Because multiple symptoms associated with “sickness behavior” have a negative impact on functional status and quality of life, increased information on the mechanisms that underlie inter-individual variability in this symptom experience is needed. The purposes of this study were to determine: if distinct classes of individuals could be identified based on their experience with pain, fatigue, sleep disturbance, and depression; if these classes differed on demographic and clinical characteristics; and if variations in pro- and anti- inflammatory cytokine genes were associated with latent class membership. Self-report measures of pain, fatigue, sleep disturbance, and depression were completed by 168 oncology outpatients and 85 family caregivers (FCs). Using latent class profile analysis (LCPA), three relatively distinct classes were identified: those who reported low depression and low pain (83%), those who reported high depression and low pain (4.7%), and those who reported high levels of all four symptoms (12.3%). The minor allele of IL4 rs2243248 was associated with membership in the “All high” class along with younger age, being White, being a patient (versus a FC), having a lower functional status score, and having a higher number of comorbid conditions. Findings suggest that LPCA can be used to differentiate distinct phenotypes based on a symptom cluster associated with sickness behavior. Identification of distinct phenotypes provides new evidence for the role of IL4 in the modulation of a sickness behavior symptom cluster in oncology patients and their FCs.

Background-Fatigue is a significant problem associated with radiation therapy (RT).

Study purposes were to determine the prevalence of persistent pain in the breast; characterize distinct persistent pain classes using growth mixture modeling, and evaluate for differences among these pain classes in demographic, preoperative, intraoperative, and postoperative characteristics. In addition, differences in the severity of common symptoms and quality of life outcomes measured prior to surgery, among the pain classes, were evaluated. Patients (n=398) were recruited prior to surgery and followed for six months. Using growth mixture modeling, patients were classified into no (31.7%), mild (43.4%), moderate (13.3%), and severe (11.6%) pain groups based on ratings of worst breast pain. Differences in a number of demographic, preoperative, intraoperative, and postoperative characteristics differentiated among the pain classes. In addition, patients in the moderate and severe pain classes reported higher preoperative levels of depression, anxiety, and sleep disturbance than the no pain class. Findings suggest that approximately 25% of women experience significant and persistent levels of breast pain in the first six months following breast cancer surgery.

Pain, fatigue, sleep disturbance, and depression are common and frequently co-occurring symptoms in oncology patients. This symptom cluster is often attributed to the release of pro-inflammatory cytokines. The purposes of this study were to determine whether distinct latent classes of patients with breast cancer (n = 398) could be identified based on their experience with this symptom cluster, whether patients in these latent classes differed on demographic and clinical characteristics, and whether variations in cytokine genes were associated with latent class membership. Three distinct latent classes were identified: All Low (61.0%), Low Pain and High Fatigue (31.6%), All High (7.1%). Compared to patients in the All Low class, patients in the ALL High class were significantly younger, had less education, were more likely to be non-White, had a lower annual income, were more likely to live alone, had a lower functional status, had a higher comorbidity score, and had more advanced disease. Significant associations were found between interleukin (IL) 6 rs2069845, IL13 rs1295686, and tumor necrosis factor alpha rs18800610 and latent class membership. Findings suggest that variations in pro- and anti-inflammatory cytokine genes are associated with this symptom cluster in breast cancer patients.

Background: Many debilitating symptoms arise from cancer and its treatment that are often unrelieved by established methods. Pranayama, a series of yogic breathing techniques, may improve cancer-related symptoms and quality of life, but it has not been studied for this purpose. Objectives: A pilot study was performed to evaluate feasibility and to test the effects of pranayama on cancerassociated symptoms and quality of life. Design: This was a randomized controlled clinical trial comparing pranayama to usual care. Setting: The study was conducted at a university medical center. Subjects: Patients receiving cancer chemotherapy were randomized to receive pranayama immediately or after a waiting period (control group). Interventions: The pranayama intervention consisted of four breathing techniques taught in weekly classes and practiced at home. The treatment group received pranayama during two consecutive cycles of chemotherapy. The control group received usual care during their first cycle, and received pranayama during their second cycle of chemotherapy. Outcome measures: Feasibility, cancer-associated symptoms (fatigue, sleep disturbance, anxiety, depression, stress), and quality of life were the outcomes. Results: Class attendance was nearly 100% in both groups. Sixteen (16) participants were included in the final intent-to-treat analyses. The repeated-measures analyses demonstrated that any increase in pranayama dose, with dose measured in the number of hours practiced in class or at home, resulted in improved symptom and qualityof-life scores. Several of these associations-sleep disturbance ( p = 0.04), anxiety ( p = 0.04), and mental quality of life ( p = 0.05)-reached or approached statistical significance. Conclusions: Yoga breathing was a feasible intervention among patients with cancer receiving chemotherapy. Pranayama may improve sleep disturbance, anxiety, and mental quality of life. A dose-response relationship was found between pranayama use and improvements in chemotherapy-associated symptoms and quality of life. These findings need to be confirmed in a larger study.

Context Sleep disturbance is a significant problem in oncology patients. Objectives To examine how actigraphy and self-report ratings of sleep disturbance changed over the course of and following radiation therapy (RT); investigate whether specific patient, disease, and symptom characteristics predicted the initial levels and/or the characteristics of the trajectories of sleep disturbance; and to compare predictors of subjective and objective sleep disturbance. Methods Patients (n=73) completed self-report questionnaires that assessed sleep disturbance, fatigue, depressive symptoms, anxiety, and pain prior to the initiation of RT through four months after the completion of RT. Wrist actigraphy was used as the objective measure of sleep disturbance. Hierarchical linear modeling (HLM) was used for data analyses. Results Mean wake after sleep onset (WASO) was 11.9% and mean total score on the General Sleep Disturbance Scale (GSDS) was 45. More than 85% of the patients had an abnormally high number of nighttime awakenings. Substantial interindividual variability was found for both objective and subjective measures of sleep disturbance. Body mass index predicted baseline levels of objective sleep disturbance. Comorbidity, evening fatigue, and depressive symptoms predicted baseline levels of subjective sleep disturbance, and depressive symptoms predicted the trajectory of subjective sleep disturbance. Conclusion Different variables predicted sleep disturbance using subjective and objective measures. The slightly elevated WASO found may be an underestimation of the degree of sleep disturbance when it is evaluated in the context of the high number of nighttime awakenings and patient’s perception of poor sleep quality and quantity.

Purpose In this prospective, longitudinal study, we extend our findings on persistent breast pain in patients (n=398) following breast cancer surgery and evaluate the prevalence and characteristics of persistent pain in the arm/shoulder In addition, differences in the severity of common symptoms and quality of life outcomes measured prior to surgery, among the arm pain classes, were evaluated. Methods and sample Patients were recruited from Breast Care Centers located in a Comprehensive Cancer Center, two public hospitals, and four community practices. Patients were assessed prior to and monthly for six months following breast cancer surgery. Results Using growth mixture modeling, patients were classified into no (41.6%), mild (23.6%), and moderate (34.8%) arm pain classes based on ratings of worst arm/shoulder pain. Compared to the no pain class, patients in the moderate pain class were significantly younger, had a higher body mass index, and were more likely to report preoperative breast pain and swelling in the affected breast. In addition, patients in the moderate pain class reported higher levels of depression, anxiety, and sleep disturbance than the no pain class. Conclusions Findings suggest that approximately 35% of women experience persistent levels of moderate arm/shoulder pain in the first six months following breast cancer surgery. Moderate arm/shoulder pain is associated with clinically meaningful decrements in functional status and quality of life.

The purposes of this study were to identify distinct latent classes of individuals based on subjective reports of sleep disturbance; to examine differences in demographic, clinical, and symptom characteristics between the latent classes; and to evaluate for variations in pro- and anti-inflammatory cytokine genes between the latent classes. Among 167 oncology outpatients with breast, prostate, lung, or brain cancer and 85 of their FCs, growth mixture modeling (GMM) was used to identify latent classes of individuals based on General Sleep Disturbance Scale (GSDS) obtained prior to, during, and for four months following completion of radiation therapy. Single nucleotide polymorphisms (SNPs) and haplotypes in candidate cytokine genes were interrogated for differences between the two latent classes. Multiple logistic regression was used to assess the effect of phenotypic and genotypic characteristics on GSDS group membership. Two latent classes were identified: lower sleep disturbance (88.5%) and higher sleep disturbance (11.5%). Participants who were younger and had a lower Karnofsky Performance status score were more likely to be in the higher sleep disturbance class. Variation in two cytokine genes (i.e., IL6, NFKB) predicted latent class membership. Evidence was found for latent classes with distinct sleep disturbance trajectories. Unique genetic markers in cytokine genes may partially explain the interindividual heterogeneity characterizing these trajectories.