activity Questionnaire ( PPAQ ). 2017.
145. Cohen TR, Plourde H, Koski KG. Use of the Pregnancy Physical Activity Questionnaire
( PPAQ ) to Identify Use of the Pregnancy Physical Activity Questionnaire ( PPAQ ) to
Identify Behaviors Associated With Appropriate Gestational Weight Gain During
Pregnancy. 2014;(September 2013). doi:10.1123/jpah.10.7.1000
146. Karam F, Bérard A, Sheehy O, et al. Reliability and validity of the 4-item perceived stress
scale among pregnant women: Results from the OTIS antidepressants study. Res Nurs
Heal. 2012;35(4):363-375. doi:10.1002/nur.21482
147. Solivan AE, Xiong X, Harville EW, Buekens P. Measurement of Perceived Stress Among
Pregnant Women : A Comparison of Two Different Instruments. Matern Child Health J.
2015;19(9):1910-1915. doi:10.1007/s10995-015-1710-5
148. Koren G, Boskovic R, Hard M, Maltepe C, Navioz Y, Einarson A. Motherisk - PUQE
(pregnancy-unique quantification of emesis and nausea) scoring system for nausea and
vomiting of pregnancy. In: American Journal of Obstetrics and Gynecology. ; 2002.
doi:10.1067/mob.2002.123054
149. Hausler N, Heinzer R, Haba-Rubio J, Marques-Vidal P. Does sleep affect weight gain?
Assessing subjective sleep and polysomnography measures in a population-based cohort
study (CoLaus/HypnoLaus). Sleep. 2019;42(6):1-9. doi:10.1093/sleep/zsz077
150. Palagini L, Gemignani A, Banti S, Manconi M, Mauri M, Riemann D. Chronic sleep loss
during pregnancy as a determinant of stress: impact on pregnancy outcome. Sleep Med.
2014;15(8):853-859.
151. Palagini L, Cipollone G, Masci I, et al. Stress-related sleep reactivity is associated with
insomnia, psychopathology and suicidality in pregnant women: preliminary results. Sleep
Med. 2019;56:145-150.
144

152. Lasikiewicz N, Hendrickx H, Talbot D, Dye L. Exploration of basal diurnal salivary
cortisol profiles in middle-aged adults: Associations with sleep quality and metabolic
parameters. Psychoneuroendocrinology. 2008;33(2):143-151.
doi:10.1016/j.psyneuen.2007.10.013
153. Hartley E, McPhie S, Skouteris H, Fuller-Tyszkiewicz M, Hill B. Psychosocial risk
factors for excessive gestational weight gain: A systematic review. Women and Birth.
2015;28(4):e99-e109. doi:10.1016/j.wombi.2015.04.004
154. Kapadia MZ, Gaston A, Van Blyderveen S, et al. Psychological factors and trimester-
specific gestational weight gain: A systematic review. J Psychosom Obstet Gynecol.
2015;36(1):15-22. doi:10.3109/0167482X.2014.993311
155. Kapadia MZ, Gaston A, Van Blyderveen S, et al. Psychological antecedents of excess
gestational weight gain: A systematic review. BMC Pregnancy Childbirth. 2015;15(1).
doi:10.1186/s12884-015-0535-y
156. Matthews J, Huberty J, Leiferman J, Buman M. Psychosocial predictors of gestational
weight gain and the role of mindfulness. Midwifery. 2018;56(November 2016):86-93.
doi:10.1016/j.midw.2017.10.008
157. Seetho IW, Wilding JPH. Sleep-disordered breathing, type 2 diabetes and the metabolic
syndrome. Chron Respir Dis. 2014;11(4):257-275.
158. Bublitz MH, Carpenter M, Amin S, et al. The role of inflammation in the association
between gestational diabetes and obstructive sleep apnea: A pilot study. Obstet Med.
2018;11(4):186-191. doi:10.1177/1753495X18780095
159. Qiu C, Enquobahrie D, Frederick IO, Abetew D, Williams MA. Glucose intolerance and
gestational diabetes risk in relation to sleep duration and snoring during pregnancy: A
pilot study. BMC Womens Health. 2010;10. doi:10.1186/1472-6874-10-17
160. de Oliveira C, de Mattos A, Biz C, Oyama LM, Ribeiro EB, Oller do Nascimento CM.
High-fat diet and glucocorticoid treatment cause hyperglycemia associated with
adiponectin receptor alterations. Lipids Health Dis. 2011;10(1):1-14.
161. Finkelstein-Fox L, Gnall KE, Park CL. Mindfulness moderates daily stress and comfort
food snacking linkages: a multilevel examination. J Behav Med. 2020;43(6):1062-1068.
162. Bruno S, Bazzani A, Marantonio S, et al. Poor sleep quality and unhealthy lifestyle during
the lockdown: an Italian study. Sleep Med. 2022;90:53-64.
163. Flanagan EW, Most J, Broskey NT, et al. Identification of changes in sleep across
pregnancy and the impact on cardiometabolic health and energy intake in women with
obesity. Sleep Med. 2021;77:120-127. doi:10.1016/j.sleep.2020.11.031
164. Sharma S, Franco R. Sleep and its disorders in pregnancy. Wis Med J. 2004;103(5):48-52.
doi:10.5005/ijsm-1-2-72
165. Kalkhoff RK. Metabolic effects of progesterone. Am J Obstet Gynecol. 1982;142(6, Part
2):735-738. doi:https://doi.org/10.1016/S0002-9378(16)32480-2
145

166. Pacheco D, Callender E. Sleeping While Pregnancy: First Trimester. Sleep Foundation.
https://www.sleepfoundation.org/pregnancy/sleeping-during-1st-trimester. Published
2022.
167. Strauss RS, Dietz WH. Low maternal weight gain in the second or third trimester
increases the risk for intrauterine growth retardation. J Nutr. 1999;129(5):988-993.
168. Hickey CA, Cliver SP, McNeal SF, Hoffman HJ, Goldenberg RL. Prenatal weight gain
patterns and birth weight among nonobese black and white women. Obstet Gynecol.
1996;88(4):490-496.
169. Brown JE, Murtaugh MA, Jacobs Jr DR, Margellos HC. Variation in newborn size
according to pregnancy weight change by trimester. Am J Clin Nutr. 2002;76(1):205-209.
170. Sridhar SB, Xu F, Hedderson MM. Trimester-specific gestational weight gain and infant
size for gestational age. PLoS One. 2016;11(7):e0159500.
171. Hill AB. The Environment and Disease: Association or Causation? Proc R Soc Med.
1965;58(5):295-300.
172. Van De Water ATM, Holmes A, Hurley DA. Objective measurements of sleep for non-
laboratory settings as alternatives to polysomnography - a systematic review. J Sleep Res.
2011;20(1 PART II):183-200. doi:10.1111/j.1365-2869.2009.00814.x
173. Jackson CL, Patel SR, Jackson WB, Lutsey PL, Redline S. Agreement between self-
reported and objectively measured sleep duration among white, black, Hispanic, and
Chinese adults in the United States: Multi-ethnic study of atherosclerosis. Sleep.
2018;41(6):1-12. doi:10.1093/sleep/zsy057
174. Zhang L, Zhao ZX. Objective and subjective measures for sleep disorders. Neurosci Bull.
2007;23(4):236-240. doi:10.1007/s12264-007-0035-9
175. Coo S, Milgrom J, Trinder J. Mood and Objective and Subjective Measures of Sleep
During Late Pregnancy and the Postpartum Period. Behav Sleep Med. 2014;12(4):317-
330. doi:10.1080/15402002.2013.801348
176. Van Ravesteyn LM, Tulen JHM, Kamperman AM, et al. Perceived sleep quality is worse
than objective parameters of sleep in pregnant women with a mental disorder. J Clin Sleep
Med. 2014;10(10):1137-1141. doi:10.5664/jcsm.4118
177. Volkovich E, Tikotzky L, Manber R. Objective and subjective sleep during pregnancy:
links with depressive and anxiety symptoms. Arch Womens Ment Health. 2016;19(1):173-
181. doi:10.1007/s00737-015-0554-8
178. Galea JT, Ramos K, Coit J, et al. The Use of Wearable Technology to Objectively
Measure Sleep Quality and Physical Activity Among Pregnant Women in Urban Lima,
Peru: A Pilot Feasibility Study. Matern Child Health J. 2020;24(7):823-828.
doi:10.1007/s10995-020-02931-5
179. Mussa BM, Schauman M, Kumar V, Skaria S, Abusnana S. Personalized intervention to
improve stress and sleep patterns for glycemic control and weight management in obese
Emirati patients with type 2 diabetes: a randomized controlled clinical trial. Diabetes,
146

Metab Syndr Obes targets Ther. 2019;12:991.
180. Beddoe AE, Paul Yang CP, Kennedy HP, Weiss SJ, Lee KA. The effects of mindfulness-
based yoga during pregnancy on maternal psychological and physical distress. JOGNN - J
Obstet Gynecol Neonatal Nurs. 2009;38(3):310-319. doi:10.1111/j.1552-
6909.2009.01023.x
181. Vanstone M, Kandasamy S, Giacomini M, DeJean D, McDonald SD. Pregnant women’s
perceptions of gestational weight gain: A systematic review and meta-synthesis of
qualitative research. Matern Child Nutr. 2017;13(4):1-18. doi:10.1111/mcn.12374
182. American Academy of Pediatrics, The American College of Obstetricians and
Gynecologists. Guidelines for Perinatal Care.; 2017. doi:10.1542/9781610020886
183. Naya CH, Eckel SP, Yang T, et al. Poor sleep quality increases gestational weight gain
rate in women with normal pre-pregnancy BMI: findings from predominantly Hispanic,
low-income women from the MADRES study. Int J Obes. 2023.
184. Hirotsu C, Tufik S, Andersen ML. Review article Interactions between sleep , stress , and
metabolism : From physiological to pathological conditions. 2015;8:143-152.
185. Szidonya L, Hunyady L. A transgenic model of visceral obesity and the metabolic
syndrome. Trends Endocrinol Metab. 2002;13(3):98.
186. Allolio B, Hoffmann J, Linton EA, Winkelmann W, Kusche M, Schulte HM. Diurnal
salivary cortisol patterns during pregnancy and after delivery: relationship to plasma
corticotropin-releasing-hormone. Clin Endocrino. 1990;(33):279-289.
187. Entringer S, Buss C, Shirtcliff EA, et al. Attenuation of maternal psychophysiological
stress responses and the maternal cortisol awakening response over the course of human
pregnancy. Stress. 2010;13(3):258-268. doi:10.3109/10253890903349501
188. Kudielka BM, Gierens A, Hellhammer DH, Wüst S, Schlotz W. Salivary cortisol in
ambulatory assessment-some dos, some don’ts, and some open questions. Psychosom
Med. 2012;74(4):418-431. doi:10.1097/PSY.0b013e31825434c7
189. Stalder T, Kirschbaum C, Kudielka BM, et al. Assessment of the cortisol awakening
response: Expert consensus guidelines. Psychoneuroendocrinology. 2016;63:414-432.
doi:10.1016/j.psyneuen.2015.10.010
190. Rotenberg S, McGrath JJ, Roy-Gagnon MH, Tu MT. Stability of the diurnal cortisol
profile in children and adolescents. Psychoneuroendocrinology. 2012;37(12):1981-1989.
doi:10.1016/j.psyneuen.2012.04.014
191. Adam EK, Kumari M. Assessing salivary cortisol in large-scale, epidemiological research.
Psychoneuroendocrinology. 2009. doi:10.1016/j.psyneuen.2009.06.011
192. Facco FL, Kramer J, Ho KH, Zee PC, Grobman WA. Sleep disturbances in pregnancy.
Obstet Gynecol Surv. 2010;65(4):220-222. doi:10.1097/01.ogx.0000371710.47597.7a
193. Christian LM, Carroll JE, Porter K, Hall MH. Sleep quality across pregnancy and
postpartum: effects of parity and race. Sleep Heal. 2019;5(4):327-334.
147

doi:10.1016/j.sleh.2019.03.005
194. Alcántara C, Patel SR, Carnethon M, et al. Stress and sleep: Results from the Hispanic
Community Health Study/Study of Latinos Sociocultural Ancillary Study. SSM - Popul
Heal. 2017;3(August):713-721. doi:10.1016/j.ssmph.2017.08.004
195. Seicean S, Neuhauser D, Strohl K, Redline S. An exploration of differences in sleep
characteristics between Mexico-born US immigrants and other Americans to address the
Hispanic paradox. Sleep. 2011;34(8):1021-1031. doi:10.5665/SLEEP.1154
196. Giuntella O, Lonsky J, Mazzona F, Stella L. Immigration Policy and Immigrants’ Sleep.
Evidence from DACA. GLO Discuss Pap. 2020;589:1-31.
197. Lewandowska M, Więckowska B, Sajdak S. Pre-Pregnancy Obesity, Excessive
Gestational Weight Gain, and the Risk of Pregnancy-Induced Hypertension and
Gestational Diabetes Mellitus. J Clin Med. 2020;9(6):1980. doi:10.3390/jcm9061980
198. Facco FL, Grobman WA, Kramer J, Ho KH, Zee PC. Self-reported short sleep duration
and frequent snoring in pregnancy: Impact on glucose metabolism. Am J Obstet Gynecol.
2010;203(2):142.e1-142.e5. doi:10.1016/j.ajog.2010.03.041
199. Cai S, Tan S, Gluckman PD, et al. Sleep quality and nocturnal sleep duration in pregnancy
and risk of gestational diabetes mellitus. Sleep. 2017;40(2):5-12.
doi:10.1093/sleep/zsw058
200. Kapadia MZ, Gaston A, Van Blyderveen S, et al. Psychological antecedents of excess
gestational weight gain: A systematic review. BMC Pregnancy Childbirth. 2015;15(1).
doi:10.1186/s12884-015-0535-y
201. Cohen at al. The Perceived Stress Scale ( 14 items ) - Cohen et al , 1983 Recommended by
The NIH Centers for PopCohen at al. (1983). The Perceived Stress Scale ( 14 items ) -
Cohen et al , 1983 Recommended by The NIH Centers for Population Health and Health
Disparitie. 1983:1983.
202. Baker JH, Rothenberger SD, Kline CE, Okun ML. Exercise during early pregnancy is
associated with greater sleep continuity. Behav Sleep Med. 2018;16(5):482-493.
doi:10.1080/15402002.2016.1228649
203. Haakstad LAH, Voldner N, Henriksen T, Bø K. Physical activity level and weight gain in
a cohort of pregnant Norwegian women. Acta Obstet Gynecol Scand. 2007;86(5):559-564.
doi:10.1080/00016340601185301
204. Chasan-Taber L, Schmidt MD, Roberts DE, Hosmer D, Markenson G, Freedson PS.
Development and validation of a pregnancy physical activity questionnaire. Med Sci
Sports Exerc. 2004. doi:10.1249/01.MSS.0000142303.49306.0D
205. Hayes AF. PROCESS: A versatile computational tool for observed variable mediation,
moderation, and conditional process modeling.
206. Bublitz MH, Bourjeily G, D’Angelo C, Stroud LR. Maternal Sleep Quality and Diurnal
Cortisol Regulation Over Pregnancy. Behav Sleep Med. 2018;16(3):282-293.
doi:10.1080/15402002.2016.1210147
148

207. Naya CH, Toledo-Corral CM, Chavez T, et al. Third trimester cortisol is positively
associated with gestational weight gain in pregnant women with class one obesity. Int J
Obes. 2021. doi:10.1038/s41366-021-01009-8
208. Garde AH, Albertsen K, Persson R, Hansen ÅM, Rugulies R. Bi-Directional Associations
Between Psychological Arousal, Cortisol, and Sleep. Behav Sleep Med. 2011;10(1):28-40.
doi:10.1080/15402002.2012.636272
209. Akinloye O, Obikoya O., Jegede A., Oparinde D., Arowojolu A. Cortisol plays central
role in biochemical changes during pregnancy. Int J Med Biomed Res. 2013;2(1):3-12.
doi:10.14194/ijmbr.212
210. ALLOLIO B, HOFFMANN J, LINTON EA, WINKELMANN W, KUSCHE M,
SCHULTE HM. Diurnal Salivary Cortisol Patterns During Pregnancy and After Delivery:
Relationship To Plasma Corticotrophin‐Releasing‐Hormone. Clin Endocrinol (Oxf).
1990;33(2):279-289. doi:10.1111/j.1365-2265.1990.tb00492.x
211. Miller R, Plessow F, Kirschbaum C, Stalder T. Classification criteria for distinguishing
cortisol responders from nonresponders to psychosocial stress: evaluation of salivary
cortisol pulse detection in panel designs. Psychosom Med. 2013;75(9):832-840.
212. Baron R, Kenny D. The moderator-mediator variable distinction in social psychological
research. J Pers Soc Psychol. 1986;51(6):1173-1182. doi:10.1037/0022-3514.51.6.1173
213. Owusu JT, Anderson FJ, Coleman J, et al. Association of maternal sleep practices with
pre-eclampsia, low birth weight, and stillbirth among Ghanaian women. Int J Gynecol
Obstet. 2013;121(3):261-265. doi:10.1016/j.ijgo.2013.01.013
214. Okun ML, Schetter CD, Glynn LM. Poor sleep quality is associated with preterm birth.
Sleep. 2011. doi:10.5665/sleep.1384
215. Bonzini M, Palmer KT, Coggon D, Carugno M, Cromi A, Ferrario MM. Shift work and
pregnancy outcomes: A systematic review with meta-analysis of currently available
epidemiological studies. BJOG An Int J Obstet Gynaecol. 2011. doi:10.1111/j.1471-
0528.2011.03066.x
216. Flenady V, Koopmans L, Middleton P, et al. Major risk factors for stillbirth in high-
income countries: a systematic review and meta-analysis. Lancet. 2011;377(9774):1331-
1340.
217. Liggins GC. The role of cortisol in preparing the fetus for birth. Reprod Fertil Dev. 1994.
doi:10.1071/RD9940141
218. Späth-Schwalbe E, Gofferje M, Kern W, Born J, Fehm HL. Sleep disruption alters
nocturnal ACTH and cortisol secretory patterns. Biol Psychiatry. 1991;29(6):575-584.
219. Anne Helene Garde, Berndt Karlson, Åse Marie Hansen RP and TÅ. CHAPTER 6 Sleep
and Salivary Cortisol. Role Saliva Cortisol Meas Heal Dis. 2012:116-128.
220. Morgan E, Schumm LP, McClintock M, Waite L, Lauderdale DS. Sleep characteristics
and daytime cortisol levels in older adults. Sleep. 2017;40(5):zsx043.
149

221. Van Lenten SA, Doane LD. Examining multiple sleep behaviors and diurnal salivary
cortisol and alpha-amylase: Within- and between-person associations.
Psychoneuroendocrinology. 2016. doi:10.1016/j.psyneuen.2016.02.017
222. Tomfohr LM, Edwards KM, Dimsdale JE. Is obstructive sleep apnea associated with
cortisol levels? A systematic review of the research evidence. Sleep Med Rev.
2012;16(3):243-249. doi:10.1016/j.smrv.2011.05.003
223. Vargas I, Vgontzas AN, Abelson JL, Faghih RT, Morales KH, Perlis ML. Altered
ultradian cortisol rhythmicity as a potential neurobiologic substrate for chronic insomnia.
Sleep Med Rev. 2018;41:234-243.
224. Ekstedt M, Åkerstedt T, Söderström M. Microarousals during sleep are associated with
increased levels of lipids, cortisol, and blood pressure. Psychosom Med. 2004;66(6):925-
931.
225. Palesh O, Zeitzer JM, Conrad A, et al. Vagal regulation, cortisol, and sleep disruption in
women with metastatic breast cancer. J Clin Sleep Med. 2008;4(5):441-449.
226. Hucklebridge FH, Clow A, Rahman H, Evans P. Cortisol response to normal and
nocturnal awakening. J Psychophysiol. 2000;14(1):24.
227. Kumari M, Badrick E, Ferrie J, Perski A, Marmot M, Chandola T. Self-reported sleep
duration and sleep disturbance are independently associated with cortisol secretion in the
Whitehall II study. J Clin Endocrinol Metab. 2009;94(12):4801-4809.
228. Stalder T, Evans P, Hucklebridge F, Clow A. Associations between psychosocial state
variables and the cortisol awakening response in a single case study.
Psychoneuroendocrinology. 2010;35(2):209-214.
229. O’Connor SG, Habre R, Bastain TM, et al. Within-subject effects of environmental and
social stressors on pre- and post-partum obesity-related biobehavioral responses in low-
income Hispanic women: protocol of an intensive longitudinal study. BMC Public Health.
2019;19(1):253. doi:10.1186/s12889-019-6583-x
230. Scholtz et al_2019_Investigations associations between momentary stress and cortisol in
daily life.pdf.
231. Hedeker D, Mermelstein RJ, Demirtas H. Modeling between-subject and within-subject
variances in ecological momentary assessment data using mixed-effects location scale
models. Stat Med. 2012. doi:10.1002/sim.5338
232. Goldstein H, Bryk AS, Raudenbush SW. Hierarchical Linear Models: Applications and
Data Analysis Methods. J Am Stat Assoc. 1993. doi:10.2307/2290750
233. SAS. SAS/STAT® 9.2 User’s Guide. 2008.
234. Pruessner JC, Wolf OT, Hellhammer DH, et al. Free cortisol levels after awakening: A
reliable biological marker for the assessment of adrenocortical activity. Life Sci.
1997;61(26):2539-2549. doi:10.1016/S0024-3205(97)01008-4
235. Bailey SL, Heitkemper MM. Morningness-eveningness and early-morning salivary
150

cortisol levels. Biol Psychol. 1991;32(2-3):181-192.
236. Gustafsson K, Lindfors P, Aronsson G, Lundberg U. Relationships between self‐rating of
recovery from work and morning salivary cortisol. J Occup Health. 2008;50(1):24-30.
237. Stalder T, Evans P, Hucklebridge F, Clow A. Associations between psychosocial state
variables and the cortisol awakening response in a single case study.
Psychoneuroendocrinology. 2010;35(2):209-214. doi:10.1016/j.psyneuen.2009.06.006
238. Kupper N, de Geus EJC, van den Berg M, Kirschbaum C, Boomsma DI, Willemsen G.
Familial influences on basal salivary cortisol in an adult population.
Psychoneuroendocrinology. 2005;30(9):857-868.
239. Griefahn B, Robens S. The normalization of the cortisol awakening response and of the
cortisol shift profile across consecutive night shifts—an experimental study.
Psychoneuroendocrinology. 2010;35(10):1501-1509.
240. Kudielka BM, Kirschbaum C. Awakening cortisol responses are influenced by health
status and awakening time but not by menstrual cycle phase. Psychoneuroendocrinology.
2003;28(1):35-47.
241. Clow A, Thorn L, Evans P, Hucklebridge F. The Awakening Cortisol Response:
Methodological Issues and Significance. Stress. 2004;7(1):29-37.
doi:10.1080/10253890410001667205
242. Wilson DL, Barnes M, Ellett L, Permezel M, Jackson M, Crowe SF. Decreased sleep
efficiency, increased wake after sleep onset and increased cortical arousals in late
pregnancy. Aust New Zeal J Obstet Gynaecol. 2011;51(1):38-46. doi:10.1111/j.1479-
828X.2010.01252.x
243. Jackowska M, Dockray S, Hendrickx H, Steptoe A. Psychosocial factors and sleep
efficiency: Discrepancies between subjective and objective evaluations of sleep.
Psychosom Med. 2011;73(9):810-816. doi:10.1097/PSY.0b013e3182359e77
244. Wright CE, Erblich J, Valdimarsdottir HB, Bovbjerg DH. Poor sleep the night before an
experimental stressor predicts reduced NK cell mobilization and slowed recovery in
healthy women. Brain Behav Immun. 2007;21(3):358-363.
245. Goodin BR, Smith MT, Quinn NB, King CD, McGuire L. Poor sleep quality and
exaggerated salivary cortisol reactivity to the cold pressor task predict greater acute pain
severity in a non-clinical sample. Biol Psychol. 2012;91(1):36-41.
246. Mrug S, Tyson A, Turan B, Granger DA. Sleep problems predict cortisol reactivity to
stress in urban adolescents. Physiol Behav. 2016;155:95-101.
247. Benson S, Arck PC, Tan S, et al. Effects of obesity on neuroendocrine, cardiovascular,
and immune cell responses to acute psychosocial stress in premenopausal women.
Psychoneuroendocrinology. 2009;34(2):181-189.
248. Chrousos GP. The role of stress and the hypothalamic–pituitary–adrenal axis in the
pathogenesis of the metabolic syndrome: neuro-endocrine and target tissue-related causes.
Int J Obes. 2000;24(2):S50-S55.
151

249. Ruttle PL, Klein MH, Slattery MJ, Kalin NH, Armstrong JM, Essex MJ. Adolescent
adrenocortical activity and adiposity: differences by sex and exposure to early maternal
depression. Psychoneuroendocrinology. 2014;47:68-77.
doi:10.1016/j.psyneuen.2014.04.025
250. Chaput J-P, Dutil C, Featherstone R, et al. Sleep timing, sleep consistency, and health in
adults: a systematic review. Appl Physiol Nutr Metab. 2020;45(10 (Suppl. 2)):S232-S247.
doi:10.1139/apnm-2020-0032
251. Zhu B, Wang Y, Yuan J, et al. Associations between sleep variability and cardiometabolic
health: A systematic review. Sleep Med Rev. 2022;66:101688.
doi:10.1016/j.smrv.2022.101688
252. Tsai S, Lee C, Wu W, Landis CA. Sleep hygiene and sleep quality of third‐trimester
pregnant women. Res Nurs Health. 2016;39(1):57-65.
253. González-Mesa E, Cuenca-Marín C, Suarez-Arana M, et al. Poor sleep quality is
associated with perinatal depression. A systematic review of last decade scientific
literature and meta-analysis. J Perinat Med. 2019;47(7):689-703. doi:10.1515/jpm-2019-
0214
254. Ross LE, Murray BJ, Steiner M. Sleep and perinatal mood disorders: A critical review. J
Psychiatry Neurosci. 2005;30(4):247-256.
255. Patel SR, Malhotra A, White DP, Gottlieb DJ, Hu FB. Association between reduced sleep
and weight gain in women. Am J Epidemiol. 2006. doi:10.1093/aje/kwj280
256. Zhu B, Bronas UG, Carley DW, et al. Relationships between objective sleep parameters
and inflammatory biomarkers in pregnancy. Ann N Y Acad Sci. 2020;1473(1):62-73.
doi:10.1111/nyas.14375
257. Okun ML, Hall M, Coussons-Read ME. Sleep disturbances increase interleukin-6
production during pregnancy: Implications for pregnancy complications. Reprod Sci.
2007;14(6):560-567. doi:10.1177/1933719107307647
258. Okun ML, Roberts JM, Marsland AL, Hall M. How disturbed sleep may be a risk factor
for adverse pregnancy outcomes: A hypothesis. Obstet Gynecol Surv. 2009;64(4):273-280.
doi:10.1097/OGX.0b013e318195160e
259. Agatisa PK, Ness RB, Roberts JM, Costantino JP, Kuller LH, McLaughlin MK.
Impairment of endothelial function in women with a history of preeclampsia: an indicator
of cardiovascular risk. Am J Physiol Circ Physiol. 2004;286(4):H1389-H1393.
260. Roberts JM, Lain KY. Recent insights into the pathogenesis of pre-eclampsia. Placenta.
2002;23(5):359-372.
261. Hall WL. The emerging importance of tackling sleep-diet interactions in lifestyle
interventions for weight management. Br J Nutr. 2022;128(3):561-568.
doi:10.1017/S000711452200160X
262. Duncan MJ, Brown WJ, Burrows TL, et al. Examining the efficacy of a multicomponent
m-Health physical activity, diet and sleep intervention for weight loss in overweight and
152

obese adults: Randomised controlled trial protocol. BMJ Open. 2018;8(10):1-14.
doi:10.1136/bmjopen-2018-026179
263. Tiwari A, Self-Brown S. Feasibility of Biological Sample Collection among a High-Risk
Maternal Population for Child Maltreatment-Qualitative Pilot Findings. Am J Fam Ther.
2018;46(2):184-194. doi:10.1080/01926187.2018.1461033
264. Mussa BM, Schauman M, Kumar V, Skaria S, Abusnana S. Personalized intervention to
improve stress and sleep patterns for glycemic control and weight management in obese
emirati patients with type 2 diabetes: A randomized controlled clinical trial. Diabetes,
Metab Syndr Obes Targets Ther. 2019;12:991-999. doi:10.2147/DMSO.S201142
265. Logue EE, Bourguet CC, Palmieri PA, et al. The better weight-better sleep study: A pilot
intervention in primary care. Am J Health Behav. 2012;36(3):319-334.
doi:10.5993/AJHB.36.3.4
266. Skouteris H, McCabe M, Milgrom J, et al. Protocol for a randomized controlled trial of a
specialized health coaching intervention to prevent excessive gestational weight gain and
postpartum weight retention in women: The HIPP study. BMC Public Health.
2012;12(1):78. doi:10.1186/1471-2458-12-78
267. C BVBFPA de BPJAPLLCFBRDB. Interventions for sleep problems during pregnancy: a
systematic review [with consumer summary]. Sleep Med Rev 2020 Apr;50101234.
268. Buysse DJ, Yu L, Moul DE, et al. Development and validation of patient-reported
outcome measures for sleep disturbance and sleep-related impairments. Sleep.
2010;33(6):781-792.
269. Dietch JR, Sethi K, Slavish DC, Taylor DJ. Validity of two retrospective questionnaire
versions of the Consensus Sleep Diary: the whole week and split week Self-Assessment of
Sleep Surveys. Sleep Med. 2019;63:127-136.
270. Tsai SY, Lee CN, Wu WW, Landis CA. Sleep Hygiene and Sleep Quality of Third-
Trimester Pregnant Women. Res Nurs Heal. 2016;39(1):57-65. doi:10.1002/nur.21705
271. Adam EK, Kumari M. Assessing salivary cortisol in large-scale, epidemiological research.
Psychoneuroendocrinology. 2009;34(10):1423-1436.
272. Kudielka BM, Gierens A, Hellhammer DH, Wüst S, Schlotz W. Salivary cortisol in
ambulatory assessment-some dos, some don’ts, and some open questions. Psychosom
Med. 2012;74(4):418-431. doi:10.1097/PSY.0b013e31825434c7
273. Patel NP, Grandner MA, Xie D, Branas CC, Gooneratne N. “Sleep disparity” in the
population: Poor sleep quality is strongly associated with poverty and ethnicity. BMC
Public Health. 2010;10. doi:10.1186/1471-2458-10-475
274. Silva-perez LJ, Gonzalez-Cardenas N, Surani S, Etindele Sosso F, Surani SR.
Socioeconomic Status in Pregnant Women and Sleep Quality During Pregnancy. Cureus.
2019;11(11). doi:10.7759/cureus.6183
275. Conlon RPK, Wang B, Germeroth LJ, Cheng Y, Buysse DJ, Levine MD. Demographic,
Pregnancy-Related, and Health-Related Factors in Association with Changes in Sleep
153

Among Pregnant Women with Overweight or Obesity. Int J Behav Med. 2020.
doi:10.1007/s12529-020-09887-4
276. Ahn S, Lobo JM, Logan JG, Kang H, Kwon Y, Sohn MW. A scoping review of
racial/ethnic disparities in sleep. Sleep Med. 2021;81:169-179.
doi:10.1016/j.sleep.2021.02.027
277. Restall A, Taylor RS, Thompson JMD, et al. Risk factors for excessive gestational weight
gain in a healthy, nulliparous cohort. J Obes. 2014;2014(Table 1).
doi:10.1155/2014/148391
278. Merkx A, Ausems M, Budé L, de Vries R, Nieuwenhuijze MJ. Weight gain in healthy
pregnant women in relation to pre-pregnancy BMI, diet and physical activity. Midwifery.
2015;31(7):693-701. doi:10.1016/j.midw.2015.04.008
279. Billings ME, Cohen RT, Baldwin CM, et al. Disparities in Sleep Health and Potential
Intervention Models: A Focused Review. Chest. 2021;159(3):1232-1240.
doi:10.1016/j.chest.2020.09.249
280. Montgomery A. Addressing Weight Stigma and Fatphobia in Public Health.
https://publichealth.uic.edu/community-engagement/collaboratory-for-health-
justice/addressing-weight-stigma-and-fatphobia-in-public-health/. Published 2023.
281. Mulherin K, Miller YD, Barlow FK, Diedrichs PC, Thompson R. Weight stigma in
maternity care: Women’s experiences and care providers’ attitudes. BMC Pregnancy
Childbirth. 2013;13. doi:10.1186/1471-2393-13-19
282. Heslehurst N, Evans EH, Incollingo Rodriguez AC, Nagpal TS, Visram S. Newspaper
media framing of obesity during pregnancy in the UK: A review and framework synthesis.
Obes Rev. 2022;23(12):1-10. doi:10.1111/obr.13511
283. Incollingo Rodriguez AC, Dunkel Schetter C, Tomiyama AJ. Weight stigma among
pregnant and postpartum women: A new context of stigmatization. Stigma Heal.
2020;5(2):209-216. doi:10.1037/sah0000191
284. Incollingo Rodriguez AC, Dunkel Schetter C, Brewis A, Tomiyama AJ. The
psychological burden of baby weight: Pregnancy, weight stigma, and maternal health. Soc
Sci Med. 2019;235(November 2018):112401. doi:10.1016/j.socscimed.2019.112401
285. Schvey NA, Puhl RM, Brownell KD. The stress of stigma: Exploring the effect of weight
stigma on cortisol reactivity. Psychosom Med. 2014;76(2):156-162.
doi:10.1097/PSY.0000000000000031
286. Skorinko JLM, Incollingo Rodriguez AC, Doyle JK. Overlapping Stigmas of Pregnancy,
Motherhood, and Weight: Policy Implications for Employment and Higher Education.
Policy Insights from Behav Brain Sci. 2020;7(2):123-131.
doi:10.1177/2372732220943233
287. Rodriguez ACI, Tomiyama AJ, Guardino CM, Schetter CD. Association of Weight
Discrimination During Pregnancy and Postpartum With Maternal Postpartum Health. Heal
Psychol. 2019;38(3):226-237. doi:10.1037/hea0000711
154

288. Nagpal TS, Tomiyama AJ, Incollingo Rodriguez AC. Beyond BMI: Pregnancy-related
weight stigma increases risk of gestational diabetes. Prim Care Diabetes.
2021;15(6):1107-1109. doi:10.1016/j.pcd.2021.07.002
289. Fox R, Park K, Hildebrand-Chupp R, Vo AT. Working toward eradicating weight stigma
by combating pathologization: A qualitative pilot study using direct contact and narrative
medicine. J Appl Soc Psychol. 2021;(May 2019):1-14. doi:10.1111/jasp.12717
290. Nagpal TS, Liu RH, Gaudet L, Cook JL, Adamo KB. Summarizing recommendations to
eliminate weight stigma in prenatal health care settings: A scoping review. Patient Educ
Couns. 2020;103(11):2214-2223. doi:10.1016/j.pec.2020.06.017
291. Incollingo Rodriguez AC, Nagpal TS. The WOMBS Framework: A review and new
theoretical model for investigating pregnancy-related weight stigma and its
intergenerational implications. Obes Rev. 2021;22(12):1-11. doi:10.1111/obr.13322

155

Abstract (if available)

Abstract Excessive gestational weight gain (GWG) increases the risk of adverse birth outcomes and long-term maternal and child health issues. Despite efforts in the last 10 years to curtail excessive GWG rates, prenatal weight gain counseling and clinical interventions aimed at increasing physical activity and promoting healthy eating have led to inconsistent results, especially among low-income minority mothers. Thus, there is a need to identify alternative modifiable predictors of excessive GWG in minority women to improve prenatal care recommendations for healthy weight gain.
Sleep health has consistently received little attention in GWG research, even though poor sleep is a putative risk factor for obesity in non-pregnant populations and is ubiquitous during pregnancy. A key regulator that has been hypothesized to drive the association between sleep health and weight gain is the hypothalamic-pituitary-adrenal (HPA) axis and its end-product cortisol. However, no study to date has examined the relationship between prenatal sleep health, cortisol, and GWG.
The overall goal of this dissertation is to examine both population-level and day-level associations among sleep health, diurnal cortisol profiles, and GWG throughout pregnancy. This project will leverage data from pregnant women enrolled in the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) Study and the real-time personal monitoring sub-study in MADRES.

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Asset Metadata

Creator Naya, Christine Hotaru (author)

Core Title Prenatal sleep health, cortisol, and gestational weight gain

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Preventive Medicine

Degree Conferral Date 2023-05

Publication Date 04/20/2023

Defense Date 04/03/2023

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag cortisol,gestational weight gain,OAI-PMH Harvest,Pregnancy,Sleep

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Dunton, Genevieve (committee chair), Bastain, Theresa (committee member), Belcher, Britni (committee member), Eckel, Sandrah (committee member), Saxbe, Darby (committee member)

Creator Email christine.h.naya@gmail.com,naya@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113057194

Unique identifier UC113057194

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Legacy Identifier etd-NayaChrist-11677

Document Type Dissertation

Format theses (aat)

Rights Naya, Christine Hotaru

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Source 20230421-usctheses-batch-1027 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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