Validation Study of the Chinese Version of the Brief Fatigue Inventory (BFI-C)

Transcription

1 322 Journal of Pain and Symptom Management Vol. 27 No. 4 April 2004 Original Article Validation Study of the Chinese Version of the Brief Fatigue Inventory (BFI-C) Xin Shelley Wang, MD, Xi-Shan Hao, MD, Ying Wang, BS, Hong Guo, MD, Yong-Qin Jiang, RN, Tito R. Mendoza, PhD, and Charles S. Cleeland, PhD Department of Symptom Research (X.S.W., H.G., T.R.M., C.S.C.), Division of Anesthesiology and Critical Care, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA; and Tianjin Medical University Cancer Hospital (X.-S.H., Y.W., Y.-Q.J.), Tianjin, People s Republic of China Abstract A cross-sectional study was conducted among 249 Chinese cancer patients with multiple diagnoses to validate a Chinese version of the Brief Fatigue Inventory (BFI-C). Cronbach s coefficient alpha was 0.92 for fatigue severity items and 0.90 for fatigue interference items. Construct validity was explored by principal factor analysis and suggested a two-factor solution: fatigue severity and fatigue interference. Internal consistency reliability was excellent. Convergent validity was examined by correlating the BFI-C with 2 subscales and 2 component scores of the MOS 36-Item Short-Form Health Survey (coefficients ranged between 0.44 and 0.71, P 0.001). Known-group validity was examined by comparing fatigue severity in patients having different scores on the Eastern Cooperative Oncology Group Performance Status Scale. Approximately 60% of patients experienced moderate to severe fatigue (4 or greater on the 0 10 scale of the BFI-C fatigue worst item). The BFI-C is a valid, reliable instrument to measure the severity and impact of cancer-related fatigue among Chinese patients. J Pain Symptom Manage 2004;27: U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved. Key Words Cancer-related fatigue, Chinese, validation, factor analysis Introduction Fatigue is the most prevalent, complex, and persistent symptom experienced by cancer patients. Because of the difficulty inherent in describing symptoms, measurement issues in the Address reprint requests to: Xin Shelley Wang, MD, Department of Symptom Research, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 221, Houston, TX 77030, USA. Accepted for publication: September 14, U.S. Cancer Pain Relief Committee Published by Elsevier Inc. All rights reserved. study of fatigue, and the lack of effective therapy, fatigue is a challenging and controversial subject for research and clinical management. Although assessment methods have not been consistent, it is widely accepted that over 80% of cancer patients experience cancer-related fatigue. 1 The development of fatigue in these patients is caused by many factors, including those related to the disease itself, those related to treatment (surgery, chemotherapy, radiation therapy, bone marrow or stem cell transplants, and immunotherapy), and those related to certain primary conditions (such as anemia, /04/$ see front matter doi: /j.jpainsymman

2 Vol. 27 No. 4 April 2004 Chinese Version of the Brief Fatigue Inventory 323 pain, distress, sleep problems, and endocrine dysfunction). 2 The precise etiologic factors of fatigue have not been identified. Fatigue has a tremendous impact on cancer patients well being and health-related quality of life. It is a significant problem as patients experiencing fatigue might withdraw prematurely from potentially curative treatment, or might be unwilling to take adequate doses of various forms of treatment. 3 Despite the impact of fatigue on patients functioning and quality of life, cancer-related fatigue is infrequently addressed in the clinic. Fatigue assessment has rarely been part of routine cancer care, as both patients and healthcare professionals historically have regarded cancer-related fatigue as an expected part of the disease. The frequency and severity of cancerrelated fatigue across diagnostic and treatment categories indicates the need for clinical interventions. 4,5 Nevertheless, the science of measuring fatigue is only recently becoming well developed. To effectively investigate and manage fatigue, researchers must be able to measure it in a reliable and valid manner. Standardized measurement tools are essential for conducting mechanism and epidemiologic studies comparing the characteristics of fatigue across diseases and treatments, and for drawing meaningful conclusions from clinical trials of fatigue interventions. There currently exists no gold standard for assessing and managing cancer-related fatigue in the United States, although guidelines have been developed. 2 On a global level, many countries, including China, have never had a valid fatigue-assessment tool. A validation study (305 patients and 290 healthy community subjects) of the English version of the Brief Fatigue Inventory (BFI) demonstrated that this fatigue assessment tool is a valid and reliable instrument for English-speaking cancer populations 6 and provides for the rapid assessment of fatigue levels in cancer patients and the identification of those patients with severe fatigue. In a fatigue study of American cancer patients, BFI responses showed that 50% of patients with hematological malignancies experienced severe fatigue (defined as 7 or greater on a 0 10 scale), as did 34% of patients with solid tumors. In contrast, severe fatigue was reported by only 17% of the community-dwelling sample. 6 8 To validate a Chinese language version of the Brief Fatigue Inventory-Chinese (BFI-C), we conducted a cross-sectional study to evaluate the instrument s construct validity and reliability. We investigated possible predictors of fatigue severity from demographic information, disease status, other cancer-related symptoms, and laboratory results. Methods Patients and Data Collection The study was approved by the Institutional Review Boards of Tianjin Medical University affiliated Cancer Hospital in Tianjin, China, and The University of Texas M. D. Anderson Cancer Center in Houston, Texas, USA. Patients eligible for this study were required to have a pathological diagnosis of cancer and had to be undergoing radiotherapy, chemotherapy, or surgery on the day of enrollment, or be under clinical observation if not undergoing active treatment. Patients were excluded if, in their physician s estimation, they could not understand the intent of the study, if they refused to participate, or if they were currently diagnosed with a major psychiatric illness. Two hundred forty-nine (249) oncology inpatients and outpatients were consecutively recruited from the departments of Medical Oncology, Surgical and Radiation Oncology, and Traditional Chinese Medicine in Tianjin Cancer Hospital. Each department enrolled no more than 50 consecutive inpatients and 50 consecutive outpatients. After giving informed consent, patients completed a set of self-administered questionnaires that included a demographics survey, the BFI-C, and a Chinese version of the MOS 36-Item Short-Form Health Survey (SF- 36-C). 9 Family members were not allowed to complete the study forms. The staff answered questions from patients and completed checklists with disease and treatment information and laboratory results culled from patients medical records. Measures and Survey Questionnaires The BFI. The BFI was developed to measure fatigue in cancer populations. The validity and reliability of the original scale is well established. 6 This one-page questionnaire uses an

3 324 Wang et al. Vol. 27 No. 4 April point scale (0 to 10) to measure the specific symptom of cancer-related fatigue in a single dimension. Nine items ask patients to rate the severity of their fatigue at its worst, usual, and now during the past 24 hours, with 0 no fatigue and 10 fatigue as bad as you can imagine. Cut points for fatigue severity were defined in two categories: a fatigue worst rating of 7 or greater indicates severe and 0 to 6 indicates non-severe. Six additional items describe how much fatigue has interfered with different aspects of the patient s life during the past 24 hours. These items include general activity, mood, walking ability, normal work (includes both work outside the home and housework), relationships with other people, and enjoyment of life. Interference is measured with 0 does not interfere and 10 completely interferes. The BFI-C (see Appendix 1). This instrument was developed using a standard translation/backtranslation process. First, the BFI items were translated into Chinese. A second translator who had not seen the original English items then back-translated the Chinese translation into English. Bilingual fluency was required of both translators. The English back-translated items were then compared to the originals. If the back-translated items and the originals did not agree, the first translator attempted a second translation after comparing the originals and the back-translations. A second backtranslation was made and compared again to the original items. This process was repeated until both translators concurred on the BFI item descriptions. SF-36-C. To determine the BFI-C s convergent validity we included the MOS 36-Item Short- Form Health Survey (SF-36), a previously validated tool that measures a patient s functional status. The SF-36 is a comprehensive short form that yields an 8-scale health profile (physical functioning, role-physical, bodily pain, general health, vitality, social function, role-emotional, and mental health) and summary measures of health-related quality of life (physical and mental health component scores). The SF-36 has proven useful in monitoring quality of life for both individual patients and the general population. 9 The Chinese version of the SF-36 has been validated Demographic Information. A demographic sheet collected the patient s date of birth, educational level, and job and marital status. Clinician Checklist. This recorded current disease and treatment information from the patient s medical record. Disease information included cancer site and stage and the presence of metastatic disease. The patient s physical activity level was evaluated using the Eastern Cooperative Oncology Group (ECOG) Performance Status Scale. 14 Treatment information included current or prior cancer treatment, medication prescribed for pain control, and laboratory data. Hemoglobin and serum albumin levels from the past week were recorded as two key laboratory variables. Statistical Analysis The psychometric properties of the BFI-C were assessed as follows. The internal consistency, a component of test reliability of the BFI-C, was evaluated by calculating the Cronbach coefficient alpha, which ranges from 0 to 1, with higher values indicating less measurement error. Convergent validity was evaluated by calculating the Pearson product moment correlation coefficient between the BFI-C scores (the single item of fatigue worst, severity composite score, and interference composite score), and related SF-36-C scores (the Physical Functioning and Vitality subscales, and the Physical and Mental Health Component scores). We hypothesized that the BFI-C scales would correlate significantly with the fatigue-related constructs of the SF-36-C. Construct validity was established using confirmatory factor analysis. 15 The number of factors was determined based on clinical interpretability and model fit. The model fit was assessed using Harman s rule. 15 Known-group validity was examined by comparing the BFI-C fatigue worst single score and composite scores in patients having different levels of ECOG performance status (ECOG PS) using one-way analyses of variance (ANOVAs). Tukey tests were used to specify significant difference (P 0.05) among pairwise comparisons of three groups of patients with different ECOG PS levels. In addition to the validation analyses, we investigated fatigue severity and prevalence, a distribution-based measure of clinical significance,

4 Vol. 27 No. 4 April 2004 Chinese Version of the Brief Fatigue Inventory 325 and fatigue interference as it related to fatigue severity. It was expected that patients who had poorer ECOG PS (2 4) would have more severe fatigue than patients having better ECOG PS (0 1). Group mean differences and their effect sizes were computed. To gauge clinically important differences, group mean differences were presented based on the half standard deviation 16 and the one standard error of measurement (SEM). 17 The formulas to calculate effect size, the half standard deviation, and the SEM were as follows: Adjacent category effect size adjacent category mean difference/ (within group mean square) 1/2 1 2 SD 1 Pooled standard deviation 2 SEM Pooled standard deviation (1 reliability of the scale) 1/2 Linear regression analysis was performed to determine possible predictors of fatigue severity. Exploratory univariate analyses were conducted to obtain candidate variables for the multivariate regression models. For categorical variables, one-way ANOVA was used to determine possible predictors of the outcome variable. The association between continuous or binary predictors and fatigue was tested for significance by calculating correlation coefficients. A predictor was considered a candidate if it had a marginal association (P 0.25). A cutoff value of 0.25 was used initially and was decreased to 0.05 in the multivariate regression model so that strong predictors were not inadvertently excluded. Possible collinearity problems were addressed by examining the tolerance of all independent variables (1 squared multiple correlation of that variable with all other variables in the model), which should be far from a lower limit of 0.02 on the tolerance level for each independent variable. 18 Multivariate regression analyses were conducted using forward and stepwise methods on clinical and symptom models. Residual diagnostics were performed to evaluate the appropriateness of using the linear regression analyses. All statistical procedures were performed using the SPSS Statistical Software Program for Windows. 19 All P values were 2-tailed. Type I error was controlled by using the Bonferroni adjustment. Results Patient Characteristics Table 1 displays the patients demographic and disease-related characteristics. Fifty-four percent of the sample were women. The mean age was 51 (18 77) years old. The majority of patients (86%) were married and approximately 40% had more than a high-school education. Only 30% of patients were working full-time and approximately 40% were retired. The patients were diagnosed with various types of cancer. The top three primary tumor types were gastrointestinal (25%), breast (24%), and lung (21%) cancer; half the sample (48%) had metastatic disease. Fifty-eight percent of the patients had a good ECOG performance status (0 1). Some patients were receiving cancer treatment, including chemotherapy (54%), radiotherapy (24%), surgery (40%), biotherapy (12%), and immunotherapy (11%). Opioid analgesics had been prescribed to 19% of patients for pain management. Upon enrollment to study, 22% had infection, 42% used antibiotics or antifungal medicine, and 30% used antiemetics. Construct Validity Principal axis factor analysis with oblimin rotation supported the 2-factor solution for all 9 items in the BFI-C by model fitting and consideration of clinical interpretability. The initial eigenvalues of 5.77, 0.95, and 0.69 for the first 3 factors accounted for 64.1%, 10.6% and 7.6% of the common variance in the exploratory factor analysis. Meaningful factors are usually associated with eigenvalues greater than However, adoption of a model ultimately depends on model fit and interpretability. Harman s criteria for a good model fit is that the standard deviation of the residuals should be less than the reciprocal of the square root of the sample size. 15 In this sample of 249 subjects, the reciprocal of the square root of the sample size was The standard deviation of the residuals for the 1-factor solution was 0.084, which was larger than 0.063, indicating model misfit.

5 326 Wang et al. Vol. 27 No. 4 April 2004 Table 1 Sample Demographic and Disease Characteristics Patients (n 249) Characteristic % n Median age in yrs (range) 51 (18 77) Sex Female Male Educational level Preschool 2 5 Primary school Middle school High school Special technical school College Employment status Employed full-time Employed part-time 6 14 Homemaker 3 7 Retired Disabled due to illness 2 6 Unemployed Other (Unspecified) 5 14 Marital status Married Divorced 2 4 Widowed 3 7 Separated Single w/another adult 6 16 Single alone 3 7 Cancer Diagnosis Gastrointestinal Breast Lung Head & Neck 6 15 Lymphoma (NHL) 4 10 Sarcoma 4 10 Gynecologic 4 9 GU 4 9 Thyroid 4 9 Lymphoma (HD) 2 5 Mesothelioma 1 3 Melanoma 1 2 Genitourinary Leukemia Myeloma Other unspecified ECOG performance status (Missing 1) Stage Stage I Stage II Stage III Stage IV Disease status (Missing 34) NED 2 5 Local Metastatic Laboratory data (Mean and range) Hgb in g/l (n 204) 11.5 ( ) Albumin in g/dl (n 154) 4.4 ( ) Because of an unacceptable model fit for a one-factor solution, we fitted a two-factor solution model (Table 2) and again tested the model fit. Six fatigue-related interference items loaded on the first factor (scores from 0.50 to 0.86). Another three fatigue severity items loaded on the second factor with high factor loadings ranging from 0.78 to With regard to the model fit, the standard deviation of the residuals was less than and indicative of good model fit using a 2-factor construct. In addition, the pattern of fatigue severity and fatigue interference loading on separate factors was more interpretable than the single-factor construct. Convergent Validity The fatigue severity composite score, the single item of fatigue worst, and the fatigue interference composite score (mean of 6 BFI-C interference items) were significantly correlated with the Physical and Mental Health Component scores, the Vitality subscale, and Physical Functioning subscale in the SF-36-C (P 0.001, Table 3). The results supported the hypothesis that the BFI-C scales would correlate significantly with the fatigue-related constructs of the SF-36-C. Correlation coefficients were moderate to high, ranging from 0.44 to 0.71 (all Ps 0.001). Moderate to large effect sizes are typically associated with moderate to large correlation coefficients, respectively. The fatigue interference composite score was more predictive of physical functioning than was fatigue worst (r 0.63, P 0.05), while the magnitude of correlation coefficients for both fatigue worst and fatigue severity composite scores with physical functioning were similar ( 0.44 vs. 0.50, P 0.05). The interference composite score of the BFI-C was Table 2 Factor Analysis for BFI-C (extract 2 factors) (n 249) Fatigue Item Factor 1 Factor 2 Enjoyment of life Relations with others Walk Work General activity Mood Fatigue usual Fatigue now Fatigue worst

6 Vol. 27 No. 4 April 2004 Chinese Version of the Brief Fatigue Inventory 327 Table 3 Correlation between BFI-C and SF-36-C (n 249) SF-36-C Physical Health Mental Health Physical Vitality BFI-C Component Score Component Score Functioning Subscale Subscale Fatigue worst Fatigue severity composite score (3 items) Fatigue interference composite score (6 items) Note: All correlation coefficients were P strongly and inversely correlated with the 2 component scores of the SF-36-C (r 0.71 and 0.66, P 0.001). The correlations of three BFI-C variables with the SF-36-C vitality subscale were similar (P 0.05, Table 3). Distribution-Based Measures of Clinical Significance and Known-Groups Validity As expected, patients having a poorer ECOG PS (2 4) consistently had significantly higher BFI scores, which included fatigue worst, mean of 3 severity items, and mean of 6 interference items (P 0.001), than did patients having a better ECOG PS (0 1). Means of the group differences and effect sizes of adjacent clinical categories and the clinically important differences are presented in Table 4. Using ECOG PS as an important clinical indicator can differentiate patients with varying fatigue severity using the BFI-C score. ECOG PS was correlated with fatigue severity at the fatigue worst item (r 0.421, P 0.001), using opioids (r 0.344, P 0.001), and disease stage (r 0.267, P 0.001). The fatigue severity composite score had a larger effect size to separate patients with an ECOG PS of 0 from those with an ECOG PS of 1; the fatigue interference composite score had larger effect size to separate patients with an ECOG PS of 1 from those with an ECOG PS of 2 to 4. To determine minimum clinically important differences, two methods of distribution-based analyses (1/2 SD and SEM) were used and are presented in the last two columns of Table 4. Spearman rank correlation between the mean of 3 fatigue severity items and the ECOG PS was 0.50 (P 0.001). Based on the severe fatigue cut point defined for the BFI English version, Figure 1 illustrates the relationship between the percentage of patients with severe fatigue ( fatigue worst of 7 10) and levels of hemoglobin (g/l). The patients with lower levels of hemoglobin ( 12 g/ L) had a greater level of fatigue severity (5.1 vs. 4.0; P 0.01) than patients with higher levels of hemoglobin ( 12 g/l). Reliability Cronbach coefficient alphas of 0.92 for 3 fatigue severity items and 0.90 for 6 fatigue interference items revealed the excellent internal consistency of the BFI-C (Table 5). Coefficient alphas for the subscales, if items were deleted, ranged from 0.86 to Table 4 Means and Standard Deviations of BFI-C Across ECOG Performance Status (n 248) (1) (2) (3) Pool The Adjacent Adjacent ECOG ECOG ECOG ECOG Reliability Category Category PS (0) PS (1) PS (2 4) PS (0 4) of the Mean Effect 1/2 Item n 63 n 82 n 103 n 248 Scale F Differences Size SD SEM Fatigue at 3.1 (2.7) a 4.1 (2.5) 6.2 (2.8) 4.7 (3.0) NA F (2,245) , , NA its worst P level (0 10) b Mean of (2.1) 3.8 (2.1) 5.8 (2.3) 4.4 (2.5) 0.92 F (2,245) , , severity P items (0 10) b Mean of (2.0) 2.9 (2.1) 5.2 (2.4) 3.6 (2.6) 0.90 F (2,245) , , interference P items (0 10) b a mean (sd). b Pairwise comparison between groups was significant at P 0.05 using the Tukey test.

7 328 Wang et al. Vol. 27 No. 4 April 2004 Fig. 1. Percentage of patients with fatigue worst score of 7 10, by hemoglobin level (g/l). Fatigue Severity and Its Relation to Interference The mean level of fatigue severity (3 items) was 4.3 (SD: 2.5; 95% CI: ). The mean of fatigue worst (1 item) was 4.7 (SD: 3.0; 95% CI: ). We did not observe any significant difference in fatigue severity between gender groups, between inpatients and outpatients, among age, marital-status, and educationallevel groups, or in patients who had had major cancer therapy within 30 days of this survey. Neither was there significant difference in fatigue severity among three major disease groups (GI, breast, and lung cancer) in the sample. However, significantly greater fatigue was reported by patients with advanced cancer (5.4 vs. 4.0; P 0.01), patients using opioids for pain control, and male patients with anemia (Hgb 12g/dL). Applying cut points established for the English version of the BFI, we found that 10% of the sample reported no fatigue (a rating of 0) on the fatigue worst item during the last 24 hours, 31% had severe fatigue ( fatigue worst of 7 or greater), and 60% had moderate to severe fatigue ( fatigue worst of 4 or greater). Figure 2 shows the relationship between the BFI-C composite interference score (mean of 6 items) and fatigue worst score. The nonparametric correlation between fatigue severity and interference scores was 0.72 (P 0.000). We observed the steepest slopes between 3 to 4 and 7 to 8. Based on a rule that optimal cut points should be associated with large increases in fatigue interference, the rate of increase (slope) can be an alternative way to help visualize the optimum cut points, which are associated with large increases in interference. Predictors of Fatigue Severity Linear regression analyses were performed to determine possible predictors of fatigue severity (Table 6). The dependent variable used was the composite score of fatigue severity (mean of three severity items on BFI-C) on the 0 10 scale. To examine any potential factors, such as demographic information, disease status, treatments, and lab data, that may be associated with fatigue severity, we initially analyzed seventeen independent variables: age, sex, marital status, grade, job status, cancer diagnosis, staging, disease status, ECOG PS, presence of infection, treatment by radiotherapy, chemotherapy, or surgery, use of opioids to treat pain, use of an emetic, hemoglobin level, and albumin level. Univariate Analysis. After screening the 17 independent variables in univariate analysis, we identified 7 of the biomedical variables (checklist, demographics, lab data) as candidates for Table 5 Reliability Analysis for BFI-C (extract 2 factors) (n 249) Fatigue Fatigue Severity Interference Item α if Item Item α if Item (α ) Deleted (α ) Deleted Fatigue now General activity Fatigue usual Mood Fatigue worst Walking Work Relations with others Enjoyment of life Fig. 2. Mean fatigue interference score (6 items) by fatigue worst score.

8 Vol. 27 No. 4 April 2004 Chinese Version of the Brief Fatigue Inventory 329 Table 6 Linear Regression Model: Predictors of Fatigue Severity Independent 95% CI for Variables in Predictors of Unstandardized Standardized Unstandardized Model (R 2, n) the Model Severe Fatigue Coefficients Coefficients Coefficients Model 1 (0.24, 174) Age, sex, ECOG PS, 1. ECOG PS (0 4) a , 2.61 disease stage, disease 2. Disease status , 1.54 status, use of opioid for pain, hemoglobin (local/advanced) b 3. Hemoglobin level b , level Model 2 (0.11, 174) Age, sex, disease stage, 1. Disease stage (1 4) c , 0.95 disease status, use of 2. Use of opioid , 1.82 opioid for pain, for pain (yes/no) b hemoglobin level Note: The dependent variable, fatigue severity, is a composite score of the worst level of fatigue, the usual level of fatigue during the last 24 hours, and the level of fatigue at present. a P b P c P the multivariate regression analysis. These candidates met either of two criteria in the correlation coefficient calculation (interval, ordinal, or binary variable) or one-way ANOVA (categorical variable with 3 choices). The first criterion was whether the P value was 0.25 and the magnitude of the correlation coefficient was Disease status, staging, ECOG PS, and use of opioids for pain were selected based on this criterion. The second criterion was whether or not the variable was clinically significant enough to be included even though it did not partially or completely meet the first criterion. Hemoglobin level (r 0.16, P 0.02), age (r 0.15, P 0.02), and sex (r 0.045, P 0.48) were selected due to their importance. Accordingly, the 7 remaining biomedical candidate variables were patient s age, sex, ECOG PS, disease stage, disease status, use of opioids for pain, and hemoglobin. Two symptom variables, nausea and vomiting, were excluded due to weak predicted relationships with severe fatigue (r 0.10, P 0.10; r 0.11, P 0.08). Multivariate Model. The predictive relationships between severe fatigue and the 7 biomedical candidate variables above were first examined using a linear regression model. Results showed that poor ECOG PS, advanced disease status, and decreased hemoglobin level were significant predictors (Model 1, Table 6). Second, we were interested in identifying other significant predictors of severe fatigue when ECOG PS was excluded from the candidate variables because of its strong relationship to fatigue, as well as its possible confounding effect. We found late-stage cancer and use of opioids for pain to be significant predictors (Model 2, Table 6). Tolerance of all independent variables ranged from 0.6 to 0.9. Thus, multiple collinearity problems did not exist in the models. 18 Standardized residuals in the three models were all normally distributed (P 0.05), meeting the assumptions of the linear regression model. Discussion Similar to cancer pain, fatigue severity and its impact on daily life and function are a concern to Chinese cancer patients. The current validation study demonstrates that the BFI-C s straightforwardness and good psychometric properties make it an excellent assessment tool for use in both symptom research and the clinical management of fatigue among Chinese cancer patients. The BFI-C compares favorably to other one-dimensional and multi-dimensional fatigue assessment tools. It has a small number of items, items that are easily understood, an easy scaling method for patients to use, standardized rules for administration and scoring, and welldocumented reliability and validity. The BFI s 0 10 rating scale creates several advantages. The scale is easy to understand for patients of all education levels and social statuses. The 0 10 numerical ratings can be entered into Internet

9 330 Wang et al. Vol. 27 No. 4 April 2004 forms or interactive voice response telephone touch-pad systems, simplifying data collection from discharged patients. It is also acceptable for patients who are severely ill. The two-factor solution provided a good model fit for the 9 BFI-C severity items (the original English, 6 Japanese, 21 and German 22 versions of the BFI utilized a single-factor solution). Both the single item of fatigue worst and the severity component score correlated well with the SF-36-C severity component score, suggesting that the single item fatigue worst could be a sensitive indicator of a patient s fatigue severity and a clinically friendly method for rapid fatigue screening or evaluation. The 6 BFI-C interference items provide a basic profile of a patient s fatigue-related functional status. The convergent validity analysis in this study confirmed that the BFI-C interference composite score was highly correlated with both the physical and mental health component scores of the SF-36-C. This interesting result encourages the use of the BFI-C in future clinical studies on fatigue, as a simple measure of fatigue-related physical and mental functional impairment. Fatigue, one of the top complaints in Western cancer patients, 7 was also highly prevalent in the Chinese cancer patients. Thirty-one percent of patients had severe fatigue (7 10) and 60% had moderate to severe fatigue (4 10) according to the fatigue severity cut points established for the English BFI. These results are quite similar to those derived from the original. BFI validation study, in which 35% of patients reported severe fatigue and 73% reported moderate to severe fatigue on the fatigue worst item. 6 There was clear evidence of a significant relationship between fatigue severity and fatigue-related interference in patients daily functioning. Consistent with Western fatigue research, the current study showed that sicker cancer patients, patients with advanced-stage disease, and those with anemia had stronger predictive relationships with fatigue severity. Results from the examination of the difference in fatigue severity and fatigue interference between adjacent ECOG PS groups were similar to reported differences in other health-related quality-of-life outcomes. Accordingly, patients whose ECOG PS improved would expect a one-half standard deviation increase in their quality of life. 16,23 In this study, we found that one-half standard deviation corresponds approximately to a onepoint difference. When ECOG PS was removed in the regression model, another interesting clinical predictor of fatigue severity, using opioids for pain management, appeared. In fact, patients on pain medicine often have advanced disease and suffer from analgesic side effects. It has been recognized by many oncology professionals that fatigue almost always clusters with other significant symptoms, either caused by disease or therapy. 24 A separate regression model in the current study demonstrated that pain, distress, drowsiness, sleep problems, and cognitive impairment were highly associated with fatigue severity. This evidence raised a new hypothesis for further fatigue research: that multiple-symptom management might improve fatigue severity. This study had two major limitations. First, test retest reliability cannot be examined in this cross-sectional study. Future studies with longitudinal design can help to further evaluate the BFI-C s reliability and responsiveness. Also, a better understanding of the interaction between fatigue and other symptoms during aggressive treatments might be gained by analyzing repeated measures of fatigue. Second, we had no normal control group to differentiate between the severity of cancer-related fatigue and typical fatigue in Chinese cancer patients. Using the BFI-C in a largescale fatigue epidemiology study across diseases and age groups in China could provide more data on the nature of fatigue. Much effort has been made toward using traditional Chinese medicine to manage fatigue and to improve patients functional status and health-related quality of life, although the efficacy of this approach has never been evaluated in the Chinese cancer population. In conclusion, BFI-C satisfies the practical and the psychometric properties for a good assessment tool to measure cancer-related fatigue in the Chinese cancer population. The BFI-C could become a critical assessment tool for further fatigue medicine trials and for providing evidence-based fatigue interventions. Because the BFI has been translated into other languages, the use of the BFI-C in the Chinese cancer population can simplify fatigue epidemiology and etiology study comparisons between countries.

10 Vol. 27 No. 4 April 2004 Chinese Version of the Brief Fatigue Inventory 331 Acknowledgments The authors gratefully acknowledge the Hawn Foundation, Dallas, Texas, for their support of this project. References 1. Blesch KS, Paice JA, Wickham R, et al. Correlates of fatigue in people with breast or lung cancer. Oncol Nurs Forum 1991;18(1): Mock V, Atkinson A, Barsevick A, et al. NCCN practice guidelines for cancer-related fatigue version National Comprehensive Cancer Network, Cleeland CS, Wang XS. Measuring and understanding fatigue. Oncology 1999;13(11A): Winningham ML, Nail LM, Burke MB, et al. Fatigue and the cancer experience: the state of the knowledge. Oncol Nurs Forum 1994;21(1): Patrick DL, Ferketich SL, Frame PS, et al. National Institutes of Health state-of-the-science conference statement: Symptom management in cancer: pain, depression, and fatigue July, 15 17, J Natl Cancer Inst 2003;95(15): Mendoza TR, Wang XS, Cleeland CS, et al. The rapid assessment of fatigue severity in cancer patients: use of the Brief Fatigue Inventory. Cancer 1999;85(5): Cleeland CS, Mendoza TR, Wang XS, et al. Assessing symptom distress in cancer patients: the M. D. Anderson Symptom Inventory. Cancer 2000;89(7): Wang XS, Giralt SA, Mendoza TR, et al. Clinical factors associated with cancer-related fatigue in patients being treated for leukemia and non-hodgkin s lymphoma. J Clin Oncol 2002;20(5): Ware JE Jr, Sherbourne CD. The MOS 36-item Short-Form Health Survey (SF-36). I. Conceptual framework and item selection. Med Care 1992;30 (6): Li L, Wang HM, Shen Y. Chinese SF-36 Health Survey: translation, cultural adaptation, validation, and normalization. J Epidemiol Community Health 2003;57(4): Li L, Wang H, Shen Y. Development and psychometric tests of a Chinese version of the SF-36 Health Survey Scales. Chinese J Prev Med 2002;36(2): Lam CL, Gandek B, Ren XS, et al. Tests of scaling assumptions and construct validity of the Chinese (HK) version of the SF-36 Health Survey. J Clin Epidemiol 1998;51(11): Ren XS, Wang XS, Liu S, et al. Psychometric and clinical evaluation of a Chinese version of the SF- 36 Health Survey among cancer patients in China. Quality of Life Newsletter 2003;30: Oken MM, Creech RH, Tormey DC, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 1982;5(6): Harman HH. Modern factor analysis, 2nd revised ed. Chicago: University of Chicago Press, Sloan JA, Cella D, Frost M, et al. Assessing clinical significance in measuring oncology patient quality of life: introduction to the symposium, content overview, and definition of terms. Mayo Clin Proc 2002;77(4): Wyrwich KW, Tierney WM, Wolinsky FD. Further evidence supporting an SEM-based criterion for identifying meaningful intra-individual changes in health-related quality of life. J Clin Epidemiol 1999;52(9): Kleinbaum DG, Kupper LL, Muller KE. Applied regression analysis and other multivariable methods. Boston: PWS-Kent Publishing Co., 1988: SPSS statistical software program for Windows. Chicago, IL: SPSS, Inc., Nunnally JC, Bernstein IH. Psychometric theory, 3rd ed. New York: McGraw-Hill, Okuyama T, Wang XS, Akechi T, et al. Validation study of the Japanese version of the Brief Fatigue Inventory. J Pain Symptom Manage 2003;25(2): Radbruch L, Sabatowski R, Elsner F, et al. Validation of the German version of the Brief Fatigue Inventory. J Pain Symptom Manage 2003;25(5): Cella D, Eton DT, Lai JS, et al. Combining anchor and distribution-based methods to derive minimal clinically important differences on the Functional Assessment of Cancer Therapy (FACT) anemia and fatigue scales. J Pain Symptom Manage 2002;24(6): Dodd MJ, Miaskowski C, Paul SM. Symptom clusters and their effect on the functional status of patients with cancer. Oncol Nurs Forum 2001;28 (3):

11 332 Wang et al. Vol. 27 No. 4 April 2004 Appendix 1. The Chinese version of the Brief Fatigue Inventory-Chinese (BFI-C).