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Theses and Dissertations--Kinesiology and Health Promotion Kinesiology and Health Promotion

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CIRCADIAN RHYTHM PHASE SHIFTS CAUSED BY TIMED CIRCADIAN RHYTHM PHASE SHIFTS CAUSED BY TIMED

EXERCISE VARY WITH CHRONOTYPE IN YOUNG ADULTS EXERCISE VARY WITH CHRONOTYPE IN YOUNG ADULTS

J. Matthew Thomas University of Kentucky, jmthomg@uky.edu Digital Object Identifier: https://doi.org/10.13023/etd.2019.406

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J. Matthew Thomas, Student

Dr. Jody L. Clasey, Major Professor

Dr. Melinda Ickes, Director of Graduate Studies

CIRCADIAN RHYTHM PHASE SHIFTS CAUSED BY TIMED EXERCISE VARY WITH CHRONOTYPE IN YOUNG ADULTS

________________________________________

DISSERTATION ________________________________________

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the

College of Education at the University of Kentucky

By J. Matthew Thomas

Lexington, Kentucky Director: Dr. Jody L. Clasey, Professor of Kinesiology and Health Promotion

Lexington, Kentucky 2019

Copyright © J. Matthew Thomas 2019

ABSTRACT OF DISSERTATION

CIRCADIAN RHYTHM PHASE SHIFTS CAUSED BY TIMED EXERCISE VARY WITH CHRONOTYPE IN YOUNG ADULTS

The circadian system controls 24-hour cycles of behavior and physiology, such as rest-activity and feeding rhythms. The human circadian system synchronizes with, or entrains to, the light/dark cycle (sunrise/sunset) to promote activity and food consumption during the day and rest at night. However, strict work schedules and nighttime light exposure impair proper entrainment of the circadian system, resulting in chronic circadian misalignment. Numerous studies have shown that chronic circadian misalignment results in poor health. Therefore, therapeutic interventions that could shift circadian rhythms and alleviate circadian misalignment could broadly impact public health. Although light is the most salient time cue for the circadian system, several laboratory studies have shown that exercise can also entrain the internal circadian rhythm. However, these studies were performed in controlled laboratory conditions with physically-active participants. The purpose of this study was to determine whether timed exercise can phase advance (shift earlier) the internal circadian rhythm in sedentary subjects in free-living conditions. Fifty-two young, sedentary adults (16 male, 24.3±0.76 yrs) participated in the study. As a marker of the phase of the internal circadian rhythm, we measured salivary melatonin levels (dim light melatonin onset: DLMO) before and after 5 days of timed exercise. Participants were randomized to perform either morning (10h after DLMO) or evening (20h after DLMO) supervised exercise training for 5 consecutive days. We found that morning exercisers had a significantly greater phase advance than evening exercisers. Importantly, the morning exercisers had a 0.6h phase advance, which could theoretically better align their internal circadian rhythms with the light-dark cycle and with early-morning social obligations. In addition, we also found that baseline DLMO, a proxy for chronotype, influenced the effect of timed exercise. We found that for later chronotypes, both morning and evening exercise advanced the internal circadian rhythm. In contrast, earlier chronotypes had phase advances when they exercised in the morning, but phase delays when they exercised in the evening. Thus, late chronotypes, who experience the most severe circadian misalignment, may benefit from exercise in the morning or evening, but evening exercise may exacerbate circadian misalignment in early

chronotypes. Together these results suggest that personalized exercise timing prescriptions based on chronotype could alleviate circadian misalignment in young adults.

KEYWORDS: Circadian Rhythms, Chronotype, Circadian Misalignment Exercise, Phase Shift

J. Matthew Thomas

October 11, 2019 Date

CIRCADIAN RHYTHM PHASE SHIFTS CAUSED BY TIMED EXERCISE VARY WITH CHRONOTYPE IN YOUNG ADULTS

By J. Matthew Thomas

Dr. Jody L. Clasey Director of Dissertation

Dr. Melinda Ickes Director of Graduate Studies

September 16, 2019 Date

iii

Table of Contents

List of Tables……………….………..……………………………………..…….………vi

List of Figures…………...………………………………..…………………………..….vii

Chapter 1: Review of Literature……………………………….………..…………1

1.1 Overview…………………………………………………………….……1

1.2 Circadian rhythms in humans…….………………………..………...……2

1.2.1 What is a circadian rhythm?……..………………………………….2

1.2.2 Fundamental properties………..……………………………….……2

1.2.3 How are circadian rhythms generated?…………...….………….…..4

1.2.3.1 Circadian pacemaker……………...………………..………..4

1.2.3.2 Molecular mechanism…..……………….…..……...……..…5

1.2.3.3 Peripheral circadian oscillators in mammals……..…..…..….7

1.2.4 Modulation of circadian rhythms…………….…….…………….....9

1.2.5 Measuring human circadian rhythms……….…………………..…12

1.2.5.1 Melatonin………….…..…..……………………....……….12

1.2.5.2 Body temperature…………....………………………..…...14

1.2.5.3 Actigraphy…….……...…….……………………….……..15

1.2.5.4 Subjective questionnaires………...………....………….….16

1.2.6 Disruption of human circadian rhythms……………………..…….17

1.2.6.1 Shift work……………..……………………….…………..17

1.2.6.2 Circadian misalignment………....……………..………….18

1.2.6.2.1 Chronotype………………..……………….……19

1.2.6.2.2 Impact of chronotype on human performance and health…………………………………………...….……...21

1.2.6.2.3 Social jetlag…….……………..……….………..23

1.2.6.2.4 Other measures of circadian misalignment…..…24

1.2.7 Interventions targeting the circadian system……………………..26

1.2.7.1 Light therapy…………..…..………….…………….……26

iv

1.2.7.2 Time restricted feeding…………..………………….…...26

1.2.7.3 Melatonin………………………..…..………….………..28

1.2.7.4 Physical activity……………..…………..…………..…...28

1.3 Physical activity and human health……………………………………….29

1.3.1 Importance of physical activity and physical fitness…….……..…..29

1.3.2 Physical activity dose-response……...….…….…..………………..30

1.3.3 Physical activity and sleep……………...………….……………….33

1.3.4 Measurement of physical fitness ……………….…….……………34

1.3.4.1 Cardiorespiratory fitness……..…..………………………..34

1.3.4.2 Body composition……..…….…………………………….35

1.3.4.3 Muscular Fitness………....…………………………..……36

1.3.4.4 Flexibility……………..…………………………..……….36

1.3.5 Measurement of physical activity………....………………..………36

1.3.5.1 Accelerometers………..…..……...……………………….36

1.4 Physical activity and circadian rhythms…………………….……………..37

1.4.1 Phase shifting the circadian clock with exercise……..………....….37

1.4.2 Mechanisms causing exercise phase shifts…….…….…….…...…..44

1.4.3 Exercise timing and performance……………....……………….….44

1.5 Conclusion…………………………………………………….……………46

Chapter 2: Circadian rhythm phase shifts caused by timed exercise vary with chronotype in young adults…………………….…………….…………..……………...47

2.1 Abstract……………………………………………………………………..47

2.2 Introduction…………………..……………………………………………..49

2.3 Results……………………………………………………………………....50

2.3.1 Study Participants…………………………………………………….50

2.3.2 The time of exercise affects the phase of circadian rhythms in young, sedentary adults………………………………………..………….....51

2.3.3 Chronotype is associated with phase shifts in evening exercisers.......55

2.4 Discussion…………………………………………………………………..57

v

2.5 Methods……………………………………………………………………..61

2.5.1 Participants……………………………………………………………61

2.5.2 Study protocol…………………………………………………………61

2.5.3 Chronotype questionnaires……...……………………………………..62

2.5.4 Anthropometric and body composition measures…….……..………...62

2.5.5 Actigraphy……………………………...…………………………...…63

2.5.6 Circadian phase measure: dim light melatonin onset (DLMO)...….….63

2.5.7 Maximal graded exercise test……………..………………………..…64

2.5.8 Exercise intervention……………..…..…………………………….....65

2.5.9 Phase shifts……………………..…………………………………..…65

2.5.10 Statistical analysis……………...………………………………….…66

2.5.11 Study Approval………………….....……………………………...…66

2.6 Supplemental information……..……………………………………………...67

Chapter 3: Expanded Methodology and Procedures……………………………………..70

3.1 Study approval………………………………………………………………..70

3.2 Subjects recruitment………..………………………………………………....70

3.3 Protocol………………………………………………………………………..71

3.3.1 Anthropometric measures and body composition...………………….….71

3.3.2 Cardiorespiratory fitness……………………………………………...….72

3.3.3 Circadian and sleep measures…..………………………………………..73

3.3.3.1 Dim light melatonin onset…..…………………………………...73

3.3.3.2 Chronotype………………………………………………………78

3.3.3.3 Actigraphy and sleep logs…………………………………….….78

3.3.4 Exercise intervention………………………….………………………….80

3.3.5 Statistical analysis……………………………………………………..…81

3.4 Excluded and withdrawn subjects………………………………………….….81

References………………………...……………………………………………………..84

Curriculum Vitae…………………………………………..…………………………...102

vi

List of Tables

Table 1.1 Summary of research investigating circadian phase shifts with exercise……..39

Table 2.1 Characteristics of study participants ……………….…………….………..….52

Table 2.2 Circadian and sleep characteristics of study participants……………….….....54

Table 2.3 Statistical model to assess covariates ………………………...………..….…..54

Table 2.4 Model of morning and evening exercise on earlier and later chronotypes ..….56

Table 2.5 Supplemental table - baseline characteristics of study participants by sex .….67

vii

List of Figures

Figure 1.1 Components of a circadian system………….…………………………………3

Figure 1.2 Hierarchical organization of the mammalian circadian system……….…...….8

Figure 1.3 Example phase response curve to light exposure…………………..……...…10

Figure 1.4 Melatonin rhythm with dim light melatonin onset (DLMO) indicated…..…..13

Figure 1.5 Circadian misalignment measured as the phase relationship between DLMO and sleep onset ……………………….………………………………………..………...25

Figure 1.6 Phase advances of the internal circadian rhythm could improve circadian alignment in late chronotypes……………………………..…………………..…………38

Figure 2.1 Participant recruitment…………………….…………………………………51

Figure 2.2 Morning exercise advances the phase of the internal circadian rhythm…..….55

Figure 2.3 Phase shift is correlated with internal phase in evening exercisers………..…56

Figure 2.4 The effects of timed exercise on phase shift depends on chronotype………..57

Figure 2.5 Study protocol………………………………………………………..………62

Figure 2.6 Supplemental figure. Chronotype is normally distributed…………..…….....68

Figure 2.7 Supplemental figure. MEQ is associated with sleep fragmentation during exercise intervention……………………………………………………………..……....68

Figure 2.8 Supplemental Figure. Baseline DLMO is associated with mid-sleep during exercise intervention…………………………………….……………………………….69

Figure 3.1 Recruitment flyer…………………………………………………...………...71

Figure 3.2 DLMO collection instruction document………………...…………………....75

Figure 3.3 DLMO setup for circadian phase measurement………………...…………....76

Figure 3.4 Dim light melatonin onset calculation……………………...………………...77

Figure 3.5 Sleep logs………………………………………………………...…………...79

Figure 3.6 Excluded subject DLMOs…………………………………………..…..……83

1

Chapter 1. Review of Literature

1.1 Overview

Exercise has many well-established benefits to physical and mental health (1-6).

Additionally, many aspects of exercise prescription, including intensity, duration,

frequency, mode, volume, and progression, have been studied to determine the optimal

approaches to benefit health (2, 7, 8). However, the proper time of day of exercise to

achieve maximal health benefits has not been elucidated. Determining the ideal time of

day to exercise could have a broad and dramatic impact on human health.

The effects of timed exercise on health are likely mediated by the circadian system.

Circadian rhythms are 24-hour cycles of physiology and behavior that are synchronized

with the daily cycles caused by the rotation of the Earth. Humans have evolved with the

24-hour light/dark cycle of sunrise and sunset. The circadian system synchronizes with

this 24-hour cycle and regulates rhythms of behavior and physiology so that they

anticipate and are synchronized with the daily availability of resources (9). Humans are

diurnal, meaning the circadian system promotes activity and arousal during the day and

rest at night.

Modern society has experienced a massive departure from reliance on environmental

cycles to shape daily lives and has limited exposure to natural light/dark cycles. The

advent of electric lighting, as well as the use of TVs, tablets, and smartphones, has also

resulted in light exposure at night. These changes in lifestyle have paralleled the increases

in the prevalence of obesity and sedentary behavior.

Numerous studies have shown that circadian disruption has negative effects on health.

For example, shift work, which chronically disrupts the circadian system, increases the

risk of cancer, obesity and cardiovascular disease (10-15). Why is the circadian system

important to human health? In this review I will first discuss the fundamental properties

of circadian rhythms, the discovery of circadian clocks in tissues, methods for measuring

circadian rhythms, and the consequences of disrupting the circadian system. Next, I will

discuss the health benefits of regular physical activity, the proper exercise dose to

improve health, and the importance of physical fitness, as well as methods of measuring

2

fitness. Finally, I will discuss the current body of evidence supporting an effect of

exercise on the circadian system.

1.2 Circadian rhythms in humans

1.2.1 What is a circadian rhythm?

Circadian rhythms are approximately 24-hour cycles of behavior, physiology, and gene

expression. Circadian rhythms have evolved to entrain to environmental cycles but can

continue to oscillate even without environmental input. Observations of circadian

rhythms go back as far as the fourth century B.C. (16). However, it wasn’t until the 18th

century when the first experiment was performed, demonstrating that Mimosa plants have

circadian rhythms of leaf movement (17). Two hundred years later, in the middle of the

20th century, there was a surge of research in the field of circadian rhythms. Work by

Colin Pittendrigh, Jorgen Aschoff, and Franz Halberg (who coined the term circadian

rhythms in 1959; cira-diem, latin for “about a day”) was pivotal in developing the field of

circadian biology. In one of Pittendrighs’ early works (18) he proposed that a circadian

system must have 3 essential components: 1. rhythmic environmental input, or zeitgeber

(German for time giver); 2. an endogenous self-sustained oscillator; and 3. output

rhythms of physiology and behavior (Figure 1.1). This is now the dogmatic model of the

circadian system.

1.2.2 Fundamental Properties

There are 3 fundamental properties of a circadian rhythm. These properties distinguish

circadian rhythms from simple responses to external stimuli and demonstrate that it

oscillates autonomously.

First, a circadian rhythm must be endogenous, such that the rhythms continue to cycle

with a ~24-hour period when placed in constant conditions without external time cues.

This is called the free-running rhythm. The free-running periods of most organisms are

near, but not exactly 24 hours. The fact that circadian rhythms are not exactly 24 hours is

evidence that circadian rhythms are generated endogenously and are not driven by an

environmental stimulus (since environmental stimuli are often exactly 24 hours). An

internal rhythm, which continues to oscillate in constant conditions, has been observed in

3

many organisms spanning many phyla. In the 1950s researchers showed that several

different organisms presented free-running rhythms, even after being in constant

conditions since birth or for multiple generations (18). In the past several decades,

research on human circadian rhythms has involved controlled study designs aimed at

observing human circadian rhythms with no time cues or environmental stimuli. These

studies have taken place in caves, bunkers, and controlled lab conditions with the goal of

limiting external perturbations that can effect circadian rhythms (19, 20).

Figure 1.1 Components of a circadian system. A circadian system has 3 essential components: A) cyclic input (zeitgeber), which is most often the light-dark cycle in humans; B) a self-sustained oscillator that can keep time in the absence of input, but is also capable of entraining to the cyclic input; and C) Output rhythms that are driven by the oscillator and entrained to the cyclic input.

The second fundamental property of a circadian rhythm is that it must entrain to

environmental stimuli that have a 24-hour rhythm. The ability of internal rhythms to

entrain to environmental inputs is critical for the adaptation to the 24-hour cycle.

Entrainment is the process in which a circadian rhythm (which is near, but not exactly 24

hours) adopts the period of an input stimuli (usually the 24-hour light/dark cycle) and

develops a stable phase relationship. Two distinct models of entrainment, parametric and

non-parametric, have been proposed by the founders of the circadian biology field,

Aschoff and Pittendrigh, respectively (21, 22). The non-parametric model, involving

4

instantaneous shifts to the internal rhythm, is the most well-established model and will be

discussed in a later section.

The third fundamental property is that a circadian rhythm must be temperature

compensated. This means that the period of the rhythm remains constant over a

physiological range of temperatures. Biological reactions are sensitive to temperature. If

a circadian rhythm was not temperature compensated, the period of the rhythm would

vary depending on the external temperature. Early evidence of temperature compensation

investigated ectothermic organisms, including bees, crabs, and Drosophila (23, 24). More

recently, temperature compensation has been observed in neural and peripheral tissues

cultured from endotherms (within normal physiological temperature ranges), including

the suprachiasmatic nuclei (SCN) and fibroblasts (25). Very little evidence is available to

demonstrate temperature compensation in humans. However, one study did show

temperature compensation in fibroblast samples from one subject (26).

1.2.3 How are circadian rhythms generated?

In the 1950s, the 3-component model of the circadian system proposed by Colin

Pittendrigh (Figure 1.1) was quite controversial (18). Specifically, the existence of a self-

sustained endogenous oscillator was a point of contention. A popular hypothesis at the

time explained daily rhythms with an “hourglass model” involving a substance that

accumulated during the night and was depleted during the day (27). Another obstacle at

that time was that the anatomical location of the hourglass or endogenous oscillator was

unknown. In the following paragraphs, I describe the discovery of circadian oscillators in

mammals, and how these discoveries supported Pittendrigh’s model, containing a self-

sustained oscillator.

1.2.3.1 The Circadian Pacemaker

Prior to the 1970s, the location of the tissue responsible for generating 24-hour biological

rhythms was a mystery. However, researchers knew that a rostral portion of the

hypothalamus controlled the rhythmic estrous cycle (28), and it therefore was a candidate

for the circadian pacemaker. Fredrick Stephan and Irving Zucker (29) lesioned an area of

the hypothalamus called the suprachiasmatic nucleus (SCN) and measured drinking and

5

activity in rats. Whereas control rats consumed 95% of their water during the dark phase,

SCN-lesioned rats completely lost their nocturnal drinking rhythm. Nocturnal wheel

running activity was also significantly reduced, thereby disrupting the activity rhythm.

Moore and Eichler (30) then provided further information about the ability of the clock to

synchronize with the light/dark cycle. They investigated the role of retinal projections in

modulating the 24-hour rhythm of corticosterone in rats. They found that the

corticosterone rhythm was not entrained to the light-dark cycle when the

retinohypothalamic tract was severed. The corticosterone rhythm was also destroyed

when the SCN was lesioned. Thus, it was concluded that the SCN was responsible for

generating neuroendocrine rhythms and the retinohypothalamic tract was essential for

entraining 24-hour rhythms with the light/dark cycle.

Even with this considerable evidence suggesting the SCN was the mammalian pacemaker

tissue, further evidence was warranted. Therefore, Michael Menaker and colleagues (31)

sought to determine if transplanting SCN tissue to an SCN-lesioned animal could restore

circadian rhythms in the recipient animal. Wild-type and tau mutant hamsters were the

perfect model organisms because of their distinctly different period lengths. Wild-types

had a 24-hour period and homozygous tau mutants had a 20-hour period. After SCN

ablation the hamsters became completely arrhythmic, as evidenced by their arrhythmic

locomotor activity. Fetal donor SCN tissue was implanted in each of the hamsters.

Following the implant, the locomotor activity of the hamsters returned. Incredibly, the

hamsters exhibited a period that was similar to the genotype of the donor SCN, therefore

providing evidence that the SCN is autonomous in the generation of mammalian

circadian rhythms. Also, this experiment showed that the SCN tissue encoded the period

of circadian rhythms and regulated output rhythms.

1.2.3.2 Molecular Mechanisms

Even before the discovery of the SCN, the first evidence of the molecular mechanisms of

the circadian clock came from Drosophila (32-34). After hatching, Drosophila emerge as

maggots, and then develop into pupae. Emergence from the pupae (termed eclosion) into

an adult form occurs at dawn and is a bona-fide circadian rhythm. Konopka and Benzer

(35) sought to determine if genes could encode the circadian rhythm of eclosion. To

6

determine this, they mutagenized Drosophila and identified mutants with abnormal

eclosion rhythms. Following observation of eclosion rhythms from thousands of flies, 3

types of mutants were observed. One mutant was arrhythmic and emerged at any time of

day or night. One had a short period of approximately 19 hours and emerged hours before

dawn. The third mutant type had a long period of about 28 hours and emerged hours after

dawn. To locate the position of the mutation, recombination experiments were performed.

The three types of mutants were all products of alterations to a single gene that affected

both eclosion and locomotor rhythms. This was the first evidence of a gene that was

responsible for the period of the circadian oscillator. Thus, Konopka and Benzer named it

the period (per) gene. The per gene was later cloned, which was a major discovery that

earned three circadian biologists, Drs. Rosbach, Hall, and Young, the 2017 Nobel Prize in

Physiology or Medicine.

Konopka and Benzer discovered a major component of the clock, but their studies did not

address how the per gene regulated circadian rhythms. To investigate this, Hardin and

colleagues (34) exposed wild-type flies to a cycle of 12 hours of light and 12 hours of

dark prior to freezing individual flies at various circadian times. They found that per

mRNA peaked at the time of lights-off and was at its lowest in the middle of the light

period. They also observed rhythmic fluctuations of per mRNA in mutant flies with

abnormal period lengths. However, in mutant flies they noticed that the mRNA

fluctuations were either earlier or later compared to the wild-types. Even in 24-hour dark

conditions per mRNA expression was rhythmic. In fact, the mRNA fluctuations were

identical in period length to the activity rhythms of the Drosophila. This study concluded

that rhythmic fluctuations in per mRNA underlie fluctuations in PER protein and

possibly overall circadian pacemaker rhythmicity. They further postulated that the per

gene product regulates its own transcription by negative feedback and drives the clock

function and the output rhythms.

Although the discovery of the period gene in Drosophila was integral to understanding

the circadian clock, much more information was needed, particularly in mammals. An

explosion of genetic findings in the mammalian circadian system began with the

discovery of the Clock gene (36). Similar to the approach used by Konopka and Benzer in

7

Drosophila, Takahashi et al. (36) mutagenized mice and measured activity rhythms. The

Clock mutant mice had a long period and became arrhythmic in constant darkness. The

position of the Clock gene was also mapped on the mammalian chromosome. Following

this, the other components of the mammalian molecular clock were discovered.

The current model of the molecular timekeeping mechanism of circadian clocks in

mammals is a transcriptional/translational feedback loop of gene and protein expression

(37-40). Two transcription factors, CLOCK and BMAL1, heterodimerize and drive the

transcription of Per and Cry mRNA. After translation of these mRNAs into protein

products they dimerize and are then able to enter the nucleus. Once in the nucleus, PER

and CRY proteins inhibit the transcription factor activity of CLOCK and BMAL1. It

takes approximately 24-hour for one cycle of this transcription/translation feedback loop.

Other mechanisms, such as post-translational modifications, refine the molecular

feedback loop to modulate period length and to permit entrainment to environmental

cycles.

1.2.3.3 Peripheral circadian oscillators in mammals

Research at the turn of the 21st century expanded our understanding of the components of

the mammalian circadian system. By this time, thousands of circadian rhythms in

behavior, physiology, biochemistry, and gene expression had been discovered. It seemed

unlikely that one solitary circadian oscillator in the SCN could directly regulate all these

output rhythms. Several lines of evidence in rodents, and other species, suggested that

peripheral tissues may contain self-sustaining circadian oscillators (see (41) for review).

If a peripheral tissue contained a self-sustained clock, then its rhythms should persist in a

dish. (42, 43). Yamazaki et al. (42) took advantage of the recent cloning of the Per1 gene

and promotor to generate a reporter animal. By linking the firefly luciferase gene to the

mouse Per gene promoter, a transgenic rat model was created. This allowed the

visualization of the rhythmic activity of this promoter in peripheral tissues in a dish by

measuring bioluminescence. This technique allowed for the observation of circadian

rhythms in any tissue ex vivo. Researchers observed that in addition to the SCN,

peripheral tissues (e.g. lung, skeletal muscle, liver) exhibited robust circadian rhythms of

Per1 promoter-driven bioluminescence when they were cultured in a dish. In addition,

8

the rhythm of each tissue peaked at a unique time, relative to the SCN rhythm, that was

similar for all rats in the study, indicating a stable phase relationships between each tissue

and the SCN. Also, large shifts (6 hours) in the light/dark cycle resulted in immediate

shifts of the SCN, but slower shifts in peripheral rhythms. In a similar study (43), even

SCN lesioned mice had intact rhythms in peripheral tissues. Importantly, SCN lesioning

resulted in large differences in the phase of peripheral rhythms in each tissue, suggesting

that the SCN coordinates the phases of peripheral tissues.

Figure 1.2 Hierarchical organization of the mammalian circadian system. A circadian pacemaker (SCN) receives light information and entrains to the light dark cycle. The SCN then coordinates the phases of peripheral oscillators. These oscillators then control local circadian output rhythms.

Many studies have contributed to the development of the hierarchical model of the

mammalian circadian system (42-44). This model includes a circadian pacemaker (SCN),

9

which receives light information and entrains to the light dark cycle. The SCN then

coordinates the phase in peripheral oscillators (Figure 1.2). The SCN and peripheral

oscillators then drive their respective output rhythms.

1.2.4 Modulation of circadian rhythms

The circadian system evolved to entrain to natural environmental cycles. Over the course

of a day an organism is exposed to many stimuli that could contribute to entrainment. The

cycle of light (sunrise) and dark (sunset) has long been considered the most salient

entrainment cue for the SCN in mammals (9). This so-called photic stimulus has been

well-studied. Decades ago researchers began to plot the response of various physiological

and behavioral rhythms to timed light stimuli (21, 45). These plots came to be known as

Phase Response Curves (PRC), which are graphical representations of phase shifts as a

function of time, unique to each organism and each stimulus. Currently, PRCs exist for

many organisms, nocturnal and diurnal, including humans (46-48).

Constructing a PRC is done by first releasing the organism into constant conditions (i.e.,

there are no rhythmic stimuli in the environment), which is typically constant darkness

for rodents (47). Then light pulses are applied at different times, and the resultant shift in

output rhythms are observed. Several studies in humans have used the circadian rhythm

of melatonin production as an output rhythm for a PRC. When PRCs are graphed, the x-

axis represents the time of the stimulus, relative to a given phase marker from a

physiological rhythm. Previous human studies have plotted the number of hours after

melatonin onset or hours since core temperature minimum to display the time of a

stimulus relative to an individual’s internal rhythm. By calculating the change in timing

of a phase marker, a phase shift is measured. The y-axis represents the magnitude of the

phase shift in the output rhythm (Figure 1.3). By convention, a positive value denotes an

advance in the phase and a negative value denotes a delay in the phase. The shape of the

PRC (i.e., the maximal phase advance or delay) shows the range of entrainment for the

pacemaker to a given stimulus. There are limits to entrainment, which are determined by

the intensity and duration of the stimulus, the circadian period of the organism, and the

shape of the PRC to that stimulus.

10

Figure 1.3 Example phase response curve to light exposure. Light pulses in the early and middle of the night, near the temperature minimum, will cause phase delays while early morning light exposure will cause phase advances (49-51).

PRCs provide us with an understanding of how we and other organisms entrain to the 24-

hour daily rotation of the earth. Two modes of entrainment have been proposed:

parametric and non-parametric (47, 52). Parametric entrainment is based on the premise

that continuous light exposure acts to compress or extend the free running period and

thereby entrain the pacemaker to the environment (45). Non-parametric entrainment is

based on the premise that the circadian pacemaker is entrained by exposure to discrete

light pulses. When these light pulses are delivered during phases when the circadian

pacemaker is capable of shifting, it will advance or delay its timing to entrain to the

external cue. Although circadian entrainment likely involves contributions of both

parametric and non-parametric entrainment, non-parametric entrainment has been well-

studied in recent decades. In support of non-parametric entrainment, several studies have

shown that humans and other organisms are able to stably entrain to brief pulses of light

(skeleton photoperiods) throughout the day (21, 51, 53).

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For adult humans who have a period slightly greater than 24 hours, the pacemaker must

experience a daily phase advance in order to entrain to the light-dark cycle (54). In

humans, a light pulse during the early part of the night will phase-delay the circadian

clock, while a light pulse during the late part of the night (early morning) will phase-

advance the circadian clock (Figure 1.3) (49-51). Therefore, exposure to early morning

light is advantageous to humans because it helps them entrain to the environmental light-

dark cycle.

The entrainment of the circadian clock to environmental stimuli regulates the magnitude

of circadian misalignment. If an individual is stably entrained with a small phase angle to

the light-dark cycle (i.e., the sleep-wake cycle is aligned with the dark-light cycle), then

the individual has minimal circadian misalignment. This is important because circadian

misalignment is associated with insulin resistance, obesity, metabolic syndrome, and

systemic inflammation (11, 13, 55-57) (see further discussion below). Thus, phase

response curves can be used to understand how we anticipate our circadian rhythms will

respond to a given stimulus. For example a condition called delayed sleep-wake phase

disorder is associated with reduced light exposure during a critical advancing window of

the PRC to light (58). Since the human period is usually slightly longer than 24 hours,

this lack of daily phase advance may exacerbate the delayed sleep/wake rhythm.

Considering the numerous health consequences of circadian misalignment (discussed

below), it is important to understand the precise effect of photic and non-photic time cues

throughout the 24-hour day.

Although many studies have investigated the impact of photic stimuli on circadian

rhythms, non-photic cues can also phase shift the circadian system. For example, there is

mounting evidence suggesting an effect of food intake, exogenous melatonin, and

physical activity on human circadian rhythms (59-63). Although it is clear that non-

photic cues can alter circadian rhythms, questions still remain regarding the optimal

timing of these stimuli to improve entrainment to the light/dark cycle. Regularly

scheduled non-photic stimuli could be an attractive intervention for late chronotypes and

others experiencing circadian misalignment.

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1.2.5 Measuring Human Circadian Rhythms

Studies in rodents and non-human primates have established that the SCN is the circadian

pacemaker (64, 65). The SCN is also believed to be the circadian pacemaker in humans,

although it is not possible to perform lesion or other experiments to validate this

hypothesis. Therefore, in humans, the internal circadian timing, or phase, is measured by

assessing rhythms that are presumably under the direct control of the SCN. These include

core body temperature and hormone (e.g., cortisol, melatonin) rhythms. Though a

multitude of rhythms exhibit circadian oscillations, few are appropriate for controlled

investigations. Many rhythms (e.g. activity, heart rate, insulin sensitivity, and cortisol)

are influenced greatly by external factors. When stimuli appear to affect a rhythm without

actually altering the pacemaker, it is called “masking”. Previous research has attempted

to limit the masking effect by conducting human studies in controlled conditions. One

experimental condition used in many human circadian studies is called constant routine

(66, 67). During the constant routine, subjects remain awake in a room in constant dim

light (temperature and humidity are also constant), in a supine position, with isocaloric

meals provided at regular intervals (e.g. every hour) for about 48-hours. This

experimental method has been used for decades in human circadian studies. However, the

highly controlled laboratory conditions make it difficult to apply any findings to free-

living conditions. Circadian rhythms can also be measured in free-living conditions. Two

well-established physiological markers of the internal phase are melatonin secretion and

core body temperature. These measures, as well as others that are ideal free-living

measures of human circadian rhythms, are discussed below.

1.2.5.1 Melatonin

The human circadian pacemaker directly controls the timing of the secretion of melatonin

from the pineal gland (68). The rhythm of melatonin typically peaks during the night for

people with normal sleep/wake activity (Figure 1.4). It is also a bonafide circadian

rhythm since it persists in the absence of light (or in constant dim light conditions). The

melatonin rhythm, measured in plasma, urine, or saliva, is a well-established method for

measuring human circadian phase (68-71). Light suppresses melatonin levels. For this

reason, experts have agreed that measurements of melatonin measured for circadian

13

purposes should be conducted in dim light of <30lux (71). When measuring the

melatonin rhythm, any time point can be used to assess the endogenous phase. However,

because melatonin onset occurs prior to sleeping, this time point is commonly chosen as

the phase marker. Dim light melatonin onset (DLMO) represents the synthesis and

secretion of melatonin. Using linear interpolation, DLMO is defined as the time when

melatonin concentration exceeds a specific threshold. Previous studies have defined

DLMO using a variety of thresholds (e.g., 3pg/ml, 4pg/ml, 5pg/ml, 10pg/ml, mean + 2SD

of daytime values, 20% of peak, 25% of peak, 40% of peak) (70-74) depending on the

biological fluid (saliva, blood, or urine) that is being collected. Often 4pg/ml is used as

the threshold for DLMO in saliva (73, 75). Previous studies have found that DLMO is

strongly associated with sleep timing (73, 76). Specifically, DLMO occurs approximately

2 hours prior to bedtime for young adults (Figure 1.4) and is significantly correlated with

mid-sleep and wake time on free days (days with no vocational responsibilities) (69, 76).

Figure 1.4 Melatonin rhythm with dim light melatonin onset (DLMO) indicated.

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Posture is an important factor to consider when measuring melatonin. Postural changes

have been shown to alter plasma and salivary melatonin levels (77). For this reason,

extraneous movement must be minimized during the DLMO assay. This is particularly

important just prior to collecting each sample. Subjects are typically asked to refrain from

caffeine, chocolate, bananas and alcohol on the day (if not longer) of the melatonin

measure (75, 76). In addition, nonsteroidal anti-inflammatory drugs (NSAIDs) have been

shown to suppress melatonin and should be excluded up to 72 hours before assessment

(78). While most studies have found that there was not an effect of menstrual cycle on the

melatonin rhythm, one study showed a delay in melatonin onset during the luteal phase

(79-82). However, the conflicting result could have been due to the large differences in

light exposure between study protocols (range: 50 to >1000 lux).

1.2.5.2 Body Temperature

Oscillations in core body temperature occur every ~24 hours. In free-living humans who

have normal sleep/wake activity, core body temperature peaks around 14:00-20:00 and

begins to rise just before waking, thereby anticipating and preparing the individual for

waking. Previous studies in rodents have demonstrated that the core body temperature

rhythm occurs as a direct output of the SCN (83, 84). Many studies in humans have

established that the rhythm of body temperature persists in controlled conditions, limiting

external time cues. Nathaniel Kleitman conducted one of the first studies to demonstrate

the human circadian rhythm of body temperature. In 1938, Kleitman and his graduate

student lived in Mammoth Cave for several weeks under a modified 28-hour day (19).

Each day consisted of 19 hours of wakefulness and 9 hours of darkness in bed. The

researchers observed robust circadian rhythms of oral temperature.

The traditional method for measuring core body temperature in humans is by an

indwelling rectal probe, which requires that the subject remain in the lab for the duration

of the experiment. Laboratory measures of the core temperature rhythm often take place

in a constant routine protocol since core body temperature is sensitive to masking effects.

However, the restriction of sleep (which is enforced in the constant routine) can reduce

the amplitude of the temperature rhythm; therefore some researchers have suggested that

15

the core temperature rhythm measured during constant routine does not truly represent

the endogenous rhythm (85, 86).

Another method to measure core body temperature is by gut temperature thermistor pills.

This method can be used in free-living subjects. These temperature sensors are

swallowed and collect core temperature information as the sensor passes through the

digestive tract. A previous study compared rectal and gut temperature measurements and

showed that they had narrow limits of agreement, while axilla temperature was very

different from rectal temperature and exhibited greater limits of agreement (87).

Disadvantages to using thermistor pills are that they are expensive, and often multiple pill

ingestions are required due to reduced transient time and evacuation from the bowels. In

addition, the subjects must wear a small transmitter around the abdomen to collect data

from the sensor.

The Thermochron iButton® has been used to non-invasively measure the circadian

rhythm of skin temperature in free-living subjects (88, 89). The iButton is a small metal

device approximately the size of a quarter which contains a temperature sensor and a

battery. The iButton is usually fixed to skin (such as the inner wrist) using medical

adhesive tape (90). The iButton allows ambulatory monitoring of the skin temperature in

the periphery. However, it is important to note that peripheral skin temperature rhythms

are out of phase with the core body temperature rhythm, because heat is lost via heat

exchange from the skin due to peripheral blood flow. Thus, iButton measurements do not

measure core body temperature and therefore do not report the direct output of the

internal circadian clock. Methodological issues associated with the iButton include a lack

of consensus on proper skin location, and that clothing and ambient temperature affect

the reliability of the data (90).

1.2.5.3 Actigraphy

One of the most well-established circadian rhythms is that of locomotor activity. The

behavioral rhythm of locomotor activity has been documented in nocturnal and diurnal

organisms, including humans (91, 92). In individuals with a normal sleep/wake rhythm,

activity is consolidated to the daytime. Activity rhythms under free-living conditions are

often measured with actigraphy. Actigraphy is the measurement of activity collected via

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accelerometers, which are devices that detect human motion in the form of accelerations,

often in three planes (tri-axial). This is a non-invasive and continuous way to detect

movement. Actigraphy devices can be worn on the wrist or on a waist belt above the hip.

Since the 1970s developments in actigraphy devices have produced devices with long

battery life and high memory storage (greater than 2 weeks). A strength of using

actigraphy is that it allows long, continuous recording under free-living conditions with

minimal participant burden. For example, one previous study continuously collected

actigraphy data for more than 1.5 years (92).

Actigraphy can also be used to indirectly detect sleep, assess sleep quality, and determine

the sleep/wake rhythm. Actigraphy is a free-living alternative to polysomnography

(PSG), which is a well-established laboratory method for measuring sleep. Many

commercially available actigraphy devices have been validated by PSG (93). For

measures of sleep, accelerometers should be worn on the wrist where they are sensitive to

movements of the extremities. It is also helpful to have participants keep recordings of

sleep/wake times and removal of the device to explain missing data or aid in data

processing. Also, many accelerometer devices measure light exposure which is often an

important measure in circadian studies. Previous studies have shown that sleep phase

measured by actigraphy (e.g. sleep onset, mid-sleep) are highly correlated with internal

circadian phase (94).

1.2.5.4 Subjective Questionnaires

Although many well-established physiological and behavioral methods have been used to

measure circadian rhythms in humans, questionnaires have also been developed to assess

circadian rhythms. Several well-established questionnaires have been used in previous

studies that query an individual’s preferred timing of sleep, food intake, and physical and

mental activities, called chronotype (95-97). Chronotype is thought to be a direct output

of the internal circadian rhythm.

The first questionnaire, the Morningness-Eveningness Questionaire (MEQ), was

developed in the mid-1970s and was designed to assess an individuals’ ideal time of sleep

and activity throughout the 24 hour day. Another questionnaire, developed more recently,

is the Munich Chronotype Questionaire (MCTQ). The MCTQ queries the time of sleep

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and wake for the previous month, assessing actual sleep behavior. There are many

benefits to including chronotype questionnaires in free-living research studies, as they are

free and strongly correlated with measures of circadian phase (98, 99). More details

regarding measuring chronotype will be discussed below.

1.2.6 Disruption of human circadian rhythms

The circadian system presumably evolved to coordinate behavior and physiology with

environmental cycles such as sunrise/sunset (9). Modern society has seen a massive

departure from reliance on environmental cycles to shape daily lives. Exposure to natural

light/dark cycles that synchronize the SCN clock to the environment is limited. This has

resulted in a society that is living in a chronic state of circadian disruption. Below I

review the evidence that circadian disruption is negatively impacting human health.

1.2.6.1 Shift Work

Shift work, or working outside of standard daytime hours, has a tremendous impact on

our circadian system. Permanent night, or rotating shift work, interrupts the finely-tuned

coordination of the circadian system with the environment (sunrise/sunset).

Epidemiological studies have found that shift work increases the risk of obesity,

cardiovascular disease, metabolic syndrome, and certain types of cancer (100-103). An

elevated risk of breast cancer has been well-established in several studies, including

airline cabin crews as well as other shift working populations (104, 105). Nakamura et al.

(106) found that rotating shift work was associated with a higher risk of coronary heart

disease, characterized by higher levels of serum cholesterol and abdominal adiposity.

Laboratory studies have also investigated the acute effect of shift work-like circadian

disruption. Morris and colleagues (107) conducted a study in which subjects participated

in an 8-day circadian misalignment protocol (they had a 12-hour shift of their sleep/wake

schedule similar to night shift workers). This shift work-like schedule increased 24-hour

systolic and diastolic blood pressure as well as inflammatory markers including CRP,

TNF-α, resistin, and IL-6. Also, the magnitude of the reduction (“dipping”) in the systolic

blood pressure rhythm was reduced. The loss of the “dip” in blood pressure during sleep

is an independent predictor of adverse cardiovascular events and all-cause mortality

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(108). Findings from the study by Morris et al. suggested that circadian misalignment is a

key mechanism causing the increased risk of cardiovascular disease in shift workers.

A protocol called forced desynchrony is often used in laboratory-based circadian studies

to separate the masking effects of sleep on the circadian rhythm. For example, a common

protocol requires a subject to live multiple days on reoccurring 28-hour days. Since the

period of the human circadian pacemaker is approximately 24 hours, this day length is

outside of the range of entrainment. Thus, the endogenous rhythm will therefore

desynchronize from the imposed light/dark or sleep/wake schedule and presumably

oscillate at its intrinsic rhythm. A study by Scheer and colleagues (109) used the forced

desynchrony protocol to investigate the metabolic and cardiovascular consequences of

circadian misalignment in 10 subjects. Subjects in this protocol were exposed to recurring

28-hour days and ate 4 isocaloric meals each day. Hourly, plasma leptin, insulin, glucose,

and cortisol were measured. On the fourth day of the protocol subjects were misaligned

by 12 hours with their baseline. Compared to when they were aligned, misalignment

resulted in a 17% decrease in leptin, a 6% increase in glucose, and a 22% increase in

insulin. Nearly 40% of subjects exhibited a prediabetic or diabetic 2-hour postprandial

glucose response.

Because sleep restriction often accompanies circadian misalignment in free-living

conditions, one study investigated the combination of forced desynchrony with sleep

restriction on glucose metabolism (110). They found that circadian misalignment

combined with sleep restriction induced a 15% increase in postprandial glucose and 27%

decrease in postprandial insulin (110).

1.2.6.2 Circadian misalignment

Circadian disruption has been defined in many ways, but in general it refers to living

against your internal rhythm. One term, circadian misalignment, has been defined as two

or more rhythms with an abnormal phase angle relative to each other (111). Circadian

misalignment can occur at many different levels, including misalignment between the

circadian pacemaker and the light/dark cycle, or between the circadian pacemaker and

sleep timing, or between rhythms in peripheral tissues and the circadian pacemaker.

Circadian misalignment is a chronic issue that occurs in everyday life. The next sections

19

discuss methods of assessing circadian misalignment in free-living conditions and how

circadian misalignment affects human health.

1.2.6.2.1 Chronotype

Years ago, in the absence of artificial light, the circadian pacemaker and the sleep/wake

cycles were tightly regulated by the environmental light/dark cycle. Modern society and

the advent of electric lighting have resulted in an extended period of illumination into the

night, and large variability in sleep/wake schedules among individuals (112). For

example, a preferred sleep onset of 3:00 AM or later has been reported in approximately

8% of the population (113).

Subjective questionnaires that query preferred timing of daily activities (e.g. sleep, wake,

physical activity, food intake) can be used to assess an individual’s entrainment to the 24-

hour day, called chronotype. The Horne and Osterberg questionnaire, also referred to as

the Morningness Eveningness Questionnaire (MEQ), assigns chronotype based on a

subjective rating of “feeling” more like a morning or evening person (95). The MEQ

consists of nineteen questions regarding preferred timing of daily activities. Output from

this questionnaire provides a composite score ranging from 16-86 that can be used to

categorize an individual as a definite morning type (70-86), moderate morning type (59-

69), neither type (42-58), moderate evening type (31-41), or definite evening type (16-

30). A reduced version of the MEQ is also available (rMEQ) (114). This version of the

chronotype questionnaire was validated using activity rhythms (measured with

accelerometers) and showed that evening types had a significantly delayed activity

rhythm versus morning and intermediate types (115).

Another questionnaire, the Munich Chronotype Questionnaire (MCTQ), assigns

chronotype quantitatively based on the middle of the sleep episode on free days (mid-

sleep free days; MSF) (96). Whereas the MEQ assigns chronotype based on preferential

timing of daily sleep and activity, the MCTQ measures chronotype based on timing of

actual behavior. While the MCTQ does not provide cut-off values to distinguish between

early and late chronotypes, comparisons within a sample or population allow for the

categorization of “earlier” or “later” chronotypes. The MSF calculated from the MCTQ

can also be “corrected” for accumulated sleep debt (MSFsc) (96). Late sleep timing and

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early alarms during the work week often lead to accumulated sleep debt, and exaggerated

and very late free day mid-sleep times. Importantly, MSFsc is unable to be calculated for

individuals who have no free days or use alarms on free days (11).

MSFsc on free days can differ by more than 4 hours between earlier and later

chronotypes (96). Although variability decreases when comparing sleep/wake timing

during the work days, early chronotypes fall asleep almost 2 hours prior to late

chronotypes. Interestingly, they still tend to wake up within 30 min of each other, leading

to a reduced sleep duration for late chronotypes (96).

Chronotype is known to be affected by environmental and social influences (116). Some

evidence also suggests that genetics could contribute to inter-individual variability of

chronotype. Duffy et al. (117) demonstrated that individuals who were more of a morning

chronotype had a shorter endogenous circadian period, and individuals who were more of

an evening chronotype had a longer period. Furthermore, period length was associated

with wake time and core body temperature phase. Another study found that a

polymorphism in the CLOCK gene was significantly associated with MEQ morningness-

eveningness score (118). Chronotype is also significantly associated with dim light

melatonin onset, a measure of endogenous phase (98, 99).

Chronotype is not fixed and can vary throughout an individual’s lifespan (119). In a

seminal study, Roenneberg and colleagues investigated the impact of age and sex on

chronotype of individuals living in Europe. They found that young children and older

adults were on average earlier chronotypes, as measured by mid-sleep on free days from

the MCTQ, while adolescents and young adults (approximately 20 years of age) had the

latest chronotypes (119). Furthermore, women were typically earlier chronotypes than

men, until the age of 50 when the gender difference disappeared. A recent study was

performed in individuals living in the United States (120). This study used mid-sleep on

free (weekend) days as the measure of chronotype. Similarly, this study showed that

chronotype continued to become later from age 15 until approximately 20. After 20 years

of age chronotype advances steadily throughout the lifespan. Furthermore, a later

chronotype was observed in men, however this sex difference was only found until 40

years of age. Following 40 years of age women displayed later chronotypes. In addition,

21

variability in chronotypes (standard deviation in average mid-sleep weekend day) was

greatest during the 20-24 age range and decreases non-linearly with age.

1.2.6.2.2 Impact of chronotype on human performance and health

Accumulating evidence has shown that chronotype affects school academic performance

(121-123). Specifically, late chronotypes perform more poorly and have lower grades.

This is particularly impactful since adolescents have the latest chronotype. The negative

association between chronotype and performance has been shown to be strongest in

“science-based” academic subjects (chemistry, geography, biology, mathematics) that

involve reasoning, logic, and abstract thinking (121). In addition, late chronotypes

performed worse on exams administered in the morning compared to morning

chronotypes. This difference in performance was abolished when exams were

administered in the afternoon (123). Late chronotypes were also sick and absent from

school more often than early chrontypes. Thus, chronotype must be taken into account in

assessment of academic performance. Some schools have implemented later start times

and have observed improvements, primarily in sleepiness and mood (124).

In both adolescents and adults, chronotype is associated with depression (125-127).

Specifically, late chronotypes are at a greater risk of having depressive symptoms (125).

In contrast one study showed that early chronotypes are more likely to have depression

symptoms (128). Late chronotype is also associated with greater likelihood to use

addictive substances (113, 125).

Several studies have investigated the relationships between chronotype and dietary

choices and obesity (113, 129, 130). Later sleep timing or late chronotype is associated

with shorter sleep duration, lower consumption of fruits and vegetables, higher BMI,

lower HDL, less frequent meals, and more calories consumed after 8:00 PM (113, 129).

After controlling for sleep timing, age, and sleep duration, caloric consumption after 8:00

PM has been shown to be an independent predictor of BMI and total caloric intake.

Maukonen et al. (130) investigated the interrelationships between a healthy diet,

chronotype, and obesity. Their results showed that evening chronotypes were younger,

more likely to smoke, more likely to be physically inactive, more likely to perceive

themselves as having poor health, and had higher levels of education.

22

Merikanto et al. (131) investigated the association between chronotype and

cardiovascular disease and type 2 diabetes, as well as outcomes associated with the

pathogenesis of these diseases. Results from this cross-sectional study showed that late

chronotypes had an increased risk of type 2 diabetes and hypertension compared to

morning types. Another study in adolescents showed that late chronotype (measured by

MSFsc) is associated with more frequent headaches, stomach aches, and backaches, as

well as drinking soft drinks, smoking, and more screen time (132). These results were

independent of sleep duration, environmental factors, and concurrent health behaviors. In

a recent study, Knutson and Schantz (133) investigated the relationship between

chronotype and morbidity and mortality using the UK Biobank data. To determine

chronotype, the researchers asked one question to place the subject in a category ranging

from definite morning to definite evening. Definite evening types had greater odds of

having comorbid conditions, including psychological and neurological disorders,

diabetes, renal disease, and CVD.

The impact of chronotype also extends to diurnal variations in physical and cognitive

performance. A recent study observed a significant interaction of time of day and

chronotype for daytime sleepiness, psychomotor vigilance, executive function, and

maximum voluntary contraction (standardized method for measuring muscle strength)

(134). In the morning (08:00), early chronotypes performed significantly better than late

chronotypes in both cognitive and physical tasks. Unexpectedly, when the data was

expressed as a function of time since waking, peak performance on cognitive tasks

occurred approximately at the time of typical awaking for early chronotypes and occurred

12.6 hours after waking for late chronotypes. Furthermore, peak performance on a

physical task occurred 6.7 hours after waking for early chronotypes and 12.6 hours after

waking for late chronotypes. Other literature has shown that chronotype can influence the

perceived exertion during exercise sessions, resulting in increased perceived exertion

during morning physical activity for late chronotypes (135).

Previous studies have shown that natural light significantly impacts chronotype.

Roenneberg and colleagues (96) found that time spent outdoors was significantly

correlated with midsleep on free days. People who spent more than 30hours/week

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outdoors had a midsleep that was almost 2 hours earlier than individuals who spent less

than 10 hours/week. In another study, Wright et al. observed melatonin rhythms and sleep

timing in 8 participants during 1 week of normal, electric lighting conditions and during 1

week of camping in the natural light/dark environment (136). Following the week of

camping, sleep timing advanced, especially in late chronotypes. The authors postulated

that natural sunlight synchronized circadian rhythms and decreased circadian disruption.

Importantly, during the camping week, physical activity levels were 70% higher, and

could have contributed to the advance of the circadian rhythms.

A recent randomized controlled trial recruited late chronotypes for an intervention in

which subjects in the experimental condition were asked to participate in practical

adjustments to shift their activities to earlier times (e.g. earlier sleep times, morning light

exposure, fixed meal times and caffeine intake) (137). The experimental group

experienced a 2-hour advance in sleep/wake time and circadian phase (DLMO),

accompanied by improvements in morning and afternoon sleepiness, cognition, and

physical performance. Most importantly, the experimental group reported significantly

reduced depression and stress.

1.2.6.2.3 Social Jetlag

Jetlag is a common form of circadian misalignment in modern society. When individuals

travel across time zones, circadian oscillators become desynchronized with the

environment. Jetlag caused by trans-meridian travel subsides after a few days, when the

phases of circadian oscillators entrain to the new environmental cycle. A phenomenon

similar to jetlag, called social jetlag, is misalignment between social obligations and

internal circadian rhythms. Social jetlag is chronic circadian misalignment that affects

70% of people living their everyday lives (11). Electrical lighting, TV watching, and

smart phone use promote staying up late at night, but work and school schedules require

early wake times. Eighty percent of people require an alarm clock to wake up on work

days, often several hours before they naturally wake up on the weekend (11). Sleep

timing on the weekend (or other days with no social obligations) represents the timing of

the internal circadian clock. Thus, social jetlag is quantified as the difference (in hours)

between the time of mid-sleep on work days and on free days (138). Similar to

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chronotype, social jetlag varies with age as well. Social jetlag is greatest in young adults

(<21 years old = 3 hours) (96).

Recent studies have found that social jetlag is associated with poor health (11, 13, 55,

139). Roenneberg and colleagues (11) found that social jetlag was positively associated

with BMI in overweight individuals. Wong et al. (13) investigated the association

between chronotype, social jetlag, and cardiometabolic risk factors in a sample of middle-

aged participants. After controlling for sleep quality and various health behaviors, social

jetlag associated positively with triglycerides, fasting insulin, insulin resistance, waist

circumference, and BMI, and negatively with HDL-cholesterol. Other studies also found

that social jetlag was positively associated with BMI, as well as fat mass, cortisol levels,

increased heart rate, and the metabolic syndrome (55, 56). In a group of individuals with

non-communicable diseases (including type 2 diabetes, hypertension, dyslipidemia, or

obesity) social jetlag was found to be significantly positively associated with fasting

glucose. In addition, having greater than one 1 hour of social jetlag resulted in twice the

likelihood of being overweight (139).

Despite the accumulating evidence of the negative health effects associated with social

jetlag, few studies have investigated interventions to reduce this form of circadian

misalignment. In a recent study, Zerbini and colleagues (140) developed two different

protocols aimed at advancing DLMO and sleep onset, and thereby reducing social jetlag.

The two methods the researchers chose were: 1) Wearing blue light-blocking glasses in

the evening prior to bed and 2) Opening curtains in the morning to allow increased dawn

light exposure. Both methods (imposed on different subjects in separate intervention

groups) were designed to expose subjects to light during the advancing portion of the

human PRC. Advances in DLMO and work day sleep onset occurred after 1-week

wearing light-blocking glasses. However, social jetlag was not significantly improved

following either protocol.

1.2.6.2.4 Other measures of circadian misalignment

Other measures to quantify circadian misalignment are also being developed. These

measures use analyses of physiological and behavioral rhythms, including DLMO and

actigraphy measured sleep data. For example, one method quantifies the relationship

25

between DLMO and sleep onset to determine the circadian alignment between the central

circadian rhythm and sleep (Figure 1.5). The average duration between DLMO and sleep

onset in young adults is approximately 2 hours. One study found greater insulin

resistance when sleep onset was closer to DLMO (e.g., less than 2h) (10).

Two other measures of misalignment that can be performed using only sleep data are

composite phase deviation (CPD) and sleep regularity index (SRI) (141, 142). Both of

these indices assess the longitudinal regularity of sleep timing. The CPD composite score

also takes into account the relationship between sleep timing and the internal circadian

rhythm. In a recent study, sleep irregularity was shown to be associated with increased

obesity, hypertension, fasting glucose, hemoglobin A1C, stress, and depression (143).

Figure 1.5 Circadian misalignment measured as the phase relationship between DLMO and sleep onset. Young adults have an average phase relationship of 2h between DLMO and sleep onset. Studies have shown that phase relationships less than 2h are associated with poor metabolic health.

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1.2.7 Interventions targeting the circadian system

1.2.7.1 Light therapy

Because light is a salient cue for synchronizing the circadian system with the

environment, clinical studies have investigated the efficacy of light in shifting the internal

circadian rhythm (50, 51). For example, one study found that exposure to the natural

outdoor light-dark cycle during a week of camping (e.g. bright sunny days and dark

nights) advanced sleep onset, especially in late chronotypes (136). Bright morning light

treatment is also used to treat maladies associated with circadian disruption, such as

delayed sleep phase disorder and seasonal affective disorder (144-146). While these and

other studies largely support the efficacy of light in alleviating circadian misalignment,

light treatments have not been widely used, in part because they require special light

boxes and optimized exposures to light (e.g., optimal brightness, spectral content, and

duration) that have not been standardized (147). In addition, some medications and

medical conditions (e.g. glaucoma) can contraindicate safe exposure to light therapy

(147) and some subjects report side effects like nausea and headaches (148).

1.2.7.2 Time-restricted feeding

The human circadian system has evolved to promote activity, wakefulness and food

intake during the day, and sleep and fasting at night. When food intake occurs during

inappropriate times, such as the biological night, it can result in deleterious health

consequences, including weight gain (149, 150).

Many studies have investigated the circadian rhythm of food intake in rodents (62, 151).

Healthy nocturnal mice eating normal chow show a robust feeding rhythm, with most of

their food consumed during the night and very little during the day. When given access to

high-fat diet, these mice lose their feeding rhythm and eat during both the day and night.

In addition, these mice become obese (152). However, if the same high fat diet is

provided only at the proper time of day, health and metabolism improves (62). Hatori et

al. (62) showed that when mice were fed high-fat diet only during the night they did not

gain excess body weight, in spite of eating the same caloric amount as their ad libitum-

fed littermates. They were also protected from hyperinsulinemia, hepatic steatosis, and

27

inflammation. In another study, time restricted (high-fat diet only during the active phase

at night) feeding was performed during the 5-day work week and mice ate ad libitum on

weekends (151). This scenario, which is relevant to human lifestyles (i.e., less structure

on weekends), resulted in the same metabolic benefits and weight improvement as

continuous time-restricted feeding.

The timing of food intake has also been explored and implemented as an intervention in

humans. A study in college students showed that later timing of food intake, relative to

DLMO, was associated with higher body fat percentage and BMI (149). No association

was found between caloric content, meal macronutrient content, activity level, or sleep

duration and body composition. These findings highlight the importance of the timing of

nutritional intake, independent of other factors. Another study tracked daily eating in

humans using a smart phone app (153). They found that more than half of the subjects ate

for 15 hours or more each day (15h from the first meal to the final meal of the day),

which means they were eating during the biological night. The researchers then instructed

a group of subjects to restrict their eating to 10-12 hours per day for 16 weeks. At the end

of the 16-week intervention, the subjects had an average weight loss of 3.27 kg. Another

study found that time of day of eating is associated with total food intake, with increased

late-night eating resulting in increased daily caloric consumption (154).

The link between food timing and health outcomes may be explained, in part, by the

effects of food timing on circadian clocks in tissues. Food can entrain some circadian

oscillators in mammals, independent of the SCN. In rodents, food intake (timing and

composition) affects circadian rhythms in peripheral tissues, including the liver (152,

155), but does not alter the SCN rhythm. Very little is known regarding the effect of meal

timing on central and peripheral circadian rhythms in humans. One recent study found

that delaying daily meals by 5 hours resulted in a similar delay in the phase of the glucose

rhythm and a small delay of 1 hour in the rhythm of Per2 mRNA in white adipose tissue

(156). The rhythms of melatonin and cortisol were unaltered, suggesting that there was

no effect of delayed meal timing on the SCN. These results suggest that, similar to

rodents, the timing of food intake can alter peripheral, but not SCN, rhythms in humans.

28

Based on these findings in mouse models and humans, the timing of food intake is an

important factor in managing weight and metabolism.

1.2.7.3 Melatonin

Melatonin is a hormone that is produced rhythmically by the pineal gland. Melatonin has

been called the “hormone of darkness” as it is produced primarily during the night. As

previously discussed, melatonin is known for its use as a marker of the phase of the

endogenous circadian pacemaker. However, melatonin is also able to shift circadian

phase (59, 60), perhaps by acting directly on the SCN since melatonin receptors are

expressed in the human SCN in tissue collected post-mortem (157). The timing of

melatonin administration is critical. The phase response curve for exogenous melatonin is

approximately 12 hours out of phase with that of light exposure. Specifically, melatonin

in the late afternoon results in phase advances and melatonin in the early morning results

in phase delays. Therefore, melatonin should be taken 4-6 hours before bed for the

greatest phase advance (158). Oral melatonin administration has no known side effects in

adults, other than sleepiness.

1.2.7.4 Physical Activity

The response to regular exercise, including weight loss and training adaptations, exhibits

large inter-individual differences, raising the possibility that time of day of exercise may

be an important factor (159, 160). Recent studies have observed significantly greater

improvements in weight loss, body composition, and energy intake following morning

exercise compared to exercise later in the day (160, 161). Specially, one study found that

a greater proportion of morning exercisers achieved clinically significant weight loss

(>5%) compared to evening exercisers (160). A potential mechanism for the effect of

timed exercise could be a phase shift of the human circadian rhythm, which will be

discussed in detail later in this review.

29

1.3 Physical Activity and Human Health

1.3.1 Importance of Physical Activity and Physical Fitness

The prevalence of obesity, cardiovascular disease, and type 2 diabetes is increasing at an

alarming rate. Cardiovascular disease is the leading cause of death in the United States

and accounts for 1 in 3 deaths (162). An inverse relationship has been shown between

physical activity habits and negative health outcomes, including coronary heart disease,

cardiovascular disease, stroke, and colon cancer (163). A large cohort study including

39,372 women showed that those who walked as little as 1 hour per week were at a lower

risk of coronary heart disease (164). In addition, higher volumes of exercise conferred

even lower risk of coronary heart disease, even for women currently at high risk (164).

Mortality rates are also 3- to 4-fold greater in low fit compared to high fit individuals

(163). The evidence concerning the health benefits of regular exercise also extends to

improvements in anxiety, depression, cognition, and overall well-being (1, 2).

Researchers have observed these improvements following both resistance and aerobic

training (4, 165). For example, Bartholomew and colleagues (4) saw an improvement in

positive well-being after one 30-minute bout of moderate intensity aerobic exercise in

individuals with major depressive disorder. Larson et al. (166) observed a 32% reduction

in the risk for dementia when older individuals exercised 3 or more times each week.

Although the role of physical activity in the prevention of chronic conditions is well-

established, nearly half of Americans do not meet the American College of Sports

Medicine (ACSM) recommendation of 150 mins of moderate intensity physical exercise

combined with muscle strengthening activity 2 days/week (2). In general, few people are

participating in physical activity during their leisure time. This change, combined with

the decreased amount of time spent in occupations requiring moderate to vigorous

physical activities, has contributed to an increase in sedentary lifestyles. Thus, physical

inactivity has been considered the biggest health concern of the 21st century (167).

Although many large epidemiological studies have shown that physical activity can

improve health and reduce the risk of deleterious conditions as well as morbidity and

mortality, it is critical to investigate the volume of exercise needed to achieve cardio-

protective benefits.

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1.3.2 Physical Activity Dose-Response

Consistent participation in physical activity decreases the risk of chronic diseases. To

provide useful guidelines to the population at large, it is important to understand the

appropriate dose to confer health benefits. The prescribed dose of physical activity is

described in terms of frequency, intensity, duration and type of physical activity. Exercise

volume is the product of frequency, intensity, and duration, and is often reported in a

continuous metric, such as metabolic equivalent (MET). MET is a measure of the energy

cost of physical activity. The MET value represents exercise intensity as a multiple of the

resting metabolic rate (1 MET = 3.5 mL/kg/min) and is expressed as MET-min or MET-

hour per week for physical activity recommendations. The response to physical activity

can be measured relative to indicators of health, fitness, or a combination of these factors.

Most evidence-based research supports the current ACSM guidelines of 150 minutes per

week of moderate physical activity or 75 minutes per week of vigorous physical activity

to promote substantial benefits. The evidence for these optimal doses is derived from

meta-analyses of large epidemiological studies (168, 169).

A number of studies have investigated the dose-response relationship between physical

activity and weight management. Brown et al. (170) investigated the determinants of

healthy weight maintenance in young Australian women over a 16-year period. They

found that women who participated in a volume of exercise equivalent to 500-1000

MET-min/week had higher odds (Odds Ratio: 1.23) of maintaining a healthy BMI at the

16-year follow-up. This volume is equivalent to 150 minutes per week of moderate

intensity exercise. In a recent meta-analysis, Liu et al. (168) investigated the dose-

response association between physical activity and the incidence of hypertension. In total,

this review included 330,222 subjects and found that the risk of hypertension was

reduced with increasing levels of physical activity. The individuals that met the minimum

physical activity guidelines of 150 min/week (approximately 10 MET hour/week), had a

6% reduced risk of hypertension. Furthermore, the risk of hypertension decreased by 6%

more for every 10 MET hour/week increase in physical activity. Huai et al. (171) also

pooled data from prospective cohort studies investigating the association between

physical activity and incidence of hypertension. They found an inverse dose-response

31

association between recreational physical activity and risk of hypertension. Also,

individuals with a greater resting blood pressure typically experienced a greater

improvement following an exercise intervention (172).

A higher volume of physical activity also reduces the risk of type 2 diabetes (173, 174).

Most meta-analyses report a curvi-linear relationship between volume of physical activity

and risk of type 2 diabetes. Thus, the greatest improvement in risk reduction occurred in

individuals who were currently sedentary. The greatest risk reduction occurred when

individuals progressed from being inactive (0 MET hour/week) to moderately active (6

MET hour/week, RR = 0.77). Therefore, the greatest benefits of increasing physical

activity on type 2 diabetes occurs when an individual progresses from sedentary to

moderate vigorous activity which meets the ACSM guidelines.

Changes in a health outcome can vary with baseline disease state. Johannsen et al. (175)

conducted a randomized trial that consisted of 9 months of training (aerobic, resistance,

or combined) to investigate the association between HbA1c improvement and changes in

lipid metabolism and skeletal muscle mitochondrial biogenesis. The novel finding from

this study was that HbA1C change was independently associated with duration of type 2

diabetes. The authors therefore proposed that physical activity interventions should be

performed early after diagnosis to achieve the maximum improvement in glycemic

control.

A plethora of literature has shown significant associations between physical activity and

cardiovascular health (176, 177). Tanasescu et al. (178) assessed leisure time exercise in

a 12-year cohort study involving 44,452 men. Every 2 years a questionnaire was

completed by participants that queried time spent in leisure time activities including

walking, hiking, jogging, running, heavy outdoor work, or weight training. When the data

were analyzed with physical activity as a continuous variable, the results demonstrated

that for every 50 MET-hour/week increase in physical activity, the risk ratio of CHD

decreased by 26%. In another meta-analysis of 1,683,693 participants, Wahid et al.

investigated the association between physical activity and CVD and diabetes (174). The

follow-up period was an average of 12.8 years. They found that increasing physical

activity by 11.25 MET hour/week was associated with a significant risk reduction of

32

CVD incidence and CVD mortality (RR = 0.83, 0.77, respectively). Even after adjusting

for body weight, these risk ratios were still significantly decreased. The greatest risk

reduction occurred when moving from inactive to moderate physical activity (an increase

in 6 MET h/week). The authors extrapolated that this risk reduction would equate to a

decrease of 4.3% risk of CVD mortality per 1 MET hour/week.

Although evidence for associations between regular physical activity and positive health

outcomes is impressive and mounting, there are some discrepancies regarding the manner

in which to achieve the appropriate dose. Many studies in recent decades have

investigated the effects of fractionation of exercise, or performing exercise in multiple

short bouts vs. one long continuous bout. Most evidence-based research suggests that

improvements in cardiovascular fitness and overall energy expenditure are similar

between short, intermittent daily exercise interventions and long duration, extended

interventions (179-181). For example, in one study 47 sedentary women were

randomized to a long (30 min) brisk walk, 3 short brisk walks (10 min ea.), or control (no

walk) for 10 weeks (181). Maximal oxygen uptake (VO2max) increased similarly between

the long and short interval groups, indicating an increase in cardiorespiratory fitness. In

another study, 49 sedentary subjects were randomized to a long daily walk, several short

daily walks, or a control (no walking prescribed) group for 18 weeks (180). Intensity and

total walk duration were held constant for both exercise groups. Reductions in heart rate

during a standard submaximal aerobic fitness test (step test) were observed for both

walking groups, indicating improved cardiovascular fitness for both groups. Thus, the

authors concluded that similar improvements in fitness can be achieved through long

duration or accumulated short duration exercise bouts. In contrast, some studies have

shown a greater effect of multiple short duration exercise bouts, versus one continuous

bout, on daily systolic blood pressure (182, 183). The researchers attributed this finding

to an acute exercise training induced reduction in systolic blood pressure referred to as

post-exercise hypotension. Thus, interspersed short duration physical activity could be an

appropriate therapy to manage hypertension.

One issue to consider concerning the dose-response of physical activity on

cardiometabolic health is the considerable inter-individual variability in response to

33

regular physical activity (184). Although evidence for the beneficial effects of regular

exercise is undeniable, the issue of inter-individual variability in response to an exercise

intervention is often understated. One factor that may contribute to the heterogeneity of

exercise response is a genetic factor that predisposes someone as a non-responder. A non-

responder can be broadly defined as an individual who experiences no significant change

in an outcome of interest. It is possible that non-responders to a particular mode of

exercise training may be a responder to another (185, 186).

1.3.3 Physical Activity and Sleep

Epidemiological studies have shown that regular physical activity is associated with

improved sleep (187, 188). Importantly, the results from epidemiological studies may not

be due to a direct effect of exercise on sleep, but rather an indirect relationship because

people who sleep better often have more energy for exercise. In addition, surveys that

query an individual’s perception of sleep quality or exercise history may be inaccurate

due to issues with recall or truthfulness.

Studies that implemented an exercise intervention found only modest effects on sleep

(189, 190). In addition, exercise intervention studies investigated the effects of chronic

and acute exercise on subjects with healthy sleep. The limited improvement may have

been because the subjects already were healthy sleepers. A meta-analysis by Youngstedt

et al. (191) showed that acute exercise had no effect on sleep latency or wake after sleep

onset, but a small increase in total sleep duration, slow-wave sleep, and REM sleep

latency. In addition, they found that the fitness level did not influence the effect of

exercise on sleep. In a study in normal sleepers who recorded physical activity and sleep

for 102 days, no association was found between daily exercise duration and sleep

variables (189). In this group, when the most active and least active days were compared

(without regard to exercise time), a small improvement in wake after sleep onset, and

sleep efficiency was observed. However, when time spent outdoors was included as a

covariate there was no longer a significant difference. In a separate study involving 7

assessment days the same researchers found (in a between-subjects analysis) only a

modest association between mean total energy expenditure and mean subjective sleep

latency as well as mean total activity and mean reported insomnia (189). However, no

34

within-subjects associations were found between sleep and daily energy expenditure.

Also, no significant differences were found between sleep measures from the most active

and least active days. Thus, for each subject, energy expenditure did not affect sleep

variables. These two studies conclude that daily physical activity does not promote sleep.

Several studies have investigated the effect of time of day of exercise on sleep parameters

with mixed results (72, 192-194). Two studies showed that evening exercise elevated

heart rate during sleep (192, 194). One study also found that evening exercise attenuated

the normal decrease in core temperature (72) and another found that high intensity

exercise near bedtime delayed sleep onset (194). Another study found that exercise

before bedtime does not disturb sleep quality (192). Interestingly, one study found that

morning exercise improved sleep quality in individuals with insomnia (193). Therefore,

overall, evening exercise may negatively impact sleep while morning exercise could

improve sleep.

1.3.4 Measurement of Physical Fitness

The American College of Sports Medicine (ACSM) provides categories and working

definitions for several components of physical fitness including cardiorespiratory fitness,

body composition, muscular strength and endurance, and flexibility (195), which I briefly

review below.

1.3.4.1 Cardiorespiratory Fitness

According to ACSM, a maximal graded exercise test (GXT) is a laboratory measure of

cardiorespiratory fitness. The goal of a max GXT is to perform incremental exercise up to

an intensity that elicits an oxygen uptake that reaches the physiological ceiling of the

individual. This physiological ceiling is typically referred to as a VO2max value. The

VO2max represents exercise tolerance as well as cardiovascular oxygen delivery and

skeletal muscle oxygen extraction. The criteria used to determine if a VO2max has been

achieved is a plateau in oxygen uptake or a change in VO2 ≤ 150 ml* min-1 despite an

increase in workload. The VO2max must not be confused with VO2peak. VO2peak is defined

as the highest oxygen uptake attained during a testing session when predetermined

physiological and subjective criteria have been achieved or exceeded. These criteria

35

include achievement of age-predicted heart rate maximum (Male max HR=220-age;

Female max HR=206-0.88*age) (196, 197), a respiratory exchange ratio ≥1.15, a rating

of perceived exertion ≥17 (6-20; Borg Scale) (198), and venous blood lactate ≥ 8 mmol.

To standardize the max GXT and increase the validity, this set of criteria is used to

determine if maximal volitional effort, and thus VO2peak is achieved for each participant.

Whether designing standardized or unique GXT protocols for a study design, the overall

protocol duration, stage length, and workload increases for each stage should be taken

into account (199). It has been suggested that the primary determinant for the differences

in VO2max for the general population is differences in stroke volume (200) and/or oxygen

extraction by the working skeletal muscle (201, 202).

1.3.4.2 Body Composition

The quantification of components of the human body is important in studying human

health. The body can be divided into several organization levels including atomic,

molecular, cellular, tissue systems, and whole body (203). Various methods have been

used to mathematically model and thus provide estimates of regional and total body

composition. Body composition is a useful measure to determine the absolute and relative

components of the fat and fat-free masses of an individual.

Anthropometric measures are frequently used in clinical and research settings as a

measure of the whole body. These measures include body weight, height, circumferences,

and skinfold measures. Body mass index (BMI) is commonly used to measure weight

relative to height by the following calculation BMI=kg/m2. BMI is often referenced in

clinics and clinical research. Although BMI is unable to distinguish between body fat,

muscle mass, or bone, it is an easily obtained measure that is significantly associated with

cardiovascular and metabolic outcomes (204).

Currently, dual energy x-ray absorptiometry (DXA) is considered one of the single

method criterion measures of total body composition. Based on tissue attenuation of

alternating x-ray currents, DXA is capable of estimating the absolute and relative fat

mass, bone mineral content mass, and mineral-free lean mass (205). While DXA

measurements have error inherit to violations of known assumptions, DXA scans can

36

provide rapid, non-invasive measures of total body and regional measures of body

composition using 3-component modeling techniques (206, 207).

1.3.4.3 Muscular Fitness

Muscular strength and endurance are another component of health and fitness (195).

Muscular fitness is important because it improves bone health, metabolism, and fat-free

mass, and the ability to maintain proper posture and carry out activities of daily living.

Tests of muscular fitness are often specific to a certain muscle group. Assessment of total

body muscle fitness is not possible. Strength testing assesses the maximal force generated

and endurance testing assesses the ability to generate a force for a prolonged period of

time.

1.3.4.4 Flexibility

According to ACSM, flexibility is also an important component of physical fitness (195).

Flexibility represents the ability to carry out a joint movement through its range of

motion. Healthy flexibility allows for proper execution of exercise movements, more

easily completing activities of daily living, and a reduction in injury risk (208).

Assessments of flexibility are specific to the joint being tested and total body assessments

do not exist. The most widely used flexibility assessment is the sit-and-reach test which

measures lower back and hamstring flexibility.

1.3.5 Measurement of physical activity

1.3.5.1 Accelerometers

Accelerometers provide objective and reliable measures of physical activity in free-living

subjects. Accelerometers are devices that detect human movement in the form of

accelerations of motion, often in three planes (tri-axial). The accelerations are converted

into activity counts using validated algorithms. Physical activity levels are estimated

based on activity counts for a specific time period. Accelerometers also provide estimates

of sleep duration and require minimal burden on the subject. These objective measuring

devices are often preferred over subjective physical activity questionnaires due to the

reduction in the error associated with completion, truthfulness, and recall (209).

37

Physical activity output measures from accelerometers include time spent in sedentary

and moderate to vigorous physical activity, estimations of energy expenditure, and the

number of steps taken within specified time intervals. Placement of the accelerometer

device depends on the desired measures (210). When estimating physical activity, these

devices are commonly worn on a waist belt at the level of the top of the iliac crest, in line

with the midpoint of the front of the thigh. For measures of sleep, accelerometers are

worn on the wrist where they are more sensitive to movements of the extremities.

Accelerometers have clocks that time-stamp activity. This is particularly important when

time of day of physical activity and sleep are desired outcome measures.

1.4 Physical Activity and Circadian Rhythms

1.4.1 Phase shifting the circadian clock with exercise

Much is known about the intensity, duration, frequency, volume and progression to

maximize the effectiveness of physical activity; however, much less is known concerning

the time of day exercise should be completed to maximize the beneficial effects of

physical activity.

It is possible that exercise has a time-dependent effect on the circadian system. Proper

entrainment of the circadian system to the environment is important for human health,

and misalignment of the human circadian system can confer numerous negative health

consequences. Exercise, if performed at the appropriate time of day, could shift the phase

of the internal circadian rhythm and therefore improve entrainment. In many individuals

(e.g., in later chronotypes), an advance of the internal circadian rhythm would better align

internal rhythms with the environment and with standard social schedules (Figure 1.6).

To date, there is little evidence-based research concerning the optimal timing of exercise.

It is known that physical activity at specific times of day alters the phase of the circadian

rhythm in rodents (211, 212). Following a single 3-hour exercise pulse, phase advances in

the golden hamster were almost 4 hours. In recent years a few studies investigated the

effect of exercise on phase shifts of the human circadian system (Table 1.1).

38

Figure 1.6 Phase advances of the internal circadian rhythm could improve circadian alignment in late chronotypes. The black line is baseline salivary melatonin rhythm for a late chronotype with a DLMO occurring after sleep onset. A phase advance shifts DLMO earlier to before sleep onset.

In one of the earliest studies, Eastman et al. investigated the ability of exercise to improve

entrainment for night shift workers by phase delaying the human circadian clock (213).

Subjects performed simulated night shift work followed by daytime sleep, thereby

delaying sleep by 9 hours. During the night shift, half of the subjects exercised hourly for

15 minutes at 60% maximum heart rate, and the remaining subjects served as controls (no

exercise). The rhythm of core body temperature was significantly phase-delayed in the

exercise group compared to the controls (6.6 ± 2.5 hrs vs. 4.2 ± 3.4 hrs, respectively).

This phase delay meant that the temperature rhythm exhibited a more normal phase

relationship with the sleep/wake episode. Furthermore, the subjects in the exercise group

reported improved sleep, alertness, and mood.

39

Table 1.1 Summary of research investigating circadian phase shifts with exercise

Time of Day (Reference)

Exercise InterventionA Condition Outcome Activity Level

Morning (214)

1 hour, 75% VO2 max, stair climber, 1 session

Lab 0.5 hour delayB Physically Active

Morning (46)

1 hour treadmill, 65-75% heart rate reserve

Lab 0.9 hour advance Physically Active

Morning (72)

2 hours cycle ergometer 65-75% heart rate max

Lab 1.2 hour delay (similar to control)

Physically Active

Morning (215)

30 min, 70% VO2 max, cycle ergometer, 1 session

Free living 1 hour advance Physically Active

Mid-day (214)

1 hour, 75% VO2 max, stair climber, 1 session

Lab 43 minute delayB Physically Active

Mid-day (216)

1 hour, indoor cardio equipment, 5 sessions

Free living 1.5 hour advance Not specified

Mid-day (46)

1 hour treadmill, 65-75% heart rate reserve

Lab 0.7-0.9 hour advance Physically Active

Evening (214)

1 hour, 75% VO2 max, stair climber, 1 session

Lab 0.5 hour advanceB Physically Active

Evening (215)

30 min, 70% VO2 max, cycle ergometer, 1 session

Free-living No Change Physically Active

Evening (216)

1 hour, indoor cardio equipment, 5 sessions

Free-living 1 hour advance Not specified

Evening (46)

1 hour treadmill, 65-75% heart rate reserve

Lab 0.5-0.7 hour delay Physically Active

Evening (72)

2 hours cycle ergometer 65-75% heart rate max

Lab 1.3 hour delay (similar to control)

Physically Active

Evening (89)

45 min, continuous running, 7 sessions

Free-living

1.75 hour delay (compared to morning exercise)

Not specified

Night (217)

3 hours, alternating arm and leg cycling

Lab 1 hour delay Good Physical Condition

40

Exercise durations used in several studies were extrapolated from rodent studies and are

far too intense for a normal daily exercise routine (e.g. 3 hours/day) (217). Buxton et al.

investigated the effect of a single nocturnal session of exercise at one of two durations

and intensities: 1 hour of exercise at high intensity or 3 hours at moderate intensity (219).

High intensity exercise was performed on a stair climber at 75% VO2 max and low

intensity was maintained at alternating 40% and 60% VO2 max and utilized an arm and

leg exerciser. All subjects (n=8 males) performed both conditions as well as a sedentary

condition in a within-subjects design. The study protocol consisted of a baseline measure

of circadian phase (dim light melatonin onset; DLMO), followed by an exercise

condition, and then a post-exercise circadian phase measure, and a 2-week washout

period. All study procedures were conducted in constant routine conditions, at a light

intensity of 70-80 lux (when not sleeping). Each exercise stimulus occurred at

approximately 01:00 hours. This study showed that delays in melatonin onset were

similar for both exercise durations and intensities (approximately 1-hour delay for each

condition), indicating that more practical exercise recommendations could be used to

phase shift the human circadian clock. Since then, other studies have also shown that an

exercise bout of 1 hour, or even 30 mins, is capable of shifting circadian rhythms (46,

215).

Time of day of an exercise stimulus can determine whether a phase advance, phase delay,

or no shift occurs. Although there is a greater consensus in the literature demonstrating

Table 1.1 Continued

Night (218)

3 hours, cycle ergometer, 40% and 60% VO2max, alternating

Lab Young – 45 min delay

Old – 30 min delay

No criteria

Night (214)

1 hour, 75% VO2 max, stair climber, 1 session

Lab 49 minute delayB Physically Active

Night (46)

1 hour treadmill, 65-75% heart rate reserve

Lab Non-significant Physically Active

Night (215)

30 min, 70% VO2 max, cycle ergometer, 1 session

Free living 1 hour delay Physically Active

AStudies involving combining exercise with additional intervention were not included. BPost-exercise circadian phase was measured on the same day as the exercise stimulus.

41

circadian phase delays from exercise, a few studies have also found phase advances (46,

214, 215). Miyazaki et al. performed the first study showing that physical exercise phase

advances the central circadian rhythm (61). The researchers conducted a controlled

laboratory study involving a shortened sleep-wake schedule (23hr 40min) for 12 cycles in

an isolation facility. Each night subjects began sleeping 20 minutes earlier than the

previous day. Subjects in the exercise group performed physical activity in the morning

and afternoon at an intensity standardized at 140 beats/min. The control group did not

perform exercise and were instructed to sit quietly during the same period of time. A

phase advance of plasma melatonin occurred in the exercise group while a phase delay

was observed in the control group (1.60 ± 0.42 hr and -0.80 ± 0.71 hr, respectively). This

study demonstrated that combined morning and afternoon exercise can phase advance the

circadian system of humans, which could be used to ameliorate circadian disruption or

disorders such as delayed sleep-phase syndrome.

Although Miyazaki and colleagues observed a phase advance following morning and

afternoon exercise, other researchers have observed conflicting results. For example,

Yamanaka et al. (72) found a similar phase delay was observed following both morning

and evening exercise, as well the control group, indicating no effect of time of exercise

on the central circadian rhythm. Buxton et al. (214) provided evidence of a phase

advancing effect of evening exercise. These researchers investigated the impact of

exercise in the morning, afternoon, evening and night on melatonin onset, and observed a

sharp break point around the time of melatonin onset (22:31) where phase advances

changed to phase delays. The evening exercise resulted in a phase advance on the day of

exercise, but this advance was transient, as a large phase delay in melatonin onset was

observed the following day. The researchers postulated that the single exercise stimulus

was not sufficient to induce a steady-state phase shift in the human circadian system. The

authors suggested that repeated exercise exposure at the appropriate circadian phase may

be critical to observe a sustained phase shift.

In rodent models, light and exercise are antagonistic when applied within a few hours of

each other (220). Youngstedt et al. (220) compared the independent phase-delaying

effects of an exercise stimulus with that of bright light exposure during the same time in

42

the night in humans. They also compared these results with that of light exposure and

exercise that was separated by a few hours, and occurred at a time when a delay was

expected from each stimuli. During each condition, the subjects were in the laboratory for

2.5 days and participated in a sleep/wake protocol consisting of repeated 3 hour cycles of

1 hour of sleep and 2 hours of wake. They collected urine every 90 minutes to measure 6-

sulphatoxymelatonin, the urinary metabolite of melatonin. They found a significantly

increased phase delay following exercise combined with light treatment (80.8 ± 11.6 min)

compared to exercise alone (47.3 ± 21.6 min), and a non-significant increase compared to

bright light treatment alone (56.6 ± 15.2 min). The exercise alone group exhibited a phase

shift that was 84% of that of the light treatment group. Although previous literature

suggested that light is a far greater modulator of the circadian clock, this study showed

that properly timed exercise may be an effective alternative.

The majority of previous research on exercise timing in human subjects was conducted in

constant routine conditions. Edwards et al. (215) investigated the effect of an exercise

stimulus in free-living subjects. The subjects performed one exercise bout on a cycle

ergometer for 30 minutes at 70% VO2max at 4 different times throughout the day. Core

body temperature data was collected continuously for 24 hours the day before and the day

after the single bout of exercise. However, during the exercise day the subjects were able

to live freely (unlike previous studies). When the exercise was performed between 4

hours before to 1 hour after the temperature minimum (middle of the night), the subjects

experienced a phase delay of the core temperature rhythm (approximately -1 hour). When

the exercise was performed between 3 hours to 8 hours after the temperature minimum

(morning/early afternoon), the subjects experienced a phase advance in the core

temperature rhythm (approximately +1 hour). Exercise performed outside of these times

had no change on the phase of the temperature rhythm. Another study investigated the

effect of morning and evening exercise in free-living conditions (89). This study involved

7 days of exercise (continuous running) at either 9:00 AM or 9:00 PM. However, exercise

intensity was not standardized. The circadian rhythm was assessed by wrist temperature.

Evening exercise resulted in a 2-hour phase delay of the wrist temperature rhythm and a

slower elevation of skin temperature at the beginning of the sleep episode.

43

A recent study constructed a human phase response curve to exercise using exercise

stimuli at eight different time points throughout the day and night (221). Younstedt et al.

(221) found a phase advance following morning and afternoon exercise and a phase delay

following evening exercise. This was a rigorous, exhausting protocol that lasted 5.5 days

in controlled lab conditions and consisted of 60-minute wake bouts and 30-minute sleep

bouts. While the study was rigorous, it does not inform exercise timing recommendations

for free-living people.

One difficulty with studying the effects of exercise on circadian rhythms is that exercise

induces acute physiological changes that mask the circadian effect. For example,

thermoregulatory responses to exercise can acutely affect measurements of core body

temperature. There is mixed evidence in the literature concerning the acute effect of

exercise on melatonin secretion. Studies have reported increases or decreases in

melatonin following evening exercise (222, 223). To avoid masking, it is important to

collect the post-exercise circadian phase at least one day after the final exercise stimulus.

Physical activity in general is associated with greater robustness in circadian rhythms.

Tranel et al. showed that total daily physical activity was associated with increased

amplitude of the wrist temperature rhythm (224). In addition, the amplitude of the

temperature rhythm was associated with adiposity and risk factors of the metabolic

syndrome. Specifically, lean individuals had a higher temperature rhythm amplitude

while obese and metabolically dysfunctional individuals had a low amplitude rhythm.

Other studies have shown that low-amplitude core temperature rhythms are associated

with neuronal (Parkinson’s, Alzheimer’s), mental (depression), and physiological

(metabolic syndrome, obesity) disorders (224-226).

In summary, the majority of reported evidence indicates that exercise can shift circadian

rhythms, and it appears that daytime physical activity has the greatest tendency to

advance these rhythms (Table 1). However, most of these prior studies were performed in

controlled lab conditions and included physically-active adults. It is possible that prior

exposure to physical activity could affect the phase shifting response. Investigations on

the effects of exercise on circadian parameters in less active individuals is warranted,

given the fact that 46% of the U.S. population do not meet the minimum recommended

44

guidelines for aerobic physical activity (227). These previous studies should be used as a

guideline for conducting studies in free-living conditions, where subjects are also

exposed to other zeitgebers.

1.4.2 Mechanisms causing exercise phase shifts

The mechanisms responsible for exercise-induced phase shifts of the human circadian

system are poorly understood. A study in mice indicated that neuropeptide Y and

serotonin were both necessary for entrainment to forced treadmill running (228). It has

been suggested that exercise-induced phase shifts could be due to an altered retinal

sensitivity to light and thus be an effect of concurrent light exposure (219). To explore

this phenomenon, Barger el al. (63) tested the effectiveness of moderate intensity exercise

to phase delay the circadian system in near constant dark conditions (<5 lux). Eighteen

healthy males completed a 49-hour constant routine protocol. Following the constant

routine, the subjects underwent a 9-hour delayed sleep-wake cycle. Subjects were

randomized to a control or exercise group for 7 days. The exercise group performed three

45-minute bouts each night on a cycle ergometer at 65-75% of heart rate max. The

exercise occurred at a time that corresponded to the biological night. When comparing

the baseline to post measures of circadian phase, all measures of the melatonin profile in

the exercise group were phase delayed by more than 3 hours. The authors noted that the

delay experienced in the control (no-exercise) group (approximately 1.5 hours) over the

course of the seven experimental days is consistent with what would be expected based

upon the intrinsic period of the human circadian system. Results of this study indicate

that exercise-induced phase shifts of the central circadian rhythm are independent from

photic mechanisms.

1.4.3 Exercise Timing and Performance

Accumulating research shows that exercise performance or capacity fluctuates with a 24-

hour rhythm. Skeletal muscles in rodents have self-sustained oscillators and their phases

are shifted by timed exercise (229). Likewise, a few studies in humans have shown a 24-

hour rhythm of skeletal muscle gene expression and oxidative capacity (230, 231). Thus,

it is hypothesized that the skeletal muscle circadian clock influences daily variations in

exercise capacity. Anaerobic power performance has been shown to be greatest in the

45

afternoon or early evening. The increase observed has been as high as 7-9% greater

during the afternoon or evening, compared to morning (232, 233). One study also found

the change in blood glucose following morning exercise was 5-fold higher than that after

evening exercise (232).

The master clock in the SCN controls the circadian rhythm of core body temperature,

with increases of nearly 1 degree Celsius from the nighttime low to the late afternoon

(36.5-37.5°C) (19, 84). Studies have shown that skeletal muscle temperature also

increases at this time. Elevated skeletal muscle temperature has been shown to increase

power output through a variety of mechanisms. One study found that passive heating of

the skeletal muscle increased cycling power output, likely through an increase in muscle

fiber conduction velocity, ATP turnover for phosphocreatine utilization, glycolysis, and

total anaerobic ATP turnover (234). Resistance exercise performance also displays 24-

hour rhythms, with force being greatest in the late afternoon and evening and lowest in

the morning (235). In one study, Kuusmaa et al. required subjects to perform combined

strength and endurance training in the morning or evening for 24 weeks (236). Following

training, isometric strength significantly improved in the evening group, but not in the

morning. Furthermore, cardiorespiratory fitness (VO2max) increased similarly for both

morning and evening exercise groups. Thus, specific training adaptations may vary based

on the timing of the exercise stimuli.

Two recent papers showed a significant effect of time of day of exercise performance and

gene expression in mice (237, 238). Greater performance in high intensity exercise

occurred in the early active period. Alternatively, greater performance in low and

moderate intensity exercise occurred in the late active period. However, this time of day

difference was eliminated when the circadian clock was disrupted (Per1 and 2 knockout

mice). The results of this study showed that the circadian clock regulates the time-of-day

effect on performance. Skeletal muscle gene expression of 513 transcripts were also

different in the morning and evening. The authors hypothesized that this could drive the

time of day differences in exercise performance. The authors concluded that exercise

alters gene transcription in a time-dependent manner, with many of the changes being

transcripts involved in metabolic pathways, such as glucose metabolism. The researchers

46

also investigated the effect of a submaximal exercise bout in humans performed at

8:00am and 6:00pm. They found that at the same workload VO2 was significantly

reduced in the evening exercise, suggesting improved exercise efficiency.

1.5 Conclusion

Modern lifestyles are characterized by limited exposure to the natural light/dark cycle,

activity long after sunset, late night eating, and early morning arousal with the use of

alarm clocks. This has resulted in a society that is living in a chronic state of circadian

disruption. Numerous studies have shown that circadian disruption is deleterious to health

and well-being. Because circadian disruption is common and is associated with poor

health, therapeutics that alleviate this disruption could broadly impact human health.

Recent studies have begun to identify an effect of time of day of exercise on human

health. However, the topic is understudied and the mechanisms are unknown. Research

has shown that exercise can shift circadian rhythms in humans. However, most of these

prior studies were performed in controlled lab conditions and used exceptionally long

exercise durations, limiting the translation to free-living individuals. Furthermore, the

previous studies included physically-active adults so the results may have been

confounded by recent non-laboratory exercise regimens. Therefore, the purpose of this

dissertation was to investigate the impact of timed exercise on circadian rhythms in free-

living, sedentary young adults.

47

Chapter 2. Circadian rhythm phase shifts caused by timed exercise vary with

chronotype in young adults

2.1 Abstract

The human circadian system synchronizes with, or entrains to, the light/dark cycle

(sunrise/sunset) to promote activity and food consumption during the day and rest at

night. However, strict work schedules and nighttime light exposure impair proper

entrainment of the circadian system, resulting in chronic circadian misalignment.

Numerous studies have shown that chronic circadian misalignment results in poor health.

Although light is the most salient time cue for the circadian system, several laboratory

studies have shown that exercise can also entrain the internal circadian rhythm. However,

these studies were performed in controlled laboratory conditions with physically-active

participants. The purpose of this study was to determine whether timed exercise can

phase advance (shift earlier) the internal circadian rhythm in sedentary subjects in free-

living conditions. Fifty-two young, sedentary adults (16 male; 24.3±0.76 yr) participated

in the study. As a marker of the phase of the internal circadian rhythm, we measured

salivary melatonin levels (dim light melatonin onset: DLMO) before and after five days

of timed exercise. Participants were randomized to perform either morning (10h after

DLMO) or evening (20h after DLMO) supervised exercise training for five consecutive

days. Analysis of covariance (ANCOVA) was used to analyze the effect of exercise time

on phase shift while adjusting for covariates. We found that morning exercisers had a

significantly greater phase advance than evening exercisers. Importantly, the morning

exercisers had a 0.6±0.2h phase advance, which could theoretically better align their

internal circadian rhythms with the light-dark cycle and with early-morning social

obligations. In addition, we found that baseline DLMO, a proxy for chronotype,

influenced the effect of timed exercise. We found that for later chronotypes (i.e. subjects

with later DLMO), both morning (0.5±0.3h) and evening (0.5±0.2h) exercise advanced

the internal circadian rhythm. In contrast, earlier chronotypes (i.e. subjects with earlier

DLMO) had phase advances when they exercised in the morning (0.5±0.2h), but phase

delays when they exercised in the evening (-0.4±0.3h). Thus, late chronotypes, who

experience the most severe circadian misalignment, may benefit from exercise in the

48

morning or evening, but evening exercise may exacerbate circadian misalignment in early

chronotypes. Collectively, these results suggest that personalized exercise timing

prescription based on chronotype could alleviate circadian misalignment in young adults.

49

2.2 Introduction

Modern lifestyles can disrupt circadian rhythms. The circadian system controls 24-hour

cycles of behavior and physiology, such as rest-activity and feeding rhythms. This system

evolved to entrain, or synchronize, to natural cycles of light (sunrise) and dark (sunset)

(9). This synchronization between internal and external rhythms has been shown to

improve fitness in model organisms (239-241). However, in a modern society, exposure

to the natural light/dark cycle is limited. People are active long after sunset (due to

electrical lighting), eat late into the night, and wake-up to alarm clocks hours before they

would naturally (11, 136). This has resulted in a society that is living in a chronic state of

circadian disruption.

Numerous studies have shown that circadian disruption is associated with poor health

(11, 57, 100, 106). Shift work, which chronically disrupts the circadian system, increases

the risk of cancer, obesity and cardiovascular disease (10-15). Non-shift workers also

experience chronic forms of circadian disruption. Seventy percent of the population

experiences social jetlag, which is a misalignment between social obligations and internal

circadian rhythms (e.g., waking-up to an early-morning alarm clock) (11). Social jetlag is

associated with increased cortisol levels and resting heart rate, insulin resistance, obesity,

metabolic syndrome, and systemic inflammation (11, 13, 55-57). Late chronotypes, or

night owls, often experience social jetlag because they have early-morning work

schedules that do not match their natural internal rhythms. Late chronotypes are less

physically active, have a higher BMI, and are at increased risk of developing type 2

diabetes and metabolic syndrome (125, 130, 242, 243). Misalignment of circadian

rhythms may be a contributing factor leading to obesity and metabolic dysfunction in late

chronotypes (244).

Because circadian disruption is common and is associated with poor health, therapeutics

that alleviate this disruption could broadly impact human health. The circadian system is

remarkably sensitive to environmental cues that synchronize internal rhythms to external

cycles. Therefore, behavioral, non-invasive therapeutics could tap into these input circuits

and align internal and external rhythms in individuals with disrupted circadian systems.

Sunlight is the dominant environmental signal in mammals (51, 245, 246). Studies in

50

rodents have shown that melanopsin-containing retinal ganglion cells in the eye detect

irradiance in the environment and send light information directly to the master circadian

clock in the suprachiasmatic nucleus (SCN) (31, 247). In rodents, the SCN controls daily

rhythms of rest-activity, feeding, body temperature, and hormones (e.g., corticosterone,

glucose, insulin, leptin) (29, 30, 83, 248-250). In addition to light, exercise can entrain

the circadian system in mammals (61, 63, 211, 220, 251). It is well-known that regular

physical activity has substantial health benefits, including improvements in

cardiometabolic health, obesity, anxiety, depression, cognition, and overall well-being (1-

6). However, the role of exercise in regulating the circadian system has been

understudied. Exercise, if performed at the appropriate time of day, could shift the phase,

or timing, of the internal circadian rhythm and therefore improve circadian misalignment.

In many individuals (e.g., in later chronotypes), an advance of the internal circadian

rhythm would better align internal rhythms with the environment and with standard social

schedules. Recent studies in mice showed that exercise performance, gene transcription,

and energy utilization depend on the time of day of exercise (237, 238). Several clinical

studies showed that morning or early afternoon exercise advances the internal circadian

rhythm (46, 61, 215) (but one study showed delays) (72). Other research demonstrated

that evening exercise delays the internal circadian rhythm (46, 72) (but one study showed

advances) (214). However, most of these prior studies were performed in controlled lab

conditions and included physically-active adults so the results may have been confounded

by recent non-laboratory exercise regimens. Therefore, in this study we investigated the

impact of timed exercise on circadian rhythms in free-living, sedentary young adults.

2.3 Results

2.3.1 Study Participants

Young adults were assessed in the present study because they have later sleep timing

(chronotype is latest in young adults) (96) and they could therefore have the greatest

benefit from timed exercise, if that exercise advanced their circadian rhythms.

Importantly, we also studied sedentary participants (≤2h moderate-vigorous structured

exercise/week) and they exercised only during the 30-minute scheduled and supervised

session. Sixty-seven participants enrolled in the study (Figure 2.1). Eleven participants

51

withdrew due to personal reasons, including unwillingness to exercise at the prescribed

times or to abstain from other physical activity. Four participants were excluded from the

analytic data set because we could not measure circadian phase (salivary melatonin levels

were erratic or did not exceed the threshold).

Figure 2.1 Participant recruitment. Participants enrolled in the study were 18 to 45 years old, healthy, medication-free (except birth control), and reported no psychiatric or sleep conditions (N=67). Participants were randomized to morning or evening exercise and 11 participants subsequently withdrew. Fifty-two participants were included in the analytic data set (after removing 4 participants because phase could not be determined from the melatonin data).

2.3.2 The time of exercise affects the phase of circadian rhythms in young, sedentary

adults

We first determined whether timed exercise shifts the phase of the internal circadian

rhythm in young sedentary adults under free-living conditions. We chose morning and

evening exercise for two reasons. First, morning exercise has been shown, in some

studies, to cause significant phase advances, which could alleviate circadian

misalignment in late chronotypes and others whose internal rhythms are delayed relative

to the environment (46, 61, 215). Second, morning and evening are the most common

times when people exercise on weekdays, so exercise at these times could be

52

implemented as feasible behavioral interventions for circadian disruption in free-living

individuals (252). Age, anthropometric (body weight, standing height, BMI, and waist,

abdominal and hip circumferences), body composition, (body fat %, fat mass, fat-free

mass, mineral-free lean mass) and cardiorespiratory fitness (VO2peak) measures did not

differ between the group of participants who exercised in the morning compared to the

group who exercised in the evening (baseline characteristics in Table 2.1).

Table 2.1. Characteristics of study participants. VariableA Morning Group

n=26 Evening Group

n=26 p-valueB

Age 24.77 ± 1.20 23.73 ± 0.96 0.50

Body Mass (kg) 66.75 ± 2.84 69.09 ± 2.28 0.52

Height (cm) 165.89 ± 1.30 168.07 ± 1.76 0.33

BMI (kg·m2-1) 24.12 ± 0.85 24.56 ± 0.85 0.72

Anthropometric

Waist Circumference (cm) 75.33 ± 1.83 77.59 ± 1.99 0.41

Abdominal Circumference (cm) 82.66 ± 2.11 85.55 ± 2.17 0.34

Hip Circumference (cm) 99.68 ± 1.85 101.03 ± 1.71 0.59

Body Composition

Body Fat Percentage (%) 31.11 ± 1.63 32.45 ± 1.81 0.58

Fat Mass (kg) 20.99 ± 1.88 22.28 ± 1.74 0.62

Fat-free Mass (kg) 44.56 ± 1.52 44.82 ± 1.42 0.90

Mineral-free lean Mass (kg) 42.17± 1.46 42.45 ± 1.36 0.89

Cardiorespiratory Fitness

VO2Peak (mL·kg-1·min-1) 38.91 ± 1.55 36.47 ± 1.70 0.29 AData are presented as mean ± SE. BBaseline participant characteristics for the morning and evening exercise groups were compared using independent sample t-tests; p < 0.05.

As a marker of the phase of the internal circadian rhythm, we measured salivary

melatonin levels. The rhythmic secretion of melatonin by the pineal gland is under the

direct control of the master circadian clock in the SCN (253). The rise in melatonin levels

before habitual bedtime (e.g. dim light melatonin onset or DLMO) is a well-established,

reliable, and widely-used marker of internal circadian phase of human participants (254).

Thus, we measured DLMO before and after 5 days of timed exercise to determine the

change in phase caused by exercise. We found there was a trend for morning exercisers to

53

have larger phase advances compared to evening exercisers (Table 2.2: Circadian Phase

Shift Unadjusted; Table 2.3: Model 1). When we compared baseline circadian phase

(baseline DLMO) between groups, there was a trend for morning exercisers to have an

earlier circadian phase (e.g., earlier DLMO) than evening exercisers (Table 2.2).

Therefore, we adjusted for the difference in baseline DLMO between the groups and

found that morning exercisers had a significantly greater phase advance than evening

exercisers (Fig. 2.2; p=0.01, Phase Shift DLMOadjusted in Table 2.3: Model 2).

Importantly, the morning exercisers had a 0.6h phase advance, which could theoretically

better align their internal circadian rhythms with the light-dark cycle and with early-

morning social obligations. All remaining models (Table 2.3) provided non-significant

results.

We also analyzed sleep using actigraphy. Sleep onset and mid-sleep during the exercise

days were significantly earlier in the morning group compared to the evening group,

which is consistent with the trending earlier circadian phase and chronotype in the

morning group (Table 2.2). Sleep fragmentation index, a measure of sleep quality, and

sleep duration did not differ between the exercise groups. Therefore, despite the earlier

timing of sleep in the morning exercise group, the quality and duration of sleep were

similar between groups.

Circadian misalignment typically occurs when the timing of sleep (which is often dictated

by social and work obligations) is not synchronized with the timing of the internal

circadian rhythm (96). One established method for measuring circadian alignment in free-

living people is to quantify the duration of time, or phase relationship, between DLMO

(the marker of the internal rhythm) and sleep onset (10, 69, 244, 255). Consistent with

previous studies of young adults that had circadian misalignment that was associated with

metabolic risk (10, 244), we found that the phase relationship was ~2h in our participants

and it did not differ between morning and evening exercise groups (Table 2.2).

54

Table 2.2 Descriptive comparison of Circadian and sleep characteristics stratified by/between exercise timing groups.

VariableA Morning Group Evening Group p-valueB

Circadian

Circadian Phase Shift Unadjusted (h) 0.51 ± 0.21 0.09 ± 0.17 0.13

MEQ Baseline Score 51.35 ± 1.79 46.92 ± 1.86 0.09

Baseline DLMO (hh:mm) 21:50 ± 00:16 22:35 ± 00:17 0.07

Phase Relationship (h) 1.95 ± 0.21 2.54 ± 0.29 0.11

Sleep

Sleep Onset (hh:mm) 23:50 ± 00:15 25:06 ± 00:15 <0.01

Mid-sleep (hh:mm) 3:20 ± 00:14 4:52 ± 00:13 <0.01

Sleep Duration (h) 7.02 ± 0.20 7.53 ± 0.26 0.13

Sleep Fragmentation Index (%) 24.50 ± 1.22 26.55 ± 1.80 0.35

Environmental Conditions

Civil Daylength (h) 12.91 ± 0.29 12.79 ± 0.29 0.77 AData are presented as mean ± SE. BBaseline participant characteristics for the morning and evening exercise groups were compared using an independent sample t-tests.

Table 2.3 Statistical model to assess covariates Variables Included in

ModelA Evening vs.

Morning Phase Shift Difference

Standard Error

p-value

Model 1 Exercise Group only -0.42 0.27 0.13 Model 2 Exercise Group adjusted for

baseline DLMO -0.64 0.25 0.01

Model 3 Exercise Group adjusted for baseline MEQ score

-0.49 0.28 0.08

Model 4 Exercise Group adjusted for midsleep on exercise days

-0.53 0.36 0.15

AData were analyzed with ANCOVA using phase shift as the dependent variable and exercise group as the independent variable.

55

Figure 2.2 Morning exercise advances the phase of the internal circadian rhythm. Young sedentary adults exercised for 5 days either in the morning (n=26) or evening (n=26). Phase shift was calculated as the difference in the timing of DLMO before and after 5 days of exercise and was adjusted for the difference in baseline DLMO between the groups. Data points are raw data. Bars are mean±SEM from ANCOVA model including exercise group and DLMO. *p<0.01

2.3.3 Chronotype is associated with phase shifts in evening exercisers

Previous studies have shown that chronotype, or preferred sleep timing (so-called

morning larks or night owls), is highly correlated with internal circadian phase.

Moreover, chronotype has been linked to changes in sensitivity of the circadian system to

light (256), so it may also modulate the circadian response to exercise. For this study we

enrolled young sedentary participants without regard to chronotype. As a result, our

participants had variable chronotypes that were normally distributed similar to previous

studies of chronotype in young adults (120, 257) (Figure 2.6). We next determined

whether the magnitude of phase shifts elicited by timed exercise depended on

chronotype. We found that there was no significant correlation between DLMO (a marker

56

of internal phase and a proxy for chronotype) and phase shifts in the morning exercise

group (Fig. 2.3A), but there was a strong association between chronotype and phase shifts

in the evening exercise group (Fig. 2.3B). Moreover, there was a trend for baseline

DLMO to be a modifier for the effect of timed exercise on circadian rhythms (p=0.20).

Because these analyses suggested that baseline internal phase modifies the relationship

between timed exercise and phase shift, we next examined the impact of morning or

evening exercise on phase shift separately in earlier and later chronotypes (Table 2.4; Fig.

2.4). We found that earlier chronotypes had phase advances with morning exercise but

phase delays with evening exercise. In contrast, later chronotypes had phase advances

with either morning or evening exercise.

Figure 2.3 Phase shift is correlated with internal phase in evening exercisers. Baseline DLMO, which is marker of internal phase and a proxy for chronotype was plotted relative to phase shift by morning (A, n=26) or evening (B, n=26) exercise. Data was analyzed by two-tailed Pearson correlation.

Table 2.4 Model of morning and evening exercise on earlier and later chronotypes Model 5

Baseline DLMO groupA

Exercise Group

Mean Standard Error

Evening vs. Morning Phase Shift Difference

p-value

Earlier Chronotypes

Evening -0.41 0.29 -0.90 0.02 Morning 0.49 0.25

Later Chronotypes

Evening 0.46 0.25 -0.08 0.83 Morning 0.54 0.29

AData were analyzed with ANCOVA using phase shift as the dependent variable and exercise group and chronotype group (earlier chronotypes <22:12 baseline DLMO; later chronotypes ≥ 22:12 baseline DLMO) as the independent variables

57

Figure 2.4 The effects of timed exercise on phase shifts depends on chronotype. Chronotype was dichotomized with a median split and the effect of morning (n=26) and evening (n=26) exercise on phase shift was separately analyzed in earlier (n=26) and later chronotypes (n=26). Data points are raw data. Bars are mean±SEM from two-way ANCOVA model with exercise group and dichotomized DLMO. *p<0.05 vs. all other groups.

2.4 Discussion

Exercise has many well-established benefits to physical and mental health (1-6).The

intensity, duration, frequency, mode, volume, and progression of exercise to optimize the

beneficial effects have been well-characterized (2, 7, 8). However, the proper timing of

exercise, and the additional benefit on circadian rhythms has been understudied.

Disruption of circadian rhythms (by shift work, social jetlag, early-morning schedules

etc.) is associated with metabolic disorders, cardiovascular disease, and cancer (10-15). If

exercise could improve circadian disruption, then it may also improve the risk factors

associated with this disruption. A tenet of the mammalian circadian system is that it is

differentially sensitive to stimuli given at different times of day. Therefore, it is likely

that the response of the internal circadian rhythm to exercise depends on the time of day

of exercise. This concept has been addressed in a few studies, and while they consistently

58

showed time-of-day effects of exercise on circadian rhythms, the results were not

consistent between the studies (i.e., in some studies morning exercise advanced the

circadian rhythm and in others it did not) and they were mostly performed in controlled

laboratory conditions that are not translatable (46, 61, 72, 214). Thus, the goal of the

present study was to rigorously test the impact of timed exercise on circadian rhythms

under free-living conditions and using exercise times and intensities that are feasible for

translation.

Overall, in the present study morning exercise advanced the phase of the internal

circadian rhythm, whereas evening exercise did not in young sedentary adults. Young

adults, on average, have the latest chronotypes and therefore the greatest potential of all

ages to experience circadian disruption caused by misalignment of their (later) internal

circadian rhythms with early morning obligations (96). Therefore, the present study

demonstrated that morning exercise has the greatest potential to alleviate circadian

misalignment in young adults. With regard to the feasibility of implementing a morning

exercise regimen in a young adult who prefers to stay up late and sleep in, it is important

to note that morning exercise does not necessarily mean that an individual has to exercise

at 6:00am. In the present study, exercise time was scheduled relative to each individual’s

internal circadian rhythm and “morning” exercise for a night owl may be at 10:00am.

Thus, a morning exercise prescription is feasible even for a late-night person.

The mechanisms by which morning exercise advance the internal circadian rhythm are

unknown. It is well established that exposure to morning light can phase advance the

internal rhythm (48, 258), therefore, the combination of exposure to morning light and

morning exercise, could have additive phase-advancing effects on young adults in free-

living conditions. Indeed, one previous study performed in controlled laboratory

conditions that combined the two stimuli observed an additive phase-shifting effect (220).

Thus, the combination of light and exercise may be a very effective treatment strategy to

alleviate social jetlag and the associated metabolic derangements.

Although morning exercise was overall the ideal time to induce phase advances, we also

found that baseline DLMO, a proxy for chronotype, influenced the effect of timed

exercise. We found that for later chronotypes, both morning and evening exercise

59

advanced the internal circadian rhythm. Thus, late chronotypes, who experience the most

severe circadian misalignment, may benefit from exercise in the morning or evening. In

contrast, earlier chronotypes had phase advances when they exercised in the morning, but

phase delays when they exercised in the evening. These results suggest that evening

exercise may exacerbate circadian misalignment in early chronotypes. These results

suggest the need for personalized exercise timing prescriptions based on chronotype.

Numerous epidemiological studies have also shown that cumulative years of exposure to

circadian disruption (e.g., >5 years) markedly increases the risk of heart disease, breast

cancer, and type 2 diabetes compared to acute exposure (259-261). Since the negative

effects of circadian disruption increases with years of exposure, it is ideal to target young

adults, who are particularly susceptible to circadian misalignment, to reduce the

cumulative years of exposure. Our study demonstrates that timed exercise is effective in

shifting the phase of the internal circadian rhythm and thus has the potential to reduce

early life exposure to circadian disruption and thereby reduce cumulative exposure over a

lifetime.

The results of our study in young adults under free-living conditions was in agreement

with previous studies in laboratory conditions, such that morning/early afternoon exercise

similarly advanced the phase of the internal circadian rhythm in and out of the laboratory

(46, 61). Our experimental design had several strengths that make our findings

particularly applicable to people in “free-living” conditions. First, the intensity was

“standardized” based on GXTmax results (moderate; a brisk walk or light jog) and duration

(30 min/day) of our exercise regimen was practical. This is in contrast to several previous

studies that did not standardize the exercise intensity relative to cardiorespiratory fitness

(61, 89), or used long (3h) exercise bouts or multiple exercise sessions per day (63, 217,

219, 262). We also chose exercise times that are common in daily routines. Morning and

evening are the most accessible times for much of the population. Other studies have

investigated the effects of exercise at all times throughout the day and even in the middle

of the night, which are not practical for translation to free-living people (61, 63, 213-215,

220, 262). Some previous studies also performed post-exercise phase assessments on the

same day as the exercise stimulus, making it difficult to differentiate between an acute

60

effect of exercise or a phase shift (214, 217). In our study, we measured the phase of the

internal circadian rhythm on the day after the 5th day of exercise, thereby avoiding the

acute masking effect of the exercise session. Finally, although we used well-established

and reliable measures of circadian rhythms (DLMO, actigraphy) and cardiorespiratory

fitness (GXTMax) and normalized exercise intensity and timing across participants to

rigorously test the effect of timed exercise on circadian phase, we can also make these

predictions and prescriptions using cheaper and easier methods. For example, we can

predict internal phase (and therefore prescribe exercise time) from simple questionnaires

(e.g., DLMO is highly correlated with preferred sleep timing) (76).

There were some limitations of our study. We did not design the study to collect baseline

sleep measures, but it would have been interesting to determine whether exercise timing

affected sleep duration or quality. Also, given that we designed our experiment to

examine individuals under free-living conditions, we did not control for other stimuli that

could affect the internal circadian phase. For example, travel to the lab for morning

exercise could have resulted in increased light exposure during this time, facilitating

phase advances independent of or in addition to exercise. We also did not control for

sleep timing or duration (e.g. individuals were instructed to sleep as they normally

would) and previous research has shown that changing sleep time can affect DLMO (76,

263).

Now that we have identified the proper exercise times to advance circadian rhythms,

which can be tailored for chronotype, future studies should investigate the effect of timed

exercise on circadian misalignment and risk factors for metabolic disorders, heart disease,

and cancer. A timed exercise intervention can easily be translated to clinical practice.

While it is not feasible to perform DLMOs on every individual (as they are time-

consuming and costly), chronotype questionnaires or self-report of preferred sleep timing

could be used as a proxy for DLMO when prescribing the appropriate time to exercise

(76). Importantly, exercising at a time of day that elicits a phase advance could increase

the already substantial benefits of exercise on health.

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2.5 Methods

2.5.1 Participants

Data were collected between July 2017 and March 2019 in Lexington, Kentucky

(38.0406° N, 84.5037° W) year-round, except during the 4 weeks following each semi-

annual daylight saving time transition. Participants were recruited via local

advertisements and were first screened by telephone to query sleep habits, medical

diagnoses, and medication use. Participants enrolled in the study were 18 to 45 years old,

healthy, medication-free (except birth control), and reported no psychiatric or sleep

conditions. All participants were sedentary which was defined as ≤2 hours of structured

physical activity per week. Participants had not traveled across more than 1 time zone in

the 4 weeks prior to beginning the study and reported no night or rotating shift work in

the previous year.

2.5.2 Study Protocol

Immediately following consent, participants completed 2 questionnaires regarding

preferred daily timing of sleep and activities, or chronotype (Figure 2.5). Each participant

completed a Physical Activity Readiness Questionnaire (PAR-Q) and Health History

Form to identify existing contraindications to exercise as specified in the American

College of Sports Medicine Guidelines for Exercise Testing and Prescription (264).

Height and weight measures were also collected during the consent session. Participants

next performed a baseline measure of circadian phase called dim light melatonin onset

(DLMO) to determine the timing of the internal circadian rhythm. Next, cardiorespiratory

fitness was determined by measuring VO2peak from a maximal graded exercise test

(GXTMax). Circumference measures as well as a DXA scan were performed to assess

body composition. Participants then performed 5 days of supervised treadmill exercise in

the morning or evening. Participant randomization was performed using Statistical

Analysis Software (SAS, Version 9.4). Participants maintained a heart rate corresponding

to 70% VO2max for 30 continuous minutes during exercise. One day after the final day of

exercise, a post-exercise DLMO was performed to assess changes in the internal rhythm

resulting from the exercise intervention.

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Figure 2.5 Study protocol. During consent, participants completed questionnaires and height and weight measures. One to 9 days later, we measured baseline circadian phase with the Dim Light Melatonin Onset (DLMO) assay. Next, we took anthropometric (Anthro) and body composition (Body comp) measures and performed a maximal graded exercise test (GXTmax) to determine peak oxygen consumption (VO2peak). Participants then performed 5 days of treadmill exercise, where they maintained a heart rate corresponding to 70% VO2peak for 30 continuous mins, in the morning or evening. One day after the final day of exercise, post-exercise circadian phase was assessed by the DLMO assay.

2.5.3 Chronotype Questionnaires

Each participant completed the Morningness-Eveningness Questionnaire (Self-

assessment version, MEQ-SA) to determine chronotype (95). We also collected the

Munich Chronotype Questionnaire (MCTQ) to estimate habitual bedtime (96). Previous

studies have used the MCTQ to calculate mid-sleep on free dayssleep corrected (MSFsc) as a

measure of chronotype, however we were unable to use this measure since 42% of our

participants used alarms on free days or had irregular work schedules (11).

2.5.4 Anthropometric and Body Composition Measures

Participants wore lightweight clothing containing no metal and removed shoes for

anthropometric and body composition measures. Standing height was determined to the

nearest 0.1 cm from a wall-fixed meter stick (Pittsburgh®; Pittsburg, PA) with the

participants’ hands positioned on the hips during a maximal inhalation. Body mass was

determined to the nearest 0.1 kg using a calibrated electric scale (Escali Corp.,

Burnsville, MN). Circumference measurements (waist, abdominal, and hip) were taken in

triplicate using a fiberglass anthropometric tape (Creative Health Products Inc.,

Plymouth, MN) and the mean measurement was used in accordance with the guidelines

established by the Airlie Conference Proceedings (265). Body composition was measured

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using total body DXA scans performed using a GE Lunar iDXA (Lunar Inc., Madison,

WI) bone densitometer. In accordance with University of Kentucky procedures and

policy, all women of reproductive status completed a urine pregnancy test immediately

prior to DXA scanning. Only women with a negative urine pregnancy test (within

established urine specific gravity ranges) were included. A single, trained investigator

analyzed all scans using the Lunar software Version 14.10. Absolute and relative fat mass

(kg and %), fat-free mass (kg), and mineral-free lean mass (kg) were determined for each

participant.

2.5.5 Actigraphy

Wrist activity was monitored using a triaxial actigraphy device (ActiGraph model

wGT3X-BT, Pensacola, FL) worn on the non-dominant wrist for the duration of the

study. The actigraphy devices were initialized at 30Hz and participants were instructed to

wear the devices at all times, except when they came in contact with water (e.g., bathing).

The times when the actigraphy watches were removed (e.g., before bathing) were

recorded by participants on their sleep logs. Actigraphy data were downloaded in 10

second epochs and analyzed using AcitLife sotware (version 6.13.3). Wear time

validation was performed by indicating non-wear times in the software. Following

reintegration to 60 second epochs, the Sadeh algorithm (93), combined with sleep logs,

was used to assess sleep timing, duration, and sleep fragmentation. Sleep Fragmentation

Index (SFI) represents sleep restlessness, taking into account number of awakenings and

number of brief (1-min) sleep bouts (266). Sleep data during the exercise days were

included in the analytic dataset. Eight participants were excluded because of missing data

when they removed their actigraphy devices during sleep. Due to the masking effect of

scheduled morning or evening exercise intervention, cosinor analysis was not appropriate

for this data set.

2.5.6 Circadian Phase Measure: Dim Light Melatonin Onset (DLMO)

Participants reported to the lab 8 hours prior to habitual bedtime, which was the sleep

timing indicated for free or weekend days (for participants that had no free days) on the

MCTQ. Participants were instructed to abstain from taking non-steroidal anti-

inflammatory drugs (NSAIDs) throughout the study and to refrain from eating bananas,

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caffeine, chocolate, and drinking alcohol on the day of the assessment because these have

been shown to alter melatonin measurements (78, 267-269). Participants were exposed to

<10 lux at eye level, and no light-emitting electronic devices were permitted during

DLMO testing to avoid light suppression of melatonin production (71). Saliva samples

were collected hourly, except between 2 to 4h before habitual bedtime, when samples

were collected every 30 mins (because this is when melatonin levels typically rise) (76),

and the final sample was collected at or just after habitual bedtime. Participants were

allowed to eat snacks (snacks contained no artificial colorants) except during the 30

minutes prior to sample collection. They were instructed to rinse with water 30 minutes

and 15 minutes prior to sampling and to remain seated for 15 mins prior to sample

collection. Saliva was collected in salivettes (Sardest, Numbrecht, Germany). Melatonin

was measured by SolidPhase Inc. using a BUHLMANN Direct Saliva Melatonin Radio

Immunoassay test kit (Schonenbuch, Switzerland). Intra-assay variation coefficients for

low, medium, and high levels of salivary melatonin are 20.1%, 4.1%, and 4.8%,

respectively. The inter-assay variation coefficients for the assay standards including 0.5,

1.5, 5.0, 15.0 and 50.0 pg·mL-1 for our sample were 6.3%, 7.0%, 5.5%, 4.1%, and 4.8%,

respectively. DLMO was calculated as the time when melatonin concentration exceeded

and remained above 4pg·ml-1 (linear interpolation) (73, 75). DLMOs were performed at

baseline and one day after the final day of exercise (Fig. 2.5). Circadian phase

relationship was calculated as the duration of time between baseline DLMO and the

averaged sleep onset over the 5 exercise days (244).

2.5.7 Maximal Graded Exercise Test

Maximal graded exercise tests (GXTMax) were completed using an indirect calorimetry

testing system with an integrated ECG and a treadmill ergometer (SensorMedics Vmax

Encore, CareFusion Corporation, San Diego, CA). During the progressive (speed and

grade) tests, oxygen consumption (VO2) and cardiovascular responses (12-lead ECG and

heartrate; Cardiosoft v6.7; GE Healthcare/CareFusion Corporation, San Diego, CA) were

continuously measured and monitored. The GXTMax tests were performed using

progressive 2-min workload stages. The initial stage of the test began with a walking

speed of 3.2 mph with a 0.4-mph increase with each subsequent stage. The grade began at

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0% and increased by 2% at each successive stage. Test termination was based on

volitional fatigue or the presence of any absolute or relative contraindications to

continuing according to the American College of Sports Medicine guidelines (264). Each

participant achieved VO2peak (ml*kg-1*min-1) determined by the participant’s ability to

obtain a minimum of two of the following criteria: respiratory exchange ratio ≥ 1.15,

estimated maximal heart rate achieved or exceeded, and/or rating of perceived exertion ≥

17 (196, 197).

2.5.8 Exercise Intervention

Exercise timing was standardized across participants by scheduling exercise relative to

baseline DLMO. During the consent session each participant was randomized to either

morning (10 hours after DLMO) or evening (20 hours after DLMO) exercise. Participants

were expected to arrive and begin exercise within 1 hour of the scheduled time (ranges in

local EST: morning: 5:34-10:17; evening: 15:43-21:43). For 8 participants, DLMO data

were not available before beginning exercise, so exercise times were estimated using

habitual sleep timing indicated on the MCTQ. Participants exercised for 30 minutes for 5

consecutive days at the designated time. Upon arrival to the lab, and immediately post-

exercise, blood pressure and heart rate were measured with an automated system (Omron

digital blood pressure monitor HEM-907XL; Hoffman Estates, IL). Exercise training was

performed on a treadmill and intensity was maintained at a heart rate corresponding to

70% of VO2peak (determined from GXTMax). Participants wore chest heart rate monitors

(Polar, Bethpage, NY) during each training session to ensure prescribed intensity was

met. Treadmill speed and/or grade was adjusted to achieve target heart rate. All training

sessions were monitored and supervised to ensure that proper heart rate intensities were

maintained, to ensure safety of the participants, and to provide motivation.

2.5.9 Phase Shifts

One day after the final day of exercise, each participant performed a post-exercise

DLMO. Phase shifts were calculated as the difference (in hours) of post-exercise DLMO

subtracted from baseline DLMO. As is convention, phase advances were expressed as

positive values and delays as negative values.

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2.5.10 Statistical Analysis

Data were analyzed using SAS, Version 9.4. Descriptive data are presented as means ±

SEM. Baseline participant characteristics for the morning and evening exercise groups

were compared using independent sample t-tests. Analysis of covariance (ANCOVA)

was used to analyze the effect of exercise time on phase shift while adjusting for the

covariates, baseline DLMO, MEQ, and mid-sleep. A test of interaction between exercise

time and baseline DLMO was conducted and Pearson correlation analysis was used to

assess associations between baseline DLMO and phase shift for each exercise group. To

further examine the effect of baseline circadian phase on phase shift in each exercise

group, DLMO was dichotomized into earlier and later chronotype based on a median split

(earlier chronotypes <22:12 baseline DLMO; later chronotypes ≥ 22:12 baseline DLMO).

A two-way ANCOVA was conducted with an interaction term to compare earlier and

later chronotypes.

2.5.11 Study Approval

Prior to participation, written consent was obtained for each participant in accordance

with the policies and procedures of the University of Kentucky Institutional Review

Board.

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2.6 Supplemental Information

Table 2.5 Supplemental Table. Baseline characteristics of study participants by sex

Morning Exercise Evening Exercise

VariableA Male (N=8)B Female (N=18) Male (N=8) Female (N=18)

Age (yr) 21.88 ± 1.49 26.06 ± 1.52 23.63 ± 1.70 23.78 ± 1.19

Body Mass (kg) 73.99 ± 3.65 63.53 ± 3.57 73.10 ± 3.81 67.31 ± 2.79

Height (cm) 171.69 ± 2.11 163.32 ± 1.22 175.54 ± 1.88 164.74 ± 1.95

BMI (kg·m-2) 25.05 ± 0.93 23.71 ± 1.17 23.70 ± 1.06 24.94 ± 1.14

Anthropometric

Waist Circumference (cm) 80.76 ± 1.63 72.92 ± 2.35 80.33 ± 3.02 76.37 ± 2.55

Abdominal Circumference (cm) 85.26 ± 1.50 81.51 ± 2.96 85.63 ± 3.86 85.52 ± 2.70

Hip Circumference (cm) 99.79 ± 2.52 99.63 ± 2.47 99.30 ± 2.64 101.79 ± 2.20

Body Composition

VO2 Peak (mL·kg-1·min-1) 46.38 ± 2.1 35.59 ± 1.47 44.30 ± 2.83 33.25 ± 1.55

Body Fat Percentage (%) 27.88 ± 1.63 32.54 ± 2.19 24.90 ± 3.10 35.81 ± 1.74

Fat Mass (kg) 20.12 ± 1.86 21.37 ± 2.62 18.30 ± 3.01 24.05 ± 2.05

Fat Free Mass (kg) 51.41 ± 1.83 41.52 ± 1.59 52.61 ± 1.86 41.36 ± 1.16

Mineral-free Lean Mass (kg) 48.84 ± 1.78 39.21 ± 1.51 50.02 ± 1.75 39.08 ± 1.09

Cardiorespiratory Fitness

VO2 Peak (mL·kg-1·min-1) 46.38 ± 2.1 35.59 ± 1.47 44.30 ± 2.83 33.25 ± 1.55

Environmental Conditions

Civil Daylength (h) 13.13 ± 0.70 12.81 ± 0.30 12.51 ± 0.39 12.91 ± 0.38

Sleep and Circadian Measures

Circadian Phase Shift (h) 0.46 ± 0.42 0.53 ± 0.25 0.25 ± 0.29 0.02 ± 0.21

Baseline DLMO (hh:mm) 22:20 ± 00:27 21:38 ± 00:20 22:33 ± 00:39 22:35 ± 00:19

MEQ Baseline Score 52.38 ± 2.78 50.89 ± 2.32 50.75 ± 2.38 45.22 ± 2.40

Circadian Phase Alignment (h)* 1.41 ± 0.16 2.10 ± 0.26 2.2 ± 0.52 2.69 ± 0.36

Mid-Sleep Exercise Days (hh:mm)*

3:43 ± 00:23 3:14 ± 00:16 4:38 ± 00:20 4:58 ± 00:17

Sleep Fragmentation Index* 27.80 ± 1.91 23.58 ± 1.41 25.96 ± 3.47 26.82 ± 2.16

Sleep Onset (hh:mm)* 24:12 ± 00:22 23:43 ± 00:19 24:42 ± 00:19 25:17 ± 00:21

Sleep Duration (h)* 7.02 ± 0.13 7.02 ± 0.26 7.89 ± 0.34 7.37 ± 0.35 AData are presented as mean ± SE. BN is reduced by 7 total participants.

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Figure 2.6 Supplemental figure. Chronotype is normally distributed. Young sedentary adults completed a MEQ questionnaire at baseline. Based on the composite score a majority of the sample was classified as neither type.

Figure 2.7 Supplemental Figure. MEQ is associated with sleep fragmentation during exercise intervention. Young sedentary adults exercised for 5 days either in the morning or evening. Sleep fragmentation, a measure of sleep restlessness, was measured during exercise days using wrist actigraphy.

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Figure 2.8 Supplemental figure. Baseline DLMO is associated with mid-sleep during exercise intervention. Young sedentary adults exercised for 5 days either in the morning or evening. Sleep was measured using a combination of wrist actigraphy and sleep logs.

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Chapter 3. Expanded Methodology and Procedures

3.1 Study Approval

The study protocol was submitted for full medical review and approved by the

Institutional Review Board at the University of Kentucky.

3.2 Subject Recruitment

Subjects were recruited for this study from Lexington and surrounding communities via

local advertisements (fliers; Figure 3.1), and verbal and email solicitation. Strategies for

recruitment were developed in collaboration with the Participant Recruitment Service

Core of the University of Kentucky’s Center for Clinical and Translational Science

(CCTS). This included UK Center for Clinical and Translational Research wall mounts,

advertisements on internet webpages, and promotion via social media, including

Facebook and Twitter advertisements. Subjects were also recruited from registry

databases, including ResearchMatch.org and CenterWatch.com.

Following email contact from an interested subject, a phone conversation was scheduled

to conduct screening, including explaining additional details to each individual and

determining study eligibility. Specifically, subjects were asked about their age, habitual

sleep timing, diagnosed conditions, prescribed medications and over-the-counter

supplementation, current smoking, recent travel, children, shift work experience, and

exercise participation. Subjects reported no psychiatric or sleep conditions and were

medication-free, except female contraceptives. All subjects met the predetermined

sedentary inclusion criteria, ≤ 2 hours of structured moderate or vigorous physical

activity each week. Subjects had not traveled across multiple time zones in the 4 weeks

prior to beginning the study and reported no night or rotating shift work in the past year.

All subjects provided signed informed consent prior to participation. Following written

informed consent, each subject completed a Physical Activity Readiness Questionnaire

(PAR-Q) and brief medical history to identify any existing contraindications to exercise

testing.

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Figure 3.1 Recruitment flyer

3.3 Protocol

3.3.1 Anthropometric measures and body composition

During the first testing session all subjects completed anthropometric measures including

standing height and body mass measured with minimal clothing and no shoes. Standing

height was determined to the nearest 0.1 cm from a wall-fixed meter stick (Pittsburgh®;

Pittsburg, PA) with the participants’ hands positioned on the hips during a maximal

inhalation. Body mass was determined to the nearest 0.1 kg using a calibrated electric

scale (Escali Corp., Burnsville, MN). BMI was calculated by dividing body mass (kg) by

height squared (m2). During a subsequent testing session, measures were collected for

waist (narrowest part of the torso), abdomen (level of the umbilicus), and hip

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circumference (maximal circumference of the buttocks) (195). Circumference

measurements were taken in triplicate and the mean measurement was used for analyses

using a fiberglass anthropometric tape in accordance with the guidelines established by

the Airlie Conference Proceedings for each subject (270).

A dual energy X-ray absorptiometry (DXA) scan was performed on each subject to

measure body composition. In accordance with University of Kentucky procedures and

policy, all women underwent a urine pregnancy test immediately prior to DXA scanning.

In additional, urine specific gravity was also measured using a hand-held refractometer

(Agtago, USA, Inc. Bellevue, WA) and compared to established ranges to ensure a valid

pregnancy test result. Only women with a negative urine pregnancy test were included as

subjects. All scans were performed by a single, trained investigator. Subjects were

instructed to wear light clothing and remove shoes, eyeglasses, the accelerometer and any

items in their pockets. Subjects were positioned on the center of the scanning platform

within the designated scanning area. A Velcro strap was positioned around the

feet/ankles, enabling the subject to limit extraneous leg movement. DXA fat mass (g),

fat-free mass (g), mineral-free lean mass (g), and percentage fat (%Fat) were assessed.

3.3.2 Cardiorespiratory fitness

To measure cardiorespiratory fitness, a maximal graded exercise test (GXTmax) was

conducted at the CCTS, within the Functional Assessment and Body Composition Core

(FABCC). The GXT was performed on a treadmill ergometer while oxygen uptake (VO2)

was measured using an indirect calorimetry testing system (SensorMedic Vmax Encore,

CareFusion Corporation, San Diego, CA). VO2 was measured breath by breath and

averaged over 1-minute intervals. Heart rate was monitored at baseline, throughout the

test, and during recovery using an integrated electrocardiogram (12-lead ECG and

heartrate; Cardiosoft v6.7; GE Healthcare/CareFusion Corporation, San Diego, CA). In

addition, another heart rate monitor (Polar T31 chest transmitter, Bethpage, NY) was

worn on the chest as a back-up to the ECG. In rare circumstances ECG electrodes will

lose adhesion and detach from the skin due to excessive sweating. Reading from this

secondary heart rate monitor was detected via a compatible watch adhered to the

treadmill (Polar A3, Bethpage, NY).

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Prior to beginning the GXT, baseline heart rate and blood pressure (assessed by manual

auscultation using an appropriate sized cuff and hand aneroid and stethoscope) were

measured while standing, and the ECG was examined to rule out any contraindications to

exercise testing according to ACSM guidelines. The treadmill test began at a walking

pace and the intensity increased in 2-minute stages. Stage 1 began with each subject

walking at 3.2 mph and 0% grade. With each subsequent 2-minute stage the speed

increased by 0.4mph and the grade increased by 2.0%. In the final minute of each stage,

blood pressure and rating of perceived exertion (RPE; 6-20 Borg Scale(198)) were

recorded. Heart rate was recorded in the last ten seconds of each stage. The test

termination was based on volitional fatigue or the presence of any absolute or relative

contraindications to continuing according to the ACSM guidelines (271). Verbal

encouragement was given throughout the test. Upon termination of the test, heart rate,

blood pressure, and VO2 measures were collected for five minutes during a passive

recovery while the subject remained standing on the treadmill. Confirmation of maximal

effort during the GXT was confirmed by meeting two of the following criteria: age

predicted heart rate max (male: 220-age (197); female: 206-0.88*age (196)), respiratory

exchange ratio ≥1.15, and RPE ≥17.

3.3.3 Circadian and sleep measures

3.3.3.1 Dim light melatonin onset

Dim light melatonin onset (DLMO) was measured using an 8-hour assessment ending at

a subject’s habitual bedtime, which was the sleep timing indicated for free or weekend

days (for subjects that had no free days) on the MCTQ. For example, if a subject

indicated that they “actually get ready to fall asleep” at 1:00am and they need 30 minutes

to fall asleep, the DLMO was scheduled for 5:30pm – 1:30am. Prior to beginning data

collection subjects were provided with a sample collection sheet that displayed saliva

collection times and reminders (Figure 3.2). Subjects were instructed (verbally during

consent session and via email) to refrain from alcohol, chocolate, bananas, and any form

of caffeine on the day of the DLMO. In addition, subjects were instructed to abstain from

taking non-steroidal anti-inflammatory drugs (NSAIDs) during the entire study.

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The entirety of this assessment occurred in dim light (≤ 10 lux at eye level) in a

bathroom. The bathroom utilized a “double door” entrance for this assessment. This

allowed the researcher to enter the dimly lit room without exposing extraneous light on

the subject. A researcher was positioned at a desk directly in front of the testing room for

the duration of the DLMO. Subjects were instructed to not have any light-emitting

electronic devices in the room during the DLMO.

To prepare the room for the DLMO a sign was first adhered to the exterior door indicated

testing was in progress. Next, antibacterial air freshener was sprayed on all surfaces of

the bathroom. Then, a lamp with an 11-watt bulb was placed in the corner of the room.

Using a light meter (Ruby Electronics, Saratoga, CA) the distance at which the light

intensity drops below 10 lux was designated with brightly colored tape. Subjects were

instructed to not bring their eyes closer to the lamp than the designated line (Figure 3.3).

A desk and office chair were placed in the bathroom, just outside of the 10 lux barrier. A

blanket was supplied to each subject due to the cold temperatures that were typical of the

bathroom. On the desk the following was placed: bottled water (for mouth rinse and

hydration), snacks (see below for details), a lab timer, labeled saliva tubes, ice, and the

sample collection document.

Snacks were available to the subject during the DLMO. In general, these snacks were dull

in color, and contained no artificial colorants. The food items were lacking caffeine,

chocolate or banana. Food included apple sauce, granola bars, peanut butter crackers,

cheese crackers, and pretzels. Subjects were provided bottles of filtered water. Subjects

were allowed to eat anytime except the 30 minutes prior to a sample. If they had recently

eaten they were instructed to rinse their mouth vigorously with water 30 minutes prior to

a sample. Fifteen minutes prior to each sample, subjects were instructed to rinse with

water (regardless if eaten or not) and remain seated until collection of the next sample.

The first saliva sample was collected following 1 hour in dim light. Next, a saliva sample

was collected each hour with the final sample collected at or just after habitual bedtime.

Between 2-4 hours before habitual bedtime, samples were collected at 30 minute

intervals to improve data resolution during this time. During this interval, melatonin

concentration was most likely to begin increasing. This time frame was chosen because

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DLMO occurs approximately 2 hours prior to sleep onset for normally entrained

individuals (76).

Figure 3.2 DLMO collection instruction document

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Lights On Dim Light

Figure 3.3 DLMO setup for circadian phase measurement.

For the collection of each saliva sample, salivettes (Sardest, Nümbrecht, Germany) were

used. At the time of the collection the subject removed the cylindrical cotton swab from

the salivette tube and placed it into his/her mouth. Subjects were instructed to “move the

cotton swab around the mouth and gently chew to stimulate saliva flow”. After

approximately 1 minute the cotton swab was removed, inserted back into the tube, and

placed on ice. Every 2-3 hours a researcher collected the samples and provided fresh ice.

Samples were centrifuged for 2 minutes at 4°C and 1.0 rcf. The first sample was collected

and centrifuged soon after saliva collection to check for appropriate saliva volume

(≥0.5mL). If volume was insufficient the subject was asked to keep the salivette in his/her

mouth for additional time.

To assess melatonin concentration in saliva, a double-antibody radioimmunoassay (RIA)

was performed by Solidphase Inc (Portland, ME) using a BUHLMANN Direct Saliva

Melatonin Radio Immunoassay test kit (Schonenbuch, Switzerland). Intra-assay variation

coefficients for low, medium, and high levels of salivary melatonin are 20.1%, 4.1%, and

4.8%, respectively. The inter-assay variation coefficients for low, medium, and high

levels of salivary melatonin are 16.7%, 6.6%, and 8.4%, respectively. DLMO was

calculated as the time when melatonin concentration exceeded (and remained above)

4pg/ml (linear interpolation; Figure 3.4).

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Figure 3.4. Dim light melatonin onset calculation. DLMO was calculated as the time when melatonin concentration exceeded (and remained above) 4pg/ml (A,B).

On the day following the last exercise session, a final post-intervention DLMO was

performed. This assessment was usually scheduled at the same time as the baseline

assessment. Procedures for this assessment were identical to baseline. Female subjects

documented phase of the menstrual cycle by indicating beginning and end of most recent

period as well as use of prescribed contraceptive.

Phase shifts were calculated as the difference (in hours) when post-exercise DLMO is

subtracted from baseline DLMO. If the post-exercise DLMO was later, that is considered

a phase delay and recorded as a negative value. If the post-exercise DLMO is earlier, this

is considered a phase advance and is recorded as a positive value.

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3.3.3.2 Chronotype

Subjects completed the Munich Chonotype Questionnaire (MCTQ) and a self-assessment

version of the Horne-Östberg Morningness-Eveningness Questionnaire (MEQ-SA) to

determine chronotype(95, 96). The MCTQ queries typical sleep time for work and work-

free days. The outcome variable for the MCTQ is mid-sleep on free days. Mid-sleep on

free days is “corrected” for accumulated sleep debt, resulting in MSFsc, by using a

previously developed equation (119). Importantly, MSFsc cannot be calculated for

individuals with irregular weekly schedules and those who use alarms on free days. Thus,

individuals must have at least one day per week of unrestricted sleep to determine

chronotype. The MEQ consists of nineteen questions that query the preferred timing for

sleep, as well as social, academic, and physical activity events. The result of this

questionnaire is a composite score (range: 16-86) separating individuals into 5 categories:

definite morning type, moderate morning type, neither type, moderate evening type,

definite evening type.

3.3.3.3 Actigraphy and sleep logs

For the duration of the study subjects were instructed to complete daily sleep logs that

queried sleep and wake times as well as occurrence of naps, whether or not they were

sleeping alone, if the TV remained on while asleep, and if each day was a work or a free

day (Figure 3.5). Subjects also wore a triaxial accelerometer (WGT3X-BT Actigraph

monitor, Pensacola, FL) on their non-dominant wrist for the entire duration of the study.

The accelerometer was initialized using Actilife software (v6.13.3). The device was

synced to local computer time, the sampling rate was set at 30 Hz, and demographic data

was entered. To preserve battery life idle sleep mode was enabled (device enters low

power state after experiencing 10 seconds of inactivity). No stop time was enabled and

the accelerometer was able to continue collecting for the life of the battery charge

(approximately 30 days). Subjects were instructed to wear the monitor at all times, while

sleeping and awake, unless they came in contact with water (e.g. bathing). The time

when the monitor was removed was recorded on the provided sleep log (Figure 3.5).

Since this monitor could measure light exposure, subjects were instructed to keep long-

sleeved clothing pulled above the monitor to allow ambient light exposure.

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Figure 3.5 Sleep logs

Actigraphy data was downloaded as 10 second epochs. Then, wear-time validation was

performed on the data using vector magnitude. Specifically, periods with a zero value of

vector magnitude for ≥5 minutes were flagged as non-wear periods. In combination with

the non-wear periods indicated on the subject sleep logs, true non-wear periods were

flagged on the actigraphy file. Additional, non-documented non-wear periods were

observed and documented.

Following wear-time validation, sleep analysis was performed on each actigraphy file.

Data was reintegrated into 60 second epochs for this analysis. This study was not

designed to measure baseline sleep data. Sleep data during the 5 exercise days were

analyzed and used in the analytic data set. The beginning of the sleep episode was first

determined by a reduction in intensity and frequency of activity counts. The end of the

sleep episode was determined by an increase in intensity and frequency of activity counts.

If sleep logs and sleep period were in concordance, sleep log was recorded as

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beginning/end of sleep period. If not in concordance, activity counts were used to

designate sleep episode times.

The Sadeh algorithm was used to determine sleep onset and offset (93). This algorithm

was developed using an adult sample with a mean age of 22.6 yr (current sample: 24.3

yr). The Sadeh algorithm analyzes each epoch to determine if it should be designated as

sleep or wake (using the five previous and the five future epochs). After completing the

sleep analysis an excel file was exported containing sleep data, including sleep onset,

sleep efficiency, total sleep time, total time in bed, wake after sleep onset (WASO),

number of awakenings, average awakening length, movement index, and sleep

fragmentation index.

3.3.4 Exercise Intervention

During the consent session each subject was randomized to an exercise time.

Randomization was determined by a randomization generator function from the SAS

statistical analysis software (SAS 9.4, Cary, NC). Results from the baseline DLMO were

used to time the exercise. Morning exercise was scheduled for 10 hours after DLMO and

evening exercise was scheduled for 20 hours after DLMO. Subjects were expected to

arrive and begin exercise within 1 hour of scheduled time. Some subjects were outside of

this range due to delays in processing baseline DLMO. Exercise time was estimated for

eight subjects using habitual sleep timing indicated on the MCTQ. Subjects exercised for

5 consecutive days at the designated time of day. The intent was to provide the exercise

stimulus at a time that was relative to the individuals’ endogenous circadian phase.

Daily exercise occurred at the designated time of day in the University of Kentucky

Pediatric Exercise Physiology (PEP) Laboratory (Seaton Building). Time of arrival was

documented. Researchers measured blood pressure (Omron digital blood pressure

monitor HEM-907XL; Hoffman Estates, IL) at resting and immediately post-exercise.

Subjects wore a heart rate monitor (Polar T31 chest transmitter, Bethpage, NY) during

each training session to ensure prescribed intensity was met. The exercise session began

with a 3-5 minute warm-up prior to beginning the 30 minute exercise bout. Target

aerobic training (treadmill walking/jogging) intensity was a heart rate corresponding to

70% of peak VO2 (determined from baseline Max GXT). Target heart rate was

81

maintained within ± 5 beats·min-1. Approximately every 5 minutes of training, heart rate

was documented to record that proper intensity was achieved. Treadmill intensity (speed

and/or grade) was adjusted to achieve target heart rate. All training sessions were

supervised to ensure safety of the subjects and to provide motivation.

3.3.5 Statistical Analysis

Data was analyzed using Statistical Analysis Software (SAS, Version 9.4). Descriptive

data were presented as mean ± standard error for all study variables. Baseline subject

characteristics for the morning and evening exercise groups were compared using

independent sample T-tests. Differences in morning and evening baseline DLMO and

MEQ were nearly significant and midsleep during exercise days was significantly

different, indicating that these variables could be considered as possible confounders.

Using analysis of covariance (ANCOVA) we assessed the effect of exercise time on

phase shift while adjusting for covariates that could be influencing the outcome (DLMO,

MEQ, midsleep on exercise days). A test of interaction between exercise time and

baseline DLMO was conducted. A Pearson correlation analysis was used to assess

associations between baseline DLMO and phase shift for each exercise group. To further

examine the effect of baseline circadian phase on phase shift in each exercise group,

DLMO was dichotomized into early and late chronotype based on a median split (earlier

chronotypes <22:12 baseline DLMO; later chronotypes ≥ 22:12 baseline DLMO). A

traditional 2-way ANOVA was conducted with an interaction term. The level of

significance was set at p<0.05 for all analyses.

3.4 Excluded and Withdrawn Subjects

One female subject (04-S, morning group, Figure 3.6A) was excluded from the analytic

data set due to post-exercise saliva melatonin data that was exceeding normal

physiological values. Even after diluting the samples 1:8 to get them to fall on a readable

portion of the standard curve, the first daytime sample was >200 pg/ml. This typically

indicates that the subject is taking exogenous melatonin supplementation. Another female

subject (26-S, morning group, Figure 3.6B) was excluded due to a baseline melatonin

profile that was very obscure, resulting in erratic increases and decreases in melatonin

concentration throughout the sampling period. Two other male subjects (61-S, 74-S,

82

evening group, Figure 3.6C, D) were excluded from the analytic data set because baseline

melatonin levels did not exceed the threshold and circadian phase was unable to be

calculated.

A male subject (16-S, evening group) withdrew due to academic and personal issues. A

female subject (23-S, morning group) withdrew from the study due to a change in her

schedule that limited her availability. A female subject (24-S, evening group) withdrew

due to a lack of willingness to remain compliant. She was not willing to refrain from

additional exercise (outside of study protocol). A male subject (30-S, evening group)

withdrew due to an unforeseen event in his family. A female subject (31-S, morning

group) withdrew due to scheduling conflicts and difficulty with transportation to testing

sessions. A female subject (36-S, morning group) withdrew due to a lack of willingness

to remain compliant. She had several physical events occurring soon and was not willing

to forgo beginning her training regime. A female subject (44-S, evening group) withdrew

due to personal matters that occurred that would adversely affect her sleep and her ability

to continue participating in the study. A female subject (45-S, morning group) withdrew

during her DLMO because she preferred not to continue with the study procedures. A

female subject (52-S, morning group) withdrew because of a separate commitment that

required her to begin physical training outside of our study protocol. A female subject

(57-S, evening group) withdrew because of an inability to attend scheduled exercise due

to work conflicts. A female subject (72-S, morning group) withdrew due to time

constraints.

83

Figure 3.6 Excluded subject DLMOs. Circadian phase was unable to be calculated for four subjects due to (A,B) eratic melatonin profiles or (C,D) low melatonin production not exceeding threshold.

84

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Vita J. Matthew Thomas

Education

INSTITUTION AND LOCATION

DEGREE

COMPLETION DATE

FIELD OF STUDY

University of Kentucky, Lexington, KY B.S 05/2011 Biology

University of Kentucky, Lexington, KY M.S. 05/2015 Exercise Physiology

Professional Positions 2019- Clinical Research Coordinator, Department of Internal Medicine-

Gastroenterology, University of Kentucky, Lexington, KY 2019 Graduate Teaching Assistant, Microbiology Lab, Dept. of Biology,

University of Kentucky, Lexington, KY 2016-2018 TL1 Predoctoral Fellow, University of Kentucky, Lexington, KY 2016 Graduate Teaching Assistant, Exercise Physiology Lab, Dept. of

Kinesiology and Health Promotion, University of Kentucky, Lexington, KY

2013-2016 Graduate Teaching Assistant, Microbiology Lab, Dept. of Biology, University of Kentucky, Lexington, KY

2012 Graduate Teaching Assistant, General Biology Lab, Dept. of Biology, University of Kentucky, Lexington, KY

Scholastic and Professional Honors 2019 Doctoral Student Research Award, Southeast American College of Sports

Medicine Annual Meeting, Greenville, SC 2019 Department of Kinesiology and Health Promotion Travel Award 2018 Arvle and Ellen Turner Thacker Dissertation Support Award 2018 Department of Kinesiology and Health Promotion Travel Award 2018 Frank G. and Elizabeth D. Dickey Graduate Fellowship in Education 2017 Hackensmith Outstanding Graduate Student Award Nominee, Department

of Kinesiology and Health Promotion 2017 Arvle and Ellen Turner Thacker Dissertation Support Award 2016-2018 TL1TR001997 – National Center for Advancing Translational Sciences

National Institutes of Health 2015 Department of Kinesiology and Health Promotion Travel Award 2010 Summer Undergraduate Research and Creativity Grant

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Publications J.J. Krupa, and J. Matthew Thomas. Is the common teasel (Dipsacus Fullonum) carnivorous or was Francis Darwin wrong? Botany. 97(6): 321-328, 2019.

J. Matthew Thomas, M. Pohl, R. Shapiro, J. Keeler, and M.G. Abel. Effect of load carriage on tactical performance in special weapons and tactics operators. Journal of

Strength and Conditioning. 32(2): 554-564, 2018. Abstracts J. M. Thomas, J. S. Pendergast, W. S. Black, P. A. Kern, J. L. Clasey. Circadian Phase is Associated with Self-reported Chronotype in Young, Sedentary Adults. American College of Sports Medicine 66th annual meeting, Orlando, Florida, May 30, 2019. J. M. Thomas, J. S. Pendergast, W. S. Black, P. A. Kern, J. L. Clasey. Fat Mass, and not Heart Rate Recovery is Associated with Cardiorespiratory Fitness in Young, Sedentary Adults. University of Kentucky Center for Clinical and Translational Science 14th Annual Spring Conference. Lexington, Kentucky. April 15, 2019. J. M. Thomas, J. S. Pendergast, W. S. Black, P. A. Kern, J. L. Clasey. Fat Mass, and not Heart Rate Recovery is Associated with Cardiorespiratory Fitness in Young, Sedentary Adults. College of Education and Human Development Spring Research Conference. Lexington, Kentucky. March 2, 2019. J. M. Thomas, J. S. Pendergast, W. S. Black, P. A. Kern, J. L. Clasey. Circadian Phase is Associated with Self-reported Chronotype in Young, Sedentary Adults. Southeast American College of Sports Medicine annual meeting, Greenville, South Carolina, February 15, 2019. J. M. Thomas, J. S. Pendergast, W. S. Black, P. A. Kern, J. L. Clasey. Fat Mass, and not Heart Rate Recovery is Associated with Cardiorespiratory Fitness in Young, Sedentary Adults. 21st Annual Saha Cardiovascular Research Day. Lexington, Kentucky, September 21, 2018. J. M. Thomas, J. S. Pendergast, W. S. Black, P. A. Kern, J. L. Clasey. Fat Mass, and not Heart Rate Recovery is Associated with Cardiorespiratory Fitness in Young, Sedentary Adults. American College of Sports Medicine 65th Annual Meeting, Minneapolis, Minnesota. May 31, 2018. J. M. Thomas, J. L. Clasey, W. S. Black, P. A. Kern, J. S. Pendergast. Effects of Timed Exercise on Circadian Rhythms in Humans. Society for Research on Biological Rhythms Annual Conference. Amelia Island, FL. May 13, 2018. J. M. Thomas, J. S. Pendergast, W. S. Black, P. A. Kern, J. L. Clasey. Metabolic Benefits of Timed Exercise. University of Kentucky Center for Clinical and Translational Science 13th Annual Spring Conference. Lexington, Kentucky, April 20, 2018.

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J. M. Thomas, J. S. Pendergast, W. S. Black, P. A. Kern, J. L. Clasey. Reducing Circadian Misalignment with Timed Exercise. College of Education and Human Development Spring Research Conference. Louisville, Kentucky. March 24, 2018. J. M. Thomas, J. S. Pendergast, W. S. Black, P. A. Kern, J. L. Clasey. Metabolic Benefits of Timed Exercise. University of Kentucky 7th Annual Obesity and Diabetes Research Day, Lexington, Kentucky. May 18, 2017. J. M. Thomas, J. S. Pendergast, W. S. Black, P. A. Kern, J. L. Clasey. Metabolic Benefits of Timed Exercise. Association for Clinical and Translational Science Annual Meeting Washington D.C., April 20, 2017. J. M. Thomas, J. S. Pendergast, W. S. Black, P. A. Kern, J. L. Clasey. Metabolic Benefits of Timed Exercise. University of Kentucky Center for Clinical and Translational Science 12th Annual Spring Conference. Lexington, Kentucky, March 30, 2017. J. M. Thomas, M. Pohl, R. Shapiro, J. Keeler, and M.G. Abel. Effect of load carriage on tactical physical ability and marksmanship in SWAT operators. National Strength and Conditioning Association National Conference. Orlando, Florida. July 9, 2015.