Free-living and in-lab effects of sedentary time on cardiac autonomic nervous system function in youth with overweight and obesity

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Content Copyright 2024 Kelsey McAlister Beland
FREE-LIVING AND IN-LAB EFFECTS OF SEDENTARY TIME ON CARDIAC
AUTONOMIC NERVOUS SYSTEM FUNCTION IN YOUTH
WITH OVERWEIGHT AND OBESITY
By
Kelsey McAlister Beland
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
(POPULATION AND PUBLIC HEALTH SCIENCES [HEALTH BEHAVIOR RESEARCH])
December 2024

ii
Acknowledgements
I would like to thank my dissertation committee for their invaluable guidance, expertise,
and encouragement throughout my graduate studies. A special thank you to my committee chair,
Dr. Britni Belcher, for her unwavering support, thoughtful feedback, and dedication to my growth
as a researcher. I am also grateful to my lab mates, whose collaboration and camaraderie made this
journey more rewarding. Finally, I want to express my immense gratitude to my family and friends
for their constant support, patience, and belief in me. Completing this journey would not have been
possible without the incredible support system I had in all of you.

iii
Table of Contents
Acknowledgements………………………………………………………………………. ii
List of Tables…………………………………………………………………………….. v
List of Figures……………………………………………………………………………. vi
Abstract…………………………………………………………………………………… vii
Chapter 1: Introduction…………………………………………………………………… 1
Background and Significance…………………………………………………….. 1
Chapter 2: Free-Living Associations of Sedentary Time with Cardiac Autonomic
Nervous System Function in Youth with Overweight and Obesity……………………..... 26
Abstract…………………………………………………………………………… 26
Introduction……………………………………………………………………….. 28
Methods…………………………………………………………………………... 30
Results…………………………………………………………………………….. 38
Discussion………………………………………………………………………… 49
Strengths and Limitations………………………………………………………… 53
Conclusions……………………………………………………………………….. 55
Chapter 3: Between-Person and Within-Person Moderators in the Associations of
Sedentary Time with Cardiac Autonomic Nervous System Function in Youth with
Overweight and Obesity………………………………………………………………….. 56
Abstract…………………………………………………………………………… 56
Introduction……………………………………………………………………….. 57
Methods…………………………………………………………………………... 59
Results…………………………………………………………………………….. 64
Discussion………………………………………………………………………… 70
Strengths and Limitations………………………………………………………… 74
Conclusions……………………………………………………………………….. 75
Chapter 4: In-Lab Effects of Breaking Up Prolonged Sitting with Physical Activity on
Cardiac Autonomic Nervous System Function in Youth with Overweight and Obesity… 76
Abstract…………………………………………………………………………… 76
Introduction……………………………………………………………………….. 78
Methods…………………………………………………………………………... 80
Results…………………………………………………………………………….. 86
Discussion………………………………………………………………………… 100
Strengths and Limitations………………………………………………………… 103
Conclusions……………………………………………………………………….. 104
Chapter 5: Discussion……………………………………………………………………... 105
Summary of Findings……………………………………………………………... 105

iv
Summary of Potential Mechanisms………………………………………………. 106
Implications………………………………………………………………………. 106
Future Research Directions………………………………………………………. 111
Strengths and Limitations………………………………………………………… 113
Conclusions………………………………………………………………………. 115
References………………………………………………………………………………... 116

v
List of Tables
Table 1: Distinguishing features of the sympathetic and parasympathetic nervous
systems……………………………………………………………………………………. 4
Table 2: HRV variables included in analyses………………….......................................... 35
Table 3: Participant descriptives for those included in the free-living assessment (study
1)…………………………………………………………………………………………..
40
Table 4: Model fit parameters for all five models for Study 1…………………………… 43
Table 5: Multilevel model results [β (SE)] for the associations of between-person and
within-person ST with time-domain HRV metrics (study 1)…………………………….. 47
Table 6: Multilevel model results [β (SE)] for the associations of between-person and
within-person ST with frequency-domain HRV metrics (study 1)………………………. 47
Table 7: Multilevel model results [β (SE)] for moderation effects of same-level
moderators on the associations of between-person and within-person ST with timedomain HRV metrics (study 2)….……………………………………………………….. 68
Table 8: Multilevel model results [β (SE)] for moderation effects of same-level
moderators on the associations of between-person and within-person ST with frequencydomain HRV metrics (study 2) ……………………………..……………………………. 68
Table 9: Participant demographics for those included in Day 1 and Day 7 analyses by
experimental condition (study 3) ………………………………………………………… 88
Table 10: Participant demographics for those included in Days 2-6 analyses by
experimental condition (study 3) ………………………………………………………… 92
Table 11: Model fit parameters for all five models for in-lab Days 2-6 analyses (study
3)..………………………………………………………………………………………… 95
Table 12: Multilevel model results [β (SE)] for the interaction of experimental condition
and time to determine differences in time-domain HRV variables between experimental
conditions (study 3).……………………………………………………………………… 98
Table 13: Multilevel model results [β (SE)] for the interaction of experimental condition
and time to determine differences in frequency-domain HRV variables between
experimental conditions (study 3).……………………………………………………… 98

vi
List of Figures
Figure 1: Pathways that demonstrate the physiological mechanisms connecting cardiac
ANS dysfunction to increased CVD risk…………………………………………………… 9
Figure 2: Conceptual model showing the hypothesized connections between ST and
increased CVD risk, what is currently known in the literature, and the major gaps in the
literature…………………………………………………………………………………......
17
Figure 3: Conceptual model showing the potential person-level moderating variables, the
connection of these moderating variables to cardiac ANS function, and the gaps in the
literature…………………………………………………………………………………….
19
Figure 4: Conceptual model showing the potential time-varying moderating variables, the
connection of these moderating variables to cardiac ANS function, and the gaps in the
literature…………………………………………………………………………………….
22
Figure 5: Conceptual model of the observational associations and experimental effects of
ST on cardiac ANS function, measured by HRV…………………………………………..
23
Figure 6: Study participant flow for the free-living assessment (study 1 and 2)………….. 39
Figure 7: Experimental conditions (study 3) ……………………………………………… 82
Figure 8: Study participant flow for those included in the in-lab week analyses (study 3)... 87

vii
Abstract
Excessive sedentary time (ST) in youth is a growing health concern, with physiological
evidence suggesting it may negatively impact the cardiac autonomic nervous system (ANS), which
may have implications for cardiovascular health. However, observational and experimental
research using gold standard measures remains limited. This dissertation investigated the freeliving and in-lab effects of ST on cardiac ANS function in youth with overweight and obesity
(OW/OB). The overarching objective of this dissertation was to increase our scientific
understanding of ST-cardiac ANS associations by taking a comprehensive approach, examining
both habitual ST in daily life and structured ST breaks in the lab. The specific aims of this
dissertation were to: 1) observationally examine the between-person and within-person
associations of ST with cardiac ANS function in a free-living, naturalistic environment, 2) explore
potential person-level and day-level moderating factors in the free-living associations of ST with
cardiac ANS function, and 3) experimentally investigate the acute effects of interrupting ST with
walking on cardiac ANS function in youth with OW/OB. Findings suggest that: 1) youth who
spend more time overall in ST, and on days when their ST is higher than usual, experience worse
cardiac ANS function, 2) ST-cardiac ANS function associations did not vary by person-level or
day-level moderating factors, and 3) breaking up ST in the lab over a three-hour period with a
longer moderate-intensity walking bout over multiple days may mitigate the adverse effects of ST
on cardiac ANS function. Taken together, these findings highlight the role of minimizing ST and
introducing regular, moderate-intensity movement breaks to support healthier cardiac ANS
function in youth with OW/OB. Methodological rigor enhances the reliability of these findings,
which could inform clinical and public health strategies to reduce cardiovascular risks from
sedentary behavior in this population, promoting healthier outcomes and guiding future research.

1
Chapter 1: Introduction
Background and Significance
Prevalence and Burden of Cardiovascular Disease
Cardiovascular disease (CVD) is defined as a group of noncommunicable disorders of the
heart and blood vessels.1 CVD poses a significant burden, accounting for almost one third of deaths
worldwide, costing the U.S. health care system approximately $216 billion per year, and causing
$147 billion in lost productivity on the job.
2-4 Despite the alarming mortality rate and
overwhelming economic burden, 90% of CVDs may be preventable.5,6 The underlying
mechanisms that lead to CVD vary, but there are several modifiable pre-existing physiological
conditions, known as CVD risk factors, that contribute to CVD development. These CVD risk
factors include hypertension, dyslipidemia, insulin resistance, and obesity.7 While each of these
risk factors individually contributes to CVD development, having multiple risk factors
significantly increases the risk for CVD.7

Although CVD development is multifaceted, it can begin as early as childhood,8-10 with
some evidence showing that increased risk can occur as early as three years old.11 Populationbased data from the U.S. suggests that 20% of youth have adverse lipid profiles,12-14 and that the
incidence of Type II Diabetes Mellitus15 and obesity16 continue to rise. Youth with
overweight/obesity (OW/OB) are at a significantly higher risk for the development of CVD,17-19
and the number of CVD risk factors increases with increased adiposity in a dose-response
manner.20,21 This relationship may be explained by the role that adipose tissue plays in the
pathogenesis of CVD.22 For example, adipose tissue releases metabolites, hormones, and proinflammatory cytokines that adversely affect the cardiovascular system and promotes the
occurrence of risk factors for CVD, including increased waist circumference, dyslipidemia,

2
hypertension, and insulin resistance.23,24 The release of these factors can lead to changes in the
liver, including changes in liver-derived clotting factors, cytokines, and lipoproteins, which
contribute to poor arterial health and therefore, poor cardiovascular health.23

Higher CVD risk during childhood can have lifelong consequences for individuals, as
tracking studies indicate that high CVD risk is a stable characteristic from childhood to
adulthood.25-28
Studies have shown that youth with several CVD risk factors in childhood tend to
have several CVD risk factors in adolescence26 and adulthood.27 In addition, studies have
demonstrated that OW/OB during childhood is significantly associated with higher CVD risk in
adulthood.29 Therefore intervening in youth at higher risk for CVD, particularly youth with
OW/OB, is critical to halt and reverse CVD morbidity and mortality.
The Cardiac Autonomic Nervous System
The cardiac autonomic nervous system (ANS) is the main regulator of the cardiovascular
system and does so without conscious thought. The cardiac ANS regulates the heart’s response to
varying stimuli the body is exposed to, in which the two branches of the cardiac ANS, the
sympathetic and parasympathetic nervous systems, exert antagonistic effects based on the
demands of these stimuli.
30,31 Table 1 outlines the distinguishing features of the sympathetic and
parasympathetic nervous systems. The physiology of the ANS is complex, so a brief overview of
the physiology as it relates to the cardiovascular system is provided below.
The sympathetic response, also referred to as the “fight or flight” response, prepares the
body for conditions like energy expenditure, emergencies, and stressful situations. The
sympathetic nervous system is critical for the control of daily energy expenditure via its regulation
of metabolic rate and thermogenesis (i.e., the production of heat) in the response to physiological
stimuli, changes in energy states, food intake, and carbohydrate consumption.32 Broadly, a stimuli

3
activates the central nervous system (i.e., the brain) to produce a sympathetic response.33 Many
bodily processes that influence the cardiovascular system then occur because of this stimulation.
For instance, the signal for sympathetic activation is carried to the adrenal glands, where large
amounts of epinephrine and norepinephrine are released directly into the bloodstream causing
global sympathetic stimulation, which in turn increases cardiac output (i.e., increased
contractibility and heart rate).30,33 An additional pathway relevant to this proposal, is the
stimulation of the sympathetic chain ganglia, which are nerves in the thoracic spine that directly
influence several sympathetic responses in the body, including the heart.30 The role of the
sympathetic chain ganglia includes the targeted release of small amounts of epinephrine and
norepinephrine to specific organs, including the heart.30,34 During the sympathetic response, the
vagal nerve is deactivated, and heart rate, contractibility, and arterial pressure increase to pump
oxygen and nutrients to tissues, such as muscle, that require it.
The parasympathetic nervous system, referred to as the “rest and digest” response,
counteracts the sympathetic response by restoring the body to a restful state. The central nervous
system receives a signal from sensory input (e.g., physiological stimuli such as maxing out during
exercise; external stimuli such as an emergent situation being alleviated) to activate the
parasympathetic response.30,33,35 This signal to the central nervous system triggers the vagus nerve
(i.e., the main nerve responsible for parasympathetic response) to release acetylcholine to specific
organs, including the heart.30,33 The acetylcholine released by the end of the vagus nerve acts on
heart’s tissues by reducing the heart’s heightened cardiac output that was stimulated during the
sympathetic response.30,33 During the parasympathetic response, the vagus nerve is activated and
heart rate, contractibility, and arterial pressure decrease.30,31

4
The sympathetic and parasympathetic nervous systems always provide some sort of
stimulation to the heart, even during reast.
33,36 However, the two systems have differing levels of
dominance throughout the day based on the various demands.36 For each organ system, including
the cardiovascular system, there may be a tendency for the organ to be in more of a sympathetic
or parasympathetic state at rest- this is known as an organ’s autonomic tone.36 Maintaining a
balance of sympathetic and parasympathetic activity (i.e., cardiac ANS function) is viewed as
having good autonomic tone, whereas the imbalance (i.e., cardiac ANS dysfunction) is viewed as
poor autonomic tone.36,37
Table 1. Distinguishing features of the sympathetic and parasympathetic nervous systems
Sympathetic Nervous System Parasympathetic Nervous System
Known as the “flight or flight” response Known as the “rest and digest” response
Predominates during emergencies, stressful
situations, and exercise Predominates during quiet and restful situations
Vagus nerve is deactivated Vagus nerve is activated
Increased heart rate Decreased heart rate
Increased heart contractibility Decreased heart contractibility
Vasoconstriction of the arterioles Vasodilatation of the arterioles
Increased blood pressure Decreased blood pressure
Release of epinephrine and norepinephrine Release of acetylcholine
Cardiac ANS function is measured by quantitative heart rate variability (HRV) indicators
of sympathetic and parasympathetic activity. HRV is defined as the fluctuation in the time intervals
between adjacent heartbeats.38 A healthy heart’s beat-to-beat fluctuations are non-linear and are
reflective of the heart’s ability to rapidly and flexibly cope with changes in ANS activity.

5
Therefore, a higher HRV (i.e., more variability in the time between adjacent heartbeats) is typically
reflective of healthier cardiac ANS function, and a lower HRV is indicative of cardiac ANS
dysfunction.39 While HRV cutoffs are commonly reported for adults in clinical and research
settings, standardized cutoffs for youth are not yet established. The capacity for HRV to change
within the context of topics covered in this dissertation are presented below (see section Sensitivity
for Cardiac Autonomic Nervous System Function to Change with Changes in Activity Behavior on
page 15).
While some heart rate monitors (e.g., Polar H10) can provide an overall measure of HRV,
electrocardiogram (ECG) is considered the gold standard for measuring HRV. Advancements in
ECG technology allow for HRV to be captured over multiple days, as lead-based monitors (e.g.,
ambulatory Holter monitor) have a short wear time (i.e., only 24-48 hours) and can yield low
compliance due to its cumbersome multi-lead, wired design.40-43 ECG provides several HRV
variables that are reflective of cardiac ANS function. Time- and frequency-domain analyses
provide precise descriptions of the heartbeat fluctuations and are the most widely-used analysis
methods for HRV data processing.39,44 Time-domain metrics of HRV quantify the amount of
variability between successive heartbeats in units of time (e.g., the standard deviation of the time
between normal-to-normal heartbeats).39,44 Frequency-domain analyses estimate the distribution
of power into research-based frequency bands (or ranges). Power refers to the signal energy found
within a frequency band in units of Hertz, and different frequency bands associate with specific
physiological processes.44 For example, the high frequency band is reflective of parasympathetic
activity.39,44 Time- and frequency-domain metrics provide crucial insight for both sympathetic and
parasympathetic activity, and highlight the importance of utilizing ECG technology to obtain these
important variables.39

6
Mechanisms Linking the Cardiac Autonomic Nervous System and Cardiovascular Disease
The sympathetic and parasympathetic nervous systems are two interacting systems in
which the balance of them (i.e., fluctuating between sympathetic and parasympathetic activity)
indicates a healthful state.30,31 Dominance of one versus the other is indicative of dysfunction and
demonstrates a lack of dynamic flexibility and health. A review paper suggested that dominance
of sympathetic activity is strongly and bidirectionally associated with several CVD risk factors
and therefore, CVD development, as demonstrated by animal studies and human studies in adults
and youth.
45 Figure 1 summarizes the physiological pathways that link cardiac ANS dysfunction
to CVD. Overactivation of the sympathetic nervous system is strongly linked to obesity in youth,46-
49 possibly due to the roles of leptin and beta-adrenoreceptors. Leptin is a hormone secreted from
adipocytes, and youth with OW/OB tend to have higher leptin levels compared to youth with
healthy weight.50-52 Leptin inhibits appetite and increases energy expenditure by activating the
sympathetic nervous system.53 However, it is additionally thought that individuals with an
overactive sympathetic nervous system may be at risk for developing obesity due to the
downregulation of beta-adrenoreceptors.32,54 Beta-adrenoreceptors are receptors that epinephrine
and norepinephrine bind to in order to signal sympathetic activity.55 When sympathetic activity is
dominant, the body compensates by reducing the number of beta-adrenoreceptors.55 This reduction
in beta-adrenoreceptors leads to excessive epinephrine and norepinephrine since there is a
decreased number of receptors to bind to. When this occurs, the adrenergic stimulation is not as
effective at initiating lipolysis (i.e., the breakdown of fat).54,55 Therefore, this downregulation of

7
beta-adrenoreceptors makes it difficult to dissipate excessive calories and leads to the
accumulation of adipose tissue.54
Several mechanisms explain the links between enhanced sympathetic activity and the CVD
risk factors of hypertension, and dyslipidemia.56-58 During the sympathetic response, the
vasoconstriction of the arteries (which increases blood pressure), increased contractibility of the
heart, and increased heart rate place a higher demand on the cardiovascular system. This higher
demand increases the cardiovascular workload and hemodynamic stresses, so spending more time
in the sympathetic response increases the duration of stress put on the cardiovascular system. In
turn, this prolonged stress predisposes one to various conditions such as hypertension, cardiac
hypertrophy, arterial remodeling, and endothelial dysfunction.
56 It has also been proposed that
increased arterial stiffness reduces arterial baroreflex activity (i.e., receptors that inform the ANS
of changes in beat-to-beat fluctuations in arterial blood pressure) due to the lack of stretch of the
arterial walls, which then leads to an increase in sympathetic activation.32 Sympathetic activation
can also directly influence dyslipidemia, as epinephrine-induced vasoconstriction via sympathetic
activity reduces blood flow, which limits chylomicron (i.e., large, triglyceride-rich lipoproteins)
clearance.58
Although complex and still unclear, a review paper focusing on animal and human adult
studies suggested that sympathetic dominance and insulin resistance are bidirectionally linked.32
It is hypothesized that vasoconstriction during sympathetic activation decreases blood flow to the
skeletal muscles.32,57 This decrease in blood flow may impair glucose uptake in the skeletal muscle
when the sympathetic nervous system is overactivated.32,57 Additional evidence in animal studies
suggests that insulin resistance from chronic overeating leads to hyperinsulinemia, which in turn
results in elevated neuropeptide Y.32,59 Given that neuropeptide Y is a transmitter of sympathetic

8
activity and acts with norepinephrine, this increase in neuropeptide Y is then thought to contribute
to enhanced sympathetic activity.32,59,60 However, more mechanistic research is needed to fully
understand these pathways.
In addition to the direct linkages between sympathetic dominance and CVD risk, there are
important benefits from increased parasympathetic activity. Benefits of parasympathetic activity
include decreased heart rate, decreased blood pressure, and vasodilation of the arterioles, which
are all aspects that contribute to a healthier cardiovascular system.56,61,62 Parasympathetic activity
promotes a state of relaxation, which fosters recovery.62 Reduced parasympathetic activity is
concerning because the physiological benefits of parasympathetic activity are protective against
CVD and these benefits are in opposition to the adverse consequences of sympathetic dominance.62
However, studies in animals and human adults show that there are benefits to sympathetic
activation, such as its vital role in the dissipation of energy following food intake.32 Therefore, the
balance of sympathetic and parasympathetic activity is key for maintaining health.30,31

9
Figure 1. Pathways that demonstrate the physiological mechanisms connecting cardiac ANS
dysfunction to increased CVD risk.
Cardiac Autonomic Nervous System Dysfunction in Youth with Overweight/Obesity
Childhood obesity is prevalent in the U.S. and is a major risk factor for CVD
development.16,25-28,63 Cardiac ANS dysfunction is highly prevalent in youth with OW/OB, as
many studies show reduced parasympathetic activity, heightened sympathetic activity, and/or
reduced cardiac ANS balance in youth with OW/OB.48,49,64-74 A study in 8-13-year-old children
showed that children with OB had lower parasympathetic nervous system activity and lower
overall HRV compared to children of a healthy weight.75 Another study showed that girls with
higher levels of central adiposity had significantly more sympathetic activity and less
parasympathetic activity compared to girls with lower levels of central adiposity.49 To date, most
studies show reduced parasympathetic activity in youth ages 7 and older, but one study

10
demonstrated reduced parasympathetic activity in a large sample (N=1,540) of youth ages 5-6
years old, suggesting that cardiac ANS dysfunction can occur at an early age. Taken together, the
research suggests that disturbances in the cardiac ANS begin early in children with OW/OB, and
likely increases their CVD risk due to the known adverse physiological consequences of cardiac
ANS dysfunction.56,61 Considering that weight status during childhood tracks into adolescence and
adulthood,76,77 strategies to promote a healthier, more dynamic cardiac ANS is warranted in youth
with OW/OB.
Cardiac Autonomic Nervous System Dysfunction and Cardiovascular Disease Risk in Youth
Studies in youth of various weight statuses show that poor cardiac ANS function is
associated with individual CVD risk factors.67,68,70,78-86 High blood pressure, insulin resistance,
obesity, and centralized obesity were independently associated with cardiac ANS dysfunction in
studies of youth with OW/OB of various ages.
70,72,87,88 The associations between cardiac ANS
dysfunction with individual CVD risk factors aligns with the physiological mechanisms that link
cardiac ANS dysfunction to each risk factor (see Mechanisms Linking the Cardiac Autonomic
Nervous System and Cardiovascular Disease section above on page 6). However, there is a dearth
of literature in this area in youth with OW/OB, limiting our understanding of the role of the cardiac
ANS function in the development of CVD early in life.
In studies that have included multiple measures of CVD risk and created combined CVD
risk scores, alterations in cardiac ANS function have been associated with a greater number of
CVD risk factors and higher overall CVD risk scores in youth of varying weight statuses.80,84-86
Among a sample of 1,152 adolescent boys, those with two or more CVD risk factors had
significantly lower HRV, which is indicative of poor cardiac ANS function, compared to those
with no CVD risk factors.84 In a sample of 443 6-to-8-year-old children, higher CVD risk scores

11
were associated with reduced parasympathetic activity and greater cardiac ANS imbalance.80 A
study in youth 9-11 years old found a dose-response relationship between the number of CVD risk
factors and several metrics of cardiac ANS function, including reduced parasympathetic activity
and increased sympathetic activity.86 While these studies did not limit their samples solely to youth
with OW/OB, 11-16% of the samples had OW/OB.80,84,85 The strong connections of cardiac ANS
function to CVD risk have made cardiac ANS function in youth an emerging area of interest, and
highlight the need for more research to understand these mechanisms in early life so we can
determine the most effective intervention windows.
Sedentary Time as a Modifiable Behavior of Interest
In sum thus far, the current literature suggests that: 1) CVD is a leading cause of morbidity
and mortality in the U.S.; 2) disruptions in cardiac ANS function contribute to CVD development;
3) youth, in particular youth with OW/OB, present with cardiac ANS dysfunction despite their
young age; and 4) cardiac ANS dysfunction is strongly linked with increased CVD risk in youth.
Strategies are needed to improve cardiac ANS function to lower CVD risk, especially in youth
who are vulnerable to higher CVD risk. In the last decade, sedentary time (ST) has become a
modifiable behavior of interest given its strong associations with several health outcomes,
including its associations with CVD risk.89,90 Therefore, ST may be a unique modifiable behavior
to target in youth for mitigating cardiac ANS dysfunction for the purposes of improving CVD risk.
Definition of Sedentary Time
ST is defined as time spent in any waking behavior that requires little to no energy
expenditure (≤1.5 metabolic equivalents).91 Among youth, behaviors that contribute to ST consist
of leisure-time activities, such as television viewing, computer use, and video gaming, and schooltime activities, such as sitting in class. The prevalence of ST remains high in youth, with current

12
estimates showing that youth spend seven to nine hours sedentary per day during and outside of
school.92-94 Advances in technology in the past decade have fostered increased time spent
sedentary. Youth ages 5-15 years old increased their use of tablets and mobile phones from 2015
to 2019, and used these devices more frequently to watch television programs.95 A longitudinal
study on data from the National Health and Nutrition Examination Survey (NHANES) showed 15-
year trends in ST varied by behavior type, of which the prevalence of television watching in
particular was high among children and adolescents.96 An additional study among 26 countries
demonstrated that several countries have increasing trends in ST accumulated during leisure-time
in youth ages 12-15 years.97 Cross-sectional analyses using data from the International Children’s
Accelerometry Database showed increases in ST after just age 5.98 Although differences appear
between types of sedentary behaviors, the large accumulation of daily ST is concerning among
youth. Fully understanding the consequences of prolonged time in ST among youth is particularly
important considering that ST from childhood is likely to track into adulthood.99
Accelerometry is a popular method for measuring time spent sedentary as well as patterns
of ST, which may have health implications. Accelerometry was first used in the 1990s, with the
original purpose of collecting better estimates of physical activity by removing self-report biases
in epidemiological studies.100,101 Large population-based studies have also used accelerometry to
collect objective measures of ST.102-104 Accelerometry measures bodily movements performed
throughout the day via three axes, including the 1) vertical, 2) anteroposterior, and 3) lateral
planes.105 While wrist- and hip-worn accelerometers provide valid estimates of movement, the
thigh-mounted activPAL (PAL Technologies, UK) monitor has numerous advantages that has led
to its frequent use in studies focusing on ST. ActivPAL monitors capture activity duration and
intensity, have the ability to differentiate sitting from standing, and collect the number of sit-to-

13
stand and stand-to-sit transitions.105,106 Large-scale epidemiological studies focusing on ST
surveillance are increasingly using the activPAL device.107,108 Additionally, the activPAL has been
validated against direct observation of ST in children.109,110 Therefore, the activPAL monitor is
considered the gold standard for measuring ST.111
Sedentary Time and Cardiovascular Disease in Youth
Strong evidence suggests that more time spent sedentary is associated with CVD risk in
adults, independent of physical activity participation.112-114 Recent longitudinal studies
demonstrated that increased ST was associated with higher overall CVD risk, increased adiposity,
and insulin resistance in youth.89,90,115 However, most studies in youth are cross-sectional, and
findings are inconsistent.116 While numerous associations between ST and CVD risk are
significant, many associations are attenuated when adjusting for physical activity.117-122 It is
currently unknown whether ST simply displaces time spent in physical activity since the amount
of time in a day is finite, or if participation in ST triggers adverse physiological processes.123 Given
that ST has increased in youth96 and that youth spend a large proportion of their time in ST,98,124
determining associations between ST and cardiac ANS function will help inform the underlying
pathology between ST and CVD.
Mechanisms Linking Sedentary Time with Cardiac Autonomic Nervous System Function
It has been hypothesized that the association between ST with overactivation of the
sympathetic nervous system and reduced parasympathetic activity is the primary cardiac ANS
pathway that links ST with CVD.125 During a bout of prolonged (>1 hour) ST, the blood pools in
the lower extremities. This pooling leads to interrupted blood flow and reduced blood pressure.126
It is thought that in order to compensate, the sympathetic nervous system increases its activity to
increase the heart rate to adapt the blood flow and blood pressure to the body’s position.126-128 This

14
increase in sympathetic activity then leads to an associated decrease in parasympathetic activity.126-
128 In addition to the increase in sympathetic activity, the decreased blood flow during a bout of
prolonged ST causes a reduction in shear stress and nitric oxide.126,129 Nitric oxide acts as a vagal
activity enhancer via augmented acetylcholine release.130 Therefore, the reduction in nitric oxide
during ST may attenuate acetylcholine release and therefore, may lead to reduced parasympathetic
activity.130 It has been proposed that the frequent exposure to these acute cardiac ANS responses
that occur during ST leads to chronic associations between high levels of ST and poorer cardiac
ANS function.125 Despite this theory, no prior studies have investigated the acute associations
using repeated measures between ST and cardiac ANS function, especially in youth populations
at risk for CVD development.
Some evidence in adults has demonstrated inverse associations between daily ST and
cardiac ANS function, suggesting that more ST is associated with sympathetic dominance and
reduced parasympathetic activity.131-133 However, there is a dearth of evidence on the relationship
between ST and cardiac ANS function in youth. A systematic review by Oliveira and colleagues
(2017), with the last search taking place in August 2016, found that there were no cross-sectional
studies investigating associations between ST and cardiac ANS in youth.134 To our knowledge,
only three cross-sectional observational studies since 2016 have investigated this relationship in
youth with mixed findings. Oliveira and colleagues (2018) found that average daily ST was not
related to cardiac ANS function in 54 adolescent youth with a mean age of 13 years.135
Veijalaninen and colleagues (2019) found that more ST (in average minutes/day) was associated
with poor cardiac ANS balance in a larger sample (N=377) of youth ages 6-9 years.136 Most
recently, Farah et al. (2020) found that spending more than two hours per day sedentary was
associated with higher odds of low parasympathetic activity in a sample of 1,149 adolescent

15
boys.137 However, there are limitations in the study methodology. Only one of these studies136 used
gold standard ECG methods for measuring cardiac ANS function, and only two used
accelerometry to obtain daily ST.135,136 Additionally, most youth were of healthy weight in all three
studies.135-137 Differing measurement methods may contribute to the mixed findings, as more
research using gold standard measures is needed if we are to draw conclusions about these
relationships.
Sensitivity for Cardiac Autonomic Nervous System Function to Change with Changes in Activity
Behavior
During an acute bout of physical activity, the sympathetic nervous system is dominant to
cope with the bodily stress of exercise. This response occurs even with less intense physical
activity (i.e., moderate intensity).138,139 Repeated activation of the sympathetic nervous system via
physical activity over time results in better cardiac ANS balance.140 These adaptations have the
capacity to acutely change. Studies in adults demonstrated these beneficial effects on the cardiac
ANS after only three sessions of resistance training141 and after a single 60-minute bout of
exercise.142 In adolescents, two weeks of high-intensity interval training improved cardiac ANS
function.143 During a bout of ST, the cardiac ANS changes acutely to cope with the hemodynamic
changes that occur during sitting, as described above (see the Mechanisms Linking Sedentary Time
and Cardiac Autonomic Nervous System Function section on page 13). Considering the acute
physiological responses that occur during a bout of ST, investigating the acute associations and
effects of ST on cardiac ANS function will lend insight toward creating interventions of longer
duration.
Gaps in Knowledge on Sedentary Time-Cardiac Autonomic Nervous System Function Associations
As discussed in the previous section, there have only been three studies that investigate the
associations between ST and cardiac ANS in youth. These prior studies are limited because they:

16
1) are cross-sectional in nature and are limited to between-person analyses (e.g., variation in the
entire sample); 2) do not combine investigations of both free-living habitual associations and
experimental approaches to manipulate ST in a single study; 3) do not address within-person (e.g.,
variation in individual responses) associations to understand the relationship between ST duration
and cardiac ANS function in youth, which should be investigated to determine if tailored
interventions that intervene when ST is expected to be higher than one’s normal are needed; and
4) do not use gold standard measurement methods for obtaining ST and cardiac ANS function in
the same study. Noninvasive ECG monitoring is considered the gold standard,144 and
advancements in ECG technology provide the unique opportunity to capture ECG data using
adhesive single-patch devices that are water-resistant, wireless, do not need to be removed, and
last for multiple days, making it less burdensome for participants.
Considering these large gaps in the current evidence, more studies are needed to understand
the underlying pathways that connect more time in ST to CVD risk in youth. Figure 2 shows the
links between ST and CVD risk and highlights the overarching gap in the current literature that
this dissertation addresses. The current literature suggests that more ST, decreased time in physical
activity, and cardiac ANS dysfunction are associated with increased CVD risk in youth
(represented in the figure with solid arrows). However, it is unknown if the associations between
ST and CVD risk are driven by time displaced from physical activity to ST, or if time in ST
prompts adverse adjustments to cardiac ANS function (represented in the figure with dotted
arrows). The three dissertation studies will contribute to elucidating the associations between ST
and cardiac ANS function.

17
Figure 2. Conceptual model showing the hypothesized connections between ST and increased
CVD risk, what is currently known in the literature, and the major gaps in the literature.
Solid arrows: what is generally understood based on the current literature.
Dotted arrows: what is unclear in the current literature.
Between-Person Moderators in the Associations of Sedentary Time with Cardiac Autonomic
Nervous System Function
The relationships between ST and cardiac ANS function may be dependent on important
person-level moderating factors. Adiposity may act as an amplifier (i.e., relationships may be
stronger in youth with higher adiposity) in the relationship between ST and cardiac ANS function,
and youth who are sedentary are more likely to have higher adiposity.145,146 As discussed above
(see Cardiac Autonomic Nervous System Dysfunction in Youth with Overweight/Obesity section
on page 9), youth with OW/OB tend to present with cardiac ANS dysfunction,48,49,64-71 and cardiac
ANS dysfunction is strongly associated with increased adiposity in a dose-response manner.46-49
Mechanisms that bidirectionally link higher adiposity to cardiac ANS dysfunction include
increased leptin secretion via adipocytes and the downregulation of beta-adrenoreceptors via
increased sympathetic activity (see Mechanisms Linking the Cardiac Autonomic Nervous System
and Cardiovascular Disease and Figure 1 on page 6). While differences in cardiac ANS function

18
between youth with OW and youth with OB is limited, one study found that youth with severe OB
had worse cardiac ANS function compared to youth with OW.70 Taken together, the current
evidence suggests that there may be important differences in cardiac ANS function that vary by
degree of adiposity.48,49,147 Therefore, adiposity status is an important moderating factor to
examine, as it is possible that higher degrees of adiposity will have stronger associations between
ST and cardiac ANS metrics compared to lower degrees of adiposity.
Cardiorespiratory fitness also has links to ST and cardiac ANS function that make it an
important moderator to consider. There are bidirectional associations reported in the literature:
greater ST is associated with lower cardiorespiratory fitness in youth, and youth who are sedentary
are more likely to have low cardiorespiratory fitness.145,148-150 Evidence suggests that lower
cardiorespiratory fitness is associated with cardiac ANS dysfunction in youth.134,136,147,151 While it
is widely known that increased cardiorespiratory fitness induces adaptations to the cardiac ANS
via increases in autonomic tone,152 the physiological mechanisms are unclear. During a bout of
physical activity, the sympathetic nervous system is activated to meet the bodily demands of
physical activity. Lack of repeated bouts of physical activity (i.e., repeated exposure to sympathetic
activity during physical activity followed by parasympathetic activity during recovery) leads to
reduced autonomic tone via reduced vagal activity and decreased cardiorespiratory fitness.140,152 It
has also been hypothesized that lower cardiorespiratory fitness contributes to decreased blood
volume (which results in lower blood pressure due to less circulating blood) and left ventricular
stroke volume (i.e., less blood being pumped out per beat; also results in lower blood pressure),
resulting in increased sympathetic activity as a compensatory response to increase the blood
pressure.138 Taken together, it is plausible that cardiorespiratory fitness level may act as a buffer in
ST-cardiac ANS function associations, and therefore should be investigated as a potential

19
moderator. Relationships between ST and cardiac ANS dysfunction may be stronger in those with
lower cardiorespiratory fitness compared to those with higher cardiorespiratory fitness, as
cardiorespiratory fitness may have protective effects in this relationship.
Figure 3 shows the potential moderating associations of person-level adiposity status and
cardiorespiratory fitness level in the associations of ST with cardiac ANS function. Figure 3
additionally includes the possible physiological pathways of adiposity status and cardiorespiratory
fitness level with cardiac ANS dysfunction. These pathways further demonstrate the importance
of exploring adiposity status and cardiorespiratory fitness as moderators.
Figure 3. Conceptual model showing the potential person-level moderating variables, the
connection of these moderating variables to cardiac ANS function, and the gaps in the literature.

20
Solid arrows: what is generally understood based on the current literature.
Dotted arrows: what is unclear in the current literature.
Within-Person Moderators in the Associations of Sedentary Time with Cardiac Autonomic
Nervous System Function
Sleep duration may moderate associations of ST with cardiac ANS function. Studies
demonstrate that increased ST is associated with shorter sleep durations and sleep disorders in
youth.153-155 Adequate sleep allows for increased time in deep and restorative sleep stages,
including non-rapid eye movement (non-REM) Stages 3 and 4.156 Non-REM Stages 3 and 4 are
collectively known as slow-wave sleep and are considered the most physically rejuvenating and
restorative phases of sleep.
156 Slow-wave sleep fosters parasympathetic activity and enhances
cardiac ANS balance.156 Longer sleep durations allow for more time in slow-wave sleep, which
supports recovery, reduces stress levels, and maintains healthy circadian rhythms- all of which
collectively contribute to enhanced cardiovascular health and better HRV.
156-159 In adults, studies
show that sleep deprivation is associated with worse cardiac ANS function.158,160 In youth, one
cross-sectional study found that subjectively-measured sleep duration was related to cardiac ANS
dysfunction in preschool children.161 A cross-sectional and longitudinal study in youth ages 5-11
years found that shorter sleep duration was associated with lower parasympathetic activity when
sleep was objectively measured.162 Moreover, a cross-sectional study found that irregular habitual
sleep patterns were associated with adverse cardiac ANS function in adolescents.163 In addition,
sleep disorders are strongly related to obesity.164-166 Considering the connections between sleep,
ST, OW/OB status, and cardiac ANS function, the associations of ST and cardiac ANS function
in youth with OW/OB may differ based on sleep duration the previous night.
In addition to sleep, moderate-to-vigorous physical activity (MVPA) also has linkages to
cardiac ANS function. During an acute bout of MVPA, the sympathetic nervous system dominates

21
to cope with the bodily stress of exercise.140 The cardiac ANS response to the stress of MVPA is
indicative of cardiac ANS flexibility.167,168 For example, a quicker transition from sympathetic
dominance to parasympathetic dominance upon the cessation of exercise is associated with better
cardiac ANS function and better health outcomes.167,168 In addition, repeated activation of the
sympathetic nervous system via physical activity over time results in better cardiac ANS
balance.140 A recent systematic review and meta-analysis found that MVPA was strongly related
to better cardiac ANS function in observational studies in children and adolescents.169 Moreover,
a systematic review found that experimental studies that included activity interventions of
moderate to vigorous intensity were successful in improving cardiac ANS function in youth with
obesity.170 Since MVPA may benefit cardiac ANS function, it may have a buffering effect in
associations of ST and cardiac ANS function, and is therefore should be investigated as a potential
moderator.
Lastly, there are important structural and contextual differences on weekdays versus
weekend days that are important to consider with respect to activity behaviors. School time
constitutes a large proportion of the day on Mondays through Fridays, and therefore plays an
important role in youth’s daily activity behaviors. Evidence suggests that youth are more sedentary
on weekend days compared to weekdays, possibly because youth have greater volitional control
over their activities on weekend days versus structured school days.171-174 Several other factors
known to influence cardiac ANS function differ on weekdays versus weekend days. For example,
evidence suggests that dietary intake is worse as marked by less vegetable consumption and larger
proportions of unhealthy foods,175,176 MVPA participation is lower,173,174 and bedtime and sleep
durations differ177,178 on weekend days compared to weekdays. Therefore, day of the week may be
an important moderator in associations of ST with cardiac ANS function and is worth exploring.

22
Figure 4 shows the potential moderating connections of within-person, time-varying sleep
duration, MVPA, and day of the week in the associations between ST and cardiac ANS function.
Figure 4 additionally shows the pathways that may connect sleep duration, MVPA, and day of the
week to cardiac ANS dysfunction.
Figure 4. Conceptual model showing the potential time-varying moderating variables, the
connection of these moderating variables to cardiac ANS function, and the gaps in the literature.
Solid arrows: what is generally understood based on the current literature.
Dotted arrows: what is unclear in the current literature.

23
Conceptual Model Connecting Sedentary Time and Cardiac ANS Function and Summary of
Contributions
Based on what is currently known and the gaps in the literature, Figure 5 shows the
hypothesized connections between ST and cardiac ANS function that appear in the three
dissertation studies. The unifying hypothesis is that more time in ST will be associated with poor
cardiac ANS function (via HRV metrics) in naturalistic and in-lab settings. The first two
dissertation studies utilized observational ST data, and the third study experimentally manipulated
patterns of ST using an experimental study design in a lab setting.
Figure 5. Conceptual model of the observational associations and experimental effects of ST on
cardiac ANS function, measured by HRV.
Study 1 investigated the observational between-person and within-person associations of
ST with cardiac ANS function under free-living conditions. We hypothesized that youth with
overall higher ST would have lower overall HRV, worse sympathetic/parasympathetic balance,
and lower parasympathetic activity compared to others. We hypothesized that on days when youth

24
had higher ST than their usual, they would have lower overall HRV, worse
sympathetic/parasympathetic balance, and lower parasympathetic activity on the corresponding
day.
Study 2 investigated between-person and within-person moderators in the observational
associations of ST with cardiac ANS function under free-living conditions. We explored adiposity
status and cardiorespiratory fitness as potential between-person moderators in the associations of
between-person ST and cardiac ANS function. We additionally explored daily sleep duration,
MVPA, and day of the week as potential time-varying moderators in the associations of withinperson ST and cardiac ANS function.
Study 3 investigated whether experimentally manipulating ST patterns in a lab setting
influences cardiac ANS function over one week. We hypothesized that those randomized to
prolonged ST (SIT condition) would exhibit lower HRV, worse sympathetic/parasympathetic
balance, and lower parasympathetic activity compared those randomized to frequent walking
breaks (SIT+WALK condition) and those randomized to a single bout of exercise (EX condition).
The three dissertation studies contribute to addressing important literature gaps and are
novel because: 1) the between-person and within-person analyses provide information on who may
be most responsive to this type of intervention and when this intervention should be delivered to
have the greatest effects on enhanced heart health; 2) the exploratory moderation analyses identify
other factors that should be considered in associations of ST with cardiac ANS function, which
provides knowledge toward tailored intervention strategies and which populations may be at
higher vulnerability; 3) they contain numerous methodological strengths, including an innovative,
rigorous randomized control trial study design and the combination of gold-standard measures of
ST and HRV; 4) the design includes both observational and experimental components to study

25
both the effects of habitual ST and experimentally manipulating ST in a controlled setting on HRV,
which is essential for ecological validity and informing precision prevention intervention
strategies; and 5) they assess whether short interruptions in ST are useful for improving cardiac
ANS function and therefore potentially CVD risk, which may be easier to attain than longer
walking bouts in youth with OW/OB. In sum, the studies presented in this dissertation help
elucidate mechanistic links between ST and cardiac ANS function, which will be instrumental in
establishing a foundation of evidence to support future behavioral studies and intervention
approaches.

26
Chapter 2: Free-Living Associations of Sedentary Time with Cardiac Autonomic Nervous
System Function in Youth with Overweight and Obesity
Abstract
Objective: Excessive sedentary time (ST) may disrupt cardiac autonomic nervous system (ANS)
function. The purpose of this study was to observationally examine between-person and withinperson associations of free-living ST with cardiac ANS function in youth with overweight/obesity
(OW/OB) in a naturalistic setting.
Methods: Data came from 25 youth with OW/OB (Mage=9.56±1.0 years, 40% girls, 76%
Hispanic) in the Sedentary Breaks Study 3. Youth wore a thigh-mounted accelerometer and
electrocardiogram (ECG) monitor for 24 hours for 7 consecutive days. Youth who had ≥3 days
with ≥8 hours of waking wear time each day were included. Day-level total ST (minutes) via
accelerometry and day-level mean HRV metrics [standard deviation of normal-to-normal intervals
(SDNN), root mean square of successive normal-to-normal interval differences (RMSSD), low
frequency (LF), high frequency (HF), LF/HF ratio] via ECG were calculated. Multilevel models
assessed whether within-person and between-person ST predicted each day-level mean HRV
variable, controlling for covariates.
Results: Across all participants, there were 126 days of valid accelerometer and ECG data.
Compared to other youth with OW/OB, those who averaged two more hours of ST per day
experienced a 4.80 millisecond decrease in SDNN (p=0.03) and RMSSD (p=0.03), a 24% decrease
in LF (p=0.008), and a 36% decrease in HF (p=0.03). On days when youth with OW/OB spent two
hours more in ST than their usual, there was a 6.0 millisecond decrease in SDNN (p=0.001), a 4.80
millisecond decrease in RMSSD (p=0.05), a 0.24 increase in the LF/HF ratio (p=0.02), a 24%
decrease in LF (p=0.001), and a 36% decrease in HF (p=0.001).

27
Conclusion: High levels of between-person and within-person ST were associated with worse
cardiac ANS function across multiple HRV metrics over one week. These findings highlight the
need to decrease both overall mean levels of ST and daily increases in ST to enhance cardiac ANS
function and potentially reduce CVD risk in this population. Future research in larger samples will
need to confirm these preliminary findings to inform future behavioral interventions to reduce the
risk of developing CVD.

28
Introduction
Although most cardiovascular diseases (CVDs) are considered preventable, CVD
morbidity and mortality remains high in the U.S.5,6 Even in youth, high rates of individual CVD
risk factors, including obesity, hypertension, type 2 diabetes, and dyslipidemia are increasingly
prevalent.15,16,179,180 Youth with overweight and obesity (OW/OB) are at higher risk for CVD
development.17-19 This is a relevant public health issue because adiposity and other CVD risk
factors track from childhood to adulthood,25-28 thus methods to mitigate CVD risk are needed,
especially for youth with OW/OB.
Excessive sitting in youth has emerged as a major public health concern given that youth
spend a large proportion of their time sedentary.92,93,181 While limiting sedentary time (ST) appears
to be a promising behavioral target to reduce CVD risk in youth,89,90,117,121,122,145,182-189 the
underlying mechanisms that connect ST to CVD are unknown. Prior observational research shows
that associations between ST and CVD risk factors are attenuated when models are adjusted for
physical activity level.117-122 Therefore, it is possible that the links between ST and CVD risk may
be attributed to ST displacing time spent in physical activity. Another potential mechanism is
through detrimental changes in cardiac autonomic nervous system (ANS) function, which has been
linked to increased CVD risk in youth67,68,70,78-80,82,84-86 and may explain associations between ST
and CVD development. It is hypothesized that sympathetic activity is increased and
parasympathetic activity is reduced during bouts of prolonged ST (e.g., sitting longer than one
hour).
126-128,130 It has been suggested that this frequent exposure to sympathetic dominance and
reduced parasympathetic activity during ST results in cardiac ANS dysfunction,125 which distally
contributes to CVD development.45

29
Despite the hypothesized physiological mechanisms linking ST and cardiac ANS function,
there is a large gap in the literature. Only three studies to date have investigated these associations
in youth of varying weight statuses, with conflicting findings.135-137 One study in adolescents with
a mean age of 13 years (mean body fat=20±7.6%) found that ST was not related to cardiac ANS
function,135 whereas a study in healthy youth ages 6-9 years136 (mean body fat=19.7±3.2%) and a
study in healthy boys ages 14-19 years137 (mean BMI=21.6±3.8 kg/m2
) suggested that more ST
was associated with worse cardiac ANS function. These studies are limited because they are in
healthy samples, are cross-sectional, utilize between-subject analyses that only observe variation
among the entire sample, and do not simultaneously combine gold standard measurement methods
for ST (i.e., accelerometry)111 and cardiac ANS function [i.e., electrocardiograph (ECG) to obtain
heart rate variability (HRV) metrics]144 in the same study over multiple days.
Studying youth with OW/OB is critically important since this population is at higher risk
for CVD,17-19 are more sedentary than youth with healthy weight,145,146 and can present with
cardiac ANS dysfunction.48,49,64-71 While both ST89,90,117,121,122,145,182-189 and poor cardiac ANS
function67,68,70,78-80,82,84-86 are linked to CVD development, it remains unknown if ST is associated
at a between- and within- person level with cardiac ANS dysfunction in youth with OW/OB. It is
important to disentangle these associations to identify potential behavioral intervention targets.
Therefore, this study has two aims and six hypotheses:
Aim 1: To observationally examine between-person associations of free-living ST with cardiac
ANS function in youth with OW/OB in a naturalistic setting.
H1A: Compared to others, youth with higher overall ST will have lower overall HRV.
H1B: Compared to others, youth with higher overall ST will have worse
sympathetic/parasympathetic balance.

30
H1C: Compared to others, youth with higher overall ST will have lower parasympathetic
activity.
Aim 2: To examine daily within-person associations between free-living ST with cardiac ANS
function in youth with OW/OB in a naturalistic setting.
H2A: Within individuals, days when youth have higher ST than usual will be associated
with lower overall HRV.
H2B: Within individuals, days when youth have higher ST than usual will be associated
with worse sympathetic/parasympathetic balance.
H2C: Within individuals, days when youth have higher ST than usual will be associated
with lower parasympathetic activity.
Methods
Participants
Youth ages 8-11 years old with OW/OB [defined as a body mass index (BMI) ≥85th
percentile]190 were recruited from the Sedentary Breaks Study 3 (ClinicalTrials.gov identifier
NCT04469790). Participants were recruited from BuildClinical, a research recruitment service that
engages the exact population needed for studies via digital advertising. Interested families first
completed an initial screening form via BuildClinical. Research staff then contacted interested
families who appeared eligible based on the screening form.
Participants were screened for eligibility over the phone prior to the first in-person visit.
For an individual to be included in the study, they had to be 8-11 years old, have good general
health, have a BMI ≥85th percentile,190 have a fasting plasma glucose <100 mg/dL, and be in preor peri-puberty. Youth were excluded from study participation if they had significant cardiac or

31
pulmonary disease likely to or resulting in hypoxia or decreased perfusion, had evidence of
impaired glucose tolerance or type 2 diabetes, had the presence of other endocrinologic disorders
leading to obesity (e.g., Cushing Syndrome), had current or past anti-psychotic drug use that would
affect metabolism, were on non-diet treatment for hypertension or dyslipidemia, had precocious
puberty, were receiving androgen and estrogen therapy, or were taking medication known to affect
body composition/weight, and/or had known allergies or family history of allergies to adhesives
and/or hydrogels.
The Sedentary Breaks Study 3 had three main components, including 1) a screening visit,
2) a 7-day free-living assessment, and 3) a 7-day in-lab experimental trial. The present study
utilized data collected during the screening visit and free-living assessment. Parents provided
informed consent and child participants provided assent. The Institutional Review Board at the
University of Southern California approved of this study.
Procedures
Screening Visit
After a preliminary phone screening, participants completed a two-hour in-person
screening visit to screen for eligibility. At this visit, the parent completed a demographic
questionnaire for child’s sex, birth date, race, ethnicity, maternal education, and socioeconomic
status. The parent also completed the Pubertal Development Scale at this visit to obtain pubertal
status.191,192 Height (in centimeters) and weight (in kilograms) were measured in duplicate and
were averaged to confirm OW/OB status.190 Cardiorespiratory fitness level was measured via a
modified Bruce treadmill protocol193 and a metabolic cart (COSMED Quark Cardio Pulmonary
Exercise Test; COSMED- The Metabolic Company, Concord, CA, USA) was used to measure

32
expired gas exchange. Lactate threshold was determined via V-slope method and dual criteria
graphs.194
Free-Living Assessment
Participants were mailed an accelerometer to assess daily time spent in ST (minutes), an
ECG monitor to assess daily cardiac ANS function metrics (i.e., HRV metrics), and device
instructions. Research staff were available by phone and video chat to help with the accelerometer
and ECG monitor placement and set-up as needed. Participants were instructed to wear the
accelerometer and ECG device for 24 hours each day over seven consecutive days, including while
at school, at summer camp/activities, while at any afterschool or extracurricular activities, when
exercising, and while showering. Two 24-hour dietary recalls using a multi-pass approach were
conducted using the Automated Self-Administered (ASA24) online tool.195 This approach has
been validated in youth196 and shows acceptable agreement with standard phone administration
methods.197 Dietary data were collected for one weekday and one weekend day to estimate habitual
dietary intake. Research staff were available by phone and by video chat to assist participants with
completing the two dietary recalls as needed. The percent of calories from macronutrients and total
caloric intake were averaged from the weekday and weekend dietary recalls and investigated as
covariates in analyses.
Measures
Objectively-Measured Sedentary Time via Accelerometry
Participants wore an activPAL micro4 accelerometer (PAL Technologies) on the right
thigh to measure activity and sleep behaviors throughout the free-living assessment. Research staff
placed a waterproof cover on the activPAL device prior to mailing the device to the participant.

33
Once the activPAL device was received, parents were instructed to place the device on
participant’s mid-right thigh and secure it using 3M Tegaderm tape.
Minute-by-minute activPAL data were downloaded using the activPal software
(PALanalysis v8.118.75).
198 Due to inaccuracies with activPAL’s algorithm,199 total sleep time for
each night was manually calculated and used to determine waking hours. First, hourly activity data
were visually inspected using the activPAL visualization sheet for each day to determine general
rise time and bedtime using the sleep band generated by activPAL. Then, the hourly visualization
sheet was compared to the minute-by-minute activPAL data files to find the exact timestamp for
rise time and bedtime for each day. “Rise time” was considered at least 20 consecutive minutes of
movement after the end of activPAL’s sleep band, and “bedtime” was considered at least 20
consecutive minutes of non-movement after the start of activPAL’s sleep band. Rise time and
bedtime were then used to determine waking time for each day. A valid day was defined as ≥8
hours of wear time during waking hours.200 Participants who had ≥3 valid days of wear time were
included in the analyses.201 Non-wear time was considered ≥60 consecutive minutes of zero
counts.202 ST was defined as activities requiring ≤1.5 metabolic equivalents.203 Week-level means
of ST (in minutes/day) and day-level ST (in total minutes) were calculated and used as the
between-person and within-person predictors in analyses, respectively. In addition, day-level sleep
duration, moderate to vigorous physical activity (MVPA; minutes spent in activities ≥3 metabolic
equivalents),204 and waking accelerometer wear-time were calculated to test as potential
covariates.
Cardiac ANS Function via ECG monitor
ECG is considered the gold standard for obtaining HRV metrics, which are metrics that
provide insight for cardiac ANS function.144 The 3-lead MyPatch-sl Holter Recorder (DMS-

34
Services, Los Angeles, CA) is a pediatric-friendly, ambulatory ECG device that allows for up to
14 consecutive days of recording. Parents and/or guardians were asked to place the MyPatch-sl
Recorder inferior to the sternal notch and aligned with the sternum on the participant using a
pediatric-sized patch to obtain HRV. The 3-lead MyPatch-sl Recorder is minimally invasive,
small, and wireless, which lessens participant burden and does not disrupt free-living activity.
Additionally, the MyPatch-sl Recorder is water-resistant, making it safe to wear while exercising
and showering. The patch design made the MyPatch-sl Recorder less cumbersome and more likely
to provide full data for the study duration compared to ECG devices that are attached to a strap,
which are uncomfortable, can move around, and would have needed to be removed for water
activities.
Data were imported and processed in the CardioScan Holter Analysis Software. All Holter
analyses were reviewed in detail to ensure that only normal beats with uniformly detected onsets
were labeled as normal. Both time- and frequency-domain HRV variables were derived.39,144
Time-domain variables were calculated based on the intervals between normal-to-normal beats,
measured in milliseconds (ms; i.e., quantifies the amount of HRV and expresses “how much” HRV
there is at various time scales). Frequency-domain variables express the magnitude of the variance
between normal-to-normal beats associated with specific bands of underlying rhythms.39 In order
to align with the accelerometer data, means of the HRV variables during waking time were
calculated based on the rise times and bed times derived from the accelerometer. Similar to the
accelerometer wear-time parameters, days were considered valid if there were ≥8 hours of wear
during waking time, and participants with ≥3 valid days were included in the analyses. Day-level
(i.e., daily average) time- and frequency-domain HRV variables that were derived are listed and
described in Table 2.
39,136,205

35
Table 2. HRV variables included in analyses
Time-Domain Metrics Frequency-Domain Metrics
Purpose: to measure how much variability in
time exists between heartbeats.
Purpose: to measure how much variability exists in heart
rate rhythms.
Variable Description Interpretation Variable Description Interpretation
Standard
deviation
of all R-R
intervals
(SDNN)
Overall HRV
Higher= better
cardiac ANS
function
Lower= poor
cardiac ANS
function
High
frequency
power
(HF)
Represents
parasympathetic
activity
Higher= more
parasympathetic activity
Lower= less
parasympathetic activity
Root mean
square of
successive
R-R
interval
differences
(RMSSD)
Marker of
parasympathetic
activity
Higher= more
parasympathetic
activity
Lower= less
parasympathetic
activity
Low
frequency
power
(LF)
Represents a
mixture of
sympathetic and
parasympathetic
activity
Higher= better cardiac ANS
function
Lower= poor cardiac ANS
function
LF/HF
ratio
Represents the
balance between
sympathetic and
parasympathetic
responses
Higher= worse
sympathetic/parasympathetic
balance
Lower= better
sympathetic/parasympathetic
balance
Covariates
Covariates were selected based on previous evidence.48,74,147,206-209 Person-level selfreported variables were age (continuous variable; years), sex (dichotomous variable; male vs.
female), race (dichotomous variable; White vs. all others), ethnicity (dichotomous variable;
Hispanic vs. Non-Hispanic), pubertal stage (continuous variable; ranges 1-3), and maternal
education level (dichotomous variable; college degree or higher vs. less than college degree).
Person-level measured variables were body composition (continuous variables; body fat percent
and trunk fat [kg]), basal heart rate in beats per minute (bpm; continuous variable), and systolic
blood pressure in mmHG (continuous variable). These variables were collected on Day 1 of the
in-lab week and were also tested as covariates based on previous evidence.32,56 Evidence indicates
that dietary intake influences HRV,210-214 so person-level dietary intake was tested as a covariate

36
(continuous variables; average of weekday and weekend day percent macronutrient intake and
total caloric intake in kilocalories). Youth with symptoms of anxiety also experience alterations in
the cardiac ANS,215,216 so person-level trait anxiety was tested as a covariate using the State-Trait
Anxiety Inventory for Children (STAIC) collected on Day 1 of the in-lab week.217 Sleep duration
(continuous variable; minutes) and MVPA (continuous variable; minutes) are also known to
influence HRV,147,162,206 so these variables were derived from the activPAL accelerometers and
tested as potential day-level covariates. Day-level accelerometer wear-time (continuous variable;
minutes) was also tested as a covariate. Lastly, we tested day of the week (dichotomous variable;
weekend vs. weekday)171-174 and time of year (dichotomous variable; school vs. summer)218,219 as
potential covariates. Covariates with p≤0.10 were retained in the models, and covariates with
p>0.10 were removed to achieve model parsimony. Collinearity was examined and addressed as
needed to maintain the integrity of the models.
Statistical Analyses
Frequencies and means were calculated for all participant demographic characteristics,
HRV metrics, accelerometer-derived activity and sleep variables, and covariates.
Sensitivity Analyses
Sensitivity analyses, including independent samples t-tests and chi-square tests, were
conducted to assess differences in demographic characteristics and body composition between
those included versus those excluded. Additionally, separate multilevel logistic regression models
were used to examine whether person-level age (continuous), sex (male vs. female), race (White
vs. all others), ethnicity (Hispanic vs. Non-Hispanic), maternal education level (college degree or
higher vs. less than college degree), body composition (continuous variables; body fat percent and

37
trunk fat [kg]), and pubertal stage (continuous variable; ranges 1-3) predicted daily activPAL
compliance (valid day: yes vs. no) and ECG compliance (valid day: yes vs. no). Separate multilevel
logistic regression models were also used to examine whether time of year (summer vs. school),
day of the week (weekend vs. weekday), and day of the study (continuous variable; range 1-7)
predicted daily activPAL compliance (valid day: yes vs. no) and ECG compliance (valid day: yes
vs. no).
Aim 1 and Aim 2
Since we collected multiple consecutive days of ST and HRV, multilevel models for
repeated measures were used to determine the associations of between-person and within-person
ST with each daily HRV outcome. Multilevel models allow for the partitioning of variances
between-subjects and within-subjects via grand-mean centering and person-mean centering,
respectively.
220 To address the between-person aim of this study (Aim 1), a between-person ST
variable was entered as the predictor and represented a participant’s deviation from the sample
grand mean (i.e., a given participant’s average ST as compared to the group’s average). To address
the within-person aim of this study (Aim 2), a within-person ST variable was entered as the
predictor and represented a participant’s daily deviation from their own mean (i.e., a participant’s
ST on a given day compared to their own average across the week). The daily average for each
HRV metric (i.e., SDNN, RMSSD, LF, HF, and LF/HF) was calculated and used as the outcome.
Therefore, a total of five models were run, one for each HRV outcome, controlling for a priori,
significant (p≤0.10) covariates. Model fit was assessed via p-value, the log likelihood test, Akaike
Information Criterion (AIC), and Bayesian Information Criterion (BIC), when appropriate.
Significance was considered p≤0.05, with p-values adjusted for multiple comparisons using the
Bonferroni correction. All analyses were conducted in SAS v.9.4. Cohen’s f
2
, a measure of local

38
effect size, was calculated using procedures specific to multilevel models.221 Final beta coefficients
and standard errors for the main predictors of interest (i.e., between-person ST and within-person
ST) were rescaled to reflect a 120-minute (2 hour) change in ST as it relates to HRV outcomes.
Results
Data Availability
Figure 6 provides an overview of the participant flow from recruitment to free-living
assessment completion. A total of 1,144 interested families completed a screening form through
BuildClinical, of which 594 appeared eligible based on the initial screening questions and were
contacted. After contact, 142 were deemed eligible to complete an in-person screening visit. Of
the 142 who were eligible for an in-person screening visit, 76 enrolled in the study and completed
the consent/assent. Sixty-eight participants then completed a screening visit, of which 19 were not
eligible and 49 were eligible. Of the 49 eligible participants, 48 were sent devices and participated
in the free-living assessment.
Of the 48 who participated, 27 had valid accelerometer and ECG wear-time (i.e., ≥3 days
of ≥8 hours of wear). Of the 21 who were excluded, 7 (33.33%) were not compliant/had missing
data for the ECG, 6 (28.57%) were not compliant/had missing data for both the accelerometer and
the ECG, 4 (19.05%) had missing data due to device malfunctions, 2 (9.52%) lost the ECG and/or
activPAL, and 2 (9.52%) dropped out of the study mid-week. Of the 27 who had valid
accelerometer and ECG wear-time, 2 (8%) had missing dietary recall data. Therefore, the final
analytic sample included 25 youth with OW/OB.

39
Figure 6. Study participant flow for the free-living assessment (Study 1 and 2).

40
Sample Characteristics
Table 3 presents sample descriptives. Twenty-five youth with OW/OB contributed 126
valid days of accelerometer and ECG wear (mean number of valid days per participant: 5.04 days).
The mean age of those included in the analyses was 9.56 years (range: 8-11 years old). The sample
was 40% female, 56% White, and 76% identified as Hispanic ethnicity. The mean body fat percent
of the sample was 44.83%. Mean basal heart rate and systolic blood pressure were 76.88 bpm and
111.15 mmHG, respectively. The mean trait anxiety score was 33.92, which reflects a moderate
level of anxiety.217,222 On average, youth spent most of their time each day in ST [699.53 minutes
(11.65 hours)] and less than an hour per day in MVPA (41.39 minutes). Compared to HRV values
from a sample of school-aged youth,223 youth in the present study had a low mean SDNN (54.25
ms), RMSSD (41.87 ms), LF (525.03 ms2
) and HF (275.40 ms2
), and high mean LF/HF ratio
(2.30), indicating poor cardiac ANS function relative to reported norms in children.223
Table 3. Participant descriptives for Study 1 and Study 2.
Variable Mean (SD) or N (%)
Person-Level (N=25)
Age (years) 9.56 (1.0)
Female sex 10 (40.0%)
Pubertal stage
Pre 12 (48%)
Early 10 (40%)
Mid 3 (12%)
Race
White 14 (56%)
Asian 1 (4%)
Black/African American 1 (4%)
More than one race 5 (20%)
Unknown/not reported 4 (16%)
Hispanic ethnicity 19 (76%)
Mother’s highest level of education Bachelor’s
degree or higher 11 (44%)
Age- and sex-adjusted BMI percentile 96.54 (2.97)
Body fat percent 44.83 (3.82)
Trunk fat (kg) 12.54 (4.09)

41
V02max (ml/kg/min) 24.66 (5.26)
Basal heart rate (bpm) 76.88 (9.93)
Basal systolic blood pressure (mmHG) 111.15 (11.22)
Total caloric intake (kcals) 1853.24 (701.99)
Proportion macronutrients from carbohydrates (%) 46.63 (8.27)
Proportion macronutrients from protein (%) 16.58 (4.85)
Proportion macronutrients from fat (%) 36.77 (6.72)
Trait anxiety score 33.92 (7.51)
Day-Level (N=126)
Mean (SD)
[range]
Activity variables (total minutes)
ST 699.53 (115.48)
[388.0-937.0]
MVPA 41.39 (26.93)
[0-110.63]
Waking time 829.89 (96.75)
[526.0-1078.0]
Sleep time 610.29 (89.19)
[375.0-895.20]
HRV variables (average)
SDNN (ms) 54.25 (11.09)
[31.04-90.65]
RMSSD (ms)
41.87 (14.67)
[18.74-92.19]
LF (ms2
)
525.03 (237.55)
[159.61-1520.75]
HF (ms2
)
275.40 (185.83)
[59.61-991.30]
LF/HF ratio 2.30 (0.91)
[0.85-4.66]
Sensitivity Analyses
Independent samples t-tests showed that there were no significant differences in personlevel age (t value=0.13, p=0.89), pubertal stage (t value=1.52, p=0.13), body fat percent (t
value=0.33, p=0.75), trunk fat (t value=0.07, p=0.95), basal heart rate (t value=-0.36, p=0.72), and
systolic blood pressure (t value=-0.02, p=0.98) between those included and excluded in the
analyses. Chi-square tests showed there were no significant differences in sex (female vs. male;
𝛘
2=0.02, p=0.88) or mother’s highest level of education (college degree or higher vs. less than
college degree; 𝛘
2=0.78, p=0.38) between those included and excluded in the analyses. White

42
(𝛘
2=4.90, p=0.03) and Hispanic participants (𝛘
2=7.97, p=0.01) had a significantly higher
proportion of included data compared to all others and non-Hispanic participants, respectively.
ActivPAL compliance (yes vs. no) was not related to age (OR=0.86, 95% CI 0.46-1.62,
p=0.64), sex (OR=0.85, 95% CI 0.22-3.26, p=0.81), race (White vs. all others; OR=3.23, 95% CI
0.84-12.44, p=0.10), pubertal status (OR=1.18, 95% CI 0.47-2.99, p=0.72), mother’s education
level (college degree or higher vs. less than college degree; OR=2.93, 95% CI 0.70-12.25), body
fat percent (OR=0.99, 95% CI 0.81-1.19, p=0.88), or trunk fat (OR=0.99, 95% CI 0.82-1.19,
p=0.89). Hispanic participants were less likely to meet the accelerometer wear-time requirements
on the day-level (OR = 0.25, 95% CI 0.07–0.87, p = 0.03), but overall, a higher proportion of
Hispanic compared to non-Hispanic participants met wear-time criteria and were included (as
mentioned above). Participants were more likely to comply with accelerometer wear on weekends
vs. weekdays (OR=3.56, 95% CI 1.65-7.69, p=0.001), over the summer vs. over the school year
(OR=5.14, 95% CI 1.58-16.78, p=0.01), and comply earlier in the study period vs. toward the end
(OR=1.34, 95% CI 1.11-1.62, p=0.003).
ECG compliance (yes vs. no) was not related to age (OR=0.88, 95% CI 0.55-1.39, p=0.58),
sex (OR=0.91, 95% CI 0.34-2.45, p=0.85), pubertal status (OR=1.08, 95% CI 0.54-2.17, p=0.83),
maternal education level (OR=0.42, 95% CI 0.16-1.13, p=0.10), body fat percent (OR=1.00, 95%
CI 0.88-1.14, p=0.99), or trunk fat (OR=1.05, 95% CI 0.91-1.20, p=0.52). Participants who were
White (OR=0.38, 95% CI 0.15-0.99, p=0.05) were less likely to comply with ECG wear. Hispanic
participants were also less likely to comply with ECG wear (OR=0.25, 95% CI 0.07-0.87, p=0.03),
but overall, a higher proportion of Hispanic compared to non-Hispanic participants met wear-time
criteria and were included (as mentioned above). Participants were more likely to comply with
ECG wear on weekends vs. weekdays (OR=3.56, 95% CI 1.65-7.71, p=0.001), over the summer

43
vs. over the school year (OR=3.69, 95% CI 1.47-9.17, p=0.001), and comply earlier in the study
period vs. toward the end (OR=1.42, 95% CI 1.22-1.66, p<0.001).
Model Fit
Model fit parameters are presented in Table 4. Final models included covariates with
p≤0.10 and the best model fit based on the log likelihood, AIC, and BIC values. The Restricted
Maximum Likelihood (REML) estimator was used for all models because it provides more
accurate and less biased estimates of variance components, particularly in small samples, by
accounting for the estimation of fixed effects separately from the random effects.224
Table 4. Model fit parameters for all five models for Study 1.
SDNN
Model 1:
Empty
Means
Model 2:
Main Predictors
(BW & WP ST)
Model 3:
Covariates
Model 4:
Random Slope
Model 5:
Covariance
Structure
Adding
means or
variance?
None Means Means Variance Variance
Estimator ML ML ML REML REML
-2LL 917.8 915.7 886.5 894.2 890.9
AIC 923.8 925.7 904.5 902.2 896.9
BIC 927.4 931.8 915.5 907.1 900.6
Comparison -- 1 vs. 2 2 vs. 3 3 vs. 4 4 vs. 5
Notes ICC=0.491 --
Retain age,
mother’s
education level,
day of the week,
and waking weartime.
Do not include
random slope
Autoregressive
covariance
structure
RMSSD
Model 1:
Empty
Means
Model 2:
Main Predictors
(BW & WP ST)
Model 3:
Covariates
Model 4:
Random Slope
Model 5:
Covariance
Structure
Adding
means or
variance?
None Means Means Variance Variance

44
Estimator ML ML ML REML REML
-2LL 926.9 919.8 872.1 886.2 882.9
AIC 932.9 929.8 894.1 894.2 888.9
BIC 936.5 935.9 907.5 899.1 892.5
Comparison -- 1 vs. 2 2 vs. 3 3 vs. 4 4 vs. 5
Result ICC=0.728 --
Retain age,
mother’s
education level,
day of the week,
basal heart rate,
MVPA, and
waking wear-time.
Do not include
random slope
Autoregressive
covariance
structure
LF
Model 1:
Empty
Means
Model 2:
Main Predictors
(BW & WP ST)
Model 3:
Covariates
Model 4:
Random Slope
Model 5:
Covariance
Structure
Adding
means or
variance?
None Means Means Variance Variance
Estimator ML ML ML REML REML
-2LL 93.2 90.5 66.6 118.9 115.1
AIC 99.2 100.5. 84.6 124.9 121.1
BIC 102.8 106.6 95.6 128.6 124.7
Comparison -- 1 vs. 2 2 vs. 3 3 vs. 4 4 vs. 5
Result ICC=0.534 --
Retain age,
mother’s
education level,
day of the week,
waking wear-time
Do not include
random slope
Autoregressive
covariance
structure
HF
Model 1:
Empty
Means
Model 2:
Main Predictors
(BW & WP ST)
Model 3:
Covariates
Model 4:
Random Slope
Model 5:
Covariance
Structure
Adding
means or
variance?
None Means Means Variance Variance
Estimator ML ML ML REML REML
-2LL 176.6 197.9 130.9 200.1 125.7

45
AIC 182.6 201.9 154.9 206.1 151.7
BIC 186.3 204.3 169.5 206.3 167.6
Comparison -- 1 vs. 2 2 vs. 3 3 vs. 4 4 vs. 5
Result ICC=0.633 --
Retain age,
mother’s
education level,
trunk fat, pubertal
status, day of
week, and waking
wear-time.
Do not include
random slope
Autoregressive
covariance
structure
LF/HF
Ratio
Model 1:
Empty
Means
Model 2:
Main Predictors
(BW & WP ST)
Model 3:
Covariates
Model 4:
Random Slope
Model 5:
Covariance
Structure
Adding
means or
variance?
None Means Means Variance Variance
Estimator ML ML ML REML REML
-2LL 214.8 205.2 180.5 234.6 179.6
AIC 220.8 215.2 200.5 240.6 201.6
BIC 224.5 221.3 212.7 244.3 215.0
Comparison -- 1 vs. 2 2 vs. 3 3 vs. 4 4 vs. 5
Result ICC=0.786 --
Retain mother’s
education level,
pubertal status,
day of the week,
and waking weartime.
Do not include
random slope
Autoregressive
covariance
structure
Covariates
Only covariates with p≤0.10 were retained in final models to achieve model parsimony.
However, day-level waking wear-time was included in the models regardless of p-value to account
for potential variability in data availability across participants. Among the five models, age (with
the exception of the ST-LF/HF ratio model), mother’s education level, and day of the week were
significant covariates (p’s≤0.10). As expected, since each HRV outcome is calculated differently

46
and measures distinct aspects of the cardiac ANS, covariates differed across models. Among timedomain HRV outcomes, basal heart rate and day-level MVPA were included as additional
covariates (p’s≤0.10) in ST-RMSSD models. Among frequency-domain HRV outcomes, trunk fat
and pubertal status were included as covariates for the ST-HF model (p’s≤0.10), whereas only
pubertal status was included in the ST-LF/HF ratio model (p≤0.10), and neither were included in
the ST-LF model (p’s>0.10).
Between-Person Associations of ST and Cardiac ANS Function (Aim 1)
Results for models assessing ST as it relates to time-domain HRV outcomes are presented
in Table 5, while models for frequency-domain HRV outcomes are presented in Table 6. LF and
HF HRV outcomes were log-transformed for final models, as they were highly skewed which led
to issues with model convergence. Higher between-person ST was associated with lower SDNN
(β=-4.80 ms, p=0.03, f
2=0.03), RMSSD (β=-4.80 ms, p=0.03, f
2=0.01), LF (β=-0.24 ms², p=0.008,
f
2=0.02), and HF (β=-0.36 ms², p=0.03, f
2=0.05), but was not associated with the LF/HF ratio
(β=0.12, p=0.74, f
2=0.01). Compared to other youth with OW/OB, those who averaged two more
hours of ST per day experienced a 4.80 ms decrease in SDNN and RMSSD, a 24% decrease in LF,
and a 36% decrease in HF.
Within-Person Associations of ST and Cardiac ANS Function (Aim 2)
Higher within-person ST was associated with lower SDNN (β=-6.0 ms, p=0.001, f
2=0.03),
RMSSD (β=-4.80 ms, p=0.05, f
2=0.01), LF (β=-0.24 ms², p=0.001, f
2=0.02), HF (β=-0.36 ms²,
p=0.001, f
2=0.05), and higher LF/HF ratio (β=0.24, p=0.02, f
2=0.01). On days when youth spent
two hours more in ST than their usual, there was a 6.0 ms decrease in SDNN, a 4.80 ms decrease
in RMSSD, a 0.24 increase in LF/HF ratio, a 24% decrease in LF, and a 36% decrease in HF.

47
Table 5. Multilevel model results [β (SE)] for the associations of between-person and withinperson ST with time-domain HRV metrics (Level-2 N=126 days; Level 1 N=25 participants)
SDNN RMSSD
Fixed
Effects
Intercept 36.25 (17.55)* 34.14 (31.36)
ST (BW)a
-4.80 (2.40)* -4.80 (2.40)*
ST (WS)b
-6.0 (1.20)** -4.80 (2.40)*
Age 4.06 (1.41)** 4.86 (1.94)**
Mother’s education level 13.29 (3.29)** 16.75 (4.94)**
Day of the week 4.27 (1.61)** 3.93 (1.46)**
Waking wear-time -0.03 (0.01)** -0.02 (0.02)
Basal heart rate -- -0.41 (0.22)^
MVPA -- 0.08 (0.04)^
Random
Effects
Intercept (τuO
2
) 29.77 (15.82)* 68.97 (27.04)**
Residual (σe
2
) 59.32 (10.99)** 45.83 (8.55)**
Autocorrelation 0.29 (0.13)* 0.27 (0.14)*
Fit
Statistics
Log Likelihood 885.3 882.9
AIC 891.3 888.9
BIC 895.0 892.5
Note: The REML estimator and the autoregressive covariance structure were used for all models. Only
covariates with a p≤0.10 were retained in models. Waking wear-time was included in models
regardless of p-value. Beta coefficients and standard errors for between-person ST and withinperson ST variables were multiplied by 120 to reflect 2-hour effects.
BW= between-person; WS= within-person.
aVariable was person-mean centered.
bVariable was grand-mean centered.
*p≤0.05
**p≤0.01
^p≤0.10
Table 6. Multilevel model results [β (SE)] for the associations of between-person and withinperson ST with frequency-domain HRV metrics (Level-2 N=126 days; Level 1 N=25 participants)
LF HF LF/HF Ratio
Fixed
Effects
Intercept 5.88 (0.69)** 6.12 (0.85)** 3.49 (0.77)**
ST (BW)a
-0.24 (0.12)** -0.36 (0.12)* 0.12 (0.24)
ST (WS)b
-0.24 (0.12)** -0.36 (0.12)** 0.24 (0.12)*
Age 0.17 (0.06)** 0.14 (0.08)^ --
Mother’s education level 0.46 (0.14)** 1.07 (0.17)** -1.49 (0.29)**
Day of the week 0.14 (0.06)* 0.22 (0.08)** -0.23 (0.09)*
Waking wear-time -0.001 (0.001)* -0.002 (0.001)** 0.001 (0.001)
Trunk fat (kg) -- -0.03 (0.02)* --
Pubertal status -- 0.21 (0.11)^ -0.32 (0.18)^
Random
Effects
Intercept (τuO
2
) 0.05 (0.03)* 0.07 (0.04)* 0.30 (0.10)**
Residual (σe
2
) 0.09 (0.02)** 0.14 (0.03)** 0.16 (0.03)**
Autocorrelation 0.32 (0.13)* 0.29 (0.13)* 0.13 (0.04)*
Log Likelihood 115.1 125.7 179.6

48
Fit
Statistics
AIC 121.1 151.7 201.6
BIC 124.7 167.6 215.0
Note: The REML estimator and the autoregressive covariance structure were used for all models. LF
and HF were log-transformed due to issues with skewness. Only covariates with a p≤0.10 were retained
in models. Waking wear-time was included in models regardless of p-value. Beta coefficients and
standard errors for between-person ST and within-person ST variables were multiplied by 120 to
reflect 2-hour effects.
BW= between-person; WS= within-person.
aVariable was person-mean centered.
bVariable was grand-mean centered.
*p≤0.05
**p≤0.01
^p≤0.10
Ancillary Analyses
To evaluate the effect of different device wear-time thresholds, which can influence the
amount of data included in analyses, we conducted additional models using a criterion of one valid
day of accelerometer and ECG wear-time. With this adjusted threshold, 37 participants were
included, contributing a total of 143 valid days of data. While within-person findings remained
statistically significant under the one-day wear criterion, between-person findings were no longer
considered significant. Higher between-person ST was only associated with lower LF (β=-0.12
ms², p=0.04, f
2=0.04), but was not associated with SDNN (β=-2.40 ms, p=0.22, f
2=0.06), RMSSD
(β=-1.20 ms, p=0.51, f
2=0.02), HF (β=-0.12 ms², p=0.24, f
2=0.07), and the LF/HF ratio (β=0.02,
p=0.84, f
2=0.01). Higher within-person ST was associated with lower SDNN (β=-6.0 ms, p<0.001,
f
2=0.06), RMSSD (β=-3.60 ms, p=0.04, f
2=0.02), LF (β=-0.24 ms², p<0.001, f
2=0.04), HF (β=-
0.36 ms², p<0.001, f
2=0.07), and higher LF/HF ratio (β=0.24, p=0.03, f
2=0.01).
As a conservative approach, additional models were conducted to include all variables
associated with accelerometer and ECG data missingness (race, ethnicity, time of year, day of
study participation) to better satisfy the Missing at Random (MAR) assumption and reduce
potential bias from non-random missingness, thereby strengthening the validity of the results.

49
When including race, ethnicity, time of year, and day of study participation in the models, none
met the criteria for inclusion as covariates (p’s>0.10). In time-domain models, the magnitude of
the betas slightly decreased, and p-values increased, especially in the ST-SDNN model. Higher
between-person ST was marginally associated with lower SDNN (β=-4.80 ms, p=0.07) and was
significantly associated with lower RMSSD (β=-8.40, p=0.03). Higher within-person ST was
marginally associated with lower SDNN (β=-3.60 ms, p=0.06) and significantly associated with
lower RMSSD (β=-3.60 ms, p=0.05). In frequency-domain models, the magnitude of the betas and
the p-values for between-person and within-person ST remained similar to those in the models
restricted to covariates with p≤0.10. Higher between-person ST was associated with lower LF (β=-
0.36 ms², p=0.01), and HF (β=-0.36 ms², p=0.03), but was not associated with LF/HF ratio (β=0.12,
p=0.70). Higher within-person ST was associated with lower LF (β=-0.24 ms², p=0.001), HF (β=-
0.36 ms², p=0.001), and higher LF/HF ratio (β=0.24, p=0.04).
Discussion
The aim of this study was to assess the associations of between-person and within-person
ST with cardiac ANS function in youth with OW/OB in a naturalistic setting. Both between-person
and within-person ST were associated with worse cardiac ANS function. Between-person
associations showed that spending more time in ST was associated with lower SDNN, RMSSD,
LF, and HF, indicating worse cardiac ANS function. However, between-person ST was not
associated with LF/HF ratio. Within-person associations showed that on days when youth spent
more time in ST than usual, they also had lower SDNN, RMSSD, LF, HF, and higher LF/HF ratio.
Age, mother’s highest education level, and day of the week were consistent covariates across
models. Basal heart rate and daily MVPA were additionally covariates in the relationship between

50
ST and cardiac ANS function for time-domain HRV outcomes, while trunk fat and pubertal status
were significant covariates for frequency-domain HRV outcomes.
To our knowledge, this study is the first to investigate the associations between ST and
cardiac ANS function in youth with OW/OB in a free-living environment using gold standard
measures. In contrast, three prior studies on similar associations reported mixed results and were
limited by healthy samples who were not at high risk, cross-sectional designs, and a lack of multiday measurements with gold standard methods for both ST and cardiac ANS function.
135-137 In the
present study, both between- and within-person results suggest that higher ST in youth with
OW/OB is associated with reduced parasympathetic activity (i.e., lower RMSSD, LF, and HF),
and on high-ST days, reduced parasympathetic activity and worse cardiac ANS balance (i.e., lower
SDNN, LF, HF, and higher LF/HF ratio). One mechanism that could explain these findings is STassociated changes in blood flow. Prolonged ST can cause blood to pool in the lower limbs,
disrupting circulation, lowering blood pressure, and increasing sympathetic nervous system
activity.126-128 This heightened sympathetic response concurrently reduces parasympathetic
activity.126-128 Additionally, decreased blood flow during ST lowers shear stress and nitric oxide
production, further impairing parasympathetic function.126,129,130 Notably, the lack of a significant
between-person association with the LF/HF ratio may indicate that overall ST impacts cardiac
ANS function differently from short-term day-to-day ST changes, which appear to trigger more
immediate shifts in cardiac ANS balance. Alternatively, limited variation in the LF/HF ratio
variable may have contributed to a lack of power to detect a relationship.
Another potential mechanism that could explain these findings may directly relate to our
high-risk sample. Increased adiposity is associated with systemic inflammation and impaired
vascular function by releasing pro-inflammatory cytokines and adipokines that disrupt metabolic

51
and vascular health,
23,24 potentially leading to heightened sympathetic activation and reduced
parasympathetic activity.32,45,225 More time spent in ST may exacerbate these conditions, as
prolonged sitting may worsen systemic inflammation and vascular function.89,90,115,226-228 Our
sample had high average daily levels of ST (11.6 hours), exceeding national estimates of seven to
nine hours per day,92-94 and high average body fat (44.83%), which may contribute to the observed
associations between ST and cardiac ANS dysfunction. Further research, including longitudinal
studies and direct measures of inflammation and vascular health, is needed to better understand
the complex interplay between adiposity and ST-cardiac ANS function associations.
The between-person and within-person findings in this study offer valuable insights into
how ST influences cardiac ANS function in youth with OW/OB, allowing for a more nuanced
understanding of how both long-term habitual behaviors and short-term daily fluctuations in ST
influence cardiac ANS function. The between-person associations, which reflect overall behavior,
suggest that youth who consistently spend more time in sedentary activities have poorer cardiac
ANS regulation, indicated by lower HRV metrics across both time- and frequency-domain
measures. In contrast, the within-person associations, which capture daily fluctuations in ST,
reveal that even small increases in ST from one day to the next acutely contributes to cardiac ANS
dysfunction. This suggests that both habitual ST and daily variations in ST can adversely influence
the cardiac ANS, emphasizing the importance of addressing both overall and daily ST to improve
autonomic regulation and potentially reduce CVD risk in youth with OW/OB.67,68,70,78-86 This
disaggregation of between- and within-person effects helps identify more precise intervention
targets, such as focusing on reducing chronic ST through long-term behavioral changes and
addressing acute daily increases in ST through activity breaks, which may provide immediate
benefits to cardiac ANS function.

52
A major strength of this study is its comprehensive examination of covariates relevant to
the associations of ST with cardiac ANS function, a depth of analysis not available in other studies.
Age, mother’s highest education level, and day of the week were consistent significant covariates
across HRV outcomes. Age was positively associated with better cardiac ANS function, likely due
to the maturation of the autonomic system as children grow.229 Older children typically exhibit
improved sympathetic activity and more efficient heart rate regulation, reflecting better overall
cardiac ANS function.230 However, it is important to note that autonomic regulation typically
declines during puberty due to hormonal changes.
231 We do not anticipate this significantly
contributed to the results as the youth in the present study had not yet reached puberty. Mother's
education was positively associated with better cardiac ANS function. Higher maternal education
is often associated with higher socioeconomic status (SES), which is a facilitator of better access
to resources and healthcare.232,233 This, in turn, can lead to better support for healthier lifestyle
choices and as our study suggests, may contribute to improved autonomic regulation.
232,233 Lastly,
we observed better cardiac ANS function on weekends compared to weekdays. This could be
explained by behavioral differences on weekends versus weekdays, such as longer sleep duration
or a less structured day that can influence the cardiac ANS.234 However, improved cardiac ANS
function on weekends might also be due to enhanced adherence to study procedures that led to
greater data availability to assess relationships.
The finding that certain covariates were significant only in time- or frequency-domain
models likely reflects differences in how these measures capture various aspects of HRV. Timedomain measures, which assess the variability in heart rate intervals directly, reflect beat-to-beat
changes in heart rate.39 Thus, it is possible that basal heart rate and day-level MVPA were
significant for time-domain outcomes because both influence the overall fluctuation in heart rate.

53
MVPA increases heart rate and its variability,147 while a higher basal heart rate, reflecting baseline
autonomic activity, often indicates greater sympathetic activation,235,236 can contribute to reduced
HRV. In contrast, frequency-domain measures analyze the frequency components of heart rate
fluctuations, separating them into different frequency bands that inform how these components
contribute to autonomic function.39 We identified trunk fat and pubertal status as significant
covariates. Higher trunk fat is associated with worse HRV through increased sympathetic
activity,49 while pubertal status correlates with higher HRV before puberty, reduced HRV during
puberty, and improving again afterward as autonomic balance stabilizes.230,231 These distinctions
highlight the importance of considering both types of HRV measures and their covariates when
evaluating the impact of ST on autonomic function, as they provide complementary insights into
different mechanisms of cardiac ANS regulation.
In summary, our results suggest that reducing both overall average and day-to-day
fluctuations in ST in youth with OW/OB may be important for improving cardiac ANS function.
Clinicians should consider interventions aimed at reducing ST in this population to help mitigate
adverse effects on cardiac ANS function, which may, in turn, reduce the risk of developing
CVD.67,68,70,78-80,82,84-86 Future research should also explore these associations using longitudinal
designs and more diverse samples (e.g., youth of healthy weight status) to further clarify the causal
relationships between ST, cardiac ANS function, and health outcomes. Additionally, evaluating
the effectiveness of interventions designed to both reduce overall ST and address daily fluctuations
in ST could help develop more precise strategies for preventing CVD in youth with OW/OB.
Strengths and Limitations
A strength of this study is the use of gold standard measures of ST and cardiac ANS
function simultaneously over multiple days of data collection. To our knowledge, no study has

54
combined these measurement methods in the same study using a repeated measures approach to
answer this research question. The assessment of ST and cardiac ANS function in a naturalistic,
free-living environment is also ecologically valid. The within-person, repeated measures approach
to address relationships between ST and cardiac ANS function also provides a novel, unique
perspective that helps to pinpoint dynamic, short-term intervention opportunities for improving
cardiac ANS outcomes in future behavioral studies. The within-person aim also provided
information on how changes in ST acutely influence HRV outcomes, which may be used for
developing adaptive intervention strategies to prevent prolonged ST as a means of improving
cardiac ANS outcomes. For example, tailored intervention strategies that target days where ST is
high and HRV is low may be necessary to mitigate CVD risk.
This study also has limitations. The sample consisted of youth with OW/OB, which limits
our ability to generalize findings to youth of all weight statuses. However, studying youth with
OW/OB was an important aspect of this study because they are at increased risk for CVD237 and
they are more likely to be sedentary compared to normal weight youth.238-240 Also, seven days may
not be considered representative of a participant’s typical behavior pattern or a longitudinal study
design. However, it is estimated that only 2-3 days are needed to estimate habitual ST201 and 4-5
days are needed to estimate habitual MVPA,
241 and youth included in this sample had an average
of 5.04 valid days of activPAL wear that aligned with valid days of ECG wear. It is possible that
wearing the activPAL led to changes in the youths' activity behavior (i.e., reactivity); however,
like other studies,242-244 our analyses revealed no significant differences in MVPA across study
days (p>.05). We also experienced difficulty with participant compliance with wearing multiple
devices at home. To minimize non-compliance, research staff offered phone calls and video chat
sessions to help place the activPAL and ECG monitor and checked in with participants regularly

55
throughout the free-living assessment. Despite this, only 56.25% of the participants had valid
activPAL and ECG data. As a result, additional models with relaxed wear-time criteria (i.e., only
one valid day of wear) were used to include more participants and valid days, but this led to weaker
between-person associations, likely due to reduced accuracy in capturing typical ST across
individuals, while within-person associations remained significant. Lastly, a sample size of N=90
was needed to power this study to detect changes in our HRV outcomes, assuming a Cohen’s d of
0.45, a type-I error rate (alpha) of 0.05, and an ICC of 0.26.
245 However, we did not reach this
sample size, and models generated smaller effect sizes (f
2
range= 0.01-0.05) and larger ICCs (ICC
range= 0.49-0.79). Post-hoc power analyses using the samplesize_mixed function of the sjstats R
package showed that sample sizes greater than 500 were needed to achieve a power of 0.80 with
the observed effect sizes and ICCs, indicating that our models were underpowered. This likely
reflects the greater-than-expected clustering of data within participants, limiting the amount of
unique information contributed by repeated measures. Therefore, the small sample size and
subsequent low statistical power of our results limits the generalizability of our findings and
warrants caution in interpretation.
Conclusion
We found that both between-person and within-person ST was associated with worse
cardiac ANS function in youth with OW/OB. These findings emphasize the importance of
reducing both overall ST and daily spikes in ST to improve cardiac ANS function and potentially
lower CVD risk in this population. Future research should explore these relationships
longitudinally and in diverse youth populations to better understand the mechanisms underlying
ST and cardiac ANS dysfunction, and to develop tailored interventions that address both habitual
and acute ST patterns.

56
Chapter 3: Between-Person and Within-Person Moderators in the Associations of
Sedentary Time with Cardiac Autonomic Nervous System Function in Youth with
Overweight and Obesity
Abstract
Objective: While sedentary time (ST) may be linked to poorer cardiac autonomic nervous system
(ANS) function, the extent to which factors such as adiposity, fitness, sleep, and physical activity
moderate this relationship remains unclear. The purpose of this study was to explore betweenperson and within-person moderators in the associations of ST with cardiac ANS function in youth
with overweight/obesity (OW/OB) in a naturalistic setting.
Methods: Participants were 25 youth (ages 8-11) with OW/OB who completed a 7-day free-living
assessment. For seven days, they wore accelerometers to obtain ST estimates, and an ECG monitor
to assess cardiac ANS function via heart rate variability (HRV) metrics [standard deviation of
normal-to-normal intervals (SDNN), root mean square of successive normal-to-normal interval
differences (RMSSD), low frequency (LF), high frequency (HF), LF/HF ratio]. Multilevel models
were used to examine adiposity, fitness, sleep duration, moderate-to-vigorous physical activity
(MVPA), and day of the week as moderators in the associations of ST and cardiac ANS function
using same-level interaction terms.
Results: No significant moderating effects of adiposity, fitness, sleep, MVPA, or day of the week
were observed on the relationship between ST and cardiac ANS function (p’s>0.05).
Conclusion: Factors such as adiposity, fitness, daily behaviors, and day of the week may not
influence the associations of higher ST with worse cardiac ANS function in youth with OW/OB.
Reducing ST may be universally beneficial for all youth with OW/OB, but more studies are needed
to corroborate our findings.

57
Introduction
Cardiovascular disease (CVD) remains the leading cause of mortality in the U.S1
and can
originate during childhood, especially in youth with overweight and obesity (OW/OB).8-10,18 One
mechanism that contributes to CVD is an imbalanced cardiac autonomic nervous system
(ANS).56,61 The ANS regulates the cardiac response to moment-to-moment situations via two
antagonistic responses, including sympathetic (i.e., “fight or flight”) and parasympathetic (i.e.,
“rest and digest”) responses.31 A balanced cardiac ANS, where the sympathetic and
parasympathetic responses work in coordination, is associated with lower CVD risk among
youth.39,48,80,84 However, youth with OW/OB tend to have poor cardiac ANS function, marked by
an overactive sympathetic nervous system and poor cardiac ANS balance,48,73,147 which is strongly
associated with CVD.79,80,84,144,246
In addition to CVD’s link to poor cardiac ANS function, prior evidence has demonstrated
that increased CVD risk is associated with increased sedentary time (ST) in youth.90,117,121,122,185,189
Reduced cardiac ANS balance may mediate the association between ST and CVD development,
as more ST may be associated with cardiac ANS imbalance.131-133,136,137 Several mechanisms may
trigger increased sympathetic and decreased parasympathetic activity during a prolonged bout of
ST, 126-128,130 but understanding of associations of ST with cardiac ANS function remains limited
in youth.
To our knowledge, only three cross-sectional observational studies have investigated this
relationship, 134,136,137 of which two demonstrated that more ST was associated with worse cardiac
ANS function among samples of children and adolescents.136,137 The current literature on
associations of ST with cardiac ANS function in youth is limited because studies are crosssectional, using data collected at a single time point. Only one of the three studies used

58
electrocardiogram (ECG) measures of cardiac ANS function via heart rate variability metrics
(HRV),136 and only two used accelerometers to obtain daily ST,135,136 which are considered the
gold standard measurement methods.111,144 These studies also did not narrow their samples to
youth with OW/OB, who should be studied considering they are at higher risk for CVD,17-19
present with cardiac ANS dysfunction,48,73,147 and accumulate more ST than youth with healthy
weight.145,146
Moreover, a major limitation of the current literature is that studies have not yet
investigated key moderating factors that may inform the tailoring of future interventions. Higher
levels of adiposity may strengthen associations of ST with cardiac ANS function,46-49,145,146
whereas higher cardiorespiratory fitness may weaken associations.134,136,145,147-149,151 Timevarying, day-level variables such as sleep duration and moderate-to-vigorous physical activity
(MVPA) have also not been examined as moderating factors and should be, as it is possible that
longer sleep duration163,247,248 and spending more time in MVPA136,249,250 may act as buffers and
weaken these associations. Lastly, day of the week may also play a role in associations of ST with
cardiac ANS function, as there are structural and contextual differences on school days versus
weekend days that may influence ST.171,172,174,251
Taken together, the gaps in the literature call for a repeated measures approach using gold
standard measures to identify key moderating variables that may strengthen or weaken associations
of ST with cardiac ANS function. Understanding moderating variables in the associations between
ST and cardiac ANS will lend insight toward inconsistencies in the literature, help identify
vulnerable populations for targeted intervention approaches, and subsequently inform the creation
of efficacious intervention strategies for future studies. Therefore, the present study has two aims:

59
Aim 1: To explore between-person moderators in the between-person associations of ST with
cardiac ANS function in youth with OW/OB in a naturalistic setting. Exploratory analyses were
conducted to test person-level adiposity status and cardiorespiratory fitness level as betweenperson moderators in between-person associations of ST with cardiac ANS function.
Aim 2: To explore time-varying moderators in the within-person associations of ST with cardiac
ANS function in youth with OW/OB in a naturalistic setting. Exploratory analyses were conducted
to test day-level sleep duration, MVPA, and day of the week as possible time-varying moderators
in within-person associations of ST with cardiac ANS function.
Methods
Participants
Participants and recruitment methods are described in Study 1 on pages 30-31. Youth ages
8-11 years old with OW/OB [defined as a body mass index (BMI) ≥85th percentile]190 were
recruited from the Sedentary Breaks Study 3. Youth were recruited through BuildClinical.
Inclusion and exclusion criteria for this study are described on pages 30-31.
The Sedentary Breaks Study 3 is comprised of three study segments: 1) a screening visit,
2) a 7-day free-living assessment, and 3) a 7-day in-lab experimental trial. Those interested were
first screened over the phone, and individuals who appeared eligible were invited to complete the
in-person screening visit. Similar to Study 1, the present study used data collected during the
screening visit and free-living assessment. Parents provided informed consent and child
participants provided assent. The Institutional Review Board at the University of Southern
California approved of this study.

60
Procedures
Screening Visit
The procedures that took place at the screening visit are described in Study 1 on page 31.
At the screening visit, parents of the participants completed a demographic and pubertal
development questionnaire.191,192 Height (in centimeters) and weight (in kilograms) were
measured to confirm OW/OB status. Cardiorespiratory fitness level (i.e., VO2max) using a
modified Bruce treadmill protocol193 and metabolic cart was obtained.
Free-Living Assessment
The procedures for the free-living assessment are described in Study 1 on page 32. During
the free-living assessment, participants wore an accelerometer and an ambulatory ECG monitor to
obtain daily activity behavior and cardiac ANS function, respectively, for seven consecutive days.
Participants also completed two 24-hour dietary recalls,195 one for a weekend day and one for a
weekday.
Measures
Objectively-Measured Sedentary Time via Accelerometry
Study 1 provides a detailed description of the accelerometer device, placement, data
processing methods, valid wear, waking, and sleep time parameters, and definitions of various
activity levels (i.e., ST, MVPA) on pages 32-33. Briefly, activPAL accelerometers placed on the
mid-right thigh were used to obtain daily activity behaviors. The activPAL was worn for 24 hours
each of the seven consecutive days. Data were downloaded using the activPAL software, in which
only days with ≥8 hours of wear time during waking hours and participants with ≥3 days of valid
wear were included in analyses. Week-level means of minutes of ST and day-level total minutes

61
in ST (predictors), sleep duration (potential moderator), minutes of MVPA (potential moderator),
and accelerometer wear-time (hours; covariate) were calculated and used in the analyses.
Cardiac ANS Function via ECG monitor
The MyPatch-sl Recorder that is described in Study 1 (pages 33-35) is an ambulatory,
water-resistant ECG monitor that was used to obtain daily cardiac ANS function via HRV metrics.
A pediatric-sized electrode patch and MyPatch-sl Recorder was placed inferior to the sternal notch
aligned with the sternum. Participants were asked to wear the ECG monitor for 24 hours each of
the seven days of the free-living assessment. Data were processed using the CardioScan Holter
Analysis Software following the procedures described in Study 1 on page 34. Artifact was removed
prior to calculating any variables. Time-domain variables included the standard deviation of all RR intervals (SDNN) and the root mean square of successive R-R interval differences (RMSSD).
Frequency-domain variables included high power frequency (HF), low power frequency (LF), and
the ratio of LF to HF (LF/HF). Descriptions of these time- and frequency-domain variables are
provided in Table 2 in Study 1 (page 35). Day-level means for each time- and frequency-domain
variable that occurred during waking hours were calculated based on the waking times and sleep
times derived from the accelerometers.
Between-Person Moderating Variables
Based on prior evidence,46-49,134,136,147,151 adiposity status and cardiorespiratory fitness were
tested as potential between-person moderators (continuous variables). A Dual-Energy X-Ray
Absorptiometry (DEXA) scan was conducted on Day 1 or Day 7 of the experimental trial that is
an additional component of the Sedentary Breaks Study 3. Adiposity status, including percent body
fat and the amount of trunk fat in kilograms, were derived and tested as moderators.
Cardiorespiratory fitness level was measured at the screening visit, in which VO2max obtained

62
from this test was used in the moderation analyses. A between-person moderator was considered
significant if p≤0.05.
Within-Person Moderating Variables
Sleep duration,163,247,248 MVPA minutes,
136,249,250 and day of the week171,172,174,251 were
tested as potential within-person moderators based on prior evidence. Within-person, day-level
sleep duration (continuous variable; minutes) and MVPA (continuous variable; minutes) were
collected during the free-living assessment and derived from the activPAL accelerometers. Since
ST differs in time and context on weekdays versus weekend days in youth,171,172,174,251 day of the
week (dichotomous variable; weekday vs. weekend day) was tested as a potential time-varying
moderator. A within-person moderator was considered significant if p≤0.05.
Covariates
Covariates were selected based on prior evidence.48,74,147,162,206-208,210-214,252 Person-level
self-reported variables were age (continuous variable; years), sex (dichotomous variable; male vs.
female), race (dichotomous variable; White vs. all others), ethnicity (dichotomous variable;
Hispanic vs. Non-Hispanic), maternal education level (dichotomous variable; college degree or
higher vs. less than college degree), and dietary intake (continuous variables; average of weekday
and weekend day percent macronutrient intake and caloric intake in kilocalories). Person-level
measured variables were body composition (continuous variables; body fat percent and trunk fat
(kg)), pubertal stage (continuous variable; ranges 1-3), basal heart rate (beats per minute (bpm);
continuous variable; collected on Day 1 of in-lab week), and systolic blood pressure (mmHg;
continuous variable; collected on Day 1 of in-lab week) were tested as potential covariates.
Accelerometer wear-time (day-level continuous variable; minutes) and time of year (dichotomous

63
variable; school vs. summer) were also tested as covariates. Covariates with p≤0.10 remained in
the models, and covariates with p>0.10 were removed to achieve model parsimony.
Statistical Analyses
Frequencies and means were calculated for all participant demographic characteristics,
HRV metrics, accelerometer-derived activity and sleep variables, moderating variables, and
covariates. The analytic approach for sensitivity analyses is described in Study 1 on page 36. In
brief, sensitivity analyses were conducted to assess differences between those included versus
excluded and device compliance (i.e., ECG and activPAL compliance).
Aim 1
In order to leverage the multiple consecutive days of HRV and ST data that were collected,
multilevel models were used to identify potential between-person moderators in the associations
of between-person ST with each daily HRV outcome. Multilevel models are advantageous because
they accommodate a repeated measures design and allow for the partitioning of variances betweensubjects (via grand-mean centering) and within-subjects (via person-mean centering).220 Each
daily mean HRV metric (i.e., SDNN, RMSSD, LF, HF, and LF/HF) was used as an outcome. For
each HRV outcome, a same-level interaction term using between-person ST (i.e., a given
participant’s average ST as compared to the group’s average) and each between-person moderator
of interest (i.e., percent body fat, trunk fat, VO2max) were entered into separate models as a
predictor. All potential between-person moderating variables were at the person-level (e.g., percent
body fat) and entered as a continuous variable. Models were adjusted for a priori, significant
(p≤0.10) covariates and additionally adjusted for within-person ST. An interaction term was
considered significant if p≤0.05 and remained in the model. Model fit was assessed via p-value,

64
the log likelihood test, Akaike Information Criterion (AIC), and Bayesian Information Criterion
(BIC), when appropriate.
Aim 2
Multilevel models were also used to identify potential within-person moderators in the
associations of within-person ST (i.e., day-level) with each daily HRV outcome. For each HRV
outcome, a same-level interaction term using within-person ST (i.e., a participant’s ST on a given
day compared to their own average) and each within-person moderator of interest (i.e., daily sleep
duration, MVPA, day of the week) were entered into the model as a predictor. All potential
moderating variables were at the day-level. Sleep duration and MVPA were entered as continuous
variables, and day of the week was entered as a dichotomous variable (weekday vs. weekend day).
Models adjusted for a priori, significant (p≤0.10) covariates and between-person ST. An
interaction term was considered significant if p≤0.05 and remained in the model. Model fit was
assessed via p-value, the log likelihood test, AIC, and BIC, when appropriate. Corrections for
multiple testing were not applied to these exploratory analyses. All analyses were conducted in
SAS v.9.4. Final interaction term beta coefficients and standard errors were rescaled to reflect a
120-minute (2 hour) change in ST.
Results
Data Availability
Data availability is presented in Study 1 (page 38). Briefly, out of 1,144 interested families,
594 appeared eligible after initial screening, of which 142 were eligible for an in-person screen
visit. From these, 76 enrolled and completed consent/assent and 68 attended a screening visit. Of
these, 49 were eligible, and 48 were sent devices for the free-living assessment. Finally, 25

65
participants had valid accelerometer and ECG wear-time (see Figure 6 in Study 1 on page 39).
Reasons for exclusion included non-compliance or missing data for the ECG (33.33%), missing
data for both the accelerometer and ECG (28.57%), device malfunctions (19.05%), lost ECG
device (9.52%), and study dropout (9.52%).
Sample Characteristics
Table 3 in Study 1 (page 40) shows the participant characteristics. Briefly, the mean age
was 9.56 years. The sample was 40% female, 56% White, and 76% identified as Hispanic ethnicity.
The mean cardiorespiratory fitness level in the sample was 24.66 ml/kg/min, which is considered
low compared to established norms in children.253 The mean basal heart rate was 76.88 bpm and
mean SBP was 111.15 mmHg. On average, participants spent 699.53 minutes (11.65 hours) in ST
and less than an hour in MVPA (41.39 minutes) daily. HRV measures showed relatively low
SDNN (mean: 54.25 ms), RMSSD (mean: 41.87 ms), and HF (mean: 275.40 ms), with a high
LF/HF ratio (mean: 2.30), indicating poor cardiac ANS function compared to documented norms
in children.223
Sensitivity Analyses
Results of the sensitivity analyses are presented in Study 1 (pages 41-43). In sum, there
were no significant differences in person-level age, sex, pubertal status, maternal education level,
body fat percent, trunk fat, basal heart rate, and SBP between those included and excluded in the
analyses (p’s>.05). However, race (White vs. all others; 𝛘
2=4.90, p=0.03) and Hispanic ethnicity
(Hispanic vs. Non-Hispanic; 𝛘
2=7.97, p=0.01) were associated with study inclusion.

66
ActivPAL compliance (yes vs. no) was not related to age, sex, race, pubertal status,
maternal education level, body fat percent, and trunk fat (p’s>.05). Hispanic participants were less
likely to comply with activPAL wear (OR=0.25, 95% CI 0.07-0.87, p=0.03). ActivPAL
compliance was higher on weekend days (OR=3.56, 95% CI 1.65-7.69, p=0.001), over the summer
(OR=5.14, 95% CI 1.58-16.78, p=0.01) and earlier in the study period (OR=1.34, 95% CI 1.11-
1.62, p=0.003).
ECG compliance (yes vs. no) was not related to age, sex, pubertal status, maternal
education level, body fat percent, and trunk fat (p’s>.05). Participants who were White (OR=0.38,
95% CI 0.15-0.99, p=0.05) and Hispanic (OR=0.25, 95% CI 0.07-0.87, p=0.03) were less likely
to comply with ECG wear. ECG compliance was higher on weekend days (OR=3.56, 95% CI 1.65-
7.71, p=0.001), over the summer (OR=3.69, 95% CI 1.47-9.17, p=0.001), and earlier in the study
period (OR=1.42, 95% CI 1.22-1.66, p<0.001).
Model Fit
Model fit statistics relevant to this study area presented in Study 1 (page 43). Briefly, only
covariates with p≤0.10 were retained in final models to achieve model parsimony. However, daylevel waking wear-time was included in the models regardless of p-value to account for potential
variability in data availability across participants. The REML estimator was used for all models
because it provides more accurate and less biased estimates of variance components, particularly
in small samples, by accounting for the estimation of fixed effects separately from the random
effects.224 The autoregressive covariance structure provided the best fit and was therefore used in
all models.

67
Between-Person Moderators of ST-HRV Associations (Aim 1)
Results for models assessing same-level moderators in the associations of between-person
and within-person ST with time-domain HRV outcomes are presented in Table 7, while models
for frequency-domain HRV outcomes are presented in Table 8. LF and HF HRV outcomes were
log-transformed for final models, as they were highly skewed which led to issues with model
convergence. Interaction terms for between-person ST and body fat percent (β=-0.24, p=0.91,
f
2=0.05), trunk fat (β=-0.12, p=0.69, f
2=0.05), and fitness level (β=0.24, p=0.35, f
2=0.04) were not
associated with SDNN. Interaction terms for between-person ST and body fat percent (β=-0.12,
p=0.76, f
2=0.02), trunk fat (β=-0.12, p=0.55, f
2=0.03), and fitness level (β=0.04, p=0.59, f
2=0.01)
were not associated with RMSSD. Interaction terms for between-person ST and body fat percent
(β=-0.02, p=0.65, f
2=0.03), trunk fat (β=-0.02, p=0.87, f
2=0.04), and fitness level (β=0.02, p=0.30,
f
2=0.04) were not associated with LF. Interaction terms for between-person ST and body fat
percent (β=-0.72, p=0.98, f
2=0.02), trunk fat (β=-0.004, p=0.56, f
2=0.02), and fitness level (β=0.02,
p=0.70, f
2=0.03) were not associated with HF. Interaction terms for between-person ST and body
fat percent (β=0.02, p=0.47, f
2=0.01), trunk fat (β=0.06, p=0.92, f
2=0.01), and fitness level (β=-
0.04, p=0.31, f
2=0.01) were not associated with the LF/HF ratio.
Within-Person Moderators of ST-HRV Associations (Aim 2)
Interaction terms for within-person ST and sleep duration (β=0.02, p=0.33, f
2=0.01),
MVPA (β=0.02, p=0.62, f
2=0.03), and day of the week (β=2.4, p=0.82, f
2=0.02) were not
associated with SDNN. Interaction terms for within-person ST and sleep duration (β=1.56, p=0.29,
f
2=0.01), MVPA (β=0.02, p=0.72, f
2=0.01), and day of the week (β=0.36, p=0.52, f
2=0.02) were
not associated with RMSSD. Interaction terms for within-person ST and sleep duration (β=0.04,

68
p=0.38, f
2=0.01), MVPA (β=0.008, p=0.72, f
2=0.02), and day of the week (β=0.04, p=0.73,
f
2=0.02) were not associated with LF. Interaction terms for within-person ST and sleep duration
(β=0.08, p=0.40, f
2=0.02), MVPA (β=0.002, p=0.65, f
2=0.01), and day of the week (β=0.004,
p=0.94, f
2=0.02) were not associated with HF. Interaction terms for within-person ST and sleep
duration (β=-0.006, p=0.72, f
2=0.01), MVPA (β=-0.002, p=0.78, f
2=0.01), and day of the week
(β=-0.12, p=0.20, f
2=0.01) were not associated with the LF/HF ratio.
Table 7. Multilevel model results [β (SE)] for moderation effects of same-level moderators on the
associations of between-person and within-person ST with time-domain HRV metrics (Level-2
N=126 days; Level 1 N=25 participants)
SDNN RMSSD
Between-Person
Moderators
ST (BW)*Body fat percent (BW)a
-0.24 (0.48) -0.12 (0.60)
ST (BW)*Trunk fat in kg (BW)a
-0.12 (0.72) -0.12 (1.2)
ST (BW)*Fitness level in ml/kg/min (BW)a 0.24 (0.36) 0.04 (0.48)
Within-Person
Moderators
ST (WS)*Sleep duration (WS)b 0.02 (0.012) 1.56 (1.20)
ST (WS)*MVPA (WS)b 0.02 (0.04) 0.02 (0.04)
ST (WS)*Day of the week (WS)b 2.4 (2.4) 0.36 (2.40)
Note: The REML estimator and the autoregressive covariance structure were used for all models.
Interaction terms were tested in separate models using the significant covariates for each model
used in Study 1. Covariates in SDNN models: age, mother’s education level, day of the week,
accelerometer waking wear-time. Covariates in RMSSD models: age, mother’s education level,
day of the week, accelerometer wear-time, basal heart rate, day-level MVPA. All interaction terms
were not significant (p>0.05). Beta coefficients and standard errors for each interaction term were
multiplied by 120 to reflect 2-hour effects.
BW= between-person; WS= within-person.
aVariables in interaction term were grand mean centered.
bVariables in interaction term were person mean centered.
Table 8. Multilevel model results [β (SE)] for moderation effects of same-level moderators on the
associations of between-person and within-person ST with frequency-domain HRV metrics
(Level-2 N=126 days; Level 1 N=25 participants)
LF HF LF/HF Ratio
BetweenPerson
Moderators
ST (BW)*Body fat percent (BW)a
-0.02 (0.02) -0.74 (0.36) 0.02 (0.04)
ST (BW)*Trunk fat in kg (BW)a
-0.02 (0.02) -0.004 (0.12) 0.06 (0.04)
ST (BW)*Fitness level in ml/kg/min (BW)a 0.02 (0.04) 0.02 (0.02) -0.04 (0.02)
WithinPerson
Moderators
ST (WS)*Sleep duration (WS)b 0.04 (0.48) 0.08 (0.60) -0.006 (0.06)
ST (WS)*MVPA (WS)b 0.008 (0.002) 0.002 (0.002) -0.002 (0.002)
ST (WS)*Day of the week (WS)b 0.04 (0.10) 0.004 (0.12) -0.12 (0.12)

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Note: The REML estimator and the autoregressive covariance structure were used for all models. LF
and HF were log-transformed due to issues with skewness. Interaction terms were tested in separate
models using the significant covariates for each model used in Study 1. Covariates in LF models:
age, mother’s education level, day of the week, accelerometer waking wear-time. Covariates in
HF models: age, mother’s education level, day of the week, accelerometer waking wear-time, trunk
fat, pubertal status. Covariates in LF/HF ratio models: age, mother’s education level, day of the
week, accelerometer waking wear-time, pubertal status. All interaction terms were not significant
(p>0.05). Beta coefficients and standard errors for each interaction term were multiplied by 120 to
reflect 2-hour effects.
BW= between-person; WS= within-person.
aVariables in interaction term were grand mean centered.
bVariables in interaction term were person mean centered.
Ancillary Analyses
Similar to Study 1, to assess the influence of varying wear-time criteria, we ran additional
models based on a threshold of one valid day of accelerometer and ECG wear-time. Under this
revised criterion, 37 participants were included, providing a total of 143 valid days of data.
Interaction terms for between-person ST and body fat percent (β=-0.12, p=0.46, f²=0.06), trunk fat
(β=-0.12, p=0.86, f²=0.07), and fitness level (β=0.36, p=0.32, f²=0.07) were not associated with
SDNN. Interaction terms for between-person ST and body fat percent (β=-0.12, p=0.71, f²=0.03),
trunk fat (β=-0.36, p=0.56, f²=0.03), and fitness level (β=0.12, p=0.92, f²=0.02) were not associated
with RMSSD. Interaction terms for between-person ST and body fat percent (β=-0.004, p=0.75,
f²=0.04), trunk fat (β=-0.012, p=0.66, f²=0.04), and fitness level (β=0.012, p=0.25, f²=0.05) were
not associated with LF. Interaction terms for between-person ST and body fat percent (β=-0.008,
p=0.75, f²=0.07), trunk fat (β=-0.002, p=0.96, f²=0.06), and fitness level (β=0.004, p=0.99, f²=0.07)
were not associated with HF. Interaction terms for between-person ST and body fat percent
(β=0.02, p=0.95, f²=0.01), trunk fat (β=0.04, p=0.44, f²=0.02), and fitness level (β=-0.12, p=0.69,
f²=0.02) were not associated with the LF/HF ratio.

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Interaction terms for within-person ST and sleep duration (β=0.012, p=0.54, f²=0.01),
MVPA (β=0.012, p=0.87, f²=0.05), and day of the week (β=0.6, p=0.77, f²=0.02) were not
associated with SDNN. Interaction terms for within-person ST and sleep duration (β=0.004,
p=0.65, f²=0.01), MVPA (β=0.12, p=0.78, f²=0.04), and day of the week (β=0.6, p=0.77, f²=0.03)
were not associated with RMSSD. Interaction terms for within-person ST and sleep duration
(β=0.004, p=0.33, f²=0.001), MVPA (β=0.04, p=0.81, f²=0.02), and day of the week (β=0.02,
p=0.80, f²=0.02) were not associated with LF. Interaction terms for within-person ST and sleep
duration (β=0.006, p=0.34, f²=0.01), MVPA (β=0.002, p=0.64, f²=0.04), and day of the week
(β=0.06, p=0.61, f²=0.02) were not associated with HF. Interaction terms for within-person ST and
sleep duration (β=-0.004, p=0.61, f²=0.02), MVPA (β=-0.01, p=0.73, f²=0.01), and day of the week
(β=-0.24, p=0.60, f²=0.02) were not associated with the LF/HF ratio.
Discussion
The purpose of this study was to explore between-person (i.e., body fat percent, trunk fat,
fitness level) and within-person (i.e., MVPA, sleep, day of the week) moderators of ST-cardiac
ANS associations. Same level interaction terms of between-person ST with body fat percent, trunk
fat, and fitness level were all null, suggesting that these person-level factors do not moderate STcardiac ANS associations. Same level interaction terms of within-person ST with MVPA, sleep
duration, and day of the week were also all null, suggesting that these time-varying, day-level
factors do not moderate ST-cardiac ANS associations. Models with relaxed wear-time criteria (i.e.,
one valid day of wear), which included a larger number of participants and days, produced similar
results. Thus, it is less likely that data availability affected results.

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Excess adiposity, particularly in the trunk, is often associated with increased sympathetic
activation and decreased parasympathetic activity, contributing to worse cardiac ANS function.46-
49 One potential explanation is that the negative impact of high adiposity on cardiac ANS function
is already pronounced in youth with OW/OB, and we had low variability in body fat percent in
this sample, making further moderating effects of ST less detectable. The high levels of adiposity
observed in our sample could potentially overwhelm any subtle differences in the ST-cardiac ANS
association, leading to a more uniform impact of ST across individuals, regardless of body fat
levels. Another possibility is that the physiological effects of ST on the ANS, such as increased
sympathetic tone and reduced parasympathetic tone,126-128 operate independently of body fat. This
could suggest that the mechanisms linking ST to impaired cardiac ANS function are more
dependent on factors like muscle inactivity or reduced venous return, which are not necessarily
influenced by the degree of adiposity.254
Fitness level is another factor that may moderate associations of ST with cardiac ANS
function, as higher levels of cardiorespiratory fitness are typically associated with improved
parasympathetic activity and greater cardiovascular resilience.140,152 However, in this study, fitness
did not appear to significantly alter the relationship between ST and cardiac ANS function. One
explanation could be that in youth with OW/OB, fitness may not provide enough of a protective
effect against the physiological consequences of ST. It is also possible that the fitness levels of
youth in this study were not high enough to create a meaningful buffering effect. This could point
to a threshold effect, where only very high levels of fitness might alter the impact of ST on cardiac
ANS regulation. However, more research is needed to determine this.
Moderating factors such as MVPA, sleep duration, and day of the week also did not show
significant interactions with the ST-cardiac ANS relationship. Regular engagement in MVPA is

72
expected to counterbalance the negative effects of ST by promoting parasympathetic activity and
reducing sympathetic tone.167,168 However, in this study, it is possible that the amount of MVPA
on high-activity days was insufficient to produce notable benefits in the cardiac ANS. Sleep is
another factor that can affect ANS regulation, as poor sleep quality and insufficient sleep duration
are linked to increased sympathetic activation and reduced parasympathetic activity.161-163 Poor
sleep quality is often associated with increased sympathetic activation and reduced
parasympathetic activity in children and adults,
162,255,256 which could exacerbate the effects of ST
on cardiac ANS function. By only examining total sleep time, this study may have missed the
nuances of how variations in sleep quality, such as sleep disturbances or fragmented sleep,
contribute to autonomic regulation. Future research should incorporate measures of both sleep
quantity and quality to better understand the role of sleep in the relationship between ST and
cardiac ANS function. Cardiac ANS response is also expected to differ by day of the week due to
differences in weekday activities versus weekend activities.171-174 Although youth typically face
more demands on weekdays (e.g., school) that can contribute to increased sympathetic activity,
the weekday demands experienced by our sample may not have been sufficient to create noticeable
fluctuations in their cardiac ANS function. However, it is important to consider that compliance
with study procedures was higher on weekends than on weekdays. This difference in compliance
may have influenced the results, potentially masking any true moderating effect of the day of the
week on the ST-cardiac ANS relationship. Future studies should aim to ensure consistent
compliance across all days to better assess if day of the week plays a role in ST-cardiac ANS
associations.
Different types of ST have been shown to have varying impacts on physiological stress and
autonomic regulation, and therefore, may explain our null findings. For instance, watching

73
television is typically associated with passive relaxation,257 potentially lowering stress levels and
having a less detrimental effect on the ANS. On the other hand, doing homework or other
cognitively demanding activities might elevate stress levels,258 increasing sympathetic activation
and reducing parasympathetic activity. By measuring ST via accelerometer, we were unable to
distinguish between types of ST and potentially important differences between these activities may
have been masked, making it difficult to detect any moderating effects. The physiological
responses to different forms of ST are likely not uniform, and without the ability to distinguish
between them, the study may not have captured their specific influences on the ST-cardiac ANS
relationship, contributing to the null moderating effects across various behavioral and
physiological factors. Future research should consider measuring different types of ST (e.g., via
ecological momentary assessment) and analyzing whether different types of sedentary activities
uniquely contribute to cardiac ANS function.
Given the null findings, there are several directions for future researchers to consider to
better understand the factors that may influence the ST-cardiac ANS association. First,
longitudinal studies that capture changes in body composition, fitness level, MVPA, and sleep
over time could provide a clearer picture of whether these factors moderate the effects of ST on
cardiac ANS function across different developmental stages. Such designs would allow for the
examination of how shifts in these variables interact with sedentary activities and their
physiological consequences. Second, future research should include larger sample sizes to capture
a broader range of variability in both ST and potential moderators like body fat, fitness, and daily
activities. A more diverse and larger sample might help uncover more subtle moderating effects
that were not detectable in this study due to potential homogeneity within the sample. Lastly, future
studies should consider capturing nuances of behaviors, such as the different types of ST and sleep

74
quality, to understand how these variations may differentially influence cardiac ANS function and
potentially moderate the ST-cardiac ANS relationship. Taken together, these future directions
could provide a more nuanced understanding of how ST interacts with various lifestyle factors and
physiological processes, ultimately offering more targeted strategies for improving cardiac ANS
health in youth.
Strengths and Limitations
The strengths and limitations of the present study are the same as those addressed in Study
1 on pages 53-55. In sum, this study’s strengths include the novelty of combining gold standard
measures of ST and cardiac ANS simultaneously over multiple days and in a free-living,
naturalistic environment. Limitations for this study include its relatively short duration (i.e., only
seven days), limited generalizability to youth of other weight statuses since only youth with
OW/OB were recruited, limited variability in some of the key variables that likely contributed to
null findings and the high rate of participant non-compliance. Lastly, our study was underpowered,
as initial power analyses indicated that N=90 was needed to detect moderate effect sizes with an
ICC of 0.26. However, the final sample included only N=25, with smaller effect sizes and higher
ICCs than expected. Post-hoc power analyses suggested that sample sizes greater than 500 were
needed to power our observed results, emphasizing the need for larger samples in future research
to detect small effects reliably (see Study 1 page 55 for more details).
Conclusion
This study aimed to explore the moderating effects of body fat, trunk fat, fitness level,
MVPA, sleep, and day of the week on the relationship between ST and cardiac ANS function in
youth with OW/OB. However, all interaction terms were null, suggesting that none of these
variables significantly moderated the ST-cardiac ANS association. It is possible that the

75
physiological mechanisms linking ST to cardiac ANS dysfunction are strong and direct enough
that it is not significantly altered by differences in these moderating factors in this population.
However, the limited variability in some of our moderating variables (e.g., adiposity, fitness, and
MVPA) likely resulted from the narrow scope of the recruited sample, which may have contributed
to the null findings. Future research should focus on longitudinal designs, larger and more diverse
samples, and capturing nuanced behaviors like different types of ST and sleep quality to better
understand potential moderators of the ST-ANS relationship. These efforts could help clarify the
pathways through which ST influences cardiac ANS function and identify potential intervention
targets for improving autonomic regulation in youth with OW/OB.

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Chapter 4: In-Lab Effects of Breaking Up Prolonged Sitting with Physical Activity on
Cardiac Autonomic Nervous System Function in Youth with Overweight and Obesity
Abstract
Objective: Youth spend a large portion of their day in sedentary time (ST), which is linked to
increased cardiovascular disease (CVD) risk, especially in those with overweight and obesity
(OW/OB) who are more sedentary and experience greater cardiac autonomic nervous system
(ANS) dysfunction. Thisstudy aimed to experimentally investigate the acute effects of interrupting
ST with walking on cardiac ANS function in youth with OW/OB.
Methods: Youth with OW/OB were randomized to one of three in-lab conditions that occurred
over the course of three hours for seven consecutive days: continuous sitting (SIT), sitting with
three-minute moderate-intensity walking breaks every 30 minutes (SIT+WALK), or a single bout
of moderate-intensity walking for 18 minutes followed by continuous sitting (EX). Cardiac ANS
function was measured using ECG, from which heart rate variability (HRV) metrics were derived.
Analysis of Covariance (ANCOVA) was used to evaluate differences in the changes in HRV
metrics between Day 1 and Day 7 during the three-hour lab visit across the experimental
conditions. Linear mixed effects models were used to assess the differential longitudinal changes
in daily HRV responses during Days 2-6 to the experimental conditions.
Results: Twenty-nine participants had complete data for both Day 1 and Day 7, while 24
participants met the wear-time criteria for inclusion in the Days 2-6 analyses. ANCOVA models
showed that participants in the EX condition had significantly greater increases in both LF
(F=4.10, p=0.01) and LF/HF ratio (F=3.17, p=0.04) from Day 1 to Day 7 compared to the SIT
condition. In the linear mixed effects models, for each additional study day, SDNN increased by
1.56 ms (p=0.08), while LF and HF increased by 40.66 ms2
(p=0.006) and 22.53 ms2
(p=0.05),
respectively, in the EX condition compared to the SIT condition. No significant differences were

77
found in any HRV metric in Day 1 and Day 7 analyses, nor in Days 2-6 analyses, between those
in the SIT+WALK condition and those in the SIT condition (p’s>0.05).
Conclusion: We unexpectedly found that youth with OW/OB in the EX condition had a greater
increase in mean LF and LF/HF ratio from Day 1 to Day 7, likely due to a sympathetic stress
response to exercise. While longer bouts of moderate-intensity walking improved cardiac ANS
function on Days 2-6, shorter, more frequent breaks did not. The pattern and duration of activity
appear important for improving cardiac ANS function in this population, but future research should
explore optimal activity break duration and frequency to enhance cardiac ANS outcomes in youth
with OW/OB.

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Introduction
It has been estimated that youth spend seven to nine hours sedentary per day.
92-94 This large
proportion of sedentary time (ST) is concerning given that ST is associated with several adverse
health outcomes in youth, including increased risk for cardiovascular disease
(CVD).89,90,121,145,182,186-188 Moreover, youth with overweight and obesity (OW/OB) are more likely
to be sedentary compared to youth with healthy weight,
238-240 and consequently are at increased
risk for CVD development.17-19 Since tracking studies suggest that activity behaviors99 and CVD
risk27,29 worsen as youth age, mitigating CVD risk in youth with OW/OB is critically important.
Disruptions in the cardiac autonomic nervous system (ANS), as measured using heart rate
variability (HRV) metrics,144 are strongly associated with increased risk for CVD in youth80,84-86
and may explain associations between ST and CVD risk. The cardiac ANS regulates the heart’s
response to varying stimuli. It is comprised of the sympathetic and parasympathetic nervous
systems, which exert antagonistic effects based on the demands of these stimuli.
30,31 Dominance
of sympathetic versus parasympathetic activity is indicative of dysfunction and demonstrates a
lack of health.30,31 During a bout of prolonged ST (>1 hour), sympathetic activity may dominate
to compensate for interrupted blood flow and reduced blood pressure caused by blood pooling in
the lower extremities.126-128 Additionally, the decreased blood flow during a bout of prolonged ST
may also cause a reduction in shear stress and nitric oxide, which in turn reduces parasympathetic
activity.126,129,130 Frequent exposure to these acute disruptions in cardiac ANS function via bouts
of prolonged ST is hypothesized to contribute to chronic cardiac ANS dysfunction, and therefore
contribute to CVD development.125
Considering the hypothesized physiological pathways that connect ST to cardiac ANS
dysfunction, reducing prolonged ST may be a promising behavioral strategy for reducing CVD

79
risk. Prior evidence in youth suggests that interrupting prolonged ST with moderate-intensity
walking acutely improves metabolic outcomes relevant to CVD risk.259-261 However, no studies
have experimentally assessed the acute influence of interrupting ST with walking breaks on cardiac
ANS function. In addition, no studies have investigated how manipulations in ST influence cardiac
ANS function in youth with OW/OB- a population vulnerable to spending more time in ST,238-240
who are susceptible to increased CVD risk,17-19 and who are likely to present with poor cardiac
ANS function.48,49,66-70 Therefore, this study has one aim and three hypotheses:
Aim 1: To experimentally examine the acute effects of interrupting ST in the lab on cardiac ANS
function in youth with OW/OB.
H1A: Youth who are exposed to three hours of uninterrupted sitting (SIT condition) will
have lower mean HRV compared to youth who are exposed to interrupted sitting with
three-minute walking bouts every 30 minutes over three hours (SIT+WALK condition) and
youth who are exposed to interrupted sitting with a single 18-minute bout of walking over
three hours (EX).
H1B: Youth who are exposed to the SIT condition will have worse
sympathetic/parasympathetic balance compared to youth exposed to the SIT+WALK and
EX conditions.
H1C: Youth who are exposed to the SIT condition will have lower mean parasympathetic
activity compared to youth who are exposed to the SIT+WALK and EX conditions.

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Methods
Participants
The targeted population of interest and recruitment methods are described in detail in Study
1 (see pages 30-31). Briefly, youth with OW/OB, as defined as a body mass index (BMI) ≥85th
percentile,
190 were recruited from the Sedentary Breaks Study. Participants were recruited from
BuildClinical, a digital research recruitment service that creates targeted ads for the population of
interest. Interested families first completed an initial screening form via BuildClinical, and then
were screened over the phone for eligibility. Eligibility criteria are described in Study 1 on pages
30-31. Those who appeared eligible on the phone were invited to complete an in-person screening
visit (described on page 30). Youth who remained eligible after the screening visit then completed
a baseline free-living assessment (described on page 32) and a week-long, in-lab experimental
trial. Data from the week-long, in-lab experimental trial were primarily used for the present study.
Parents provided informed consent and child participants provided assent. The Institutional
Review Board at the University of Southern California approved of this study.
Procedures
In-Lab Experimental Trials
After completion of the in-person screening visit and the free-living assessment,
participants were randomized to complete one of three conditions, each of which occurred over
three hours for seven consecutive days:
1) SIT condition: Participants sat for three hours with limited movement and only rose for
bathroom use.

81
2) SIT+WALK condition: Participants walked on a treadmill at a tailored speed and grade
reflective of moderate-intensity for three minutes every 30 minutes. This totaled in 18
minutes of moderate-intensity walking over three hours.
3) EX condition: Participants walked on the treadmill at a tailored speed and grade reflective
of moderate-intensity one time for 18 minutes. After the participant completed the single
bout of walking, the participant remained seated for the rest of the three hours.
For those in the SIT+WALK and EX conditions, moderate-intensity was defined as 60%
of the maximum oxygen consumed during the fitness test completed at the in-person screening
visit (see Study 1, page 31 for details). Throughout the duration of the in-lab experimental trial,
participants wore an ECG monitor to obtain daily cardiac ANS function and an activPAL
accelerometer to obtain daily activity behaviors. The ECG device and accelerometer were worn
continuously for 24 hours across seven days while in the lab and at home. Both devices were placed
on the participant on Day 1 prior to starting the experimental trial and removed on Day 7 after the
completion of the experimental trial. Figure 7 outlines the experimental conditions and measures
that were collected over the seven days.

82
Figure 7. Experimental conditions.
On the first or last day of the in-lab experimental trials, participants completed a dualenergy X-ray absorptiometry (DEXA) scan to obtain body composition. A 24-hour dietary recall
was obtained on each of the seven days using a multi-pass approach using the Automated SelfAdministered (ASA24) online tool.
195 Additionally, during the in lab visits on Days 2-6,
participants were provided a standardized snack. On Days 1 and 7, participants fasted for
procedures that are not applicable for this study.
Measures
Cardiac ANS Function via ECG monitor
The same electrocardiogram (ECG) device as described in Studies 1 and 2 was used to
obtain cardiac ANS function via HRV metrics. The MyPatch-sl Recorder is described in detail in

83
Study 1 (see pages 33-35). Briefly, a pediatric-sized patch with an attached recorder was placed
on the participant’s chest on Day 1 of the in-lab experimental trials. The electrode patch and ECG
device were worn throughout the duration of the experimental trials and removed after the
completion of the in-lab protocol on Day 7.
The same data processing procedures previously described in Studies 1 and 2 were utilized
to import the data, remove artifact, and derive time- and frequency-domain HRV variables (see
page 35 for details). Time-domain variables that were derived include the standard deviation of all
R-R intervals (SDNN) and the root mean square of successive R-R interval differences (RMSSD).
Frequency-domain variables that were derived include high power frequency (HF), low power
frequency (LF), and the ratio of LF to HF (LF/HF). Table 2 in Study 1 (page 35) provides a
description and interpretation of each of these variables.
Covariates
A priori covariates were selected based on prior evidence.48,74,147,206-208 Covariates tested
in all statistical models were age (continuous variable; years), sex (dichotomous variable; male vs.
female), race (dichotomous variable; White vs. all others), ethnicity (dichotomous variable;
Hispanic vs. Non-Hispanic), maternal education level (dichotomous variable; college degree or
higher vs. less than college degree), body composition (continuous variables; body fat percent and
trunk fat (kg)), pubertal stage (continuous variable; ranges 1-3), basal heart rate in beats per minute
(bpm; continuous variable), systolic blood pressure in mmHg (continuous variable), and time of
year (dichotomous variable; school vs. summer). Additional covariates tested in day-level models
only were daily moderate-to-vigorous physical activity (MVPA; continuous variable; minutes),
sleep (continuous variable; minutes), dietary intake (continuous variables; percent macronutrient
intake and total caloric intake in kilocalories), and day of the week (dichotomous variable;

84
weekend vs. weekday). Covariates with p≤0.10 were retained in the models, and covariates with
p>0.10 were removed to yield the most parsimonious model. Collinearity was examined and
addressed as needed to maintain the integrity of the models.
Statistical Analysis
Frequencies and means were calculated for all participant demographic characteristics,
HRV variables, and covariates.
Sensitivity Analyses
Sensitivity analyses, including independent samples t-tests, were conducted to assess
differences in demographic characteristics, body composition, and HRV metrics between those
included versus those excluded. One-way analysis of variance (ANOVA) was used to assess
differences in baseline characteristics (e.g., demographics, body composition, fitness level)
between the three experimental groups to determine if randomization was successful. Additionally,
separate multilevel logistic regression models were used to examine whether person-level age
(continuous), sex (male vs. female), race (White vs. all others), ethnicity (Hispanic vs. NonHispanic), maternal education level (college degree or higher vs. less than college degree), body
composition (continuous variables; body fat percent and trunk fat (kg)), and pubertal stage
(continuous variable; ranges 1-3) predicted ECG compliance (valid day: yes vs. no). Separate
multilevel logistic regression models were used to examine whether time of year (school vs.
summer), day of the week (weekend vs. weekday), and day of the study (continuous variable;
range 1-7) predicted ECG compliance (valid day: yes vs. no).

85
Aim 1
To account for participants who dropped out during the in-lab week, all analyses were
intent-to-treat (ITT). All participants initially allocated to a specific experimental condition were
included in the analysis, regardless of adherence or attrition during the study period.
Analysis of Covariance (ANCOVA) was used to compare pre- and post-test changes in
SDNN, RMSSD, LF, HF, and LF/HF (defined in Table 2 on page 35) between the three
experimental groups (analyses hereinafter referred to as Day 1 and Day 7 analyses). Covariates
tested in the models were age, sex, race, ethnicity, maternal education level, body fat percent, and
pubertal stage. For the ANCOVA models, the mean of each HRV variable was calculated for the
three-hour experimental trial on Day 1 (i.e., pre-test) and Day 7 (i.e., post-test). Post-hoc Tukey
tests with a Bonferroni correction to account for multiple testing were used to assess pairwise
differences in the experimental conditions. Only participants with complete data on Day 1 and Day
7 were included in the ANCOVA analyses.
Since we have 24 hours of HRV data on Days 2-6 of the in-lab experimental trial, linear
mixed models were used to assess the differential longitudinal changes in HRV responses to the
experimental conditions over these five days (analyses hereinafter referred to as Days 2-6
analyses). Daily waking-time means for each HRV variable were calculated and used as the
outcome of interest. A cross-level interaction term between experimental condition (betweenperson) and time (within-person; sequential day, with a day being defined as waking time only)
was used to determine differences between experimental condition. Covariates listed above, with
the addition of daily MVPA minutes, sleep duration, and dietary intake (kcal), were tested in the
models and remained if p≤0.10. Significance was considered p≤0.05, with p-values adjusted for
multiple comparisons using the Bonferroni correction.

86
Results
Data Availability
Figure 8 outlines the participant flow from recruitment to the completion of the in-lab
experimental trials. Briefly, out of 1,144 interested families who completed a screening form via
BuildClinical, 594 appeared eligible and were contacted. From these, 142 were eligible for an inperson screening visit, with 76 enrolling and completing consent/assent. Fifty-five completed the
screening visit, resulting in 49 eligible participants. Of these, 48 were sent devices for the freeliving assessment.
Of the 48 who were sent devices for the free-living assessment, 45 were randomized (n=3
dropped out during the free-living assessment week). Of the 45 randomized to one of the three
study conditions (i.e., SIT, SIT+WALK, EX), 37 completed the protocol, of which 29 were
included in the Day 1 and Day 7 analyses (i.e., person-level change in HRV analyses). Of the 8
who were excluded from Day 1 and Day 7 analyses, 7 (18.92%) were not compliant (i.e., did not
wear the ECG) or had missing data for the ECG (i.e, poor ECG hook-up), and 1 (2.7%) was missing
ECG data due to device malfunction. Twenty-four participants had valid ECG wear (i.e., 8 hours
a day of wear for at least 3 days) and were included in the Day 2-6 analyses, contributing 101 days.
Of those excluded from Day 2-6 analyses, 10 (27%) were not compliant (i.e., did not wear the
ECG) or had missing data for the ECG (i.e, poor ECG hook-up), and 3 (8.1%) were missing ECG
data due to device malfunction.

87
Figure 8. Study participant flow for those included in the in-lab week analyses (Study 3).

88
Day 1 and 7 Analyses: Sample Characteristics
Table 9 shows the participant demographics for those included in Day 1 and 7 analyses
across the three conditions: SIT (N=13), SIT+WALK (N=8), and EX (N=8). The average ages
were 9.9, 9.3, and 9.5 years, respectively. Female participants made up 30.7% of the SIT group,
50.0% of SIT+WALK group, and 37.5% of EX group. Most participants were in the pre or early
pubertal stages across all groups. Half of the participants in the SIT (53.9%) and SIT+WALK
conditions (50.0%) were Hispanic, whereas only a third (37.5%) were Hispanic in the EX
condition.
Those in the SIT condition experienced a decrease in their in-lab mean SDNN (mean=-
7.54 ms), RMSSD (mean=-2.93 ms), LF (mean=-252.71 ms2
), HF (mean=-95.54 ms2
), and LF/HF
ratio (mean=-0.13) from Day 1 to Day 7. Those in the SIT+WALK condition experienced a
decrease in their in-lab mean SDNN (mean=-1.02 ms), LF (mean=-52.39 ms2
), and HF (mean=-
10.20 ms2
), but experienced an increase in RMSSD (mean=5.98 ms) and LF/HF ratio (mean=0.08)
from Day 1 to Day 7. Those in the EX condition experienced an increase in their in-lab mean
SDNN (mean=3.06 ms), RMSSD (mean=3.90 ms), LF (mean=124.26 ms2
), and LF/HF
(mean=0.46), but a decrease in HF (mean=-19.83 ms2
) from Day 1 to Day 7.
Table 9. Participant demographics for those included in Day 1 and Day 7 analyses by
experimental condition (N=29)
SIT
N=13
SIT+WALK
N=8
EX
N=8
Variable Mean (SD) or
N (%)
Mean (SD) or
N (%)
Mean (SD) or
N (%)
Age (years) 9.92 (1.04) 9.25 (1.04) 9.50 (1.20)
Female sex 4 (30.77)% 4 (50.00%) 3 (37.50%)
Pubertal stage
Pre 6 (45.26%) 4 (50.00%) 3 (37.50%)
Early 5 (38.46%) 4 (50.00%) 2 (25.00%)
Mid 2 (15.38%) 0 (0.00%) 3 (37.50%)
Race

89
White 4 (30.77%) 2 (25.00%) 4 (50.00%)
Asian 2 (15.38%) 1 (12.50%) 0 (0.00%)
Black/African American 4 (30.77%) 2 (25.00%) 3 (37.50%)
More than one race 1 (7.69%) 1 (12.50%) 1 (12.50%)
Unknown/not reported 1 (7.69%) 2 (25.00%) 0 (0.00%)
Hispanic ethnicity 7 (53.85%) 4 (50.00%) 3 (37.50%)
Mother’s highest level of
education 6 (46.15%) 2 (28.57%) 2 (25.00%)
Age- and sex-adjusted BMI
percentile 95.65 (3.92) 97.63 (2.50) 95.96 (3.67)
Body fat percent 43.90 (5.72) 44.80 (4.22) 43.68 (4.36)
Trunk fat (kg) 11.79 (5.22) 11.81 (2.95) 11.58 (2.96)
Basal heart rate (bpm) 76.95 (10.08) 73.75 (7.65) 73.06 (10.61)
Basal systolic blood pressure 107.95 (12.24) 116.24 (2.50) 108.45 (3.26)
Trait anxiety 34.00 (6.78) 32.88 (4.94) 32.00 (8.70)
Changes from Day 1 to 7
SDNN change (ms) -7.54 (14.29) -1.02 (17.89) 3.06 (13.98)
RMSSD change (ms) -2.93 (9.90) 5.98 (13.12) 3.90 (22.85)
LF change (ms2
) -252.71 (240.87) -52.39 (313.95) 124.26 (328.18)
HF change (ms2
) -95.54 (158.39) -10.20 (166.46) -19.83 (188.60)
LF/HF ratio change -0.13 (0.37) 0.08 (0.30) 0.46 (0.48)
Note: HRV change variables reflect the change in the metric from Day 1 to Day 7 during the 3-
hour lab visit only. Mean changes were calculated by subtracting Day 1 from Day 7, so a negative
mean difference indicates the HRV metric decreased from Day 1 to Day 7, and a positive mean
difference indicates the HRV metric increased from Day 1 to Day 7.
Day 1 and 7 Analyses: Sensitivity Analyses
Independent samples t-tests showed that there were no significant differences in personlevel age (t value=-1.30, p=0.20), pubertal stage (t value=0.92, p=0.37), body fat percent (t
value=1.07, p=0.29), trunk fat (t value=0.57, p=0.57), basal heart rate (t value=1.31, p=0.20), and
systolic blood pressure (t value=0.48, p=0.64) between those included and excluded in the
analyses. Chi-square tests showed there were no significant differences in sex (female vs. male;
𝛘
2=0.62, p=0.43), race (White vs. all others; 𝛘
2=1.40, p=0.24), and mother’s highest level of
education (college degree or higher vs. less than college degree; 𝛘
2=0.28, p=0.60) between those

90
included and excluded in the analyses. Hispanic ethnicity (Hispanic vs. Non-Hispanic; 𝛘
2=4.67,
p=0.03) was associated with study inclusion, with Hispanics being more likely to be included.
ANOVA models were used to test differences in continuous demographic variables
between experimental conditions. There were no significant differences in age (F-value=1.03,
df=2, p=0.37), pubertal status age (F-value=0.90, df=2, p=0.42), body fat percent (F-value=0.12,
df=2, p=0.89), trunk fat (F-value=0.01, df=2, p=0.99), BMI percentile (F-value=0.82, df=2,
p=0.45), basal heart rate (F-value=0.49, df=2, p=0.62), and basal systolic blood pressure (Fvalue=2.34, df=2, p=0.13) between experimental conditions. Fisher’s exact test was used to test
differences in categorical demographic variables between experimental conditions, as cell counts
were too small for chi-square analyses. Experimental conditions marginally differed by sex
(female vs. male; Fisher’s p=0.09), race (White vs. all others Fisher’s p=0.08), Hispanic ethnicity
(Hispanic vs. Non-Hispanic; Fisher’s p=0.10), and maternal education level (college degree or
higher vs. less than college degree; Fisher’s p=0.08).
Day 1 and 7 Analyses: Results
To address skewness and kurtosis in all HRV variables, rank transformation was performed
prior to conducting ANCOVA models. ANCOVA was selected for the analysis as the assumptions
of normality (after rank transformation) and equal variances were met, despite unequal group sizes
among the experimental conditions. The ANCOVA for LF revealed a significant main effect
(F=4.10, p=0.007; f=0.52), with race, body fat percent, trunk fat, and time of year emerging as
significant covariates (p≤0.10). In the ANCOVA for the LF/HF ratio, a significant main effect was
observed (F=3.17, p=0.04; f=0.57), with race being the only significant covariate (p<0.10). For
both LF and LF/HF ratio, post hoc comparisons showed that the EX condition had a significantly

91
greater increase in LF (mean difference in ranks=10.79, p=0.01) and LF/HF ratio (mean difference
in ranks=9.81, p=0.04) from Day 1 to Day 7 compared to the SIT condition. The main effect for
SDNN (F=1.13, p=0.33; no significant covariates p’s≤0.10; f=0.29), RMSSD (F=1.41, p=0.26; sex
the only significant covariate p≤0.10; f=0.62), and HF (F=2.22, p=0.11; sex the only significant
covariate p≤0.10; f=0.75) were not significant, indicating that there were no differences between
the three conditions in their changes from Day 1 to Day 7.
Days 2-6 Analyses: Sample Characteristics
Table 10 summarizes the demographic characteristics of participants included in Days 2-
6 analyses across the three experimental conditions: SIT (N=10), SIT+WALK (N=7), and EX
(N=7). The average ages of participants were 9.6 years in the SIT condition, 9.3 years in the
SIT+WALK condition, and 9.3 years in the EX condition. Female participants made up 30% of
the SIT group, 57.1% of the SIT+WALK group, and 28.6% of the EX group. Most participants
were in the pre or early pubertal stages. Notably, 80% of the SIT group identified as Hispanic,
compared to 42.9% in the SIT+WALK group and 54.1% in the EX group.
In the SIT condition, participants exhibited a day-level mean SDNN of 51.64 ms, RMSSD
of 41.71 ms, LF of 480.62 ms², HF of 261.13 ms², and an LF/HF ratio of 2.36. Participants in the
SIT+WALK condition demonstrated a day-level mean SDNN of 58.82 ms, RMSSD of 45.57 ms,
LF of 580.66 ms², HF of 336.85 ms², and an LF/HF ratio of 1.94. Lastly, those in the EX condition
showed a day-level mean SDNN of 58.23 ms, RMSSD of 44.93 ms, LF of 663.67 ms², HF of
306.29 ms², and an LF/HF ratio of 2.33.

92
Table 10. Participant demographics for those included in Days 2-6 analyses by experimental
condition (N=24)
SIT
N=10
SIT+WALK
N=7
EX
N=7
Person-level (N=24)
Mean (SD) or
N (%)
Mean (SD) or
N (%)
Mean (SD) or
N (%)
Age (years) 9.60 (1.17) 9.28 (1.11) 9.28 (1.11)
Female sex 3 (30.00%) 4 (57.14%) 2 (28.57%)
Pubertal stage
Pre 6 (60.00%) 4 (57.14%) 2 (28.57%)
Early 2 (20.00%) 3 (42.86%) 2 (28.57%)
Mid 2 (20.00%) 0 (0.00%) 3 (42.86%)
Race
White 5 (50.00%) 2 (28.57%) 5 (71.43%)
Asian 2 (20.00%) 1 (14.29%) 0 (0.00%)
Black/African American 1 (10.00%) 1 (14.29%) 1 (14.29%)
More than one race 1 (10.00%) 2 (28.57%) 1 (14.29%)
Unknown/not reported 1 (10.00%) 1 (14.29%) 0 (0.00%)
Hispanic ethnicity 8 (80.00%) 3 (42.86%) 4 (54.14%)
Mother’s highest level of education
Bachelor’s degree or higher 4 (40.00%) 2 (33.33%) 3 (42.86%)
Age- and sex-adjusted BMI percentile 95.40 (4.27) 98.06 (1.33) 98.06 (1.33)
Body fat percent 43.46 (5.15) 45.57 (3.23) 45.57 (3.23)
Trunk fat (kg) 11.81 (5.63) 11.55 (3.05) 11.55 (3.05)
Basal heart rate (bpm) 77.06 (6.10) 79.23 (9.56) 79.23 (9.55)
Basal systolic blood pressure 111.20 (8.59) 111.41 (16.97) 111.41 (16.97)
Trait anxiety 34.50 (5.76) 32.86 (3.89) 32.86 (3.89)
Day-level variables (n=101 days) Mean (SD)
n=46 days
Mean (SD)
n=28 days
Mean (SD)
n=27 days
Total caloric intake (kcals) 1818.32 (573.03) 1804.55 (644.41) 2149.84 (1051.76)
Proportion macronutrients from
carbohydrates (%) 46.60 (8.63) 49.28 (10.99) 49.65 (11.72)
Proportion macronutrients from protein (%) 18.40 (6.64) 17.10 (6.73) 15.10 (6.32)
Proportion macronutrients from fat (%) 34.99 (6.71) 33.63 (8.97) 35.25 (11.22)
MVPA (mins) 31.33 (15.49) 28.14 (18.97) 32.70 (21.92)
Sleep time (hours) 9.49 (1.22) 9.99 (1.51) 9.27 (1.14)
SDNN (ms) 51.64 (8.76) 58.82 (9.35) 58.23 (14.11)
RMSSD (ms) 41.71 (9.46) 45.57 (11.04) 44.93 (14.90)
LF (ms2
) 480.62 (164.98) 580.66 (198.35) 663.67 (327.84)
HF (ms2
) 261.13 (188.20) 336.85 (174.95) 306.29 (172.79)
LF/HF ratio 2.36 (0.93) 1.94 (0.64) 2.33 (0.62)

93
Days 2-6 Analyses: Sensitivity Analyses
Independent samples t-tests showed that there were no significant differences in personlevel age (t value=-0.13, p=0.89), pubertal stage (t value=0.95, p=0.35), body fat percent (t
value=0.21, p=0.83), trunk fat (t value=-0.52, p=0.60), basal heart rate (t value=0.02, p=0.98), and
systolic blood pressure (t value=-0.23, p=0.82) between those included and excluded in the
analyses. Chi-square tests showed there were no significant differences in sex (female vs. male;
𝛘
2=0.86, p=0.35), race (White vs. all others; 𝛘
2=0.69, p=0.41), mother’s highest level of education
(college degree or higher vs. less than college degree; 𝛘
2=0.13, p=0.71), and ethnicity (Hispanic
vs. Non-Hispanic; 𝛘
2=1.44, p=0.23).
ANOVA models were used to test differences in continuous demographic variables
between experimental conditions. There were no significant differences in age (F-value=0.23,
df=2, p=0.79), pubertal stage (F-value=1.61, df=2, p=0.22), body fat percent (F-value=0.47, df=2,
p=0.63), trunk fat (F-value=0.41, df=2, p=0.67), BMI percentile (F-value=1.16, df=2, p=0.33),
basal heart rate (F-value=1.03, df=2, p=0.38), and basal systolic blood pressure (F-value=0.01,
df=2, p=0.99) between experimental conditions. Fisher’s exact test was used to test differences in
categorical demographic variables between experimental conditions, as cell counts were too small
for chi-square analyses. Experimental conditions significantly differed by sex (female vs. male;
Fisher’s p=0.05) and Hispanic ethnicity (Hispanic vs. Non-Hispanic; Fisher’s p=0.05), marginally
differed by race (White vs. all others; Fisher’s p=0.07), and did not differ maternal education level
(college degree or higher vs. less than college degree; Fisher’s p=0.15).
ActivPAL compliance (yes vs. no) was not related to age (OR=1.02, 95% CI 0.09-11.14,
p=0.98), sex (OR=2.06, 95% CI 0.02-22.45, p=0.71), race (OR=0.96, 95% CI 0.01-124.80,
p=0.99), ethnicity (OR=0.73, 95% CI 0.01-95.19, p=0.90), maternal education level (OR=0.11,

94
95% CI 0.0001-167.49, p=0.55), pubertal stage (OR=3.56, 95% CI 0.12-96.85, p=0.48), body fat
percent (OR=1.11, 95% CI 0.63-1.94, p=0.72), trunk fat (OR=1.11, 95% CI 0.63-1.97, p=0.71),
day of the week (OR=4.85, 95% CI 2.43-9.70, p=0.73), or time of year (OR=0.75, 95% CI 0.25-
2.26, p=0.77). ActivPAL compliance was related to study day (OR=2.45, 95% CI 1.04-5.77,
p=0.04), such that participants were more likely to comply later in the week.
ECG compliance (yes vs. no) was not related to age (OR=1.12, 95% CI 0.52-2.36, p=0.77),
sex (OR=1.24, 95% CI 0.26-6.02, p=0.79), race (OR=0.60, 95% CI 0.13-2.86, p=0.52), ethnicity
(OR=0.73, 95% CI 0.01-95.20, p=0.90), maternal education level (OR=0.55, 95% CI 0.11-2.93,
p=0.49), pubertal stage (OR=1.65, 95% CI 0.57-4.74, p=0.35), body fat percent (OR=1.14, 95%
CI 0.95-1.37, p=0.16), trunk fat (OR=1.06, 95% CI 0.87-1.28, p=0.56), day of the week (OR=1.18,
95% CI 0.39-3.56, p=0.76), or time of year (OR=2.08, 95% CI 0.43-9.97, p=0.36). ECG
compliance was related to study day (OR=1.62, 95% CI 1.22-2.17, p=0.001), such that participants
were more likely to comply later in the week.
Days 2-6 Analyses: Model Fit
Model fit parameters are presented in Table 11. Final models included covariates with
p≤0.10 and the best model fit was based on the log likelihood, AIC, and BIC values. The Restricted
Maximum Likelihood (REML) estimator was used for all models because it provides more
accurate and less biased estimates of variance components, particularly in small samples, by
accounting for the estimation of fixed effects separately from the random effects.224

95
Table 11. Model fit parameters for all five models for in-lab Days 2-6 analyses.
SDNN
Model 1:
Empty
Means
Model 2:
Main Predictors
(condition*time)
Model 3:
Covariates
Model 4:
Random Slope
Model 5:
Covariance
Structure
Adding
means or
variance?
None Means Means Variance Variance
Estimator ML ML ML REML REML
-2LL 687.7 677.8 666.9 687.1 658.0
AIC 693.7 693.8 688.9 675.1 662.8
BIC 697.3 695.3 701.9 682.6 665.1
Comparison -- 1 vs. 2 2 vs. 3 3 vs. 2 3 vs. 5
Notes ICC=0.79 Retain model 2
Retain basal heart
rate, basal systolic
blood pressure,
and MVPA
Remove
random slope.
Variance
components fits
best
RMSSD
Model 1:
Empty
Means
Model 2:
Main Predictors
(condition*time)
Model 3:
Covariates
Model 4:
Random Slope
Model 5:
Covariance
Structure
Adding
means or
variance?
None Means Means Variance Variance
Estimator ML ML ML REML REML
-2LL 718.6 713.9 709.2 712.4 694.9
AIC 724.6 729.9 733.2 716.4 698.9
BIC 728.2 731.5 747.4 717.1 701.3
Comparison -- 1 vs. 2 2 vs. 3 3 vs. 4 3 vs. 5
Result ICC=0.70 Retain model 2
Retain age, basal
heart rate, basal
systolic blood
pressure, and
MVPA
Remove
random slope.
Variance
components fits
best
LF
Model 1:
Empty
Means
Model 2:
Main Predictors
(condition*time)
Model 3:
Covariates
Model 4:
Random Slope
Model 5:
Covariance
Structure
Adding
means or
variance?
None Means Means Variance Variance

96
Estimator ML ML ML REML REML
-2LL 1327.4 1318.5 1309.9 1339.1 1245.3
AIC 1333.4 1334.5 1331.9 1347.1 1249.3
BIC 1337.0 1343.9 1344.9 1351.9 1251.6
Comparison -- 1 vs 2. 2 vs. 3 3 vs. 4 3 vs. 5
Result ICC=0.74 Retain model 2
Retain basal heart
rate, basal systolic
blood pressure,
and MVPA.
Remove
random slope.
Variance
components fits
best
HF
Model 1:
Empty
Means
Model 2:
Main Predictors
(condition*time)
Model 3:
Covariates
Model 4:
Random Slope
Model 5:
Covariance
Structure
Adding
means or
variance?
None Means Means Variance Variance
Estimator ML ML ML REML REML
-2LL 1228.8 1224.1 1219.3 1235.4 1166.4
AIC 1234.8 1236.1 1237.3 1241.4 1170.4
BIC 1238.3 1243.2 1247.9 1250.8 1172.7
Comparison -- 1 vs 2. 2 vs. 3 3 vs. 4 3 vs. 5
Result ICC=0.84 Retain model 2.
Retain basal
systolic blood
pressure
Remove
random slope.
Variance
components fits
best
LF/HF
Ratio
Model 1:
Empty
Means
Model 2:
Main Predictors
(condition*time)
Model 3:
Covariates
Model 4:
Random Slope
Model 5:
Covariance
Structure
Adding
means or
variance?
None Means Means Variance Variance
Estimator ML ML ML REML REML
-2LL 190.3 183.9 179.3 183.2 176.9
AIC 196.3 207.9 197.3 202.6 196.9
BIC 199.8 222.0 207.9 210.4 203.3
Comparison -- 1 vs 2. 2 vs. 3 3 vs. 4 3 vs. 5

97
Result ICC=0.61 Retain model 2. Retain age. Remove
random slope.
Variance
components fits
best
Days 2-6 Analyses: Results
Only covariates with p≤0.10 were retained in final models to achieve model parsimony.
Since each HRV outcome is calculated differently and measures distinct aspects of the cardiac
ANS, significant covariates differed across models. Among time-domain HRV outcomes, basal
heart rate, basal systolic blood pressure, and day-level MVPA minutes were significant covariates
in the ST-SDNN model, and age was an additional covariate in the ST-RMSSD model (p’s≤0.10).
Among frequency-domain HRV outcomes, basal heart rate, basal systolic blood pressure, and daylevel MVPA minutes were significant covariates in the ST-LF model, whereas only basal systolic
blood pressure was significant in the ST-HF ratio model, and only age in the ST-LF/HF ratio model
(p’s≤0.10).
Results for models assessing the effects of experimental condition over time and timedomain HRV outcomes are presented in Table 12, while models for frequency-domain HRV
outcomes are presented in Table 13. When comparing SIT+WALK to SIT among time-domain
HRV outcomes, the interaction term for SIT+WALK and time was not significant for SDNN
(β=0.10 ms, p=0.91, f
2=0.01) and RMSSD (β=-0.71 ms, p=0.52, f
2=0.02). Similarly, when
comparing SIT+WALK to SIT among frequency-domain HRV outcomes, the interaction term for
SIT+WALK and time was not significant for LF (β=12.91 ms2
, p=0.55, f
2=0.01), HF (β=5.22 ms2
,
p=0.67, f
2=0.02), or LF/HF ratio (β=0.13, p=0.15, f
2=0.002).
When comparing the EX condition to the SIT condition among time-domain HRV
outcomes, the interaction term for EX and time was marginally significant for SDNN (β=1.56 ms,
p=0.08, f
2=0.01), but not significant for RMSSD (β=0.12 ms, p=0.53, f
2=0.02). For each additional

98
study day, SDNN increased by 1.56 ms in the EX group compared to the SIT group. When
comparing EX to SIT among frequency-domain HRV outcomes, the interaction term for EX and
time was significant for LF (β=40.66 ms2
, p=0.006, f
2=0.01) and HF (β=22.53 ms2
, p=0.05,
f
2=0.02), but was not significant for LF/HF ratio (β=0.06, p=0.46, f
2=0.002). For each additional
study day, LF increased by 40.66 ms2
and HF increased by 22.53 ms2
in the EX group compared
to the SIT group.
Table 12. Multilevel model results [β (SE)] for the interaction of experimental condition and time
to determine differences in time-domain HRV variables between experimental conditions (Level2 N=101 days; Level 1 N=24 participants)
SDNN RMSSD
Fixed
Effects
Intercept 137.25 (33.80)** 110.35 (41.95)*
SIT+WALK 8.81 (6.06) 8.26 (7.17)
EX -4.48 (6.22) 2.33 (7.27)
Study day 0.55 (0.53) 1.12 (0.69)
SIT+WALK*Study day 0.10 (0.87) -0.71 (1.11)
EX*Study day 1.56 (0.87)^ 0.12 (1.13)
Age -- 0.09 (0.05)^
Basal heart rate -0.55 (0.26)^ -0.32 (0.19)^
Basal systolic blood pressure -0.39 (0.21)^ -0.23 (0.14)^
MVPA 0.07 (0.04)^ 0.04 (0.01)^
Random
Effects
Intercept (τuO
2
) 95.56 (33.34)** 115.46 (42.56)**
Residual (σe
2
) 24.16 (4.01)** 39.85 (6.61)**
Fit
Statistics
Log Likelihood 658.0 694.9
AIC 662.8 698.9
BIC 665.1 701.3
Notes: SIT condition is the reference group. REML and VC used.
*p≤0.05
**p≤0.01
^p≤0.10
Table 13. Multilevel model results [β (SE)] for the interaction of experimental condition and time
to determine differences in frequency-domain HRV variables between experimental conditions
(Level-2 N=101 days; Level 1 N=24 participants).
LF HF LF/HF Ratio
Fixed
Effects
Intercept 2275.87 (734.54)** 963.29 (387.05)** 0.30 (1.17)
SIT+WALK 82.01 (137.91) 76.41 (98.31) -0.89 (0.46)^
EX -19.11 (141.08) -38.70 (95.58) -0.39 (0.45)
Study day 2.34 (13.45) -1.59 (7.88) -0.09 (0.05)^
SIT+WALK*Study day 12.91 (21.78) 5.22 (12.86) 0.13 (0.09)

99
EX*Study day 40.66 (21.89)** 22.53 (10.77)* 0.06 (0.09)
Age -- -- -0.26 (0.12)*
Basal heart rate -10.67 (5.62)^ -- --
Basal systolic blood pressure -8.45 (4.50)^ -6.31 (3.44)^ --
MVPA 1.38 (0.79)^ -- --
Random
Effects
Intercept (τuO
2
) 43984 (15843)** 28103 (9285.94)** 0.33 (0.13)**
Residual (σe
2
) 15020 (2498.76)** 5229.59 (859.34)** 0.24 (0.04)**
Fit
Statistics
Log Likelihood 1245.3 1166.4 176.9
AIC 1249.3 1170.4 196.9
BIC 1251.6 1172.7 203.3
Notes: SIT condition is the reference group. REML and VC used.
*p≤0.05
**p≤0.01
^p≤0.10
Days 2-6 Analyses: Ancillary Analyses
Additional sensitivity analyses were conducted to evaluate the effect of different wear-time
thresholds, which can influence the amount of data included in analyses. These models used a
criterion of one valid day of ECG wear-time. With this adjusted threshold, 35 participants were
included, contributing a total of 123 valid days of data. SIT+WALK and SIT condition
comparisons remained non-significant across all HRV outcomes. When comparing SIT+WALK
to SIT conditions among time-domain HRV outcomes, the interaction term for SIT+WALK and
time was not significant for SDNN (β=1.47 ms, p=0.18, f
2=0.02) and RMSSD (β=-1.91 ms,
p=0.12, f
2=0.02). Similarly, when comparing SIT+WALK to SIT among frequency-domain HRV
outcomes, the interaction term for SIT+WALK and time was not significant for LF (β=9.18 ms2
,
p=0.68, f
2=0.03), HF (β=3.80 ms2
, p=0.77, f
2=0.03), and LF/HF ratio (β=0.05, p=0.51, f
2=0.01).
However, results for EX and SIT condition comparisons slightly changed. When
comparing the EX condition to the SIT condition among time-domain HRV outcomes, the
interaction term for EX and time was not significant for SDNN (β=1.31 ms, p=0.26, f
2=0.02) and
RMSSD (β=0.02 ms, p=0.98, f
2=0.02). When comparing EX to SIT conditions among frequency-

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domain HRV outcomes, the interaction term for EX and time was marginally significant for LF
(β=42.67 ms2
, p=0.07, f
2=0.03), significant for HF (β=27.73 ms2
, p=0.04, f
2=0.03), but was not
significant for LF/HF ratio (β=0.03, p=0.71, f
2=0.01). For each additional study day, LF increased
by 42.67 ms2
and HF increased by 27.73 ms2
in the EX compared to the SIT group.
Discussion
The purpose of this study was to experimentally investigate the acute effects of interrupting
ST on cardiac ANS function in youth with OW/OB. We hypothesized that those in the SIT
condition would experience lower overall HRV, worse sympathetic/parasympathetic balance, and
lower parasympathetic activity compared to those in the SIT+WALK and EX conditions. When
observing changes in mean HRV during the lab visit on Day 1 to Day 7, those in the EX condition
experienced a greater increase in LF and the LF/HF ratio compared to the SIT condition. However,
no significant differences were found for other HRV metrics between the EX and SIT conditions,
nor for any HRV metric when comparing SIT+WALK and SIT conditions, when looking at
changes from Day 1 to Day 7. Across Days 2-6, the EX condition showed significant increases in
their daily mean SDNN, LF, and HF over time, while no significant differences were found
between the SIT+WALK and SIT conditions over time.
The observed increase in in-lab mean LF/HF ratio in the EX group from Day 1 to Day 7
suggests that the balance of the cardiac ANS worsened in the EX condition. This unexpected
finding may reflect the acute autonomic response to physical exertion. During the three-hour
protocol, it is likely that the physical activity elicited a sympathetic stress response, which is a
normal physiological reaction.140 This acute increase in sympathetic activity, reflected in higher
LF and LF/HF ratio, could be explained by the body’s immediate need to regulate cardiovascular

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function and maintain homeostasis during and shortly after physical activity.140
This finding is in
line with a review paper that found a longer bout of exercise may delay HRV recovery,152 but the
evidence is inconclusive. In youth with OW/OB, parasympathetic activity is often diminished,68,75
which may limit the cardiac ANS’s ability to quickly return to a parasympathetic state postexercise.167,168 Youth with OW/OB also often exhibit chronic low-grade inflammation,
81,262 which
has been linked to altered autonomic regulation, including reduced parasympathetic activity and
increased sympathetic activity.246 This inflammatory state may exacerbate the body's stress
response to exercise,246 making it harder for the cardiac ANS to shift back to parasympathetic
activity, thus contributing to the observed increase in LF and LF/HF ratio from Day 1 to 7 during
the 3-hour protocol. To fully understand these dynamics, more research is needed to assess how
physiological factors (e.g., body composition, inflammation) influence cardiac ANS-activity bout
relationships and on what time scales. In addition, our sample was unique in that they were highly
sedentary (mean day-level ST=11.04 hours) and had high body fat percent (mean body fat percent
>40% across all groups). As a result, our sample may not have been accustomed to physical activity
and may have experienced both physiological and psychological stress during the longer single
bout of physical activity. Therefore, it is possible that these behavioral and body characteristics
exacerbated cardiac ANS dysfunction, leading to the observed differences in autonomic regulation
between the EX and SIT conditions between Days 1 and 7.
Across Days 2 to 6, we observed that youth in the EX condition showed improvements in
SDNN, LF, and HF throughout the entire day, despite only breaking up sitting in a single bout of
walking during a 3-hour period. SDNN, a marker of overall HRV, along with HF, a marker of
parasympathetic activity, showed increases over time, suggesting that repeated exposure to the
intervention might lead to acute improvements in cardiac ANS function. This aligns with literature

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showing that regular physical activity enhances both sympathetic and parasympathetic function in
the long term, even if the short-term effects elicit sympathetic activation.140,169,170 A potential
mechanism to explain this finding is that walking increases muscle activity and enhances blood
circulation, which immediately boosts venous return.263 In contrast, youth in the SIT+WALK
compared to the SIT condition did not show significant changes in HRV, indicating that short 3-
minute walking breaks, despite totaling the same amount of walking as the EX condition, may not
provide sufficient cardiac ANS benefits. These brief moderate-intensity walks may lack the
duration needed to mitigate the effects of prolonged sitting. More research is needed to determine
which intervention approaches, such as increased bout length or intensity, could effectively
counteract the negative effects of ST on cardiac ANS function.
Our results lend to insights regarding the patterning of ST and moderate-intensity physical
activity. Our results suggest that accumulating moderate-intensity physical activity through short
bouts may not be adequate for achieving acute improvements in HRV. Instead, our data indicate
that longer walking sessions are likely necessary to elicit these acute benefits. This aligns with
existing literature that suggests bout lengths of 20 minutes to up to 2-hours of exercise is beneficial
for the cardiac ANS in youth <18 years old.167 However, more research is needed to fully
understand the optimal patterns of ST and physical activity for improving cardiac ANS function.
Future studies should continue to explore how different patterns of physical activity, including
varying the duration, intensity, and frequency of activity, might influence cardiac ANS function.
Additionally, research should investigate whether these effects are sustained over time and across
larger, more diverse populations to better inform intervention strategies for youth with OW/OB.

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Strengths and Limitations
A strength of this study is the rigorous RCT experimental design over multiple days that
allowed for the control of context, potential confounding factors (i.e., dietary intake) and the dose
and timing of ST and physical activity. Because there is limited evidence that in-field interventions
to reduce ST are efficacious,264 in-lab studies with fewer confounders are needed to lay the
foundation for understanding mechanisms and dosing to inform in-field interventions.
Additionally, an in-lab trial is warranted considering the lack of information regarding the link
between ST and cardiac ANS function in youth with OW/OB. This helps improve our
understanding of causal relationships between variables. In addition, we used gold standard
measurement methods to address this novel research question, which allowed us to test the efficacy
of interrupting ST on cardiac ANS function for the first time.
However, there are also some important limitations to be considered. The in-lab setting
limits ecological validity and likely doesn’t represent the effects of the real world. The
experimental conditions used in this study were limited to three hours, which is not reflective of
the amount of time youth typically spend sitting per day (approximately 11 hours/day in this
sample). There were also issues with device non-compliance. To reduce non-compliance and
capture as much data as possible, the research staff checked devices each day during the in-lab
visit and were available by phone if participants had issues with the devices at home. Despite these
efforts, only 78.37% of participants provided valid data for Day 1 and Day 7 analyses, and 64.86%
for Days 2-6 analyses of those who completed the entire in-lab protocol. Although we attempted
to account for this in our analyses, it is possible that the results may be biased. Another limitation
included our narrow sample, which was sedentary 8-11 year old youth with OW/OB. This limits
our ability to generalize findings to youth of other activity levels, age ranges, and weight statuses.

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However, considering that youth with OW/OB are typically sedentary,238-240 present with cardiac
ANS dysfunction,48,49,66-70 and are at increased risk for CVD development,17-19 it is crucial to study
this population to prevent and/or reverse CVD risk. Lastly, we did not reach the needed sample
size to power this study (N=90; n=30 per group), which resulted in some models having
insufficient power. Post-hoc power analyses for Day 1 and Day 7 models (i.e., ANCOVA models),
conducted in G*Power, indicated that power ranged from 0.20 to 0.89. This wide range of power
likely reflects the varying number of covariates in each model, as covariates were retained only if
p≤0.10. Post-hoc power analyses for Days 2-6 models, conducted in R using the sjstats package
(samplesize_mixed function), showed that larger sample sizes (greater than N=90) were needed
yield a power of 0.80. This is likely because our model generated smaller effect sizes (f
2
range=
0.002-0.02) and larger ICCs (ICC range= 0.61-0.84) than we expected. Therefore, our results must
be interpreted with caution and should be replicated in a larger sample.
Conclusion
Breaking up ST with longer, continuous bouts of moderate-intensity exercise may lead to
improvements in cardiac ANS in youth with OW/OB. Although it accumulated to the same amount
of activity, shorter walking breaks did not result in significant changes in cardiac ANS function.
The pattern and duration of physical activity appear to play a key role in promoting better cardiac
ANS function when breaking up ST. Further research is needed to explore the optimal ST-activity
patterns and their long-term effects on cardiovascular health in this population.

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Chapter 5: Discussion
Summary of Findings
The overall goal of this dissertation was to examine relationships between sedentary time
(ST) and cardiac autonomic nervous system (ANS) function in youth with overweight and obesity
(OW/OB) using both observational and experimental study designs. The three studies increased
our understanding of these associations in youth with OW/OB, who are vulnerable to increased
cardiovascular disease (CVD) risk. The dissertation addresses several methodological limitations
of the current literature, including the limited knowledge regarding ST-cardiac ANS function
associations, the absence of free-living investigations and rigorous randomized control trials in
this topic area, and the lack of studies combining gold standard measures for both ST and cardiac
ANS function.
The purpose of Study 1 was to investigate the observational associations of between-person
and within-person ST with cardiac ANS function under free-living conditions. Although
longitudinal studies are needed, Study 1 shows that youth with OW/OB who engage in more ST
overall, as well as on days when their ST is higher than usual, exhibit worse cardiac ANS function.
Building upon Study 1, the aim of Study 2 was to explore moderating factors that may play a role
in the free-living associations of ST and the cardiac ANS. Person-level (i.e., body fat percent, trunk
fat, and fitness level) and day-level factors (i.e., sleep, MVPA, and day of the week) did not
moderate ST-cardiac ANS function associations, suggesting that associations between ST and
cardiac ANS may be independent of these moderators. Lastly, the purpose of Study 3 was to
examine the effects of experimentally manipulating ST on cardiac ANS function in a lab setting
across three hours for seven consecutive days. We provide preliminary evidence that interrupting

106
sitting time with longer walking breaks may mitigate poor cardiac ANS function over time in this
population.
Summary of Potential Mechanisms
The findings of this dissertation lend insight toward the hypothesized mechanisms through
which ST affects cardiac ANS function. Prolonged ST (defined as sitting continuously > 1 hour)
may lead to blood pooling in the lower extremities, which disrupts blood flow, reduces blood
pressure, and increases compensatory sympathetic nervous system activity.126-128 This
overactivation of the sympathetic nervous system concurrently reduces parasympathetic
activity.126-128 Additionally, decreased blood flow during ST reduces shear stress and nitric oxide
production, which in turn diminishes parasympathetic function.126,129,130 Our findings suggest that
the accumulation of these acute changes in autonomic balance during ST may lead to cardiac ANS
dysfunction over time. Chronic overactivation of the sympathetic nervous system and reduced
parasympathetic activity can lead to persistent autonomic imbalance, which is associated with
various CVD outcomes.67,68,70,78-86 Prolonged sympathetic dominance can contribute to
hypertension, increased adiposity, insulin resistance, and arterial stiffness, all of which are risk
factors for developing CVD.70,72,87,88 Therefore, our results indicate the cardiac ANS may serve as
a crucial mechanism linking ST to increased CVD risk. Furthermore, by elucidating how ST is
linked to cardiac ANS function, this dissertation lays the groundwork for future investigations to
continue to disentangle the complex relationships of ST with cardiac ANS function and CVD risk.
Implications
This dissertation employed several methodological approaches that advance the field. We
are the first to demonstrate the feasibility of combining gold standard measures of ST and cardiac
ANS over multiple days in both free-living and in-lab settings. The use of gold standard measures

107
reduces measurement error and increases the reliability and validity of our findings. However, we
acknowledge that we experienced issues with missing HRV data. Missing HRV data was largely
due to participant non-compliance and poor ECG attachment because of weak patch adhesion.
Future work should identify smaller, research-grade ECG devices to improve data collection and
enhance data completeness. In addition, the observational and experimental approaches used offer
first insights into ST-cardiac ANS function associations. Findings from the observational studies
(Study 1 and Study 2) provide clarity since extant cross-sectional studies in youth are
inconsistent135-137 and offer results with enhanced ecological validity since data were collected in
free-living environments. The experimental manipulation of ST in the lab (Study 3) provides an
analysis of these associations in a controlled setting, which allows for fewer confounders to
identify dosing of ST and ST breaks. Lastly, this work includes between-person and within-person
investigations and manipulations of ST patterns, which provides a robust analysis of how ST
influences cardiac ANS function. Taken together, the methodological rigor in this dissertation
establishes a foundation for future studies to build upon.
Our findings extend the limited literature on the relationship between ST and cardiac ANS
function in youth. Similar to Veijalainen et al. (2019) and Farah et al. (2020),136,137 we found that
higher ST was associated with worse cardiac ANS function in free-living settings. However, our
study provides additional insights by demonstrating both between-person and within-person
associations in youth with OW/OB, a population underrepresented in prior studies investigating
these relationships.
135-137 Unlike Oliveira et al. (2018), who found no associations,135 our study
detected significant associations, possibly due to differences in sample characteristics and
measurement precision. In the present study, higher ST, whether compared to others or to a youth’s
usual behavior, was linked to lower SDNN, RMSSD, LF, and HF, indicating worse cardiac ANS

108
function. Our in-lab findings further support these associations, showing that a single, longer bout
of moderate-intensity walking (EX condition) improved HRV metrics over time compared to
continuous sitting (SIT condition). This suggests that cumulative ST and daily fluctuations in ST
may impair cardiac ANS function, while breaking up ST with a longer moderate-intensity walking
bout may improve cardiac ANS function. However, it remains unclear at what point and under
which circumstances ST triggers adverse physiological responses, such as increased sympathetic
activity. More research is needed to understand how both short- and long-term patterns of ST
influence cardiac ANS function to identify when interventions are most effective in improving the
cardiac ANS.
Studying youth with OW/OB adds important value to the literature by addressing a group
at high risk for poor cardiovascular outcomes both in childhood and adulthood.17-19,25-28 Given the
rising rate of OW/OB in childhood,265,266 it is critical to understand how behavioral factors, like
ST, relate to mechanisms driving CVD risk since youth with OW/OB experience different
physiological responses compared to their healthy-weight peers.75,267 Our sample had distinct
physiological and behavioral characteristics, such as high body fat (mean >40%) and ST (mean
~11 hours per day), which likely contributed to our findings. Elevated adiposity can impair cardiac
ANS function by promoting chronic inflammation, which can trigger sympathetic
overactivation.23,24,32,45,225 Adipose tissue contributes to endothelial dysfunction by impairing
blood vessel dilation, which may disrupt parasympathetic regulation.268,269 Additionally, our
sample averaged only ~40 minutes of MVPA per day, falling short of the recommended 60 minutes
per day,
270 which may have left participants without the benefits of physical activity, such as
improved cardiac ANS balance and flexibility.
140,167,168 Future studies should explore these
associations in youth with different weight statuses to determine whether the relationship between

109
ST and cardiac ANS function is unique to those with OW/OB or consistent across broader
populations.
Demonstrating that increased ST is associated with poorer cardiac ANS function in both
free-living and in-lab settings underscores the urgent need for early and targeted interventions
aimed at mitigating the adverse effects of prolonged ST. Findings from our study indicate that a
longer, uninterrupted bout of moderate-intensity walking may be effective, at least in the shortterm, in improving cardiac ANS function in youth with OW/OB. This provides further support and
aligns with a systematic review that found that bout lengths of 20 minutes to up to 2-hours of
exercise are needed to elicit improvements in cardiac ANS function among youth <18 years old.167
Future work should focus on further exploring the optimal duration and frequency of moderateintensity walking, an intensity more that may be more attainable for youth with OW/OB, that can
improve cardiac ANS function. Additionally, future work should also explore the best type of
intervention for breaking up ST to improve cardiac ANS function. For example, just-in-time
adaptive interventions that capture real-time cardiac ANS function and ST, and prompt youth to
participate in physical activity accordingly, could be investigated as a potential strategy. Future
studies could also investigate interventions implemented as after-school programs or leverage
smartphone applications that can track ST and cardiac ANS function and intervene (e.g., send
reminders, provide gamified elements). By identifying the most effective ST-ST break patterns
and the best ways to translate in-lab interventions to real-world solutions, we can develop
impactful and practical intervention programs that maximize cardiac ANS benefits for youth and
ultimately promote healthier, more active lifestyles at an early age.
This dissertation also explored moderating factors that underscore the need for tailored
intervention strategies. Body fat percent, trunk fat, and fitness level were not significant

110
moderators of the ST-cardiac ANS relationship. This suggests that the associations between ST
and cardiac ANS function are relatively consistent across youth with different levels of adiposity
and fitness. Similarly, day-level factors such as MVPA, sleep, and day of the week did not
moderate these associations, indicating that the effects of ST on cardiac ANS function remain
stable throughout the week despite changes in activity behaviors each day. Although these factors
are important for overall health, their lack of significant impact on the ST-cardiac ANS function
relationship indicates that interventions do not need to be overly complex. This allows for more
straightforward and implementable designs that directly target ST reduction, making it easier for
youth to adopt and maintain these changes in their daily lives.
Our findings have clinical implications, highlighting the need for behavioral and health
monitoring in youth with OW/OB beyond the conventional monitoring methods (e.g., blood
sampling, in-person evaluations). Integrating regular monitoring of ST and cardiac ANS function
can provide valuable insights into early markers of CVD risk, even in the absence of the typical
CVD risk factors (e.g., insulin resistance, dyslipidemia). Clinicians can also play a pivotal role in
educating youth with OW/OB and their caregivers about the potential harms of prolonged ST.
Lastly, using our findings and those from other studies, clinicians may also be able to provide
personalized recommendations for managing ST and practical strategies for breaking it up
throughout the day, especially as evidence continues to emerge in this topic area.
This dissertation and future work will play an important role in the creation of public health
policies aimed at improving health throughout the lifespan. We now have a better understanding
of how ST is potentially linked to CVD and dosing of how to break up ST to potentially improve
cardiac ANS function in youth with OW/OB. Considering that youth often have limited control
over their behaviors during the school day, implementing policies to restructure the school day

111
with scheduled ST breaks could benefit cardiac ANS function. This may be particularly beneficial
for youth with OW/OB who may struggle to incorporate breaks and accumulate physical activity.
Efforts are also needed to create safe environments where youth can safely implement ST breaks
throughout their day or implement programs that teach youth and their caregivers about how to
accumulate moderate-intensity ST breaks in their home with little to no equipment. Lastly, this
study provides foundational insights into how ST independently relates to a physiological
mechanism that is strongly connected to poor CVD outcomes, yet further research is needed before
developing guidelines. Future research should focus on determining the optimal dose of ST breaks
to improve cardiac ANS function. Additionally, there are several contextual factors that should be
explored before developing specific recommendations, such as the type of sedentary activity,
environmental context, and the presence of other behaviors that typically coincide with ST
(discussed in further details in the Future Research Directions section below).
Future Research Directions
To further advance our understanding of the associations of ST and cardiac ANS function
in youth, several key areas warrant investigation. Researchers should continue to combine gold
standard measurement methods for ST and cardiac ANS function to minimize measurement error
and ensure accuracy and reliability of findings. It is also important for investigators to leverage
research-grade ECGs to mitigate compliance issues and reduce the likelihood of missing data.
Utilizing smaller devices with less adhesive area can prevent skin-related issues such as rashes,
improving participant comfort and compliance. While some wearable devices (e.g., Polar heart
rate monitors) have been validated against ECG recordings on short timescales (e.g., 5 min, 15
min),72,271-276 to our knowledge, none have been validated against 24-hour ECG recordings. Future
studies should consider validating wearable devices, such as the Fitbit, against the gold standard

112
ECG on longer timescales (i.e., across 24 hours) so researchers have more options for measuring
daily cardiac ANS function. This validation would provide researchers with more feasible and
realistic options for measuring cardiac ANS function, potentially increasing the accessibility and
scalability of such measurements in various study designs.
Future studies should also investigate ST-cardiac ANS relationships using other study
designs. Longitudinal studies are essential for advancing our understanding of ST and the cardiac
ANS. These studies should include mediation analyses to determine if poor cardiac ANS function
mediates the relationship between more ST and increased CVD risk. Additionally, tracking youth
over extended periods will help elucidate the long-term effects of ST on cardiac ANS, which can
also help identify key timepoints for intervention. Investigating the associations of 24-hour
movement behaviors on cardiac ANS is also critical. This comprehensive approach will provide
insights on how movements behaviors (i.e., physical activity, ST, and sleep) collectively influence
cardiac ANS function. Furthermore, in-lab interventions can corroborate and expand preliminary
evidence, helping to identify optimal ST break dosing. In-field interventions are also necessary to
find practical ways to translate in-lab findings to real-world settings.
Several contextual factors related to ST may influence its association with the cardiac ANS
and warrant further investigation. The type of sedentary activity may have differential impacts on
cardiac ANS function. For instance, the cognitive engagement required for homework or reading
might result in different physiological responses compared to the passive nature of watching
television.277-282 Other behaviors that typically coincide with ST may also impact the relationship
between ST and cardiac ANS function. Unhealthy dietary intake (e.g., high intakes of saturated
and trans-fat and high-glycemic carbohydrates) often accompanies sedentary activities like
watching television283,284 and is known to adversely affect the cardiac ANS.213 Environmental

113
factors also may play a crucial role in these associations. For instance, being alone versus with
others or at home versus at school can influence the amount and type of sedentary behavior285,286
and may have differential effects on ST-cardiac ANS function linkages. Understanding the
nuances of how different types of sedentary behaviors, concurrent activities, and environmental
contexts influence the ST-cardiac ANS association is essential for informing intervention
strategies and national guidelines.
Our findings are limited to youth with OW/OB, and extant literature is among youth of
varying weight statuses.135-137 Future studies should expand to other populations to understand
these associations across a variety of youth to ensure generalizability, develop comprehensive
guidelines, address health disparities, and aid in disease management. Studies could investigate
these associations in youth with normal weight status, adolescents, and clinical populations such
as youth with diabetes or metabolic disorders. Investigating diverse groups will provide a more
complete and equitable understanding of how ST influences cardiac ANS function in all youth,
which could then be leveraged to improve overall health outcomes in this population.
Strengths and Limitations
This dissertation has several strengths worth noting. This research addresses a critical gap
in the current evidence and focuses on an at-risk population, specifically youth with OW/OB. The
dissertation comprehensively investigates the overarching research question by using both
observational and experimental study designs. This approach allows for the identification of realworld relationships between ST and cardiac ANS function, as well as potential manipulations in
ST that could improve cardiac ANS function. Lastly, by employing gold standard measurement
methods for the main predictor (ST) and outcome (cardiac ANS function), this dissertation reduces
measurement error and improves the reliability and validity of the results.

114
This dissertation also has limitations. Although Study 1 and Study 2 collected multiple
days of data, their observational nature limits the ability to draw causal inferences about the
relationships between ST and cardiac ANS function. The findings may have limited
generalizability due to the specific focus on youth with OW/OB and may not be applicable to other
populations. However, this dissertation provides a strong foundation for future similar
investigations among larger, more diverse samples than those presented here. The short duration
of the studies limits the ability to elucidate whether the observed ST-cardiac ANS links would
subsequently result in reduced CVD risk over the long term. Lastly, the small sample size and
challenges with compliance and missing data across all three studies, primarily due to the ECGs
used, may impact the robustness of the findings. While imputation was considered to address
missing data, it was deemed inappropriate for this dissertation because: 1) over 60% of the daylevel data for Study 1 and 2 and over 50% for Study 3 were missing, which would require excessive
imputation that would compromise the reliability of the results;287,288 2) only 52% of participants
for Study 1 and 2, 64% for Day 1 and 7 analyses and 53% for Days 2-6 analyses in Study 3 had
sufficient data for inclusion, meaning the missing data was too extensive and likely non-random,
increasing the risk of bias and undermining the validity of imputation;287,288 3) with a small sample
size, even with imputation, it would be harder to examine between-person associations;289 4)
demographic and other variables (e.g., ethnicity, time of day, day of the week) were associated
with missing accelerometer and ECG data, suggesting non-random patterns of missingness that
imputation may not adequately address;289 and 5) if someone failed to complete a measure entirely,
there was no data available to "recreate" the missing information, making imputation ineffective
in those cases.290 However, several steps were taken across all three studies to address missing
data. Sensitivity analyses were used to compare participants included versus excluded to assess

115
potential biases. Additional sensitivity analyses were used to identify demographic and behavioral
variables associated with missingness, which were tested as covariates. These variables were
retained in ancillary models, even when not statistically significant, to ensure result robustness.
Furthermore, we ran additional models with relaxed wear-time parameters (i.e., 1 day with at least
8 hours of waking wear) to increase sample size and further evaluate the reliability of our findings.
Conclusions
This dissertation has significantly advanced our understanding of the relationship between
ST and cardiac ANS function in youth with OW/OB. Through a combination of observational and
experimental designs, the three studies have elucidated that increased ST is associated with poorer
cardiac ANS function in this population. Although no significant moderators were found,
examining person-level factors (e.g., body composition, fitness level) and day-level factors (e.g.,
MVPA, sleep) provides additional context for understanding the effects of ST on cardiac ANS
function. Moreover, experimental findings suggest that interventions aimed at reducing ST and
promoting moderate-intensity activity breaks may mitigate cardiac ANS dysfunction, with longer
bouts showing potential for cardiac ANS improvement. Methodological rigor, including rigorous
study designs and gold standard measurements, enhances the reliability and applicability of these
findings. Moving forward, integrating these insights into clinical practice and public health
strategies may help mitigate cardiovascular risks associated with sedentary lifestyles in youth with
OW/OB, fostering healthier outcomes and guiding future research endeavors.

116
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Asset Metadata

Creator Beland, Kelsey McAlister (author)

Core Title Free-living and in-lab effects of sedentary time on cardiac autonomic nervous system function in youth with overweight and obesity

Contributor Electronically uploaded by the author (provenance)

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Health Behavior Research

Degree Conferral Date 2024-12

Publication Date 01/10/2025

Defense Date 12/17/2024

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

Tag activity behaviors,children,heart rate variability,OAI-PMH Harvest,sitting time

Format theses (aat)

Language English

Advisor Belcher, Britni (committee chair), Dunton, Genevieve (committee member), Miller, Kimberly (committee member), Page, Kathleen (committee member), Unger, Jennifer (committee member)

Creator Email kelseylmcalister@gmail.com,kmcalist@usc.edu

Unique identifier UC11399F8N0

Identifier etd-BelandKels-13735.pdf (filename)

Legacy Identifier etd-BelandKels-13735

Document Type Dissertation

Format theses (aat)

Rights Beland, Kelsey McAlister

Internet Media Type application/pdf

Type texts

Source 20250112-usctheses-batch-1233 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email uscdl@usc.edu

Abstract (if available)

Abstract Excessive sedentary time (ST) in youth is a growing health concern, with physiological evidence suggesting it may negatively impact the cardiac autonomic nervous system (ANS), which may have implications for cardiovascular health. However, observational and experimental research using gold standard measures remains limited. This dissertation investigated the free-living and in-lab effects of ST on cardiac ANS function in youth with overweight and obesity (OW/OB). The overarching objective of this dissertation was to increase our scientific understanding of ST-cardiac ANS associations by taking a comprehensive approach, examining both habitual ST in daily life and structured ST breaks in the lab. The specific aims of this dissertation were to: 1) observationally examine the between-person and within-person associations of ST with cardiac ANS function in a free-living, naturalistic environment, 2) explore potential person-level and day-level moderating factors in the free-living associations of ST with cardiac ANS function, and 3) experimentally investigate the acute effects of interrupting ST with walking on cardiac ANS function in youth with OW/OB. Findings suggest that: 1) youth who spend more time overall in ST, and on days when their ST is higher than usual, experience worse cardiac ANS function, 2) ST-cardiac ANS function associations did not vary by person-level or day-level moderating factors, and 3) breaking up ST in the lab over a three-hour period with a longer moderate-intensity walking bout over multiple days may mitigate the adverse effects of ST on cardiac ANS function. Taken together, these findings highlight the role of minimizing ST and introducing regular, moderate-intensity movement breaks to support healthier cardiac ANS function in youth with OW/OB. Methodological rigor enhances the reliability of these findings, which could inform clinical and public health strategies to reduce cardiovascular risks from sedentary behavior in this population, promoting healthier outcomes and guiding future research.