The prevalence and moderators of fatigue in people who have been successfully treated for cancer

Journal of Psychosomatic Research 60 (2006) 29 – 38

The prevalence and moderators of fatigue in people who have been successfully treated for cancer Katharine E. Younga,b,T, Craig A. Whitec,d a Section of Psychological Medicine, University of Glasgow, Glasgow Royal Infirmary, United Kingdom Clinical Psychology Department, Canniesburn Plastic Surgery Unit, Glasgow Royal Infirmary, United Kingdom c Macmillan Consultant in Psychosocial Oncology, Consulting and Clinical Psychology Services, NHS Ayrshire and Arran, Ayrshire Central Hospital, United Kingdom d University of Glasgow, United Kingdom b

Received 10 May 2004; accepted 29 March 2005

Abstract Objective: The aims of this study were to estimate the prevalence of severe fatigue in disease-free breast cancer patients according to draft International Classification of Disease, Tenth Edition (ICD-10) criteria for cancer-related fatigue (CRF) and to obtain further information on the validity of these criteria. Furthermore, hypotheses derived from psychosocial theories of fatigue regarding the association of fatigue with activity level, psychological distress, and cognitive constructs were also tested. Methods: Sixty-nine disease-free breast cancer patients were assessed at least 6 months posttreatment, using self-report questionnaires and a structured interview.

Results: Nineteen percent of the sample met criteria for CRF. This subgroup differed significantly from the rest of the sample on multiple measures of fatigue and interference. Self-reported activity level bore no relationship to fatigue. Fear of recurrence (FOR) contributed to fatigue indirectly, whilst psychological distress and beliefs about activity appeared to predict fatigue directly. Conclusion: The validity of the draft ICD-10 criteria for CRF was supported. Further research is required into the relationship between fatigue, emotional distress, and cognitive– behavioural factors. D 2006 Elsevier Inc. All rights reserved.

Keywords: Breast neoplasms; Fatigue; Activity level; Psychological distress; Cognitions; Neuroticism

Introduction Fatigue is one of the most common unrelieved symptoms in cancer patients [1] and is a major factor affecting quality of life, both during and following treatment [2– 4]. Cancerrelated fatigue (CRF) differs from dnormalT or everyday fatigue in terms of its severity and persistence in the presence of adequate amounts of sleep and rest [5]. Furthermore, it is experienced as a multidimensional phenomenon, affecting physical, cognitive, affective, and behavioural domains [6,7]. For a significant proportion of cancer survivors, fatigue does not decline to premorbid levels following treatment T Corresponding author. Canniesburn Plastic Surgery Unit, Clinical Psychology Department, Room 2/98, Floor 2, Jubilee Building, Glasgow Royal Infirmary, G4 OSF Glasgow, Scotland. Tel.: +44 141 211 5639. 0022-3999/06/$ – see front matter D 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.jpsychores.2005.03.011

and can persist for months or even years [8,9]. A recent review reported that four out of six controlled prevalence studies found significantly higher levels of fatigue in disease-free cancer patients (D-FCP; [10]). The overall prevalence of significant fatigue in D-FCP was estimated as one in three, with figures ranging from 17% to 30% [10]. Differing methods of measurement and definitions of severe fatigue were proposed to account for much of this variation [10]. In an attempt to standardise the definition, diagnostic criteria for CRF have been proposed [11] and submitted for inclusion in the International Classification of Diseases, Tenth Edition (ICD-10; [12]). In preliminary validation studies, Cella et al. [5] and Sadler et al. [13] reported prevalence estimates of 17% and 21% respectively, in samples of posttreatment cancer survivors (mainly breast cancer patients). These comparatively low preva-

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lence estimates indicate that the draft ICD-10 criteria may identify a smaller subset of fatigued D-FCP than do questionnaire-based estimates. Further research is required to determine the applicability of these guidelines both clinically and in research. There is a dearth of high-quality, theory-driven research investigating the aetiology of fatigue in D-FCP [14]. The majority of work to date suggests little association of the type of cancer [4,8], type and extent of cancer treatment [15,16], and time since termination of treatment [15,16] with the level of fatigue. Psychosocial factors, such as psychological distress, sleep problems, and activity level, appear to contribute more strongly to fatigue in D-FCP [8,15,17]. The Psychobiologic Entropy Model of Winningham [19] is one of the most helpful theoretical frameworks in this field, as it is specific enough to generate testable predictions [14,18]. Fatigue is proposed to become problematic when the primary effects of cancer, including physical side effects of the disease and its treatment and psychological responses such as depression and anxiety, have the effect of reducing activity levels. Too little activity is suggested to lead to decreased denergetic capacityT and, thus, greater levels of fatigue. For DFCP, the psychosocial effects of cancer may be more relevant in determining activity level because psychological sequelae are likely to persist long after the immediate physical consequences of disease and treatment have passed [20]. Low activity levels show a consistent association with fatigue in cancer patients during treatment [21,22], and exercise programs have been successful in decreasing fatigue during the treatment phase [23–25]. Although few studies have investigated the association between activity and fatigue in D-FCP, the majority report a significant inverse association [17,26], notwithstanding some inconsistent findings [27]. Potential pathways to reduced activity levels in D-FCP have not been investigated. The model of Winningham [19] predicts that reduced activity and, thus, increased fatigue are largely a consequence of the effect of factors such as depression and anxiety in D-FCP. Consistent with this prediction, reduced activity levels are a wellrecognised component of depression [28], and anxiety can also cause individuals to become less active through avoidance behaviours [29]. Fatigue in D-FCP bears some similarities with chronic fatigue syndrome (CFS), e.g., in terms of the chronic, debilitating nature of the fatigue and its uncertain aetiology [17,30]. Like the theory of Winningham [19], cognitive– behavioural models of CFS propose that inactivity plays a key role in maintaining fatigue [31,32]. CFS sufferers are hypothesised to show low activity levels partly due to their belief that activity should be avoided [33]. Sufferers commonly believe that a physical disease underlies their symptoms and the fatigue produced by activity may be misinterpreted as a sign of drelapseT or worsening of the physical disease [33,34]. Similar processes may be relevant to fatigue in D-FCP. Following treatment for cancer, patients may believe that activity/exercise is harmful for

them and may interpret fatigue as a sign of relapse [30]. Thus, patients for whom fear of recurrence (FOR) is particularly prominent may hold stronger negative beliefs about activity, resulting in reduced activity and, ultimately, increased fatigue. FOR has been linked to higher levels of psychological distress in D-FCP [35,36], and because psychological distress itself is likely to be related to fatigue, FOR may also contribute to fatigue via its association with increased depression and anxiety. When constructing theories regarding adjustment and coping with illness, there is a danger of conceptual overlap and redundancy emerging [37]. For example, the association between particular beliefs and fatigue may be better accounted for by the presence of higher order or trait variables such as neuroticism. Thus, increased neuroticism may represent a more parsimonious explanation for the potential associations between constructs such as FOR, negative beliefs about activity, and fatigue. The present study aimed to assess the prevalence of fatigue in a sample of D-FCP according to the draft ICD10 criteria for CRF [11] and to provide further information on the validity of the guidelines. A second aim was to test hypotheses derived from theories of CRF [19] and CFS [31,32] in this patient group regarding the relationship between fatigue and psychosocial variables, including activity.

Hypotheses The following predictions were made: 1.

2.

3.

A subgroup of D-FCP would meet draft ICD-10 criteria for CRF and would experience significantly greater fatigue severity, frequency, and disruptiveness when compared with the remainder of the sample. Lowered activity levels would be directly associated with higher fatigue levels and would mediate the relationship between other psychosocial variables and fatigue. Higher levels of psychological distress, stronger negative beliefs about activity, and stronger FOR of cancer would also be associated with greater severity of fatigue.

Fig. 1. Hypothesised relationships between study variables.

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4.

The influence of neuroticism would not better account for any of the relationships between the study variables.

Fig. 1 diagrammatically represents the hypotheses tested by the study.

Methodology Sample Participants were women with previous diagnoses of breast cancer who had completed curative treatment for cancer (surgery alone or with chemotherapy and/or radiotherapy) a minimum of 6 months prior to participation and who were judged at the time of participation to be diseasefree. Although many studies report no association between hormonal therapy and fatigue levels [10,15], some controversy exists [38]. Those on antihormonal therapy (i.e., Tamoxifen) were therefore included; however, tamoxifen was included as a covariate in relevant analyses. Exclusion criteria were the presence of another physical condition significantly affecting physical functioning (e.g., osteoarthritis, cardiac conditions), presence of a physical disability, currently taking medication with fatigue as a side effect, evidence of significant cognitive impairment or psychotic disorder, age of less than 16 years, and inability to speak English. Recruitment The relevant ethics committees granted approval. All participants were attending routine review appointments at surgery or oncology clinics in the west of Scotland. Patients meeting the inclusion criteria were approached by the researcher and were given verbal information about the study and a pack to take home, including an information sheet, consent form, questionnaires, and a prepaid envelope. Completed consent forms and questionnaires were returned by post. Consenting participants agreed to being contacted by telephone for the purpose of a short follow-up interview (see below) and to allow the researcher to inform participants where their scores indicated clinically significant fatigue or distress, so that appropriate advice could be given. Measures Fatigue was measured firstly using the Multidimensional Fatigue Symptom Inventory (MFSI; [39]), an 83-item self-report measure. Items were rated on a fivepoint scale indicating agreement with each statement in terms of the preceding week. The MFSI was chosen because it taps into the multidimensional aspects of fatigue, in line with recent thinking on the nature of fatigue [6,7]. It shows acceptable test–retest reliability and

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good internal consistency (a for all subscales N.85) in breast cancer patients [39]. Subscales were derived tapping the global, somatic, affective, cognitive, and behavioural aspects of fatigue, along with vigour. Higher scores indicated more severe fatigue in each domain, with the exception of higher scores on the vigour subscale, which indicated less severe fatigue. The Fatigue Symptom Inventory (FSI; [40]), a 14-item questionnaire assessing intensity, frequency, daily pattern, and impact of fatigue, was also administered. Each of the items can be examined as an individual scale and is rated on a 10-point scale, except for the qualitative item on daily pattern of fatigue. Higher scores indicate greater intensity/frequency/impact. The seven items assessing impact of fatigue were summed to form the Disruption Index. The FSI is valid and reliable in breast cancer patients posttreatment (a=.95 for the interference subscale; [40,41]) and was selected particularly for its strengths in operationalising the impact of fatigue on participants’ lives. Additionally, the draft ICD10 criteria for CRF [11] were applied (see Table 1). An initial screening question was included in the questionnaire pack, to screen out those who did not have the potential to meet Criterion A. Participants were asked bOver the past month have you experienced: (a) significant fatigue; (b) reduced energy; (c) increased need to rest?Q. Those answering positively to one or more of these questions were then contacted by telephone. A structured interview was conducted [13] to determine the extent to which participants met criteria for CRF. Following the procedures employed by Cella et al. [5], only Criteria A and B were applied in the present study, as difficulties have been noted

Table 1 Draft ICD-10 criteria for CRF [11] A1. Significant fatigue, diminished energy, or increased need to rest, disproportionate to any recent change in activity level A2. Complaints of generalised weakness or limb heaviness A3. Diminished concentration or attention A4. Decreased motivation or interest to engage in usual activities A5. Insomnia or hypersomnia A6. Experience of sleep as unrefreshing or nonrestorative A7. Perceived need to struggle to overcome inactivity A8. Marked emotional reactivity (e.g., sadness, frustration, or irritability) to feeling fatigued A9. Difficulty completing daily tasks attributed to feeling fatigued A10. Perceived problems with short-term memory A11. Postexertional malaise lasting several hours B. The symptoms cause clinically significant distress or impairment in social, occupational, or other important areas of functioning C. There is evidence from the history, physical examination or laboratory findings that the symptoms are a consequence of cancer or cancer therapy. D. The symptoms are not primarily a consequence of comorbid psychiatric disorders such as major depression, somatization disorder, somatoform disorder, or delirium. Six (or more) of the following symptoms have been present everyday or nearly everyday during the same 2-week period in the past month, and at least one of the symptoms is (A1) significant fatigue.

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in applying Criteria C and D without taking a detailed medical and psychiatric history from patients. Activity level was measured using the 7-day recall section of the Scottish Physical Activity Questionnaire (SPAQ; [42]). Estimates of total, leisure, and occupational physical activity were yielded by prompting participants to note the number of minutes spent each day in nine different categories of activity (walking within and outside work, manual labour within and outside work, active housework, dancing, cycling, sport/leisure activities, and other physical activities). Acceptable test–retest reliability, strong concurrent validity, and limited criterion validity have been demonstrated in samples from various sectors of the Scottish general public (including aerobic class and slimming class members; [42]). However, reliability and validity estimates have been reported as somewhat unsatisfactory with regard to the occupational walking subsection of the SPAQ [42]. Psychological distress was assessed using the Hospital Anxiety and Depression Scale (HADS; [43]). This 14-item, multiple-choice scale does not rely on the somatic features of anxiety and depression that can artificially inflate scores in general medical patients. The HADS has been recommended as the instrument of choice in screening for anxiety and depression in D-FCP [44]. Good internal consistency has been demonstrated in a large sample of cancer patients with mixed diagnoses (anxiety scale a=.83; depression scale a=.79; [45]). A higher order factor corresponding to psychological distress was found in a recent factor analysis of the scale in this patient group [45]. The subscales were therefore combined in the analyses, producing an overall index of psychological morbidity, with higher scores indicating greater distress. Beliefs about activity were measured using the Tampa Scale of Kinesiophobia-Fatigue (TSK-F; [35]), a 17-item self-report questionnaire. Participants rated their agreement with each item on a four-point scale. Only the beliefs about activity subscale, which comprises six items, was included in the analysis. A higher subscale score represents more strongly negative beliefs about activity. The TSK-F, which was developed for use with CFS patients, demonstrates acceptable internal consistency (a=.70 for the beliefs about activity subscale) and test–retest reliability. It has been shown to predict avoidance of exercise in CFS sufferers [33]. FOR of cancer was assessed using the Fear of Relapse/ Recurrence Scale, a five-item scale devised by Kornblith [46]. This questionnaire has the advantage of brevity and has been shown to have satisfactory reliability (a=.75) and validity in adult leukaemia survivors [46,47]. Agreement with each item is rated on a five-point scale. All items, except Number 5, are reverse scored, so that a higher total score indicates greater FOR. Neuroticism was measured using the Neuroticism subscale of the Eysenck Personality Questionnaire-Revised Short Scale (EPQ-R-S; [48]), which is composed of 48

items with a yes/no response format. Higher scores indicate greater levels of neuroticism. The reliability and validity of the scale have been established in several studies [49,50], and the scale has demonstrated good reliability in a large sample of cancer patients with varied diagnoses (neuroticism subscale a=.85; [51]). Data analysis The effect size for the squared multiple correlation coefficient in the current study was estimated to be large (i.e., R 2z.26; [53]), based on the results of previous studies utilising psychosocial variables to predict fatigue in D-FCP [15,16]. The minimum sample size to achieve 80% power (a=.05) for six predictor variables and a large expected effect size was determined as n=46, according to calculations based on the guidelines of Cohen [53] for power analysis [52]. Data were analysed using SPSS Version 10. As there was no evidence of nonrandom distribution among the small number of missing values in the data set, cases with missing values were excluded pairwise for the analyses [54]. Nonparametric tests of difference (Mann–Whitney U Test) were conducted when comparing scores on fatigue indices for those meeting and not meeting the criteria for CRF. For subsequent parametric analyses, square root transformations were performed on the following variables to correct nonnormal distributions and reduce the influence of univariate outliers [54]: all SPAQ subscales, all MFSI subscales (excluding vigor), FSI Disruption Index, and HADS total score. Transformed data were used to calculate Pearson’s r. Biserial correlations were calculated for study variables that were nominal and could not be meaningfully ranked, i.e., work status, marital status, current tamoxifen use, and previous episodes of cancer. Sequential (hierarchical) multiple regression analysis was used to investigate further the nature of the relationships between fatigue and psychosocial variables. During sequential multiple regression, predictor variables are entered into the regression model in a predetermined order, allowing a specific hypothesis to be tested [54]. Variables entering the model are assigned both the unique and overlapping variance left to them at their point of entry. Thus, variables entered later are assessed in terms of how much variance they contribute to the dependent variable, over and above that which can be attributed to variables entered earlier [54]. Demographic and medical variables were entered into the model first to control for their potential confounding effects. The psychosocial variables were then entered in steps to test the hypotheses.

Results Seventy-nine (62.7%) questionnaire packs were returned completed, out of 126 packs distributed. Data from 10

K.E. Young, C.A. White / Journal of Psychosomatic Research 60 (2006) 29–38 Table 2 Correlations between fatigue and demographic, medical, and treatment-related characteristics Variable Age Education Work status (biserial correlation) Marital status (biserial correlation) Treatment type Time since end of treatment Current tamoxifen use (point biserial correlation) Previous episodes of cancer (biserial correlation)

MFSI global fatigue (SR)

FSI fatigue disruptiveness (SR)

.301T .154 .095

.331TT .156 .076

.021

.119

.243T .011 .122

.215 .015 .097

.007

.033

SR=square root. * Pb.05. ** Pb.01 (two-tailed).

further participants were then excluded due to them being found to meet exclusion criteria, giving a final sample size of 69. All participants were female breast cancer patients, British, of Caucasian origin, and all were disease-free, according to the most recent correspondence in their medical records. They ranged in age from 26 to 81 years, with a mean age of 59 years. Forty-nine participants (71%) were married or with a partner, 12 (17%) were widowed, and the remaining 8 (12%) were single. Twenty-eight (41%) had completed further or higher education. Thirty-six (54%) were retired, 19 (28%) were in employment, with the remaining 12 (18%) unemployed or on leave. Twenty-eight (41%) of the participants had received surgery along with either chemotherapy or radiotherapy, 24 (35%) received surgery and both adjuvant therapies, whilst 16 (24%) received surgery alone. Thirty (44%) had completed their surgery 1 to 2 years prior to completing the study. Twenty-six (38%) of the sample had finished their treatment at least 2 years prior to completing the study, whilst 12 (18%) had completed treatment between 6 months and 1 year previously. Fifty (74%) were taking tamoxifen, whilst 3 (4.4%) were taking tamoxifen alongside the progestational agent Megace.

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With regard to the symptoms tapping into Criteria A2 to A11, insomnia or hypersomnia was the most commonly endorsed symptom, being reported by 72%. Postexertional malaise lasting several hours was the least frequently reported symptom, being endorsed by only 4% of those completing the interview. The remaining symptoms were reported by between 40% and 56% of those interviewed. Sixteen (23.3%) of the sample met the criterion of experiencing at least six of these symptoms, including significant fatigue, continuously during the same 2-week period in the past month. Thirteen (18.8%) of the sample subsequently endorsed the final criterion, indicating significant distress or impairment in functioning, and thus met Criteria A and B of the draft ICD-10 criteria for CRF. Those meeting criteria for CRF showed significantly higher levels of fatigue on all dimensions of the MFSI (all PV.001, except for somatic fatigue: PV.01) and significantly lower levels of vigour ( PV.001). On the FSI, those meeting criteria scored significantly higher on all questions, indicating greater intensity and frequency of fatigue and greater interference of fatigue with multiple life domains (all PV.01, except the item referring to proportion of the day fatigued: PV.05). Further data regarding individual subscale scores are available from the author. The most common daily pattern of fatigue among those meeting criteria for CRF was dworst in the afternoonT, whilst the most common rating on this item for those not meeting criteria was dno consistent daily pattern of fatigueT. The group meeting CRF criteria also scored significantly higher on the HADS scales measuring symptoms of anxiety, depression, and total psychological distress (all PV.001). Relation of demographic, medical, and treatment-related variables to fatigue The relationship of demographic, medical, and treatmentrelated variables to global fatigue (measured on the MFSI) and fatigue disruptiveness (measured on the FSI) is displayed in Table 2. Of the demographic variables, only Table 3 Correlations between psychosocial variables and fatigue

Prevalence of CRF according to draft ICD-10 criteria and validity of criteria Forty-six (66.7%) of the sample answered positively to at least one of the screening questions described in the dMethodology sectionT. Two could not be contacted, and the remaining 44 were interviewed by telephone. Twentyfive (56.8%) participants answered positively to the first question tapping Criterion A1, indicating that they had suffered significant fatigue, reduced energy, or increased need to rest continuously for a 2-week period in the past month.

Variable SPAQ total (SR) SPAQ leisure (SR) SPAQ work (SR) TSK-F beliefs about activity HADS depression (SR) HADS anxiety HADS total (SR) FOR total EPQ-R-S neuroticism TPb.01 (one tailed).

MFSI global fatigue (SR)

FSI fatigue disruptiveness (SR)

.165 .114 .122 .389T

.186 .142 .116 .384T

.775T .701T .769T .560T .581T

.792T .751T .802T .514T .646T

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Table 4 Summary of model parameters for sequential regression analyses on the dependent variable of the square root of MFSI global fatigue Predictor variables

R

R2

Adjusted R 2

R 2 change

F

Step 1 Age Treatment type Step 2 Age Treatment type EPQ-R-S neuroticism Step 3 Age Treatment type EPQ-R-S neuroticism FOR total Step 4 Age Treatment type EPQ-R-S neuroticism FOR total HADS total (SR) TSK-F beliefs about activity

.334

.112

.083

.112T

3.96T

.594

.672

.819

.353

.451

.671

.322

.415

.637

.241TT

.098TTT

.220TT

Betaa

Beta significance

.0125 .106

.058 .225

.00184 .0756 .0864

.759 .314 b.001

.00352 0.0217 .0600 .05528

.530 .761 .002 .002

.00558 .0429 .00607 .0210 .266 .0339

.271 .448 .737 .126 b.001 .020

11.28TT

12.55TT

20.05TT

a

Unstandardised beta coefficient. T PV.05. TT PV.01. TTT PV.001.

age was significantly related to fatigue, showing an inverse relationship with fatigue severity and disruptiveness. There was also a significant correlation between the number of prior treatment modalities and global fatigue, so that fatigue levels increased with the number of treatment modalities received (i.e., surgery, radiotherapy, and chemotherapy). No significant associations were found between fatigue severity or disruptiveness and current tamoxifen use, time since the end of treatment, or the presence of previous episodes of cancer. Association of psychosocial factors with fatigue Table 3 displays the bivariate associations between indices of fatigue and psychosocial variables. There were no significant associations between the level of self-reported physical activity and any of the indices of fatigue. Correlations between fatigue and beliefs about activity, psychological distress, FOR, and neuroticism were all positive and significant ( Pb.01). Correlations were recalculated between fatigue measures and the HADS total score minus the fatigue-related item on this scale (bI feel as if I am slowed downQ). The resulting coefficients did not differ in their level of significance from the original coefficients; thus, this item was retained for subsequent analyses.

R 2. Neither variable alone had a significant effect on the model. Neuroticism, when entered into the model in Step 2, was a significant predictor producing a highly significant change in R 2 and increasing the proportion of explained variance to approximately 32%. The addition of FOR to the model in Step 3 produced a further significant change in R 2, increasing the proportion of explained variance to approximately 42%. Consistent with the hypotheses, FOR was thus a significant predictor in the equation, independent of its overlap with neuroticism. In the final step, with the addition of HADS total and TSK-F beliefs about activity, there was a highly significant change in R 2, increasing the proportion of explained variance to approximately 64%. After this step, the remaining variables, including neuroticism and FOR, became nonsignificant. This indicated that the influence of both psychological distress and beliefs about activity on fatigue was direct and not better accounted for by either neuroticism or FOR. Fig. 2 diagrammatically represents the obtained relationships.

Multiple regression analyses The parameters of the regression model are displayed in Table 4. Age and treatment type, when entered into the equation together in Step 1, explained approximately 8% of the variance in fatigue and produced a significant increase in

Fig. 2. Obtained relationships between study variables.

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Discussion The prevalence of the clinical syndrome of CRF [11] in the current study was 19%, a rate comparable with the figures of 17% and 21% reported by Cella et al. [5] and Sadler et al. [13], respectively. A large proportion (57%) of the current sample met criterion A1; that is, they reported regularly experiencing significant fatigue. This underlines the importance of applying rigorous criteria when attempting to determine the presence of clinically significant CRF and supports the use of the draft ICD-10 guidelines for this purpose. In support of the validity of the draft diagnostic criteria for CRF, there was evidence in the current study that their application identified two clearly demarcated groups. Those meeting criteria for CRF reported more severe fatigue in multiple dimensions, more intense and frequent fatigue, and perceived greater disruption to their lives due to fatigue. They also reported being fatigued an average of 2.7 more days per week and rated their average fatigue in the past week as 85% greater and their current fatigue as 136% greater than the group not meeting criteria, demonstrating the clinical significance of the group differences. Those meeting criteria for CRF also reported significantly greater symptoms of both anxiety and depression. In contrast to the present study, Sadler et al. [13] applied Criterion D and excluded only one participant on the basis this person having a comorbid psychiatric disorder. However, significant group differences remained between the groups in terms of emotional problems, consistent with the strong evidence for a concurrent association between fatigue and psychological distress in D-FCP. [14] At present, there is not sufficient evidence on the direction of causality between these variables to justify the assumption made in the draft criteria that fatigue is secondary to psychopathology [14]. Thus, a more appropriate procedure may be to specify concurrent psychiatric disorder as a subtype in the diagnostic criteria, consistent with procedures for diagnosing CFS [55,56]. Association of fatigue with psychosocial variables The results did not support the Psychobiologic Entropy Hypothesis of Winningham [19] and the cognitive–behavioural models of CFS [31,32], which predict a significant inverse association between activity and fatigue, although a nonsignificant trend in the expected direction was observed. The reliance on a self-report measure of activity in the current study is one explanation for the lack of significant association. It is possible that had the objective activity level data been collected, a clearer relationship with fatigue may have been demonstrated. However, the pattern of results in previous studies suggests that, in fact, associations between fatigue and activity are more likely to be found when subjective, general estimates of physical functioning are used [14,27]. The SPAQ prompted partic-

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ipants to recall the amount of time spent in specific activities over the preceding week and, as such, may have been less subjective than scales yielding general estimates of physical functioning. Future studies should test the hypothesis that the strength of association between activity and fatigue varies with the degree of subjectivity of the measure of activity utilised. In the present study, psychological distress was strongly associated with fatigue severity and emerged as the strongest predictor of fatigue, consistent with previous studies [14]. The prediction, stemming from the theory of Winningham [19], that this association was indirect and mediated by the association of both variables with activity level, was not supported. However, bearing in mind the difficulties in measuring activity, this explanatory mechanism cannot be rejected on the basis of the present findings alone. The strength and robustness of the association between distress and fatigue may indicate that fatigue is, in fact, an index of, or proxy for, distress. It is further possible that self-reports of reduced functioning, e.g., in terms of activity level and cognitive functioning, also reflect expressions of underlying distress [14,26,27,56]. Further research should test the hypothesis that self-reports of fatigue and functional impairment are linked to underlying distress and should investigate personality traits or coping styles, e.g., alexithymia and somatization, which may mediate these links [37]. The present study found that negative beliefs about activity were significantly associated with fatigue and emerged as a significant predictor on regression analysis. The results did not support the competing hypothesis that this association would be better accounted for by the trait factor neuroticism, as beliefs about activity contributed a significant portion of unique variance. The most obvious mechanism by which negative beliefs about activity may contribute to fatigue is via restricted activity. Due to the potential difficulties measuring activity level in this study, this hypothesis could not be confirmed or rejected with confidence. However, this mechanism seems less likely in light of the pattern of results described above with regard to the association of subjective and objective measures of activity with fatigue. It is possible that negative beliefs about activity represent a more general style of negative thinking that accompanies depressed mood [58] and, thus, fatigue. The current results, however, suggested that beliefs about activity contributed a significant proportion of unique variance to fatigue, independent of psychological distress. Further research is required to elucidate the mechanisms of this link. Fear of cancer recurrence was also associated with greater severity of fatigue, emerging as a significant predictor of fatigue in Step 3 of the regression. However, this association became nonsignificant when psychological distress and beliefs about activity entered the equation. This indicated that the association of FOR with fatigue was indirect and mediated by one or both of the new variables.

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This suggestion is supported further by the fact that interrelations existed among FOR, psychological distress, and beliefs about activity. Thus, a tentative explanation of these results is that FOR may contribute to fatigue via its association with psychological distress and may also feed worries or concerns about activity. The findings with regard to the links between specific cognitions and fatigue are preliminary and require further evaluation. This is a particularly important avenue for future research, given the potential to adapt cognitive–behavioural treatments for CFS for this patient group [58]. However, important differences may exist between CFS patients and D-FCP [30]; thus, more information is required regarding the key cognitive and behavioural mechanisms that may contribute to the perpetuation of fatigue in D-FCP. A number of limitations to this study must be considered. The sample consisted entirely of female breast cancer survivors, and thus, the findings may not generalise to DFCP in general. The response rate (62.7%), although not atypical in this field, may have introduced the risk of a systematic sampling bias, with a particular risk of biasing the prevalence estimate of CRF. Personal and clinical data on nonresponders were not collected in the present study, as this was contrary to local NHS R&D procedures. Smets et al. [4] reported that nonresponders to their follow-up assessment phase were significantly more fatigued than were the responders. However, it is equally possible that, in the current study, fatigued participants were more likely to respond due to the study’s relevance to them. For this reason, there is a need to interpret the current findings with a degree of caution. As already discussed, the method of measurement of activity in the present study was subjective and may not have corresponded closely to objective activity. Practical and financial constraints meant that an objective measure of activity could not be obtained. Had this been available, firmer conclusions could have been drawn with regard to the relationship of activity to fatigue and other study variables. Like the majority of studies in this field, the present study was cross-sectional in design. Future prospective research, tracking the association of fatigue and psychosocial variables over time, would allow clearer conclusions to be drawn regarding causality and the investigation of risk factors for fatigue which may be amenable to early intervention. Such studies would require to assess and control for treatment and disease-related variables at different time points, to assess the specific impact of psychosocial variables on fatigue. Finally, the use of structural equation modeling techniques would have allowed greater insight into the complex interrelationships between study variables [54]. However, the present study was not adequately powered for such techniques. Despite these limitations, this study provided further support for the draft ICD-10 diagnostic criteria for CRF and highlighted important avenues for future research. Clinically, preliminary support was found for the role of cognitive factors in fatigue in D-FCP, indicating the

potential for the adaptation of CBT approaches for managing fatigue in this population.

Acknowledgments We acknowledge Dr. Diana Ritchie, Consultant Oncologist, Crosshouse Hospital, for supporting this research study. Mr. Anthony Newland, Miss Phillipa Whitford, and Professor David George, Consultant Surgeons, and Sister Carol MacGregor, Clinical Nurse Specialist, and the nursing staff at surgical and oncology clinics of Crosshouse Hospital and the Beatson Oncology Centre, Glasgow, also provided invaluable support. The first author carried out this research work as part of the requirements for the Doctor of Clinical Psychology at the University of Glasgow.

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