Objective Causes of Cancer-Related Fatigue: Roles of Neuromuscular Dysfunction and Sleep
Mary Elizabeth Medysky
JUNE, 2016
© Mary Elizabeth Medysky 2016
Cancer-related fatigue (CRF) is a common and debilitating symptom of cancer-treatment,
described as a severe, feeling of fatigue, not improved by rest. A number of factors contribute to
the occurrence of CRF. It has been observed using a variety of subjective scales, focusing on the
psychological aspect. Few studies have assessed if neuromuscular function is related to CRF. It
is unclear if sleep disorders, are associated with CRF. The purposes of this thesis were to 1)
examine if neuromuscular variables are related to subjective feelings of fatigue and 2) determine
if sleep disturbances are associated with CRF in cancer patients and survivors. Independent ttests found no significant differences between subjective fatigued and non-fatigued groups in
both neuromuscular and sleep parameters. However, sleep efficiency had a medium significant
correlation with FACT-F scores (r= 0.31, p<0.05). While the results should be considered
preliminary, it is suggested that sleep but not resistance to acute muscle fatigue due to exercise
plays a role in CRF.
To Dr. Guillaume Millet, thank you for giving me the opportunity to conduct this research and
your guidance throughout. Thank you for taking a chance on me!
Thank you to Dr. Nicole Culos-Reed for your contributions to this project and your direction
over the past two years.
To my committee members and examiners, Dr. Lianne Tomfohr, Dr. Chester Ho, and Dr.
Michael Speca, thank you for your constructive input to this thesis.
Dr. John Temesi, thank you for your endless support, kindness and laughs over the past two
years. I hope we will meet up on the other side of the world someday!
To my family, thank you for showing me that I can be both a beach girl and a career woman.
Eddie, thank you for always being my rock.
Thank you Doug, Renata, Felipe and the CEPs for your assistance in collecting some of the
included data. Thibault, thank you for analyzing the neuromuscular data.
To many of my office-mates, thank you for your support, laughs and putting up with my office
To the cancer patients and survivors who participated in this study, you are my reminder to
always be brave.
Table of Contents
Abstract................................................................................................................................ ii
Acknowledgements ............................................................................................................iii
Dedication........................................................................................................................... iv
Table of Contents ................................................................................................................ v
List of Tables ....................................................................................................................viii
List of Figures and Illustrations .......................................................................................... ix
List of Symbols, Abbreviations and Nomenclature ............................................................ x
Epigraph ............................................................................................................................ xii
CHAPTER ONE: INTRODUCTION................................................................................ 1
1.1 Cancer-Related Fatigue ............................................................................................. 1
1.2 Neuromuscular Fatigue due to Physical Activity ...................................................... 7
1.2.1 Central Fatigue ................................................................................................ 10
1.2.2 Peripheral Fatigue ........................................................................................... 12
1.2.3 Muscle Function Recovery ............................................................................. 12
1.2.4 Neuromuscular Fatigue Research in Cancer Patients and Survivors .............. 13
1.3 Sleep Disorders and Cancer-Related Fatigue .......................................................... 15
1.3.1 Insomnia in Cancer Patients and Survivors .................................................... 17
1.3.2 Sleep Quantification ........................................................................................ 19 Polysomnography .................................................................................. 19 Actigraphy ............................................................................................. 21 Subjective Assessment Scales ............................................................... 22
1.3.3 Exercise and Sleep Improvements in the General Population ........................ 23
1.3.4 Exercise and Sleep in the Cancer Population .................................................. 24
1.3.5 Factors Related to Sleep Disorders ................................................................. 35 Treatment Type and Status .................................................................... 35 Medications ........................................................................................... 36 Psycho-Social Factors ........................................................................... 37 Demographics and Lifestyle Factors ..................................................... 37
1.4 Objectives ................................................................................................................ 37
1.4.1 Specific Objectives.......................................................................................... 38
1.5 Significance and clinical relevance ......................................................................... 39
CHAPTER TWO: METHODS ........................................................................................ 40
2.1 Participants and Recruitment ................................................................................... 40
2.1.1 Sample Size ..................................................................................................... 43
2.1.2 Experimental Design ....................................................................................... 45
2.1.3 Appointment #1............................................................................................... 47 Subjective Questionnaires ..................................................................... 47 Dual-X-Ray Absorptiometry ................................................................. 47 Neuromuscular Experimental Set-Up and Familiarization Protocol ..... 47 Daily Subjective Fatigue ....................................................................... 48 Sleep Assessment .................................................................................. 48
2.1.4 Appointment #2............................................................................................... 49 Neuromuscular function at rest ............................................................. 49
v Fatiguing Cycle Ergometer Protocol ..................................................... 51
2.1.5 Data Analysis .................................................................................................. 51 Force ...................................................................................................... 51 Actigraphs.............................................................................................. 53 Subjective Questionnaires ..................................................................... 53
2.1.6 Statistical Analysis .......................................................................................... 55 Neuromuscular Analysis ....................................................................... 56 Sleep Analysis ....................................................................................... 56
CHAPTER THREE: RESULTS ....................................................................................... 58
3.1 Participant Characteristics ....................................................................................... 58
3.2 Preliminary Analysis ............................................................................................... 59
3.3 Primary Analysis ..................................................................................................... 60
3.3.1 Objective One: Neuromuscular function ........................................................ 60
3.3.2 Objective Two: Sleep ...................................................................................... 63
3.4 Exploratory Analysis ............................................................................................... 65
CHAPTER FOUR: DISCUSSION ................................................................................... 67
4.1 Neuromuscular Fatigue ........................................................................................... 67
4.1.1 Isometric Fatigue Prior to Exercise ................................................................. 68
4.1.2 Neuromuscular Fatigue Following Cycling .................................................... 69 Fatigue Characteristics in the Whole Sample ........................................ 69 Fatigue Characteristics between Groups ............................................... 70 Limitations............................................................................................. 72
4.2 Sleep ........................................................................................................................ 73
4.2.1 Limitations ...................................................................................................... 76
REFERENCES .................................................................................................................. 82
APPENDIX A: DETAILS OF MAIL-OUT ...................................................................... 93
APPENDIX B: DEMOGRAPHICS SURVERY .............................................................. 94
.................................................................................................................................. 97
APPENDIX D: SOCIAL PROVISION SCALE ............................................................... 98
GENERAL AND BREAST CANCER SPECIFIC ................................................ 100
................................................................................................................................ 103
APPENDIX G: FATIGUE THERMOMETER ............................................................... 104
APPENDIX J: 24- SLEEP PATTERNS INTERVIEW .................................................. 107
APPENDIX K: INSOMNIA SEVERITY INDEX.......................................................... 108
FATIGUE SUBSCALE.......................................................................................... 109
List of Tables
Table 1. Uni-dimensional and multi-dimensional scales used to assess cancer-related fatigue...... 4
Table 2. Factors related to insomnia in the cancer population. ..................................................... 18
Table 3. Studies examining the relationship between exercise, sleep and cancer-related
fatigue (CRF). Measurements of sleep are in bold. ............................................................... 29
Table 4. Patient demographic frequencies for the total sample of cancer patients and
survivors. ............................................................................................................................... 41
Table 5. Medical information for the full sample of cancer patients and survivors. ..................... 43
Table 6. Subject characteristics for subgroup of participants who neuromuscular testing on
the chair. ................................................................................................................................ 44
Table 7. Subject characteristics for subgroup of participants who neuromuscular testing on
the chair. ................................................................................................................................ 44
Table 8. Subject characteristics for subgroup of participants included in sleep analysis. ............. 44
Table 8. Descriptive statistics of neuromuscular variables measured on the bike. ....................... 59
Table 9. Descriptive statistics of sleep variables measured via actigraph. .................................... 60
Table 11. Independent-samples t-tests between fatigued and non-fatigued groups, based on
the FACT-F groupings for fatigue, at rest on the chair, and on the ergometer. .................... 61
Table 12. Independent-samples t-tests between fatigued and non-fatigued groups, based on
the fatigue thermometer groupings for fatigue, at rest on the chair, and on the ergometer... 63
Table 13. Independent samples t-test between fatigued and non-fatigued groups based on
FACT-F subjective ratings of fatigue. ................................................................................... 64
Table 14. Independent samples t-test between fatigued and non-fatigued groups based on
fatigue thermometer subjective ratings of fatigue. ................................................................ 64
Table 15. Pearson Product-moment correlations between measures of neuromuscular fatigue
and subjective ratings of fatigue (FACT-F and fatigue thermometer). ................................. 65
Table 16. Pearson Product-moment correlations between sleep variables .................................... 66
List of Figures and Illustrations
Figure 1. Schematic diagram of factors associated with cancer-related fatigue. Adapted from
McNeely and Courneya (2010). .............................................................................................. 2
Figure 2. The relationship between decreased fatigue resistance and time as related to daily
workloads (rectangles) for a subject with normal fatigue resistance (solid line) and a
patient with deteriorated fatigue resitance (dashed line). ........................................................ 3
Figure 3. Potential sites of neuromuscular fatigue. (Bigland-Ritchie, 1981). ................................. 9
Figure 4. Force trace depicting components used to calculate voluntary activation (VA). .......... 11
Figure 5. Schematic of the complete protocol used during the first testing session. .................... 46
Figure 6. Schematic of the complete protocol used during the second testing session. ................ 46
Figure 7. Dual X-ray absorptiometry scan illustrating sub-area analyzed to include force
producing lean body mass during knee extension (indicated in red area). ............................ 53
Figure 8. Changes from Pre (black stripes) to exhaustion (Post, white dots) in MVC, twitch
force, and voluntary activation (VA). .................................................................................... 60
Figure 9. Differences between fatigued (black stripes) and non-fatigued (white dots) subjects
in 1) MVC, 2) twitch force, and 3) percent voluntary activation (VA) measured at rest
(on the chair) .......................................................................................................................... 62
Figure 10. Medium significant correlation between sleep efficiency (SE) and FACT-F scores... 66
List of Symbols, Abbreviations and Nomenclature
7-day PAR
6-Minute walk distance
7 day physical activity recall
American College of Sport Medicine
Activity measure for post-acute case-computer
adaptive test
Action potential
Alcohol use disorders identification test
Beck depression inventory II
Biceps femoris
Bioelectrical impedance
Body mass index
Blood pressure
Brief Pain Inventory
Cancer rehabilitation evaluation system short-form
Certified exercise physiologist
Centre for Epidemiological Studies - depression
Cancer related fatigue
Canadian Society of Exercise Physiology
Dual X-ray absorptiometry
European Organization for Research and
Treatment of Cancer
Edmonton Symptom Assessment Scale
Emotional wellbeing
The Functional Assessment of
Chronic Illness Therapy – Fatigue
Functional assessment of chronic illness therapy general
Functional assessment of chronic illness therapy specific
Functional wellbeing
Godin Leisure-Time Exercise Questionnaire
Heart rate
International Classification of Sleep Disorders
Insomnia Severity Index
Interpolated twitch technique
Leisure score index
Compound muscle action potential
M.D. Anderson Symptom Assessment Inventory
Memorial Symptom Assessment
Maximal voluntary contraction
Thrive Centre
Non-rapid eye movement
Cooper aerobics center longitudinal study Physical
activity questionnaire
Physical activity readiness medical examination
Physical activity readiness questionnaire plus
Profile of mood states
Patient reported outcome information system
Pittsburgh Sleep Quality Inventory
Personal wellbeing
Quality of life
Randomized controlled trial
Rapid eye movement
Rectus femoris
Root mean squared
Fourth stage of cycling protocol
Sleep efficiency
Short form 36 health survey
Sleep onset latency
Social provision scaled
Selective serotonin re-uptake inhibitors
Slow wave sleep
Transcranial Magnetic Stimulation Adult Safety
Exercise centre for cancer patients and survivors
Transcranial magnetic stimulation
Total sleep time
Voluntary activation
Vastus lateralis
Maximal oxygen consumption
Peak volume of oxygen consumed
Watts per kilogram
Wake after sleep onset
Waist circumference
Cancer Support Centre
Yoga therapy
It always seems impossible until it’s done.
Nelson Mandela
Chapter One: Introduction
1.1 Cancer-Related Fatigue
Cancer related fatigue (CRF) is a highly common and debilitating symptom experienced
by cancer patients and survivors (Prue, Rankin, Allen, Gracey, & Cramp, 2006). CRF has been
described as a severe, unrelenting feeling of fatigue that is not improved by rest or sleep,
differentiating it from fatigue in the general population. CRF affects 70-100% of individuals with
cancer (Cella, Davis, Breitbart, & Curt, 2001). It can begin as a symptom of cancer, continue
throughout treatment and last months and even years after treatment cessation in up to 1/3 of
survivors (McNeely & Courneya, 2010; Prue et al., 2006). CRF is a widely experienced
condition with no universally accepted definition. This is reflected in the development and usage
of the many and varying methods of measurement. Notably, the prevalence of CRF can differ
based on the measurement tool used. CRF has a phenomenal effect on the person as a whole –
physically, emotionally and mentally.
There are a multiplicity of factors affecting the onset and occurrence of CRF (Figure 1).
For example, Ryan and colleagues (2007) described CRF as a cancer treatment toxicity that
includes the following factors: sleep disorders, activity level, malnutrition, pain, anemia, noncancer comorbidities (e.g. renal dysfunction, cardiac dysfunction etc.), as well as emotional
distress (depression and anxiety). As of yet, no one factor has been suggested as the primary root
cause of CRF. This specific type of fatigue is multifactorial, making it far more difficult to
address than acute fatigue. In order to fully understand and appropriately address CRF, a
comprehensive study is necessary to examine each potentially related factor.
Figure 1. Schematic diagram of factors associated with cancer-related fatigue. Adapted
from McNeely and Courneya (2010).
It is important to consider the difference between fatigue resistance during exercise and
activities of daily living, in contrast with chronic fatigue. The later one can encompass the
former one. For example, in individuals with reduced fatigue resistance, one simple bout of
physical activity, such as carrying groceries, can cause exaggerated acute fatigue. As the
individual needs to perform an additional task, for example carrying children, lifting laundry, or
climbing stairs, non-optimal recovery occurs leading to fatigue accumulation. Figure 2 illustrates
this relationship.
Figure 2. The relationship between decreased fatigue resistance and time as related to daily
workloads (rectangles) for a subject with normal fatigue resistance (solid line) and a
patient with deteriorated fatigue resitance (dashed line).
CRF has been observed using a variety of subjective scales, focusing primarily on the
psychological aspect of the condition. CRF can have a detrimental impact on physical, cognitive
and emotional aspects of quality of life. It also has a socio-economic impact since it can delay
return to work, decrease productivity, and limit social provision. Although there are a variety of
validated, and well-used subjective measures of CRF, the field currently has no validated
objective measure. For instance, although the effects of exercise to reduce CRF have been
heavily studied within the literature (Cramp & Byron-Daniel, 2012; Rajotte et al., 2012), few
studies have considered the physiological mechanisms of CRF. It is common for studies to
discuss both aerobic capacities and muscular strength gains from various training interventions;
however, the role of strength loss, declines in physical functioning and neuro-physiological
causes are not yet at the forefront of CRF research. Due to the multi-dimensional aspect of this
highly debilitating issue, it is important to measure it not only with subjective measures, but to
also investigate it from a physiological/physical perspective. It is clear that the most robust
method of CRF assessment is not yet agreed upon, causing a lack of understanding for the most
appropriate diagnosis, prevention and treatment of this common cancer treatment-related side
CRF has been observed using a variety of subjective scales, focusing primarily on the
psychological aspect of the condition. A recent systematic review by Minton and Stone (2008)
revealed 22 subjective scales for fatigue measurement, 14 of which were included in the review
(9 were not as they did not meet the minimum requirements of the review for reasons including:
the scale was not validated in English, not specifically for use in cancer patients, or only pilot
studies have tested the validity of the measurement tool). The most commonly reviewed and
used scales include: EORTC-Fatigue subscale, FACT-F, Brief Fatigue Inventory, Profile of
Mood States Fatigue Subscale Fatigue Severity Scale, Fatigue Questionnaire, Fatigue Symptom
Inventory, Lee Fatigue Scale, Multidimensional Assessment of Fatigue, (short form version as
well), Revised Piper Fatigue Scale, Schwartz Cancer Fatigue Scale, and the Wu Cancer Fatigue
Scale. Table1 provides a detailed representation of common one-dimensional and multidimensional scales to assess CRF.
Table 1. Uni-dimensional and multi-dimensional scales used to assess cancer-related
EORTC-Fatigue Subscale
Key Considerations
3 item unidimensional,
converted to score
minimal time for
measures physical
fatigue over the past
brief and simple to
ceiling effect –
questionable for use
in palliative setting,
not to be used as a
single measure
40/100 cut point for
clinically significant
Brief Fatigue Inventory
Profile of Mood States
Fatigue Subscale (POMS-F)
13 item unidimensional scale: 5
point Likert Scale
Fatigue scale part of
20 item anemia
Higher scores = less
Time for
Measures physical
fatigue over the past
one dimensional
scale 9 item VAS
5 minute time for
fatigue severity and
measures fatigue
severity and
interference over the
past week, current
and past 24hs
one dimensional 7
item subscale
used for cancer and
non cancer
Fatigue Severity Scale
9-item uni5
properties, but
simplicity may
outweigh this
recommended for
use with
intervention studies
can be used
independently or
with FACT-general
34/52 cut point for
significant CRF
suggested cut points
for mild (1-3),
medium (4-6), and
severe fatigue (7-10)
– not validated
scores but useful for
limited ongoing use
has defined minimal
clinically significant
not originally
validated in cancer
no clear advantage
over other scales
could provide
helpful baseline
measures in healthy
limited use in cancer
Fatigue Questionnaire (FQ;
aka Chalder Fatigue Scale)
Fatigue Symptom Inventory
(Hann, Denniston, & Baker,
dimensional scale
validated in a noncancer chronic
disease population
11 item
subscales: 7-item
physical fatigue and
4-item mental
5-10 minutes
completion time
measures physical
and mental fatigue
over the last month
versus when the
patient felt well
13-item scale
measures the
intensity and
duration of fatigue
and it’s interference
with quality of life
time for completion:
5 minutes
Lee Fatigue Scale (Visual
Analogue Scale for Fatigue,
(Lee, Hicks, & NinoMurcia, 1991)
Assessment of Fatigue
(Belza, 1995)
Multidimensional Fatigue
Symptom Inventory short
16-item scale
30-item scale
18-item scale
measures fatigue
not recommended
for use in CRF
measures both
subjective physical
and mental fatigue
originally developed
for use with CFS
useful for screening
CRF (normative
data available)
cut point for fatigue
brief and easy to use
questionable testretest reliability
limited to patients
undergoing active
treatment and
to be used with
originally validated
for patients with
sleep disorders
not recommended
for CRF
measurement due to
sleep disorder
poorly validated
not recommended
form (MFSI-30)
(Stein, Martin, Hann, &
Jacobsen, 1998)
Multidimensional Fatigue
Inventory (MFI-20)
(Smets, Garssen, Bonke, &
De Haes, 1995)
Revised Piper Fatigue Scale
(Piper et al., 1998)
Schwartz Cancer Fatigue
(Schwartz & Meek, 1999)
Wu Cancer Fatigue Scale
(Wu, Wyrwich, &
McSweeney, 2006)
measures global,
somatic, affective,
cognitive and
symptoms of fatigue
time for completion:
5-10 minutes
previous clinical
use limited
20-item scale
designed for use in
cancer patients
27-item scale
15-20 minute
completion time
shorter version than
original PFS
28 item multidimensional fatigue
measures fatigue
associated with
behavioral, affective
meaning, sensory
and cognitive item
normative data
available for
use in various
studies with small
patients numbers
limited data on
properties in cancer
9 item multidimensional scale
psychometric data
not used often
minimum significant
difference has been
psychometric data
not used in other
studies, therefore
not recommended
1.2 Neuromuscular Fatigue due to Physical Activity
The development of acute fatigue with physical activity is a common occurrence in
human life. Fatigue has been defined as a decline in one or more biological systems, which is
reversible and may or may not occur before task failure arises (Williams & Ratel, 2009). This
frequently occurs in the general population following multiple or single bouts of exercise,
leading to skeletal muscle fatigue (Davis & Walsh, 2010). Also, a worsened resistance to fatigue
is experienced in various diseases (including cancer, Parkinson’s disease, multiple sclerosis).
There are various factors effecting the occurrence and amount of fatigue experienced
during exercise. Such factors include the type of task (e.g. prolonged, maximal, short, dynamic),
the status of the subject (e.g. healthy, diseased, untrained) or the environment. Among these
variables, the task is regarded as heavily important in the magnitude of fatigue. Critical variables
include: muscle activation pattern, type of muscle group and muscle contraction, intensity,
duration, cardiorespiratory demands (e.g. temperature, glucose, catecholamine concentration and
cerebral oxygenation), as well as (even partial) ischemia.
One of the most well-known models identifying the cause of fatigue is the Chain of
Command Model, developed by Edwards (1981). This model deduces that fatigue can occur as
the result of various body systems beginning at the brain, leading towards the muscle
sarcolemma and eventually to the output of force/power from the muscle. This model is used in
the literature to describe various physiological systems effecting fatigue. Current research has
used this model to investigate the effect of each link on fatigue, in order to unravel the most
prominent cause of this regularly occurring phenomenon.
The Chain of Command model allows physiological systems to be separated into two
distinct groups, i.e. either proximal (central) or distal (peripheral) to the neural muscular junction
(Froyd, Millet, & Noakes, 2013). Central and peripheral fatigue can occur both dependent and
independent of each other. These two variations of fatigue can be distinguished by comparing
force generated by a maximal voluntary contraction (MVC) with an electrically stimulated
contraction (Gibson & Edwards, 1985). Furthermore, central fatigue can be differentiated as
spinal or supraspinal fatigue through the use of transcranial magnetic stimulation (TMS) over the
primary motor cortex (Gandevia, Allen, Butler, & Taylor, 1996).
Figure 3 (Bigland-Ritchie, 1981) provides a detailed representation of the potential
different sites of fatigue. As labelled on the diagram, central fatigue may occur as a result of
changes in: (1) activation of the primary cortex; (2) command propagation from the central
nervous system to the motor neurons; (3) activation of the motor units; (4) neuromuscular
propagation (including the neuromuscular junction). Peripheral fatigue includes alterations in (5)
excitation-contraction coupling; (6) availability of substrates; (7) state of intracellular medium;
(8) performance of contractile apparatus; and (9) blood flow.
Figure 3. Potential sites of neuromuscular fatigue. (Bigland-Ritchie, 1981).
1.2.1 Central Fatigue
Mental fatigue, involving motivation or an alteration of cognitive function, is a form of
central fatigue. Yet central can also be related to neuromuscular function. In that case, central
fatigue is defined as a decrease in motor unit recruitment and can be caused by a variety of
factors within the central nervous system. Both a decline in discharge frequency below the
tetanic fusion or to a spatial de-recruitment can explain the decrease in maximal voluntary
activation. Environmental conditions, such as hypoxia, may exacerbate central fatigue (Millet,
Martin, Martin, & Vergès, 2011). Feedback from afferent fibres can signal to the central nervous
system to reduce descending drive as a result of muscle stretch, (type Ia and II), hypoxemia,
ischemia, and biochemical factors (type III and IV) (Gandevia, 2001).
Voluntary activation (VA) is defined as the level of voluntary drive to the muscles
(Gandevia, 2001). The standard method (Merton, 1954), used to assess level of maximal
voluntary activation (%VA) is the twitch interpolation technique (ITT). More specifically, a
stimulus is delivered during an MVC to the nerve or the muscle, which is referred to as a
superimposed twitch. The superimposed twitch is normalized to a second stimulus, delivered on
relaxed muscle, termed the resting or control twitch. Figure 4 illustrates this method. The same
technique can be applied while using high-frequency electrical or magnetic doublets applied
rather than single twitch.
Figure 4. Force trace depicting components used to calculate voluntary activation (VA).
Supraspinal VA can be assessed using TMS (Gandevia et al., 1996). This method is
similar to the ITT, however the resting twitch is not measured directly on relaxed muscles due to
prior potentiation of the motor pathway. This method requires extrapolation from the linear
regression between the superimposed twitch and voluntary force at different levels (i.e. 100%,
75%, and 50% of MVC). Yet the principle stays the same, i.e. comparing a superimposed twitch
to a control twitch. Supraspinal VA has been performed in this study but the results are not
provided in this thesis.
VA is considered a semi-quantitative method and the validity of its use is still debated
(Taylor, 2009), but authors agree that it is useful in detecting altered drive to the muscles
(Gandevia, 2001). Additional techniques of ITT include: central activation ratio superimposing a
train of stimuli to MVC (Bigland-Ritchie, Jones, Hosking, & Edwards, 1978), comparing MVC
response with high frequency tetanus (Martin, Carpentier, Guissard, Van Hoecke, & Duchateau,
1999), as well as examining changes using normalization of the M-wave with root mean square
(RMS) (Millet et al., 2011).
1.2.2 Peripheral Fatigue
Peripheral fatigue occurs at the level of the muscles and may be caused by action
potential (AP) transmission along the sarcolemma, excitation-contraction coupling (EC) failure,
as well as the actin-myosin interaction. More specifically, a peripheral governor theory has been
proposed, suggesting that peripheral fatigue is the result of decreasing membrane excitability,
inhibiting calcium release through ryanodine receptors, and decreasing the availability of
sarcoplasmic reticulum calcium reuptake (MacIntosh & Shahi, 2010).
Peripheral fatigue is measured by stimulating the muscle or motor nerve with a supramaximal intensity to attain maximal spatial recruitment identified through isometric twitch
torque or muscle action compound (M-wave) response (Millet et al., 2011). A change in peak
twitch amplitude illustrates general contractile property changes. To further locate the site of
peripheral fatigue, the ratio difference between low- and high-frequency doublets can be used.
Any decrease in this ratio shows the presence of low-frequency fatigue (Verges, et al., 2009),
generally interpreted as excitation-contraction coupling failure.
The M-wave shows the muscles response (action potential propagation changes) to a
supramaximal stimulus. Any changes of M-wave characteristics (i.e. decreased amplitude or
area, longer duration) may be indicative of changes in AP propagation.
1.2.3 Muscle Function Recovery
A study by Froyd, Millet, & Noakes (2013), found that muscle function recovers
substantially within the first 2 minutes after exercise cessation, suggesting that previous research
may be underestimating the inherent fatigue. Specifically, typical techniques of measurement
that require the subject to be transferred from the exercise equipment, to a position in which
neuromuscular tests can be carried out often require more than two minutes. During this time,
muscle function recovers substantially, resulting in the underestimation of fatigue. Thus, a
method to eliminate this unnecessary recovery time would be ideal to acquire the most accurate
fatigue data. For this reason, an innovative ergometer has been developed for the purpose of the
present study.
1.2.4 Neuromuscular Fatigue Research in Cancer Patients and Survivors
To date, few studies have assessed neuromuscular function in the cancer population. In
the few studies conducted, there are a series of methodological concerns. For example, in the
study by Yavuzsen et al. (2009) it is concluded that the fatigue experienced in 10 fatigued cancer
patients is central in origin. Although the study demonstrated that MVC force prior to the
fatiguing protocol was lower in the CRF group than the age, and gender matched healthy
controls, the researchers failed to measure central fatigue directly. Instead, the authors concluded
that because the fatigue group had a greater resting twitch following the sustained contraction,
muscle contractile properties were preserved, therefore the fatigue must be of central origin.
Including VA as a measure of central fatigue is quite simple to administer – it is not clear why
the researchers chose to forgo this method. The study found decreased endurance time during
sustained elbow flexion to exhaustion in the CRF group as compared to controls, however
factors such as motivation (Marcora, Staiano, & Manning, 2009), depression (Yavuzsen et al.,
2009), and increased rate of perceived exertion as a result of sleep disturbance (Temesi et al.,
2013) could confound this finding. Ratings of perceived exertion are likely related in part to
peripheral fatigue because muscle dysfunction and pain can lead to feedback and feed forward
mechanisms (Millet et al., 2011) and resultantly decreasing time to exhaustion.
Similarly, a second study by the same group (Kisiel-Sajewicz et al., 2012) concluded that
neuromuscular fatigue was central in origin rather than peripheral without actually measuring
central fatigue. As in Yavuzsen et al. (2009), this study assumed that because evoked contractile
properties, indicative of peripheral fatigue (peak twitch) were preserved in the CRF group (as
compared to age, gender matched controls) so that the fatigue must be of central origin.
Although both studies indicate that fatigue aetiology in CRF is of central origin, central fatigue
was never actually accounted for.
A study by Cai et al. (2014) utilized intermittent isometric elbow flexion until exhaustion
and found that force generating capability was less deteriorated in the CRF group than age and
gender matched controls. Furthermore, it was stated that the CRF patients elicited a lower degree
of muscle fatigue (measured via twitch force) at exhaustion, indicating central mechanisms of the
main contributor of fatigue in CRF patients. Although the addition of intermittent contractions
was beneficial, this group failed to directly measure central fatigue.
It is noteworthy that the three previously mentioned studies utilized isometric elbow
flexion as the testing modality. Elbow flexion is not a common activity detrimental to tasks of
daily living. It is suggested that neuromuscular function be tested in a whole body, dynamic
movement that resembles daily tasks (Millet & Lepers, 2004; Place, Yamada, Bruton, &
Westerblad, 2010), especially exercise prescription given to relieve fatigue. Similarly, both
studies utilized sustained isometric contractions. Many activities of daily living require
intermittent contractions.
Neil and colleagues (2013) conducted a study comparing sustained isometric quadriceps
contractions between fatigued and non-fatigued breast cancer survivors. The study concluded
that there were no differences in VA, nor half relaxation time in twitch (indicative of peripheral
fatigue) between fatigued and non-fatigued cancer patients, and that the protocol induced a
comparable amount of fatigue as demonstrated by similar declines in MVC. Notably, this study
utilized a sample of cancer survivors rather than those on treatment. It is possible that fatigue
aetiology in patients on treatment may differ than those who had previously completed treatment.
Interestingly, the authors noted the fatigued subjects had low VO2peak (maximal oxygen
consumption), indicative of reduced cardiorespiratory fitness, leading to feelings of fatigue
during activities of daily living. More specifically, the fatigued patients may be completing such
activities above lactate threshold, a physiological marker of exercise capacity (Binder et al.,
2008). Completing any activity above lactate threshold requires more effort, illustrating reduced
fatigue resistance in this group of subjects.
1.3 Sleep Disorders and Cancer-Related Fatigue
Sleep is a homeostatically controlled behavioural state of reduced movement and sensory
responsiveness (Allada & Siegel, 2008), described as stints of decreased consciousness, lessened
movement of skeletal muscles, as well as slower metabolism (Zisapel, 2007). Sleep improves
energy, wellbeing, and is key for learning and memory. More specifically, it has been shown to
regulate key molecular mechanisms (i.e. transcriptional regulatory proteins) (Allada & Siegel,
2008; Fullagar et al., 2015), and plays an essential role in metabolic homeostasis (Xie et al.,
2013). It is well known and commonly experienced that a lack of sleep will leave humans feeling
unwell. Anecdotally, it must be of extreme importance as it has survived many years of evolution
(Sehgal & Mignot, 2011). Humans require 7-8 hours of sleep, usually at night (Zisapel, 2007),
however disordered sleep is often prevalent in the cancer population.
Indeed, in this population, the prevalence of sleep disturbance is reported to be 17-70%,
with the difference attributed to the method of measurement used (Davis & Goforth, 2014). To
put this in context, only 20% of the general population experiences poor quality of sleep
(Miaskowski et al., 2011; Savard & Morin, 2001). Sleep disorders can begin at diagnosis and
continue into survivorship for the “significant minority” (Savard & Morin, 2001).
Currently, it is not clear how sleep disorders and CRF are associated (Curran, Beacham,
& Andrykowski, 2004; Harris, Ross, & Sanchez-Reilly, 2014; Savard et al., 2005). Research on
this relationship is limited, illustrating a gap in the literature. Roscoe et al. (2007) found that
sleep disturbances are strongly correlated with fatigue, such that disorders are more severe in
fatigued patients and baseline sleep disturbance may be a predictor of fatigue. More research
must be done to understand the difference between sleep disorders as a part of the CRF
spectrum, or if sleep disorders occur independently of CRF. Interestingly, the treatment of one
may have a reciprocal effect on the other (Roscoe et al., 2007). Symptom cluster studies have
reported several factors of CRF including pain, depression, loss of concentration and other
cognitive functions as interrelated, therefore it is possible CRF and sleep disorders may also
share a common aetiology (Roscoe et al., 2007).
Humpel and Iverson (2010) and Rogers et al. (2014) are two studies that have found
associations between disordered sleep and fatigue in cancer patients. Humpel and Iverson (2010)
found that poor sleep quality was associated with worse fatigue. Rogers et al. (2014) completed
the first study to report the predictors of fatigue as a response to exercise, finding that sleep
quality mediated and enhanced the exercise intervention. In the general population, Van Dongen
et al. (2003) found that even relatively modest sleep restriction can impair waking
neurobehavioral functions (i.e. cognitive impairments) in healthy adults. Similarly, it was found
that sleep extension improved performance in basketball players (sprint time, shooting accuracy,
reaction time, increased vigour, and reduced fatigue) (Mah, Mah, Kezirian, & Dement, 2011).
Arnal and colleagues (2014) found that six nights of extended sleep improves sustained attention
and reduces sleep pressure (homeostatic sleep drive).
These findings demonstrate the idea of a reciprocal relationship between exercise,
disordered sleep and both physiological and psychological fatigue (Chennaoui, Arnal, Sauvet, &
Léger, 2015). As explained below, exercise is associated with improved sleep (Buman & King,
2010; Kline, 2014; Youngstedt, 2005; Youngstedt & Kline, 2006). For example, moderateintensity aerobic exercise is recommended to treat and prevent sleep disorders (Chennaoui et al.,
2015). Better sleep results in higher levels of physical activity (Baron, Reid, & Zee, 2013; Kline,
2014; Youngstedt & Kline, 2006). This relationship demonstrates the necessity of an active
lifestyle. Exercise benefiting sleep hygiene and improved sleep assists in increasing daytime
activity, and reciprocally, as daytime activity increases, sleep improves. As previously
mentioned, exercise has been proven to reduce CRF, therefore the relationship between exercise,
CRF and sleep may be of related mechanisms. Mechanisms related to exercise and sleep are
addressed in section 1.3.4.
1.3.1 Insomnia in Cancer Patients and Survivors
The International Classification of Sleep Disorders (ICSD) lists over 80 different sleep
disorders (Medicine, 2005). Insomnia, a sleep disorder where patients experience extreme
difficulty falling and/or staying asleep, occurs in approximately 10% of the general population
(Health, 2005), however in cancer patients this number can be as high as 50% (Savard & Morin,
2001), making it a highly prevalent and debilitating sleep disorder experienced by the cancer
population. Insomnia is also highly correlated with depression and anxiety (Palesh et al., 2010).
The high occurrence of insomnia in CRF has been reported as a result of a combination of
factors. Such factors include: physical, emotional, and environmental issues, as well as treatment
and surgery side effects. Table 2 provides examples of each factor that may potentially cause
Table 2. Factors related to insomnia in the cancer population.
Harris, et al. (Harris et al., 2014)
Pain, nausea, vomiting, pruritus, dyspnea
Treatment Side Effects
Ancoli-Israel (Ancoli-Israel, 2009)
Steroids, hormone-treatments, medications
(opiates, chemotherapy agents, sedativehypnotics)
Depression, anxiety, performance anxiety
(worried about not being able to initiate
Roscoe et al. (Roscoe et al., 2007)
Harris, et al. (Harris et al., 2014)
Excessive lighting, temperature, noise
Alters sleep patterns, affect melatonin
Hansen, et al. (Hansen, Madsen, secretion
Wildschiødtz, Rosenberg, & Gögenur,
Cancer type, stage and diagnosis
Harris, et al. (Harris et al., 2014)
Cancer site (breast versus brain), stage 4
versus stage 1
Lack of exercise
Chikahisa and Séi (Chikahisa & Séi, 2011)
Less ADP accumulated
It is common for one factor to cause a cyclical effect thereby affecting other factors. For
example, pain can worsen depressive symptoms, thereby causing insomnia (Harris et al., 2014).
Cancer patients tend to have more time to sleep (e.g. long hospital visits), or they tend to increase
their sleep opportunities, thereby self-perpetuating insomnia (Theobald, 2004). There is a
detrimental, imbalance between sleep opportunities and sleep ability, thereby decreasing sleep
efficiency. Sleep therapies, including cognitive behavioural therapy, as well as exercise
interventions, attempt to create more balance between sleep opportunities and wake, thereby
creating improved sleep hygiene to decrease the occurrence of insomnia. Beliefs and attitudes
also contribute to insomnia (Palesh et al., 2010). Individuals may have unrealistic sleep
requirement expectations and wrongly assess their own sleep difficulties. As a result they will
exercise less and devote more time to resting in bed, which can increase sleep latency (Palesh et
al., 2010).
1.3.2 Sleep Quantification Polysomnography
PSG is the gold standard in quantifying sleep (Ancoli-Israel et al., 2003). This method
simultaneously measures and records sleep stages and arousals, respiration, limb movements,
snoring, oximetry, body position and cardiac rhythm disturbances. A basic PSG recording
(hypnogram) is done through the use of electroencephalography (EEG) used primarily to monitor
brain activity (arousal, wakefulness and sleep stages), electromyography (EMG) which measures
muscle activity and tone, electrocardiography (ECG) to measure heart rhythm, as well as
electrooculogram (EOG) which monitors rolling eye movements. Additional measures in PSG
may include: airflow, respiratory effort, oximetry, snoring, as well as position analysis. PSG is
carried out by a sleep specialist and physician at a sleep facility, or by a sleep specialist at the
patient’s home through the use of a portable PSG device.
Notably, PSG is useful to assess the sleep stages, as marked by a hypnogram. Sleep is
divided into two phases of sleep, which alternate in 90-minute cycles: rapid eye movement sleep
(REM) and non-rapid eye movement sleep (NREM) (Harris et al., 2014). NREM is further
divided into three stages. Different physiological processes occur in the different phases of sleep.
For example, individuals lacking REM sleep are predisposed to impairment of learning ability,
which is key for early childhood development. Slow wave sleep (SWS), or the third phase of
NREM, is considered to be the highest quality, and most restorative phase of sleep (Harris et al.,
2014). For example, protein synthesis for tissue reparation occurs during this phase of sleep.
Furthermore, in men, SWS is associated with a secretion of growth hormone which stimulates
growth, cell reproduction, and cell regeneration.
Quality of sleep, is more important than the quantity of sleep; it is important for humans
to progress through the stages of sleep to attain the deep, restorative sleep required (CoreyBloom, 2005). For example, SWS which occurs during NREM sleep, is known to be extremely
beneficial (Fullagar et al., 2015). Interestingly a study by Parker et al (2008), found that 114
cancer patients who underwent 42 hours of ambulatory PSG experienced nearly a total absence
of SWS. This finding is associated with a detrimental impact on the patient’s quality of life, and
thus demonstrates the benefit of assessing time spent in each of the sleep stages, acquired
through the use of PSG.
Although highly beneficial, PSG is limited in use with cancer patients and survivors as it
invokes a certain level of subject burden, can be quite cumbersome for re-use in studies with a
large sample size (Ancoli-Israel et al., 2003) and can be costly. Minimal information on sleep
disturbances has been demonstrated using PSG in the cancer population, regardless of PSG being
held as the gold standard in quantifying sleep (Parker et al., 2008). Actigraphy
Actigraphy is the prevailing objective measure of sleep (Berger, 2009). Actigraphy is a
validated, convenient and cost effective method to monitor sleep and wake, which has been used
increasingly over the past 20 years. An actigraph is a device resembling a wristwatch, which
contains a miniature accelerometer that translates movement into a numerical expression (Sadeh
& Acebo, 2002). Currently there is no consensus as to which specific device or algorithm (used
for analysis) is best (Sadeh & Acebo, 2002).
Actigraphy is often implemented because of its ease of use – it can monitor activity for a
period of hours up to weeks, which compared to PSG is quite a lengthy period of time. Data
output has been compared with PSG (Ancoli-Israel et al., 2003; Blood, Sack, Percy, & Pen,
1997; Jean-Louis, Kripke, Cole, Assmus, & Langer, 2001), along with daily logs (Krahn, Lin,
Wisbey, Rummans, & O'Connor, 1997; Lockley, Skene, & Arendt, 1999).
Several studies have assessed sleep disorders in cancer patients using actigraphy. For
example, one study examined the relationship between fatigue, sleep and rhythms in cancer
patients, and concluded that circadian rhythm disruption plays a role in the psychological aspect
of fatigue (Morrow et al., 2000). Furthermore, Fernandes et al. (2006) compared sleep
disturbances in cancer in-patients and healthy controls. It was found that in fatigued patients
sleep efficiency and wake after sleep onset was significantly worse than in healthy controls, even
though there was no difference in self-reported insomnia scores (Fernandes et al., 2006). This
finding illustrates the benefit of assessing sleep objectively, although more objective sleep
studies have been recommended (Ancoli-Israel, Moore, & Jones, 2001)
Although actigraphy has been proven as a reliable method of quantifying sleep activity,
daily logs are complementary to this method. Actigraphy gives an objective measure of what the
subject cannot tell the researcher (i.e. if they were laying still watching television in bed).
Conversely, sleep logs give a subjective report of sleep related experiences, “editing” actigraphic
data, and allowing the researcher to remove possible artifacts etc. (Sadeh, Hauri, Kripke, &
Lavie, 1995). Subjective Assessment Scales
Although not recommended to assess sleep, Sateia and Lang (2008) noted that most
studies have used single-item subjective assessments, such as the Memorial Symptom
Assessment Scale (MSA), the M.D. Anderson Symptom Inventory (MDSAI), and the Edmonton
Symptom Assessment System (ESAS). Such measures do not take into consideration the related
symptoms of fatigue and excessive sleepiness. The use of single measures is due to the fact that
sleep is not commonly the primary outcome of cancer symptom studies, therefore brief measures
are usually chosen in order to reduce participant burden (Page, Berger, & Johnson, 2006). In fact,
Medysky et al. (submitted for publication) reported that sleep was the primary outcome in few of
the exercise intervention studies reviewed (Chandwani et al., 2014; Coleman et al., 2012; Dodd
et al., 2010; Humpel & Iverson, 2010; Payne, Held, Thorpe, & Shaw, 2008; Wenzel et al., 2013),
as compared to other factors related to CRF such as depression, quality of life and pain.
In addition to the single-item subjective assessments described above (MSA, MDSAI,
ESAS), multi-item subjective questionnaires have been proposed to assess sleep. The Pittsburgh
Sleep Quality Index (PSQI) is the standard in self-reported sleep quality assessment (Berger,
2009), widely used in a variety of cancer and exercise intervention studies (Chandwani et al.,
2014; Cormie et al., 2014; Humpel & Iverson, 2010; Payne et al., 2008). Less used measures
seen in sleep and CRF studies include the Symptom Numeric Rating (Cheville et al., 2013), the
Epworth Sleepiness Scale (Coleman et al., 2003), Symptom Assessment Scale (Mock et al.,
1997), as well as the Brief Insomnia Rating (Cheville et al., 2013).
Different individuals may have diverse perceptions of their own sleep. For example, an
adult who has always slept poorly may perceive their sleep quality as “normal”, whereas another
individual may consider their poor sleep as a disorder. Such differences in perception make it
important to include an objective measure of sleep, capturing sleep quality as accurately as
possible. It is noteworthy that the intervention completed by Dodd and colleagues (2010)
reported the lack of inclusion of an objective measure of sleep as a limitation to their results,
which showed no differences in sleep disturbances post-intervention. Optimal sleep
measurement should include a subjective measure complemented with an objective measure,
such as actigraphy or PSG (Erickson & Berger, 2011).
1.3.3 Exercise and Sleep Improvements in the General Population
In the general, healthy population exercise is an accepted and recommended (by the
American Sleep Disorders Association) non-pharmacological intervention to improve sleep.
Furthermore, it has been reported that total sleep time influences next day exercise (Baron et al.,
2013) illustrating a possible bi-directional relationship. Exercise has been recommended and
discussed as one of the most closely associated daytime behaviours with sleep (Driver & Taylor,
2000; Hauri, 1993; Youngstedt, 2005).
There are several possible exercise-related mechanisms that may promote sleep. Exercise
has been shown to reduce anxiety. Specifically, poor sleep has been linked to anxiety
(Association, 2000); therefore improvements in anxiety as a result of exercise may also improve
sleep (Youngstedt, 2005). Secondly, a link between thermogenic effects and sleep has been
examined (Lack, Gradisar, Van Someren, Wright, & Lushington, 2008). Sleep is promoted by a
decrease in body temperature (0.5-1.0 degree Celsius) (Edinger et al., 1993). Exercise is said to
increase central, skin and cerebral temperature. This initial rise in temperature is lowered by
peripheral heat dissipation, resultant of vasodilation, causing a rapid decrease in core
temperature. This can then assist with sleep onset and enable entry into deeper sleep stages
(Chennaoui et al., 2015). Third, it is possible that exercise may have a major impact on sleep as
exercise easily depletes energy stores. In turn, the amount of beneficial SWS increases as a result
of increased energy expenditure. A fourth possible mechanism of exercise on sleep is circadian
rhythm shifting. Specifically, exercise can potentially be used to resynchronize a shifted
circadian system (Youngstedt, 2005).
Due to the wide range of these sleep specific benefits, as well as additional health
benefits, exercise is a cost effective, accessible, and relatively simple method of improving sleep,
with minimal, if any, negative side effects (Kline, 2014).
1.3.4 Exercise and Sleep in the Cancer Population
With quite a vast body of literature on sleep improvements as a result of exercise, a
smaller portion of the literature has recently reported improvements in sleep quantity and quality
as a result of exercise interventions in cancer patients and survivors. As previously mentioned,
exercise has been proven to reduce CRF, therefore if CRF and sleep disorders are indeed related,
it is intuitive that exercise may also improve sleep disorders.
Table 3 provides a summary of studies that have been conducted assessing sleep
outcomes following an exercise intervention or self reported exercise in cancer survivors on or
off-treatment. Humpel and Iverson (Humpel & Iverson, 2010) were one of the first groups to
suggest exercise as a potential beneficial remedy to decrease sleep disorders in cancer survivors.
Similarly, Berger (Berger, 2009) reported that those who were less active in the day had more
restless sleep experiences, and more intense fatigue. Of the limited interventions focusing on the
improvement of sleep through exercise, only a few (Chandwani et al., 2014; Coleman et al.,
2012; Dodd et al., 2010; Kampshoff et al., 2015; Wenzel et al., 2013) reported no significant
change in sleep outcome. Thus far, the impact of exercise on sleep in the cancer population
seems to be positive.
Interventions that used aerobic exercises (Cheville et al., 2013; Cormie et al., 2014;
Donnelly et al., 2011; Mock et al., 1997; Payne et al., 2008; Rajotte et al., 2012; Rogers et al.,
2014; Wang, Boehmke, Wu, Dickerson, & Fisher, 2011) (walking, cycling, swimming) as well
as resistance training, elicited positive sleep benefits, while on the contrary one of two
interventions using yoga therapy did not illicit any changes in sleep outcomes (Chandwani et al.,
2014). For example, Tang et al. (2010), employed an eight-week brisk walking program (based
on perceived exertion) three days a week, for 30 minutes, in the evenings prior to dinner
(4:00pm-6:00pm). Patients also completed a diary allowing researchers to check for compliance
to the exercise prescription. Significantly improved sleep was reported via PSQI (β=-3.54,
P=<0.01). Interestingly, the improvements in sleep as a result of the walking program also
corresponded with reduced bodily pain and improvements in mental health. Walking proved to
be the most used modality of exercise across all of the studies (Table 3), likely due to its ease of
use and cost-effectiveness.
Exercise is beneficial in both patients on treatment (Chandwani et al., 2014; Cheville et
al., 2013; Coleman et al., 2003; Coleman et al., 2012; Cormie et al., 2014; Dodd et al., 2010;
Mock et al., 1997; Payne et al., 2008; Sprod et al., 2010; Tang et al., 2010; Wang et al., 2011;
Wenzel et al., 2013) and survivors (off-treatment) (Donnelly et al., 2011; Humpel & Iverson,
2010; Kampshoff et al., 2015; Lin, Rau, & Lin, 2015; Mustian et al., 2013; Rajotte et al., 2012;
Rogers et al., 2014), of a variety of different cancer types (breast, lung, colorectal, multiple
myeloma, pancreatic, ovarian, prostate, lymphoma); see Table 3. More specifically, Payne et al.
(2008) saw significant sleep improvements, as measured by actigraphy and the PSQI, in 20
breast cancer patients receiving hormonal therapy, who completed moderate walking for 20
minutes four-times per week. Similarly, survivors who completed 12-weeks of 90 minute
resistance and aerobic exercise sessions twice per week reported improved sleep (Rajotte et al.,
Exercise in a supervised group setting (Cormie et al., 2014; Rajotte et al., 2012) as well
as a home-based location (Cheville et al., 2013; Coleman et al., 2003; Donnelly et al., 2011)
prompts positive sleep improvements. Group-based exercise is beneficial because social support
has been shown to increase adherence to exercise (Coleman et al., 2003). Supervised activities,
such as the four-week yoga (2 × 75 minute sessions per week) program implemented by Mustian
and colleagues (2013) yielded positive sleep improvements. Home-based exercise is beneficial to
reduce barriers to exercise such as transportation, cost and scheduling (Pinto, Frierson, Rabin,
Trunzo, & Marcus, 2005). This type of exercise intervention has produced positive
improvements in sleep as seen by the home-based walking and strengthening intervention
implemented by Donnelly et al. (2011).
Most exercise interventions to promote sleep use moderate exercise intensity to yield
positive sleep outcomes (Cormie et al., 2014; Donnelly et al., 2011; Rabin, Pinto, Dunsiger,
Nash, & Trask, 2009; Rogers et al., 2014; Sprod et al., 2010; Wang et al., 2011; Wenzel et al.,
2013). However, the study by Kamphoff and colleagues (2015) compared high, low to moderate
intensity and waitlist control. No between-group differences were found, however it was noted
that sleep disturbances were minimal at baseline, therefore a ceiling effect may have occurred. It
is possible that with more pronounced sleep disturbances, moderate intensity exercise would
elicit positive improvements in sleep, as described in many other studies in Table 3. Altogether,
these results indicate that exercise is a safe and beneficial method to improve sleep in cancer
patients and survivors.
There are several possible limitations that may help to explain the few negative (or no
change) outcomes in sleep improvements via exercise. Primarily, Dodd et al. (2010) noted that
the subjects had mild levels of sleep disturbances causing a ceiling effect. It is possible with a
sample of “okay” sleepers came less occasion for the intervention to impact a significant change.
Similarly, Coleman and colleagues (2012) saw statistically significant improvements in sleep
quality, due to the exercise intervention, only as the quality of sleep worsened throughout
treatment. This could be impacted further by the use of simple measurement tools, rather than
objective measures coupled with a subjective measure.
Another limitation in the reviewed studies may be the exercise prescription used. The
study by Dodd et al. (2010) had participants follow the ACSM 1998 guidelines, which
recommended exercise 3 times per week for a 20-minute duration, at a moderate intensity. The
ACSM 2006 guidelines, however, recommend exercise 5 times per week, for 30 minutes at a
moderate intensity. The difference in these two sets of recommendations add up to
approximately 50 minutes more exercise per week. Interventions such as Rogers et al. (2014)
implementation of a 12-week, individualized, resistance program of 90-minute sessions,
occurring 2 days per week, showed positive changes in sleep outcome. This intervention
prescribed 120 minutes more exercise than the intervention by Dodd et al. (2010). A similar
difference in exercise prescription can be seen in the intervention by Chandwani et al. (2014).
This study prescribed either yoga, (involving synchronized breathing, deep relaxation and
meditation) or stretching and did not see changes in sleep outcome. It seems that a more stringent
exercise program, consisting of moderate-to high-intensity exercise for a longer duration of time
(closer to the 2006 ACSM guidelines) may be more effective in producing positive sleep
Finally, compliance to exercise (or lack of) has been reported as a key limitation. For
example, Wenzel, et al. (2013), noted that 32.4% of participants “dropped out” of exercise, while
12% of controls “dropped in” to exercise, demonstrating the exercise crossover effect.
Understandably, control patients are not advised to refrain from exercise (Coleman et al., 2012).
These low adherence rates can mask clinically significant effects. Compliance can be increased
by group exercise programs (Dodd et al., 2010), significant others attending exercise sessions
(Coleman et al., 2012) and via phone calls from researchers (Dodd et al., 2010).
Table 3. Studies examining the relationship between exercise, sleep and cancer-related fatigue (CRF). Measurements of sleep
are in bold.
Study Design
et al., 2014)
Breast cancer
patients stages 0-3
radiotherapy within
the next 6 weeks
al., 2013)
Stage 4 lung or
colorectal patients
Exercise Type
Yoga (YT): preparatory
warm up &
synchronized breathing,
selected postures, deep
Deep relaxation,
alternate nostril
breathing, meditation
Stretching (ST):
exercise recommended
for breast cancer
Incremental walking
and home-based
strength training, 4+
days/week, 8 week trial
Sleep Outcome
SF-36, BFI, PSQI, Cortisol,
No differences
Mobility and Activities Short
Forms, FACT-G, FACT-F, Pain
and Sleep Quality: Symptom
Numeric Rating
(Coleman et
al., 2003)
Pilot RCT
Multiple Myeloma
patients on
treatment (n=24)
Home based exercise,
resistance combined
with aerobic training
Body composition via air
displacement plethysmography,
POMS, muscle strength &
aerobic capacity: 4 strength tests
(Kaiser pneumatic training
equip), 1RM, Modified Balke
protocol, Actigraphy, Epworth
Sleepiness Scale-daytime
Positive (small
sample size)
(Coleman et
al., 2012)
Multiple Myeloma
patients on
treatment (n=187)
Home-based, aerobic
walking at 65%-80% of
maximum heart rate
(via RPE), stretching,
and strength/resistance
training at 60%-80%
POMS-Fatigue, FACT-F,
Actigraph, 6-minute Walk Test,
No difference
difference only
after sleep
worsened during
Case Study
Stage2b Pancreatic
Cancer Patient
Supervised moderatehigh intensity resistance
and aerobic exercise,
twice/week for 6
400m walk, 1RM leg press,
repeated chai raise, stair climb,
usual & fast 6 minute walk,
sensory organization test, DXA
Scan, SF-36, FACT-Hep,
FACT-F, PSQI, Brief Symptom
Inventory-18, GodinLeisureTime Exercise Questionnaire
(Dodd et al.,
3-arm RCT
Breast, colorectal,
or ovarian cancer
patients, beginning
first CTX cycle
Home-based, aerobic
exercise, 3-5x/week at
60-80% VO2peak, 2030minutes
al., 2014)
GSDS, CES-D, Worst Pain
Intensity Scale (numeric rating improvements in
scale 0-10), Karnofsky
sleep disturbance
Performance Status (KPS) Scale,
Piper Fatigue Scale
(Donnelly et
al., 2011)
cancer intensity, home-based
survivors (n=33)
strengthening exercises,
Breast cancer
Not an intervention (n=32) & prostate
PA was measured an
cancer (n=59)
correlated to sleep
composition: WC, BMI, 12minute walk test, PSQI, 7-day
Less PA has
poorer sleep
et al., 2015)
survivors (n=277)
High intensity (n=91)
Low to moderate
intensity (n=95)
Waitlist control (n=91)
Resistance and
endurance interval
training 2x/week (same
exercise for each group,
different in intensity
only), 12 weeks
No between
(Lin et al.,
Lung cancer
survivors (n=186)
MDASI-T, 12 min walk test,
Symptom assessment scale
(Mock et al.,
- 2 group pretest, post-test
Early stage breast
cancer patients,
beginning radiation
program (n=46)
Not an intervention PA was measured
(GLTEQ) and
compared to sleep
Individualized selfpaced, home based
al., 2013)
(Payne et al.,
clinical trial
(Rabin et al.,
Single armed
Cancer survivors
(n=410) with
reported sleep
Breast cancer
patients, receiving
treatment, ages 55+
4-week yoga
intervention (2x/week,
75 minutes each)
PSQI, Actigraphy
Home-based walking,
20 minute duration, 4
times per week (14
PSQI, CES-D, Blood sample:
serum cortisol, serotonin, IL-6,
bilirubin, Actigraphy
Stage 0-2 breast
cancer survivors,
treatment (n=19)
Combined PA and
12 weeks of PA
counseling via
Moderate HR activities
such as swimming,
brisk walking, biking
Sent tip sheet
10 min, on at least 2
day/week, increased
over 12wk to 30 min of
accumulated PA per
day at least 5
PSQI, Intervention Feasibility
and Acceptability, 7-day
physical activity recall (7-day
PAR), Stage of Motivational
Readiness for PA measure,
al., 2012)
clinical trial
Cancer survivors
(off treatment 90+
days) (n=221)
12 weeks, 90 minute
session, 2 days/week
Group session,
resistance, 10 minute
aerobic warm-up
SF-36, Fatigue Symptom
Inventory, Muscle and Joint
Measure, Brief Insomnia
Rating, Social Support,
ENRICHD study, resting HR,
BP, weight, WC, 6-min walk
test, 1RM, Sit & Reach
al., 2014)
Pilot RCT
breast cancer
survivors (n=46),
<stage 2, off
3 month 160 min/week
aerobic walking &
2/week resistance with
exercise bands
3-day diet record
Patient Reported Outcomes
Information System (PROMIS)
Body Composition: height,
weight, BIA, leg strength via
dynamometer,Submax treadmill
test: Naughton protcol
Serum samples (IL-6, IL-10,
Fatigue Symptom Inventory
Breast and prostate
patients (n=38)
4-week, home-based
Progressive moderate
intensity walking and
resistance exercises
Biological Sleep Markers: IL6, TNA-α, sTNF-R
(Sprod et al.,
2-arm pilot
Positive (not
(Tang et al.,
(n=71) Taiwanese
patients of any
cancer diagnosis
Brisk walking, with
speed tailored to
individuals RPE
MOS-SF36, exercise logs
(Wang et al.,
(n=62) stage 1, or 2
breast cancer
patients, expecting
following surgery
Moderate intensity
home-based walking
program, tailored to 4060% max HR
PSQI, FACT-G, Godin Leisure
Time Exercise Questionnaire, 6
minute walk distance (6MWD),
FACT-F, Exercise Self Efficacy
al., 2013)
(n= 138) prostate,
breast, other solid
tumors patients
(n=62) Patients of
all types and stages
of cancer
Modified Piper Fatigue Scale,
Profile of Mood States Scale,
PSQI, PAQ, Pedometers, Pain,
Physical Function, VO2max
Exercise tolerance with graded
exercise test, Actigraphy
QOL: cancer rehab evaluation
system-short form (CARES-SF)
No difference
Home-based walking,
20-30 minutes at 50%70% maximum heart
12 week, Stage 2
cardiac rehab program
et al., 2003)
RCT: Randomized Controlled Trial. YT: Yoga Therapy. ST: Stretching. SF-36: Short Form-36 Health Survey. CES-D: Center for Epidemiologic Studies – Depression. AM-PAC
CAT: Activity Measure for Post-Acute Care – Computer Adaptive Test. FACT-G: Functional Assessment of Chronic Illness Therapy-General. FACT-F: Functional Assessment of
Chronic Illness Therapy – Fatigue. POMS: Profile of Mood States. 1 RM: One Repetition Maximum. DXA: Dual X-Ray Absorptiometry. FACT-F: Functional Assessment of
Chronic Illness Therapy – Hepatobiliary. GSDS: General Sleep Disturbance Scale. PSQI: Pittsburgh Sleep Quality Index. PFS: Piper Fatigue Scale. PROMIS: Patient Reported
Outcomes Information System. HR: Heart Rate. BP: Blood Pressure. MOS-SF36: Medical Outcome Study short-Form Health Survey. QOL: Quality of Life. CARES-SF: Cancer
Rehabilitation Evaluation System Short-Form. 6MWD: 6-Minute Walk Distance. BIA: Bioelectrical Impedance. 7-day PAR: Seven Day Physical Activity Recall. PAQ: Cooper
Aerobics Center Longitudinal Study Physical Activity Questionnaire. WC: Waist Circumference. BMI: Body Mass Index. MFSI-SF: Multi-dimensional Fatigue Symptom
Inventory-Short Form. BDI-II: Beck Depression Inventory II. PANAS: Positive and Negative Affect Scale.
1.3.5 Factors Related to Sleep Disorders
There are a variety of covariates that can influence the results of sleep studies. Similar to
the multi-dimensionality of CRF, the prevalence and severity of sleep disorders can be affected
by many of the factors caused by, or independent of cancer treatment. Treatment Type and Status
Women receiving radiation treatment for breast cancer have a high prevalence (85%) of
increased episodes of wake after sleep onset (WASO). Conversely, hormone therapy for breast
(Desai et al., 2013) and prostate (Savard, Hervouet, & Ivers, 2013) cancer can worsen a preexisting sleep disorder or precipitate insomnia, while accompanied by hot flashes, night sweats,
and anxiety. As negative side-effects of hormone therapy occur (i.e. impaired body image), sleep
disturbances become more prevalent (Desai et al., 2013). Chemotherapy has been associated
with poor sleep quality and daytime sleepiness (Maquet, 1995). As recorded by actigraphy,
episodes of WASO increase during treatment (Ancoli-Israel et al., 2006).
Sleep disorders occur at diagnosis of cancer, as well as during cancer treatment, and can
persist into survivorship (Cai et al., 2014). For example, in a study examining sleep quality in
breast cancer patients prior to, during and after completion of chemotherapy, poor sleep quality
was prevalent at all time points in at least half of the patients (Sanford et al., 2013). Sleep
disorders that predate treatment can also be worsened (Liu et al., 2009). A study by Berger and
colleagues (2010) used actigraphy to assess sleep throughout three cycles of chemotherapy
treatment. It was found that breast cancer patients experience more fatigue and disrupted sleep
during chemotherapy, and less so following treatment (Berger et al., 2010). Similarly,
Miaskowski and Lee (1999) found that as radiation therapy progressed, subjective sleep
complaints increased and sleep efficiency declined. These findings conclude that poor sleep is
exacerbated while on treatment. Medications
Cancer patients and survivors are often required to take a plethora of prescribed
medications. Such medications, specifically opioids, and selective serotonin reuptake inhibitors
(SSRI) can alter both SWS and REM sleep, markers of homeostatic function, as well as circadian
regulation (Meijer et al., 2000; Raymond-Shaw, Lavigne, Mayer, & Choiniere, 2005).
Medicinal cannabis has been used since ancient times to improve sleep (Mechoulam,
1986). Recent studies have shown promising improvements in subjective sleep parameters
(Barratt, Beaver, & White, 1974; Russo, Guy, & Robson, 2007), however contradictory results
have also been noted (Johnson & Potts, 2005)
Hypnotic sleep aids are the giants of the pharmaceutical market (Kripke, 2000). A review
conducted over 30 years ago determined that the most frequent psychotropic prescription was for
hypnotics (48% of all prescriptions), and that out of 814 prescriptions for hypnotics, sleep was
the reason for the prescription in 85% of cases, as compared to 14% for a medical procedure, 1%
for nausea/vomiting, 1% for psychological distress, and none for pain (Derogatis et al., 1979). A
decade later, similar results were found (Stiefel, Kornblith, & Holland, 1990). Pharmacological
interventions are only really effective for a short period of time (4-5 weeks or 35 days). These
types of interventions are not recommended in insomnia guidelines as there may be potential
adverse side effects (Harris et al., 2014; Youngstedt, 2005). Specifically, following long-term
use, hypnotics may make sleep worse (Kripke, Hauri, Ancoli-Israel, & Roth, 1990; Scharf, Roth,
Vogel, & Walsh, 1994).
36 Psycho-Social Factors
Depression is commonly reported in patients with CRF (Bruera et al., 1989; Ryan et al.,
2007). Studies have noted that depression is one of the strongest correlates of fatigue among
cancer patients, which in turn is associated with disordered sleep (Stone, Richards, A'hern, &
Hardy, 2000). Similarly, Dew et al. (1996) found that individuals with early stage depression are
more likely to have impaired sleep continuity. Quality of life (QOL) has also been associated
with persistent sleep problems (Dow, Ferrell, Leigh, Ly, & Gulasekaram, 1996). Finally, those
who lack social support are at higher risk for having disordered sleep (Koopman et al., 2002). Demographics and Lifestyle Factors
Demographic factors can determine lifestyle or psychological health factors affecting
sleep habits. For example, participants living with someone (married/common law) or those who
are more educated, experience less sleep/wake disturbances (Ohayon, Caulet, & Guilleminault,
1997). Other demographic factors such as gender (Krishnan & Collop, 2006) and race (Durrence
& Lichstein, 2006) can impact sleep outcomes. Variables such as total sleep time, SWS, and
sleep efficiency decrease with age (Koopman et al., 2002). Lifestyle factors, such as reduced
activity, can contribute to altered sleep (Vena, Parker, Cunningham, Clark, & McMillan, 2004).
1.4 Objectives
The general objective of the research described in this thesis is to determine whether
neuromuscular function and sleep disorders are associated with subjective feelings of fatigue in
cancer patients and survivors. Neuromuscular function and sleep were chosen as the specific
variables related to CRF to be assessed in this thesis for two specific reasons. The first was to
improve upon the previous studies that looked at both neuromuscular function and sleep in the
cancer population. For example, only five studies have assessed neuromuscular function as
related to CRF, and the results were inconclusive. Utilizing rigorous methodologies to assess
neuromuscular function as well as sleep was a key component of this research, as a lack of
understanding regarding these relationships may be in part due to the usage of ineffective
measurement techniques. The majority of sleep research in this population used a single-item
subjective measurement method therefore it was important to improve upon the previous
literature by utilizing an objective measurement tool in this population. The second reason for
selecting these particular variables was simply administrative. A large, comprehensive study was
conducted aiming to assess each of the variables possibly related to CRF, however to assess each
of these variables for a master’s thesis is beyond my scope. For this, two variables within my
scope of practice, as well as time frame for thesis completion were selected.
1.4.1 Specific Objectives
The specific purposes of this thesis were to:
To investigate whether a commonly used subjective measure of CRF (FACT-F scale) is
correlated with several objective measures of neuromuscular fatigue at rest and during
exercise, including MVC and twitch force, as well as VA.
2. To determine whether sleep disturbances in cancer patients and survivors are associated
with subjective CRF.
1.5 Significance and clinical relevance
Schwartz (2008) reported the following statement from a 59 year old man with prostate
cancer: “Cancer fatigue is a crushing, all-encompassing, incapacitating fatigue that is
indescribable other than to say that it is completely draining.” There is a body of work noting
the prevalence and severity of CRF, however as of yet solutions for CRF have not been created.
Although CRF has been measured successfully using subjective scales, such as the FACT-F,
objective indices of CRF have yet to be demonstrated in the literature, or correlated with widely
used subjective tools. A method to diagnose and fully understand the multitude of factors
contributing to CRF would allow clinicians, nurses, patients and others involved in the lives of
cancer patients to deal more effectively with this debilitating level of fatigue. Although large
strides in treating cancer itself have been made, CRF remains an extremely problematic issue in
the lives of cancer patients. The physical and psychological deficits can lead to reduced quality
of life. Therefore, the results of this study could be useful in expanding clinical implications for
the treatment of fatigue in cancer patients and survivors.
Chapter Two: Methods
2.1 Participants and Recruitment
A convenience sample of participants were recruited through several cancer-related
events and centres including: The Tom Baker Cancer Centre Head and Neck support group
meetings, Breast Cancer Symposium, Breast Reconstruction awareness day, The Running Room
Survivor Clinic, The Holy Cross Oncological Physiotherapy Unit, and The Science Cafe. Flyers
advertised the study at The University of Calgary Kinesiology complex, The Tom Baker Cancer
Centre, Wellspring, The Thrive Centre and Lab, as well as Breast Cancer Supportive Care. A
mail-out to 750 cancer patients and survivors through The Alberta-Cancer Registry was
conducted. Recipients of the mail-out were preferentially selected with the following criteria:
diagnosed with cancer between 2010 and 2013, fewer breast cancer patients, ages 18-65, 400
men, 350 women stratified for age distribution, and postal codes in close proximity to The
University of Calgary. Please see Appendix A for further details. Many participants were
recruited through referrals from previous participants.
Participants, both males and females, ages 18 to 85 years old, of any cancer type,
stage, diagnosis, as well as those on and off treatment were recruited for participation. A total of
(n=53) participants were recruited and included in the study. Four subjects were on treatment and
49 were survivors. Subject demographics are presented in Table 4, and additional medical
information is provided in Table 5. All subjects were cleared for participation by a Certified
Exercise Physiologist (CSEP-CEP) by the Physical Activity Readiness Questionnaire Plus
(PARQ+) (Warburton, Jamnik, Bredin, & Gledhill, 2011) and if necessary, by the Physical
Activity Readiness Medical Examination Questionnaire (PARMED-X) (Warburton et al., 2011)
forms. Approval for all procedures was obtained by The Conjoint Health Research Ethics Board
from The University of Calgary (REB14-0398).
Table 4. Patient demographic frequencies for the total sample of cancer patients and
Age (years)
Marital Status
Married/Common Law
Education Status
Secondary School
Employment Status
Annual Income
20 000 – 39 999
40 000 – 59 999
60 000 – 79 999
>80 000
Alcohol Intake (AUDIT-C)
At-risk (men >4, women>4)
Not at-risk
Table 5. Medical information for the full sample of cancer patients and survivors.
Cancer Type
Head and Neck
Treatment Status
Treatment Type
Hormone Therapy
Fatigue (FACT-F)
Fatigued (<34)
Non-Fatigued (>34)
2.1.1 Sample Size
A total sample size of n=53 cancer patients and survivors (on and off) treatment were
assessed; see Table 4 for complete demographics. In order to allow for separate analyses, the
following subgroups were created:
1. Neuromuscular (chair) n=34 ; fatigued n=12, non-fatigued n=22 (Table 6)
2. Neuromuscular (bike) n=18 ; fatigued n=8, non-fatigued n=10 (Table 7)
3. Sleep n= 48; fatigued=16, non-fatigued n=32 (Table 8)
Due to the small sample size, results, specifically those including subjective data, should be
considered as pilot data, which will contribute to a larger data set for future publications.
Table 6. Subject characteristics for subgroup of participants who neuromuscular testing on
the chair.
Table 7. Subject characteristics for subgroup of participants who neuromuscular testing on
the chair.
Table 8. Subject characteristics for subgroup of participants included in sleep analysis.
Insomnia Severity Index
Above Cut Off
Below Cut Off
2.1.2 Experimental Design
The subjects attended two separate testing appointments, approximately two weeks apart.
During the first appointment the subjects completed a battery of subjective questionnaires, and
underwent a neuromuscular familiarization session. They were given an actigraph and sleep
diary with instructions for use, subjective ratings of sleep questionnaire, and subjective fatigue
and energy thermometer. During the second session the subjects completed four questionnaires
(two of which are not included in this thesis), the neuromuscular fatigue protocol, and returned
the actigraph and corresponding subjective ratings. See Figures 5 and 6 for a complete schematic
of the protocol, including measures not used in the present thesis (questionnaires: TASS, BPI,
Edinburgh Handedness; blood sample, ultrasound, VO2max, TMS).
Figure 5. Schematic of the complete protocol used during the first testing session.
PARQ+: Physical Activity Readiness Questionnaire Plus, FACT-G/specific: Chronic Illness Therapygeneral/specific, SPS: Social Provision Scale, CES-D: Centre for Epidemiological Studies-Depression, GLTEQ:
Godin Leisure Time Exercise Questionnaire, TASS: Transcranial Magnetic Stimulation Adult Screen, PSQI:
Pittsburgh Sleep Quality Index. *not included in the present thesis
Figure 6. Schematic of the complete protocol used during the second testing session.
FACT-F: Functional Assessment of Chronic Illness Therapy-fatigue, ISI: Insomnia severity index, BPI: Brief pain
inventory, MVC: Maximal voluntary contraction, PNS: Peripheral nerve stimulation, TMS: Transcranial magnetic
stimulation. *not included in the present thesis.
2.1.3 Appointment #1 Subjective Questionnaires
Upon arrival to the laboratory for the first testing appointment, the subjects completed the
following questionnaires: demographics survey, Centre for Epidemiological Studies –
Depression (CES-D), Social Provision Scale (SPS), The Functional Assessment of Chronic
Illness Therapy – General (FACT-G), The Functional Assessment of Chronic Illness Therapy –
Cancer Specific (FACT-Specific), Modified-Godin Leisure Time Exercise Questionnaire
(GLTEQ); see Appendices B-F. Dual-X-Ray Absorptiometry
Dual X-Ray Absorptiometry (DXA) provides precise assessments of body-composition
parameters including: lean and fat body mass, body mineral density (Mazess, Barden, Bisek, &
Hanson, 1990). A whole body scan was conducted on each subject using the Discovery, Hologic
DXA System (Hologic, Inc., Sunnyvale, CA). Neuromuscular Experimental Set-Up and Familiarization Protocol
The subjects were familiarized with the neuromuscular techniques that would be used to
assess their neuromuscular function during the second appointment. Subjects performed a brief
warm-up of ten, 3-5 second voluntary knee extension contractions at increasing intensities, with
the last few nearing maximum. Following 30 seconds of rest, 2 × 5-s MVCs were performed,
each separated by 30 seconds of rest. The subjects performed a second set of MVCs with
electrical stimulation during (at maximum force) as well as immediately following the
contraction (interpolated twitch protocol). Although not considered in the present thesis, the
subjects were familiarized with a range of TMS intensities (20-80%) using a magnetic stimulator
(Magstim 2002; The Magstim Company Ltd., Whitland, UK) at rest, and while contracting to
30% of their maximal force. The subjects completed two sets of 100%, 75% and 50%
contractions with a single magnetic stimulus delivered during each contraction. Exact
experimental set-up is illustrated in detail in Figures 5 and 6 and detailed in sections and Daily Subjective Fatigue
Subjects were provided with a fatigue and energy thermometer (Appendix G) to complete
each evening prior to dinner. The subjects were instructed to rate their fatigue and energy on a
scale of zero (no fatigue, no energy) to ten (extreme fatigue, extreme energy). Sleep Assessment
Subjects were given a CamnTech MotionWatch 8 actigaph (CamnTech Ltd., Cambridge,
United Kingdom) to wear, without removing, for the following two-weeks. Following the
previous literature, the actigraph was placed on the non-dominant wrist (Sadeh & Acebo, 2002;
Sadeh et al., 1995; Webster, Messin, Mullaney, & Kripke, 1982). Two-weeks of sleep data was
suggested by Sadeh and Acebo (2002) and used to obtain aggregated measures that reliably
characterize individuals, as well as quantify sleep over both weeknights and weekend-nights. The
actigraphs were set to 30-s epochs (the sleep analysis is validated using 30 second epoch, as per
manufacturer guidelines) and recorded light quantified as lux. The subjects were asked to fill out
the American Academy of Sleep Medicine two-week sleep diary (Appendix H). Subjects were
given verbal instruction of how to appropriately fill out the sleep diary. Subjects were also asked
to fill out a subjective rating of sleep each morning (to correspond to the previous night sleep),
which corresponded with question #6 from the PSQI (“During the past month, how would you
rate your sleep quality overall?”); see Appendix I. Subjects were called once per week (as
available) and the 24-Hour Sleep Patterns Interview (Meltzer, Mindell, & Levandoski, 2007) was
conducted to increase subject compliance to wearing the actigraph and completing the sleep
diary. Data from these calls were not included in this thesis due to limited number of conducted
interviews (for reasons 1) subjects were often unavailable, 2) interviews implemented mid-way
through data collection), however the interview script used can be seen in Appendix J.
2.1.4 Appointment #2 Subjective Questionnaires
Upon arrival in the laboratory, subjects completed the following questionnaires: The
Insomnia Severity Index (ISI), The Functional Assessment of Chronic Illness Therapy – Fatigue
(FACT-F); see Appendices K and L. Neuromuscular function at rest
Femoral nerve electrical stimulation was the method of stimulation referred to throughout
this protocol (Digitimer DS7A Constant Current Stimulator, Hertfordshire, United Kingdom).
Measurements were recorded using Power Lab and Lab Chart 8 Software (ADI Instruments,
Bella Vista, Australia). The cathode electrode (30-mm diameter, MediTrace, Covidien, Quebec,
Canada) was placed over the femoral nerve, high in the femoral triangle, medial to the inguinal
tendon. The electrode was secured using tape and gauze. The anode electrode (50 × 90 mm
rectangular electrode) was placed over the gluteal fold.
The skin was prepared (hair removal, gentle abrasion, cleaned with alcohol) prior to
EMG electrode placement. Two surface electrodes (10-mm diameter silver chloride, MediTrace,
Covidien, Quebec, Canada) were placed at the distal end of each muscle of interest on the right
lower limb (vastus lateralis (VL), biceps femoris (BF), rectus femoris (RF)). The left limb was
measured in four subjects who had an injury to the right limb. The impedance between the
electrodes yielded light resistance (<5 kΩ). A reference electrode was placed over the patella.
EMG was recorded with BioAmp (ADI Instruments, Bella Vista, Australia).
The subject sat in the chair with right ankle (3-5 cm above the right malleoli) attached to
a strain gauge (Omega Engineering Inc., Stamford, Connecticut, USA), which was positioned
parallel to the floor. There was a 90-degree angle at the right knee. Movement of the upper body
was minimized using three belts across the thorax and waist. Subjects were asked to keep their
arms across the chest during each contraction.
The optimal intensity for electrical femoral nerve stimulation was determined by finding
a plateau in the evoked force response (twitch) and M-wave amplitude. The intensity was
verified using a supramaximal (130% of optimal intensity) stimulation.
Subjects completed a brief warm up of five contractions at 10% and 30% as well as three
contractions at 50% of their pre-determined maximum (determined during familiarization).
Finally one “near” maximum contraction was performed. Following the warm up the subject
completed two MVCs, separated by one minute. During each contraction, experimenters
provided vigorous verbal encouragement to ensure subjects full motivation. The interpolated
twitch technique, as described in section 1.2.1, was performed twice, separated by one minute
between contractions. All isometric measurements on the chair were recorded using Lab Chart 8
and Power Lab (ADI Instruments, Bella Vista, Australia).
50 Fatiguing Cycle Ergometer Protocol
Following the isometric measurements subjects were moved, with electrodes attached, to
a recumbent cycle ergometer engineered for the study (see Figure 6 for the cycle ergometer
protocol). The stimulus intensity was verified at rest, on the cycle ergometer. The subject
performed two MVCs without, and with stimulation (interpolated twitch technique),
respectively, while at rest. The subject began pedalling at a pre-determined power output scaled
to body weight (0.3 W/Kg). The power output was increased by this increment for the first 5
stages, by 0.4 W/Kg for stages 6-10, and by 0.5 W/Kg for the remaining stages until task failure.
At the end of every 3-min stage the pedals were immobilized to isometric mode, and subjects
performed one MVC. Electrical stimulation of the femoral nerve was superimposed during, and
following each contraction (interpolated twitch technique). Force was measured through the
instrumented pedals (Radlabor, Freiburg, Germany) and recorded with Imago software
(Radlabor, Freiburg, Germany). At the end of each 3-min stage, the subjects were also stimulated
with TMS over the motor cortex during a 50% of maximal force contraction (data not shown in
the present thesis). EMG was recorded throughout the test. The break between cycling stages to
conduct contractions paired with stimulation was normalized to 45 seconds. Heart rate and blood
pressure were monitored throughout the test for subject safety.
2.1.5 Data Analysis Force
All force measurements were analyzed using Labchart 8 software (ADI Instruments,
Bella Vista, Australia). The resting twitch amplitude, MVC and superimposed twitch force were
determined at the following time points for each subject:
1. Prior to exercise while at rest seated in chair
2. Prior to exercise while at rest seated on cycle ergometer
3. Immediately following each 3-minute stage of cycling
4. Post exercise, immediately at task failure
Force produced prior to exercise on the chair was presented as: absolute values, normalized to
body mass and normalized to right upper leg lean mass, as described below. Dual-energy X-ray absorptiometry
DXA scans were analyzed to attain lean mass of the right upper leg (quadriceps, gluteal
and hamstring muscles) to correspond to the force producing muscles used during each isometric
contraction. Figure 7 provides a sample of the area analyzed. The lean body mass was used in
order to normalize force (MVC and twitch) to the amount of force produced by the subject.
Figure 7. Dual X-ray absorptiometry scan illustrating sub-area analyzed to include force
producing lean body mass during knee extension (indicated in red area). Actigraphs
Actigraph data was analyzed with Motionware version 1.1.20. Software (CamnTech Ltd.,
Cambridge, United Kingdom). The data was edited to correspond loosely with the completed
paper sleep diary. In cases when the actigraphic data and diary disagreed for the initiation of
sleep, the light sensor data was used to determine the approximate “lights out” time, or when the
subject first actually tried falling to sleep. Similarly, when the actigraph data and the sleep diary
did not match, the light data, as well as the first large increase in activity onset was used to
determine the end of the sleep window. This type of data editing was utilized in previous studies
(Landry, Best, & Liu-Ambrose, 2015). Subjective Questionnaires
The CES-D scale for depression is a 20-item self-report measure for clinical depression.
Each item is rated on a 4-point scale. Score range from 0 to 60, with high scores reflecting more
depression symptoms. A score ≥ 16 indicates need for a clinical revision (Radloff, 1977). The
scale has established reliability and validity estimates in cancer patients receiving surgery or
radiation (Dodd, Miaskowski, & Paul, 2001; Miaskowski et al., 2006). It has excellent internal
consistency in the cancer population, perfect sensitivity and strong specificity in a study with
thirty-three cancer patients (Hopko et al., 2007).
The Social Provision Scale (SPS) was scored according to the guidelines (asterisk
statements reversed), with higher scores indicating better social provision (Russell, Cutrona,
Rose, & Yurko, 1984). The scale has six sub-groups: guidance, reassurance of worth, social
integration, attachment, nurturance and reliable alliance, and responses are rated on a four-point
scale rating from (1) strongly disagree to (4) strongly agree. The scale is reliable (α = 0.915)
(Cutrona & Russell, 1987), has internal consistency ranging from 0.85 to 0.92 across varying
populations, and has been used in a variety of cancer and exercise studies (Baron, Cutrona,
Hicklin, Russell, & Lubaroff, 1990; Dukes Holland & Holahan, 2003).
The Modified Godin Leisure-Time Exercise Questionnaire (GLTEQ), (Godin &
Shephard, 1985) reported in average minutes per week over the past month, was broken down
into four categories: mild effort (minimal effort), moderate exercise (not exhausting), and
strenuous exercise (heart beats rapidly), as well as resistance (weight bearing exercise). This
version is termed the Leisure Score Index (LSI) and has been used successfully in adult cancer
patients and survivors (Courneya & Friedenreich, 1997). The questionnaire was scored three
different ways.
1. Total Activity = Vigorous + Moderate + Mild + Resistance
2. Physical Activity Guidelines = Moderate + (Vigorous x 2)
Moderate + Vigorous
The FACT-G is a twenty-seven-item scale assessing four areas of quality of life: Physical
Wellbeing (PWB), Social/family Wellbeing (SWB), Emotional Wellbeing (EWB), Functional
Wellbeing (FWB), and a list of additional concerns for each specific type of cancer. Subscales
were not presented in this thesis due to inadequate sample size. The FACT-G was validated on a
sample of 620 cancer patients, has sound psychometric properties, an internal consistency rating
of 0.89, and test-re-test reliability coefficients ranging from 0.82 to 0.92 (Cella et al., 1993).
The FACIT-F is a 5-point likert, 13-item scale used to assess fatigue. The scale has a cut
off of 34 out of 52 to distinguish fatigued patients from non-fatigued patients. The scale has high
internal consistency (a = 0.93 – 0.95) (Yellen, Cella, Webster, Blendowski, & Kaplan, 1997) and
is widely used in the literature (Andersen et al., 2013; Stone et al., 2000).
A fatigue and energy thermometer, similar to a linear analogue scale assessment allowed
subjects to subjectively rate their fatigue on a day-to-day basis. A two-week average of
subjective fatigue was recorded for each subject (energy is not presented in this thesis). The
fatigue thermometer has been used to rate fatigue in previous studies (Quirt et al., 2001;
Zahavich, Robinson, Paskevich, & Culos-Reed, 2013).
The Insomnia Severity Index is a seven-item instrument used to provide a global index of
the severity of insomnia (Morin, Savard, Ouellet, & Daley, 1993), and has been found to be a
reliable and valid instrument to assess primary insomnia (Bastien, Vallieres, & Morin, 2001;
Blais, Gendron, Mimeault, & Morin, 1997). It has been validated in cancer patients, with a
clinical cut-off score of eight associated with optimal sensitivity and specificity for the detection
of sleep difficulties (Savard et al., 2005).
2.1.6 Statistical Analysis
All data was analyzed on IBM SPSS version 23 statistical software (IBM Corporation,
Chicago, USA). Descriptive statistics were completed for demographics of the total sample, as
well as individual subgroups for neuromuscular and sleep analyses. An independent sample t-test
was used to assess if there was a significant difference between fatigued and non-fatigued group
scores from the FACT-F.
55 Neuromuscular Analysis
Paired t-tests were conducted to look for differences between the neuromuscular
variables (MVC, twitch and VA) prior to and following the cycling test. Each variable was tested
at task failure, as well as following the fourth stage of cycling, i.e. the last common stage
completed by all subjects.
Independent samples t-tests were conducted between fatigued and non-fatigued groups to
determine if any differences in neuromuscular variables MVC, twitch exist between groups. The
same was done for VA, however the Mann Whitney-U (non-parametric equivalent to the
independent t-test) was used.
Exploratory correlational tests looked for relationships between each of the
neuromuscular variables and subjective ratings of fatigue. Correlations were categorized as
following: 1) small correlation: r=0.10-0.29, medium correlation: r=0.30-0.49, large correlation:
r=0.50-1.0 (Cohen) Sleep Analysis
Each of the sleep variables (TST, SE, SOL) was compared between fatigued and notfatigued groups using independent t-tests.
Stepwise regression was used to assess if any independent variables (age, minutes of
exercise per week, social provision, treatment status, medication and quality of life) were
predictors for any of the three sleep outcomes (TST, SE, SOL). Stepwise regression was chosen,
because it is powerful in building a regression model by sifting through a large set of potential
independent variables, and fine-tuning the model by pulling variables in or out based on the
Correlation was used as an exploratory measure to assess if any of the sleep measures
were associated with subjective fatigue and sleep ratings.
Chapter Three: Results
3.1 Participant Characteristics
Fifty-three cancer patients, on and off treatment, participated in the study. Three
subgroups were created for the separate analysis of A) neuromuscular variables seated at rest
(Table 6), B) neuromuscular variables following cycling (Table 7) and C) sleep analysis (Table
8). Subject demographics for the whole sample can be seen in Table 4. The average age of
participants was 56 years, with the vast majority being between 51 and 60 (30.2%) and 61 and 70
(37.7%) years of age. The majority of participants were female (62.3%), married or in a common
law relationship (83.0%) and more than half held a university education (56.6%). The sample
was primarily Caucasian (92.5%) ethnicity. Employment status was variable across the sample.
The majority of subjects were retired (32.1%), however many worked full-time (21.1%), parttime (18.9%), were on disability (11.3%), and a notable amount of participants were unemployed
(5.7%). Annual income was high (67.9% earned more than $80,000 per year). All of the 30
subjects to complete additional demographic questions were non-smokers, and 7 were at risk for
alcohol intake affecting their health and wellbeing (AUDIT-C). More than half the sample
(67.9%) was not fatigued as reported by the FACT-F.
The majority of participants were diagnosed with breast cancer (47.2%) or prostate
cancer (18.9%). The remainder of the subjects were diagnosed with one of eight other types of
cancer. The vast majority of participants were off-treatment (92.5%). Most of the subjects
reported that their treatment included surgery (92.4%), chemotherapy (60.4%), radiation (38.6%)
and/or hormone therapy (20.7%). Table 5 provides an overview of medical information
pertaining to the complete sample of participants.
3.2 Preliminary Analysis
Descriptive statistics can be seen in Tables 8 and 9 for neuromuscular and sleep data
respectively. Prior to performing each of the statistical techniques used in the following section,
the data with the exception of VA, was determined to be normally distributed. Non-parametric
statistical techniques were used while assessing VA. The assumptions for comparing means,
(level of measurement, independence of observations, normal distribution and homogeneity of
variance) was satisfied. Levene’s test for equal variances was determined to be >0.05 for each of
the tested variables indicating that parametric t-tests were appropriate. The neuromuscular
subgroups on the chair and the bike had a small sample size (n=34, n=18 respectively) therefore
the results must be interpreted with caution. The assumptions for performing a step-wise
regression analysis were fulfilled. The assumptions (normality, linearity, homoscedasticity) for
Pearson Product-moment correlation were carefully considered upon exploring correlations
between variables. Spearman’s Rho was used to test VA. An independent t-test found a
significantly different level of fatigue between the fatigued and non-fatigued groups as measured
by the FACT-F (P<0.001).
Table 8. Descriptive statistics of neuromuscular variables measured on the bike.
Minimum Maximum Mean
MVC: maximal voluntary contraction. VA: Voluntary activation.
Mean Δ PrePost (%)
Mean Δ PreS4 (%)
Table 9. Descriptive statistics of sleep variables measured via actigraph.
Minimum Maximum Mean StandardDeviation
TST: total sleep time. SOL: sleep onset latency. SE: sleep efficiency. Min/night: minutes per night.
3.3 Primary Analysis
3.3.1 Objective One: Neuromuscular function
Tables 6 and 7 describe the subgroup used for the neuromuscular analysis at rest while
seated, as well as during cycling, respectively. There was a significant decline in MVC and peak
twitch force (P<0.000), as reported by paired t-tests, as well as VA (P=0.002) as reported by the
Man Whitney U Test, indicating that the whole sample that completed the neuromuscular testing
experienced fatigue following a bout a cycling (Figure 8).
Force (N)
Voluntary activation (%)
Figure 8. Changes from Pre (black stripes) to exhaustion (Post, white dots) in MVC, twitch
force, and voluntary activation (VA).
There were no significant differences between fatigued and non-fatigued groups based on
the FACT-F cut-off for clinically significant fatigue, in MVC or twitch normalized to total body
mass, at rest on the chair (Figure 9). Similarly, there were no significant differences seen
between groups in MVC (P=0.499), twitch (P=0.596) at rest on the chair with force normalized
to the lean mass of the sub-area corresponding to the force generating muscles (Table 11).
Finally, there were no differences in VA at rest between fatigued and non-fatigued groups.
Table 11. Independent-samples t-tests between fatigued and non-fatigued groups, based on
the FACT-F groupings for fatigue, at rest on the chair, and on the ergometer.
n Average SD
Average SD
MVClean (N/Kg)
11.1 22 76.7
48.4 -0.7
MVCtotalmass (N/Kg)
22 5.9
Twitchlean (N/Kg)
22 25.4
17.7 -0.5
Twitchtotalmass (N/Kg)
VAa (%)
12 1.8
22 1.9
0.35 -1.0
ΔMVC pre-S4 bike
12.2 10 -14.1
10.0 -0.5
ΔMVC pre-post bike
13.0 10 -32.0
ΔTwitch pre-S4 bike
17.1 10 -27.1
19.1 -0.3
ΔTwitch pre-post
bike (%)
10.2 10 -57.9
14.4 0.4
ΔVA pre-S4 bikea
ΔVA pre-post bikea
Performance (s)
41.5 10 858.0
50.7 -0.3
MVC: maximal voluntary contraction. VA: voluntary activation. N/Kg: Newtons normalized to Kg or either total
body mass, or lean body mass of the previously described subgroup. S4: 4th stage, a common stage of completion.
Post:task failure. a Man Whitney U Test for nonparametric VA.
Voluntary activation (%)
Force N / kg BW
Figure 9. Differences between fatigued (black stripes) and non-fatigued (white dots)
subjects in 1) MVC, 2) twitch force, and 3) percent voluntary activation (VA) measured at
rest (on the chair)
There were no significant differences between groups in percent change from pre to
cycling task failure in MVC (P=0.856), peak twitch (P=0.686) or %VA (P=0.180) (Figure 9).
Similar results were found from pre to stage 4 in percent change in MVC (P=0.964), percent
change in peak twitch (P=0.263) or percent change in VA (P=0.180). Performance (time to
exhaustion reported in seconds) was not significantly different between groups (P=0.774).
Similarly, when using the median score of the two-week fatigue thermometer average to
separate the subjects into two groups, fatigued and non-fatigued, there was no statistically
significant difference between groups in any of the neuromuscular variables (at rest on the chair,
as well in cycling; Table 12).
Table 12. Independent-samples t-tests between fatigued and non-fatigued groups, based on
the fatigue thermometer groupings for fatigue, at rest on the chair, and on the ergometer.
Average SD
Fatigue Thermometer
Not Fatigued
N Average SD
MVClean (N/Kg)
14.1 15
MVCtotalmass (N/Kg)
Twitchlean (N/Kg)
1.38 15
2.8 15
1.6 -0.382
21.3 0.858
Twitchtotalmass (N/Kg)
VAa (%)
ΔMVC pre-S4 bike (%) 10
0.24 15
12.0 7
ΔMVC pre-post bike
ΔTwitch pre-S4 bike
ΔTwitch pre-post bike
ΔVAa pre-S4 bike (%)
ΔVAa pre-post bike (%)
Performance (s)
49.4 -0.118
MVC: maximal voluntary contraction.VA: voluntary activation. N/Kg: Newtons normalized to Kg or either total
body mass, or lean body mass of the previously described subgroup. S4: fourth stage, a common stage of
completion. Post: task failure. aMan Whitney U Test for nonparametric VA.
3.3.2 Objective Two: Sleep
Table 8 describes the subgroup used for the sleep analysis. Based on the FACT-F cut off
point for clinically significant fatigue, the subgroup contained 16 fatigued subjects, and 32 nonfatigued subjects. An independent t-test revealed no significant differences between TST, SE or
SOL between groups (Table 13). Similarly, there was no statistical significance between any of
the sleep variables and the fatigue thermometer ranking (Table 14).
Table 13. Independent samples t-test between fatigued and non-fatigued groups based on
FACT-F subjective ratings of fatigue.
TST (min/night)
37.8 32
52.8 -0.7
SE (%)
SOL (min/night) 16
13.5 32
TST: total sleep time. SE: percent sleep efficiency. SOL: sleep onset latency. Min/night: minutes per night.
Table 14. Independent samples t-test between fatigued and non-fatigued groups based on
fatigue thermometer subjective ratings of fatigue.
Fatigue Thermometer
TST (min/night) 25
38.8 22
53.9 0.8
SE (min/night)
SOL (%)
11.3 22
13.3 0.7
TST: total sleep time. SE: percent sleep efficiency. SO: sleep onset latency. Min/night: minutes per night.
Step-wise regression was used to assess if age, minutes of exercise per week, social
provision, treatment status, medication and quality of life are predictors of TST, SE and SOL.
The variables were entered into the aggression analysis together, rather then in a specific order. It
was found that depression was the only significant predictor for SE (B=-0.129, t=-2.066,
p=0.045). Age, minutes of exercise per week, social provision, treatment status, medication and
quality of life, were found not to be significant predictors for TST, SOL or SE. A table of values
was not provided because depression was the only significant variable identified by the
regression model.
3.4 Exploratory Analysis
The FACT-F scores were correlated with the two-week average of fatigue thermometer
score (r=0.51, p=0.01). Several correlation models were explored to see if any of the
neuromuscular factors correlated with either the subject’s FACT-F score, or the two-week
average of the fatigue thermometer. None of the variables were significantly correlated with
either of the subjective measures of fatigue (Table 15).
Table 15. Pearson Product-moment correlations between measures of neuromuscular
fatigue and subjective ratings of fatigue (FACT-F and fatigue thermometer).
MVCpre-post (N)
Twitchpre-post (N)
VApreposta (%)
MVC4 (N)
Twitch4 (N)
VA4a (%)
Performance (s)
Fatigue Thermometer
MVCpre-post: maximal voluntary contraction changes from Pre to Post. Twitchpre-post : twitch force changes from Pre
to Post VApre-post: voluntary activation changes from Pre to Post, MVC4: maximal voluntary contraction changes
from Pre to stage 4. Twitch4: twitch force changes from pre to stage 4. VA4: voluntary activation changes from Pre
to stage 4. aSpearman’s rho for nonparametric data.
Correlations were also explored between both subjective scores of fatigue (FACT-F and
fatigue thermometer) and the three sleep variables: TST, SE, SOL. It was found that SE had a
medium significant correlation with FACT-F scores (r= 0.31, p<0.05; Figure 10). There was also
a tendency toward a medium correlation (r=0.354, P=0.055) between SE and the average rating
from 6th question from the PSQI (How would you rate the quality of your sleep?), which
provides a subjective rating of sleep. There were weak correlations between each of the other
variables and the subjective ratings of sleep and fatigue (Table 16).
Figure 10. Medium significant correlation between sleep efficiency (SE) and FACT-F
Table 16. Pearson Product-moment correlations between sleep variables
FACT-F Fatigue Thermometer PSQI #6
TST (min)
SE (%)
SOL (min) -0.173
TST: total sleep time. SE: percent sleep efficiency. SOL: sleep onset latency. Subjective rating of fatigue: FACT-F
and fatigue thermometer. Subjective rating of sleep: 6th question from The Pittsburgh Sleep Quality Index (PSQI).
*Correlation is significant at the 0.05 level.
Chapter Four: Discussion
CRF is a cancer-treatment side effect with no universally accepted definition. It is likely
that a combination of several factors work together and/or independently of each other to cause
CRF. To date, CRF is quantified mostly through questionnaires (i.e., as a patient-reported
outcome). There have been few studies to assess neuromuscular function in the cancer
population (Cai et al., 2014; Kisiel-Sajewicz et al., 2012; Neil et al., 2013; Yavuzsen et al.,
2009), none of which assessed fatigue following a whole body exercise that mimics activities of
daily living. Similarly, only one of the previous neuromuscular studies directly assessed central
fatigue (Neil et al., 2013) and it is unknown whether neuromuscular deficits are of central or
peripheral origin in fatigued patients and survivors. It is also unknown if neuromuscular deficits
are a determinant of feelings of fatigue. To address these gaps in the literature, the 1st objective
of this thesis was to assess whether variables of neuromuscular fatigue are associated with
commonly used ratings of subjective fatigue.
It is well known that sleep disorders are extremely prevalent in the cancer population. It
is less well known if poor sleep is associated with CRF, or if it acts independently of CRF. Few
studies have utilized subjective and objective measurement tools together to assess sleep. The the
2nd objective of the present thesis aimed to decipher if sleep is in fact related to subjective
feelings of fatigue using both subjective and objective measurement tools.
4.1 Neuromuscular Fatigue
For clarity, section 4.1.1 will discuss the results of the neuromuscular measurements “at
rest”, while seated on the chair, prior to exercise. Such results illustrate neuromuscular physical
function prior to performing exercise. Section 4.1.2 will interpret the results in neuromuscular
function testing from the cycle ergometer protocol. These results are meant to mimic an activity
of daily living.
4.1.1 Isometric Fatigue Prior to Exercise
The null results between groups at rest indicate that there are no neuromuscular deficits in
fatigued patients and survivors versus non-fatigued patients and survivors while performing a
single isometric contraction of the knee extensors, prior to performing any further exercise. As
illustrated in Figure 9, there was no significant difference between fatigue and non-fatigued
subjects in MVC and twitch force, nor VA at rest. These results are not in accordance with the
single previous study to test neuromuscular function in the lower limbs. Specifically, the study
by Neil et al (2013) found a statistically significant differences in VA (P=0.04) between nonfatigued age and sex-matched control cancer survivors and fatigued cancer survivors, while
performing a single contraction of the knee extensors at rest. The sample in the present thesis
consisted of primarily subject’s off-treatment. Had we recruited patients undergoing treatment it
is possible that differences in central fatigue may have occurred. A second possible reason for no
difference in VA between groups is a lack of subject motivation, rather than a lack of central
fatigue. Failure to contract to ones’ true maximum can result in error in VA, as the formula for
VA assumes that the subject is contracting maximally. However, it is unlikely that this has
played a role since it is unlikely the fatigued subjects were more motivated that non-fatigued
ones, i.e. compensating their central deficit. Also, researchers attempted to minimize this
limitation by giving verbal encouragement throughout the contraction. Similar to the study by
Neil et al (2013) no differences in MVC were seen between groups at rest. As discussed above,
this could be related to motivation or treatment status.
Upper limbs MVC at rest was also found to be significantly lower in a fatigued group of
cancer patients than the control at rest (Kisiel-Sajewicz et al., 2012; Yavuzsen et al., 2009).
Sample size is likely not the culprit for the difference in these findings, yet the results in the
present thesis should be interpreted with caution. Yavuzsen (2009) tested 16 cancer patients.
Similarly, Kisiel-Sajewicz et al (2012) tested 10 fatigued patients and 12 healthy controls. Both
studies grouped patients based on subjective ratings of fatigue. The present thesis tested 34
subjects on and off treatment.
The main reason for this discrepancy is probably the treatment status. Indeed, the subjects
in Yavuszen et al (2009) were advanced cancer patients, referred to palliative medicine. The
deficits incurred on the body by both the disease and treatment toxicities are presumably worse
in palliative patients than in the present sample of mostly cancer survivors. These subjects may
have truly experienced a deficit in central drive which was not the case in the present study.
4.1.2 Neuromuscular Fatigue Following Cycling
To the best of our knowledge, there has yet to be a study to assess neuromuscular fatigue
in the cancer population during and following a dynamic movement that closely resembles
activities of daily living. For this, the results of the present study are quite novel. Fatigue Characteristics in the Whole Sample
There was a significant decline in neuromuscular function from the beginning of cycling
to task failure (MVC, P<0.000; twitch, P<0.000; VA, p=0.002) indicating that the subjects were
substantially fatigued following the bout of cycling. Figure 8 illustrates reductions in these 3
parameters Pre to immediately following cycling.
When all subjects are considered, there was a 32% reduction in MVC force following
cycling. Currently, there are no studies that describe changes in neuromuscular function while
cycling in the cancer population. Twitch, indicative of muscle force generating capacity, was
reduced by 56%. This indicates a great deal of muscle fatigue following the bout of cycling.
Cycling, a concentric movement, generally induces lower muscular damaged compared to
weight bearing exercises such as running or walking (Millet & Lepers, 2004). This is important
to consider, as activities of daily living, such as walking, may induce greater muscle damage than
seen here. Surface EMG recording (i.e. compound muscle action potential), not presented in the
current thesis, could be beneficial in further examining the changes in muscle fatigue, i.e. action
potential propagation, as opposed to contractile properties.
There was only a small reduction in VA (∼10%) indicating that central fatigue was not
the main cause of force loss. However, the occurrence of central fatigue in this test makes it
suitable to potentially detect differences between subjects with CRF and non-CRF. From these
preliminary results examining the whole sample, it seems that reductions in force are mostly
related to peripheral fatigue. Fatigue Characteristics between Groups
Table 12 shows the changes in MVC, twitch and VA from both Pre to Post and Pre to
Post stage 4 for fatigued and non-fatigued subjects. The results indicate no differences in any of
the variables. The twitch force changes, indicative of muscle fatigue, did not differ between
groups following a maximal bout of cycling (P=0.411). Similarly, central fatigue was measured
in the present thesis via VA. No significant differences between groups at stage 4 of the test, or
at exhaustion (Z=0.081, Z=0.464) were found, therefore it is concluded that central fatigue
during acute exercise may not be the cause of subjective feelings of chronic fatigue. Similar
results were found in Neil et al (2013) which is the only other study to have assessed central
fatigue using VA. These findings may at a first glance be contrary with other published studies.
Indeed, a study by Kisiel-Sajewicz, et al (2012) reported significant decreases in upper limbs
twitch (indicative of muscle contractility impairment) in the control group, however preserved
twitch force in the fatigue group. The authors concluded that the fatigued group must experience
a central deficit, which would explain the lower initial force produced. It is however important to
note that the study by Kisiel-Sajewicz, et al (2012) did not actually measure central fatigue,
therefore assuming that the fatigue was central may be overly speculative and could be only
related to an early withdraw from the CRF patients.
In the present study, there was no difference in performance (total exercise duration)
between groups (p=0.774). A similar result was found in Neil et al, (2013), however not in
Yavuzsen et al, (2009) or Kisiel-Sajewicz et al, (2012). Neither of the aforementioned studies
used a dynamic exercise modality. Exercise modality can result in differences in fatigue (Millet
& Lepers, 2004) therefore the results between studies must be compared while taking this into
consideration. However, the difference between the present study and both Yavuzsen et al,
(2009) and Kisiel-Sajewicz et al, (2012) is most likely mainly related to the fact that these last
two studies were advanced cancer patients, which was not the case in our subjects.
The results provide a snapshot into neuromuscular fatigue during and following a task
requiring a large muscle group required in common daily tasks, which has never been done
before. With a greater sample size it is possible that the differences between groups may be
significant, especially as treatment type and status, as well as cancer type and status, subgroups
can be formed.
71 Limitations
There are a few limitations to the neuromuscular aspect of this study. First, the study used
a convenience sample of both cancer patients and survivors. With this, it is possible that
sampling bias occurred. Many of the subjects (even some of those in the fatigue group) were
reportedly quite active. Physical activity is well known to reduce fatigue (Cramp & ByronDaniel, 2012). It is difficult to recruit already fatigued subjects. Some individuals decided not to
partake in this study for reasons reported as: 1) a “fatigue doctor” asked a subject not to raise
their heart rate above resting levels, 2) insomnia caused fatigue so immense that daily activities
were limited, 3) fatigue caused one subject to sleep through the study appointment. In the future
this limitation could be minimized by offering prospective subjects education on how to reduce
fatigue, or by implementing an exercise intervention with the hopes of reducing fatigue. Other
cancer co-morbidities that could not be cleared via PARMED-X form eliminated some
reportedly fatigued individuals study from participating. A study focused on recruiting only
extremely fatigued patients may find differences in many of the neuromuscular variables
reported above.
A second limitation is the method of measurement used on the bike, specifically the
immobilized pedals and force measurement device. This is a new and innovative approach to
measuring fatigue, which has not yet been used in the literature. A validity study has been
conducted and analysis of the results is in progress. Based on preliminary data, it is likely that
this method of measurement will be a valid and effective way to measure neuromuscular fatigue,
without a delay causing muscle recovery. With this, the results of this thesis must be considered
pilot results at this time.
This thesis does not present EMG data, however it would be beneficial in further
examining muscle contractile properties related to peripheral fatigue (see above: compound
muscle action potential as a surrogate of action potential propagation). Also, an additional
technique not utilized in this project is comparing high-frequency and low-frequency doublets,
indicative of low-frequency fatigue caused by EC coupling failure. This technique was not used
as it can be found slightly more painful than single stimuli and not suitable with fragile
Finally, motivation could have caused lower MVC force than actually possible, affecting
the resultant VA. The researchers attempted to increase motivation by vigorously encouraging
subjects to produce as much force as possible. Also, each of the testing sessions took place in the
morning, in order to limit the reduction of pools of motivation.
4.2 Sleep
Reports over the past two decades have started to unravel the relationship between
cancer-related sleep disorders and CRF (Roscoe et al., 2007). Sleep disorders and CRF can occur
simultaneously, however to date the relationship has yet to be completely defined. For example,
Savard et al. (2005) found no significant relationship between sleep and CRF, whereas Anderson
et al. (2003) found a significant correlation between fatigue severity and sleep disturbance in 354
cancer patients.
Notably, objective measures of sleep in the cancer population are not common in the
literature. This study assessed two-weeks of wrist actigraphy. The sleep outcomes reported in
Table 10 suggest problematic sleep across the whole sample. Subjects were acquiring, on
average, 432 minutes (7.2 hours) of sleep per night. Recommended sleep time per night is 7-9
hours in adults, and 7-8 hours in older adults, in order to attain maximal sleep benefits
(Hirshkowitz et al., 2015). This study indicates that the subjects were acquiring the very
minimum amount of recommended sleep. SOL ranged from 0 minutes to 56 minutes (average
=12.69 +/- 12.20). SOL indicates how quickly individuals can initiate sleep, with shorter SOL an
indicator of better sleep. The actigraphic data showed that SE was 85.73%, indicative of a
problematic to bad night sleep. Notably, none of the subjects attained a good nights sleep as
illustrated by the maximum SE value of 93%. All three results are comparable to previously
reported results (Ancoli-Israel et al., 2006; Berger et al., 2007).
The results of the present study found no significant differences in any of the three sleep
parameters (SE, SOL and TST) between fatigued and non-fatigued groups, based on both the
FACT-F grouping, as well as the fatigue thermometer. However, a significant correlation
between an objective measure of disordered sleep and chronic subjective fatigue in cancer
population was found. Indeed, there was a significant medium correlation between SE and
FACT-F scores (P=0.031), meaning that as sleep efficiency was reduced, fatigue increased
(Figure 10). There was also a non-significant medium correlation between SE and a 2-week
subjective rating of sleep (P=0.055) (PSQI question #6). Ancoli-Israel and colleague’s (2006)
study on breast cancer patients found that subjective ratings of sleep (PSQI) and fatigue (MFSISF) were correlated, however reports of fatigue and objective measures of sleep (i.e. TST) did
not correlate. Similarly, Landry et al. (2015) found that in older adults subjective sleep quality as
reported by the PSQI did not correlate with objective sleep quality. Interestingly, it has been
found that subjective estimates of SOL are significantly over-estimated (Lakshminarayana
Tadimeti, Caruana-Montaldo, Wallace, & Mendelson, 2000). Perspectives of sleep clearly differ
from the objective data. It would be interesting to see if improving the perceptions of the
subjects’ sleep would improve their rating of fatigue.
One interpretation of the increased fatigue with no difference in sleep parameters
between groups could be that the subjects were depressed due to their cancer diagnosis, therefore
the depression manifested in fatigue. This explanation is supported by Ancoli-Israel (2006).
Here, step-wise regression confirmed that depression is a significant predictor for reduced SE,
such that as depression increased (CES-D), SE decreased. This would indicate that fatigue results
in depression, and depression causes the sleep disturbance, rather than fatigue directly causing
the sleep disturbance. From this interpretation alone, it could be concluded that sleep and CRF
are not related, rather CRF and depression are related, a relationship previously noted in the
literature (Brown & Kroenke, 2009). This relationship could be affected by the subject’s status at
which time the measurement was taken. For example, the study by Ancoli-Israel, et al. (2006)
noted that the depression in their sample could be caused by the subject’s recent cancer
diagnosis. The sample in this thesis was mostly survivors; therefore this likely was not the case
here. With this said, time of measurement throughout the cancer-journey is an important point to
consider in future studies.
A final interesting aspect to assess is the possibility of desynchronized circadian rhythms.
The circadian rhythm, governed by each individual’s biological clock, regulates the body’s
internal processes and alertness levels. Sleep-wake homeostasis is an accumulation of sleep
inducing substances in the brain, generating homeostatic sleep drive. Homeostatic sleep drive is
countered and moderated by the circadian drive, however in some individuals the circadian
rhythm can be desynchronized. For example, in the study by Ancoli-Israel, et al. (2006) it was
hypothesized that women with desynchronized rhythms would experience more fatigue. It was
found that those with worse functional outcomes had activity rhythms that peaked later in the
day, and switched form high activity, to low activity later in the afternoon, suggesting that the
women were more phase delayed (biological clock was delayed compared to the environment).
Although this aspect was not assessed in the current thesis, it would be an interesting component
to assess in future studies.
4.2.1 Limitations
Admittedly, there are a few limitations inherent in these results. First, it is possible that,
despite Table 10 suggesting problematic sleep across the whole sample, a ceiling effect occurred
as a result of few self-reported “poor sleepers” in the sample. As reported by the ISI, only 22%
of individuals reported being above the threshold for clinically relevant insomnia. (Bastien et al.,
2001; Savard et al., 2005) Based on this measure alone, most of the subjects were self-reportedly
already okay sleepers, which may have contributed to the lack of correlation between sleep
disturbance and fatigue. With this said, sleep quantity and quality on average as reported by the
actigraph was considered problematic.
There is a risk of bias due to lack of compliancy in subjects filling out the sleep diary.
The sleep log was used to carefully edit the actigraphic data to improve its accuracy. For
example, subjects who were laying still in bed reading could report this on their sleep diary so
that the actigraph data could be adjusted accordingly. The limit of paper diaries (as opposed to
electronic diaries) is that the participants are able to enter data at any time point, and as reported
in the literature will often wait until directly before their study appointment to fill out the sleep
diaries (Jungquist, Pender, Klingman, & Mund, 2015). This limitation was minimized by using
the light sensor data to indicate sleep and wake onset when the sleep diary and actigraph data did
not match up. Providing a wrist-worn device to electronically input subjective sleep data has
been shown to improve this limitation as it is more convenient and an alarm can be set to remind
the subject to input a subjective rating (Jungquist et al., 2015). With this technique, subject
burden may be increased.
A third limitation is that this is a descriptive and/or exploratory study; therefore many
statistical calculations were performed. All of the results were reported in order to balance type
one and type two errors. A Bonferroni, or other correction factor, was not applied, as the results
were mainly insignificant.
The use of actigraphy has been debated in the literature. Although it is validated against
PSG (Kosmadopoulos, Sargent, Darwent, Zhou, & Roach, 2014; Kushida et al., 2001), as well as
manual scoring of actigraphy has proven to be in agreement with PSG (Mullaney, Kripke, &
Messin, 1979), there are some limitations of actigraphy use. The lack of standard equipment,
procedures and analytic methods limits cross-study comparison (Sadeh, 2011). Moreover, the
accuracy in detecting wakefulness is reportedly lower than 60% in many studies, compromising
derived sleep indices of TST, SE and wake after sleep onset (Sadeh, 2011). Actigraphy seems to
over-estimate sleep time because of the efforts of individuals to lay motionless to achieve sleep
for extended periods of time (Sadeh, 2011). For this, the results indicated in this thesis should be
interpreted with caution.
We were unable to control for medications that may affect sleep outcomes. Specifically,
medications used for sleep disturbances, depression, anxiety and those diseases alike may alter
sleep outcomes. Medications were recorded and those that were considered sleep-enhancing
medications (as described by The Cleveland Clinic: “Drugs used for Insomnia” webpage) were
entered into the step-wise regression analysis. Notably, it was not found that these medications
were predictors for altered sleep. Opposite to medication, physical activity has been noted to
improve sleep (Mock et al., 1997; Rajotte et al., 2012). The subjective rating of physical activity
was also entered into the step-wise regression analysis and was found not to be a predictor for
sleep outcomes. For this, these co-variants may not necessarily impact our findings, however the
potential impact of such items should be carefully considered in future studies.
Finally, the sample included 6 individuals (12.5%) who reportedly had obstructive sleep
apnea (OSA), 4 of which (8.3%) did not use their continuous positive airway pressure machine.
These individuals were not removed from the analysis presented in the results, however an
exploratory statistical analysis was run with these individuals excluded and no differences in the
non-significant result were seen. For this, these individuals were left in the analysis in order to
attain a greater sample size.
Chapter Five: Conclusions and Future Directions
The results of this exploratory study conclude that neuromuscular fatigue and sleep
disorders may not necessarily be directly linked to subjective feelings of CRF. The results of this
thesis are exploratory in nature and provide the framework to inform the design of future studies.
It is important for these results to be confirmed with a greater number of subjects. The use of
neuromuscular measures in CRF research is a beneficial addition of objective data that may
guide future research questions.
An interesting addition to neuromuscular research tools used in the present thesis is the
use of TMS. Although not included in the present thesis, TMS was used during the testing
protocol. With this technique corticospinal excitability can be assessed. In the previous literature,
corticospinal excitability has been related to perceived fatigue in stroke (Kuppuswamy, Clark,
Turner, Rothwell, & Ward, 2015) and chronic fatigue syndrome (Sacco, Hope, Thickbroom,
Byrnes, & Mastaglia, 1999), therefore this is an additional important facet to assess within the
cancer population.
Although this thesis reports mostly null results, it was of upmost importance to test
neuromuscular fatigue in a task relevant to activities of daily living. Previous studies have
assessed neuromuscular fatigue parameters in single joint, isometric movements using small
muscle groups and such movements do not necessarily mimic activities of daily living. The
partially null results of this thesis are an important contribution to the literature and should spur
future research questions and methodologies.
Future studies should focus on looking for differences in fatigue related outcomes such as
sleep and neuromuscular deficits in subgroups of patients and survivors based on treatment
status. The few studies on neuromuscular function that have been conducted have also informed
this idea. Likewise, looking for differences in treatment type would be informative. For example,
there could be differences in the severity of neuromuscular fatigue and/or sleep disorders in an
individual who underwent chemo, radiation and surgery, versus someone who underwent only
one surgery. It would be beneficial to selectively sample for those who are severely fatigued,
rather than including those who are mildly fatigued. Including a few individuals who are mildly
fatigued could act as outliers in data analysis.
The benefit of this study was that it used objective measures to assess both
neuromuscular and sleep variables. The novel neuromuscular methodology used was quite
advanced as compared to the previous studies (Kisiel-Sajewicz et al., 2012; Yavuzsen et al.,
2009) and future studies should use neuromuscular testing that includes VA as a measure of
central fatigue. In addition, TMS could provide important information regarding the location of
corticospinal excitability, a more in-depth index of central fatigue. Also, studies should use a
whole-body movement that resembles activities of daily living, rather than a single joint,
isometric movement. It was beneficial in this study that an objective measure, completed with a
subjective measure of sleep were both used. These tools yielded some interesting results that will
inform future studies. Future studies should use a similar strategy to appropriately assess sleep,
rather than using a single-item measurement tool, or only a subjective measure.
Forthcoming studies should attempt to comprehensively and systematically assess each
aspect of CRF. This type of fatigue is like a puzzle with many pieces working either together, or
independently of each other to cause the varying levels of fatigue. In addition, each patient or
survivor might experience fatigue in a different way.
Learning exactly why and how CRF works is imperative to diagnosing and treating this
cancer-treatment side effect better. Although researchers are well aware that exercise improves
CRF, it is not known exactly why this occurs. Future research should attempt to look for reasons
why these mechanisms are related to CRF, rather than simply studying that they are indeed
related to CRF. This work is fundamental. Just as vision is not corrected by the same lens for
each person, CRF may not be caused by the same underlying reason in each person so that the
training prescription must be tailored to the real causes of fatigue. For instance, if an individual
experiences a neuromuscular deficiency at rest, resistance training should be focused on to
strengthen muscle groups. Conversely, if a survivor suffers from a sleep disorder, moderate
intensity aerobic exercise during the late afternoon, should be prescribed. An interesting
component is the idea that fatigued individuals are performing tasks of daily living above
anaerobic threshold, indicating severe deconditioning. For this deficit, high-intensity endurance
exercise should be recommended as a major training component. There is seemingly no “one
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Cancer-Related Fatigue Study: Subject History
Do not fill out: lab use only
Weight (kg):
Phone Number:
Height (cm):
Blood Pressure (mm/Hg):
Resting Heart Rate (BPM):
Marital Status:
Married/Common Law
Secondary School
Employment Status:
Annual Family Income:
< 20 000
20 000 – 39 999
40 000 – 59 999
African American
60 000 – 79 999
> 80 000
Other: ________________
How often do you have a drink containing alcohol?
Monthly or less
2-4 times a month
2-3 times a week
4 or more times a week
Other: ____
How many standard drinks containing alcohol do you have on a typical day?
1 or 2
3 or 4
5 or 6
7 to 9
10 or more
How often do you have six or more drinks on one occasion?
Less than monthly
Daily or almost daily
Do you smoke products containing tobacco?
Not at all
Has a health care professional ever diagnosed you with obstructive sleep apnea (OSA)?
Yes à Do you use your CPAP machine? Yes No
Cancer Diagnosis and Treatment
Type of Cancer
Type of Treatment
No Yes (what kind?)
No Yes (what agents?)
No Yes (to where?)
Hormonal Therapy
No Yes
No Yes (what kind?)
Yes (what agents?)
No Yes (to where?)
Hormonal Therapy
No Yes
No Yes (what kind?)
No Yes (what agents?)
No Yes (to where?)
Hormonal Therapy
No Yes
Have you been screened for nutrition?
Please state nutrition abnormalities: ______________________________
Name of Medication