Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 May 1.
Published in final edited form as: J Health Psychol. 2017 Oct 27;25(6):819–829. doi: 10.1177/1359105317736577

Somatic symptom presentations in women with fibromyalgia are differentially associated with elevated depression and anxiety

Katherine Hadlandsmyth 1, Dana L Dailey 2, Barbara A Rakel 3, M Bridget Zimmerman 4, Carol G T Vance 2, Ericka N Merriwether 2, Ruth L Chimenti 2, Katharine M Geasland 2, Leslie J Crofford 5, Kathleen A Sluka 2
PMCID: PMC6287969  NIHMSID: NIHMS998466  PMID: 29076404

Abstract

This study examined whether depression and anxiety differentially relate to fatigue, sleep disturbance, pain catastrophizing, fear of movement, and pain severity in women with fibromyalgia. Baseline data from the Fibromyalgia Activity Study with TENS (FAST) were analyzed. Of 191 participants, 50% reported high anxiety and/or depression (17% high anxiety, 9% high depression, and 24% both). Fatigue and sleep impairment were associated with high depression (p < .05). Pain severity, pain catastrophizing, and fear of movement were associated with high anxiety and high depression (p < .05). Possible implications for underlying mechanisms and the need for targeted treatments are discussed.

Introduction

Fibromyalgia is a chronic pain condition associated with fatigue, sleep disturbance, and cognitive difficulties (Wolfe et al., 2010). For many, this condition is disabling, interfering with multiple functional domains, including physical, social, and mental health (Birtane, Uzunca, Tastekin, & Tuna, 2007). Pain, fatigue, and impaired sleep are reported in nearly all individuals with fibromyalgia (Bigatti, Hernandez, Cronan, & Rand, 2008; Kleinman et al., 2014; Lukkahatai, Walitt, Espina, Gelio, & Saligan, 2016; Salaffi et al., 2016), and are associated with reduced function and quality of life (Dailey et al., 2016; Theadom, Cropley, & Humphrey, 2007). Additionally, pain catastrophizing and fear of movement, two important pain-related psychological constructs, are associated with pain-related disability and pain severity (Nijs et al., 2013; Sullivan, 2009; Sullivan, Adams, Rhodenizer, & Stanish, 2006; Sullivan & Bishop, 1995). Thus, people with fibromyalgia have widespread symptoms across multiple domains and associated psychological distress and pain-specific distress, which can directly impact function and quality of life.

People with fibromyalgia can also present with high levels of anxiety and depression with prevalence estimates ranging between 20–80% (Fietta, Fietta, & Manganelli, 2007; Marangell et al., 2011; Thieme, Turk, & Flor, 2004). Anxiety, which can be characterized by anticipatory fear (Association, 2013), is related to pain catastrophizing and fear of movement in chronic pain (Sullivan, 2009; Vlaeyen, Kole-Snijders, Boeren, & van Eek, 1995; Vlaeyen, Kole-Snijders, Rotteveel, Ruesink, & Heuts, 1995), including those with fibromyalgia (Martinez, Sanchez, Miro, Medina, & Lami, 2011). The effects of anxiety may be amplified by the effects of depression, which are often accompanied by fatigue and sleep disturbance (Association, 2013; Lukkahatai et al., 2016), and in those with fibromyalgia, depression is associated with fatigue (Lukkahatai et al., 2016; Nicassio, Moxham, Schuman, & Gevirtz, 2002). Thus, different psychological constructs, like depression and anxiety, are likely associated with differing symptomology. The current study will examine the relationships of depression and anxiety together, to somatic symptoms and pain-related psychological variables.

Fibromyalgia is a heterogenous pain syndrome, with a variety of symptomology and psychological dysfunction. Prior studies have examined this heterogeneity and show multiple sub-groups among individuals with fibromyalgia. These studies generally found several sub-groups that spanned from low symptoms and psychological dysfunction to those that were high in all categories (Docampo et al., 2013; Lipkovich, Choy, Van Wambeke, Deberdt, & Sagman, 2014; Luciano et al., 2016; Lukkahatai et al., 2016; Salaffi et al., 2016; Vincent et al., 2014; Yim et al., 2017). Two common presentations of psychological distress, anxiety and depression, are conceptually distinct entities (Association, 2013) that can be treated with different cognitive behavioral approaches (CBT) (Norton & Price, 2007; Veale, 2007). Identifying such subsets of patients with fibromyalgia will provide insights for targeted therapeutic approaches, such as tailored CBT interventions, which can address both psychological distress and specific somatic symptom management. Understanding the relationships between symptomatology and psychological distress will assist in tailoring interventions to maximize effectiveness for patients with fibromyalgia.

The current study will examine whether somatic symptoms (fatigue, sleep disturbance, and pain severity) and pain-related psychological variables (pain catastrophizing and fear of movement) differentially relate to depression and anxiety in participants with fibromyalgia. It is hypothesized that: 1. a) Fatigue and sleep disturbance will be associated with high levels of depression but not anxiety, b) In the presence of high depression and high anxiety, the effect of depression will be magnified, and 2. a) Pain catastrophizing, fear of movement, and pain severity will be associated with high levels of anxiety but not depression, b) The effect of anxiety will be magnified by the presence of depression.

Methods and Measures

This study is a supplementary analysis of baseline data from a clinical trial investigating Transcutaneous Electrical Nerve Stimulation (TENS) for women with fibromyalgia: the Fibromyalgia Activity Study with TENS (FAST). Participants completed informed consent procedures then provided baseline demographic and survey data. Data was provided through the Research Electronic Data Capture (REDCap) system. Additional details about the parent study have been reported previously (Noehren et al., 2015).

Participants

Participants were recruited from a Midwestern and a Southern medical center. This study was approved by Institutional Review Boards at both study sites. Participants met the following inclusion criteria: 1) Females aged 18–70 years; 2) Fibromyalgia diagnosis by ACR 1990 criteria with 11 of 18 tender points on examination, 3) Pain greater than or equal to 4/10 at initial screening; 4) Stable treatment regimen for the last four weeks and projected stable treatment regimen for the next 2 months; 5) English speaker, and 6) Completed baseline measures of depression and anxiety. Participants were excluded due to: 1) Current or history of cardiac, pulmonary, neurological, endocrine, or renal disease that would preclude study participation; 2) TENS use in the last 5 years; 3) Pacemaker; 4) Uncontrolled blood pressure or diabetes; 5) Neuropathic pain condition; 6) Systemic autoimmune disorder; 7) Cervical or lumbar fusion or metal implants; 8) Severe skin allergy to adhesive; 9) Allergy to nickel; 10) Pregnancy; 11) Epilepsy; 12) Unstable medical or psychiatric condition; 13) Chest pain with activity; or 14) Use of assistive device for ambulation.

Measures

Depression.

Depression was assessed with the National Institutes of Health Patient Reported Outcome Measurement Information System (PROMIS) Short form (8b). This scale assesses negative mood, views of self, social cognitions, and decreased positive affect or engagement. This 8-item questionnaire assesses depressive symptoms across the past 7 days. Each item is scored from 1–5 with a maximum total of 40 on the measure. Raw scores are then converted into standardized T-scores, based on a normative sample with a mean of 50 and standard deviation of 10 (Patient-Reported Outcomes Measurement Information System (PROMIS) Available at: http://www.nihpromis.org/.). This measure has demonstrated good internal consistency (Cronbach’s alpha = 0.95) as well as convergent and discriminant validity (Bjorner et al., 2014). For the current study, scores more than one standard deviation above the mean (≥60) was used to indicate a significant degree of depressive symptoms; scores more than one standard deviation above the mean on a PROMIS measure indicate moderate to severe symptoms (Cella, 2017).

Anxiety.

Anxiety was assessed with the PROMIS short form (8a). This scale assesses self-reported fear, anxious misery, hyperarousal, and somatic symptoms related to arousal across 8 items. Each item is rated on a scale of 1–5 on symptoms across the past five days. Raw scores are converted into T-scores, based on a normative sample with a mean of 50 and a standard deviation of 10 (Patient-Reported Outcomes Measurement Information System (PROMIS) Available at: http://www.nihpromis.org/.). PROMIS anxiety measures have demonstrated internal consistency and alternate forms (long and short) reliability and construct validity (Cella et al., 2010). This measure has also demonstrated clinical validity across diverse medical samples (including back pain, chronic obstructive pulmonary disease, chronic heart failure, and cancer) (Schalet et al., 2016). For the current study, scores more than one standard deviation above the mean (≥60) were used to indicate a significant degree of anxious symptoms; scores more than one standard deviation above the mean on a PROMIS measure indicate moderate to severe symptoms (Cella, 2017).

Fatigue (Multidimensional Assessment of Fatigue, MAF).

The MAF is a self-report measure of fatigue in chronic illness. This 16 item measure includes four subscales: severity, distress, timing, and impact on activities of daily living (ADLs). It also includes a total composite score (Global Fatigue Index, GFI) from the subscales; scores range from 1 (no fatigue) to 50 (severe fatigue). The GFI was used for this study to capture a more comprehensive assessment of fatigue. The MAF has good internal consistency (r = 0.93) and convergent validity with a fatigue Visual Analogue Scale (VAS: r = 0.80, p < .05) (Lentz, Barabas, Day, Bishop, & George, 2009).

Sleep Disturbance (Pittsburgh Sleep Quality Assessment: PSQI).

The PSQI is a 19-item measure which assesses self-reported sleep quality over the previous month. The measure produces 7 clinical component scores which combine to create a global score between 0–21; this global score was used in the current study. This scale has demonstrated high test-retest reliability (r = 0.87) and convergent validity with sleep log data (r =.81, for sleep duration and r =.71 for sleep onset latency) (Backhaus, Junghanns, Broocks, Riemann, & Hohagen, 2002).

Pain Catastrophizing Scale (PCS).

The PCS is a 13-item measure of pain catastrophizing; it assesses thoughts and feelings related to pain. The PCS includes three subscales: rumination, magnification and helplessness. A total score can also be calculated and the total pain catastrophizing scale score was used for this study. Scores range from 0–52 on this measure and 30 is considered clinically significant (Sullivan & Bishop, 1995). The PCS has good internal consistency (Cronbach’s alpha = 0.95) and criterion validity; in a sample of 60 patients with chronic pain and 85 community participants without chronic pain, the PCS total score correctly classified 77% of cases (Osman et al., 2000).

Fear of Movement (Tampa Scale of Kinesiophobia, TSK).

The TSK measures fear of movement in patients with chronic pain. This 17-items are rated from 1 = strongly disagree to 4 = strongly agree; scores range from 17 to 68; the total score was used in this study. This scale demonstrates good test-retest reliability (r = 0.78) and good internal consistency: Chronbach’s alpha = 0.76 (Burwinkle, Robinson, & Turk, 2005; Roelofs, Goubert, Peters, Vlaeyen, & Crombez, 2004).

Pain Severity.

Pain severity was assessed at rest with an 11-point Numeric Rating Scale (NRS). The scale was anchored with 0 = “no pain” and 10 = “worst pain imaginable”. The pain NRS demonstrates good construct validity; it has demonstrated high correlations in patients with chronic pain to the Visual Analogue Scale (from 0.86 – 0.95) (Hawker, Mian, Kendzerska, & French, 2011).

Data Analysis

Descriptive characteristics were calculated for demographics and all variables of interest. Since this study did not include diagnostic assessments of depression and anxiety, in order to increase clinical relevance, anxiety and depression scores were dichotomized into “high” and “low” at one standard deviation above the mean (t = 60), using established cut-points (Cella, 2017), with this classification being used in the analyses. Fatigue, sleep disturbance, pain catastrophizing, and fear of movement were treated as continuous variables. Two-way ANOVAs were run to examine for main effects of depression (high versus low) and of anxiety (high versus low) on sleep disturbance, fatigue, pain catastrophizing, fear of movement, and pain severity. Interaction effects between depression and anxiety on the somatic symptoms (fatigue, sleep disturbance, and pain severity) and pain-related psychological variables (pain catastrophizing and fear of movement) were also examined within the ANOVAs. The tests for interactions determined whether depression and anxiety modified the effect of the other. The pain severity variable was non-normally distributed, so a two-way ANOVA based on ranks was conducted to determine main and interaction effects of depression and anxiety on pain severity.

Results

A total of 191 women met inclusion and exclusion criteria for this study. Participants’ mean age was 49 years (SD = 12), 53% were single, divorced, or widowed, mean BMI was 34.6 kg/m2 (SD = 9.0), the majority (63%) held a Bachelor’s degree, 70% reported an income of $60,000 or less, and 65% reported having had the diagnosis of fibromyalgia for at least 5 years (see Table 1 for further details). Participants generally reported moderate levels of pain catastrophizing and high levels of fatigue, sleep disturbance, pain severity, and fear of movement (see Table 1). Half of the sample (50%) reported less than moderate to severe depression and anxiety, 17% reported high levels of anxiety (moderate to severe) without high levels of depression, 9% reported high levels of depression (moderate to severe) without high levels of anxiety, and 24% reported high levels (moderate to severe) of both depression and anxiety (see Table 1).

Table 1.

Demographics and Clinical Characteristics.

Variable N = 191
Age
Mean (SD) 49.1 (11.9)
Range 20-70
(n = 186)
Race (Caucasian) 175 (94%)
Marital Status: (n = 187)
Significant other 87 (47%)
Single/Divorced/Widowed 100 (53%)
Education (n = 189)
High school or less 35 (19%)
Bachelor’s degree/Some college 120 (63%)
Post graduate 34 (18%)
Income (n = 182)
<20,000 66 (36%)
20,000-<60,000 62 (34%)
60,000-<100,000 37 (20%)
≥100,000 17 (9%)
Years since Diagnosis
<5 years 67 (35%)
5-9 years 40 (21%)
10-19 years 60 (31%)
≥20 years 24 (13%)
BMI
Mean (SD) 34.6 (9.0)
Range 19-84
Depression (PROMIS)
% with high depression 33%
Anxiety (PROMIS)
% with high anxiety 41%
Sleep Quality (PSQI total)
 Mean (SD) 12.8 (3.5)
 Range 4-21
Fatigue (MAF GFI)
 Mean (SD) 36.3 (7.7)
 Range 0-50
Fear of Movement (TSK)
 Mean (SD) 36.6 (8.0)
 Range 18-58
Pain Severity (NRS)
 Median (IQR) 6 [5-7]
 Range 2-10
Pain Catastrophizing (PCS)
 Mean (SD) 20.9 (13.2)
 Range 0-52
Open in a new tab

All ns are the same as the heading unless otherwise noted.

Fatigue.

Participants meeting the cut off for high depression reported significantly higher levels of fatigue on the MAF: [main effect for depression: F (1, 187) = 4.84, p = 0.03]. There was no main effect for anxiety on fatigue [F (1, 187) = 1.71, p = 0.19] and no significant interaction effect between anxiety and depression on fatigue [F (1, 187) = 0.78, p = 0.38]. The mean fatigue score was 2.87 points higher on the MAF (95% CI: 0.30, 5.45) for participants in the high depression group compared to those in the low depression group (see Table 2).

Table 2.

ANOVA results: Means and standard deviations (or medians and interquartile ranges) by high and low depression and anxiety subgroups.

Anxiety Depression
Low High Main effect for anxiety
Fatigue Low 34.33 (7.85) 38.36 (8.34) 34.94 (8.02)
High 37.19 (6.12) 38.91 (7.31 38.19 (6.85)
Main effect for depression 35.06 (7.53) 38.76 (7.53)*
Sleep Quality Low 11.80 (3.33) 14.00 (3.10) 12.13 (3.38)
High 13.33 (3.28) 14.04 (3.56) 13.75 (3.44)
Main effect for depression 12.20 (3.37) 14.03 (3.42)*
Pain Catastrophizing Low 14.59 (10.46) 24.24 (13.82 16.05 (11.50)
High 21.67 (11.30) 32.07 (11.42) 27.72 (12.42)**
Main effect for depression 16.41 (11.08) 29.95 (12.50)**
Fear of Movement Low 34.03 (7.01) 35.59 (8.86) 34.27 (7.29)
High 37.67 (8.58) 41.70 (6.90) 40.01 (7.85)**
Main effect for depression 34.97 (7.58) 40.05 (7.89)*
Pain Severity Low 5.5 (4.5 – 7.0) 7.0 (5.0 – 7.5) 6.0 (4.75–7.00)
High 6.0 (5.0 – 7.0) 7.0 (5.0 – 7.5) 6.0 (5.00–7.00)*
Main effect for depression 6.0 (5.00–7.00) 7.0 (5.00–7.50)**
Open in a new tab
*

p < .05

**

p < .01

Sleep quality.

Participants meeting the cut off for high depression reported significantly worse sleep quality on the PSQI: [main effect for depression: F (1, 187) = 6.18, p = 0.01]. There was no main effect for anxiety on sleep quality [F (1, 187) = 1.81, p = 0.18], and no significant interaction effect between anxiety and depression on sleep [F (1, 187) = 1.62 p = 0.20]. The mean sleep score was 1.46 points higher on the PSQI (95% CI: 0.30, 2.61) for participants in the high depression group compared to the low depression group (see Table 2).

Pain Catastrophizing.

Participants meeting the cut off for high depression reported significantly greater pain catastrophizing on the PCS: [main effect for depression: F (1, 187) = 26.57, p < 0.0001)]. Those meeting the cut off for high anxiety also reported significantly higher pain catastrophizing [main effect for anxiety: F (1, 187) = 14.70, p = 0.0002], but there was no significant interaction effect between anxiety and depression on pain catastrophizing [F (1, 187) = 0.04, p = 0.85]. Mean pain catastrophizing was 10.02 points higher (95% CI: 6.19, 13.86) for participants in the high depression group compared to the low depression group and 7.45 points higher (95% CI: 3.62, 11.29) for participants in the high anxiety group compared to the low anxiety group (see Table 2).

Fear of Movement.

Participants meeting the cut off for high depression reported significantly greater fear of movement on the TSK: [main effect for depression: F (1, 187) = 4.64, p = 0.04)]. Those meeting the cut off for anxiety also reported significantly higher fear of movement [main effect for anxiety: F (1, 187) = 14.11, p = 0.0002], but there was no significant interaction effect between anxiety and depression on fear of movement [F (1, 187) = 0.91, p = 0.34]. Mean fear of movement was 2.79 points higher (95% CI: 0.23, 5.35) for participants in the high depression group compared to the low depression group and 4.87 points higher (95% CI: 2.31, 7.43) for participants in the high anxiety group compared to low anxiety group (see Table 2).

Pain Severity.

Participants meeting the cut off for high depression reported significantly greater pain severity on the NRS [main effect for depression: p = 0.01]. Those meeting the cut off for high anxiety also reported significantly greater pain severity [main effect for anxiety: p = 0.04)], but no significant interaction effect was found between anxiety and depression on pain severity (i.e., anxiety and depression did not amplify the effect of the other: p = 0.65, see Table 2).

Discussion

This study uniquely examined the relationships between multiple somatic and pain-related psychological variables to depression and anxiety in women with fibromyalgia. Findings indicate that fatigue and sleep disturbance were associated with high depression but not high anxiety, while pain catastrophizing, fear of movement, and pain severity were associated with both high depression and high anxiety. Contrary to our hypotheses, depression and anxiety did not have an amplifying interaction effect on one another in relation to somatic and psychological variables.

The prevalence of anxious and depressive symptoms in this study were within previously reported ranges (Fietta et al., 2007; Marangell et al., 2011; Thieme et al., 2004). Additionally, our data support the heterogeneity of individuals with fibromyalgia. This is consistent with multiple previous cluster studies indicating diverse clinical presentations (Aktas, Walsh, & Rybicki, 2010; de Souza et al., 2009; Docampo et al., 2013; Giesecke et al., 2003; Hurtig, Raak, Kendall, Gerdle, & Wahren, 2001; Loevinger, Shirtcliff, Muller, Alonso, & Coe, 2012; Luciano et al., 2016; Rehm et al., 2010; Rutledge, Mouttapa, & Wood, 2009; Wilson, Robinson, & Turk, 2009). This diversity in clinical presentation has resulted in the suggestion that fibromyalgia presents an overlap of syndromes as opposed to a distinct entity (Bennett et al., 2010). The wide variability in this population indicates a need for better identification of subgroups, to allow for targeted treatments (Turk, Okifuji, Sinclair, & Starz, 1998). The current findings contribute to identification of clinically-relevant sub-groups of patients with fibromyalgia.

The current study identified relationships of fatigue and sleep disturbance uniquely to depression. Previous findings suggest that sleep problems are prevalent in this population and may relate to depression through the impact of poor sleep on pain severity and lower physical functioning (Bigatti et al., 2008). Another possibility is that there may be a common mechanism underlying the symptom triad of depression, fatigue, and sleep disturbance in persons with fibromyalgia. Possible underlying mechanisms could include central nervous system sensitization or systemic inflammation (Sluka & Clauw, 2016). Chronic systemic inflammation, for example, has been implicated in both fibromyalgia (Mendieta et al., 2016; Sturgill, McGee, & Menzies, 2014) and in depression (Berk et al., 2013; Dantzer, O’Connor, Freund, Johnson, & Kelley, 2008; Miller, Maletic, & Raison, 2009); however this picture is complicated because markers of inflammation have also been reported in relation to pain severity (Dawes, Andersson, Bennett, Bevan, & McMahon, 2013). Better understanding of how underlying mechanisms interact to impact the overlapping symptoms of depression, fatigue, and sleep could inform better differentiation between the wide ranges of clinical presentations among patients with fibromyalgia and inform treatment approaches; greater understanding of this symptom triad may contribute to developing targeted treatments.

In contrast to this symptom triad, pain catastrophizing, fear of movement, and pain severity were associated with both depression and anxiety, which may indicate a relationship between these variables and an underlying construct of general distress. This concept is supported by previous work in samples of patients with chronic pain which demonstrated associations between pain catastrophizing and depression, anxiety, and fear (Borsbo, Peolsson, & Gerdle, 2008; Drahovzal, Stewart, & Sullivan, 2006; Edwards, Bingham, Bathon, & Haythornthwaite, 2006; Leeuw et al., 2007), and between pain severity and a range of measures of psychological distress (Gatchel & Turk, 1996). The relationship between distress to pain catastrophizing, fear of movement, and pain in patients with fibromyalgia may indicate a vicious cycle between these variables (such that they serve to amplify one another).

These results suggest the possible benefit of tailored targeted treatments for symptom management in persons with fibromyalgia. The differences identified in fatigue and sleep quality in relation to depression versus anxiety, provide direction for such targeted clinical interventions. CBT interventions may be tailored for different psychological presentations in conjunction with various fibromyalgia symptoms. For example, CBT for individuals with fibromyalgia and significant depressive symptoms may emphasize behavioral activation (Veale, 2007) and behavioral sleep management (Morin et al., 2006). Conversely, pain catastrophizing, fear of movement, and pain severity may need to be targeted more generally across distressed persons with fibromyalgia. Further research is needed to deterime whether such targeted treatments result in improved outcomes.

In conclusion, this study is unique in its organization of somatic and pain-related psychological symptoms specifically by depression and anxiety. This study identified that half of participants (50%) presented with moderate to severe levels of depression and/or anxiety, including 24% of the sample who presented with both. This study highlights that while pain catastrophizing, fear of pain, and pain severity are associated with both anxiety and depression, fatigue and sleep disturbance are only assoicated with depression. Overall, this study provides support for differing clinical presentations in depressed compared to anxious persons with fibromyalgia.

Acknowledgements:

This work was funded by National Institutes of Health (NIH) UM1 AR06338 and NIH UM1 AR06338-S1. Data was collected with REDCap electronic data capture tools hosted at University of Iowa (supported by NIH 54TR001013). Study data collection was completed through the Clinical and Translational Science Awards (CTSA) program at University of Iowa (supported by NIH U54TR001356) and Vanderbilt University.

Footnotes

Disclosure and conflicts of interest: Kathleen A. Sluka, PT, PhD, FAPTA serves as a consultant for Bayer, Inc. Dr. Sluka has an active research grant from Medtronic, Inc and receives royalties from IASP Press. Dana Dailey, PT, PhD, serves as a consultant for Bayer, Inc. The remaining authors of this manuscript have no conflicts of interest to report.

References

  1. Aktas A, Walsh D, & Rybicki L (2010). Symptom clusters: myth or reality? Palliat Med, 24(4), 373–385. doi: 10.1177/0269216310367842 [DOI] [PubMed] [Google Scholar]
  2. Association AP (2013). Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Arlington, VA: American Psychiatric Association. [Google Scholar]
  3. Backhaus J, Junghanns K, Broocks A, Riemann D, & Hohagen F (2002). Test-retest reliability and validity of the Pittsburgh Sleep Quality Index in primary insomnia. J Psychosom Res, 53(3), 737–740. [DOI] [PubMed] [Google Scholar]
  4. Bennett RM, Russell J, Cappelleri JC, Bushmakin AG, Zlateva G, & Sadosky A (2010). Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis. BMC Musculoskelet Disord, 11, 134. doi: 10.1186/1471-2474-11-134 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Berk M, Williams LJ, Jacka FN, O’Neil A, Pasco JA, Moylan S, . . . Maes M (2013). So depression is an inflammatory disease, but where does the inflammation come from? BMC Med, 11, 200. doi: 10.1186/1741-7015-11-200 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bigatti SM, Hernandez AM, Cronan TA, & Rand KL (2008). Sleep disturbances in fibromyalgia syndrome: relationship to pain and depression. Arthritis Rheum, 59(7), 961–967. doi: 10.1002/art.23828 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Birtane M, Uzunca K, Tastekin N, & Tuna H (2007). The evaluation of quality of life in fibromyalgia syndrome: a comparison with rheumatoid arthritis by using SF-36 Health Survey. Clin Rheumatol, 26(5), 679–684. doi: 10.1007/s10067-006-0359-2 [DOI] [PubMed] [Google Scholar]
  8. Bjorner JB, Rose M, Gandek B, Stone AA, Junghaenel DU, & Ware JE Jr. (2014). Method of administration of PROMIS scales did not significantly impact score level, reliability, or validity. J Clin Epidemiol, 67(1), 108–113. doi: 10.1016/j.jclinepi.2013.07.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Borsbo B, Peolsson M, & Gerdle B (2008). Catastrophizing, depression, and pain: correlation with and influence on quality of life and health - a study of chronic whiplash-associated disorders. J Rehabil Med, 40(7), 562–569. doi: 10.2340/16501977-0207 [DOI] [PubMed] [Google Scholar]
  10. Burwinkle T, Robinson JP, & Turk DC (2005). Fear of movement: factor structure of the tampa scale of kinesiophobia in patients with fibromyalgia syndrome. J Pain, 6(6), 384–391. doi: 10.1016/j.jpain.2005.01.355 [DOI] [PubMed] [Google Scholar]
  11. Cella D (2017). Health Measures: Transforming how health is measured. Retrieved from http://www.healthmeasures.net/score-and-interpret/interpret-scores/promis [Google Scholar]
  12. Cella D, Riley W, Stone A, Rothrock N, Reeve B, Yount S, . . . Group, P. C. (2010). The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. J Clin Epidemiol, 63(11), 1179–1194. doi: 10.1016/j.jclinepi.2010.04.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dailey DL, Frey Law LA, Vance CG, Rakel BA, Merriwether EN, Darghosian L, . . . Sluka KA (2016). Perceived function and physical performance are associated with pain and fatigue in women with fibromyalgia. Arthritis Res Ther, 18, 68. doi: 10.1186/s13075-016-0954-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dantzer R, O’Connor JC, Freund GG, Johnson RW, & Kelley KW (2008). From inflammation to sickness and depression: when the immune system subjugates the brain. Nat Rev Neurosci, 9(1), 46–56. doi: 10.1038/nrn2297 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dawes JM, Andersson DA, Bennett DLH, Bevan S, & McMahon SB (2013). Inflammatory mediators and modertors of pain Wall and Melzack’s Textbook of Pain, 6th Edition Philadelphia, PA: Elsevier. [Google Scholar]
  16. de Souza JB, Goffaux P, Julien N, Potvin S, Charest J, & Marchand S (2009). Fibromyalgia subgroups: profiling distinct subgroups using the Fibromyalgia Impact Questionnaire. A preliminary study. Rheumatol Int, 29(5), 509–515. doi: 10.1007/s00296-008-0722-5 [DOI] [PubMed] [Google Scholar]
  17. Docampo E, Collado A, Escaramis G, Carbonell J, Rivera J, Vidal J, . . . Estivill X (2013). Cluster analysis of clinical data identifies fibromyalgia subgroups. PLoS One, 8(9), e74873. doi: 10.1371/journal.pone.0074873 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Drahovzal DN, Stewart SH, & Sullivan MJ (2006). Tendency to catastrophize somatic sensations: pain catastrophizing and anxiety sensitivity in predicting headache. Cogn Behav Ther, 35(4), 226–235. doi: 10.1080/16506070600898397 [DOI] [PubMed] [Google Scholar]
  19. Edwards RR, Bingham CO 3rd, Bathon J, & Haythornthwaite JA (2006). Catastrophizing and pain in arthritis, fibromyalgia, and other rheumatic diseases. Arthritis Rheum, 55(2), 325–332. doi: 10.1002/art.21865 [DOI] [PubMed] [Google Scholar]
  20. Fietta P, Fietta P, & Manganelli P (2007). Fibromyalgia and psychiatric disorders. Acta Biomed, 78(2), 88–95. [PubMed] [Google Scholar]
  21. Gatchel RJ, & Turk DC (1996). Psychological approaches to pain management: A practitioner’s handbook New York, NY, US: Guilford Press. [Google Scholar]
  22. Giesecke T, Williams DA, Harris RE, Cupps TR, Tian X, Tian TX, . . . Clauw DJ (2003). Subgrouping of fibromyalgia patients on the basis of pressure-pain thresholds and psychological factors. Arthritis Rheum, 48(10), 2916–2922. doi: 10.1002/art.11272 [DOI] [PubMed] [Google Scholar]
  23. Hawker GA, Mian S, Kendzerska T, & French M (2011). Measures of adult pain: Visual Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire (MPQ), Short-Form McGill Pain Questionnaire (SF-MPQ), Chronic Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF-36 BPS), and Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care Res (Hoboken), 63 Suppl 11, S240–252. doi: 10.1002/acr.20543 [DOI] [PubMed] [Google Scholar]
  24. Hurtig IM, Raak RI, Kendall SA, Gerdle B, & Wahren LK (2001). Quantitative sensory testing in fibromyalgia patients and in healthy subjects: identification of subgroups. Clin J Pain, 17(4), 316–322. [DOI] [PubMed] [Google Scholar]
  25. Kleinman L, Mannix S, Arnold LM, Burbridge C, Howard K, McQuarrie K, . . . Symonds T (2014). Assessment of sleep in patients with fibromyalgia: qualitative development of the fibromyalgia sleep diary. Health Qual Life Outcomes, 12, 111. doi: 10.1186/s12955-014-0111-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Leeuw M, Goossens ME, Linton SJ, Crombez G, Boersma K, & Vlaeyen JW (2007). The fear-avoidance model of musculoskeletal pain: current state of scientific evidence. J Behav Med, 30(1), 77–94. doi: 10.1007/s10865-006-9085-0 [DOI] [PubMed] [Google Scholar]
  27. Lentz TA, Barabas JA, Day T, Bishop MD, & George SZ (2009). The relationship of pain intensity, physical impairment, and pain-related fear to function in patients with shoulder pathology. J Orthop Sports Phys Ther, 39(4), 270–277. doi: 10.2519/jospt.2009.2879 [DOI] [PubMed] [Google Scholar]
  28. Lipkovich IA, Choy EH, Van Wambeke P, Deberdt W, & Sagman D (2014). Typology of patients with fibromyalgia: cluster analysis of duloxetine study patients. BMC Musculoskelet Disord, 15, 450. doi: 10.1186/1471-2474-15-450 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Loevinger BL, Shirtcliff EA, Muller D, Alonso C, & Coe CL (2012). Delineating psychological and biomedical profiles in a heterogeneous fibromyalgia population using cluster analysis. Clin Rheumatol, 31(4), 677–685. doi: 10.1007/s10067-011-1912-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Luciano JV, Forero CG, Cerda-Lafont M, Penarrubia-Maria MT, Fernandez-Vergel R, Cuesta-Vargas AI, . . . Rubio-Valera M(2016). Functional Status, Quality of Life, and Costs Associated With Fibromyalgia Subgroups: A Latent Profile Analysis. Clin J Pain, 32(10), 829–840. doi: 10.1097/AJP.0000000000000336 [DOI] [PubMed] [Google Scholar]
  31. Lukkahatai N, Walitt B, Espina A, Gelio A, & Saligan LN (2016). Understanding the Association of Fatigue With Other Symptoms of Fibromyalgia: Development of a Cluster Model. Arthritis Care Res (Hoboken), 68(1), 99–107. doi: 10.1002/acr.22626 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Marangell LB, Clauw DJ, Choy E, Wang F, Shoemaker S, Bradley L, . . . Wohlreich MM (2011). Comparative pain and mood effects in patients with comorbid fibromyalgia and major depressive disorder: secondary analyses of four pooled randomized controlled trials of duloxetine. Pain, 152(1), 31–37. doi: 10.1016/j.pain.2010.05.029 [DOI] [PubMed] [Google Scholar]
  33. Martinez MP, Sanchez AI, Miro E, Medina A, & Lami MJ (2011). The relationship between the fear-avoidance model of pain and personality traits in fibromyalgia patients. J Clin Psychol Med Settings, 18(4), 380–391. doi: 10.1007/s10880-011-9263-2 [DOI] [PubMed] [Google Scholar]
  34. Mendieta D, De la Cruz-Aguilera DL, Barrera-Villalpando MI, Becerril-Villanueva E, Arreola R, Hernandez-Ferreira E, . . . Pavon, L. (2016). IL-8 and IL-6 primarily mediate the inflammatory response in fibromyalgia patients. J Neuroimmunol, 290, 22–25. doi: 10.1016/j.jneuroim.2015.11.011 [DOI] [PubMed] [Google Scholar]
  35. Miller AH, Maletic V, & Raison CL (2009). Inflammation and its discontents: the role of cytokines in the pathophysiology of major depression. Biol Psychiatry, 65(9), 732–741. doi: 10.1016/j.biopsych.2008.11.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Morin CM, Bootzin RR, Buysse DJ, Edinger JD, Espie CA, & Lichstein KL (2006). Psychological and behavioral treatment of insomnia:update of the recent evidence (1998–2004). Sleep, 29(11), 1398–1414. [DOI] [PubMed] [Google Scholar]
  37. Nicassio PM, Moxham EG, Schuman CE, & Gevirtz RN (2002). The contribution of pain, reported sleep quality, and depressive symptoms to fatigue in fibromyalgia. Pain, 100(3), 271–279. [DOI] [PubMed] [Google Scholar]
  38. Nijs J, Roussel N, Van Oosterwijck J, De Kooning M, Ickmans K, Struyf F, . . . Lundberg M (2013). Fear of movement and avoidance behaviour toward physical activity in chronic-fatigue syndrome and fibromyalgia: state of the art and implications for clinical practice. Clin Rheumatol, 32(8), 1121–1129. doi: 10.1007/s10067-013-2277-4 [DOI] [PubMed] [Google Scholar]
  39. Noehren B, Dailey DL, Rakel BA, Vance CG, Zimmerman MB, Crofford LJ, & Sluka KA (2015). Effect of transcutaneous electrical nerve stimulation on pain, function, and quality of life in fibromyalgia: a double-blind randomized clinical trial. Phys Ther, 95(1), 129–140. doi: 10.2522/ptj.20140218 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Norton PJ, & Price EC (2007). A meta-analytic review of adult cognitive-behavioral treatment outcome across the anxiety disorders. J Nerv Ment Dis, 195(6), 521–531. doi: 10.1097/01.nmd.0000253843.70149.9a [DOI] [PubMed] [Google Scholar]
  41. Osman A, Barrios FX, Gutierrez PM, Kopper BA, Merrifield T, & Grittmann L (2000). The Pain Catastrophizing Scale: further psychometric evaluation with adult samples. J Behav Med, 23(4), 351–365. [DOI] [PubMed] [Google Scholar]
  42. Patient-Reported Outcomes Measurement Information System (PROMIS) Available at: http://www.nihpromis.org/.
  43. Rehm SE, Koroschetz J, Gockel U, Brosz M, Freynhagen R, Tolle TR, & Baron R (2010). A cross-sectional survey of 3035 patients with fibromyalgia: subgroups of patients with typical comorbidities and sensory symptom profiles. Rheumatology (Oxford), 49(6), 1146–1152. doi: 10.1093/rheumatology/keq066 [DOI] [PubMed] [Google Scholar]
  44. Roelofs J, Goubert L, Peters ML, Vlaeyen JW, & Crombez G (2004). The Tampa Scale for Kinesiophobia: further examination of psychometric properties in patients with chronic low back pain and fibromyalgia. Eur J Pain, 8(5), 495–502. doi: 10.1016/j.ejpain.2003.11.016 [DOI] [PubMed] [Google Scholar]
  45. Rutledge DN, Mouttapa M, & Wood PB (2009). Symptom clusters in fibromyalgia: potential utility in patient assessment and treatment evaluation. Nurs Res, 58(5), 359–367. doi: 10.1097/NNR.0b013e3181b499d2 [DOI] [PubMed] [Google Scholar]
  46. Salaffi F, Mozzani F, Draghessi A, Atzeni F, Catellani R, Ciapetti A, . . . Sarzi-Puttini P (2016). Identifying the symptom and functional domains in patients with fibromyalgia: results of a cross-sectional Internet-based survey in Italy. J Pain Res, 9, 279–286. doi: 10.2147/JPR.S100829 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Schalet BD, Pilkonis PA, Yu L, Dodds N, Johnston KL, Yount S, . . . Cella D (2016). Clinical validity of PROMIS Depression, Anxiety, and Anger across diverse clinical samples. J Clin Epidemiol, 73, 119–127. doi: 10.1016/j.jclinepi.2015.08.036 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Sluka KA, & Clauw DJ (2016). Neurobiology of fibromyalgia and chronic widespread pain. Neuroscience, 338, 114–129. doi: 10.1016/j.neuroscience.2016.06.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Sturgill J, McGee E, & Menzies V (2014). Unique cytokine signature in the plasma of patients with fibromyalgia. J Immunol Res, 2014, 938576. doi: 10.1155/2014/938576 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Sullivan MJ (2009). The Pain Catastrophizing Scale: User Manual. Montreal, Quebec: McGill University. [Google Scholar]
  51. Sullivan MJ, Adams H, Rhodenizer T, & Stanish WD (2006). A psychosocial risk factor--targeted intervention for the prevention of chronic pain and disability following whiplash injury. Phys Ther, 86(1), 8–18. [DOI] [PubMed] [Google Scholar]
  52. Sullivan MJ, & Bishop SR (1995). The Pain Catastrophizing Scale: Development and Validation. Psychological Assessment, 4, 524 – 532. [Google Scholar]
  53. Theadom A, Cropley M, & Humphrey KL (2007). Exploring the role of sleep and coping in quality of life in fibromyalgia. J Psychosom Res, 62(2), 145–151. doi: 10.1016/j.jpsychores.2006.09.013 [DOI] [PubMed] [Google Scholar]
  54. Thieme K, Turk DC, & Flor H (2004). Comorbid depression and anxiety in fibromyalgia syndrome: relationship to somatic and psychosocial variables. Psychosom Med, 66(6), 837–844. doi: 10.1097/01.psy.0000146329.63158.40 [DOI] [PubMed] [Google Scholar]
  55. Turk DC, Okifuji A, Sinclair JD, & Starz TW (1998). Differential responses by psychosocial subgroups of fibromyalgia syndrome patients to an interdisciplinary treatment. Arthritis Care Res, 11(5), 397–404. [DOI] [PubMed] [Google Scholar]
  56. Veale D (2007). Behavioural activation for depression. Advances in Psychiatric Treatment, 14(1), 29–36. [Google Scholar]
  57. Vincent A, Hoskin TL, Whipple MO, Clauw DJ, Barton DL, Benzo RP, & Williams DA (2014). OMERACT-based fibromyalgia symptom subgroups: an exploratory cluster analysis. Arthritis Res Ther, 16(5), 463. doi: 10.1186/s13075-014-0463-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Vlaeyen JW, Kole-Snijders AM, Boeren RG, & van Eek H (1995). Fear of movement/(re)injury in chronic low back pain and its relation to behavioral performance. Pain, 62(3), 363–372. [DOI] [PubMed] [Google Scholar]
  59. Vlaeyen JW, Kole-Snijders AM, Rotteveel AM, Ruesink R, & Heuts PH (1995). The role of fear of movement/(re)injury in pain disability. J Occup Rehabil, 5(4), 235–252. doi: 10.1007/BF02109988 [DOI] [PubMed] [Google Scholar]
  60. Wilson HD, Robinson JP, & Turk DC (2009). Toward the identification of symptom patterns in people with fibromyalgia. Arthritis Rheum, 61(4), 527–534. doi: 10.1002/art.24163 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Wolfe F, Clauw DJ, Fitzcharles MA, Goldenberg DL, Katz RS, Mease P, . . . Yunus MB (2010). The American College of Rheumatology preliminary diagnostic criteria for fibromyalgia and measurement of symptom severity. Arthritis Care Res (Hoboken), 62(5), 600–610. doi: 10.1002/acr.20140 [DOI] [PubMed] [Google Scholar]
  62. Yim YR, Lee KE, Park DJ, Kim SH, Nah SS, Lee JH, . . . Lee SS (2017). Identifying fibromyalgia subgroups using cluster analysis: Relationships with clinical variables. Eur J Pain, 21(2), 374–384. doi: 10.1002/ejp.935 [DOI] [PubMed] [Google Scholar]

RESOURCES