identifying clinically meaningful fatigue with the fatigue symptom inventory

480 Journal of Pain and Symptom Management Vol. 36 No. 5 November 2008

Original Article

Identifying Clinically Meaningful Fatiguewith the Fatigue Symptom InventoryKristine A. Donovan, PhD, Paul B. Jacobsen, PhD, Brent J. Small, PhD,Pamela N. Munster, MD, and Michael A. Andrykowski, PhDHealth 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 ina number of clinical populations. The purpose of the present study was to further establish itsutility by examining its operating characteristics and determining the optimal cutoff score foridentifying clinically meaningful fatigue. The MOS 36-Item Short Form Vitality scale,a measure widely used to identify individuals with significant fatigue-related disability, wasused to determine the sensitivity and specificity of the Fatigue Symptom Inventory. Resultsindicate that a score of 3 or greater on those items assessing fatigue in the past week is the optimalcutoff score for identifying clinically meaningful fatigue. Individuals who scored at or above thecutoff 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 FatigueSymptom Inventory can be used to discriminate effectively between individuals with andwithout clinically meaningful fatigue. J Pain Symptom Manage 2008;36:480e487.� 2008 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved.
Key Words
Fatigue, Fatigue Symptom Inventory
sclerosis,2 and psychiatric disorders such as de-
IntroductionFatigue is generally defined as a sense of per-
sistent tiredness or exhaustion that is often dis-tressing to the individual. It is a commonsymptom of many diseases, including cancer,1

neurological disorders such as multiple

This work was supported by National Cancer Insti-tute Grant R01 CA82822.

Address correspondence to: Kristine A. Donovan, PhD,Health Outcomes and Behavior Program, H. LeeMoffitt Cancer Center & Research Institute, 12902Magnolia Drive, MRC-PSY, Tampa, FL 33612, USA.E-mail: [email protected]

Accepted for publication: December 4, 2007.

� 2008 U.S. Cancer Pain Relief CommitteePublished by Elsevier Inc. All rights reserved.

pression.3 Among adult cancer patients, fa-tigue is often the most common symptomreported.4e6 Fatigue also is common in thegeneral population.7,8 One epidemiologicalstudy of working adults found that 98% re-ported some degree of fatigue and one infive reported substantial fatigue.9

Fatigue is a subjective phenomenon and isthus assessed most accurately by individualself-report. To this end, researchers have pub-lished a plethora of self-report instrumentsdesigned to assess and measure fatigue. A re-cent survey of fatigue measurement scalespublished between 1975 and 2004 identifieda total of 71 scales focusing specifically on

0885-3924/08/$esee front matterdoi:10.1016/j.jpainsymman.2007.11.013

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Vol. 36 No. 5 November 2008 481Fatigue Symptom Inventory

fatigue used in 416 studies.10 The informationobtained via these measures depends on thedeveloper’s conceptualization of fatigue andthe respondents’ interpretation of the ques-tions being asked.11 The utility of any one scalerests ultimately on its reliability and validity. Areview by Dittner et al.11 of 30 publishedfatigue scales noted that many fatigue scaleshave been published without basic data abouttheir reliability or evidence of sensitivity tochange. Further, few scales have demonstratedan ability to discriminate clinical cases of fa-tigue from noncases, with acceptable levels ofsensitivity and specificity.11 That is, few scaleshave established cutoff scores to determineclinically meaningful fatigue.

The Fatigue Symptom Inventory (FSI), firstpublished in 1998,12 has been used extensivelyto assess fatigue, especially among cancerpatients. Its psychometric properties were orig-inally established in women undergoing treat-ment for breast cancer, women who havecompleted treatment for breast cancer, andwomen with no history of cancer.12 It was fur-ther validated in a study of males and femaleswith a variety of different cancer diagnoses.13

The scale has been used since to assess fatiguein a number of clinical populations includingbreast cancer patients,14 patients undergoinghematopoietic stem cell transplantation,15

hepatocellular cancer patients undergoingstereotactic radiotherapy,16 and patients withchronic fatigue syndrome.17 The FSI hasproven to be a valid and reliable measure offatigue in medically ill patients and healthy in-dividuals, and reviewers have suggested that itis a useful tool for the assessment of fatigue.11

The purpose of the present study was to fur-ther establish the usefulness of the FSI byexamining its operating characteristics anddetermining the optimal cutoff score for iden-tifying clinically meaningful fatigue. To accom-plish this, we recruited a relatively large sampleof women with no history of cancer who com-pleted both the FSI and the MOS 36-ItemShort Form Vitality scale (SF-36).18 We usedreceiver operating characteristic (ROC) curveanalyses of FSI scores to determine the optimalFSI cutoff score relative to the established SF-36 Vitality scale. ROC analysis has been usedpreviously to establish cutoff scores on generalmeasures of fatigue including the Schedule ofFatigue and Anergia19 and the Checklist

Individual Strength,20 and on disease-specificmeasures such as the Bath Ankylosing Spondyli-tis Disease Activity Index.21 Although there isnot an accepted standard for the assessment offatigue, the SF-36 Vitality scale is commonlyused to validate instruments designed to assessfatigue in the general population and in patientsamples (see, e.g., Kleinman et al.22). Thus, re-searchers have suggested that using the SF-36Vitality scores of the general population as refer-ence data is a valid approach for establishingcutoff scores on measures of fatigue.21 To in-dicate significant health-related limitations,previous studies23e25 dichotomized the Vitalityscale based on the 25th percentile. That is,individuals scoring at or below the 25th percen-tile were considered to be experiencing limita-tions due to fatigue while those scoring abovethe 25th percentile were not considered to besuffering such limitations. Once the optimalFSI cutoff score was identified, we sought to ex-plore whether interference related to fatigue,the duration of fatigue, and demographic fac-tors differentiated individuals who scored aboveor below this cutoff score.

MethodsParticipants

Participants were recruited as part of a largerstudy comparing quality of life in women beingtreated for early stage breast cancer andwomen with no history of cancer. Eligibility cri-teria for women with no history of cancer werethat they must (a) be within five years of theage of the breast cancer patient to whomthey would be matched in the larger study;(b) reside within the same zip code as thepatient to whom they would be matched; (c)have no discernable psychiatric or neurologi-cal disorders that would interfere with studyparticipation; (d) be able to speak and readstandard English; (e) report no history ofcancer (other than basal cell skin carcinoma)or other potentially life-threatening diseases(e.g., AIDS); and (f) report no history of acondition in which fatigue is a prominentsymptom (e.g., multiple sclerosis or chronicfatigue syndrome).

ProcedurePotential participants were identified using

a database maintained by Marketing Systems

482 Vol. 36 No. 5 November 2008Donovan et al.

Group, Inc. (Fort Washington, PA) that drawsfrom all listed telephone households in theUnited States and is estimated to includedemographic and contact information for ap-proximately two-thirds of the U.S. population.For each patient who completed the six-monthassessment in the larger study, up to 25 womenwho resided in the same zip code and werewithin five years of the patient0s age wereselected randomly from the database. One ofthese women was selected at random andsent a letter of introduction describing thestudy. If this woman did not opt out by callinga toll-free telephone number or returneda postcard expressing interest in the study,telephone contact was initiated to furtherdetermine eligibility. If she met all eligibilitycriteria and verbally agreed to participate, anappointment was set up to obtain written in-formed consent and conduct an assessment.If the first woman selected could not bereached, was ineligible, refused to participate,or did not keep the appointment, anotherwoman on the list was selected randomly untila woman matched to the patient was recruitedand completed the assessment.

MeasuresDemographic data were obtained via a stan-

dardized self-report questionnaire. Variablesassessed were age, race/ethnicity, marital sta-tus, annual household income, educationallevel, height, weight, and menopausal status.

The FSI12 is a 14-item measure that assessesthe frequency and severity of fatigue and itsperceived interference. The measure includesthree items specific to fatigue severity in thepast week. Participants rate on 11-point scales(0¼ not at all fatigued, 10¼ as fatigued as Icould be) their level of fatigue: (a) on averagein the past week (FSI average), (b) on the daythey felt most fatigued in the past week (FSImost), and (c) on the day they felt leastfatigued in the past week (FSI least). A compos-ite fatigue score (FSI composite) was derived bycalculating the average across the three severityitems. This composite fatigue score showedhigh internal consistency (alpha¼ 0.84). Anal-yses focused on the operating characteristics ofthe FSI average score and FSI composite score.Analyses also were conducted using partici-pants’ average rating of the degree (0¼ nointerference, 10¼ extreme interference) to

which fatigue interfered with their general ac-tivity, ability to bathe and dress, normal workactivity, ability to concentrate, relations withothers, enjoyment of life, and mood (FSI inter-ference); participants’ ratings of the numberof days in the past week (0e7) they feltfatigued (FSI days); and participants’ ratingsof what percent of each day (0e100), on aver-age, they felt fatigued in the past week (FSIpercent).

The Acute (past week) Version of the MOS36-Item Short Form18,26 (SF-36) is a widelyused self-report measure designed to assess per-ceived health and functioning. The instrumentconsists of eight scales: Physical Functioning,Role-Physical; Bodily Pain; General Health;Vitality; Social Functioning; Mental Health;and Role-Emotional. Each scale is standardizedon a 0e100 metric, with higher scores indicat-ing better functioning. Analyses focused onthe Vitality scale, which consists of four itemsassessing how much of the time in the pastweek participants felt ‘‘full of pep,’’ had ‘‘a lotof energy,’’ felt ‘‘worn out,’’ and felt ‘‘tired.’’The latter two items are reverse coded priorto scoring. Responses range from ‘‘all of thetime’’ to ‘‘none of the time.’’ In analyses fo-cused on the operating characteristics of theFSI, participants were classified as fatigued iftheir Vitality scale score was less than or equalto 45. This score corresponds to the 25th per-centile for females in the U.S. general popula-tion,18 and is consistent with previousresearch demonstrating that the 25th percen-tile is the most appropriate dichotomous indi-cator of health-related limitations.23 Althoughprevious research has demonstrated that a scoreof 50 is indicative of biologic and psychologicdifferences in fatigue,27e32 we chose the morestringent score of 45 as the criterion to increasethe robustness of our results.

ResultsDemographic Characteristics

The demographic characteristics of the sam-ple are presented in Table 1. The mean age ofthe women was 56 years (range, 28e79). Thevast majority was white, married, and nearlyhalf had a college degree. More than two-thirdshad annual household incomes $$40,000. Theaverage body mass index was 27 and 72% of thewomen were postmenopausal.

Table 1Demographic Characteristics of the Sample

(n ¼ 265)

Characteristic n (%)

Age in years (mean � SD) 56.34 � 9.42

Race/ethnicityWhite 252 (95.1)Nonwhite 13 (4.9)

Marital statusMarried or marriage-like 184 (69.4)Not married 81(30.6)

EducationCollege 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 statusPremenopausal 70 (27.9)Peri- or postmenopausal 181 (72.1)

Body mass index (mean � SD) 27.69 � 6.85

Table 3Frequency Distribution of FSI Composite Scores

Score Frequency % Cumulative %

0 27 10.2 10.2>0 # 1 47 17.7 27.9>1 # 2 54 20.4 48.3>2 # 3 48 18.1 66.4>3 # 4 41 15.5 81.9>4 # 5 25 9.4 91.3>5 # 6 11 4.2 95.5>6 # 7 8 3.0 98.5>7 4 1.5 100.0

0.6

0.8

1

ity

FSI average = 3


Establishment of a Fatigue Cutoff ScoreTables 2 and 3 list the frequency distribution

of FSI average scores and FSI composite scores,respectively. The mean FSI average score forthe sample was 2.40 (standard deviation¼ 2.01)and the mean FSI composite score was 2.51(standard deviation¼ 1.84). ROC curves wereconstructed for sensitivity and 1�specificity forthe range of possible scores on FSI averageand FSI composite compared with normativedata for females in the U.S. general population(Figs. 1 and 2). Based on established norms, thecutoff for fatigue-related disability was definedas a Vitality score >45.18,23 The ROC curves aregraphic representations of the trade-off be-tween the sensitivity (true-positive rate) andspecificity (true-negative rate) for every possi-ble cutoff score on FSI average and FSI com-posite. The area under the curve (AUC) ineach ROC curve provides an estimate of the

Table 2Frequency Distribution of FSI Average Scores

Score Frequency % Cumulative %

0 43 16.2 16.21 65 24.5 40.82 48 18.1 58.93 40 15.1 74.04 31 11.7 85.75 18 6.8 92.56 7 2.6 95.17 6 2.3 97.48 6 2.3 99.610 1 0.0 100.0

overall discriminative accuracy of these itemsrelative to the established cutoff score for theVitality scale. In ROC analysis, an AUC of 1represents a test with perfect accuracy relativeto the established criterion, whereas an AUCof 0.5 represents a test with no apparent accu-racy relative to the established criterion. In thecurrent study, the AUC for each FSI fatiguemeasure was 0.75, using the 25th percentileon the Vitality scale as the criterion. This valueis in the range typically characterized as repre-senting good overall accuracy. Visual inspec-tion of the ROC curves for FSI average andFSI composite suggests that a score $3 is theoptimal cutoff for identifying significantfatigue, using the Vitality scale as the criterion.

The classification of participants based ona cutoff score of 3 on FSI average and FSI com-posite relative to the 25th percentile of the Vi-tality scale is illustrated in Table 4. This cutoffscore on FSI average yielded a sensitivity of0.81 and a specificity of 0.69 relative to the

0

0.2

0.4

0 0.2 0.4 0.6 0.8 1

1 - Specificity

Sen

sitiv

Fig. 1. Receiver operating characteristic curve anal-ysis comparing FSI average scores with establishedVitality cutoff score of >45.

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 11- Specificity

Sen

sitivity

FSI composite = 3

Fig. 2. Receiver operating characteristic curve anal-ysis comparing FSI composite scores with estab-lished Vitality cutoff score of >45.


25th percentile of the Vitality scale. On FSIcomposite, it yielded a sensitivity of 0.81 andspecificity of 0.70 relative to the 25th percen-tile of the Vitality cutoff score. Other cutoffscores yielded less optimal results. For exam-ple, a cutoff score of 4 on FSI average yieldeda sensitivity of 0.62 and a specificity of 0.83relative to the 25th percentile of the Vitalityscale. On FSI composite, it yielded a sensitivityof 0.56 and specificity of 0.83 relative to the25th percentile of the Vitality cutoff score.

Relation of Fatigue $3 Cutoff Scoreto Demographic Characteristics

Chi-squared analyses and analysis of variancewere conducted to explore the relation of theFSI average and FSI composite cutoff score of3 to demographic characteristics. As shown inTable 5, none of the demographic characteris-tics assessed were related significantly to the

Table 4Correspondence of FSI Average and FSI

Composite with the Vitality Scale of the SF-36

SF-36 Vitality Scale Frequency (%)

> 45 # 45

FSI averagea

<3 146 (55.1) 10 (3.8)$3 67 (25.3) 42 (15.9)

FSI compositeb

<3 149 (56.2) 10 (3.8)$3 64 (24.2) 42 (15.9)

aChi-square ¼ 41.98, P < 0.0001.bChi-square ¼ 44.80, P < 0.0001.

FSI average cutoff score. Similarly, none ofthe demographic characteristics were associ-ated with the FSI composite cutoff score of 3.

Relation of Fatigue $3 Cutoff Scoreto Fatigue Interference

Analyses of variance indicated that womenwho scored above the FSI average cutoff re-ported significantly greater FSI interferencecompared to women who scored below the cut-off (2.29� 1.80 vs. 0.41 � 0.58, P< 0.0001).Similarly, women who scored above the FSIcomposite cutoff reported significantly greaterfatigue interference compared to women whoscored below the cutoff (2.31 � 1.82 vs. 0.42� 0.61, P< 0.0001).

Relation of Fatigue $3 Cutoff Scoreto Fatigue Duration

Analyses of variance also indicated that theFSI average cutoff score of 3 was significantlyassociated with differences in both FSI daysand FSI percent. Women who scored abovethe FSI average cutoff reported that they feltfatigued an average of 4.11 � 1.97 days inthe past week vs. 1.56 � 1.65 days for womenbelow the cutoff (P< 0.0001). Compared towomen below the cutoff, women above the cut-off also reported significantly greater FSI per-cent; they felt fatigued a significantly greaterproportion of the day in the past week: anaverage of 36.9% vs. 14.0%, (P< 0.0001).

Similar results were obtained for the FSIcomposite cutoff. Compared to women belowthe cutoff, women above the cutoff reportedsignificantly more days of fatigue on average:4.06 � 2.03 vs. 1.6 � 1.70 (P< 0.0001). Womenabove the cutoff also reported that they felt fa-tigued a significantly greater proportion of theday in the past week: an average of 37.5% vs.14.2% (P< 0.0001).

Relation of Fatigue $3 Cutoff Score to VitalityFinally, analysis of variance was conducted to

examine whether there were differences in theVitality continuous score between women belowand above the FSI average and FSI compositecutoff score of 3. With respect to the FSI aver-age cutoff, women above the cutoff reportedsignificantly higher average Vitality scores thanwomen below the cutoff: 71.8 � 15.27 com-pared to 49.27 � 19.40, (P< 0.0001). Likewise,women above the FSI composite cutoff reported

Table 5Relation of the FSI Average and Composite Cutoff Score of 3 to Demographic Characteristics

FSI Average FSI Composite

<3n (%)

$3n (%) P

<3n (%)

$3n (%) P

Age in years (mean � SD) 56.5 � 9.6 56.2 � 9.2 0.82 57.1 � 9.6 55.3 � 9.0 0.13

Race/ethnicityWhite 7 (2.6) 6 (2.3) 0.71 6 (2.3) 7 (2.6) 0.30Nonwhite 149 (56.2) 103 (38.9) 153 (57.7) 99 (37.4)

Marital statusMarried or Marriage-like 110 (41.5) 74 (27.9) 0.65 112 (42.3) 72 (27.2) 0.66Not married 46 (17.4) 35 (13.2) 47 (17.7) 34 (12.8)

EducationCollege degree 78 (29.4) 48 (18.1) 0.34 81 (30.6) 45 (17.0) 0.18Less 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) 0.50 42 (15.9) 30 (11.3) 0.74$$40,000 per year 116 (43.8) 77 (29.1) 117 (44.2) 76 (28.7)

Menopausal statusPremenopausal 43 (17.1) 27 (10.8) 0.62 43 (17.1) 27 (10.8) 0.80Peri- or postmenopausal 105 (41.8) 76 (30.3) 108 (43.0) 73 (29.1)

Body mass index (mean � SD) 27.7 � 5.9 27.6 � 8.0 0.92 27.5 � 6.3 28.0 � 7.6 0.60


significantly higher average Vitality scores:71.64 � 15.06 compared to 48.82 � 19.60,(P< 0.0001).

DiscussionThe results of the current study indicate that

a score of 3 or greater for FSI average or theFSI composite is the optimal cutoff for identi-fying clinically meaningful fatigue using theFSI. That is, this score yielded the optimal sen-sitivity and specificity relative to the establishedcutoff score on the SF-36 Vitality scale. Therewere no demographic characteristics associ-ated with scoring at or above the cutoff of 3.Individuals who scored at or above the cutoffreported significantly greater fatigue interfer-ence, more days of fatigue on average, andfatigue a greater proportion of each day. As ex-pected, individuals who reported a 3 or greaterfatigue score also had significantly higherVitality scores.

The FSI compares favorably with the SF-36Vitality scale. This conclusion is based on theAUC statistics obtained when comparing thefull range of FSI average and FSI compositescores with the established cutoff score onthe Vitality scale (AUC¼ 0.75 in both cases).These results show that the FSI, specificallythose items concerning fatigue severity in thepast week, can discriminate between those

individuals with and without clinically mean-ingful fatigue.

As noted previously, few published measuresof fatigue include a cutoff score by which to de-termine the presence or absence of clinicallymeaningful fatigue.11 Thus, study findingsmake the FSI relatively unique among fatigueassessment measures. The establishment ofa cutoff score on the FSI greatly expands theinstrument’s utility. For example, researchersmay find it useful to dichotomize samplesbased on a cutoff score of 3 into groups withand without clinically meaningful fatigue. Sub-sequent analyses would then focus on elucidat-ing those physiological and psychosocialfactors that contribute to the developmentand persistence of fatigue. A score of 3 orgreater also might be used as an eligibility cri-terion for participation in intervention trialsfocused on treating clinically meaningful fa-tigue. Finally, the cutoff score may be usefulclinically in screening for fatigue among med-ically ill patients. A ‘‘positive’’ screen for clini-cally meaningful fatigue could initiate a morecomprehensive work up or assessment andthe identification of contributing factors ortreatable causes of the fatigue.

Strengths of the current study should benoted. The sample size was relatively largeand was recruited using an outreach proce-dure designed to limit participation bias.


We compared the FSI to the SF-36 Vitalityscale, a measure that has been widely usedwith both healthy and medically ill populationsand has established norms for identifying indi-viduals with significant fatigue. In addition, weused statistical methods appropriate for theidentification of an optimal cutoff score. Thecurrent study also has several noteworthy limi-tations. Only women were included in thestudy sample and the majority was peri- orpostmenopausal. There also was limited diver-sity within the sample with respect to ethnicity,education, and socioeconomic status. Thus,the operating characteristics of the FSI cutoffscore are unknown in men and in minoritypopulations and low-literacy populations ofwomen. Finally, the finding that a cutoff scoreof 3 on FSI average or the FSI composite mea-sure yielded the optimal combination of sensi-tivity and specificity was not cross-validated ina second sample of individuals. Findings thata similar cutoff score was obtained in anothersample of healthy individuals would increaseconfidence in our findings.

In conclusion, the present study further es-tablishes the usefulness of the FSI by determin-ing that an FSI average or FSI composite scoreof 3 or greater is indicative of clinically mean-ingful fatigue. This cutoff score yielded the op-timal sensitivity and specificity relative to thewidely used SF-36 Vitality scale. The cutoffscore also classified effectively those individ-uals with greater fatigue-related interferenceand fatigue duration. These findings supportthe continued use of the FSI not only asa means of assessing fatigue but also as a meansof distinguishing those individuals with clini-cally meaningful fatigue.

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identifying clinically meaningful fatigue with the fatigue symptom inventory

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