Identifying Clinically Meaningful Fatigue with the Fatigue Symptom Inventory

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1 480 Journal of Pain and Symptom Management Vol. 36 No. 5 November 2008 Original Article Identifying Clinically Meaningful Fatigue with the Fatigue Symptom Inventory Kristine A. Donovan, PhD, Paul B. Jacobsen, PhD, Brent J. Small, PhD, Pamela N. Munster, MD, and Michael A. Andrykowski, PhD Health Outcomes and Behavior Program (K.A.D., P.B.J., B.J.S) and Breast Cancer Program (P.N.M.), Moffitt Cancer Center & Research Institute, Tampa, Florida; Department of Psychology (P.B.J.) and School of Aging Studies (B.J.S.), University of South Florida, Tampa, Florida; and Department of Behavioral Science (M.A.A.), University of Kentucky College of Medicine, Lexington, Kentucky, USA Abstract The Fatigue Symptom Inventory has been used extensively to assess and measure fatigue in a number of clinical populations. The purpose of the present study was to further establish its utility by examining its operating characteristics and determining the optimal cutoff score for identifying clinically meaningful fatigue. The MOS 36-Item Short Form Vitality scale, a measure widely used to identify individuals with significant fatigue-related disability, was used to determine the sensitivity and specificity of the Fatigue Symptom Inventory. Results indicate that a score of 3 or greater on those items assessing fatigue in the past week is the optimal cutoff score for identifying clinically meaningful fatigue. Individuals who scored at or above the cutoff also reported significantly greater fatigue interference, more days of fatigue on average, and fatigue a greater proportion of each day in the past week. Findings suggest that the Fatigue Symptom Inventory can be used to discriminate effectively between individuals with and without clinically meaningful fatigue. J Pain Symptom Manage 2008;36:480e487. Ó 2008 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved. Key Words Fatigue, Fatigue Symptom Inventory Introduction Fatigue is generally defined as a sense of persistent tiredness or exhaustion that is often distressing to the individual. It is a common symptom of many diseases, including cancer, 1 neurological disorders such as multiple This work was supported by National Cancer Institute Grant R01 CA Address correspondence to: Kristine A. Donovan, PhD, Health Outcomes and Behavior Program, H. Lee Moffitt Cancer Center & Research Institute, Magnolia Drive, MRC-PSY, Tampa, FL 33612, USA. kristine.donovan@moffitt.org Accepted for publication: December 4, Ó 2008 U.S. Cancer Pain Relief Committee Published by Elsevier Inc. All rights reserved. sclerosis, 2 and psychiatric disorders such as depression. 3 Among adult cancer patients, fatigue is often the most common symptom reported. 4e6 Fatigue also is common in the general population. 7,8 One epidemiological study of working adults found that 98% reported some degree of fatigue and one in five reported substantial fatigue. 9 Fatigue is a subjective phenomenon and is thus assessed most accurately by individual self-report. To this end, researchers have published a plethora of self-report instruments designed to assess and measure fatigue. A recent survey of fatigue measurement scales published between 1975 and 2004 identified a total of 71 scales focusing specifically on /08/$esee front matter doi: /j.jpainsymman

2 Vol. 36 No. 5 November 2008 Fatigue Symptom Inventory 481 fatigue used in 416 studies. 10 The information obtained via these measures depends on the developer s conceptualization of fatigue and the respondents interpretation of the questions being asked. 11 The utility of any one scale rests ultimately on its reliability and validity. A review by Dittner et al. 11 of 30 published fatigue scales noted that many fatigue scales have been published without basic data about their reliability or evidence of sensitivity to change. Further, few scales have demonstrated an ability to discriminate clinical cases of fatigue from noncases, with acceptable levels of sensitivity and specificity. 11 That is, few scales have established cutoff scores to determine clinically meaningful fatigue. The Fatigue Symptom Inventory (FSI), first published in 1998, 12 has been used extensively to assess fatigue, especially among cancer patients. Its psychometric properties were originally established in women undergoing treatment for breast cancer, women who have completed treatment for breast cancer, and women with no history of cancer. 12 It was further validated in a study of males and females with a variety of different cancer diagnoses. 13 The scale has been used since to assess fatigue in a number of clinical populations including breast cancer patients, 14 patients undergoing hematopoietic stem cell transplantation, 15 hepatocellular cancer patients undergoing stereotactic radiotherapy, 16 and patients with chronic fatigue syndrome. 17 The FSI has proven to be a valid and reliable measure of fatigue in medically ill patients and healthy individuals, and reviewers have suggested that it is a useful tool for the assessment of fatigue. 11 The purpose of the present study was to further establish the usefulness of the FSI by examining its operating characteristics and determining the optimal cutoff score for identifying clinically meaningful fatigue. To accomplish this, we recruited a relatively large sample of women with no history of cancer who completed both the FSI and the MOS 36-Item Short Form Vitality scale (SF-36). 18 We used receiver operating characteristic (ROC) curve analyses of FSI scores to determine the optimal FSI cutoff score relative to the established SF- 36 Vitality scale. ROC analysis has been used previously to establish cutoff scores on general measures of fatigue including the Schedule of Fatigue and Anergia 19 and the Checklist Individual Strength, 20 and on disease-specific measures such as the Bath Ankylosing Spondylitis Disease Activity Index. 21 Although there is not an accepted standard for the assessment of fatigue, the SF-36 Vitality scale is commonly used to validate instruments designed to assess fatigue in the general population and in patient samples (see, e.g., Kleinman et al. 22 ). Thus, researchers have suggested that using the SF-36 Vitality scores of the general population as reference data is a valid approach for establishing cutoff scores on measures of fatigue. 21 To indicate significant health-related limitations, previous studies 23e25 dichotomized the Vitality scale based on the 25th percentile. That is, individuals scoring at or below the 25th percentile were considered to be experiencing limitations due to fatigue while those scoring above the 25th percentile were not considered to be suffering such limitations. Once the optimal FSI cutoff score was identified, we sought to explore whether interference related to fatigue, the duration of fatigue, and demographic factors differentiated individuals who scored above or below this cutoff score. Methods Participants Participants were recruited as part of a larger study comparing quality of life in women being treated for early stage breast cancer and women with no history of cancer. Eligibility criteria for women with no history of cancer were that they must (a) be within five years of the age of the breast cancer patient to whom they would be matched in the larger study; (b) reside within the same zip code as the patient to whom they would be matched; (c) have no discernable psychiatric or neurological disorders that would interfere with study participation; (d) be able to speak and read standard English; (e) report no history of cancer (other than basal cell skin carcinoma) or other potentially life-threatening diseases (e.g., AIDS); and (f) report no history of a condition in which fatigue is a prominent symptom (e.g., multiple sclerosis or chronic fatigue syndrome). Procedure Potential participants were identified using a database maintained by Marketing Systems

3 482 Donovan et al. Vol. 36 No. 5 November 2008 Group, Inc. (Fort Washington, PA) that draws from all listed telephone households in the United States and is estimated to include demographic and contact information for approximately two-thirds of the U.S. population. For each patient who completed the six-month assessment in the larger study, up to 25 women who resided in the same zip code and were within five years of the patient 0 s age were selected randomly from the database. One of these women was selected at random and sent a letter of introduction describing the study. If this woman did not opt out by calling a toll-free telephone number or returned a postcard expressing interest in the study, telephone contact was initiated to further determine eligibility. If she met all eligibility criteria and verbally agreed to participate, an appointment was set up to obtain written informed consent and conduct an assessment. If the first woman selected could not be reached, was ineligible, refused to participate, or did not keep the appointment, another woman on the list was selected randomly until a woman matched to the patient was recruited and completed the assessment. Measures Demographic data were obtained via a standardized self-report questionnaire. Variables assessed were age, race/ethnicity, marital status, annual household income, educational level, height, weight, and menopausal status. The FSI 12 is a 14-item measure that assesses the frequency and severity of fatigue and its perceived interference. The measure includes three items specific to fatigue severity in the past week. Participants rate on 11-point scales (0 ¼ not at all fatigued, 10 ¼ as fatigued as I could be) their level of fatigue: (a) on average in the past week (FSI average), (b) on the day they felt most fatigued in the past week (FSI most), and (c) on the day they felt least fatigued in the past week (FSI least). A composite fatigue score (FSI composite) was derived by calculating the average across the three severity items. This composite fatigue score showed high internal consistency (alpha ¼ 0.84). Analyses focused on the operating characteristics of the FSI average score and FSI composite score. Analyses also were conducted using participants average rating of the degree (0 ¼ no interference, 10 ¼ extreme interference) to which fatigue interfered with their general activity, ability to bathe and dress, normal work activity, ability to concentrate, relations with others, enjoyment of life, and mood (FSI interference); participants ratings of the number of days in the past week (0e7) they felt fatigued (FSI days); and participants ratings of what percent of each day (0e100), on average, they felt fatigued in the past week (FSI percent). The Acute (past week) Version of the MOS 36-Item Short Form 18,26 (SF-36) is a widely used self-report measure designed to assess perceived health and functioning. The instrument consists of eight scales: Physical Functioning, Role-Physical; Bodily Pain; General Health; Vitality; Social Functioning; Mental Health; and Role-Emotional. Each scale is standardized on a 0e100 metric, with higher scores indicating better functioning. Analyses focused on the Vitality scale, which consists of four items assessing how much of the time in the past week participants felt full of pep, had a lot of energy, felt worn out, and felt tired. The latter two items are reverse coded prior to scoring. Responses range from all of the time to none of the time. In analyses focused on the operating characteristics of the FSI, participants were classified as fatigued if their Vitality scale score was less than or equal to 45. This score corresponds to the 25th percentile for females in the U.S. general population, 18 and is consistent with previous research demonstrating that the 25th percentile is the most appropriate dichotomous indicator of health-related limitations. 23 Although previous research has demonstrated that a score of 50 is indicative of biologic and psychologic differences in fatigue, 27e32 we chose the more stringent score of 45 as the criterion to increase the robustness of our results. Results Demographic Characteristics The demographic characteristics of the sample are presented in Table 1. The mean age of the women was 56 years (range, 28e79). The vast majority was white, married, and nearly half had a college degree. More than two-thirds had annual household incomes $$40,000. The average body mass index was 27 and 72% of the women were postmenopausal.

4 Vol. 36 No. 5 November 2008 Fatigue Symptom Inventory 483 Table 1 Demographic Characteristics of the Sample (n ¼ 265) Characteristic n (%) Age in years (mean SD) Race/ethnicity White 252 (95.1) Nonwhite 13 (4.9) Marital status Married or marriage-like 184 (69.4) Not married 81(30.6) Education College degree 126 (47.5) Less than college degree 139 (52.5) Household income <$40,000 per year 72 (27.2) $$40,000 per year 193 (72.8) Menopausal status Premenopausal 70 (27.9) Peri- or postmenopausal 181 (72.1) Body mass index (mean SD) Establishment of a Fatigue Cutoff Score Tables 2 and 3 list the frequency distribution of FSI average scores and FSI composite scores, respectively. The mean FSI average score for the sample was 2.40 (standard deviation ¼ 2.01) and the mean FSI composite score was 2.51 (standard deviation ¼ 1.84). ROC curves were constructed for sensitivity and 1 specificity for the range of possible scores on FSI average and FSI composite compared with normative data for females in the U.S. general population (Figs. 1 and 2). Based on established norms, the cutoff for fatigue-related disability was defined as a Vitality score >45. 18,23 The ROC curves are graphic representations of the trade-off between the sensitivity (true-positive rate) and specificity (true-negative rate) for every possible cutoff score on FSI average and FSI composite. The area under the curve (AUC) in each ROC curve provides an estimate of the Table 2 Frequency Distribution of FSI Average Scores Score Frequency % Cumulative % Table 3 Frequency Distribution of FSI Composite Scores Score Frequency % Cumulative % >0 # >1 # >2 # >3 # >4 # >5 # >6 # > overall discriminative accuracy of these items relative to the established cutoff score for the Vitality scale. In ROC analysis, an AUC of 1 represents a test with perfect accuracy relative to the established criterion, whereas an AUC of 0.5 represents a test with no apparent accuracy relative to the established criterion. In the current study, the AUC for each FSI fatigue measure was 0.75, using the 25th percentile on the Vitality scale as the criterion. This value is in the range typically characterized as representing good overall accuracy. Visual inspection of the ROC curves for FSI average and FSI composite suggests that a score $3 is the optimal cutoff for identifying significant fatigue, using the Vitality scale as the criterion. The classification of participants based on a cutoff score of 3 on FSI average and FSI composite relative to the 25th percentile of the Vitality scale is illustrated in Table 4. This cutoff score on FSI average yielded a sensitivity of 0.81 and a specificity of 0.69 relative to the Sensitivity FSI average = Specificity Fig. 1. Receiver operating characteristic curve analysis comparing FSI average scores with established Vitality cutoff score of >45.

5 484 Donovan et al. Vol. 36 No. 5 November 2008 Sensitivity FSI composite = Specificity Fig. 2. Receiver operating characteristic curve analysis comparing FSI composite scores with established Vitality cutoff score of >45. 25th percentile of the Vitality scale. On FSI composite, it yielded a sensitivity of 0.81 and specificity of 0.70 relative to the 25th percentile of the Vitality cutoff score. Other cutoff scores yielded less optimal results. For example, a cutoff score of 4 on FSI average yielded a sensitivity of 0.62 and a specificity of 0.83 relative to the 25th percentile of the Vitality scale. On FSI composite, it yielded a sensitivity of 0.56 and specificity of 0.83 relative to the 25th percentile of the Vitality cutoff score. Relation of Fatigue $3 Cutoff Score to Demographic Characteristics Chi-squared analyses and analysis of variance were conducted to explore the relation of the FSI average and FSI composite cutoff score of 3 to demographic characteristics. As shown in Table 5, none of the demographic characteristics assessed were related significantly to the Table 4 Correspondence of FSI Average and FSI Composite with the Vitality Scale of the SF-36 SF-36 Vitality Scale Frequency (%) > 45 # 45 FSI average a <3 146 (55.1) 10 (3.8) $3 67 (25.3) 42 (15.9) FSI composite b <3 149 (56.2) 10 (3.8) $3 64 (24.2) 42 (15.9) a Chi-square ¼ 41.98, P < b Chi-square ¼ 44.80, P < FSI average cutoff score. Similarly, none of the demographic characteristics were associated with the FSI composite cutoff score of 3. Relation of Fatigue $3 Cutoff Score to Fatigue Interference Analyses of variance indicated that women who scored above the FSI average cutoff reported significantly greater FSI interference compared to women who scored below the cutoff ( vs , P < ). Similarly, women who scored above the FSI composite cutoff reported significantly greater fatigue interference compared to women who scored below the cutoff ( vs , P < ). Relation of Fatigue $3 Cutoff Score to Fatigue Duration Analyses of variance also indicated that the FSI average cutoff score of 3 was significantly associated with differences in both FSI days and FSI percent. Women who scored above the FSI average cutoff reported that they felt fatigued an average of days in the past week vs days for women below the cutoff (P < ). Compared to women below the cutoff, women above the cutoff also reported significantly greater FSI percent; they felt fatigued a significantly greater proportion of the day in the past week: an average of 36.9% vs. 14.0%, (P < ). Similar results were obtained for the FSI composite cutoff. Compared to women below the cutoff, women above the cutoff reported significantly more days of fatigue on average: vs (P < ). Women above the cutoff also reported that they felt fatigued a significantly greater proportion of the day in the past week: an average of 37.5% vs. 14.2% (P < ). Relation of Fatigue $3 Cutoff Score to Vitality Finally, analysis of variance was conducted to examine whether there were differences in the Vitality continuous score between women below and above the FSI average and FSI composite cutoff score of 3. With respect to the FSI average cutoff, women above the cutoff reported significantly higher average Vitality scores than women below the cutoff: compared to , (P < ). Likewise, women above the FSI composite cutoff reported

6 Vol. 36 No. 5 November 2008 Fatigue Symptom Inventory 485 Table 5 Relation of the FSI Average and Composite Cutoff Score of 3 to Demographic Characteristics FSI Average FSI Composite <3 n (%) $3 n (%) P <3 n (%) $3 n (%) P Age in years (mean SD) Race/ethnicity White 7 (2.6) 6 (2.3) (2.3) 7 (2.6) 0.30 Nonwhite 149 (56.2) 103 (38.9) 153 (57.7) 99 (37.4) Marital status Married or Marriage-like 110 (41.5) 74 (27.9) (42.3) 72 (27.2) 0.66 Not married 46 (17.4) 35 (13.2) 47 (17.7) 34 (12.8) Education College degree 78 (29.4) 48 (18.1) (30.6) 45 (17.0) 0.18 Less than college degree 78 (29.4) 61 (23.0) 78 (29.4) 61 (23.0) Household income <$40,000 per year 40 (15.1) 32 (12.1) (15.9) 30 (11.3) 0.74 $$40,000 per year 116 (43.8) 77 (29.1) 117 (44.2) 76 (28.7) Menopausal status Premenopausal 43 (17.1) 27 (10.8) (17.1) 27 (10.8) 0.80 Peri- or postmenopausal 105 (41.8) 76 (30.3) 108 (43.0) 73 (29.1) Body mass index (mean SD) significantly higher average Vitality scores: compared to , (P < ). Discussion The results of the current study indicate that a score of 3 or greater for FSI average or the FSI composite is the optimal cutoff for identifying clinically meaningful fatigue using the FSI. That is, this score yielded the optimal sensitivity and specificity relative to the established cutoff score on the SF-36 Vitality scale. There were no demographic characteristics associated with scoring at or above the cutoff of 3. Individuals who scored at or above the cutoff reported significantly greater fatigue interference, more days of fatigue on average, and fatigue a greater proportion of each day. As expected, individuals who reported a 3 or greater fatigue score also had significantly higher Vitality scores. The FSI compares favorably with the SF-36 Vitality scale. This conclusion is based on the AUC statistics obtained when comparing the full range of FSI average and FSI composite scores with the established cutoff score on the Vitality scale (AUC ¼ 0.75 in both cases). These results show that the FSI, specifically those items concerning fatigue severity in the past week, can discriminate between those individuals with and without clinically meaningful fatigue. As noted previously, few published measures of fatigue include a cutoff score by which to determine the presence or absence of clinically meaningful fatigue. 11 Thus, study findings make the FSI relatively unique among fatigue assessment measures. The establishment of a cutoff score on the FSI greatly expands the instrument s utility. For example, researchers may find it useful to dichotomize samples based on a cutoff score of 3 into groups with and without clinically meaningful fatigue. Subsequent analyses would then focus on elucidating those physiological and psychosocial factors that contribute to the development and persistence of fatigue. A score of 3 or greater also might be used as an eligibility criterion for participation in intervention trials focused on treating clinically meaningful fatigue. Finally, the cutoff score may be useful clinically in screening for fatigue among medically ill patients. A positive screen for clinically meaningful fatigue could initiate a more comprehensive work up or assessment and the identification of contributing factors or treatable causes of the fatigue. Strengths of the current study should be noted. The sample size was relatively large and was recruited using an outreach procedure designed to limit participation bias.

7 486 Donovan et al. Vol. 36 No. 5 November 2008 We compared the FSI to the SF-36 Vitality scale, a measure that has been widely used with both healthy and medically ill populations and has established norms for identifying individuals with significant fatigue. In addition, we used statistical methods appropriate for the identification of an optimal cutoff score. The current study also has several noteworthy limitations. Only women were included in the study sample and the majority was peri- or postmenopausal. There also was limited diversity within the sample with respect to ethnicity, education, and socioeconomic status. Thus, the operating characteristics of the FSI cutoff score are unknown in men and in minority populations and low-literacy populations of women. Finally, the finding that a cutoff score of 3 on FSI average or the FSI composite measure yielded the optimal combination of sensitivity and specificity was not cross-validated in a second sample of individuals. Findings that a similar cutoff score was obtained in another sample of healthy individuals would increase confidence in our findings. In conclusion, the present study further establishes the usefulness of the FSI by determining that an FSI average or FSI composite score of 3 or greater is indicative of clinically meaningful fatigue. This cutoff score yielded the optimal sensitivity and specificity relative to the widely used SF-36 Vitality scale. The cutoff score also classified effectively those individuals with greater fatigue-related interference and fatigue duration. These findings support the continued use of the FSI not only as a means of assessing fatigue but also as a means of distinguishing those individuals with clinically meaningful fatigue. References 1. Prue G, Rankin J, Allen J, Gracey J, Cramp F. Cancer-related fatigue: a critical appraisal. Eur J Cancer 2006;42(7):846e MacAllister WS, Krupp LB. Multiple sclerosis-- related fatigue. Phys Med Rehabil Clin N Am 2005;16:483e Baldwin DS, Papakostas GI. Symptoms of fatigue and sleepiness in major depressive disorder. J Clin Psychiatry 2006;67:9e Andrykowski MA, Cordova MJ, Hann DH, et al. Patients psychosocial concerns following stem cell transplantation. Bone Marrow Transplant 1999;24: 1121e Baker F, Denniston M, Smith T, West MM. Adult cancer survivors: how are they faring. Cancer 2005; 104:2565e Fox SW, Lyon DE. Symptom clusters and quality of life in survivors of lung cancer. Oncol Nurs Forum 2006;33:931e Loge JH, Ekeberg O, Kaasa S. Fatigue in the general Norwegian population: normative data and associations. J Psychosom Res 1998;45:53e Watt T, Groenvold M, Bjorner JB, et al. Fatigue in the Danish general population. Influence of sociodemographic factors and disease. J Epidemiol Community Health 2000;54:827e Bültmann U, Kant I, Kasl SV, Beurskens AJ, van den Brandt PA. Fatigue and psychological distress in the working population: psychometrics, prevalence, and correlates. J Psychosom Res 2002;52:445e Hjollund NH, Andersen JH, Bech P. Assessment of fatigue in chronic disease: a bibliographic study of fatigue measurement scales. Health Qual Life Outcomes 2007;5:12e Dittner AJ, Wessely SC, Brown RG. The assessment of fatigue: a practical guide for clinicians and researchers. J Psychosom Res 2004;56:157e Hann DM, Jacobsen PB, Azzarello LM, et al. Measurement of fatigue in cancer patients: development and validation of the Fatigue Symptom Inventory. Qual Life Res 1998;7:301e Hann DM, Denniston MM, Baker F. Measurement of fatigue in cancer patients: further validation of the Fatigue Symptom Inventory. Qual Life Res 2000;9:847e Donovan KA, Jacobsen PB, Andrykowski MA, et al. Course of fatigue in women receiving chemotherapy and/or radiotherapy for early stage breast cancer. J Pain Symptom Manage 2004;28:373e Hacker ED, Ferrans CE. Ecological momentary assessment of fatigue in patients receiving intensive cancer therapy. J Pain Symptom Manage 2007;33: 267e Lai YH, Shun SC, Hsiao YL, et al. Fatigue experiences in hepatocellular carcinoma patients during six weeks of stereotactic radiotherapy. Oncologist 2007;12:221e Siegel SD, Antoni MH, Fletcher MA, et al. Impaired natural immunity, cognitive dysfunction, and physical symptoms in patients with chronic fatigue syndrome: preliminary evidence for a subgroup. J Psychosom Res 2006;60:559e Ware JE. SF-36 Health Survey: Manual and interpretation guide. Boston, MA: The Health Institute, New England Medical Center, Hadzi-Pavlovic D, Hickie IB, Wilson AJ, et al. Screening for prolonged fatigue syndromes:

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