Prediction of morning fatigue severity in outpatients receiving chemotherapy: less may still be more.

INTRODUCTION: Fatigue is the most common and debilitating symptom experienced by cancer patients undergoing chemotherapy (CTX). Prediction of symptom severity can assist clinicians to identify high-risk patients and provide education to decrease symptom severity. The purpose of this study was to predict the severity of morning fatigue in the week following the administration of CTX. METHODS: Outpatients (n = 1217) completed questionnaires 1 week prior to and 1 week following administration of CTX. Morning fatigue was measured using the Lee Fatigue Scale (LFS). Separate prediction models for morning fatigue severity were created using 157 demographic, clinical, symptom, and psychosocial adjustment characteristics and either morning fatigue scores or individual fatigue item scores. Prediction models were created using two regression and five machine learning approaches. RESULTS: Elastic net models provided the best fit across all models. For the EN model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out," "exhausted") were the strongest predictors. CONCLUSIONS: This study is the first to use machine learning techniques to accurately predict the severity of morning fatigue from prior to through the week following the administration of CTX using total and individual item scores from the Lee Fatigue Scale (LFS). Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict morning fatigue severity.

Epigenetic Regulation of Inflammatory Mechanisms and a Psychological Symptom Cluster in Patients Receiving Chemotherapy.

BACKGROUND: A psychological symptom cluster is the most common cluster identified in oncology patients. Although inflammatory mechanisms are hypothesized to underlie this cluster, epigenetic contributions are unknown. OBJECTIVES: This study's purpose was to evaluate associations between the occurrence of a psychological symptom cluster and levels of DNA methylation for inflammatory genes in a heterogeneous sample of patients with cancer receiving chemotherapy. METHODS: Prior to their second or third cycle of chemotherapy, 1,071 patients reported the occurrence of 38 symptoms using the Memorial Symptom Assessment Scale. A psychological cluster was identified using exploratory factor analysis. Differential methylation analyses were performed in two independent samples using Illumina Infinium 450K and EPIC microarrays. Expression-associated CpG (eCpG) loci in the promoter region of 114 inflammatory genes on the 450K and 112 genes on the EPIC microarray were evaluated for associations with the psychological cluster. Robust rank aggregation was used to identify differentially methylated genes across both samples. Significance was assessed using a false discovery rate of 0.05 under the Benjamini-Hochberg procedure. RESULTS: Cluster of differentiation 40 ( CD40 ) was differentially methylated across both samples. All six promoter eCpGs for CD40 that were identified across both samples were hypomethylated in the psychological cluster group. CONCLUSIONS: This study is the first to suggest associations between a psychological symptom cluster and differential DNA methylation of a gene involved in tissue inflammation and cell-mediated immunity. Our findings suggest that increased CD40 expression through hypomethylation of promoter eCpG loci is involved in the occurrence of a psychological symptom cluster in patients receiving chemotherapy. These findings suggest a direction for mechanistic studies.

Symptom clusters in outpatients with cancer using different dimensions of the symptom experience.

PURPOSE: Relatively few studies have evaluated for symptom clusters across multiple dimensions. It is unknown whether the symptom dimension used to create symptom clusters influences the number and types of clusters that are identified. Study purposes were to describe ratings of occurrence, severity, and distress for 38 symptoms in a heterogeneous sample of oncology patients (n = 1329) undergoing chemotherapy; identify and compare the number and types of symptom clusters based on three dimensions (i.e., occurrence, severity, and distress); and identify common and distinct clusters. METHODS: A modified version of the Memorial Symptom Assessment Scale was used to assess the occurrence, severity, and distress ratings of 38 symptoms in the week prior to patients' next cycle of chemotherapy. Symptom clusters for each dimension were identified using exploratory factor analysis. RESULTS: Patients reported an average of 13.9 (±7.2) concurrent symptoms. Lack of energy was both the most common and severe symptom while "I don't look like myself" was the most distressing. Psychological, gastrointestinal, weight gain, respiratory, and hormonal clusters were identified across all three dimensions. Findings suggest that psychological, gastrointestinal, and weight gain clusters are common while respiratory and hormonal clusters are distinct. CONCLUSIONS: Psychological, gastrointestinal, weight gain, hormonal, and respiratory clusters are stable across occurrence, severity, and distress in oncology patients receiving chemotherapy. Given the stability of these clusters and the consistency of the symptoms across dimensions, the use of a single dimension to identify these clusters may be sufficient. However, comprehensive and disease-specific inventories need to be used to identify distinct clusters.

Quality of life of patients with gastrointestinal cancers undergoing chemotherapy.

PURPOSE: Findings regarding changes in the quality of life (QOL) of patients with gastrointestinal cancers (GI) undergoing chemotherapy (CTX) are inconclusive. Purpose was to evaluate for changes in QOL scores of patients with GI cancers over two cycles of CTX. METHODS: Patients (n = 397) completed disease-specific [i.e., Quality of Life-Scale-Patient Version (QOL-PV)] and generic [12-item Medical Outcomes Study Short Form Survey (SF-12)] measures of QOL a total of six times over two cycles of CTX. Changes in these QOL scores were evaluated using bootstrapped multilevel regression with full information maximum likelihood estimation. Treatment group (i.e., with or without targeted therapy), age, number of metastatic sites, time from cancer diagnosis, number of prior cancer treatments, GI cancer diagnosis (i.e., colon/rectum/anal vs. other), and CTX regimen were evaluated as covariates in the conditional models for each of the QOL scores. RESULTS: During the second cycle of CTX, QOL-PV scores decreased in the week following CTX administration, and then increased the following week. For both cycles of CTX, the physical component summary and mental component summary scores of the SF-12 decreased in the week following CTX administration and then increased the following week. Increased time from cancer diagnosis and a higher number of prior cancer treatments resulted in worse QOL-PV and SF-12 scores at enrollment. CONCLUSIONS: While changes in QOL scores over the two CTX cycles were statistically significant, the differences were not clinically meaningful. Future studies need to determine the optimal timing of QOL assessments to assess changes associated with cancer treatments.

Modifiable and non-modifiable characteristics associated with sleep disturbance in oncology outpatients during chemotherapy.

PURPOSE: In a sample of outpatients with breast, gastrointestinal, gynecological, and lung cancer who received at least two cycles of chemotherapy (CTX), the purposes were to evaluate for inter-individual differences in the severity of sleep disturbance and determine which demographic, clinical, and symptom characteristics were associated with initial levels as well as the trajectories of sleep disturbance. METHODS: A total of 1331 patients completed study questionnaires in their homes, at six time points over two cycles of CTX (prior to CTX administration, approximately 1 week after CTX administration, and approximately 2 weeks after CTX administration). Questionnaires included demographic, clinical, and symptom assessments (i.e., General Sleep Disturbance Scale, Lee Fatigue Scale, Center for Epidemiological Studies-Depression Scale, Spielberger State-Trait Anxiety Inventories, Attentional Function Index). Hierarchical linear modeling based on full maximum likelihood estimation was performed. RESULTS: Characteristics associated with higher initial levels of sleep disturbance included higher body mass index, poorer functional status, higher trait anxiety, higher depressive symptoms, and higher evening fatigue. Characteristics associated with the worse trajectories of sleep disturbance were higher levels of education and higher sleep disturbance at enrollment. Characteristics associated with both higher initial levels and worse trajectories of sleep disturbance were higher morning fatigue and worse attentional function. CONCLUSIONS: A large amount of inter-individual variability exists in sleep disturbance during CTX. The modifiable and non-modifiable characteristics found in this study can be used to identify higher risk patients and provide earlier interventions to reduce sleep disturbance.

Cytokine gene variations associated with trait and state anxiety in oncology patients and their family caregivers.

PURPOSE: Anxiety is common among cancer patients and their family caregivers (FCs) and is associated with poorer outcomes. Recently, associations between inflammation and anxiety were identified. However, the relationship between variations in cytokine genes and anxiety warrants investigation. Therefore, phenotypic and genotypic characteristics associated with trait and state anxiety were evaluated in a sample of 167 oncology patients with breast, prostate, lung, or brain cancer and 85 of their FCs. METHODS: Using multiple regression analyses, the associations between participants' demographic and clinical characteristics as well as variations in cytokine genes and trait and state anxiety were evaluated. RESULTS: In the bivariate analyses, a number of phenotypic characteristics were associated with both trait and state anxiety (e.g., age, functional status). However, some associations were specific only to trait anxiety (e.g., number of comorbid conditions) or state anxiety (e.g., participation with a FC). Variations in three cytokine genes (i.e., interleukin (IL) 1 beta, IL1 receptor 2 (IL1R2), nuclear factor kappa beta 2 (NFKB2)) were associated with trait anxiety, and variations in two genes (i.e., IL1R2, tumor necrosis factor alpha (TNFA)) were associated with state anxiety. CONCLUSIONS: These findings suggest that both trait and state anxiety need to be assessed in oncology patients and their FCs. Furthermore, variations in cytokine genes may contribute to higher levels of anxiety in oncology patients and their FCs.

Association between an interleukin 1 receptor, type I promoter polymorphism and self-reported attentional function in women with breast cancer.

Subgroups of patients with breast cancer may be at greater risk for cytokine-induced changes in cognitive function after diagnosis and during treatment. The purposes of this study were to identify subgroups of patients with distinct trajectories of attentional function and evaluate for phenotypic and genotypic (i.e., cytokine gene polymorphisms) predictors of subgroup membership. Self-reported attentional function was evaluated in 397 patients with breast cancer using the Attentional Function Index before surgery and for six months after surgery (i.e., seven time points). Using growth mixture modeling, three attentional function latent classes were identified: High (41.6%), Moderate (25.4%), and Low-moderate (33.0%). Patients in the Low-moderate class were significantly younger than those in the High class, with more comorbidities and lower functional status than the other two classes. No differences were found among the classes in years of education, race/ethnicity, or other clinical characteristics. DNA was recovered from 302 patients' samples. Eighty-two single nucleotide polymorphisms among 15 candidate genes were included in the genetic association analyses. After controlling for age, comorbidities, functional status, and population stratification due to race/ethnicity, IL1R1 rs949963 remained a significant genotypic predictor of class membership in the multivariable model. Carrying the rare "A" allele (i.e., GA+AA) was associated with a twofold increase in the odds of belonging to a lower attentional function class (OR: 1.98; 95% CI: 1.18, 3.30; p=.009). Findings provide evidence of subgroups of women with breast cancer who report distinct trajectories of attentional function and of a genetic association between subgroup membership and an IL1R1 promoter polymorphism.

Variations in potassium channel genes are associated with breast pain in women prior to breast cancer surgery.

Preoperative breast pain in women with breast cancer may result from a number of causes. Previous work from our team found that breast pain occurred in 28.2% of women (n = 398) who were about to undergo breast cancer surgery. The occurrence of preoperative breast pain was associated with a number of demographic and clinical characteristics, as well as variation in two cytokine genes. Given that ion channels regulate excitability of sensory neurons, we hypothesized that variations in potassium channel genes would be associated with preoperative breast pain in these patients. Therefore, in this study, we evaluated for associations between single-nucleotide polymorphisms and inferred haplotypes among 10 potassium channel genes and the occurrence of preoperative breast pain in patients scheduled to undergo breast cancer surgery. Multivariable logistic regression analyses were used to identify those genetic variations that were associated with the occurrence of preoperative breast pain while controlling for age and genomic estimates of and self-reported race/ethnicity. Variations in four potassium channel genes: (1) potassium voltage-gated channel, delayed rectifier, subfamily S, member 1 (KCNS1); (2) potassium inwardly rectifying channel, subfamily J, member 3 (KCNJ3); (3) KCNJ6; and (4) potassium channel, subfamily K, member 9 (KCNK9) were associated with the occurrence of breast pain. Findings from this study warrant replication in an independent sample of women who report breast pain following one or more breast biopsies.

A scoping review of Ayurvedic rasayana adaptogens in oncology.

INTRODUCTION: Rasayanas are Ayurvedic natural products that have adaptogenic effects. The extensive research on rasayanas in oncology is not currently well summarized. The aim of this review is to investigate the range and nature of the current body of research, identify gaps in knowledge, and to summarize the existing literature as it relates to Ayurvedic rasayanas and oncology. MATERIALS AND METHODS: A comprehensive literature search of fifteen Ayurvedic adaptogen rasayanas was conducted using three main concepts: Ayurvedic herbal terms, neoplasm terms, and oncological pathways. After screening was performed, key variables were extracted (tagged) including type of adaptogen, cancer type, type of study design, constituent type, and mechanisms of action (MOA). The results were synthesized and summarized using descriptive statistics and narrative summaries. RESULTS: Five hundred and eighty-four articles were reviewed and tagged. The two most tagged adaptogens were Glycyrrhiza glabra (Yashthimadhu/licorice) (n = 166 (28.4 %)) and Withania somnifera (Ashwagandha) (n = 151 (25.9 %)). The most frequently tagged cancer diagnostic categories were gastrointestinal (n = 175 (30 %)), and breast (n = 126 (21 %)). Most of the articles focused on in vitro studies (n = 470 (80.3 %)). Of the 12 MOA tags, the most frequently tagged was apoptosis (n = 298 (29.2 %)). CONCLUSION: A large body of pre-clinical literature exists on adaptogen rasayanas in oncology, indicating this field of research is still in its early phase. Comparatively few studies focused on the effects on the immune system. Given the growing interest in immuno-oncology therapeutics and the potential impact of adaptogen rasayanas on the immune system, future research may focus more in this area, along with work that is more directly linked to future clinical studies.

Mindfulness-Based Group Medical Visits: Strategies to Improve Equitable Access and Inclusion for Diverse Patients in Cancer Treatment.

Background: Mindfulness-based interventions (MBIs) are supported by clinical practice guidelines as effective non-pharmacologic interventions for common symptoms experienced by cancer patients, including anxiety, depression, and fatigue. However, the evidence predominately derives from White breast cancer survivors. Racial and ethnic minority patients have less access to integrative oncology care and worse cancer outcomes. To address these gaps, we designed and piloted a series of mindfulness-based group medical visits (MB-GMVs), embedded into comprehensive cancer care, for racially and ethnically diverse patients in cancer treatment. Methods: As a quality improvement project, we launched a telehealth MB-GMV series for patients undergoing cancer treatment, delivered as four weekly 2-hour visits billable to insurance. Content was concordant with evidence-based guidelines and established MBIs and adapted to improve cultural relevance and fit (eg, access-centered, trauma-informed, with inclusive communication practices). Program structure was adapted to address barriers to participation, with ≥50% slots per series reserved for racial and ethnic minority patients. Intake surveys incorporated a demographic questionnaire and symptom assessments. Evaluations were sent following the visits. Results: In our first ten cohorts (n = 78), 80% of referred patients enrolled. Participants were: 22% Asian, 14% Black, 17% Latino, 45% non-Latino White; 65% female; with a median age of 54 years (range 27-79); and 80% had metastatic cancer. Common baseline symptoms included lack of energy, difficulty sleeping, and worrying. Most patients (90%) attended ≥3 visits. On final evaluations, 87% patients rated the series as "excellent"; 81% "strongly agreed" that they liked the GMV format; and 92% would "definitely" recommend the series to others. Qualitative themes included empowerment and connectedness. Conclusion: Telehealth GMVs are a feasible, acceptable, and financially sustainable model for increasing access to MBIs. Diverse patients in active cancer treatment were able to participate and reported high levels of satisfaction with this series that was tailored to center health equity and inclusion.

Gastrointestinal Symptom Cluster is Associated With Epigenetic Regulation of Lymphotoxin Beta in Oncology Patients Receiving Chemotherapy.

OBJECTIVES: While the gastrointestinal symptom cluster (GISC) is common in patients receiving chemotherapy, limited information is available on its underlying mechanism(s). Emerging evidence suggests a role for inflammatory processes through the actions of the nuclear factor kappa B (NF-κB) signaling pathway. This study evaluated for associations between a GISC and levels of DNA methylation for genes within this pathway. METHODS: Prior to their second or third cycle of chemotherapy, 1071 outpatients reported symptom occurrence using the Memorial Symptom Assessment Scale. A GISC was identified using exploratory factor analysis. Differential methylation analyses were performed in two independent samples using EPIC (n = 925) and 450K (n = 146) microarrays. Trans expression-associated CpG (eCpG) loci for 56 NF-κB signaling pathway genes were evaluated. Loci significance were assessed using an exploratory false discovery rate (FDR) of 25% for the EPIC sample. For the validation assessment using the 450K sample, significance was assessed at an unadjusted p-value of 0.05. RESULTS: For the EPIC sample, the GISC was associated with increased expression of lymphotoxin beta (LTB) at one differentially methylated trans eCpG locus (cg03171795; FDR = 0.168). This association was not validated in the 450K sample. CONCLUSIONS: This study is the first to identify an association between a GISC and epigenetic regulation of a gene that is involved in the initiation of gastrointestinal immune responses. Findings suggest that increased LTB expression by hypermethylation of a trans eCpG locus is involved in the occurrence of this cluster in patients receiving chemotherapy. LTB may be a potential therapeutic target for this common cluster.

Perturbations in common and distinct inflammatory pathways associated with morning and evening fatigue in outpatients receiving chemotherapy.

BACKGROUND: Moderate to severe fatigue occurs in up to 94% of patients with cancer. Recent evidence suggests that morning and evening fatigue are distinct dimensions of physical fatigue. The purposes of this study were to evaluate the transcriptome for common and distinct perturbed inflammatory pathways in patients receiving chemotherapy who reported low versus high levels of morning or low versus high levels of evening cancer-related fatigue. METHODS: Patients completed questionnaires during the week prior to their chemotherapy treatment. Severity of morning and evening fatigue was evaluated using the Lee Fatigue Scale. Gene expression and pathway impact analyses (PIA) were performed in two independent samples using RNA-sequencing (n = 357) and microarray (n = 360). Patterns of interactions between and among these perturbed pathways were evaluated using a knowledge network (KN). RESULTS: Across the PIA, nine perturbed pathways (FDR < 0.025) were common to both morning and evening fatigue, six were distinct for morning fatigue, and four were distinct for evening fatigue. KN (19 nodes, 39 edges) identified the phosphatidylinositol 3-kinase (PI3K)-Akt pathway node (perturbed in evening fatigue) with the highest betweenness (0.255) and closeness (0.255) centrality indices. The next highest betweenness centrality indices were seen in pathways perturbed in evening fatigue (i.e., nuclear factor kappa B: 0.200, natural killer cell-mediated cytotoxicity: 0.178, mitogen-activated protein kinase: 0.175). CONCLUSIONS: This study describes perturbations in common and distinct inflammatory pathways associated with morning and/or evening fatigue. PI3K-Akt was identified as a bottleneck pathway. The analysis identified potential targets for therapeutic interventions for this common and devastating clinical problem.

Association between pro- and anti-inflammatory cytokine genes and a symptom cluster of pain, fatigue, sleep disturbance, and depression.

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.

Advances in Conceptual and Methodological Issues in Symptom Cluster Research: A 20-Year Perspective.

Two conceptual approaches are used to evaluate symptom clusters: "clustering" symptoms (ie, variable-centered analytic approach) and "clustering" patients (ie, person-centered analytic approach). However, these methods are not used consistently and conceptual clarity is needed. Given the emergence of novel methods to evaluate symptom clusters, a review of the conceptual basis for older and newer analytic methods is warranted. Therefore, this article will review the conceptual basis for symptom cluster research; compare and contrast the conceptual basis for using variable-centered versus patient-centered analytic approaches in symptom cluster research; review their strengths and weaknesses; and compare their applications in symptom cluster research.

Symptom clusters in patients receiving chemotherapy: A systematic review.

BACKGROUND AND PURPOSE: Since 2001, symptom cluster research has grown considerably. However, because multiple methodological considerations remain, ongoing synthesis of the literature is needed to identify gaps in this area of symptom science. This systematic review evaluated the progress in symptom clusters research in adults receiving primary or adjuvant chemotherapy since 2016. METHODS: Eligible studies were published in English between 1 January 2017 and 17 May 2021; evaluated for and identified symptom clusters 'de novo;' and included only adults being treated with primary or adjuvant chemotherapy. Studies were excluded if patients had advanced cancer or were receiving palliative chemotherapy; symptoms were measured after treatment; symptom clusters were pre-specified or a patient-centred analytic approach was used. For each study, symptom instrument(s); statistical methods and symptom dimension(s) used to create the clusters; whether symptoms were allowed to load on more than one factor; method used to assess for stability of symptom clusters and associations with secondary outcomes and biomarkers were extracted. RESULTS: Twenty-three studies were included. Memorial Symptom Assessment Scale was the most common instrument and exploratory factor analysis was the most common statistical method used to identify symptom clusters. Psychological, gastrointestinal, and nutritional clusters were the most commonly identified clusters. Only the psychological cluster remained relatively stable over time. Only five studies evaluated for secondary outcomes. DISCUSSION: While symptom cluster research has evolved, clear criteria to evaluate the stability of symptom clusters and standardised nomenclature for naming clusters are needed. Additional research is needed to evaluate the biological mechanism(s) for symptom clusters. PROSPERO REGISTRATION NUMBER: CRD42021240216.

Symptom clusters in oncology outpatients: stability and consistency across a cycle of chemotherapy.

OBJECTIVES: Improved understanding of the stability and consistency of symptom clusters across time, symptom dimensions and cancer diagnoses will lead to refinements in symptom assessments and management, and provide direction for mechanistic studies. Study purposes were to describe the occurrence, severity and distress of 38 symptoms; evaluate the stability and consistency of symptom clusters across a cycle of chemotherapy, three symptom dimensions and four distinct cancer types; and identify common and distinct symptom clusters. METHODS: Oncology outpatients (n=1329) completed the Memorial Symptom Assessment Scale prior to their next cycle of chemotherapy (T1), 1 week after chemotherapy (T2) and 2 weeks after chemotherapy (T3). Symptom clusters were identified using exploratory factor analysis using unweighted least squares. GEOMIN rotated factor loadings with absolute values ≥0.40 were considered meaningful. Clusters were stable if they were identified across each time point and/or dimension. Clusters were consistent if the same two or three symptoms with the highest factor loadings were identified across each time point and/or dimension. RESULTS: Patients reported 13.9 (±7.2) symptoms at T1, 14.0 (±7.0) at T2 and 12.2 (±6.8) at T3. Psychological, weight gain, gastrointestinal and respiratory clusters were stable across time and dimensions. Only the psychological, weight gain and respiratory clusters were consistent across time and dimensions. CONCLUSION: Given the stability of the psychological, weight gain and gastrointestinal clusters across cancer diagnoses, symptoms within these clusters need to be routinely assessed. However, respiratory and hormonal clusters are unique to specific cancer types and the symptoms within these clusters are variable.

Digital Life Coaching During Stem Cell Transplantation: Development and Usability Study.

BACKGROUND: For patients with multiple myeloma receiving high-dose chemotherapy followed by autologous stem cell transplantation (SCT), acute life disruptions and symptom burden may lead to worsened quality of life (QOL) and increased emotional distress. Digital life coaching (DLC), whereby trained coaches deliver personalized well-being-related support via phone calls and SMS text messaging, has been shown to improve QOL among SCT survivors. However, DLC has not been investigated during the acute peri-SCT period, which is generally characterized by symptomatic exacerbations and 2-week hospitalizations. OBJECTIVE: We launched a single-arm pilot study to investigate the feasibility of patient engagement with DLC during this intensive period. METHODS: We approached English-speaking adult patients with multiple myeloma undergoing autologous SCT at our center. Enrolled patients received 16 weeks of virtual access to a life coach beginning on day -5 before SCT. Coaches used structured frameworks to help patients identify and overcome personal barriers to well-being. Patients chose the coaching topics and preferred communication styles. Our primary endpoint was ongoing DLC engagement, defined as bidirectional conversations occurring at least once every 4 weeks during the study period. Secondary endpoints were electronic patient-reported outcome assessments of QOL, distress, and sleep disturbances. RESULTS: Of the 20 patients who were screened, 17 (85%) chose to enroll and 15 (75%) underwent SCT as planned. Of these 15 patients (median age 65 years, range 50-81 years), 11 (73%) demonstrated ongoing DLC engagement. The median frequency of bidirectional conversations during the 3-month study period was once every 6.2 days (range 3.9-28 days). During index hospitalizations with median lengths of stay of 16 days (range 14-31 days), the median frequency of conversations was once every 5.3 days (range 2.7-15 days). Electronic patient-reported outcome assessments (94% adherence) demonstrated an expected QOL nadir during the second week after SCT. The prevalence of elevated distress was highest immediately before and after SCT, with 69% of patients exhibiting elevated distress on day -5 and on day +2. CONCLUSIONS: DLC may be feasible for older patients during intensive hospital-based cancer treatments such as autologous SCT for multiple myeloma. The limitations of our study include small sample size, selection bias among enrolled patients, and heterogeneity in DLC use. Based on the positive results of this pilot study, a larger phase 2 randomized study of DLC during SCT is underway to investigate the efficacy of DLC with regard to patient well-being. TRIAL REGISTRATION: ClinicalTrials.gov NCT04432818; https://clinicaltrials.gov/ct2/show/NCT04432818.

Feasibility and Acceptability of a Physical Activity Tracker and Text Messages to Promote Physical Activity During Chemotherapy for Colorectal Cancer: Pilot Randomized Controlled Trial (Smart Pace II).

BACKGROUND: We conducted a pilot 2-arm randomized controlled trial to assess the feasibility of a digital health intervention to increase moderate-to-vigorous physical activity in patients with colorectal cancer (CRC) during chemotherapy. OBJECTIVE: This study aimed to determine whether a digital health physical activity intervention is feasible and acceptable during chemotherapy for CRC. METHODS: Potentially eligible patients with CRC expected to receive at least 12 weeks of chemotherapy were identified in person at the University of California, San Francisco, and on the web through advertising. Eligible patients were randomized 1:1 to a 12-week intervention (Fitbit Flex, automated SMS text messages) versus usual care. At 0 and 12 weeks, patients wore an Actigraph GT3X+ accelerometer for 7 days and completed surveys, body size measurements, and an optional 6-minute walk test. Participants could not be masked to their intervention arm, but people assessing the body size and 6-minute walk test outcomes were masked. The primary outcomes were adherence (eg, Fitbit wear and text response rate) and self-assessed acceptability of the intervention. The intervention would be considered feasible if we observed at least 80% complete follow-up and 70% adherence and satisfaction, a priori. RESULTS: From 2018 to 2020, we screened 240 patients; 53.3% (128/240) of patients were ineligible and 26.7% (64/240) declined to participate. A total of 44 patients (44/240, 18%) were randomized to the intervention (n=22) or control (n=22) groups. Of these, 57% (25/44) were women; 68% (30/44) identified as White and 25% (11/44) identified as Asian American or Pacific Islander; and 77% (34/44) had a 4-year college degree. The median age at enrollment was 54 years (IQR 45-62 years). Follow-up at 12 weeks was 91% (40/44) complete. In the intervention arm, patients wore Fitbit devices on a median of 67 out of 84 (80%) study days and responded to a median of 17 out of 27 (63%) questions sent via SMS text message. Among 19 out of 22 (86%) intervention patients who completed the feedback survey, 89% (17/19) were satisfied with the Fitbit device; 63% (12/19) were satisfied with the SMS text messages; 68% (13/19) said the SMS text messages motivated them to exercise; 74% (14/19) said the frequency of SMS text messages (1-3 days) was ideal; and 79% (15/19) said that receiving SMS text messages in the morning and evening was ideal. CONCLUSIONS: This pilot study demonstrated that many people receiving chemotherapy for CRC are interested in participating in digital health physical activity interventions. Fitbit adherence was high; however, participants indicated a desire for more tailored SMS text message content. Studies with more socioeconomically diverse patients with CRC are required. TRIAL REGISTRATION: ClinicalTrials.gov NCT03524716; https://clinicaltrials.gov/ct2/show/NCT03524716.

Multi-Staged Data-Integrated Multi-Omics Analysis for Symptom Science Research.

The incorporation of omics approaches into symptom science research can provide researchers with information about the molecular mechanisms that underlie symptoms. Most of the omics analyses in symptom science have used a single omics approach. Therefore, these analyses are limited by the information contained within a specific omics domain (e.g., genomics and inherited variations, transcriptomics and gene function). A multi-staged data-integrated multi-omics (MS-DIMO) analysis integrates multiple types of omics data in a single study. With this integration, a MS-DIMO analysis can provide a more comprehensive picture of the complex biological mechanisms that underlie symptoms. The results of a MS-DIMO analysis can be used to refine mechanistic hypotheses and/or discover therapeutic targets for specific symptoms. The purposes of this paper are to: (1) describe a MS-DIMO analysis using "Symptom X" as an example; (2) discuss a number of challenges associated with specific omics analyses and how a MS-DIMO analysis can address them; (3) describe the various orders of omics data that can be used in a MS-DIMO analysis; (4) describe omics analysis tools; and (5) review case exemplars of MS-DIMO analyses in symptom science. This paper provides information on how a MS-DIMO analysis can strengthen symptom science research through the prioritization of functional genes and biological processes associated with a specific symptom.

Prediction of evening fatigue severity in outpatients receiving chemotherapy: less may be more.

BACKGROUND: Fatigue is the most common and debilitating symptom experienced by oncology patients undergoing chemotherapy. Little is known about patient characteristics that predict changes in fatigue severity over time. PURPOSE: To predict the severity of evening fatigue in the week following the administration of chemotherapy using machine learning approaches. METHODS: Outpatients with breast, gastrointestinal, gynecological, or lung cancer (N=1217) completed questionnaires one week prior to and one week following administration of chemotherapy. Evening fatigue was measured with the Lee Fatigue Scale (LFS). Separate prediction models for evening fatigue severity were created using clinical, symptom, and psychosocial adjustment characteristics and either evening fatigue scores or individual fatigue item scores. Prediction models were created using two regression and three machine learning approaches. RESULTS: Random forest (RF) models provided the best fit across all models. For the RF model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out", "exhausted") were the strongest predictors. CONCLUSION: This study is the first to use machine learning techniques to predict evening fatigue severity in the week following chemotherapy from fatigue scores obtained in the week prior to chemotherapy. Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict evening fatigue severity.