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An Examination of Pain's Relationship to Sleep Fragmentation and Disordered Breathing across Common Sleep Disorders

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Title:
An Examination of Pain's Relationship to Sleep Fragmentation and Disordered Breathing across Common Sleep Disorders
Creator:
Mundt, Jennifer M
Place of Publication:
[Gainesville, Fla.]
Florida
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University of Florida
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english
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1 online resource (56 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Psychology
Clinical and Health Psychology
Committee Chair:
ROBINSON,MIKE E
Committee Co-Chair:
PEREIRA,DEIDRE B
Committee Members:
DOTSON,VONETTA M
EBNER,NATALIE CHRISTINA

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Subjects / Keywords:
apnea -- insomnia -- pain -- polysomnography -- sleep
Clinical and Health Psychology -- Dissertations, Academic -- UF
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bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Psychology thesis, Ph.D.

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Abstract:
Short sleep duration and insomnia have been linked to higher pain and an increased risk of developing chronic pain, but relatively little research has examined the contribution of sleep disordered breathing (SDB) to pain. This study examined the unique contributions of SDB and insomnia to chronic pain. Patients who presented to the University of Florida Health Sleep Center for overnight polysomnography were invited to participate, provided they had no history of using positive airway pressure. Participants (N = 105) completed additional questionnaires about their sleep (Insomnia Severity Index) and pain (Medical College of Virginia Pain Questionnaire, pain locations, chronic pain diagnoses) before undergoing overnight polysomnography. They subsequently completed an online sleep/pain diary for two weeks. Physicians diagnosed 52.38% with obstructive sleep apnea (OSA) and 4.76% with insomnia, though 20.95% were classified as having chronic insomnia based on sleep diaries used for the study. In a hierarchical regression, polysomnography-measured total sleep time, but not measures of sleep fragmentation (apnea-hypopnea index, spontaneous arousals) or hypoxemia (SaO2 nadir), was related to pain. The majority of participants (80.00%) reported chronic pain, with musculoskeletal pain (28.57%) and chronic headaches (24.76%) being the most frequent. Although the likelihood of having chronic pain did not differ by sleep disorder, there was a significant difference in pain intensity; individuals with comorbid OSA/insomnia (12.38% of the sample) reported an average pain intensity that was 20 points (out of 100) higher than individuals with insomnia or no diagnosis and 28 points higher than those with OSA, controlling for participant sex (ps < .05). Thus, although polysomnography measures of SDB severity were unrelated to pain intensity, individuals with comorbid OSA/insomnia had the most severe pain. ( en )
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In the series University of Florida Digital Collections.
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Includes vita.
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Includes bibliographical references.
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Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2017.
Local:
Adviser: ROBINSON,MIKE E.
Local:
Co-adviser: PEREIRA,DEIDRE B.
Electronic Access:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2018-06-30
Statement of Responsibility:
by Jennifer M Mundt.

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UFRGP
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Applicable rights reserved.
Embargo Date:
6/30/2018
Classification:
LD1780 2017 ( lcc )

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DISORDERED BREATHING ACROSS COMMON SLEEP DISORDERS By JENNIFER MARIE MUNDT A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2017

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201 7 Jennifer Marie Mundt

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3 ACKNOWLEDGMENTS I would like to thank the members of my committee and especially my research mentor, Dr. Michael Robinson, for their guidance and support in completing this research. I would also like to thank Dr. Stephan Eisenschenk and the staf f at the UF Health Sleep Center for their assistance during the recruitment process. Finally, I am grateful to have had the support of my lab mates, friends, and family.

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4 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 3 LIST OF TABLES ................................ ................................ ................................ ............ 6 LIST OF FIGURES ................................ ................................ ................................ .......... 7 LIST OF ABBREV IATIONS ................................ ................................ ............................. 8 ABSTRACT ................................ ................................ ................................ ..................... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 11 Sleep Disordered Breathing and Insomnia ................................ ............................. 11 Prevalence ................................ ................................ ................................ ....... 11 Clinical Presentation ................................ ................................ ......................... 11 Assessment and Treatment ................................ ................................ .............. 12 Interaction of Sleep Disordered Breathing and Insomnia ................................ 13 Sleep Disturbance and Pain ................................ ................................ ................... 14 Sleep Duration, In somnia, and Pain ................................ ................................ 15 Sleep Disordered Breathing and Pain ................................ .............................. 18 Sleep Disordered Breathing, Insomnia, and Pain ................................ ............. 20 Aims and Hypotheses ................................ ................................ ............................. 21 2 METHODS ................................ ................................ ................................ .............. 23 Participants ................................ ................................ ................................ ............. 23 Measures ................................ ................................ ................................ ................ 23 Demographics and Med ical History ................................ ................................ .. 24 Polysomnography ................................ ................................ ............................. 24 Sleep Questionnaires ................................ ................................ ....................... 25 Sleep Diaries ................................ ................................ ................................ .... 25 Pain ................................ ................................ ................................ .................. 26 Procedures ................................ ................................ ................................ ............. 26 Statistical Analyses ................................ ................................ ................................ 26 Aims 1 and 2 ................................ ................................ ................................ .... 27 Aim 3 ................................ ................................ ................................ ................ 27 3 RESULTS ................................ ................................ ................................ ............... 29 Participant Characteristics ................................ ................................ ...................... 29 Sleep Characteristics ................................ ................................ .............................. 29 Sleep Disordered Breathing ................................ ................................ ............. 29

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5 In somnia ................................ ................................ ................................ ........... 29 Pain Characteristics ................................ ................................ ................................ 30 Aims 1 and 2: Contributions of Fragmentation and Hypoxemia to Pain .................. 31 Aim 3: Risk of Pain Across Sleep Disorders ................................ ........................... 31 Differences in Pain Intensity by Sleep Diagnosis ................................ ............. 31 Likelihood of Chronic Pain by Sleep Diagnosis ................................ ................ 32 Identification and Validation of Sleep Disorder Clusters ................................ ... 32 Differences in Pain Intensity by Cluster ................................ ............................ 34 4 DISCUSSION ................................ ................................ ................................ ......... 44 Contributions of Sleep Characteristics and Disorders to Pain ................................ 44 Clinical Implications ................................ ................................ ................................ 46 Limitations and Future Directions ................................ ................................ ........... 47 Conclusions ................................ ................................ ................................ ............ 48 LIST OF REFERENC ES ................................ ................................ ............................... 50 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 56

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6 LIST OF TABLES Table page 3 1 Demographic characteristics for all enrolled participants ( N = 105). ................... 36 3 2 Frequencies of sleep disorders. ................................ ................................ .......... 37 3 3 Frequency of chronic pain across body regions. ................................ ................ 37 3 4 Chronic pain diagnoses reported by participants. ................................ ............... 38 3 5 MCV ratings of pain and accompanying negative em otions for the preceding week. ................................ ................................ ................................ .................. 38 3 6 Hierarchical regression predicting average pain intensity. ................................ .. 38 3 7 Correlation matrix for pain intensity and polysomnography variables. ................ 39 3 8 Pain characteris tics by sleep diagnosis. ................................ ............................. 39 3 9 Means for variables used in hierarchical cluster analysis. ................................ .. 39 3 10 Cluster means for demographic and mood variables. ................................ ........ 40

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7 LIST OF FIGURES Figure page 3 1 Standardized means for variables used in hierarchical cluster analysis. ............ 41 3 2 Frequency of sleep disorders in groups identified through hierarchical cluster analysis. ................................ ................................ ................................ ............. 42 3 3 Sex distribution across groups identified through hierarchical cluster analysis. 43

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8 LIST OF ABBREVIATIONS AASM American Academy of Sleep Medicine AHI Apnea hypopnea index ANCOVA Analysis of covariance ANOVA Analysis of variance BMI Body mass index CBT I Cognitive behavioral therapy for insomnia CPAP Continuous positive airway pressure CSA Central sleep apnea ESS Epworth Sleepiness Scale ISI Insomnia Severity Index MCV M edical College of Virginia Pain Questionnaire MSLT Multiple Sleep Latency Test OSA Obstructive sleep apnea PAP Positive airway pressure PSG P olysomnography REM Rapid eye movement SaO 2 A rterial oxyge n saturation SDB Sleep disordered breathing TMD T emporomandibular disorder UF University of Florida VAS Visual analog scale

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9 Abstra ct of Disserta tion P resented t o t he Graduate S chool of t he Un iversity o f Flo rida in Partial Fulfillmen t o f the Requirements for the Degree o f Do ctor of Philosophy DISORDERED BREATHING ACROSS COM MON SLEEP DISORDERS By Jennifer Marie Mu ndt December 2017 Chair: Michael Robinson Major: Psychology Short sleep duration and in somn ia have b een linke d t o higher pain and an increased ri sk of d eveloping ch ron ic pain, b ut re latively l ittle re search h as e xamined t he contribution of sle ep disordered b reathing (S DB) to p ain. This study examined the unique co ntributions of S DB and in somn ia to chronic pain. P atien ts who p resented to the U niversity o f Florida Health S leep Ce nte r for overnigh t polysomnography were invited to p articipate provided t hey h ad no h istory o f using p ositive airway p ressure. Participan ts ( N = 1 05 ) completed a dditional questionnaire s abou t their sle ep (Insomnia Severity I ndex ) and p ain (Medical College o f Virginia Pa in Questionnaire pa in locations, chronic pa in diagnoses) before u ndergoing o vernigh t polysomnography They subsequently co mpleted a n o nline sle ep/pa in diary f or two w ee ks. Physicians diagnosed 5 2.38% w ith o bstructive sle ep apnea (OSA) and 4.76 % with in somnia, though 20.95 % were classified as having ch ronic insomn ia based o n sleep diaries used for the st udy. In a hierarchical regression, polysomnography-measured t otal sleep time, bu t no t measu res of sl eep fragmentation (apnea-hypopnea in dex spontaneous arousals) o r hypoxemia (SaO 2 nadir), was related t o pain. The ma jority of p articipants

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10 (80.00%) reported chronic pain with musculoskeletal pain (28.57%) and chronic headaches (24.76%) being the most frequent Although the likelihood of having chronic pain did not differ by sleep disorder, there was a significant difference in pain intensity; individuals with comorbid OS A/insomnia (12.38% of the sample) reported an average pain intensity that was 20 points (out of 100) higher than individuals with insomnia or no diagnosis and 28 points higher than those with OSA, controlling for participant sex ( p s < .05). Thus, although polysomnography measures of SDB severity were unrelated to pain intensity, individuals with comorbid OSA/insomnia had the most severe pain.

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11 CHAPTER 1 INTRODUCTION Sleep Disordered Breathing and Insomnia Prevalence Sleep disordered breathing (SDB) and ins omnia represent the most common sleep disorders, affecting millions of individuals in the United States. Estimates of prevalence vary due to differences in research diagnostic criteria, but studies have shown that approximately 2 4% of adults have some for m of SDB, most commonly obstructive sleep apnea (OSA). 1 The prevalence of insomnia with associated daytime impairment among adults is approximately 10 15%, though 33 50% of adults report some insomnia symptoms. 56 Although extensive literatures exist on each disorder, a relatively small number of studies have examined comorbid SDB and insomnia. These studies have demonstrated high rates of co occurrence, though estimates again vary considerably due to inconsis tent criteria. Among individuals with OSA, 22 55% have comorbid insomnia 1 but estimates for the broader category of SDB reach up to 84%. 52 For individuals with insomnia, the rates of SDB and OSA range from 16 83% and 7 75%, respectively. 1 52 However, as insomnia researchers commonly screen out individuals with SDB symptoms, these numbers likely underestimate the true prevalence of SDB among individuals with insomnia. 52 Clinical Presentation In terms of symptoms and daytime impairments, SDB and insomnia are both characterized by fragmented sleep and a shortened sleep duration, associated with complaints of impaired daytime function such as fatigue or sleepiness. 22 56 While individuals with SDB are often unaw are of these awakenings, individuals with insomnia

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12 are by definition aware of and often distressed by their awakenings. While both SDB and insomnia may be accompanied by subjective reports of daytime fatigue, typically only SDB is characterized by objectiv e sleepiness (i.e., the propensity to fall asleep if given an opportunity). 22 32 Although ind ividuals with insomnia may report being sleepy, they tend not to fall asleep during the daytime due to an elevated level of physiological arousal. By contrast, individuals with apnea fall asleep quite easily, even in situations when it is dangerous to do s o, such as while driving. While both conditions share some aspects of fragmentation and daytime impairments, the key distinguishing feature is the cause of the sleep disturbance. In the case of SDB, awakenings are caused by the repeated cessation of breath ing. Insomnia, by contrast, is characterized by difficulty falling or staying asleep due to elevated physiological arousal and sleep interfering habits (e.g., consumption of caffeine or other stimulants, bright light exposure, learned associations). Asses sment and Treatment Patients with SDB and insomnia are both likely to present with complaints of unrefreshing and inadequate sleep. However, different treatments are indicated for each disorder in order to target the unique underlying causes of the present ing complaints. SDB is typically treated with positive airway pressure (PAP), while insomnia can be alleviated with cognitive behavioral therapy or sedating medications. 22 56 A thorough assessment and accurate diagnosis is therefore crucial to de termine the best course of treatment. This is typically accomplished by means of a clinical interview and a sleep study (polysomnography; PSG), which quantifies the frequency and type of apneic events. PSG can readily identify the presence of SDB, but dail y sleep diaries are necessary in order to adequately assess for symptoms of insomnia. As sleep may vary

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13 greatly from night to night in insomnia, standard research practice includes the completion of two weeks of sleep diaries. 56 Insomnia related sleep disturbance includes subjective perceptions of difficulty falling asleep, staying asleep, or waking too early. In addition to sleep disturb ance, a diagnosis of insomnia requires self reported daytime impairment (fatigue, concentration, occupational functioning, mood, etc.). 2 Interaction of Sleep Disordered Breathing and Insomnia Despite the fact that these conditions frequently co occur, researchers have typically examined one or the other in isolation. However, recent investigations have highlighted the comorbidity of SDB and insomnia and proposed possible mechanisms for a bidirectional interaction. Given evidence that treating OSA often improves nocturia, OSA may precipitate and perpetuate insomnia via its impact on nocturia. 1 It is also possible that restricted airflow leads to insomnia like alpha intrusions and sympathetic activation which make res uming sleep more difficult. 51 72 Such repeated awakenings from SDB itself or from poor tolerance to PAP treatment may also lead to the development of dysfunctional sleep behaviors that perpetuate insomnia (e.g., ruminating in bed during awakenings). 6 Conversely, it is also conceivable that insomnia may exacerbate sleep apnea. Specifically, sleep deprivation and fragmentation have been sh own to reduce pharyngeal muscle tone, and this mechanism could potentially link insomnia to an increase in obstructive apneic events. 1 6 These hypotheses remain to be tested empirically, however. 1 52 The treatment of comorbid SDB and insomnia also deserves further attention, as there are presently no treatment standards for this population. 52 Researchers have suggested that SDB should be addressed first 1 though treating both conditions is likely to lead to the best outcomes. 42 Some studies have highlighted possible complications of

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14 treating comorbid SDB and insomnia. Studies have found that initial 7 and middle 7 72 insomnia predict poorer adherence to PAP. Bjornsdottir and colleagues 7 also demonstrated that while initial and late insomnia tended to persist regardless of PAP use, middle insomnia was most likely to improve significantly with PAP use. This finding may suggest that for some individuals, frequent awakenings associated with middle insomnia may be better characterized as a symptom of untreated OSA. This same study revealed that new cases of insomnia among PAP users were most likely to be la te insomnia, potentially due to being awakened by the PAP machine itself during the lighter stages of sleep which predominate at the end of the night. Independently, SDB and short sleep duration have each been linked to an increased risk for a number of h ealth problems, including cancer, cardiovascular disease, diabetes, and obesity. 13 16 26 28 31 However, little is known about the combined impact of comorbid SDB and insomnia on health outcomes. Therefore, the goal of the present study is to examine the relationship of these two common sleep disorders to chronic pain, which is itself an extremely common health problem affecting nearly one third of the population of the United States. 33 Sleep Disturbance and Pain The relationship of p ain with short sleep duration or insomnia has been characterized fairly extensively. However, relatively little is known regarding how SDB impacts chronic pain. Furthermore, it is unclear how pain is related to different types of SDB (i.e., obstructive vs. central apnea) or different features of SDB (i.e., fragmentation vs. hypoxemia).

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15 Sleep Duration, Insomnia and P ain Cross sectionally, high rates of comorbidity between chronic pain and sleep problems have been observed. Half of patients with insomnia ha ve chronic pain, and between 50 88% of patients with chronic pain have sleep disturbance. 24 65 A great deal of evidence suggest s there is a bidirectional relationship between sleep and pain 58 though research in the last decade has more strongly supported the role of sleep in predicting pain than vice versa. 24 Evidence for the interaction of sleep and pain, including the temporal trajectory, comes from studies on short term sleep deprivation, longitudinal observations, and treatment studies. E xperimental manipulation of sleep has demonstrated that acute sleep deprivation (either partial or total deprivation) results in increased pain sensitivity the following day. This has been shown in healthy (i.e., pain free) individuals as well as those wit h pre existing chronic pain using a number of different experimental pain protocols, including heat, cold, and laser induced pain (e.g., 4 11 27 ). There is e ven some evidence that individuals with chronic pain are more sensitive to the hyperalgesic effects of sleep deprivation. A study by Irwin and colleagues 34 found a greater degree of hyperalgesia in individuals with rheumatoid arthritis compared to healthy individuals. S ome studies have examined whether selectively depriving individuals of particular sleep stages has greater effects on pain per ception. Results have been mixed, though there is some evidence that, controlling for total sleep time, selective deprivation of rapid eye movement (REM) or slow wave sleep is enough to increase pain sensitivity. 24 Some research points to disrupted sleep architecture in individuals with chronic pain conditions, though results have not been entirely consistent. For example, rheumatic conditions have been associated with problems in nearly al l

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16 aspects of sleep, including total sleep time, sleep onset latency, wake time, and arousals. 40 The primary complaint of many chronic pain patients, particularly fibromyalgia, is unrefreshing or nonrestorative sleep. It has been suggested that this may be related to increased number of arousals during slow wave sleep, but this has not be en borne out by all published PSG studies. Observational studies have shown that short sleep predicts greater next day pain. Th is has been demonstrated in the general population 21 as well as individuals with chronic pain. 53 63 Microlongitudinal studies with daily measurement of pain and sle ep have shown that sleep reliably predicts next day pain, while pain does not necessarily predict sleep that same night. 21 63 In a sample representative of the gen eral population, average daytime pain predicted sleep duration 21 but in a heterogeneous chronic pain sample, presleep pain did not predict subsequent sleep. 63 Sleep has also been found to predict pain months later, while pain generally has not shown the same predictive power for later sleep disturbance. However, there are exceptions to this, such as a study of individuals hospitaliz ed for burns. 59 In that sample, sleep at discharge predicted later pain, and pain at discharge also predicted later sleep. In studies that have observed participants for months or years, baseline sl eep predicts the onset of new pain as well as the exacerbation of existing pain. 24 Conversely, restorative sleep at baseline has been shown to predict the resolution of chronic widespread pain. 15 Sl eep complaints are also intimately tied to many headache disorders and seem to be both a symptom as well as a potential exacerbating factor. 8 Some headaches have circadian patterns, suggesting their occurrence is tied to aspects of the sleep cycle. Hypnic headaches occur only during s leep, usually at the same time each night.

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17 M igraine headaches occur more frequently in the morning and can be triggered by sleep loss or alleviated with sleep. Despite evidence of a relationship between sleep and pain, insomnia treat ment studies have gener ally not demonstrated significant improvements in pain. Although cognitive behavioral therapy for insomnia ( CBT I ) improves sleep among those with comorbid chronic pain and insomnia, only a couple of studies have shown any effect on pain. One study showed improvement in pain related functional impairment. 43 Another study showed that although pain was not initially impr oved in patients with osteoarthritis, sleep improvement at post treatment predicted improvement at later assessments (9 and 18 months). 67 In that same study, pain improvement at post treatment was not associated with later sleep improvement. Thus, sim ilar to observational studies showing delayed effects of good sleep on pain, there is some evidence that improvement in insomnia may lead to improved pain if long enough follow up is available. Some pharmacological treatments for chronic pain are believed to impact pain partially through their ability to improve sleep. For example, gabapentin and tricyclic antidepressants lead to increased slow wave sleep and more consolidated sleep. 50 Sodium oxybate, a drug only available for narcolepsy at this time due to abuse concerns, has shown promising preliminary results for fibromyalgia patients in terms of increasing slow wave sleep and p roducing sleep that is subjectively rated as more refreshing. 61 62 The accumulation of research during recent years supports the conclusion that insufficient or poor sleep serves as an exacerbating factor for most types of acute and chronic pain. Data on the relationship between pain and insomnia or short sleep

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18 duration suggests that there is a bidirectional relationship, but that sleep is a stronger and more reliable predictor of pain. 24 Sleep Disordered Breathing and Pain Although a great deal of research has demonstrated connections of pain with insomnia and sleep duration, less research has focused on SDB. Thus far, results have been mixed in terms of whether SDB is related to increased or decreased pain, perhaps owing to the fact that SDB involves both fragmentation and hypoxemia, which may exert opposite effects on pain sensitivity. C ross sectional studies have shown that the presence of OSA is linked to higher odds of pain and vice versa In one study, a decrease in minimum SaO 2 (arterial oxygen saturation) from 92% to 72% doubled the odds of pain (i.e., morning headaches, sleep disrupting pain, or chest pain while in bed) 17 Similarly, another study demonstrated an association between lower oxygen saturation and higher sensitivity during fibromyalgia tender point testing. 66 Interestingly, there was no relationship between AHI and pain in that study. In terms of comorbidity, it has been reported that p atients with fibromyalgia we re more than twice as likely to have a diagnosis of OSA than controls matched for age, sex, and body mass index ( BMI ) 71 Likewise, compared to those without chronic pain, a higher percentage of individuals with chronic musculoskeletal pain were classified as having clinically probable OSA based on a self report screening measure (48% vs. 69%). 47 Conversely, Nadeem and colleagues 46 found that 51% of individuals with OSA had comorbid chronic musculoskeletal pain. P rospective studies have shown that the presence of OSA predicts a greater likelihood of later developing temporomandibular disorder (TMD) 55 and blad der pain. 12

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19 In contrast, several studies have found that SDB is either not associated with pain or is associated with decreased pain. In a sample of individuals with OSA, the severity of OSA was not related to either pain intensity or pain duration. 3 A recent study showed mixed results such that more severe apn ea (as indicated by AHI and desaturation) predicted higher pain intensity but lower pain sensitivity (forearm pressure pain threshold) the next morning. 70 Results of this study also suggested a long term hypoalgesic effect of SDB, as more severe AHI and desaturation were associated with lower pain intensity over the preceding day and six months (based on retrospective reporting of pain on the Brief Pain Inventory and Chronic Pain Grade Scale, respectively). Smith and colleagues 60 found that while insomnia was related to hyperalgesia (i.e., decreased pain threshold during thermal and mechanical pain protocols), apnea was related to hypoalgesia (during the mechanical protocol only). Additionally, in a study of women with TMD, higher average pain was related to fewer res piratory event related arousals 19 Another experiment showed an association between lower minimum SaO 2 and increased analgesic response to remifentanil, suggesting that hypoxemia potentiates the analgesic effect of opioids. 18 The opioid receptors in response to hypoxemia may be the mechanism underlying this effect, which has been observed in other studies. 9 10 Opioid pain medications represent one mechanism by which pain may interact with SDB. Research has found that opioids h ave a dose dependent effect on SDB and other measures of sleep disturbance. 38 50 Opioids depress respiration and therefore represent a risk factor for SDB, particularly central sleep apnea (CSA). Recent

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20 summaries have concluded that opioid use increases the frequency of central apneic events (i.e., those caused by a cessation of respiratory effort due to central nervous system factors) but does not increase obstructive apneic events (i.e., those caused by loss of musc le tone or physical obstruction). 23 38 Contrary to earlier literature suggesting that individuals become tolerant to the respiratory depressive effects of opioids, newer evidence suggests that patients on long term opioid therapy continue to have increased rates of SDB. 69 Treatment studies have been mixed but provide some support for the impact of improved SDB on pain. A retrospective examination of veterans using CPAP did not find reductions in ether pain intensity or opioid consumption after 12 months. 35 However, i n pain free patients with OSA, treatment with PAP has been shown to decrease experimental pain sensitivity. 39 49 In one study 39 when PAP was temporarily stopped after two months of use, pain sensitivity increased nearly to baseline levels but went down again after resuming PAP use. These studies provide evidence that improving OSA leads to improvement in pain sensitivity at least in indivi duals free of chronic pain and parallel research findings on the negative implications of insomnia or short sleep duration for pain. Sleep Disordered Breathing, Insomnia, and Pain Given conflicting findings in the literature on SDB and pain, there is not yet a clear understanding of how sleep apnea and its component features interact with chronic pain. Smith and Finan 57 rec ently noted the physiological connections between hypoxemia and sleep fragmentation are distinct sleep disordered breathing phenotypes that may exert potentially differential effects on pain sensitivity and symptoms

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21 respiration and not examined the contribution of SDB related fragmentation to pain. Additionally, although SDB and insomnia frequently co occur in clinical samples, they are usually studied in isolation. However, they both present with some degree of fragmented and shortened sleep, and it remains unknown whether disruptions associated with each disorder have similar effects on pain. Aim s and Hypotheses The present study aim ed to address these gaps in the literature by examining the relationship of pain to both SDB and insomnia related sleep disturbance. In order to better understand the unique contributions of each disorder to pain, thi s study include d individuals with symptoms of SDB and insomnia. Aim 1: To investigate whether SDB related sleep fragmentation and nocturnal hypoxemia are uniquely related to pain. Hypothesis 1A: Controlling for total sleep time, sleep fragmentation will be positively related to pain intensity. Hypothesis 1B: Controlling for total sleep time, hypoxemia will be negatively related to pain intensity. Aim 2: To examine whether SDB and insomnia relat ed sleep fragmentation are uniquely related to pain. Hypothesis 2: Controlling for total sleep time and hypoxemia, both SDB and insomnia related sleep fragmentation will contribute to higher pain intensity. Given limited prior research linking SDB to pain no hypothesis is made as to whether SDB or insomnia will more strongly predict pain.

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22 Aim 3: To determine whether the presence of both SDB and insomnia creates an increased risk for pain. Hypothesis 3: Compared to individuals with either SDB or insomnia in isolation, individuals with both sleep disorders will report higher pain intensity and will be more likely to report having a chronic pain condition.

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23 CHAPTER 2 METHODS Participants Patients referred to the U niversity of F lorida (UF) Health Sleep Center for overnight PSG w ere recruited to participate in this study. Individuals were eligible for participation if they met the following criteria: a) 18 years of age or older, b) scheduled to undergo diagnostic PSG (i.e., measurement only, no PAP administration) or split night PSG (i.e., PAP administered during the latter portion of the night ) at the Sleep Center, c) access to a compute r with internet in order to complete daily sleep diaries, and d) able to read and write English. Individua ls w ere excluded from participation for the following reasons: a) previous and/or current treatment with PAP or b) undergoing treatment PSG (i.e., PAP administered the entire night). Individuals were not included/excluded on the basis of pain type or durat ion; the study aim ed to sample a broad range of pain types and the hypotheses d id not specify different predictions based on pain type. Similarly, potential participants could present with any type of sleep complaint, as the study s ought to examine continu ous measures of sleep related respiration and fragmentation that may be found in a variety of disorders. Measures Some data for this study w er e taken from measures routinely administered to patients undergoing PSG at the UF Health Sleep Center. Participan ts w ere also asked to complete additional measures on the night of the PSG and two weeks of sleep diaries following the PSG, as described below.

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24 Demographics and Medical History Basic background information collected from clinic paperwork include d sex, ag e, marital statu s, shift work, body mass index caffeine intake, medications, and medical history. Study specific measures ask ed for race, ethnicity, and education level. P olysomnography PSG was performed and scored by registered polysomnographic technologists according to American Academy of Sleep Medicine ( AASM ) scoring guidelines. Final diagnoses of OSA, CSA, parasomnias, periodic limb movements, or other sleep disorders was determined by phys icians at the UF Health Sleep Center. The following PSG channels were recorded: frontal, central, and occipital electroencephalography (used in the determination of sleep stage s ) ; electro oculography (measures eye movements associated with REM sleep) ; chin electromyography ( measures muscle tone, which decreases during REM ); electrocardiogram; respiration ( airflow [ nasal oral airflow by thermal sensor ], nasal pressure and respiratory effort; to detect apneas, hypopneas, and other sleep related breathing problems); arterial oxygen saturation ( SaO 2 ; to determine severity of sleep related breathing disorders) ; leg electromyography (measures leg movements associated with restless legs syndrome and periodic limb movement disorder); and body position (to deter mine whether apnea is exacerbated in the supine position). For a small number of patients, daytime assessment (Multiple Sleep Latency Test; MSLT) was also performed during the day following PSG in order to assess for narcolepsy. MSLT raw data were not used for the present study, though the results of MSLT were used by physicians in determining patient diagnoses.

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25 Sleep Questionnaires Clinic questionnaires provide d schedule, nighttime and daytime symptoms, and r esponses to the Epworth Sleepiness Scale (ESS). The ESS asks respondents to rate their propensity to fall asleep in various situations, such as while watching television. 36 Adequate internal consistency has been 37 Study participants were also ask ed to complete the Insomnia Severity Index (ISI) on the night of their PSG. The ISI is a brief measure used for insomnia screening and assessing treatment outcomes. Adequate internal consistency has been demonstrated among patients referred to a sleep diso 5 and among primary care patients, 2 5 Sleep Diaries Insomnia w as further assessed via two weeks of sleep diaries, in keeping with suggested guid elines. 56 In order to ease participant burden and encourage timely completion, participants received a daily email with a link to comp lete the sleep diary using the REDCap web application. The daily sleep diaries yield ed the following variables which were used in statistical analyses for aim 3 : time in bed total sleep time sleep onset latency, wake time after sleep onset, sleep efficie ncy (the percentage of The presence or absence of chronic insomnia w as determined from sleep diary data. Based on the International Classifica tion of Sleep Disorders Third Edition 2 and research diagnostic criteria 41 participants w ere d iagnosed with insomnia if they report ed difficulty initiating or maintaining sleep (a) lasting > 30 minutes 41 (b) occurring at least

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26 three nights per week 2 41 and (c) having persisted for at least three months 2 along with (d) a complaint of daytime distress or dysfunction due to insomnia 2 41 Pain Pain intensity and unpleasantness ratings were also obtained from the daily online survey. On the night of the PSG, participants were asked to complete the Medical College of Virginia (MCV) Pain Questionnaire. 54 68 The MCV provides visual analog scale (VAS) ratings of pa in intensity, pain unpleasantness, the impact of pain on the frustration, anger, fear). Participants also indicate d whether they ha d any chronic pain conditions, the name of the condition(s), and the location(s) of their pain during the last three months. Procedures All procedures were approved by the U F Institutional Review Board. Recruitment took place at the UF Health Sleep Center during the evening when patients arrive d for overnight PSG. A technologist ask ed the patient for permission to be approached by a researcher who describe d the study and provide d time for the patient to review the informed consent form. Individuals who consented to participate were asked to complete questionnaires (described above) and to complete daily sleep diaries online during the next two weeks. Compensation (a $25 gift card) was provided to participants after all procedures were completed. Statistical Analyses Analyses were performed using SPSS v2 2 .0 (SPSS Inc., Chicago, IL, USA ). For participants who underwent split night PSG ( n = 23), only data from the diagnostic

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27 portion of the PSG were used (e.g., the AHI reflects the value during the untreated portion of th e night, not the value during PAP titration). Aims 1 and 2 Aims 1 and 2 examine d 1) whether prominent characteristics of SDB (i.e., fragmentation and nocturnal hypoxemia) were uniquely related to pain and 2) whether SDB and insomnia related sleep fragment ation were uniquely related to pain. These aims were examined with a single hierarchical regression with the outcome variable of average pain intensity obtained via daily diaries. The predictors entered into the regression were obtained from PSG In order to control for total sleep time it w as entered in the first predictor block of the r egression. The second block add ed minimum SaO 2 as a measure of nocturnal hypoxemia, spontaneous arousals as a measure of insomnia related fragmentation, and the apnea hypo pnea index (AHI) as a measure of SDB related fragmentation. Prior research has shown that the AHI correlates strongly ( r = .97, p < .0001) with the number of respiratory arousals. 64 Aim 3 For the final aim, the relationship of SDB and insomnia to pain w as examined in several ways. To begin, sleep was examined from the standpoint of conventional diagnostic categories. Although sleep disorder diagnoses use somewhat arbitrary cutoffs, they are nonetheless useful in that they identify what is belie ved to represent clinically significant levels of sleep disturbance. Examining individuals who f all into these categories provide s a picture of the differences in pain experienced by clinical populations. We first used a one way ANOVA to compare the pain i ntensity (mean obtained from sleep diaries) of individuals with no sleep diagnosis and those meeting diagnostic criteria for OSA insomnia, comorbid OSA /insomnia or no sleep disorder

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28 Secondly, chi square was used to compare the likelihood of individuals in those same diagnostic categories having chronic pain (i.e., self report of pain more days than not during the preceding three months) Finally, as an exploratory analysis, we perform ed a cluster analysis. In contrast to using diagnostic categories, this data driven approach is useful for identifying potential subtypes w ithin the categories of SDB and insomnia. 45 73 A hierarchical agglomerative method squared Euclidian distance as a measure of similarity ) w as used in order to identify clusters of individuals based on a number of sleep variables including daytime sleepiness (ESS total score) and insomnia severit y (ISI total sc ore). PSG derived variables include d total sleep time apnea hypopnea index, duration of oxygen desatur ation less than or equal to 88% ( duration as a percentage of total sleep time) minimum SaO 2 spontaneous arousals, and REM latency. The f ollowing sleep diary variables derived from sleep diaries were included: total wake time, total sleep time, and sleep quality. After clusters were identified, they were validated by examining the distribution of sleep disorders across clusters. Cluster dif ferences were characterized through a series of ANOVAs on the clustering variables ANOVAs were also used to examine differences in continuous demographic variables (age, BMI) and pain related mood (depression, anxiety) A chi square analysis examined differences in the sex distribution across clusters A discriminant function analysis was performed to determine which variables had contributed most strongly to the formation of clusters. To address the hypothesis of this aim diffe rences in pain intensity were examined using an ANOVA and t he likelihood of each group endorsing the presence of chronic pain was examined using chi square.

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29 CHAPTER 3 RESULTS Participant Characteristics A total of 105 individuals were enrolled in the study, with 94 participants completing all study procedures Of the 105 enrolled participants, 41.9 0 % were male and 58. 10 % were female. The age of p articipants ranged fro m 18 to 81, with a mean age of 43.8 3 ( SD = 16. 5 2 ). Participants self identified as belonging to the following racial and ethnic groups: 7 0. 48 % White, 17.1 4 % Black / African American, 5.7 1 % Hispanic / Latino, and 5.7 1 % Asian / Asian American. A summary of other demographic characteristics (education, employment status, relationship status) can be found in Table 3 1 Sleep Characteristics Sleep Disordered Breathing Diagnoses of sleep related breathing disorders and other sleep disorders were made by physici ans at the UF Health Sleep Center. OSA represented the most common diagnosis, comprising 52.38% of the sample ( n = 55). A substantial proportion of individuals many of whom were referred for suspected sleep apnea were given no diagnosis ( n = 36, 34.29%). S ee table 3 2 for a complete list of diagnoses given to participants by physicians following PSG (and MSLT, if applicable). Insomnia Physicians infrequently made a diagnosis of insomnia ( n = 5 4.76 %) based on a PSG results T his is perhaps not surprising, given standards of practice dictating that insomnia is best evaluated through a detailed sleep history and sleep diaries rather than PSG 56 U sing sleep diaries and other self report data obtained for the

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30 study a diagnosis of chronic insomnia was assigned based on AASM criteria 2 and research diagnostic criteria 41 as specified above Using these criteria 22 participants ( 20.95 % ) wer e classified as having chronic insomnia. The duration of insomnia ranged from 6 months to 55 years with a mean of 10.89 years ( SD = 13.03). Over half of those with insomnia (59.09%) had a comorbid diagnosis of OSA from their physician ( n = 13, 12.38% of the total sample). There were no other comorbidities among individua ls determined to have chronic insomnia. Scores from the ISI indicated that 14.29% of all participants could be classified as not having clinically significant insomnia, while 40.95% had subthreshold insomnia, 35.24% had moderately severe clinical insomnia, and 9.52% had severe clinical insomnia. Pain C haracteristics Participants indicated the body regions in which they had pain more days than not over the preceding three months (see Table 3 3 ) On this basis, 80.00% of the sample was determined to have chr onic pain in at least one body region Lower back pain was the most common, with nearly half of the sample reporting chronic pain on the left side ( n = 51, 48.57%) and the right side (n = 49, 46.67%) Participants also reported what diagnoses, if any they had received with regard to their chronic pain. The most common diagnoses were musculoskeletal pain ( n = 30, 28.57%) and chronic headaches ( n = 26, 24.76%). These data are available in Table 3 4 Using the visual analog scales of the MCV (0 100 with 0 re presenting none ) participants reported their usual pain intensity ( M = 36.41 SD = 2 4.24 ) and usual pain unpleasantness over the preceding week ( M = 33.98, SD = 25.78). Table 3 5 ratings of lowest and highest pain as well as negative emotions that accompanied their pain.

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31 Aim s 1 and 2: C ontributions of F ragmentation and Hypoxemia to P ain A hierarchical regression was conducted t o de termine whether sleep fragmentation and nocturnal hypoxemia contributed uniquely to pain intensity ( above and b eyond total sleep time) As expected, PSG measured total sleep time was a signific ant predictor of pain intensity F (1, 95 ) = 4.00 p = .05 R 2 = .0 4 However, measures of fragmentation (AHI spontaneous arousals) and hypoxemia ( SaO 2 nadir ) were not significant predictors (see Table 3 6 ) Table 3 7 provides a correlation matrix for these variables. Ai m 3: Risk of Pain Across Sleep Disorders Diffe rences in P ain I ntensity by S leep D iagnosis A one way ANOVA revealed significant differences in pain intensity ( mean obtained from daily diaries) among individuals with OSA insomnia, comorbid OSA /insomnia, or no sleep diagnosis, F (3, 84) = 6.00, p < .01, = .18. Bonferroni corrected post hoc comparisons showed that the comorbid group had a significantly higher ( p < .001) pain intensity than the OSA group and the no diagnosis group ( p = .05). Group means are reported in table 3 8 Potential covariates were examined, and neither age nor BMI were significantly correlated with pain intensity ( p s > .05). However, there was a significant sex difference in pain intensity, t (86) = 2.57, p < .05 (male M = 20.25, SD = 21.07; female M = 33.42, SD = 25.95). Sex w as subsequently added to the model as a covariate In this ANCOVA, sex was significantly related to pain intensity, F (1, 83) = 4.26, p < .05, = .05. After controlling for sex, there continued to be a significant effect of diagnostic group on pain intensity, F (3, 83) = 5.14, p < .01, = .16. With the addition of sex as a

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32 covariate, Bonferroni corrected post hoc comparisons continued to show s ignificantly higher pain intensity in the comorbid group compared to those with OSA or no diagnosis ( p s < .01 ). Additionally, the comorbid group had higher pain intensity compared to the insomnia group ( p < .05). Covariate adjusted means are reported in ta ble 3 8 Likelihood of Chronic P ain by S leep D iagnosis The prevalence of chronic pain was 81.25% among the subset of participants ( n = 96) with either OSA, insomnia, comorbid OSA/insomnia, or no sleep disorder The prevalence of chronic pain within diagnos tic groups was as follows: OSA 76.19%, insomnia 88.89%, comorbid OSA/insomnia 100%, and no diagnosis 78.13%. B ased on a chi square analysis, t here was no significant difference in the likelihood of individuals in these diagnostic categories having chronic pain, 2 (3) = 4.26, p = .24 w = .43 Identification and V alidation of Sleep Disorder C lusters The agglomeration coefficients for the hierarchical cluster analysis showed a large increase between four and five clusters. We therefore used a four cluster solution which appeared based on group means on the variables used to create clusters to roughly c orrespond to the following sleep diagnostic groups: 1) OSA, n = 48; 2) insomnia, n = 11; 3) comorbid OSA/insomnia, n = 12; and 4) other/no diagnosis, n = 26. Figure 3 1 shows the profiles of the four clusters with the standardized means plotted for each of the variables. A chi square was performed to determine if the distribution of sleep disorders differed across clusters. The chi square was significant, 2 (12) = 42.26, p < .001. Inspection of the standardized residuals confirmed that each of the clusters had higher than expected frequencies of the diagnoses corresponding to the labels given them (e.g., cluster 2 had significantly more individuals with insomnia than expected). Figure 3 2 shows the frequency of sleep disor ders in each cluster.

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33 To better characterize the clusters and understand which variables contributed to the formation of the clusters, we conducted a series of one way ANOVAs on each of the variables. All of these ANOVAs were significant ( p s < .05). Bonfer roni correct ed pairwise comparisons showed that cluster 2 (insomnia) had significantly lower PSG and diary total sleep time and higher diary total wake time than all other clusters ( p s < .01). Cluster 3 (comorbid OSA/insomnia) was unique in terms of the se verity of apnea related variables; this cluster demonstrated significantly worse AHI, percent time below 88% SaO 2 and minimum SaO 2 than all other groups ( p s < .001). Finally, cluster 4 (other/no diagnosis) reported higher levels of daytime sleepiness on t he ESS than all other groups ( p < .05). Cluster means for all variables are presented in Table 3 9 A discriminant function analysis was also performed in order to determine which variables had the greatest contribution to the clusters. Function 1 was sign ificant ( 2 [33] = 255.96, p < .001) with a canonical correlation of .85. Based on the standardized canonical coefficients, the AHI w as the strongest predictor of cluster membership ( .77), followed by diary total wake time (.48), minimum SaO 2 (.47), daytime sleepiness (.46), and PSG total sleep time ( .39). We further characterized the clusters by examining differences in demographic variables (sex, age, BMI) and pain related mood (depression, anxiety). A chi square analysis indicated a signif icant difference in sex distribution across clusters, 2 (3) = 19.10 p < .001 w = 53 Based on standardized res iduals, men were overrepresented in the OSA cluster and underrepresented in the insomnia and other/no diagnosis clusters. Figure 3 3 shows the frequencies of males and females across clusters. An ANOVA showed that age also differed across clusters, F (3, 93) = 3.48, p < .05, = .10,

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34 with the other /no diagnosis cluster being on average significantly younger than the OSA cluster ( p < .05; Bonferroni corrected for this and all subsequent comparisons). The ANOVA for BMI was also significant, F (3, 93) = 4.48, p < .01, = .13, and post hoc analyses showed that the comorbid OSA/insomnia cluster had a significantly higher BMI than the OSA and other/no diagnosis clusters ( p Pain related d epression differed significantly among the clusters, F (3, 88) = 5.55, p < .01, = .16, as did pain related anxiety, F (3, 90) = 5.82, p < .01, = .16. The OSA cluster displayed lower dep ression than the insomnia and OSA/insomnia clusters ( p s < .05). Individuals in the OSA cluster were also less anxious compared to all other clusters ( p means for age, BMI, depression, and anxiety are available in Table 3 10 Differences in P ain Intensity by C luster An ANOVA demonstrated that the clusters differed significantly on pain intensity (mean obtained from daily diaries), F (3, 93 ) = 6.21 p < .01, = .1 7. Specifically, Bonferroni corrected post hoc comparisons showed that the OSA cluster had significantly lower pain ( M = 18.19, SD = 19.76 ) than the insomnia ( M = 45.70, SD = 31.21 ) and other/no diagnosis clusters ( M = 33.99, SD = 22.17 ). The pain intensity of the comorbid OSA/insomnia cluster did not differ from other groups ( M = 35.02, SD = 26.00). Potential covariates were examined for inclusion in an ANCOVA Among the participants included in the cluster analysis ( n = 97), pain intensity was unrelated to age ( r = .14, p = .18) but significantly related to BMI ( r = .21, p < .05). Additionally, females reported higher average pain intensity, t (95) = 2.75, p < .01 (male M = 19.74, SD = 21.04; female M = 33.17, SD = 25.31). When BMI and sex were added to the model as

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35 covariates, the clusters continued to differ significantly on pain intensity F (3, 91 ) = 3.88, p < .0 5 = .1 1 In Bonferroni corrected post hoc comparisons, the only significant ( p < .05) group comparison that remained after the inclusion of covariates was a higher pain i ntensity for the insomnia cluster (cov ariate adjusted M = 42.10, S E = 6.95 ) compared to the OSA cluster (covariate adjusted M = 19.60, S E = 3.34 ).

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36 Table 3 1 Demographic characteristics for all enrolled participants ( N = 105). n % Mean SD Age --43.83 16.52 Male 44 41.90 Female 61 58.10 White 74 70.48 Black or African American 18 17.14 Hispanic or Latino 0 6 0 5.71 Asian or Asian American 0 6 0 5.71 Education Did not complete high school 0 5 0 4.76 High school 42 40.00 16 15.24 20 19.05 13 12.38 Doctoral degree 0 9 0 8.57 Employment status Working 57 54.29 Disabled 15 14.29 Student 12 11.43 Retired 10 0 9.52 Unemployed (looking for work) 0 5 0 4.76 Homemaker 0 4 0 3.81 Relationship status Married 51 48.57 Single 22 20.95 Divorced 12 11.43 Cohabiting 10 0 9.52 Widowed 0 5 0 4.76 Dating 0 4 0 3.81

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37 Table 3 2 Frequencies of sleep disorders. Diagnosis n % Obstructive sleep apnea 55 52.38 Insomnia (diaries) 22 20.95 Insomnia 5 0 4.76 Periodic limb movement disorder 4 0 3.81 Idiopathic h ypersomnia 3 0 2.86 Narcolepsy 2 0 1.90 Restless legs syndrome 2 0 1.90 REM behavior disorder 1 0 0.95 Central sleep apnea 1 0 0.95 No diagnosis 3 6 34.29 Note: Five participants received two diagnoses from their physicians. Frequency of insomnia as classified using sleep diaries ( based on AASM and research diagnostic criteria ) All other categories represent diagnoses made by Sleep Center physicians following polysomnography (and M ultiple S leep L atency T est if applicable) Table 3 3 Frequency of chronic pain across body regions. Left Right n % n % Hand 18 17.14 19 18.10 Arm 10 9.52 14 13.33 Shoulder 29 27.62 29 27.62 Neck 28 26.67 30 28.57 Head 20 19.05 21 20.00 Face 0 5 0 4.76 0 4 0 3.81 Chest 10 0 9.52 10 0 9.52 Stomach 0 8 0 7.62 10 0 9.52 Upper back 18 17.14 19 18.10 Lower back 51 48.57 49 46.67 Pelvis 13 12.38 12 11.43 Hip 20 19.05 18 17.14 Knee 25 23.81 26 24.76 Leg ( other than knee) 19 18.10 16 15.24 Foot 30 28.57 26 24.76 Ankle 18 17.14 15 14.29

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38 Table 3 4 Chronic pain diagnoses reported by participants. Diagnosis n % Musculoskeletal pain 30 28.57 Chronic headaches 26 24.76 Osteoarthritis/Degenerative Joint Disease 19 18.10 Neuropathic pain 14 13.33 Chronic Fatigue Syndrome 0 6 0 5.71 Irritable Bowel Syndrome 0 6 0 5.71 Fibromyalgia 0 5 0 4.76 Rheumatoid Arthritis 0 4 0 3.81 Chronic pelvic pain 0 3 0 2.86 Temporomandibular disorder 0 3 0 2.86 Inflammatory Bowel Disease 0 2 0 1.90 Cancer pain 0 2 0 1.90 Spondylitis 0 2 0 1.90 Pain due to an other medical condition 0 4 0 3.81 Table 3 5 MCV r atings of pain and accompanying negative emotions for the preceding week. Scale* Usual Mean ( SD ) Lowest Mean ( SD ) Highest Mean ( SD ) Pain intensity 36.41 (24.24) 21.32 (19.66) 52.60 (27.99) Pain unpleasantness 33.98 (25.78) 22.72 (21.81) 52.47 (29.92) Depression 24.79 (29.62) --Anxiety 31.30 (30.85) --Frustration 42.91 (31.93) --Anger 26.57 (30.76) --Fear 20.86 (27.56) --* All scales rated using a 0 100 visual analog scale (0 = none). Table 3 6 Hierarchical regression predicting average pain intensity. PSG derived predictors R 2 change F change B SE B t p Model 1 .0 4 4.00 Total sleep time .0 8 .04 .2 0 2. 00 .0 5 Model 2 .0 1 18 Total sleep time .0 7 .0 4 20 1.78 .0 8 Minimum SaO 2 21 .37 .0 8 58 .5 7 Spontaneous arousals/h ou r .10 .3 8 .0 3 2 7 79 Apnea hypopnea index .02 .1 9 .01 .0 8 .9 3

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39 Table 3 7 Correlation matrix for p ain intensity and polysomnography variables. Pain intensity Total sleep time Minimum SaO 2 Spontaneous arousals/h ou r Total sleep time .2 0 ---Minimum SaO 2 .04 .1 1 --Spontaneous arousals/h ou r .11 .37** .00 -Apnea hypopnea index .04 .0 2 .6 1 ** .2 2 * p < .05 ** p < .001 Table 3 8 Pain characteristics by sleep diagnosis Diagnosis n Pain intensity Covariate adjusted pain intensity* Chronic pain Mean SD Mean SE n % OSA 42 18.89 19.33 20.28 3.60 32 0 76.19 Insomnia 0 9 32.91 26.03 28.24 7.79 0 8 0 88.89 OSA/insomnia 13 49.17 25.99 48.56 6.21 13 100.00 None 32 28.31 24.92 28.10 4.39 25 0 78.13 Note: OSA = obstructive sleep apn ea. Means controlling for participant sex as a covariate. Table 3 9 M eans for variables used in hierarchical cluster analysis. Variable Cluster 1 (OSA) Mean ( SD ) Cluster 2 (Insomnia) M ean ( SD ) Cluster 3 (OSA/insomnia) Mean ( SD ) Cluster 4 (Other / no diagnosis) Mean ( SD ) ESS 7.18 (4.12) 8.09 (5.44) 10.25 (4.45) 14.8 0 (4.06) ISI 10.85 (5.01) 17.36 (5.25) 14.25 (7.18) 17.15 (4.28) TST (PSG) 367.27 (54.22) 301.09 (103.64) 415.5 0 (33.97) 403.11 (43.8) AHI 11.8 0 (12.82) 2.39 (2.75) 43.2 0 (24.84) 2.04 (2.24) Time <88% 4.14 (6.88) 0.9 0 (1.78) 19.89 (26.12) 0.17 (0.12) Minimum SaO 2 85.64 (6.49) 90.54 (4.41) 70.75 (10.73) 90.73 (3.21) Spont. Arousals 12.17 (8.97) 10.4 0 (7.14) 8.35 (3.59) 7.48 (3.46) REM latency 127.54 (77.41) 211.9 0 (106.21) 167.91 (88.91) 107.19 (41.57) TWT (diary) 49.37 (28.08) 194.41 (92.39) 79.27 (67.4) 77.08 (35.48) TST (diary 433.4 0 (58.28) 321.8 0 (83.21) 403.57 (108.41) 419.12 (38.66) Quality 3.15 (0.59) 2.51 (0.65) 3.01 (0.78) 2.86 (0.43) Notes: ESS = Epworth Sleepiness Scale, ISI = Insomnia Severity Index, TST = total sleep time (minutes) PSG = polysomnography, AHI = apnea/hypopnea index, Time <88% = percentage of total sleep time with arterial oxygen saturation below 88%, Spont. Arl. = spontaneous arousals per hour REM latency = rapid eye movement latency (minutes) TWT = total wake tim e (minutes) Quality = sleep quality rating.

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40 Table 3 10 Cluster m eans for demographic and mood variables. Variable Cluster 1 (OSA) Mean ( SD ) Cluster 2 (Insomnia) M ean ( SD ) Cluster 3 (OSA/insomnia) Mean ( SD ) Cluster 4 (Other/no diagnosis) Mean ( SD ) Age 47.94 (17.85) 48.27 (14.90) 43.25 (11.93) 35.88 (14.12) BMI 32.58 (8.19) 36.34 (10.35) 39.81 (7.02) 29.95 (8.10) Depression 13.70 (18.82) 41.36 (36.46) 43.00 (40.22) 31.60 (32.83) Anxiety 18.55 (24.04) 44.36 (34.28) 46.27 (36.40) 40.68 (29.85) Notes: BMI = body mass index

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41 Figure 3 1 Standardized means for variables used in hierarchical cluster analysis. Notes: ESS = Epworth Sleepiness Scale, ISI = Insomnia Severity Index, TST = total sleep time, PSG = polysomnography, AHI = apnea/hypopnea index, Time <88% = percentage of total sleep time with arterial oxygen saturation below 88%, Spont. Arl. = spontaneous arousa ls per hour REM = rapid eye movement, TWT = total wake time, Quality = sleep quality rating. -2.00 -1.50 -1.00 -.50 .00 .50 1.00 1.50 2.00 ESS ISI TST (PSG) AHI Time <88% Min. SaO2 Spont. Arl. REM latency TWT (diary) TST (diary) Quality Z score Cluster 1 (OSA) Cluster 2 (Insomnia) Cluster 3 (OSA/insomnia) Cluster 4 (Other/no diagnosis)

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42 Figure 3 2 Frequency of sleep disorders in groups identified through hierarch ical cluster analysis Note: OSA = obstructive sleep apnea 0 5 10 15 20 25 30 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Frequency OSA Insomnia OSA/insomnia Other None

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43 Figure 3 3 Sex distribution across groups identified through hierarchical cluster analysis. Note: OSA = obstructive sleep apnea 0 5 10 15 20 25 30 Cluster 1 (OSA) Cluster 2 (Insomnia) Cluster 3 (OSA/insomnia) Cluster 4 (Other/no diagnosis) Frequency Male Female

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44 CHAPTER 4 DISCUSSION Contributions of Sleep Characteristics and Disorders to Pain This study investigated the unique contributions of sle ep disorders and their distinct features, particularly symptoms of SDB, to pain. There was a high prevalence of chronic pain in this sample (80.00%), and perhaps in part due to this high base rate of chronic pain the likelihood of having chronic pain did n ot differ by sleep diagnosis. However, controlling for participant sex, individuals with comorbid OSA/insomnia reported an average pain intensity that was 20 points (out of 100 ) higher than individuals with insomnia or no diagnosis and 28 points higher tha n those with OSA. This magnitude of difference is likely to be noticeable and meaningful to patients 20 and as such would have implications for clinical care (discussed below). The results of this study thus indicate that there is a high likelihood that any patient being referred for sleep evaluation h as chronic pain but that patients who meet criteria for both OSA and insomnia may have significantly higher pain than their counterparts with a single sleep disorder. A small number of prior studies have found mixed associations of SDB with pain intensity or experimental pain sensitivity, including indications that nocturnal hypoxemia and SDB related fragmentation may differ in their relationships to pain. Our study found no relationship between SDB related variables (AHI, SaO 2 nadir ) and clinical pain int ensity in a sample of individuals referred to a sleep clinic Additionally, a measure of non apnea fragmentation (spontaneous arousals) was not related to pain intensity. Although these specific aspects of sleep were unrelated to pain, lower PSG measured t otal sleep time predicted higher pain intensity.

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45 The hierarchical cluster analysis identified four groups that coalesced roughly arou nd sleep diagnostic categories. However, t his classification method provided groups that were empirically derived from a v ariety of objective and self report measures rather than being based on arbitrary cutoffs used in traditional diagnoses. An examination of the sleep, demographic, and mood characteristics of the four groups allowed us to better characterize the unique feat ures of these groups beyond a diagnostic label. Specifically, cluster 1 (OSA) had a disproportionate number of men and reported lower pain related anxiety and depression than the other groups. The insomnia group (cluster 2) was notable for having the least amount of sleep on both objective and subjective measures as well as for consisting almost entirely of females. Cluster 3 (comorbid OSA/insomnia) had more severe apnea than all other groups, including cluster 1. This group also had a higher average BMI F inally, cluster 4 (other/no diagnosis) was predominantly female, younger than the other groups, and reported the highest daytime sleepiness. The latter finding perhaps represents the fact that this group contained four of the five individuals diagnosed wit h idiopathic hypersomnia or narcolepsy In terms of pain intensity, after controlling for confounding variables ( BMI and participant sex ) only one significant group difference was evident the insomnia cluster reported pain intensity 22.50 points higher (o n a 100 point scale) than the OSA cluster As noted above the insomnia cluster had the lowest total sleep time, and the higher pain intensity among this group therefore mirrors the results of the hierarchical regression which found that total sleep time w as a significant predictor of pain intensity.

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46 C linical Implications In this sample of patients referred to a sleep center, most were diagnosed with OSA (52.38%). Although physicians diagnosed only 4.76% with insomnia, the inclusion of sleep diaries in this study allowed us to classify 20.95% as meeting criteria for chronic insomnia. Of particular interest for this study, we found that 12.38% of participants had comorbid OSA/insomnia. Although these results may only generalize to other sleep clinics and not the general population our rates of comorbidity fall within the ranges previously reported 1 52 Specifically, we found that 23.64% of participants with OSA also met criteria for insomnia, and 59.09% of those with insomnia also m et criteria for OSA Most individuals with insomnia were not identified as such though the standard sleep center evaluation, suggesting the potential importance of more thorough insomnia assessment through either screening measures or sleep diaries when feasible. Effective treatments exist for both OSA and insomnia, but it is importan t to identify whether both disorders are present in an individual, as this may complicate treatment I nitial and middle insomnia are associated with poorer PAP adherence 7 72 and it may therefore be necessary to treat insomnia concurrently. Additionally, insomnia comorbid with OSA is likely to require treatment, as pr evious research has shown that insomnia (with the possible exception of middle insomnia) tend s to persist and may even emerge (in the case of late insomnia) with PAP treatment. 7 The high frequency of chronic pain (80.00%) in a population referred for sleep evaluation is striking but nonetheless aligns with what is already known about high rates of chronic pain in patients with sleep disorders. 24 M usculoskeletal pain (28.57%) and chronic headaches (24.76%) were the most common types of pain reported In keeping with population norms 48 the back was the most frequently reported location of chronic

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47 pain (48.57%). The high frequency of chronic pain in this sample is of clinical importance for treating sleep disorders, as pain intensity has been shown to predict PAP nonadherence. 35 In the present study the majority of patients diagnosed with OSA had chronic pain ( 81.82 %). Moreo ver, individuals with insomnia in addition to OSA had significantly higher average pain intensity than individuals with only OSA Thus, while pain may be a barrier to PAP adherence in many patients with OSA, adherence is likely to be especially challenging among individuals with comorbid OSA/insomnia. Improved management of a comorbid pain co ndition would therefore be indicated when pain impedes adherence to treatment for a sleep disorder. Similarly, initial or concurrent treatment of comorbid insomnia may aid adherence to OSA treatment, though this remains to be tested in clinical trials. 52 Limitations and Future Directions The results of this study may apply only to the population from which they were drawn a tertiary sleep center and may not generalize to the broader population or even to patients seen in ot her medical sett ings Specific chronic pain diagnoses used to characterize the sample were solicited from participants and were not verified by providers or with medical records However, diagnoses were collected only in order to characterize the sample, and determination of the presence of chronic pain was accomplished through separate questions ascertaining whether the participant had experienced persistent pain in any body part over the preceding three months. This study did not explore whether assoc iations between slee p features and pain are modified by the type of pain condition In our broad clinical sample, w e did not find evidence that SBD related fragmentation and hypoxemia are associated with pain intensity. However, future researchers may be interested in

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48 exploring relationships between SDB characteristics and pain in specific pain populations, such as individuals with spinal conditions noted to have higher rates of OSA than the general population. 44 Such investigations may shed light on the mechanisms of the sleep pain interaction. The present stud y did not find associations of pain to specific SDB symptoms, but it did reveal that individuals who had comorbid OSA/insomnia had more intense pain than individuals with either disorder in isolation. This population deserves further study in order to bett er understand what mechanisms might account for the higher pain associated with comorbid OSA/insomnia. Additionally, a n important area for future research concerns the clinical care of patients with comorbid OSA/insomnia. Treatment may be complicated by th e presence of a comorbid sleep disorder, and future research (such as an ongoing clinical trial by Ong and colleagues 14 ), should aim to develop empiricall y based guidelines regarding whether comorbid OSA and insomnia are best treated concurrently or in succession, including the optimal order for treatment, which may depend on the type of insomnia (i.e., initial, middle, or late). Conclusions This investigation found that PSG measured total sleep time, but not measures of sleep fragmentation or hypoxemia, was related to pain intensity among individuals referred to a sleep center. The majority of participants reported chronic pain. Although the likelihood of chronic pain did not differ by sleep disorder, individuals with comorbid OSA/in somnia had significantly higher average pain than their counterparts with OSA, insomnia, or no sleep diagnosis. This suggests that the most severe pain is likely t o be found in individuals whose sleep is disturbed in a greater number of ways at least when looking at t he most common sleep disorders rather than a specific pain to type

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49 of sleep disturbance relationship This study highlights the importance of identifying individuals with comorbid OSA/insomnia as effective treatment of their sleep problems may be complicated by the existence of comorbid sleep disorders 7 72 and chronic pain 35 which is likely to be present in this population.

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56 BIOGRAPHICAL SKETCH Jennifer Mundt earned her B.A. in psychology with a minor in sociology from Seattle Pacific University in 2005. She subsequently attended Central Washington University, earning an M.S. in m ental h ealth c ounseling with additional certification in school counseling. Her master's thesis research was on the topic of empathy and bullying among adolescents. After graduating in 2008, she worked as a Research Associat e for a Seattle e learning company. Her work there involved project management and study coordinat ion for CDC and NIH funded projects related to chronic pain, including a CBT based self help program for patients with chronic low back pain and continuing e ducation for health professionals on topics related to pain management. In 2012, she began studying clinical health psychology at the University of Florida Research and Behavioral Health. She completed her Ph.D. in 2017. Her research and clinical interests are focused on the area of health and chronic illness, particularly the interaction of sleep and pain.