UNIVERSITY OF CALGARY Objective Causes of Cancer-Related Fatigue: Roles of Neuromuscular Dysfunction and Sleep Disorders by Mary Elizabeth Medysky A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE GRADUATE PROGRAM IN KINESIOLOGY CALGARY, ALBERTA JUNE, 2016 © Mary Elizabeth Medysky 2016 Abstract 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. ii Acknowledgements 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 chatter. iii Dedication To the cancer patients and survivors who participated in this study, you are my reminder to always be brave. iv 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 1.3.2.1 Polysomnography .................................................................................. 19 1.3.2.2 Actigraphy ............................................................................................. 21 1.3.2.3 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 1.3.5.1 Treatment Type and Status .................................................................... 35 1.3.5.2 Medications ........................................................................................... 36 1.3.5.3 Psycho-Social Factors ........................................................................... 37 1.3.5.4 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 2.1.3.1 Subjective Questionnaires ..................................................................... 47 2.1.3.2 Dual-X-Ray Absorptiometry ................................................................. 47 2.1.3.3 Neuromuscular Experimental Set-Up and Familiarization Protocol ..... 47 2.1.3.4 Daily Subjective Fatigue ....................................................................... 48 2.1.3.5 Sleep Assessment .................................................................................. 48 2.1.4 Appointment #2............................................................................................... 49 2.1.4.2 Neuromuscular function at rest ............................................................. 49 v 2.1.4.3 Fatiguing Cycle Ergometer Protocol ..................................................... 51 2.1.5 Data Analysis .................................................................................................. 51 2.1.5.1 Force ...................................................................................................... 51 2.1.5.2 Actigraphs.............................................................................................. 53 2.1.5.3 Subjective Questionnaires ..................................................................... 53 2.1.6 Statistical Analysis .......................................................................................... 55 2.1.6.1 Neuromuscular Analysis ....................................................................... 56 2.1.6.2 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 4.1.2.1 Fatigue Characteristics in the Whole Sample ........................................ 69 4.1.2.2 Fatigue Characteristics between Groups ............................................... 70 4.1.2.3 Limitations............................................................................................. 72 4.2 Sleep ........................................................................................................................ 73 4.2.1 Limitations ...................................................................................................... 76 CHAPTER FIVE: CONCLUSIONS AND FUTURE DIRECTIONS .......................... 79 REFERENCES .................................................................................................................. 82 APPENDIX A: DETAILS OF MAIL-OUT ...................................................................... 93 APPENDIX B: DEMOGRAPHICS SURVERY .............................................................. 94 APPENDIX C: CENTRE FOR EPIDIOLOGICAL STUDIES SCALE FOR DEPRESSION .................................................................................................................................. 97 APPENDIX D: SOCIAL PROVISION SCALE ............................................................... 98 APPENDIX E: THE FUNCTION ASSESSMENT OF CHONIC ILLNESS THERAPY – GENERAL AND BREAST CANCER SPECIFIC ................................................ 100 APPENDIX F: THE MODIFIED GODIN LESURE TIME EXERCISE QUESTIONNAIRE ................................................................................................................................ 103 APPENDIX G: FATIGUE THERMOMETER ............................................................... 104 vi APPENDIX H: AMERICAN ACADEMY OF SLEEP MEDICINE SLEEP DIARY ... 105 APPENDIX I: PITTSBURGH SLEEP QUALITY INDEX QUESTION #6 ................. 106 APPENDIX J: 24- SLEEP PATTERNS INTERVIEW .................................................. 107 APPENDIX K: INSOMNIA SEVERITY INDEX.......................................................... 108 APPENDIX L: FUNCTIONAL ASSESSMENT OF CHRONIC ILLNESS THERAPY FATIGUE SUBSCALE.......................................................................................... 109 vii 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 viii 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 ix List of Symbols, Abbreviations and Nomenclature 6MWD 7-day PAR ACSM AM-PAC CAT AP AUDIT- C BDI-II BF BIA BMI BP BPI CARES-SF CEP CES-D CRF CSEP DXA EC EEG EMG EOG EORTC ESAS EWB FACT-F FACT-G FACT-Specific FWB GLTEQ HR ICSD ISI ITT LSI M-wave MDSAI MSA MVC 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 Excitation-contraction Electroencephalography Electromyography Electrooculography 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 x NREM PAQ PARMED-X PARQ+ POMS PROMIS PSG PSQI PWB QOL RCT REM RF RMS S4 SE SF-36 SOL SPS SSRI ST SWS Symbol TASS Thrive Centre TMS TST VA VL VO2max VO2peak W/Kg WASO WC Wellspring YT Non-rapid eye movement Cooper aerobics center longitudinal study Physical activity questionnaire Physical activity readiness medical examination questionnaire Physical activity readiness questionnaire plus Profile of mood states Patient reported outcome information system Polysomnography 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 Stretching Slow wave sleep Definition Transcranial Magnetic Stimulation Adult Safety Screen 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 xi Epigraph It always seems impossible until it’s done. Nelson Mandela xii 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. 1 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. 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). 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 3 appropriate diagnosis, prevention and treatment of this common cancer treatment-related side effect. 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 fatigue. Scale EORTC-Fatigue Subscale Features - - Key Considerations 3 item unidimensional, converted to score /100 minimal time for completion measures physical fatigue over the past week 4 - - brief and simple to administer ceiling effect – questionable for use in palliative setting, not to be used as a single measure 40/100 cut point for clinically significant CRF - FACT-F - Brief Fatigue Inventory (BFI) - Profile of Mood States Fatigue Subscale (POMS-F) - 13 item unidimensional scale: 5 point Likert Scale Fatigue scale part of 20 item anemia scale Higher scores = less fatigue Time for completion=5-10 minutes Measures physical fatigue over the past week one dimensional scale 9 item VAS 5 minute time for completion fatigue severity and interference measures fatigue severity and interference over the past week, current and past 24hs one dimensional 7 item subscale used for cancer and non cancer population - - - - Fatigue Severity Scale - 9-item uni5 - weaker psychometric properties, but simplicity may outweigh this disadvantage recommended for use with intervention studies can be used independently or with FACT-general scale 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 screening limited ongoing use reasonable psychometric properties has defined minimal clinically significant difference not originally validated in cancer patients no clear advantage over other scales could provide helpful baseline measures in healthy population limited use in cancer Fatigue Questionnaire (FQ; aka Chalder Fatigue Scale) - - Fatigue Symptom Inventory (FSI) (Hann, Denniston, & Baker, 2000) - - dimensional scale validated in a noncancer chronic disease population 11 item multidimensional scale subscales: 7-item physical fatigue and 4-item mental fatigue 5-10 minutes completion time measures physical and mental fatigue over the last month versus when the patient felt well multi-dimensional 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, VASF) (Lee, Hicks, & NinoMurcia, 1991) - Multidimensional Assessment of Fatigue (MAF) (Belza, 1995) Multidimensional Fatigue Symptom Inventory short - multi-dimensional 16-item scale - - multi-dimensional 30-item scale - - multi-dimensional 18-item scale measures fatigue severity 6 - patients 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 >4.0 brief and easy to use reasonable psychometric properties questionable testretest reliability limited to patients undergoing active treatment and survivors to be used with MFSI-30 brief originally validated for patients with sleep disorders not recommended for CRF measurement due to sleep disorder overlap poorly validated not recommended favorable psychometrics form (MFSI-30) (Stein, Martin, Hann, & Jacobsen, 1998) - Multidimensional Fatigue Inventory (MFI-20) (Smets, Garssen, Bonke, & De Haes, 1995) - Revised Piper Fatigue Scale (PFS) (Piper et al., 1998) - - - Schwartz Cancer Fatigue Scale (Schwartz & Meek, 1999) - Wu Cancer Fatigue Scale (Wu, Wyrwich, & McSweeney, 2006) - measures global, somatic, affective, cognitive and behavioral symptoms of fatigue time for completion: 5-10 minutes - previous clinical use limited multi-dimensional 20-item scale designed for use in cancer patients - multi-dimensional 27-item scale 15-20 minute completion time shorter version than original PFS 28 item multidimensional fatigue scale measures fatigue associated with behavioral, affective meaning, sensory and cognitive item - normative data available for reference use in various studies with small patients numbers limited data on psychometric properties in cancer patients 9 item multidimensional scale - - - - extensive psychometric data not used often minimum significant difference has been determined limited 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 7 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 8 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). 9 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. 10 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). 11 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 12 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 13 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. 14 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 15 (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 16 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 17 (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 insomnia. Table 2. Factors related to insomnia in the cancer population. Factor Examples Physical 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 sleep/perform) Emotional Roscoe et al. (Roscoe et al., 2007) Environmental Harris, et al. (Harris et al., 2014) Excessive lighting, temperature, noise Surgery Alters sleep patterns, affect melatonin Hansen, et al. (Hansen, Madsen, secretion Wildschiødtz, Rosenberg, & Gögenur, 2013) 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 18 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 1.3.2.1 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 19 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 20 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). 1.3.2.2 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 21 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). 1.3.2.3 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 22 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). 23 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, 24 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 25 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., 2012). 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 26 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 27 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 outcomes. 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). 28 Table 3. Studies examining the relationship between exercise, sleep and cancer-related fatigue (CRF). Measurements of sleep are in bold. Study Study Design (Chandwani et al., 2014) RCT Breast cancer patients stages 0-3 undergoing radiotherapy within the next 6 weeks (n=163) (Cheville al., 2013) RCT Stage 4 lung or colorectal patients (n=66) et Sample Exercise Type Yoga (YT): preparatory warm up & synchronized breathing, selected postures, deep relaxation Deep relaxation, alternate nostril breathing, meditation Stretching (ST): exercise recommended for breast cancer patients Incremental walking and home-based strength training, 4+ days/week, 8 week trial 29 Measures Sleep Outcome SF-36, BFI, PSQI, Cortisol, CES-D No differences AM-PAC CAT, AM-PAC Mobility and Activities Short Forms, FACT-G, FACT-F, Pain and Sleep Quality: Symptom Numeric Rating Positive (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 Sleepiness Positive (small sample size) (Coleman et al., 2012) RCT 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% 1RM POMS-Fatigue, FACT-F, Actigraph, 6-minute Walk Test, Hemoglobin No difference (significant difference only after sleep worsened during treatment) et Case Study Stage2b Pancreatic Cancer Patient (n=1) Supervised moderatehigh intensity resistance and aerobic exercise, twice/week for 6 months 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 Positive (Dodd et al., 2010) 3-arm RCT Breast, colorectal, or ovarian cancer patients, beginning first CTX cycle (n=119) Home-based, aerobic exercise, 3-5x/week at 60-80% VO2peak, 2030minutes (Cormie al., 2014) 30 GSDS, CES-D, Worst Pain No Intensity Scale (numeric rating improvements in scale 0-10), Karnofsky sleep disturbance Performance Status (KPS) Scale, Piper Fatigue Scale (Donnelly et al., 2011) (Humpel Iverson, 2010) & RCT Randomized controlled intervention trial Gynecologi Moderate cal cancer intensity, home-based survivors (n=33) walking and strengthening exercises, 30 minutes, 5 days/week Breast cancer Not an intervention (n=32) & prostate PA was measured an cancer (n=59) correlated to sleep survivors quality MFSI-SF, FACT-F, FACT-G, BDI-OO, PANAS, Body composition: WC, BMI, 12minute walk test, PSQI, 7-day PAR FACT-F, PSQI Positive Less PA has poorer sleep quality (Kampshoff et al., 2015) RCT Breast,colon, ovarian,cervix, testis,lymphoma 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 PSQI No between group differences (Lin et al., 2015) Longitudinal Lung cancer survivors (n=186) MDASI-T, 12 min walk test, Symptom assessment scale Positive (Mock et al., 1997) Experimental - 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 quality Individualized selfpaced, home based walking PFS Positive 31 (Mustian al., 2013) et RCT (Payne et al., 2008) Longitudinal randomized clinical trial (Rabin et al., 2009) Single armed pilot Cancer survivors (n=410) with reported sleep disturbance Breast cancer patients, receiving hormonal treatment, ages 55+ (n=20) 4-week yoga intervention (2x/week, 75 minutes each) PSQI, Actigraphy Positive Home-based walking, 20 minute duration, 4 times per week (14 weeks) PSQI, CES-D, Blood sample: serum cortisol, serotonin, IL-6, bilirubin, Actigraphy Positive Stage 0-2 breast cancer survivors, completed treatment (n=19) Combined PA and relaxation 12 weeks of PA counseling via telephone 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 days/week PSQI, Intervention Feasibility and Acceptability, 7-day physical activity recall (7-day PAR), Stage of Motivational Readiness for PA measure, Accelerometers Positive 32 (Rajotte al., 2012) et Pre-post clinical trial Cancer survivors (off treatment 90+ days) (n=221) 12 weeks, 90 minute session, 2 days/week Group session, individualized 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 (Rogers al., 2014) et Pilot RCT Post-menopausal breast cancer survivors (n=46), <stage 2, off treatment 3 month 160 min/week moderate-intensity aerobic walking & 2/week resistance with exercise bands 3-day diet record Actigraphy 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, TNF-alpha) Fatigue Symptom Inventory Breast and prostate patients (n=38) 4-week, home-based Progressive moderate intensity walking and resistance exercises PSQI Biological Sleep Markers: IL6, TNA-α, sTNF-R (Sprod et al., 2010) 2-arm pilot 33 Positive Positive Positive (not significant) (Tang et al., 2010) RCT (n=71) Taiwanese patients of any cancer diagnosis Brisk walking, with speed tailored to individuals RPE MOS-SF36, exercise logs RPE, PSQI Positive (Wang et al., 2011) RCT (n=62) stage 1, or 2 breast cancer patients, expecting chemotherapy 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 (ESES) Positive (Wenzel al., 2013) RCT (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 Prospective, repeated measures Home-based walking, 20-30 minutes at 50%70% maximum heart rate 12 week, Stage 2 cardiac rehab program et (YoungMcCaughan et al., 2003) Positive 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. 34 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. 1.3.5.1 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 35 complaints increased and sleep efficiency declined. These findings conclude that poor sleep is exacerbated while on treatment. 1.3.5.2 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 1.3.5.3 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). 1.3.5.4 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 37 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: 1. 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. 38 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. 39 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) 40 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 survivors. Demographic Age (years) 21-30 31-40 41-50 51-60 61-70 71-80 81-90 Mean n Gender Female Male n Race Caucasian Asian Latin n Marital Status Married/Common Law Divorced/Separated Single n Education Status University College Secondary School n Employment Status Part-Time Full-Time Retired Unemployed Disability/Leave n 41 N % 2 4 9 16 20 1 1 56 53 3.8 7.5 17.0 30.2 37.7 1.9 1.9 33 20 53 62.3 37.7 49 3 1 53 92.5 3 1 44 5 4 53 83.0 9.7 5.4 30 14 9 53 56.6 26.4 17.0 10 17 17 3 6 53 18.9 21.1 32.1 5.7 11.3 Annual Income 20 000 – 39 999 40 000 – 59 999 60 000 – 79 999 >80 000 n Smoker Yes No n Alcohol Intake (AUDIT-C) At-risk (men >4, women>4) Not at-risk n 42 5 5 6 36 52 9.4 9.5 11.3 67.9 0 30 30 0 100 7 23 30 23.3 76.7 Table 5. Medical information for the full sample of cancer patients and survivors. Variable Cancer Type Breast Prostate Lymphoma Head and Neck Colorectal Pancreatic Penile Kidney Testicular Endometrial n Treatment Status On-treatment Off-treatment n Treatment Type Chemotherapy Hormone Therapy Surgery Radiation Fatigue (FACT-F) Fatigued (<34) Non-Fatigued (>34) n N % 25 10 3 5 5 1 1 1 1 1 53 47.2 18.9 5.7 9.4 9.4 1.9 1.9 1.9 1.9 1.9 4 49 53 7.5 92.5 32 11 49 21 60.4 20.7 92.4 38.6 17 36 53 31.1 67.9 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) 43 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. Variable N Gender Male Female Age Height Weight Fatigued Mean(SD) 12 4 8 52.6(9.9) 165.1(11.0) 76.1(29.6) Non-Fatigued Mean(SD) 22 7 15 57.9(12.0) 167.6(10.7) 71.7(17.8) Table 7. Subject characteristics for subgroup of participants who neuromuscular testing on the chair. Variable N Gender Male Female Age Height Weight Fatigued Mean(SD) 8 2 6 51.5(11.6) 163.7(10.8) 69.5(16.0) Non-Fatigued Mean(SD) 10 3 7 56.2(14.5) 165.9(9.9) 67.5(14.7) Table 8. Subject characteristics for subgroup of participants included in sleep analysis. Variable N Age Gender Male Female Insomnia Severity Index Above Cut Off Below Cut Off N Fatigued Mean(SD) 16 52.1(9.6) 5 11 5 3 8 44 Non-Fatigued Mean(SD) 32 57.2(12.4) 12 20 4 14 18 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). 45 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. 46 2.1.3 Appointment #1 2.1.3.1 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. 2.1.3.2 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). 2.1.3.3 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 47 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 2.1.4.2 and 2.1.4.3. 2.1.3.4 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). 2.1.3.5 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 48 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 2.1.4.1.1 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. 2.1.4.2 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. 49 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 2.1.4.3 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 2.1.5.1 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: 51 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. 2.1.5.1.1 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. 52 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). 2.1.5.2 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). 2.1.5.3 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 53 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) 3. 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). 54 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 2.1.6.1 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) 2.1.6.2 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 significance. 56 Correlation was used as an exploratory measure to assess if any of the sleep measures were associated with subjective fatigue and sleep ratings. 57 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. 58 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. Outcome MVC(N) Twitch (N) VA(%) Minimum Maximum Mean 120.9 26.4 264.1 102.3 178.9 61.4 Standard Deviation 45.7 18.5 88.0 100.0 96.0 3.7 MVC: maximal voluntary contraction. VA: Voluntary activation. 59 Mean Δ PrePost (%) 31.7 56.3 Mean Δ PreS4 (%) 14.2 28.2 10.8 4.6 Table 9. Descriptive statistics of sleep variables measured via actigraph. Outcome Minimum Maximum Mean StandardDeviation TST(min/night) 333.0 638.0 432.3 48.1 SOL(min/night) 0.0 56.0 13.7 12.2 SE(%) 76.4 92.8 85.7 3.6 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). 250 200 Force (N) 80 150 60 100 40 50 20 0 Voluntary activation (%) 100 0 MVC Twitch VA 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 60 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. Fatigue Rest Outcome FACT-F Non-Fatigued n Average SD n Fatigued Average SD T /Z DF MVClean (N/Kg) 11 66.6 11.1 22 76.7 48.4 -0.7 31 0.499 MVCtotalmass (N/Kg) 12 5.2 1.1 22 5.9 1.7 -1.3 32 0.199 Twitchlean (N/Kg) 11 22.5 2.9 22 25.4 17.7 -0.5 31 0.596 Twitchtotalmass (N/Kg) VAa (%) 12 1.8 12 0.3 22 1.9 22 0.35 -1.0 -0.1 32 0.298 0.885 ΔMVC pre-S4 bike (%) 8 -14.3 12.2 10 -14.1 10.0 -0.5 16 0.964 ΔMVC pre-post bike (%) 8 -31.0 13.0 10 -32.0 8.8 16 0.856 ΔTwitch pre-S4 bike (%) 8 -29.4 17.1 10 -27.1 19.1 -0.3 16 0.796 ΔTwitch pre-post bike (%) 8 -54.9 10.2 10 -57.9 14.4 0.4 16 0.686 0.2 P ΔVA pre-S4 bikea (%) -1.3 0.201 ΔVA pre-post bikea (%) -1.3 0.200 Performance (s) 8 851.5 41.5 10 858.0 61 50.7 -0.3 16 0.8 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. 8 6 80 60 4 40 2 20 0 Voluntary activation (%) Force N / kg BW 100 0 MVC Twitch VA 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). 62 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. Outcome Fatigue Rest n Fatigued Average SD Fatigue Thermometer Not Fatigued T/Z N Average SD DF P MVClean (N/Kg) 15 69.0 14.1 15 79.4 58.1 0.674 28 0.506 MVCtotalmass (N/Kg) Twitchlean (N/Kg) 16 15 5.83 22.4 1.38 15 2.8 15 5.6 27.2 1.6 -0.382 21.3 0.858 29 28 0.705 0.398 Twitchtotalmass (N/Kg) 16 VAa (%) 15 ΔMVC pre-S4 bike (%) 10 1.91 1.9 .43 -10.5 9.0 -0.185 -0.298 1.167 29 -16.8 0.24 15 15 12.0 7 15 0.854 0.766 0.262 ΔMVC pre-post bike (%) 10 -32.5 12.1 7 -30.1 9.5 0.433 15 0.671 ΔTwitch pre-S4 bike (%) 10 -33.7 20.7 7 -21.3 11.8 0.167 15 0.173 ΔTwitch pre-post bike (%) 10 -57.6 14.3 7 -55.0 11.0 0.402 15 0.694 ΔVAa pre-S4 bike (%) -1.745 0.081 ΔVAa pre-post bike (%) -0.732 0.464 Performance (s) 10 853.6 45.4 7 850.9 49.4 -0.118 15 0.907 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 63 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. FACT-F Fatigued Not-Fatigued SD n Average t DF P Outcome n Average SD TST (min/night) 16 425.8 37.8 32 435.6 52.8 -0.7 46 0.512 SE (%) 16 84.6 3.3 32 86.3 3.7 -1.5 46 0.141 SOL (min/night) 16 16.5 13.5 32 12.3 11.5 1.1 46 0.263 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 Fatigued Outcome n Average Not-Fatigued SD n Average t DF P SD TST (min/night) 25 429.3 38.8 22 440.3 53.9 0.8 45 0.421 SE (min/night) 25 85.5 3.4 22 86.1 4.0 0.5 45 0.583 SOL (%) 25 12.2 11.3 22 14.1 13.3 0.7 45 0.462 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 64 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). Scale MVCpre-post (N) Twitchpre-post (N) VApreposta (%) MVC4 (N) Twitch4 (N) VA4a (%) Performance (s) FACT-F 0.226 -0.043 -0.214 0.095 0.210 -0.21 0.125 Fatigue Thermometer -0.085 -0.067 0.074 -0.136 -0.349 0.074 0.189 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 65 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 scores. Table 16. Pearson Product-moment correlations between sleep variables Scale FACT-F Fatigue Thermometer PSQI #6 TST (min) 0.194 -0.17 0.24 SE (%) 0.312* -0.06 0.35 SOL (min) -0.173 -0.24 -0.24 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. 66 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 67 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. 68 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. 4.1.2.1 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. 69 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. 4.1.2.2 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 70 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 4.1.2.3 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. 72 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 populations. 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 73 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 74 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 75 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 76 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 77 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. 78 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. 79 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 80 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 size fits all” approach to effectively alleviate CRF. 81 References Allada, R., & Siegel, J. M. (2008). Unearthing the phylogenetic roots of sleep. Curr Biol, 18(15), R670-R679. Ancoli-Israel, S. 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Never Monthly or less 2-4 times a month 2-3 times a week 4 or more times a week 94 Other: ____ Disability/Leave 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? Never Less than monthly Monthly Weekly Daily or almost daily Do you smoke products containing tobacco? Daily Occasionally Not at all Has a health care professional ever diagnosed you with obstructive sleep apnea (OSA)? No Yes à Do you use your CPAP machine? Yes No Cancer Diagnosis and Treatment Type of Cancer Type of Treatment Surgery No Yes (what kind?) Chemotherapy No Yes (what agents?) Radiation No Yes (to where?) Hormonal Therapy No Yes Other: ___________________________________________________ ___ Surgery No Yes (what kind?) Chemotherapy 95 No Yes (what agents?) Radiation No Yes (to where?) Hormonal Therapy No Yes Other: ___________________________________________________ ___ Surgery No Yes (what kind?) Chemotherapy No Yes (what agents?) Radiation No Yes (to where?) Hormonal Therapy No Yes Other: ___________________________________________________ ___ Nutrition: Have you been screened for nutrition? Yes Please state nutrition abnormalities: ______________________________ No Medications: Name of Medication Dosage 96 APPENDIX C: CENTRE FOR EPIDIOLOGICAL STUDIES SCALE FOR DEPRESSION 97 APPENDIX D: SOCIAL PROVISION SCALE 98 99 APPENDIX E: THE FUNCTION ASSESSMENT OF CHONIC ILLNESS THERAPY – GENERAL AND BREAST CANCER SPECIFIC 100 101 102 APPENDIX F: THE MODIFIED GODIN LESURE TIME EXERCISE QUESTIONNAIRE 103 APPENDIX G: FATIGUE THERMOMETER 104 APPENDIX H: AMERICAN ACADEMY OF SLEEP MEDICINE SLEEP DIARY 105 APPENDIX I: PITTSBURGH SLEEP QUALITY INDEX QUESTION #6 106 APPENDIX J: 24- SLEEP PATTERNS INTERVIEW 107 APPENDIX K: INSOMNIA SEVERITY INDEX 108 APPENDIX L: FUNCTIONAL ASSESSMENT OF CHRONIC ILLNESS THERAPY FATIGUE SUBSCALE 109