Fatigue and Depression in Cancer Patients Undergoing Chemotherapy: An Emotion Approach

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1 Vol. 32 No. 4 October 2006 Journal of Pain and Symptom Management 311 Original Article Fatigue and Depression in Cancer Patients Undergoing Chemotherapy: An Emotion Approach Youngmee Kim, PhD, Jane T. Hickok, MD, MPH, and Gary Morrow, PhD, MS University of Rochester School of Medicine, Rochester, New York, USA Abstract Fatigue and depressive symptoms are common in cancer patients, but the nature of the relationship between the two remains unclear. We examined the degree to which two dimensions of emotion assessed as psychological factors (i.e., arousal and valence) predicted changes in fatigue and depressive symptoms over four cycles of chemotherapy in cancer patients who participated in a randomized clinical trial. Among 549 patients enrolled in the study, 525 provided data from a minimum of two treatments and were included in the multilevel modeling analyses. Multilevel models were used to identify significant predictors of initial levels and changes of fatigue and depressive symptoms and to determine the relationship between fatigue and depressive symptoms independent of other predictors proposed in this study. Multiple factors, including age, gender, and cancer site, predicted the initial levels. More importantly, the two dimensions of psychological factors significantly predicted changes in fatigue and depressive symptoms, in similar patterns but to different degrees. Specifically, changes in fatigue depended more on the valence dimension, whereas changes in depressive symptoms depended on both the valence and arousal dimensions. Theoretical and practical implications of the current findings are discussed and suggestions for interventions to alleviate fatigue and depressive symptoms in cancer patients are proposed. J Pain Symptom Manage 2006;32:311e321. Ó 2006 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved. Key Words Fatigue, depression, cancer, chemotherapy, emotion approach This study was supported by National Cancer Institute grant U10CA GlaxoSmithKline provided study medication and an unrestricted educational grant to help support investigator meeting expenses. The first author dedicates this research to the memory of Heekyoung Kim. Address reprint requests to: Youngmee Kim, PhD, Behavioral Research Center, American Cancer Society, 1599 Clifton Road, NE, Atlanta, GA , USA. youngmee.kim@cancer.org Accepted for publication: May 11, Ó 2006 U.S. Cancer Pain Relief Committee Published by Elsevier Inc. All rights reserved. Introduction Fatigue is one of the most common and debilitating symptoms that cancer patients experience. 1 Cancer-related fatigue has been described as an unusual, excessive, and pervasive whole-body experience that is unrelated to activity or exertion and is not alleviated by rest or sleep, thus differing from the fatigue that healthy individuals experience following periods of exertion or insufficient sleep. 2 Cancer-related fatigue occurs in 75%e100% of patients undergoing chemotherapy. 3, /06/$esee front matter doi: /j.jpainsymman

2 312 Kim et al. Vol. 32 No. 4 October 2006 Fatigue in cancer patients can interfere with self-care activities, 5 be extreme enough at times to postpone or reduce treatment, 6,7 and disturb daily life. 2,4 The level of fatigue reported by cancer patients is more than twice that reported by age-matched healthy comparison groups. 8,9 In recent years, the etiology of cancer-related fatigue has received increased attention. 4,10,11 Numerous studies have reported that fatigue correlates significantly with the level of depression in cancer patients. 10,12,13 Less common than fatigue, depressive symptoms are reported by 2%e50% of cancer patients. 14 Both fatigue 15,16 and depression 17,18 have been conceptualized from multidimensional perspectives as having physiologic, behavioral, emotional, and cognitive components. Fatigue and depression also share empirically common features. Among demographic variables, higher levels of fatigue 19,20 or depression were reported by younger cancer patients and female cancer patients. Among medical variables, prior surgery, 21,22 distant metastases, 23 and cancer site 19,24 have been related to higher levels of fatigue or depression. Psychological factors, such as heightened anxiety, anger, and confusion, or lowered vigor, have been associated with greater levels of fatigue 8,25 and depression 26 in cancer patients. Pharmacological intervention studies have found that drugs that block the reuptake of 5HT, namely the selective serotonin reuptake inhibitors, are effective in reducing depressive symptoms of individuals receiving cytokine therapy for cancer. 27 Such drugs have been particularly effective in reducing anxiety and cognitive components of depressive symptoms and less effective in reducing neurovegetative and somatic components of depressive symptoms. 28e30 These findings suggest that anxiety and cognitive dysfunction may be core features of depression, whereas somatic or arousal components might be tertiary features of depression, that instead may be core features of fatigue, an entity which is similar to yet distinct from depression. The role of psychological factors in the development of fatigue and depression among cancer patients, however, has been understudied. Complementing the above hypothesis is the theory that emotion is comprised of two dimensions: arousal (alertness vs. boredom) and valence (positive vs. negative). 31e33 Using this two-dimensional approach, the findings of a differential impact of pharmacological interventions on changes in depressive symptoms can be interpreted to suggest that changes in depressive symptoms may depend more on the valence dimension of emotion (e.g., anxiety and confusion), whereas changes in fatigue may depend more on the arousal dimension (e.g., anger and vigor). To better understand the complex nature of fatigue and depressive symptoms in cancer patients undergoing chemotherapy, we took the two-dimensional approach described above as a conceptual framework, used data from a double-blind longitudinal study of an antidepressant drug given for fatigue, 34 and used a multilevel modeling approach to examine the independent role of patients individual characteristics and medical treatment characteristics on the initial levels of and changes in fatigue and depressive symptoms in cancer patients. We first explored the associations between demographic and medical factors and the initial levels of fatigue (Research Question 1-1) and depression (Research Question 1-2). Based on previously published research, 11,29 we hypothesized that psychological factors would be associated with changes in fatigue and depression (Hypothesis 1). Specifically, we proposed that the arousal dimension of psychological factors (e.g., anger and vigor) would be more strongly associated with changes in fatigue than in depression (Hypothesis 1-1), and the valence dimension of psychological factors (e.g., anxiety and confusion) would be more strongly associated with changes in depressive symptoms than in fatigue (Hypothesis 1-2). We also explored whether or not the association between changes in fatigue and changes in depression across treatments would remain significant after taking into consideration differences in the initial level of fatigue based on the patient s demographic and medical characteristics, and variances accounted for by psychological variables (Research Question 2). Methods Participants Data were collected as a part of a doubleblind, placebo-controlled clinical trial

3 Vol. 32 No. 4 October 2006 Fatigue and Depression in Cancer 313 examining the efficacy of an antidepressant medication (paroxetine) in attenuating the development of depression and fatigue in cancer patients during chemotherapy treatment. 34 Patients were recruited from 18 community oncology practices affiliated with the University of Rochester Cancer Center Community Clinical Oncology Program Research Base. To be eligible for the project, participants had to a) have a diagnosis of cancer, b) be scheduled to receive a minimum of four cycles of chemotherapy a minimum of 3 weeks apart, without concurrent radiation therapy or interferon treatment, c) be 18 years of age or older, d) be able to speak and read standard English, and e) have adequate renal, hepatic, and cardiac function, as determined by the treating oncologist. Patients who received chemotherapy prior to the present study were excluded. Of 549 patients who were initially randomized into either the paroxetine or placebo group, eight patients in the paroxetine group and 16 from the placebo group failed to provide data from a minimum of two treatments. Thus, results are reported for the 525 patients who were randomized into either the paroxetine group (n ¼ 269) or the placebo (n ¼ 256) group, provided complete demographic and treatment data, and completed questionnaires at a minimum of two treatments were available. Procedures The Institutional Review Board of the University of Rochester and each participating site approved the protocol in accordance with an assurance filed with and approved by the Department of Health and Human Services. Written informed consent was obtained from each subject prior to study enrollment. One week following their first through fourth chemotherapy treatments, participants completed questionnaires that included measures of fatigue, depression, and psychological variables. Patients completed the questionnaires at home and mailed the completed questionnaires to the study site in a self-addressed stamped envelope. Reminder phone calls were made by study personnel on the day patients were scheduled to complete the questionnaires. Between Day 5 and Day 7 after the second chemotherapy treatment, the presence of fatigue during the past week was assessed by study personnel via telephone. The time of assessment was chosen to avoid including any temporary fatigue effect of prior surgery or radiation therapy. A Pearson zeroorder correlation coefficient between a single item from the Multidimensional Assessment of Fatigue (MAF) 35 ( To what degree have you experienced fatigue during the past week ) and a total score of the MAF was 0.96, which indicates that this item is a highly representative and reliable measure of the presence of fatigue. Thus, the single item using a 10-point Likert-style format (1 ¼ not at all, 10 ¼ a great deal) was used to assess the presence of fatigue. If a patient did not experience fatigue at all, that patient was assigned to the no-drug group and these patients were excluded from the current analyses. Patients who had experienced any level of fatigue were randomly assigned by a computer-generated random numbers table to one of the two drug groups (i.e., paroxetine and placebo groups). Measures Demographic and Medical Variables. Self-report demographic (age, gender, marital status, education, and race) information was obtained and medical (prior surgery or radiation therapy, cancer site, and whether the cancer had metastasized) information was abstracted from medical records. Psychological Variables. Four subscales of the 30-item version of the Profile of Mood States- Short Form (POMS-SF) 36 were used to assess the patient s psychological status during the past week, using a five-point Likert-style format (0 ¼ not at all, 4 ¼ extremely). To avoid overlap with the study outcome variables, fatigue and depression subscales were excluded, leaving only four subscales of the POMS-SF: anger, anxiety, confusion, and vigor. Anger and vigor subscale scores served as indicators of the arousal dimension of psychological factors; anxiety and confusion subscale scores served as indicators of the valence dimension of psychological factors. Fatigue. The 30-item Fatigue Symptom Checklist (FSCL) 37 was used to assess fatigue. The scale has been used in studies to investigate fatigue in cancer patients and has been

4 314 Kim et al. Vol. 32 No. 4 October 2006 demonstrated to have good reliability and validity. 38,39 Participants indicated the presence and intensity of each item on a five-point Likert-style format (0 ¼ absence of, 4 ¼ a great deal). Depressive Symptoms. The 20-item Center for Epidemiological Studies Depression Scale (CES-D) 40 was used to assess depressive symptoms during the past week, using a four-point Likert-style format (0 ¼ rarely or none of the time, 3 ¼ most or all of the time). The CES-D has been widely used with a variety of populations, including cancer patients. 26 Analysis Plan: Multilevel Modeling Analysis The main research hypothesis and questions were examined using a hierarchical linear modeling (HLM) approach. HLM 41 allows the examination of the independent effects of both between-person variables and withinperson variables that are nested below the between-person variables. 42,43 The demographic, medical, and intervention group variables are a patient s unique characteristics (person-level variables: between-person factors), whereas levels of psychological variables and depressive symptoms or fatigue at a certain treatment cycle are characteristics uniquely observed at the treatment (treatment-level variables: within-person factors). Thus, variations in an outcome variable depend on characteristics associated with a certain treatment, which also depend on characteristics of the patient. The results from this multilevel analysis should reveal unique effects of each of the following groups of predictors on the initial level and changes of fatigue: a) demographic variables (i.e., age, gender, marital status, education, and ethnicity), b) medical variables (i.e., if the patient had surgery or radiation therapy before the current study started, if the cancer had metastasized, and cancer site), c) intervention group (i.e., to which intervention group the patient was assigned), d) changes in psychological variables (i.e., changes in anger, anxiety, confusion, and vigor), and finally e) the change in depressive symptoms. Demographic and medical variables were dummy coded (1 vs. 0), except age (continuous variable) and treatment cycle (categorical variables) (see Appendix). HLM is effective in dealing with unequal numbers of unevenly distributed data points across the four treatment cycles. HLM also allows regression intercepts (i.e., baseline score of a variable) and slopes (i.e., changes in a variable) to vary across participants. The pair-wise deletion method was used for dealing with missing values in treatment-level variables. 42 Each variable effect was estimated simultaneously and independently within each level and across levels by HLM. The model for predicting fatigue and depressive symptoms used in this study has two different levels of analysis, individual differences in study variables (person level) and differences in the study variables by consecutive treatment cycles within each person (treatment level). The treatment-level variables, except linear and curvilinear effects of treatment cycle, were centered around the individual s means, and age in the person-level was centered around the sample mean. Dummy or categorical variables were not centered. 42 Results Descriptive Statistics of Study Variables Descriptive information regarding the proposed predictor variables is shown in Table 1. Table 2 shows means and standard deviations of the outcome measures of fatigue (i.e., FSCL) and depressive symptoms (i.e., CES-D) from the first to the fourth chemotherapy treatments (average scores per treatment and by cancer sites). In addition, the zero-order correlations between frequency and depressive symptoms at each chemotherapy treatment are reported in Table 2. The association between fatigue and depressive symptoms was significantly positive at each treatment and the magnitude of the association remained the same. Multilevel Analysis for Fatigue and Depression at the Initial Level Results using HLM for fatigue are shown under the fatigue columns of Table 3. The between-person factors rows show the results examining Research Question 1-1: the association between demographic and medical variables and the initial level (i.e., intercept) of fatigue. Among demographic variables, only male gender was significantly associated with

5 Vol. 32 No. 4 October 2006 Fatigue and Depression in Cancer 315 Table 1 Descriptive Statistics of Proposed Predictor Variables Mean (SD) or Frequency (%) Demographic variables (n ¼ 525) Age (12.43) Gender (female) 388 (73.9%) Married 369 (70.3%) High school or less education 280 (53.3%) Ethnicity (white) 465 (88.6%) Medical variables (n ¼ 525) Presurgery 346 (68.5%) Preradiation therapy 52 (10.4%) Metastasis 160 (30.5%) Cancer sites Breast cancer 269 (51.2%) Hematologic neoplasms 73 (13.9%) Lung cancer 80 (15.2%) Other 103 (19.7%) (Alimentary tract, 7.2%; gynecologic, 7.0%) Tx1 (n ¼ 523) Tx2 (n ¼ 522) Tx3 (n ¼ 462) Tx4 (n ¼ 428) Psychological Variables Mean (SD) Mean (SD) Mean (SD) Mean (SD) Anger 2.86 (3.53) 3.04 (3.70) 2.49 (3.39) 2.24 (3.23) Vigor 4.30 (4.21) 3.67 (3.66) 3.85 (3.81) 3.94 (3.88) Anxiety 4.52 (3.89) 4.30 (3.95) 3.68 (3.81) 3.21 (3.32) Confusion 0.94 (3.23) 1.18 (3.24) 0.77 (2.94) 0.64 (2.77) Tx ¼ Chemotherapy treatment. a higher initial level of fatigue. Among medical variables, patients with lung cancer reported a higher level of fatigue than patients with other types of cancer. The analysis was repeated for depressive symptoms. Results are shown under the depression columns of Table 3. Regarding Research Question 1-2: the association between demographic and medical factors and the initial levels of depression. Among demographic variables, only age was significantly associated with the initial level of depressive symptoms, with younger-adult patients reporting a higher initial level of depressive symptoms. Among medical variables, patients with lung cancer reported significantly higher levels of depressive symptoms than patients with other types of cancer. Multilevel Analysis for Changes in Fatigue and Depression Hypothesis 1-1 was to examine whether changes (i.e., slopes) in psychological variables would be significantly associated with changes Table 2 Levels of Fatigue and Depression by Cancer Sites Tx1, Mean (SD) Tx2, Mean (SD) Tx3, Mean (SD) Tx4, Mean (SD) Fatigue (FSCL) n ¼ 525 n ¼ 520 n ¼ 464 n ¼ 427 All (19.65) (19.58) (19.45) (18.32) Breast (18.14) (19.29) (19.82) (18.36) Hematologic neoplasms (21.17) (18.89) (19.68) (18.84) Lung (22.52) (21.80) (20.89) (19.79) Depression (CES-D) n ¼ 516 n ¼ 520 n ¼ 457 n ¼ 425 All (10.28) (10.32) (10.26) (9.68) Breast (10.52) (10.71) (10.36) (10.39) Hematologic neoplasms (8.43) (8.45) (9.17) (8.90) Lung (11.90) (10.22) (11.43) (9.69) Zero-order correlation between Fatigue (FSCL) and Depression (CES-D) Tx ¼ Chemotherapy treatment. n ¼ 516 n ¼ 518 n ¼ 457 n ¼

6 316 Kim et al. Vol. 32 No. 4 October 2006 Table 3 Unstandardized Coefficients (B) of Hierarchical Linear Modeling Analyses on Fatigue and Depression Fatigue Depression Predictors B t B t Between-person (person) factors: Predictors of the initial level Demographic variables Age a Gender (female ¼ 1, male ¼ 0) b Married High school or less education Ethnicity (white) Medical variables Presurgery Preradiation therapy Metastasis Cancer sites Breast Hematologic neoplasms Lung c c Within-person (treatment) factors: Predictors of the change Psychological variable effects Anger a Vigor a a Anxiety a a Confusion a a Depression/fatigue effect a a Linear effect By paroxetine Curvilinear effect By paroxetine c Married ¼ 1 for married, 0 for nonmarried; High school or less education ¼ 1 for high school or less education, 0 for greater than high school education; Ethnicity (white) ¼ 1 for white, 0 for nonwhite; Presurgery ¼ 1 for having surgery before participating in the study, 0 for no surgery before this study; Preradiation therapy ¼ 1 for receiving radiation therapy before participating in the study, 0 for no radiation therapy before this study; Metastasis ¼ 1 for the cancer had metastasized, 0 ¼ no metastasis; Paroxetine ¼ 1 for paroxetine, 0 for placebo groups. a P < b P < c P < (i.e., slopes) in fatigue across treatments. These results are shown in the psychological variable effects rows under the fatigue columns of Table 3. With the exception of anger, changes in psychological variables were all significantly associated with changes in fatigue. An increase in anxiety or confusion was related to an increase in fatigue, whereas an increase in vigor was associated with a decrease in fatigue. Change in fatigue was more strongly associated with change in anxiety (h 2 ¼ 0.36) or confusion (h 2 ¼ 0.42), which are indicators of the valence dimension of psychological factors, compared to the change in anger (h 2 ¼ 0.05) or vigor (h 2 ¼ 0.11), which are indicators of the arousal dimension of psychological factors. These findings did not support Hypothesis 1-1. The results of testing Research Question 2: the unique association between changes in fatigue and changes in depressive symptoms across treatments, is shown in the depression effect row under the fatigue columns of Table 3. Although the change in depressive symptoms was a significant predictor of changes in fatigue, the association was weaker (h 2 ¼ 0.14) than the associations with the changes in psychological factors. Next, the psychological variable effects rows under the depression columns of Table 3 show the results of testing Hypothesis 1-2: the associations between changes in psychological variables and changes in depressive symptoms. An increase in anger, anxiety, or confusion was related to an increase in depressive symptoms, whereas an increase in vigor was related to

7 Vol. 32 No. 4 October 2006 Fatigue and Depression in Cancer 317 a decrease in depressive symptoms. The change in depressive symptoms was fairly equally associated with changes in anger (h 2 ¼ 0.19), vigor (h 2 ¼ 0.11), anxiety (h 2 ¼ 0.21), or confusion (h 2 ¼ 0.20), which suggested that changes in depressive symptoms are evenly accounted for by both the arousal and valence dimensions of psychological factors. These results partially supported Hypothesis 1-2. The change in fatigue was a significant predictor of the change in depression (Research Question 2: fatigue effect row under depression columns), but the association (h 2 ¼ 0.07) was the weakest compared to other associations with changes in psychological variables. Discussion The findings, which used a two-dimensional approach to assessing emotion and multilevel modeling, illustrated that independent multiple factors predicted initial levels and changes in fatigue and depressive symptoms. Male gender and a diagnosis of lung cancer significantly predicted the initial level of fatigue, whereas younger age and lung cancer diagnosis significantly predicted initial levels of depressive symptoms. The other demographic variables studied were not significant predictors of fatigue or depressive symptoms. Our findings suggest that demographic and medical characteristics of fatigue or depressive symptoms in cancer patients are nonspecific. 2 More interestingly, our results show that changes in fatigue depend more on the valence dimension (e.g., anxiety and confusion), whereas changes in depressive symptoms depend on both the valence and arousal dimensions. It is also noteworthy that the magnitude of the associations between each psychological variable and fatigue or depressive symptoms varied significantly in the present study. The degree to which changes in anxiety or confusion predicted the change in fatigue was considerably greater than the degree to which changes in anxiety or confusion predicted the change in depressive symptoms (d ¼ 0.15 for anxiety; d ¼ 0.18 for confusion). In contrast, the degree to which anger or vigor predicted the change in fatigue was notably smaller than the degree to which anger or vigor predicted the change in depressive symptoms (d ¼ 0.14 for anger; d ¼ 0.12 for vigor). We further found the association between changes in fatigue and changes in depressive symptoms across treatments to remain significant, after controlling for the effects of demographic and medical variables on initial levels of fatigue and depressive symptoms, as well as effects of psychological variables on changes in fatigue and depressive symptoms. However, among statistically significant predictors of changes in fatigue or depressive symptoms, in the present study, depressive symptoms or fatigue, respectively, was one of the weakest predictors. These findings indicate that although fatigue was a significant predictor of depressive symptoms and depression was a significant predictor of fatigue, clear and noteworthy differences exist in the association between fatigue and depressive symptoms among cancer patients who are undergoing chemotherapy. The findings in the present study have valuable practical implications to assist clinicians in developing effective interventions for reducing fatigue and depressive symptoms. Because lung cancer patients are more likely to experience higher levels of fatigue and depressive symptoms from the beginning of chemotherapy treatment, these patients would benefit not only from their physician s particular attention to the symptoms of fatigue and depressive symptoms during the course of treatments but also from special guidance and education about self-care strategies in relieving those symptoms. Jacobsen et al. 44 demonstrated the efficacy of a patient self-administered form of stress management training in improving multidimensional aspects of quality of life in patients undergoing chemotherapy. This intervention may also effectively reduce fatigue and depressive symptoms. Our results also provide useful insight as to which components of guidance and education are essential. The finding that fatigue among cancer patients is associated with increases in anxiety and confusion suggests that strategies often used to lower anxiety, such as relaxation with guided imagery, the use of positive coping strategies, and avoidance of negative thinking, 45 may hold promise in reducing fatigue among cancer patients undergoing chemotherapy. Other strategies targeted at restoring direct attention and the capacity for

8 318 Kim et al. Vol. 32 No. 4 October 2006 concentration, 46 minimizing cognitive impairment that might be due in part to sleep deprivation, providing information regarding the disease and related medical treatment to the patients, and educating them on how to manage the side effects of cancer treatments, 11,47 may also be effective in reducing the patients feelings of anxiety, confusion, and bewilderment. Limitations and Directions for Future Studies Because both fatigue and depressive symptoms are subjective phenomena, clinicians have relied mainly on patients self-report to assess the severity of fatigue and depressive symptoms. Objective measures could also improve the reliability of assessing fatigue and depressive symptoms. Future studies could include biophysiological markers of fatigue and depressive symptoms such as circadian rhythm, levels of cytokines and serotonin or serum cortisol, degree of anemia, changes in hydrogen ion concentration, ph, or levels of muscle metabolic end products (determined by magnetic resonance spectroscopy). In addition, patients dispositional characteristics that may make them more susceptible to changes in fatigue and depressive symptoms or more likely to report such symptoms 48 should be evaluated in future studies. Research on chronic fatigue syndrome has suggested that the personality traits of emotionality, neuroticism, and perfectionism 49,50 are particularly associated with increased complaints of fatigue. Reliance on catastrophic coping strategy was also associated with worsened fatigue among women with breast cancer who had previously been treated with adjuvant chemotherapy. 51 By including both biophysiological and psychological indicators of fatigue and depressive symptoms, the complex nature of fatigue and depressive symptoms may be better understood. Future studies also need to investigate whether the current study model would hold the same for male and female patients or for different types of cancer (e.g., lung cancer patients compared to breast cancer patients). Further examination of mechanisms of the curvilinear effect of paroxetine on changes in depressive symptoms will be also fruitful. Conclusion Fatigue and depressive symptoms are commonly experienced by cancer patients undergoing treatment. Understanding the etiology of fatigue and how to manage this symptom effectively is a challenge for oncology practitioners and researchers 16 as well as for patients and their family caregivers. Although effective treatments for fatigue have not yet been developed, 1 our resultsdthat changes in fatigue depend more on the valence dimension (i.e., anxiety and confusion) whereas changes in depressive symptoms depend on both the valence and arousal (i.e., anger and vigor) dimensiondenrich the literature by providing the first demonstration of the distinct association of both fatigue and depressive symptoms with psychological dimensions. In addition, the results present valuable information for initiating new methods to alleviate cancer patients fatigue and depressive symptoms during their course of chemotherapy. Patients will benefit from comprehensive intervention programs designed to identify the demographic, medical, and psychological factors that may render them more vulnerable to changes of fatigue and depressive symptoms. In addition, programs designed to encourage patients to engage in activities for emotion regulation and self-care may help them minimize development of cancer-related fatigue and depressive symptoms. References 1. Ream E, Richardson A. From theory to practice: designing interventions to reduce fatigue in patients with cancer. Oncol Nurs Forum 1999;26: 1295e Winningham ML, Nail LM, Burke MB, et al. Fatigue and the cancer experience: the state of the knowledge. Oncol Nurs Forum 1994;21:23e Richardson A. Fatigue in cancer patients: a review of the literature. Eur J Cancer Care 1995;4:20e Stone P, Richards M, Hardy J. Fatigue in patients with cancer. Eur J Cancer 1998;34:1670e Buccheri GF, Ferrigno D, Tamburini M, Brunelli C. The patient s perception of his own quality of life might have an adjunctive prognostic significance in lung cancer. Lung Cancer 1995;12: 45e Brophy L, Sharp E. Physical symptoms of combination biotherapy: a quality of life issue. Oncol Nurs Forum 1991;18(Suppl 1):25e30.

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11 Vol. 32 No. 4 October 2006 Fatigue and Depression in Cancer 321 b 2 ¼ g 20 þ g 21 ðageþþg 22 ðfemaleþ þg 23 ðmarriedþþg 24 ðeducationþ þg 25 ðwhiteþþg 26 ðsurgeryþ þg 27 ðrtþþg 28 ðmetastasisþ þg 29 ðbreastþþg 210 ðhemaþ þg 211 ðlungþþg 212 ðparoxetineþþy 2 b 3 ¼ g 30 b 4 ¼ g 40 b 5 ¼ g 50 b 6 ¼ g 60 b 7 ¼ g 70 where education refers to having only high school or less education; white refers to Caucasian; surgery and RT refer to having surgery or radiation therapy before the current study started, respectively; breast refers to breast cancer, hema refers to hematologic neoplasms; lung refers to lung cancer; paroxetine refers to the group of patients who received Paxil (coded 1) compared to patients who received placebo (coded 0). The linear effect (b 1 ) and curvilinear effect (b 2 ) of treatment cycle among the treatment-level variables were treated as random effects, thereby permitting generalization of the findings to the population, whereas effects of psychological variable and depression (b 3, b 4, b 5, b 6, and b 7 ) were treated as fixed effects.