<%BANNER%>

Predictors of Clinical Pain in Fibromyalgia

Permanent Link: http://ufdc.ufl.edu/UFE0042990/00001

Material Information

Title: Predictors of Clinical Pain in Fibromyalgia Examining the Role of Sleep
Physical Description: 1 online resource (36 p.)
Language: english
Creator: ANDERSON,RYAN J
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: CHRONIC -- CLINICAL -- FIBROMYALGIA -- SLEEP
Clinical and Health Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Clinical pain intensity can be highly variable across patients with fibromyalgia syndrome (FM), and past research has shown that a predictive model of spatial extent of pain, negative mood, and after-sensation accounts for 40-50% of the variance in clinical pain. Poor sleep is hypothesized to have a causal relationship with pain, and over 75% of individuals with FM report disturbed sleep. In the present study, I hypothesized that the existing model of clinical pain in FM would be replicated, and the addition of sleep measures would significantly increase the predictive ability of the model. Fifty-nine adults with FM participated in the study. Participants indicated the extent of their pain by shading areas on a body diagram, and a sum of pain areas was calculated. Negative mood was measured with the Beck Depression Inventory?II (BDI-II) and State Trait Anxiety Inventory Form Y-1 (STAI). As a proxy measure of central sensitization, ratings of painful after-sensation were obtained 30 seconds following stimulation with a series of heat pulses. Participants also wore an actigraph and kept sleep and pain diaries for 14 days. Data were averaged over the 14 days and 4 measures of sleep were obtained for each participant: average total sleep time and average total wake time (i.e. duration of insomnia), each measured objectively (actigraph) and subjectively (diary). Hierarchical linear regression was used to determine the variance in clinical pain (average of 14 daily pain ratings) accounted for by each predictor. Greater spatial extent (R2=.21), higher after-sensation ratings (R2=.07), and higher BDI-II total scores (R2=.06) were each significant predictors of more clinical pain and together accounted for 34.0% of its variance. Decreased objective total sleep time was the only sleep variable to account for significant additional variance (R2=.09). The results replicate previous research and suggest that spatial extent of pain, central sensitization, and depressed mood play important roles in FM pain. The data provided limited support for the hypothesis that measures of sleep would improve the predictive ability of the model. Future research should examine the daily, intra-individual variation between sleep and pain in FM to better clarify this relationship.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: 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.
Statement of Responsibility: by RYAN J ANDERSON.
Thesis: Thesis (M.S.)--University of Florida, 2011.
Local: Adviser: Robinson, Michael E.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-04-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0042990:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042990/00001

Material Information

Title: Predictors of Clinical Pain in Fibromyalgia Examining the Role of Sleep
Physical Description: 1 online resource (36 p.)
Language: english
Creator: ANDERSON,RYAN J
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: CHRONIC -- CLINICAL -- FIBROMYALGIA -- SLEEP
Clinical and Health Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Clinical pain intensity can be highly variable across patients with fibromyalgia syndrome (FM), and past research has shown that a predictive model of spatial extent of pain, negative mood, and after-sensation accounts for 40-50% of the variance in clinical pain. Poor sleep is hypothesized to have a causal relationship with pain, and over 75% of individuals with FM report disturbed sleep. In the present study, I hypothesized that the existing model of clinical pain in FM would be replicated, and the addition of sleep measures would significantly increase the predictive ability of the model. Fifty-nine adults with FM participated in the study. Participants indicated the extent of their pain by shading areas on a body diagram, and a sum of pain areas was calculated. Negative mood was measured with the Beck Depression Inventory?II (BDI-II) and State Trait Anxiety Inventory Form Y-1 (STAI). As a proxy measure of central sensitization, ratings of painful after-sensation were obtained 30 seconds following stimulation with a series of heat pulses. Participants also wore an actigraph and kept sleep and pain diaries for 14 days. Data were averaged over the 14 days and 4 measures of sleep were obtained for each participant: average total sleep time and average total wake time (i.e. duration of insomnia), each measured objectively (actigraph) and subjectively (diary). Hierarchical linear regression was used to determine the variance in clinical pain (average of 14 daily pain ratings) accounted for by each predictor. Greater spatial extent (R2=.21), higher after-sensation ratings (R2=.07), and higher BDI-II total scores (R2=.06) were each significant predictors of more clinical pain and together accounted for 34.0% of its variance. Decreased objective total sleep time was the only sleep variable to account for significant additional variance (R2=.09). The results replicate previous research and suggest that spatial extent of pain, central sensitization, and depressed mood play important roles in FM pain. The data provided limited support for the hypothesis that measures of sleep would improve the predictive ability of the model. Future research should examine the daily, intra-individual variation between sleep and pain in FM to better clarify this relationship.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: 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.
Statement of Responsibility: by RYAN J ANDERSON.
Thesis: Thesis (M.S.)--University of Florida, 2011.
Local: Adviser: Robinson, Michael E.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-04-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0042990:00001


This item has the following downloads:


Full Text

PAGE 1

1 PREDICTORS OF CLINICAL PAIN IN FIBROMYALGIA: EXAMINING THE ROLE OF SLEEP By RYAN JAMES ANDERSON A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2011

PAGE 2

2 2011 Ryan James Anderson

PAGE 3

3 ACKNOWLEDGEMENTS I sincerely thank my chair, Dr. Michael Robinson, and members of my supervisory committee (Drs. David Janicke, Patricia Durning and Vonetta Dotson) for their mentoring. I also thank Dr. Christina McCrae for allowing me to analyze the data, and to the National Institute of Health for their financial support. This project was sponsored by a grant from the National Institutes of Health (R01 AR055160).

PAGE 4

4 TABLE OF CONTENTS page ACKNOWLEDGEMENTS ............................................................................................... 3 LIST OF TABLES ............................................................................................................ 5 LIST OF FIGURES .......................................................................................................... 6 LIST OF ABBREVIATIONS ............................................................................................. 7 ABSTRACT ..................................................................................................................... 8 CHAPTER 1 INTRODUCTION .................................................................................................... 10 2 METHODS .............................................................................................................. 13 Participants ............................................................................................................. 13 Clinical Pain Intens ity .............................................................................................. 14 TP Testing .............................................................................................................. 14 Body Pain Areas (SLAP) ......................................................................................... 14 TSSP and AS .......................................................................................................... 15 Sleep ....................................................................................................................... 16 Subjective Sleep: Diaries .................................................................................. 16 Objective Sleep: Actigraph ............................................................................... 16 Negative Mood ........................................................................................................ 17 Statistical Analyses ................................................................................................. 18 3 RESULTS ............................................................................................................... 21 4 DISCUSSION ......................................................................................................... 23 Central Sensitization and AS .................................................................................. 23 SLAP ....................................................................................................................... 24 Negative Mood ........................................................................................................ 25 Sleep ....................................................................................................................... 26 Clinical Application .................................................................................................. 28 Limitations ............................................................................................................... 29 Conclusion and Futur e Directions ........................................................................... 29 LIST OF REFERENCES ............................................................................................... 31 BIOGRAPHICAL SKETCH ............................................................................................ 36

PAGE 5

5 LIST OF TABLES Table page 2 1 Demographic characteristics of the sample (N = 59) .......................................... 19 3 1 Clinical characteristics of the sample .................................................................. 22 3 2 Results of the hierarchical regression predicting clinical pain intensity ............... 22

PAGE 6

6 LIST OF FIGURES Figure page 2 1 Body diagram used by participants to shade areas of current pain ................... 20

PAGE 7

7 LIST OF ABBREVIATION S AS After sensation BDI Beck Depression Inventory C Degrees Celcius FM Fibromyalgia syndrome NMDA N Methyl D aspartic acid SD Standard deviation SLAP Sum of local areas of pain STAI State Trait Anxiety Inventory TP Tenderpoint TSSP Temporal summation of second pain TST Total sleep time TWT Total wake time

PAGE 8

8 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial F ulfillment of the Requirements for the Degree of Master of Science PREDICTORS OF CLINICAL PAIN IN FIBROMYALGIA: EXAMINING THE ROLE OF SLEEP By Ryan J ames Anderson May 2011 Chair: Michael E. Robinson Major: Psychology Clinical pain intensity can be highly variable across patients with fibromyalgia syndrome (FM), and past research has shown that a predictive model of spatial extent of pain, negative mood, and after sensation accounts for 4050% of the variance in clinical pain Poor sleep is hy pothesized to have a causal relationship with pain, and over 75% of individuals with FM report disturbed sleep. In the present study, I hypothesized that the existing model of clinical pain in FM would be replicated, and the addition of sleep measures woul d significantly increase the predictive ability of the model Fifty nine adults with FM participated in the study. Participants indicated the extent of their pain by shading areas on a body diagram, and a sum of pain areas was calculated. Negative mood was measured with the Beck Depression Inventory II (BDIII) and State Trait Anxiety Inventory Form Y 1 (STAI). As a proxy measure of central sensitization, ratings of painful after sensation were obtained 30 seconds following stimulation with a series of heat pulses. Participants also wore an actigraph and kept sleep and pain diaries for 14 days. Data were averaged over the 14 days and 4

PAGE 9

9 measures of sleep were obtained for each participant: average total sleep time and average total wake time ( i.e. duration o f insomnia) each measured objectively (actigraph) and subjectively (diary). Hierarchical linear regression was used to determine the variance in clinical pain (average of 14 daily pain ratings) accounted for by each predictor. Greater spatial extent (R2= .21 ), higher after sensation ratings (R2=.07 ), and higher BDI II total scores (R2=.06) were each significant predictors of more clinical pain and together accounted for 34.0% of its variance. Decreased o bjective total sleep time was the only sleep variable to account for significant additional variance (R2=.09 ). The results replicate previous research and suggest that spatial extent of pain, central sensitization, and depressed mood play important roles in FM pain. The data provided limited support for the hypothesis that measures of sleep would improve the predictive ability of the model. Future research should examine the daily, intraindividual variation between sleep and pain in FM to better clarify this relationship.

PAGE 10

10 CHAPTER 1 INTRODUCTION Fibromyalgia syndrome ( FM ) is defined by chronic, widespread musculoskeletal pain across the four body quadrants and decreased pressure pain thresholds The American College of Rheumatology identif ied 9 bilaterally paired tender point (TP) sites to charac terize this mechanical allodynia (a perception of pain in response to a normally nonpainful stimulus like gentle touch) ; a diagnosis of FM is considered when at least 11 of these 18 points are endorsed as painful during palpation (Wolfe, et al., 1990) While TP counts have diagnostic utility for FM their correlation with clinical pain is low to modest in both general population (Croft, et al., 1996; Croft, Schollum, & Silman, 1994) and FM samples (Nicassio, Weisman, Schuman, & Young, 2000; Staud, Price, Robinson, & Vierck, 2004; Wolfe, 1997) Clinical pain intensit y can be conceptualized as a rating of overall bodily pain, and has strong associations with health care usage and quality of life. D ue to the complex interplay between physical, psychological, and social factors involved with this condition, clinical pain intensity can be highly variable across FM patients and is difficult to predict. In an effort to identify predictors of clinical pain and thereby elucidate potential treatment targets Staud et al. (Staud, et al., 2004; Staud, Robinson, et al., 2003) found that n egative mood after sensation (AS) ratings taken duri ng experimental induction of second pain [using a temporal summation of second pain (TSSP) protocol] and a measure of spatial extent of pain [ the sum of local areas of pain (SLAP) ] were significant predictors of clinical pain intensity in FM patients and together accounted for 40 50% of its variance. T he results suggest that physiological and psychological variables are relevant to FM clinical pain. Second pain is the delayed sensation of pain

PAGE 11

11 associated with slower conducting C fibers, and AS is a type of second pain often conceptualized as pain rating taken 15 30 seconds following a pain stimulus. TSSP refers to an increased perception of second pain evoked by repetitive noxious stimuli at constant intensities, and has been found to be mediated by central nervous system processes (Price, Hu, Dubner, & Gracely, 1977) Thus, TSSP and its maintenance (AS) are thought to be proxy measures of a centrally mediated hypersensitivity to pain stimuli (centr al sensitization), a mechanism hypothesized to underlie FM and other chronic p ain conditions (Yunus, 2007) Relative to normal controls, FM patients show enhanced TSSP as well as prolonged and enhanced AS following repetitive st imulation with thermal heat (Price, et al., 2002; Staud, Cannon, et al., 2003; Staud, Vierck, Cannon, Mauderli, & Price, 2001) Along with widespread pain and allodynia, these psychophysical studies suggest that central sensitization may play an important role in the FM pain experience. Other subjective health complaints (fatigue, stiffness, irritable bowel syndrome, etc.) represent core syndromal symptoms of FM and are often considered by clinicians when making a diagnosis Chief among these complaints are those of poor sleep. In subjective assessments of sleep quality, over 75% of individuals with FM r eport disturbed and nonrestorative sleep (Moldofsky, 2008; Wolfe, Ross, Anderson, Russell, & Hebert, 1995) Objective findings fr om polysomnagraphic studies demonstrate that FM patients have abnormal sleep architecture including an increased sleep onset latency (Horne & Shackell, 1991) an increased number of nighttime arousals (Branco, Atalaia, & Paiva, 1994) reduced amounts of restorative stage 3/4 sleep (Branco, et al., 1994) and greater alpha wave intrusion (Moldofsky, Scarisbrick, England, & Smythe,

PAGE 12

12 1975) Poor sleep may have a re ciprocal relationship with pain, as there is evidence to suggest that it is both a consequence of (Nicassio, Moxham, Schuman, & Gevirtz, 2002) and a causal or maintenance mechanism (Affleck, Urrows, Tennen, Higgins, & Abeles, 1996; Bigatti, Hernandez, Cronan, & Rand, 2008; Moldofsky & Scarisbrick, 1976) for chronic pain conditions The current study built upon the Staud et al. (Staud, et al., 2004; Staud, Robinson, et al., 2003) predictive model of clinical pain in FM (negative mood AS ratings taken after a TSSP protocol, and SLAP ) by examining the role of sleep I hypothesized that results would replicate those of Staud et al., and that the addition of sleep (measured subjectively and objectively) would significantly increase the predictive ability of the model.

PAGE 13

13 CHAPTER 2 METHODS Participants Written informed consent was obtained from all participants before evaluation, and the University of Florida Institutional Review Board reviewed and approved all procedures described in this report. Adults with FM ( N = 59) were recruited to participate in a cognitive behavioral treatment trial for pain and insomnia. Details of the intervention are beyond the scope of this report as it e xamined pretreatment, baseline data only. Subject s were recruited by t elevision and radio advertising around the Gainesvil le Florida area, as well as referrals from tertiary care clinics (Rheumatology, Sleep Disorders Center ) associated with the University of Florida. Participants meeting inclusion criteria were those who were able to read and understand Eng lish and currently suffering from FM Subjects were excluded from the study if they were unable to provide informed consent, did not endorse at least 11 sites as painful during TP testing or did not report pain in all four body quadrants. To maximize the generalizeability of findings, c omorbid medical or psychiatric conditions were not exclusion factors for this pretreatment analysis Medication use was also allowed; however, participants were asked to remain stable on all medications during the study period, and refrain from changing their existing regimen or initiating a new medication. Demographic characteristics of the sample are presented in Table 2 1 The average age o f the sample was 52.4 years Consistent with population estimates of FM the majority (93.2%) of participants were female. The racial composition was as follows: 88.1% Caucasian, 10.1% African American, and 1.7 % Asian. The marital status of the

PAGE 14

14 sample consisted of the following: 50.8% married, 16.9% single, 20.3% divorced, 3.4% separated, and 8.5 % widowed. Clinical Pain Intensity Participants provided ratings of current clinical pain intensity once a day in the evening for 14 days. Ratings were made using a visual analogue scale anchored with no pain sensation on the left side, and most intense pain imaginable on the right side. The visual analogue scale was instantiated on paper as a 10 centimeter horizontal line with the tw o anchors, and participants indicated their pain level by marking a spot on the line. To obtain a numerical score for this rating, a ruler was used to measure the distance (in centimeters, to the tenth decimal) between the mark and the left end of the line. This value was then mult iplied by 10 to rescale it to a 0 100 range. The 14 daily values were averaged to get one rating of clinical pain per person. TP Testing On day 1 of the 14day observation period, TP sensitivity was assessed at 9 bilaterally paired sites as specified by the A merican College of Rheumatology criteria for FM (Wolfe, et al., 1990) The evaluati on was done by trained investigators using a Wagner Force One FDIX Dolorimeter ( Wagner Instruments, Greenwich, CT ) The rubber tip (7/16 inch diameter) of the Dolorimeter was placed at each TP site and pressure was applied with a force of 1 kg/s until the participant indicated pain, or until 4 kg of force had been reached. The TP was considered positive if pain was indicated at 4 kg of force A total number of positive TPs was calculated for each person. Body Pain Areas (SLAP) Participants indicated the location of their current pain by shading the corresponding areas on a diagram depicting the front and back of a human body ( Figure

PAGE 15

15 2 1 ) (Staud, et al., 2004) on day 1 of the observation period. T he diagrams were divided into 50 areas, indicated by letters and numbers in the Figure. If any part of an individual body area was shaded, it was scored as 1 (painful). An unshaded area was scored as 0 (not painful). A sum of local areas of pain (SLAP) was calculated for each subject. TSSP and AS Subjects underwent quantitative sensory testing using a co mputer controlled Medoc Thermal Sensory Analyzer ( Model TSA II; Ramat Yishai, Israel) on day 7 of the observation period. A TSSP protocol was used as train s of 8 heat stimuli were delivered via a contact thermode (30x30mm) placed on the thenar eminence of the pal m. Each heat stimulus started at a baseline temperature of 39C, peaked at 49C, then returned to baseline with a rise and decline rate of 10C/ s. T he duration of each stimulus was 2.4 seconds with an inter puls e interval (onset to onset) of 3s Subjects were asked to attend to the peak of delayed pain sensation ( i.e. second pain) felt after every pulse, and cued to verbally rate the intensity of that second pain after the 2nd, 4th, 6th, and 8th pulses using a numerical rating scale anchored with 0 (no pain sensation) and 100 (most intense pain sensation imaginable). Using the same numerical rating scale, ratings of painful AS were obtained at 15s and 30s following the 8th and final heat stimulus AS ratings at 30s have been shown to be a strong predictor of FM clinical pa in in past research (Staud, Robinson, et al., 2003) and were used as a predictor of clinical pain intensity in the regression analyses. Each subject completed two trials of the TSSP protocol using their nondominant palm, and the AS ratings from the two trials were averaged. Prior to the two trials, subjects completed one training trial on their dominant palm to familiarize them to the rating system and the range of heat.

PAGE 16

16 Sleep Insomnia can be divided into 3 stages: early (difficulty falling asleep), middle (waking up in the night), and late (waking too early). Total wake time (TWT) is an aggregate of the time spent in each stage, and can serve as a q uantification of insomnia. Total sleep time (TST) is the total time spent asleep. Information obtained from subjectively reported sleep diaries, and objective data from an actigraph was used to calculate TWT and TST Subjective Sleep: Diaries Self reported sleep diaries were kept daily for 14 days. Each morning p articipants recorded information about their previous nights sleep, including: bed time, sleep onset latency ( number of minutes it took to fall asleep) minutes spent awake after sleep onset, time of last morning awakening, and arise time. TWT (in minutes) was calculated as the sum of sleep onset latency, time spent awake in the night, and the time between the last morning awakening and arise time. TST (in minutes) was calculated as TWT subtracted from the time spent in bed. TST and TWT from the 14 diaries were averaged to obtain 1 mean for each participant, and will be referred to as subjective TST (TSTs) and subjective TWT (TWTs) Objective Sleep: Actigraph Participants wore an actigraph, the Actiwatch 2 (Phillips Respironics ), on their nondominant wrist for the 14 days coincident to completing the sleep diaries The Actiwatch 2 records data on gross motor activity using a solidstate piezoelectric accelerometer. The accelerometer continually measures the intensity and frequency of wrist movement at a sampling rate of 32 cycles per second. The sum of all wrist movements in a 30 second interval is recorded as an activity count. The act ivity counts are downloaded

PAGE 17

17 o nto a PC and analyzed using Actiware Sleep Analysis Softw are v.5.3.2 which classifies each 30second epoch as a sleep or wake state using validated algorithms. The bedtime and morning arise times reported on the sleep diaries were inserted into the corresp onding actigraph day and represented the timein bed period. Actiware determined the start of sleep by searching this time in bed period for the first 10 minute interval during which no more than 1 epoch was scored as awake. Similarly, sleep end was signi fied by the last 10 minute interval containing no more than 1 wakestate epoch. Nightly TWT, as measured by actigraphy, is the sum of all the wake epochs within the time in bed period. TST is the sum of all sleep epochs within the timein bed period. TST and TWT from the 14 days of actigraph were averaged to obtain 1 mean for each participant, and will be referred to as objective TST (TSTo) and objective TWT ( TWTo) Negative Mood N egative mood was assessed with the Beck Depression Inventory II (BDI II) (Beck, Steer, & Brown, 1996) and the State Trait Anxiety Inventory State Version Form Y1 (STAI Y 1) (Spielberger, Gorsuch, & Lushene, 1970) On day 14 of the observation period, p articipants were asked to think about their mood over the previous 14 days (corresponding to the time period assessed by the sleep and pain diaries) when responding to the questions on the BDI II and STAI Y1. The BDI II is a 21 item self report inventory that measures the severi t y of current depressive symptomotology, including cognitive, affective, and vegetative symptoms. Each item consists of a group of 4 descriptive statements centering around one symptom, and participants choose the statement that most accurately characteriz es them. Each item is scored on a 0 3 scale, and total scores range from 0 to 63. The STAI Y1 is a 20item self report questionnaire that measures current levels of anxiety. Each item consists of a self -

PAGE 18

18 descriptive stateme nt (I feel ___, nervous, calm, etc), and p articipants rate their agreement with the statement on a 4point liker t scale (1 = not at all to 4 = extremely). A total score is obtained, ranging from 20 80 The BDI II and STAI have been used extensively in a variety of populations, incl uding those with chronic pain and other medical conditions. The instruments are well vali dated, show good reliability (alpha coefficients > .8 ), and can accurately distinguish between clinical and nonclinical populations. Statistical Analyses The Statist ical Package for the Social Sciences (SPSS, version 17.0) was used for all statistical analyses. Descriptive statistics were calculated for demographic and clinical variables across the sample. H ierarchical linear regression was used to determine the variance in clinical pain intensity accounted for by AS ratings, SLAP, negative mood (BDI II and STAI total scores), and sleep. Each i ndependent variable was entered as a separate block in the hierarchical regression, and the associated R2 change value established t heir unique contribution to the dependent variable, clinical pain intensity. Four hierarchical regression analyses were run. In all analyses, t he variables found to be predictive of clinical pain in past research ( AS rat ings SLAP, and negative mood) were entered into the first four blocks Then each of the 4 analyses entered a different sleep variable (TSTs, TWTs, TSTo, or TWTo) into the final block to determine whether measures of sleep increased the predictive ability of the model.

PAGE 19

19 Table 21. Demographic characteristics of the sample (N = 59) N % Years Mean Age in Years 52.4 (SD = 8.4 ) Female Gender 55 93. 2 Race Caucasian 5 2 88 1 African American 6 10 1 Asian 1 1 7 Marital Status Married 30 5 0.8 Divorced 1 2 20 3 Separated 2 3. 4 Widowed 5 8 5 Single 10 16 9 Mean Years of Education 14.4 (SD = 2.5 ) Employment Employed 29 49 2 Unemployed 30 50 8

PAGE 20

20 Figure 21 Body diagram used by participants to shade areas of current pain. Each shaded area was coded as 1. Each nonshaded area was coded as 0. A sum of shaded areas (SLAP) was calculated for each participant.

PAGE 21

21 CHAPTER 3 RESULTS Fifty nine adults with FM provided data for the analysis. P ain mood, and sleep characteristics of the sample are presented in Table 31 The mean rating of clinical pain intensity across the sample was 50.6 (rang e: 8.7 88.5, SD = 19.3). Su bjects endorsed an average of 16.0 painful TPs (SD = 2.3) and shaded 21.7 (SD = 11.2) areas of pain on the body diagram. Following experimental induction of second pain, 30 second AS ratings ranged from 0 77.5, with a mean of 29.6, SD = 22.6 P articipants reported moderate levels of depressive and anxiety symptoms ( mean BDI II : 15 .2 SD = 10.5 ; mean STAI Y1 : 41.8 SD = 12.9). S elf report data from the sleep diaries indicated that the TSTs across the sample was 397.7 minutes (range: 213.5 631.9, SD = 74.1) and the TWTs was 122.2 minutes (range: 31.9 361.3, SD = 67.1). A ctigraphy data indicated that the TSTo across the sample was 397.9 minutes (range: 221.6 546.6, SD = 58.9 ) and the TWTo was 104.0 minutes (range: 39.6 305.2, SD = 49.5). Table 3 2 presents the results of the hierarchical regression analyses Higher AS ratings ( 2 =.07, p = .05) more bodily pain areas (SLAP) ( 2 =.21, p < .001 ) and higher BDI scores ( 2 =.06 p = .04 ) were each significant predictors of higher clinical pain and together accounted for 34.0% of its variance. STAI was not a significant predictor of clinical pain ( 2 =. 0 0 p = .78 ) Among the four measures of sleep, only lower TSTo was a significant predictor of higher clinic al pain ( 2 =.09, p = .01 ).

PAGE 22

22 Table 3 1. Clinical ch aracteristics of the sample Mean SD Min Max Clinical Pain Intensity 50. 6 19. 3 8.7 88.5 AS 29.6 22.6 0 77.5 SLAP 21.7 11.2 4.0 50.0 TP Count 16.0 2.3 11.0 18.0 BDI Total Score 15.2 10.5 0 44 STAI Total Score 41.8 12.9 20 78 TST o 397.9 58.9 221.6 546.6 TWT o 104.0 49.5 39.6 305.2 TST s 397.7 74.1 213.5 631.9 TWT s 122.2 67.1 31.9 361.3 Table 32. Results of the hierarchical regression predicting clinical pain intensity Block Variable R 2 F change P 1 AS .07 3.98 .05 2 SLAP .21 15. 09 <.001 3 STAI .00 .08 .78 4 BDI .06 4.3 0 .04 5 a TST o .09 7.11 .01 5 b TWT o .01 .35 .56 5 c TST s .02 1.68 .20 5 d TWT s .01 .34 .56 Full Model Beta (Standardized) T P AS .23 1.93 .06 SLAP .40 3.19 .002 STAI .19 1.16 .25 BDI .34 2.07 .04 TST o .31 2.67 .01 TWT o .08 .59 .56 TST s .15 1.30 .20 TWT s .07 .59 .56 *For blocks 1 4, full model = AS, SLAP, STAI, and BDI For block 5a, full model = AS, SLAP, STAI, BDI, and TSTo For block 5b, full model = AS, SLAP, STAI, BDI, and TWTo For block 5c, full model = AS, SLAP, STAI, BDI, and TSTs For block 5d, full model = AS, SLAP, STAI, BDI, and TWTs

PAGE 23

23 CHAPTER 4 DISCUSSION The purpose of this study was to determine the predictors of clinical pain intensity in adults with FM and it represented a replication and extension of work done by Staud et al. (Staud, et al., 2004; Staud, Robinson, et al., 2003) R esults confirm that a predictive model of AS, SLAP, and negative mood account s for si gnificant amount of variance (34.0%) in clinical pain. Only 1 of the 4 measures of sleep (TSTo) accounted for significant additional variance. Central Sensitization and AS Central sensitization has been hypothesized to be the primary pathophysiological mechanism for the maintenance of FM pain as well as other musculoskeletal pain conditions (Yunus, 2007) In central sensitization, neurons in the dorsal horn of the spinal cord become hyper excitable and subsequently hyper responsive due to prolonged noxious stimuli. These neuroplastic changes are manifested as increased pain sensitivity (hyperalgesia), a perception of pain in response to a normally nonpainful stimulus like gentle touch (allodynia), referred or widespr ead pain (i.e. a lack of spatial localization), and prolonged electrophysiological discharge resulting in post stimulus pain (Meeus & Nijs, 2007; Yunus, 2007) All of these are relevant characteristics of FM pain. Most relevant to t his analysis, c entral sensitization is thought to disrupt the endogenous pain inhibitory systems (NMDA receptors and substance P mechanisms, in particular), thereby resulting in prolonged post stimulus pain due to an inability to adequately stop the pai n r esponse (Dickenson & Sullivan, 1991; Meeus & Nijs, 2007) Painful AS ratings taken during experimental heat pain induction are a measure of this

PAGE 24

24 inhibitory dysfunction, and therefore a psychophysical correlate to central sensitization. In this study, higher AS ratings predicted more cl inical pain and accounted for 7% of its variance. This result is a confirmation of Stauds findings (Staud, Robinson, et al., 2003) and taken together with findings of abnormal TSSP in FM (Staud, Cannon, et al., 2003) it suggests that central sensitization is an important mechanism in FM clinical pain. SLAP Widespread pain is the diagnostic hallmark for FM It is also an indication of central sensitization, and was m easured in this study by SLAP and TP count. SLAP was used as t he measure of spatial extent in the analysis and proved to be a powerful predictor of daily clinical p ain by accounting for 21% of its variance. This confirms past research suggesting that spatial extent plays an important role in determining clinical pain intensity in FM (Staud, et al., 2004) and c omplements findings of spatial summation in pain threshold and tolerance during experimental pain induction (Coghill, Mayer, & Price, 1993; Douglass, Carstens, & Watkins, 1992; Price, McHaffie, & Larson, 1989) Additionally, the result supports the clinical utility of shading areas on a body diagram as a n indicator of the magnitude of daily pain in FM patients and to potentially guide localized treatment targets. TP palpation is an evaluation of sensitivity to mechanic al stimulation of muscle t issue, and can suffer from measurement error d ue to inter rater variability in assessment Statistically, the usefulness of TP count in predicting FM clinical pain is limited by a restriction of range due to the FM diagnosis requiring at least 11 positive TPs, and due to a hypersensitivity to nociceptive stimulation in FM patients (i.e. most FM patients endorse all or nearly all TP sites as painful). For these reasons, TP count may not be a us eful correlate to clinical pain, and

PAGE 25

25 may actually be a better ind icator of distress (Wolfe, 1997) Indeed in this study, TP count was significantly correlated with both BDI II (r =.32, p =.01) and STAI (r =.31, p = .02 ) total scores Negative Mood Higher BDI II scores predicted more clinical pain and accounted for 6% of its variance. The result confirms Stauds (Staud, et al., 2004; Staud, Robinson, et al., 2003) findings of the importance of negative mood in FM clinical pain, and adds evidence to a large literature linking depressi on with chronic pain (Bair, Rob inson, Katon, & Kroenke, 2003) Longitudinal studies have demonstrated that depression can be both a predictive antecedent to (Leino & Magni, 1993; Magni, Moreschi, Rigatti Luchini, & Merskey, 1994) and consequence of (Atkinson, Slater, Patterson, Grant, & Garfin, 1991; Brown, 1990) chronic pain. Additionally, the presence of depression portends worse pain outcomes and greater functional limitations in those with a chronic pain condition (Engel, von Korff, & Katon, 1996; Wells, Golding, & Burnam, 1989) Given their frequent co occurrence, it follows that pain and depression share similar biological and behavioral mechanisms. Regions of the brain involved with emotion regulation (amygdala, hypothalamus, medial prefrontal cortex) are intricately connected to those involved with pain modulation ( periaqueductal gray) Thus, the negative expectations and emotions of depression may amplify pain sig nals, increasing the intensity and duration of pain experienced. Indeed, s imilar functional and structural changes in limbic formations of the brain ( amygdala and hippocampus) have been found in patients with major depression (Frodl, et al., 2008) and FM (Emad, et al., 2008) Behaviorally, depression is associated with a lack of motivation and physical inactivity. Physical inactivity can contribute to the muscle stiffness experienced by

PAGE 26

26 patients with FM and conversely, exercise has been shown to have a beneficial effect on sym ptoms of FM (Fontaine, Conn, & Clauw, 2010; Mannerkorpi, Nordeman, Cider, & Jonsson, 2010) Finally, g iven the common comorbidity between chronic pain and anxiety (Gureje, 2008) and between anxiety and depression, i t is somewhat surprising that STAI total scores did not significantly predict clinical pain. It is possible that the vegetative symptoms of depression (inactivity, fatigue, insomnia) play a more important role in modulating the daily pain of FM than the symptoms of autonomic and psychological arousal that are assessed by the STAI. Sleep The examination of sleep represented an extension of Stauds predictive model of clinical pain in FM Two primary measures of sleep were examined in this study: a measure of sleep duration (TST), and a measure of insomni a duration (TWT), each assessed subjectively and objectively over the course of 14 days. To date this was the first study to examine TST and TWT as a predictor of clinical pain at the betweenperson level in an FM population. O nly lower TSTo was found to be a significant predictor of more clinical pain, accounting for 9% of its variance. Though the study designs and populations differ longitudinal within person findings on the effect of sleep duration on pain are generally supportive of this result Edwards et al. (Edwar ds, Almeida, Klick, Haythornthwaite, & Smith, 2008; Edwards, et al., 2009) demonstrated that longer TST predicted better pain inhibitory control the next morning in temporomandibular joint disorder patients, and shorter TST predicted higher next day pain in a general population. Wilson et al. (Wilson, Watson, & Currie, 1998) found that more pain during the day predicted shorter TST at night in a chronic musculoskeletal pain population. And i n a study of older adults with insomnia, Dzier zewski et al.

PAGE 27

27 (Dzierzewski, et al., 2010) found that above average TST predicted below average pain the following morning. Both measures of insomnia (TWTo and TWTs) failed to predict clinical pain in our study This result is contrary to studies demonstrating that disturbed sleep predicts higher pain in an FM population (Wilson, et al., 1998) and in hospitalized burn patients (Raymond, Ancoli Israel, & Choiniere, 2004) Additionally, laboratory studies in healthy subjects have found that sleep deprivation results in decreased mechanical and heat pain thresholds the fol lowing day (Kundermann, Spernal, Huber, Krieg, & Lautenbacher, 2004; Onen, Alloui, Gross, Eschallier, & Dubray, 2001) Three of the four measures of sleep failed to predict clinical pain, thus giving limited support to the hypothesis that sleep would increase the predictive ability of the model of clinical pain in FM Several potent ial explanations exist for the lack of significance in the findings It is possible that a sleep pain relationship was washed out at the betweenperson level using 14day averages. Rather, it may be that an individual night of poor sleep is followed by a day of higher pain or vice versa Ind eed, evidence for this daily variation between sleep and pain exists (Edwards, et al., 2008; Edwards, et al., 2009; Raymond, et al., 2004; Wilson, et al., 1998) and two studies have found significant relationships at the within person level but not at the group level (Affleck, et al., 1996; Dzierzewski, et al., 2010) It also may be that the effects of poor sleep (e.g. daytime fatigue, inactivity or perceived sleep quality ) rather than measures of sleep or insomnia duration, are more important in determining clinical pain There is evidence that fatigue and pain are related in FM samples (Nicassio, et al., 2002) and the perception of sleep quality has been shown to be a predictor of clinical pain in FM

PAGE 28

28 as well (Raymond, Nielsen, Lavigne, Manzini, & Choiniere, 2001) Finally, it is possible that a sleep pain relationship is depen dent on disruption of specific sleep stages. Muldofsky et al. (Moldofsky & Scarisbrick, 1976) and others (Onen, et al., 2001) have demonstrated that selective disruption of Stage 4, slow wave sleep in healthy subjects resulted in symptoms similar to FM pain (e.g. localized areas of muscle tenderness). Addit ionally, FM patients have been shown to exhibit abnormal patterns of alpha activity during slow wave sleep compared to normal controls (Roizenblatt, Moldofsky, BeneditoSilva, & Tufik, 2001) Statistically, TSTs and TWTs demonstrated weak zero order correlation with the dependent variable, clinical pain intensity, as well as with the other independent variables in the regression analyses. Therefore, it is unlikely that their inability to significantly predict clinical pain can be explained by multicollinearity with other predictors. TWTo was moderately correlated (r = .48) with BDI at the zero order. Thus, depressed mood and objectively measured insomnia likely shared some variance in the regression analysis pote ntially limiting the predictive ability of TWTo. Clinical Application In clinical application, the results of the present study suggest that FM patients may benefit from a threepronged approach to pain management The significance of SLAP to clinical pain indicates that peripheral nociceptive sources are playing an important role in the generalized pain hypersensitivity of FM To decrease the spatial extent of pain, p atients may benefit from anesthetic injection to or manual manipulation of myofascial trigger points, or lidocaine injection to local pain areas To address the implication of central sensitization in the AS finding, treatments should be aimed at normalizing the hyperexcitability of neurons in the central nervous system. Pregabalin

PAGE 29

29 and duloxetine are centrally acting medications that have demonstrated some success in treating FM pain (Arnold, et al., 2005; Crofford, et al., 2005) Finally, psychobehavioral therapies should be considered to treat those with mood dysfunction and maladaptive pain coping. There is evidence to suggest that exercise (Gowans, et al., 2001; Lemstra & Olszynski, 2005) and cognitive behavioral therapy (Lemstra & Olszynski, 2005; Turk, Okifuji, Sinclair, & Starz, 1998) are successful in treating depression as well as improving pain related variables in FM pat ients. Limitations There are several limitations to the current study. The modest sample size limits the generalizeability of findings and may have decreased our power to detect significan ce in the regression analyses. Due to incomplete data and power considerations the ana lyses did not control for the potential effect of sleep medication use among the participants. Use of s leep medications may have an e ffect on the duration of sleep or insomnia, particularly the time it takes to fall asleep or the time spent awake after sleep onset. Finally, the timing of clinical pain assessment ( i.e. in the evening before bed) may have affected its relationship with the independent variables. FM pain can vary widely throughout the day, and morning or midday pain may have had stronger or weaker relationships with sleep than night pain. Conclusion and Future Directions The study confirmed that a model of AS, SLAP, and negative mood is a strong predictor of clinical pain intensity in FM The data provided limited support to the hypothesis t hat measures of sleep and insomnia duration would account for additional, significant variance. With an eye toward treatment targets and mechanisms of action, f uture research should examine other clinical correlates of FM to strengthen the

PAGE 30

30 predictive ability of the model. These might include known correlates like cognitive dysfunction, physical activity level, and symptoms of irritable bowel syndrome, or other sleeprelated variables like fatigue, perception of sleep quality, or stagespecific abnormalities during sleep. Future research should also longitudinally examine the intraindividual, daily variation between sleep and pain. This can help to clarify whether a sleep pain relationship in FM exists on a daily level, and if so, which of the two variables are driving the relationship.

PAGE 31

31 LIST OF REFERENCES Affleck, G., Urrows, S., Tennen, H., Higgins, P., & Abeles, M. (1996). Sequential daily relations of sleep, pain intensity, and attention to pain among women with fibromyalgia. Pain, 68(2 3), 363368. doi: 10.1016/S03043959(96)032265 Arnold, L. M., Rosen, A., Pritchett, Y. L., D'Souza, D. N., Goldstein, D. J., Iyengar, S., et al. (2005). A randomized, doubleblind, placebocontrolled trial of duloxetine in the treatment of women with fibromyalgia with or without major depressive disorder. Pain, 119(1 3), 5 15. doi: 10.1016/j.pain.2005.06.031 Atkinson, J. H., Slater, M. A., Patterson, T. L., Grant, I., & Garfin, S. R. (1991). Prevalence, onset, and risk of psychiatric disorders in men with chronic low back pain: a controlled study. Pain, 45(2), 111121. doi: 03043959(91)90175W Bair, M. J., Robinson, R. L., Katon, W., & Kroenke, K. (2003). Depression and pain comorbidity: a literature review. Arch Intern Med, 163(20), 24332445. doi: 10.1001/archinte.163.20.2433 Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Manual for the Beck Depression Inventory II. San Antonio, Texas: Psychological Corporation. Bigatti, S. M., Hernandez, A. M., Cronan, T. A., & Rand, K. L. (2008). Sleep disturbances in fibromyalgia syndrome: relationship to pain and depression. Arthritis Rheum, 59(7), 961967. doi: 10.1002/art.23828 Branco, J., Atalaia, A., & Paiva, T. (1994). Sleep cycles and alphadelta sleep in fibromyalgia syndrome. J Rheumatol, 21(6), 11131117. Brown, G. K. (1990). A causal analysis of chronic pain and depression. J Abnorm Psychol, 99 (2), 127137. doi: 10.1037//0021843X.99.2.127 Coghill, R. C., Mayer, D. J., & Price, D. D. (1993). The roles of spatial recruitment and discharge frequency in spinal cord coding of pain: a combined electrophysiological and imaging investigation. Pain, 53(3), 295309. doi: 10.1016/03043959(93)90226F Crofford, L. J., Rowbotham, M. C., Mease, P. J., Russell, I. J., Dworkin, R. H., Corbin, A. E., et al. (2005). Pregabalin for the treatment of fibromyalgia syndrome: results of a randomized, doubleblind, placebocontrolled trial. Arthritis Rheum, 52 (4), 12641273. doi: 10.1002/art.20983 Croft, P., Burt, J., Schollum, J., Thomas, E., Macfarlane, G., & Silman, A. (1996). More pain, more tender points: is fibromyalgia just one end of a continuous spectrum? Ann Rheum Dis, 55(7), 482485. doi: 10.1136/ard.55.7.482

PAGE 32

32 Croft, P., Schollum, J., & Silman, A. (1994). Population study of tender point counts and pain as evidence of fibromyalgia. BMJ, 309(6956), 696699. Dickenson, A. H., & Sullivan, A. F. (1991). NMDA receptors and central hyperalgesic states. Pain, 46 (3), 344 346. doi: 10.1016/03043 959(91)90118H Douglass, D. K., Carstens, E., & Watkins, L. R. (1992). Spatial summation in human thermal pain perception: comparison within and between dermatomes. Pain, 50(2), 197 202. doi: 03043959(92)901614 Dzierzewski, J. M., Williams, J. M., Rodit i, D., Marsiske, M., McCoy, K., McNamara, J., et al. (2010). Daily variations in objective nighttime sleep and subjective morning pain in older adults with insomnia: evidence of covariation over time. J Am Geriatr Soc, 58(5), 925 930. doi: 10.1111/j.1532 5415.2010.02803.x Edwards, R. R., Almeida, D. M., Klick, B., Haythornthwaite, J. A., & Smith, M. T. (2008). Duration of sleep contributes to next day pain report in the general population. Pain, 137(1), 202 207. doi: 10.1016/j.pain.2008.01.025 Edwards, R. R ., Grace, E., Peterson, S., Klick, B., Haythornthwaite, J. A., & Smith, M. T. (2009). Sleep continuity and architecture: associations with paininhibitory processes in patients with temporomandibular joint disorder. Eur J Pain, 13(10), 10431047. doi: 10.10 16/j.ejpain.2008.12.007 Emad, Y., Ragab, Y., Zeinhom, F., El Khouly, G., Abou Zeid, A., & Rasker, J. J. (2008). Hippocampus dysfunction may explain symptoms of fibromyalgia syndrome. A study with singlevoxel magnetic resonance spectroscopy. J Rheumatol, 35(7), 13711377. doi : 10.1016/S10903801(09)605036 Engel, C. C., von Korff, M., & Katon, W. J. (1996). Back pain in primary care: predictors of high healthcare costs. Pain, 65 (2 3), 197204. doi: 10.1016/03043959(95)001646 Fontaine, K. R., Conn, L., & Clauw, D. J. (2010). Effects of lifestyle physical activity on perceived symptoms and physical function in adults with fibromyalgia: results of a randomized trial. Arthritis Res Ther, 12(2), R55. doi: 10.1016/03043959(95)001646 Frodl, T., Jager, M., Sma jstrlova, I., Born, C., Bottlender, R., Palladino, T., et al. (2008). Effect of hippocampal and amygdala volumes on clinical outcomes in major depression: a 3year prospective magnetic resonance imaging study. J Psychiatry Neurosci, 33 (5), 423 430. Gowans S. E., deHueck, A., Voss, S., Silaj, A., Abbey, S. E., & Reynolds, W. J. (2001). Effect of a randomized, controlled trial of exercise on mood and physical function

PAGE 33

33 in individuals with fibromyalgia. Arthritis Rheum, 45(6), 519529. doi: 10.1002/15290131(2 00112)45:6<519::AID ART377>3.0.CO;2 3 Gureje, O. (2008). Comorbidity of pain and anxiety disorders. Curr Psychiatry Rep, 10(4), 318 322. doi: 10.1007/s1192000800510 Horne, J. A., & Shackell, B. S. (1991). Alphalike EEG activity in non REM sleep and the fibromyalgia (fibrositis) syndrome. Electroencephalogr Clin Neurophysiol, 79(4), 271276. doi: 10.1016/00134694(91)90122K Kundermann, B., Spernal, J., Huber, M. T., Krieg, J. C., & Lautenbacher, S. (2004). Sleep deprivation affects thermal pain thresholds but not somatosensory thresholds in healthy volunteers. Psychosom Med, 66(6), 932 937. doi: 10.1097/01.psy.0000145912.24553.c0 Leino, P., & Magni, G. (1993). Depressive and distress symptoms as predictors of low back pain, neck shoulder pain, and other musculoskeletal morbidity: a 10year follow up of metal industry employees. Pain, 53(1), 89 94. doi: 10.1016/03043959(93)900603 Lemstra, M., & Olszynski, W. P. (2005). The effectiveness of multidisciplinary rehabilitation in the treatment of fibromyalgia: a randomized controlled trial. Clin J Pain, 21(2), 166174. doi: 10.1097/0000250820050300000008 Magni, G., Moreschi, C., Rigatti Luchini, S., & Merskey, H. (1994). Prospective study on the relationship between depressive symptoms and chronic musculoskeletal pain. Pain, 56(3), 289 297. doi: 10.1016/0304 3959(94)901678 Mannerkorpi, K., Nordeman, L., Cider, A., & Jonsson, G. (2010). Does moder ate to high intensity Nordic walking improve functional capacity and pain in fibromyalgia? A prospective randomized controlled trial. Arthritis Res Ther, 12(5), R189. doi: 10.1186/ar3159 Meeus, M., & Nijs, J. (2007). Central sensitization: a biopsychosocial explanation for chronic widespread pain in patients with fibromyalgia and chronic fatigue syndrome. Clin Rheumatol, 26(4), 465473. doi: 10.1007/s1006700604339 Moldofsky, H. (2008). The significance, assessment, and management of nonrestorative sleep in fibromyalgia syndrome. CNS Spectr, 13(3 Suppl 5), 2226. Moldofsky, H., & Scarisbrick, P. (1976). Induction of neurasthenic musculoskeletal pain syndrome by selective sleep stage deprivation. Psychosom Med, 38 (1), 35 44. Moldofsky, H., Scarisbrick, P., England, R., & Smythe, H. (1975). Musculosketal symptoms and nonREM sleep disturbance in patients with "fibrositis syndrome" and healthy subjects. Psychosom Med, 37(4), 341 351.

PAGE 34

34 Nicassio, P. M., Moxham, E. G., Schuman, C. E., & Gevirtz, R. N. (2002). The contribution of pain, reported sleep quality, and depressive symptoms to fatigue in fibromyalgia. Pain, 100(3), 271 279. doi: 10.1016/S03043959(02)003007 Nicassio, P. M., Weisman, M. H., Schuman, C., & Young, C. W. (2000) The role of generalized pain and pain behavior in tender point scores in fibromyalgia. J Rheumatol, 27(4), 1056 1062. Onen, S. H., Alloui, A., Gross, A., Eschallier, A., & Dubray, C. (2001). The effects of total sleep deprivation, selective sleep interr uption and sleep recovery on pain tolerance thresholds in healthy subjects. J Sleep Res, 10(1), 35 42. doi: 10.1046/j.13652869.2001.00240.x Price, D. D., Hu, J. W., Dubner, R., & Gracely, R. H. (1977). Peripheral suppression of first pain and central summ ation of second pain evoked by noxious heat pulses. Pain, 3(1), 57 68. Price, D. D., McHaffie, J. G., & Larson, M. A. (1989). Spatial summation of heat induced pain: influence of stimulus area and spatial separation of stimuli on perceived pain sensation intensity and unpleasantness. J Neurophysiol, 62(6), 12701279. Price, D. D., Staud, R., Robinson, M. E ., Mauderli, A. P., Cannon, R., & Vierck, C. J. (2002). Enhanced temporal summation of second pain and its central modulation in fibromyalgia patients. Pain, 99(1 2), 4959. doi: 10.1016/S03043959(02)000532 Raymond, I., Ancoli Israel, S., & Choiniere, M. (2004). Sleep disturbances, pain and analgesia in adults hospitalized for burn injuries. Sleep Med, 5(6), 551 559. doi: 10.1016/j.sleep.2004.07.007 Raymond, I., Nielsen, T. A., Lavigne, G., Manzini, C., & Choiniere, M. (2001). Quality of sleep and its dai ly relationship to pain intensity in hospitalized adult burn patients. Pain, 92(3), 381388. doi: 10.1016/S03043959(01)002822 Roizenblatt, S., Moldofsky, H., Benedito Silva, A. A., & Tufik, S. (2001). Alpha sleep characteristics in fibromyalgia. Arthritis Rheum, 44(1), 222230. doi: 10.1002/15290131 Spielberger, C. D., Gorsuch, R. L., & Lushene, R. E. (1970). Manual for the StateTrait Anxiety Inventory ("Self Evaluation Questionnaire"). Palo Alto, California: Consulting Psychologists Press. Sta ud, R., Cannon, R. C., Mauderli, A. P., Robinson, M. E., Price, D. D., & Vierck, C. J., Jr. (2003). Temporal summation of pain from mechanical stimulation of muscle

PAGE 35

35 tissue in normal controls and subjects with fibromyalgia syndrome. Pain, 102(1 2), 8795. d oi: 10.1016/s03043959(02)003445 Staud, R., Price, D. D., Robinson, M. E., & Vierck, C. J., Jr. (2004). Body pain area and painrelated negative affect predict clinical pain intensity in patients with fibromyalgia. J Pain, 5 (6), 338343. doi: 10.1016/j.jpain.2004.05.007 Staud, R., Robinson, M. E., Vierck, C. J., Jr., Cannon, R. C., Mauderli, A. P., & Price, D. D. (2003). Ratings of experimental pain and painrelated negative affect predict clinical pain in patients with fibromyalgia syndrome. Pain, 105 (1 2 ), 215222. doi: 10.1016/S03043959(03)002082 Staud, R., Vierck, C. J., Cannon, R. L., Mauderli, A. P., & Price, D. D. (2001). Abnormal sensitization and temporal summation of second pain (windup) in patients with fibromyalgia syndrome. Pain, 91(1 2), 16 5 175. doi: 10.1016/S03043959(00)004322 Turk, D. C., Okifuji, A., Sinclair, J. D., & Starz, T. W. (1998). Interdisciplinary treatment for fibromyalgia syndrome: clinical and statistical significance. Arthritis Care Res, 11(3), 186 195. doi: 10.1002/art.1790110306 Wells, K. B., Golding, J. M., & Burnam, M. A. (1989). Affective, substance use, and anxiety disorders in persons with arthritis, diabetes, heart disease, high blood pressure, or chronic lung conditions. Gen Hosp Psychiatry, 11 (5), 320 327. doi: 10.1016/01638343(89)901199 Wilson, K. G., Watson, S. T., & Currie, S. R. (1998). Daily diary and ambulatory activity monitoring of sleep in patients with insomnia associated with chronic musculoskeletal pain. Pain, 75(1), 75 84. doi: 10.1016/S03043959(9 7)002078 Wolfe, F. (1997). The relation between tender points and fibromyalgia symptom variables: evidence that fibromyalgia is not a discrete disorder in the clinic. Ann Rheum Dis, 56(4), 268 271. doi: 10.1136/ard.56.4.268 Wolfe, F., Ross, K., Anderson, J., Russell, I. J., & Hebert, L. (1995). The prevalence and characteristics of fibromyalgia in the general population. Arthritis Rheum, 38(1), 1928. doi: 10.1002/art.1780380104 Wolfe, F., Smythe, H. A., Yunus, M. B., Bennett, R. M., Bombardier, C., Goldenberg, D. L., et al. (1990). The American College of Rheumatology 1990 Criteria for the Classification of Fibromyalgia. Report of the Multicenter Criteria Committee. Arthritis Rheum, 33(2), 160172. doi: 10.1002/art.1780330203 Yunus, M. B. (2007). Role of central sensitization in symptoms beyond muscle pain, and the evaluation of a patient with widespread pain. Best Pract Res Clin Rheumatol, 21(3), 481 497. doi: 10.1016/j.berh.2007.03.006

PAGE 36

36 BIOGRAPHICAL SKETCH Ryan Anderson rec eived his B.A. in psychology from Washington University in St. Louis in 1999. After graduating, Ryan spent eight years doing research in the Department of Psychiatry at Washington University School of Medicine on depressive and anxiety disorders in patient s with diabetes and prediabetic obesity. This included work on clinical trials examining the efficacy of various antidepressants in patients with comorbid diabetes and depression, as well as metaanalytic reviews of the prevalence of depression and anxiet y in diabetes. In 2009, he began his doctoral study in clinical and health psychology at the University of Florida, and joined Dr. Michael Robinsons Center for Pain Research and Behavioral Health to continue pursuing his interest in medical psychology Ry ans research interests include the interactions of negative mood, sleep, and pain in patients with chronic pain conditions. His clinical interests are in the assessment and treatment of psychological disorders in patients with medical conditions, and more broadly, in the treatment of adults with depressive disorders.