The contribution of pain, reported sleep quality, and depressive symptoms to fatigue in fibromyalgia

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1 Pain 100 (2002) The contribution of pain, reported sleep quality, and depressive symptoms to fatigue in fibromyalgia Perry M. Nicassio a,b, *, Ellen G. Moxham c, Catherine E. Schuman d, Richard N. Gevirtz a a California School of Professional Psychology, San Diego, Pomerado Road, San Diego, CA 92131, USA b Department of Psychiatry, University of California, San Diego, CA, USA c Baron Center, Inc., Scripps Trail, PMB 122, San Diego, CA 92131, USA d University of Vermont, and Fletcher Allen Health Care, Psychological Services, Patrick 406, 111 Colchester Avenue, Burlington, VT , USA Received 11 January 2002; accepted 26 July 2002 Abstract The major objective of this research was to evaluate the predictors of fatigue in patients with fibromyalgia (FM), using cross-sectional and daily assessment methodologies. In the cross-sectional phase of the research involving a sample of 105 FM patients, greater depression and lower sleep quality were concurrently associated with higher fatigue. While pain was correlated with fatigue, it did not independently contribute to fatigue in the regression equation. For a subset of patients from the cross-sectional sample ðn ¼ 63Þ who participated in a week of prospective daily assessment of their pain, sleep quality, and fatigue, multiple regression analysis of aggregated (averaged) daily scores revealed that previous day s pain and sleep quality predicted next day s fatigue. Depression from the cross-sectional phase was not related to aggregated daily fatigue scores. A path analytic framework was tested with disaggregated (removing between subjects variability) data in which pain was predicted to contribute to lower sleep quality which, in turn, was predicted to lead to greater fatigue. The results revealed that poor sleep quality fully accounted for the positive relationship between pain and fatigue, thus substantiating the mediational role of sleep quality. The findings are indicative of a dysfunctional, cyclical pattern of heightened pain and non-restful sleep underlying the experience of fatigue in FM. q 2002 International Association for the Study of Pain. Published by Elsevier Science B.V. All rights reserved. Keywords: Pain; Sleep Quality; Depression; Fatigue; Fibromyalgia 1. Introduction Fibromyalgia (FM) is an enigmatic chronic pain syndrome of unknown etiology and the second most prevalent condition in rheumatological practice (Goldenberg, 1987). FM is characterized by widespread musculoskeletal pain of at least 3 months duration, accompanied by tenderness upon palpation in 11 out of 18 anatomical sites (Wolfe et al., 1990). While these criteria are diagnostically specific for FM, they do not account for commonly reported associated symptoms such as fatigue and non-restorative sleep. Although the hallmark symptom of FM is chronic, unrelenting pain, fatigue constitutes one of the disorder s most troublesome and common problem (Wolfe et al., 1996). Previous studies have shown that between 78 and 94% of FM patients report being fatigued (Wolfe et al., 1990, 1996). In contrast, the prevalence of fatigue reported in community surveys of the general population ranges between 10 and * Corresponding author. Tel.: ; fax: address: pnicassio@alliant.edu (P.M. Nicassio). 28% (David et al., 1990; Lewis and Wessely, 1992). Fatigue also appears to be more prevalent in FM than in other rheumatogical conditions. For example, a study by Wolfe et al. (1996) showed that 76% of FM patients reported a clinically significant level of fatigue, in contrast to 41% of osteoarthritis patients and 42% of rheumatoid arthritis patients. Moreover, these authors also found that patients who reported clinically significant levels of fatigue were six times more likely to report being unable to work. In a study examining daily fatigue and pain in FM, Hendriksson et al. (1992) reported that fatigue constituted a greater impediment to the accomplishment of daily tasks than did pain. Furthermore, FM fatigue tends to be chronic and unremitting like FM pain (Wolfe et al., 1997). The high prevalence rates of fatigue in FM may in part be due to the overlap between FM and chronic fatigue syndrome (CFS). Muscle pain and multiple joint pain without joint swelling or redness are among the symptom criteria for making a CFS diagnosis (see Fukuda et al., 1994). As many as 58% of women with FM may also be afflicted with CFS, based on epidemiological data (White et al., 2000). FM /02/$20.00 q 2002 International Association for the Study of Pain. Published by Elsevier Science B.V. All rights reserved. PII: S (02)

2 272 P.M. Nicassio et al. / Pain 100 (2002) patients, however, show abnormalities in allodynia (heightened pain sensitivity) and functional brain activity that are not characteristics of patients with CFS (Bradley et al., 2000). Thus, FM and CFS are distinguishable disorders. Ambiguity surrounding the etiology of FM also contributes to the difficulty in understanding the genesis of fatigue. While there is no known pathophysiological cause of FM, several physiological differences have been found between FM patients and controls. These include low platelet serotonin levels (Russell et al., 1992), elevated levels of substance P in spinal fluid (Russell et al., 1994), and autonomic nervous system dysfunction (Bennett et al., 1997). However, the role of these abnormalities in FM fatigue has not been determined. The evaluation of the independent and conjoint influences of physiological and psychological factors in FM fatigue is an important focus for future research. An opposing view is that depression itself is the cause of FM pain and that the major symptoms of FM may be viewed as depressive equivalents, or somatic indicators of an underlying affective disorder. This position is not as tenable as the former since the majority of FM patients do not meet diagnostic criteria for depression (Russell, 2001), and their psychological functioning has been found to be highly variable (Schoenfield-Smith et al., 1995). In this regard, many FM patients are not depressed, while all have chronic pain (Buckwald, 1996). Collectively, the data suggest that a dysfunctional pattern may characterize the plight of many FM patients, typified by increasing pain and associated sleep and mood problems, all of which may contribute to fatigue. At this juncture, the independent and interactive roles of pain, sleep disturbance, and depression in fatigue remain speculative since these variables have not been evaluated in the same study Multimodal perspective of FM fatigue The present research was based on the premise that multiple factors may contribute to FM fatigue. This study focused on the roles of pain, depression, and sleep disturbance since they are common problems in FM and all have the potential to lead to, maintain, and exacerbate fatigue. Pain and fatigue have been positively correlated in research in other rheumatic conditions (Crosby, 1991), but it has not been determined if pain leads to fatigue directly, or indirectly, by exacerbating mood or sleep problems. In this regard, Moldofsky et al. (1975) reported on the relationship between pain and sleep disturbance in FM, providing evidence that alpha-delta intrusions in slow-wave sleep are associated with increased musculoskeletal pain. Although these data suggested that a primary sleep disturbance may underlie FM symptomatology, an alternate view is that pain may lead to disruptions in slow-wave sleep, causing patients to feel unrefreshed upon awakening (Carrette, 1995), and fatigued during the day. In a study of 50 FM patients assessed over 30 consecutive days, Affleck et al. (1996) found a recursive relationship between pain and sleep in which poor sleep led to more pain the following day and poor sleep that night. Depression, like sleep disturbance, may also give rise to FM fatigue. Insomnia and fatigue are hallmark symptoms of depression, and very high rates of depression have been found in a number of FM studies (Alfici et al., 1989; Hudson et al., 1985; Nicassio et al., 1995a; Russell, 2001). History of affective disorder has been linked with higher fatigue in patients with rheumatoid arthritis (Fifield et al., 1998), and a substantial correlation between pain and depressive symptomatology in FM has been reported (Nicassio et al., 1995a; Wolfe et al., 1996). Chronic FM pain may lead to depression directly, or the effects of pain may be mediated by dysfunctional illness beliefs such as perceived helplessness (Nicassio et al., 1999) Study objectives The purpose of this research was to examine the independent contribution of pain, sleep quality, and depression to FM fatigue in a sample of clinically diagnosed FM patients. The research was conducted in two phases. First, the relationship between variables was explored in a cross-sectional design in which summary indices of these variables were adopted. The purpose of this phase of the research was to describe statistically whether pain, sleep disturbance, and depression would each contribute unique variability to concurrent fatigue. The second phase of the research explored the relationships among variables based on daily assessments of pain, sleep, and fatigue that were conducted prospectively over a week. This phase had two primary purposes. First, we attempted to replicate the cross-sectional findings using aggregated (averaged) scores across days. Secondly, we tested two models using disaggregated data that clarified the dynamic relationships among pain, sleep quality, and fatigue. In the first model, we hypothesized that previous day s pain would adversely affect sleep quality which would contribute to greater fatigue the next day. An alternate model was also evaluated in which poor sleep quality was expected to lead to greater pain the next day, followed by greater fatigue. The use of disaggregated data allowed for an analysis of the daily patterning of variables after subject variability from each score was removed (Eckenrode, 1984), thereby allowing for a determination of directional relationships among variables at the withinpersons level of analysis. 2. Methods 2.1. Sampling procedures This study is based on data gathered from a comprehensive psychosocial research program for FM that has been

3 P.M. Nicassio et al. / Pain 100 (2002) described in detail elsewhere (Nicassio et al., 1995b). Participants diagnosed with FM were recruited from private and university-based rheumatology clinics, FM support groups, and the community via public service announcements placed in local newspapers. Criteria for eligibility in this study were: (a) must be at least 18 years of age; (b) specific diagnosis of FM by the participant s rheumatologist or physician, including written confirmation, from a physician, of the FM diagnosis; and (c) confirmation of the FM diagnosis by a physical tender point examination conducted by a trained rheumatology nurse in which at least 11 of 18 tender points were found, and the verification that the participant had diffuse upper and lower body pain for a duration of 3 months or more (Wolfe et al., 1990). Exclusionary criteria were: (a) the existence of concomitant rheumatological disorders, including rheumatoid arthritis, systemic lupus erythematosus, Hashimoto s disease, Sjogren s syndrome or scleroderma; (b) any other serious medical illness or pregnancy; and (c) the existence of a psychotic disorder or any other serious psychiatric condition. In addition, patients needed to be stable on analgesic or antidepressant medications for a minimum of 30 days preceding the study. This research focused exclusively on the recruitment of FM patients, and extreme care was exercised in applying ACR criteria (Wolfe et al., 1990), confirming the FM diagnosis with referring physicians, and conducting the rheumatological examination. While no specific screening for CFS was implemented and it is likely that the sample included patients who also met diagnostic criteria for CFS, the sample did not include patients who had CFS only Participants A total of 122 patients met the above criteria; however, 17 patients were eliminated due to missing data on study variables. The total sample consisted of 105 adults, including 96 women and nine men, with an average age of years ðsd ¼ 11:82Þ. Average reported illness duration (time since diagnosis) was years ðsd ¼ 13:38Þ. Sixty-three percent were married, 20% were divorced or separated, 14% were single, and 3% were widowed. Eighty-eight percent of the participants were Caucasian, with the remainder being of Hispanic, Asian, or other ethnic origin. The average Hollingshead Index score of socioeconomic status was 49, indicative of middle to upper-middle socioeconomic standing. Eighty-seven percent of the participants reported usage of analgesics, and 37% reported usage of antidepressants to manage their FM symptoms. All participants resided in San Diego or neighboring counties in Southern California Phase I The total sample of 105 patients participated in a crosssectional evaluation of their psychosocial adjustment to their FM. Doctoral-level psychology research assistants administered a battery of measures to patients at the Clinical Research Center of a major university medical center. The battery assessed FM symptoms, pain, psychological functioning, and social functioning. The data reported in this research are based on measures of pain, depression, sleep, and fatigue Predictor variables Pain FM pain was assessed via a composite index score comprising the following measures which were administered at the clinic pretreatment evaluation: (a) the Pain Rating Index (PRI), consisting of the sum of the ranked values associated with adjectives depicting the severity of pain from the McGill Pain Questionnaire (MPQ) (Melzack, 1975); (b) a body area score, reflecting the number of body areas endorsed as painful on a human figure drawing from the MPQ; (c) the Pain Subscale of the Fibromyalgia Impact Assessment Questionnaire (FIA) (Mason et al., 1992), measuring the severity of FM pain over the preceding month; and (d) a flare-up index, created by multiplying the frequency of pain flares over the past month by the square of their average intensity derived from a 10 cm visual analogue rating scale (Brown and Nicassio, 1987). The Pain Index is the sum of the standardized scores of the individual pain measures. This composite measure has been effectively employed in other FM research (Nicassio et al., 1995a,b, 1999) Sleep Patient self-report of sleep over the past week was assessed using the Sleep Subscale from the FIA Questionnaire (Mason et al., 1992). While the Sleep Subscale comprises four items, only the item addressing how well rested patients felt upon awakening was adopted in this research. As an index of the quality of sleep, this item was chosen for its relevance to the FM syndrome. Responses on this item ranged from 1 ¼ no day, to 5 ¼ all days (reverse scored for data analysis) Depression Depressive symptoms over the past week were assessed using the Center for Epidemiological Studies Depression Scale (CES-D), developed by Radloff (1977). The CES-D is a self-report, 20-item measure that assesses the prevalence of depressive symptomatology in community samples and has been adopted in research investigating depression in FM and arthritis populations (Nicassio et al., 1999; Tayer et al., 2001). Participants respond to such items as I felt depressed and I felt my life was a failure by indicating one of the following responses: rarely or never, some of the time, occasionally, or most or all of the time. A score of 16 or more is indicative of depression in a community-based sample, whereas a cutoff score of 19 has been recommended for chronic pain populations (Turk and Okifuji, 1994). Within

4 274 P.M. Nicassio et al. / Pain 100 (2002) the CES-D are two items associated with fatigue: Item 11, My sleep was restless and Item 20, I could not get going. To prevent confounding with the criterion variable, fatigue, these items were removed from the CES-D for statistical analyses Criterion variable Fatigue This variable was assessed by the Vanderbilt Fatigue Scale (VFS), a measure that was adapted from the Multidimensional Assessment of Fatigue (MAF), developed by Belza (1995). This self-report measure consists of five items; the number of days the patients experienced fatigue in the preceding week, and four Likert items reflecting degree and severity of fatigue, impact of fatigue on daily functioning, and impact of fatigue on distress. The Likert items are summed, divided by 0.25, and then multiplied by the number of days in which patients reported fatigue. The summary score thus reflects a combination of the frequency and intensity of the patient s fatigue. The internal consistency reliability of this measure is high (0.93) (Tayer et al., 2001) Phase 2 In the second phase of this research, a sample of 63 patients participated in a daily study assessing prospective relationships among self-reported pain, non-restorative sleep, and fatigue. These patients comprised a subset of the larger cross-sectional sample who agreed to participate in a clinical trial to evaluate coping skills intervention for their FM pain. The comparability of the samples was evaluated by t-tests conducted on clinical variables, age, and illness duration. The samples did not differ in depression ðp ¼ 0:76Þ, pain ðp ¼ 0:73Þ, sleep quality ðp ¼ 0:39Þ, fatigue ðp ¼ 0:17Þ, age ðp ¼ 0:51Þ, or illness duration ðp ¼ 0:26Þ. Chi-square tests revealed no differences between the samples in gender ðp ¼ 0:39Þ, race ðp ¼ 0:60Þ, or marital status ðp ¼ 0:90Þ. Thus, the samples were highly similar in clinical status and sociodemographic characteristics. Patients were mailed a packet of measures and then contacted by telephone by a research assistant to review the assessment protocol. For 6 consecutive days, patients completed the sleep measure upon arising, and the pain and fatigue measures just before retiring for the night Predictor variables Pain Upon retiring, patients rated their daily pain on two 10 cm visual analogue scales, reflecting their experience of pain that day. The two items assessed pain severity and pain unpleasantness. Scores for pain severity ranged from 0 ¼ no pain, to 100 ¼ extremely severe. Scores for pain unpleasantness ranged from 0 ¼ not at all unpleasant, to 100 ¼ extremely unpleasant. The ratings of the two items were added to form a pain summary score Sleep At 10 min after awakening each morning, patients answered the following three questions about their sleep the previous night. Patients estimated how long it took them to fall asleep in minutes, evaluated the quality of their sleep, and indicated how rested they felt upon awakening. The latter two items were assessed on 10 cm visual analogue scales ranging from 0 to 100. Given the relevance of non-restorative sleep to FM and to achieve consistency with the cross-sectional data, only the item on how rested patients felt upon awakening was used in data analyses. Scores on this item ranged from 0 ¼ very tired, unrested, to 100 ¼ refreshed, fully rested Criterion variable Fatigue The Fatigue Subscale of the Visual Analogue Scale to Evaluate Fatigue (VAS-F) (Lee et al., 1991) was adopted to assess fatigue. The VAS-F comprises 13 items describing fatigue arranged on 10 cm visual analogue lines ( not at all tired to absolutely tired ). The measure was developed to assess fatigue in both patient and non-clinical populations. The internal consistency reliability of the VAS-F is high, ranging from 0.91 to 0.96 in studies measuring fatigue in patients with HIV (Lee et al., 1999), and in research comparing patients being evaluated for sleep disorders with healthy controls (1991) Statistical analyses Two approaches for analyzing the data were adopted. In Phase I, hierarchical multiple regression analysis was conducted to examine the independent contribution of depression, pain, and sleep quality on global pain scores reported over the previous week. This analysis allowed for an evaluation of the separate contribution of each predictor variable while controlling for the other two. In Phase 2, hierarchical regression analysis, employing path analytic techniques, was used to clarify the day-to-day relationships between pain, sleep quality, and fatigue, and to test specifically whether sleep quality would mediate the relationship between previous day s pain and next day s fatigue. 3. Results 3.1. Phase I findings The major objective of the cross-sectional phase of the research was to evaluate the independent contribution of pain, depression, and sleep quality to FM fatigue. Because the data were collected concurrently, potential causal patterning among the variables was not examined. Pearson

5 P.M. Nicassio et al. / Pain 100 (2002) Table 1 Pearson correlations between Phase I study variables: cross-sectional findings a Pain Depression Fatigue Sleep quality Pain Depression 0.52** Fatigue 0.45** 0.63** Sleep quality 20.27* 20.28* 20.35** a *P, 0:01, **P, 0:001. Table 3 Correlations between Phase II study variables: aggregated (averaged) data a Pain Depression b Fatigue Sleep quality Pain Depression b 0.36** Fatigue 0.40** 0.26* Sleep quality 20.36* * a *P, 0:05; **P, 0:01. b From Phase I. product moment correlations among these variables are presented in Table 1. All correlations among pain, depression, fatigue, and sleep quality reached significance (P values,0.01). As anticipated, pain, depression, and fatigue were positively correlated, whereas the relationships between these variables and sleep quality were negative. Fatigue and depression were the most strongly correlated variables ðr ¼ 0:63Þ. Thus, greater pain, greater depression, and lower sleep quality were associated with higher fatigue scores. In order to determine the relative significance of pain and sleep quality in predicting fatigue, two separate hierarchical multiple regression analyses were conducted in which the order of entry of pain and sleep quality was varied in the regression equation. In each analysis, the effect of depression was controlled on the first step, owing to the large bivariate relationship between depression and fatigue and the potential overlap in the concurrent assessment of these constructs. On the first step of both analyses, depression was highly significant, Fð1; 105Þ ¼62:65, P, 0:001), accounting for 37.4% of the variance in fatigue. In the first analysis, when pain was entered before sleep quality, its contribution to fatigue fell short of significance (Fð1; 104Þ ¼3:30, P ¼ 0:07). On the next step, sleep quality made a significant contribution to fatigue (Fð1; 103Þ ¼5:30, P, 0:05). When sleep quality was entered before pain, its contribution to fatigue also was significant, Fð1; 104Þ ¼6:51, P, 0:05. However, when pain followed sleep quality, it did not add unique variability to fatigue Fð1; 103Þ ¼2:16, P. 0:10. Table 2 presents a summary of the regression analysis after all the variables have entered the model. Thus, the findings from Phase I substantiate the important roles of depression and poor sleep quality in explaining Table 2 Multiple regression analysis predicting fatigue: cross-sectional findings, final model a Predictor Standardized b t sr 2 Depression ** 0.18 Pain Sleep quality * 0.03 a sr 2 ¼ unique variance; *P, 0:05, **P, 0:001; standardized b ¼ standardized regression coefficient. current FM fatigue. Sleep quality predicted fatigue before and after taking into account the effect of pain Phase II findings Phase II of the research examined prospective relationships between daily pain, daily sleep quality, and daily fatigue from data collected over 1 week. Both aggregated and disaggregated analyses were conducted Aggregated analyses In these analyses, data were summed across days for each participant. For pain measured each evening, summary scores were obtained for Days 1 through 6, whereas for sleep quality and fatigue, scores were obtained for Days 2 through 7. The relationships between preceding day s pain, sleep quality reported the following morning, and fatigue the following evening were then examined. Correlations between aggregated variables and depression from Phase I (see Table 3) indicate that depression, pain, and sleep quality were related to fatigue in a manner that was consistent with the cross-sectional findings. However, while the correlations between daily pain, daily sleep quality, and daily fatigue were very similar, the relationship between depression and daily fatigue was substantially lower ðr ¼ 0:26Þ. Accordingly, the aggregated data demonstrate that prior day s pain was associated with poorer sleep quality and higher fatigue reported the next day. As per the cross-sectional findings, hierarchical multiple regression analysis was conducted to examine the independent contribution of predictor variables to fatigue. Prior pain and sleep quality were evaluated for their contribution to fatigue after controlling for depression from Phase I. On the first step, depression did not predict fatigue, P ¼ 0:10; however, pain, entered on the next step, made a highly significant contribution Fð1; 61Þ ¼11:64, P, 0:01, accounting for 16% of the variance in fatigue. Sleep quality, entered on the final step, fell just short of significance, Fð1; 60Þ ¼ 3:20, P ¼ 0:08. Thus, in contrast to Phase I findings, daily pain was a more significant predictor of daily fatigue than depression or daily sleep quality. Phase I depression scores and daily sleep quality were not related independently to daily fatigue when they competed with daily pain in the regression model (see Table 4).

6 276 P.M. Nicassio et al. / Pain 100 (2002) Table 4 Hierarchical multiple regression analysis of aggregated (averaged) daily fatigue (Phase II) a Step Predictor R 2 R 2 change F change df b 1 Depression b , Pain * 1, Sleep quality , a *P, 0:01; R 2, variance accounted for by predictor variables in the regression equation; R 2 change, variance accounted for by predictor variable at each step of the regression equation; F change, F ratio testing the significance of R 2 change; b ¼ standardized regression coefficient. b From Phase I Disaggregated analyses Disaggregated analyses examined the temporal sequencing of pain, sleep quality, and fatigue, and provided information on the dynamic relationships among variables over time. Disaggregated analysis examines the day-to-day fluctuation among independent and dependent variables after controlling for between-persons variability and autocorrelation. Two steps are necessary to conduct this analysis in an unbiased manner. First, variance due to individual differences between persons was removed by dummy coding participants (n-1 participants) and then examining their contribution in the regression equation. Next, autocorrelation, indicative of the serial dependency among measures taken over consecutive days, was estimated using the Durbin Watson first-order autocorrelation statistic (Caspi et al., 1987). For each analysis, autocorrelation rho variables were created and entered in the regression equation. Controlling for between-persons variability and autocorrelation allowed for an examination of the patterning of the data over time by eliminating the effect of each participant s response style and serial correlations among measured constructs. Correlations between disaggregated pain, sleep quality, and fatigue appear in Table 5. Consistent with aggregated findings, higher pain and lower sleep quality were associated with greater fatigue (P values,0.01). In the first model, path analytic multiple regression analysis tested whether previous day s high pain (Day 1) would be associated with poorer sleep quality reported the following morning (Day 2), which would contribute to greater fatigue reported that evening (Day 2). Table 5 Correlations among disaggregated variables a The first analysis evaluated the path from pain to sleep quality. The entry of the participant s variable on the first step accounted for 51% of the variance in sleep quality, Fð60; 303Þ ¼5:20, P, 0:001. Pain, entered on the next step, accounted for an additional 5% of the variance, thus confirming the significance of the hypothesized path from pain to sleep quality. The second regression analysis tested the direct effect of pain on next day s fatigue. After removing the effect of participants which accounted for 56% of the variance, Fð60; 303Þ ¼6:29, P, 0:001, pain made a significant contribution, accounting for 3% unique variance in next day s fatigue, Fð61; 302Þ ¼7:00, P, 0:001. The direct relationship between pain and fatigue was thus established. The final analysis examined the mediational role of sleep quality. In order for mediation to occur, the path from sleep quality to fatigue would be significant, and the positive, significant relationship between pain and fatigue would be eliminated after both variables entered the regression equation. After controlling for individual differences, the contribution of pain and sleep quality to fatigue was significant, Fð62; 301 ¼ 3:83, P, 0:001, explaining an additional 3% of the variance. Lower sleep quality was associated with higher fatigue (b ¼ 20:13, P, 0:01), while the contribution of pain to fatigue became negative (b ¼ 20:15, P, 0:001). Therefore, the relationship between sleep quality and fatigue fully accounted for the positive, direct relationship between pain and fatigue, thus substantiating the hypothesized mediational significance of sleep quality (Fig. 1). Because of the potentially dynamic, recursive relationship between pain and sleep quality, an alternate disaggregated model was tested in which the effect of poor sleep quality on fatigue was examined via the mechanism of worsening pain. This analysis focused on sleep quality reported in the morning (Day 2 a.m.), and pain and fatigue reported later that evening (Day 2 p.m.). In testing the path from sleep quality to pain, participant variables accounted for 28% of the variance in pain, Fð60; 304Þ ¼1:95, P, 0:001. Sleep quality, on the next step, was also significant, Fð61; 303Þ ¼2:37, P, 0:001, contributing 5% unique variance to pain. Lower sleep quality was associated with greater pain reported that evening. The direct path from sleep quality to fatigue was then examined. After taking into account the participants variable which accounted for 33% of the variance in fatigue, Fð60; 304Þ ¼2:44, P, 0:001, sleep quality, on the next Variable Pain Fatigue Sleep quality Pain Fatigue 0.28* Sleep quality 20.27* 20.36* a *P, 0:01. Disaggregated refers to daily scores from which betweensubjects variability was removed. Fig. 1. Model depicting relationships between Day 1 pain, Day 2 sleep quality, and Day 2 fatigue with disaggregated data. *P, 0:05, **P, 0:01, ***P, 0:001.

7 P.M. Nicassio et al. / Pain 100 (2002) Fig. 2. Model depicting relationships between Day 2 sleep quality, Day 2 pain, and Day 2 fatigue with disaggregated data. *P, 0:001. step, independently predicted fatigue, contributing 2.5% unique variance. Lower sleep quality was associated with greater fatigue that evening. The final analysis tested the mediational role of pain. After removing participant variance, sleep quality and pain jointly accounted for 6.5% of the variance in fatigue, Fð62; 302Þ ¼2:83, P, 0:001. However, only the path from pain to fatigue was significant, b ¼ 0:26, tð302þ ¼ 4:75, P, 0:001. Pain contributed 4.7% of the variance in fatigue, whereas the relationship between sleep quality and fatigue became non-significant. These analyses indicate that pain fully mediated the effect of sleep quality on fatigue (Fig. 2). 4. Discussion Fatigue is a poorly understood symptom that has a high degree of prevalence in several rheumatic conditions (Wolfe et al., 1996). Like chronic FM pain, fatigue is associated with considerable social and vocational disability (Wolfe et al., 1996), and thus constitutes a major clinical problem for patients and health care providers. Equally troublesome is the lack of understanding concerning the etiology of fatigue and the recommended approach to its management. While considerable research has focused on FM pain, FM fatigue has received sparse empirical scrutiny. The present study attempted to fill the void in the research literature by investigating the correlates of FM fatigue. A multidimensional perspective was adopted, based on the premise that several variables may contribute to fatigue, and that no single variable by itself would be likely to explain all of the variability in this symptom (Engel, 1980). Accordingly, this research analyzed the roles of depression, sleep quality, and pain with the intent of determining their independent contributions. An advantage of this research was that two separate methodologies were employed, enabling the acquisition of information on correlates of fatigue with cross-sectional and day-to-day approaches. The cross-sectional phase of the research provided evidence for the important contributions of depression and sleep quality to fatigue. Depression was the dominant factor, explaining 18% of the variability in fatigue. However, lower sleep quality was still related to higher fatigue, indicating that its role was not explained by mood disturbance. Although pain and fatigue were significantly correlated, pain did not contribute independently to fatigue when sleep quality and depression were taken into account. Cross-sectional pain and depression shared considerable variance, and the role of pain in fatigue was explained by its convergence with depression. The cross-sectional findings indicated that concurrent reports of fatigue overlapped considerably with depressive mood despite the fact that fatigue-related items were removed from the CES-D. It is possible, however, that when patients were subjected to a subjective recall of their experiences over the past month, an affective bias may have contributed to a spuriously high correlation between these two variables. Biases such as neuroticism, for example, may account for such variance in retrospective self-report measures when negative symptomatology is assessed (Larson, 1992). Nonetheless, the independent contribution of poor sleep quality to fatigue argues against an underlying reporting bias that could have explained the relationships among all of these variables. Phase II clarified the relationships among the predictors of fatigue since data were collected prospectively. Although different measures of pain, sleep quality, and fatigue were used, the correlations among these measures were similar to those found in the cross-sectional phase of the study, thus enhancing the validity of the relationships among the constructs assessed. Higher daily pain was positively correlated with higher daily fatigue, and, as expected, both daily pain and daily fatigue were negatively correlated with sleep quality. However, the correlation between Phase I CES-D scores and daily fatigue was substantially weaker, and depression scores made no contribution to the prediction of fatigue in regression analyses of aggregated data. In contrast to cross-sectional results, aggregated multiple regression analysis revealed that prior day s pain was a highly significant predictor of subsequent day s fatigue. Pain overshadowed the role of sleep quality in predicting fatigue, a finding which supports the primacy of the role of pain in FM and its potential impact on other symptoms of the condition. Chronic, diffuse body pain is the cardinal complaint of FM patients (Wolfe et al., 1990) and the source of considerable distress and disability. Daily pain shared less variance with CES-D scores, thus enhancing its independent contribution to daily fatigue and explaining the departure in these results from cross-sectional findings. Disaggregated analyses, which controlled for individual differences, examined potential causal patterning among the variables and clarified the relative significance of predictors of fatigue. Specifically, a model was tested in which pain was hypothesized to affect next day s fatigue by interfering with sleep quality. Path analytic regression analyses provided substantial support for this framework. Higher prior day s pain was related to higher fatigue the next day through the intervening influence of poor sleep quality. These data are consistent with the position that FM pain can have negative consequences on sleep which, in turn, contributes to fatigue. As a departure from the aggregated analyses, the findings provided greater support for importance of sleep quality in that the positive, direct relationship

8 278 P.M. Nicassio et al. / Pain 100 (2002) between pain and fatigue was totally eliminated when sleep quality was examined in the regression equation. An alternate disaggregated model, in which the effect of sleep quality was examined on next day s pain and fatigue with pain as a mediator, shed light on the recursive nature of the relationship between pain and sleep, and their conjoint impact on fatigue. Affleck et al. (1996) had previously found evidence of a recursive pattern between FM pain and sleep disturbance using a within-subjects methodology. The present study demonstrated that poor sleep quality was associated with greater pain and fatigue the next day; however, pain fully mediated the relationship between sleep quality and fatigue in the regression analysis predicting to fatigue. It should be noted, however, that the data do not substantiate the causal primacy of either pain or sleep in examining their relationship with each other or in their association with fatigue. Both pain and sleep disturbances may be caused, for example, by physical or emotional trauma that has been shown to have a high degree of prevalence in the histories of FM patients (Gardner, 2000). Rather, the two disaggregated models illustrate a disruptive, cyclical pattern of previous day s high pain leading to poor sleep quality, which in turn, is associated with higher pain and fatigue the following day. The continuous nature of this disruptive cycle contributes to an explanation of the significant degree of behavioral and social impairment that is frequently seen in FM patients. It should be recognized, however, that this research did not include a polysomnographic assessment of sleep that could be used to determine whether patients were sleep deprived or were affected by an abnormality in sleep architecture. Subjective sleep complaints may not correspond very closely with objective sleep findings, and it is possible that complaints of poor sleep quality in the present study may not have reflected objective abnormalities in sleep. The use of more objective indicators of sleep would thus make a significant contribution in future research which attempts to carefully delineate the roles of mood disturbance, pain, and sleep in the analysis of FM fatigue. An additional caveat in interpreting the findings from this research is that patients were not diagnostically evaluated for the presence of CFS. A significant percentage of patients with FM also meet diagnostic criteria for CFS (White et al., 2000), and it is likely that a substantial number of the FM patients in this research also would have been diagnosed with CFS. Because of the potential diagnostic overlap, the findings of this research apply to patients with FM alone and to patients who meet diagnostic criteria for both disorders. Since the factors contributing to fatigue in CFS alone may be different than those found in the present study, it is essential for future research focusing on fatigue to carefully distinguish between patients with FM alone, CFS alone, and those having both FM and CFS, using objective diagnostic criteria (Fukuda et al., 1994). In summary, this research has provided evidence of the contribution of depressive mood, pain, and sleep quality to fatigue in FM. It has also illustrated the value of using daily assessment methodology to extend our knowledge of the dynamic relationships among these variables and as a means of testing cross-sectional hypotheses. In future research, it is recommended that a longer period of daily assessment be adopted to evaluate the reliability of these dynamic patterns. In addition, the adoption of daily measures of mood would clarify how mood disturbance may influence the patterning of pain, sleep, and fatigue across time. 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