Insomnia and Fatigue Symptom Trajectories in Breast Cancer: A Longitudinal Cohort Study

Effects of yoga interventions on the fatigue-pain-sleep disturbance symptom cluster in breast cancer patients: A systematic review

OBJECTIVE

This systematic review aimed to evaluate the effectiveness of yoga intervention on the fatigue-pain-sleep disturbance symptom cluster in breast cancer patients.

METHODS

Ten electronic databases (Medline, Embase, PubMed, Cochrane Central Register of Controlled Trials, CINAHL, Web of Science, Scopus, British Nursing Index, China National Knowledge Infrastructure, and Wan Fang database) were searched to identify randomized controlled trials from inception to October 2023. Two independent reviewers evaluated study eligibility, extracted data, and assessed the risk of bias using the Cochrane Risk of Bias tool. The findings were synthesized narratively. This systematic review has been registered (PROSPERO ID: CRD42023391269).

RESULTS

A total of 1389 studies were identified, and 18 studies were included in this systematic review. Two studies reported significant alleviation of fatigue-pain-sleep disturbance symptoms, and two studies indicated a significant reduction in fatigue-sleep disturbance symptoms compared to the control group. Commonly employed yoga contents included breathing exercise and posture practice. The effective intervention components encompassed the combination of in-person sessions and home-based sessions delivery mode, with intervention sessions lasting 50-120 min each and dosages ranging from once per week to twice daily, spanning 6-16 weeks.

CONCLUSIONS

Yoga intervention can be beneficial in alleviating the fatigue-pain-sleep disturbance symptom cluster in breast cancer patients. Future research should be tailored to design yoga interventions addressing different treatment stages and preferences of breast cancer patients.

Sleep disturbance among breast cancer survivors and controls from midlife to early older adulthood: Pink SWAN

PURPOSE

To compare sleep disturbance from 5 years pre- to 5 years post-diagnosis between breast cancer survivors (BCS) and women without cancer over the same period and to identify BCS subgroups exhibiting different sleep trajectories.

METHODS

Analyses included data from 152 BCS and 2163 controls from 20 years of follow-up in the longitudinal Study of Women's Health Across the Nation (SWAN), a multi-racial/ethnic cohort study. SWAN participants were assessed approximately annually from 1995 to 2015 using a standardized protocol. Pink SWAN focused on women who reported no cancer at SWAN enrollment and developed incident breast cancer after enrollment or did not develop breast cancer. Nonparametric locally weighted scatterplot smoothing plots and linear mixed models were used to compare the prevalence of the most frequently reported sleep problem, frequently waking several times a night (a sleep maintenance problem) during the previous 2 weeks, between BCS and controls in the 5 years pre- to 5 years post-diagnosis. We characterized heterogeneity among BCS on this sleep problem using group-based trajectories and examined pre-diagnosis variables as predictors of group membership.

RESULTS

No differences were found between BCS and controls in prevalence of frequent nighttime awakenings either before or after diagnosis. Among BCS, three trajectory groups were identified. Thirty-seven percent of BCS had consistently low prevalence of waking several times per night, 30% had high prevalence, and 33% had increasing prevalence which started 2 years pre-diagnosis. Prevalence of pre-diagnosis vasomotor symptoms, anxiety, depressive symptoms, and smoking differed among these groups.

CONCLUSION

Among mid-aged women diagnosed with breast cancer, this diagnosis did not trigger/amplify a sleep maintenance problem. The majority of BCS had similar levels of this sleep problem from pre- to post-diagnosis.

IMPLICATIONS FOR CANCER SURVIVORS

Although sleep maintenance problems were not amplified by a cancer diagnosis, a subset of BCS may have sleep issues that should be monitored and treated, as indicated.

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

BACKGROUND

People with cancer frequently experience severe and distressing symptoms associated with cancer and its treatments. Predicting symptoms in patients with cancer continues to be a significant challenge for both clinicians and researchers. The rapid evolution of machine learning (ML) highlights the need for a current systematic review to improve cancer symptom prediction.

OBJECTIVE

This systematic review aims to synthesize the literature that has used ML algorithms to predict the development of cancer symptoms and to identify the predictors of these symptoms. This is essential for integrating new developments and identifying gaps in existing literature.

METHODS

We conducted this systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. We conducted a systematic search of CINAHL, Embase, and PubMed for English records published from 1984 to August 11, 2023, using the following search terms: cancer, neoplasm, specific symptoms, neural networks, machine learning, specific algorithm names, and deep learning. All records that met the eligibility criteria were individually reviewed by 2 coauthors, and key findings were extracted and synthesized. We focused on studies using ML algorithms to predict cancer symptoms, excluding nonhuman research, technical reports, reviews, book chapters, conference proceedings, and inaccessible full texts.

RESULTS

A total of 42 studies were included, the majority of which were published after 2017. Most studies were conducted in North America (18/42, 43%) and Asia (16/42, 38%). The sample sizes in most studies (27/42, 64%) typically ranged from 100 to 1000 participants. The most prevalent category of algorithms was supervised ML, accounting for 39 (93%) of the 42 studies. Each of the methods-deep learning, ensemble classifiers, and unsupervised ML-constituted 3 (3%) of the 42 studies. The ML algorithms with the best performance were logistic regression (9/42, 17%), random forest (7/42, 13%), artificial neural networks (5/42, 9%), and decision trees (5/42, 9%). The most commonly included primary cancer sites were the head and neck (9/42, 22%) and breast (8/42, 19%), with 17 (41%) of the 42 studies not specifying the site. The most frequently studied symptoms were xerostomia (9/42, 14%), depression (8/42, 13%), pain (8/42, 13%), and fatigue (6/42, 10%). The significant predictors were age, gender, treatment type, treatment number, cancer site, cancer stage, chemotherapy, radiotherapy, chronic diseases, comorbidities, physical factors, and psychological factors.

CONCLUSIONS

This review outlines the algorithms used for predicting symptoms in individuals with cancer. Given the diversity of symptoms people with cancer experience, analytic approaches that can handle complex and nonlinear relationships are critical. This knowledge can pave the way for crafting algorithms tailored to a specific symptom. In addition, to improve prediction precision, future research should compare cutting-edge ML strategies such as deep learning and ensemble methods with traditional statistical models.

Early change in fatigue, insomnia, and cognitive impairment and symptom severity 3 years post-treatment in breast cancer survivors

PURPOSE

Breast cancer is the most common form of cancer among Canadian women. Survivorship challenges include fatigue, sleep disturbance, and cognitive impairment. This study examined (1) symptom trajectory from diagnosis to 3 years; (2) whether symptom change in the first 4 months was associated with prolonged difficulties after 3 years; and (3) which factors were associated with deterioration in symptoms during the first 4 months.

METHODS

This prospective observational cohort study examined 53 women (Mage = 58.6, 96.2% White, 67.9% stage I) with newly diagnosed breast cancer over 3 years. Women completed assessments before starting treatment, 4 months, and 3 years after diagnosis. Three-way repeated-measures ANOVAs evaluated symptom trajectories. A repeated-measures mediation analysis was performed to determine if change from pre-treatment to 4 months accounted for change from pre-treatment to 3 years. A series of between-subjects ANOVAs were used to determine what variables significantly differed by deterioration status.

RESULTS

Perceived cognitive impairment and fatigue increased linearly from diagnosis to 3 years. Change in fatigue in the first 4 months fully accounted for its change over 3 years. Insomnia severity and sleep quality deteriorated from diagnosis to 4 months, but returned to pre-treatment levels at 3 years. Those whose fatigue and cognitive ability deteriorated during the first 4 months were younger.

CONCLUSION

Efforts to identify those who are at risk of experiencing fatigue, sleep disturbance, and cognitive impairment; monitor patients early after receiving a diagnosis; and provide targeted interventions may prevent long-term deterioration and improve well-being.

Cancer-related fatigue trajectories up to 5 years after curative treatment for oesophageal cancer

BACKGROUND

Whether cancer-related fatigue develops differently after curative-intended oesophageal cancer treatment and the related modifiable factors are unclear.

METHODS

This population-based and longitudinal cohort included 409 oesophageal cancer patients who underwent curative oesophagectomy in 2013-2020 in Sweden. The main outcome was cancer-related fatigue trajectories with measurements at 1, 1.5, 2, 2.5, 3, 4 and 5 years postoperatively by validated EORTC QLQ-FA12 questionnaire, and analysed using growth mixture models. Weighted logistic regressions provided odds ratios (OR) with 95% confidence intervals (95% CI) for underlying sociodemographic, clinical, and patient-reported outcome factors in relation to the identified trajectories.

RESULTS

Two distinct overall cancer-related fatigue trajectories were identified: low level of persistent fatigue and high level of increasing fatigue, with 64% and 36% of patients, respectively. The odds of having high level of fatigue trajectory were increased by Charlson comorbidity index (≥ 2 versus 0: OR = 2.52, 95% CI 1.07-5.94), pathological tumour Stage (III-IV versus 0-I: OR = 2.52, 95% CI 1.33-4.77), anxiety (OR = 7.58, 95% CI 2.20-26.17), depression (OR = 15.90, 95% CI 4.44-56.93) and pain (continuous score: OR = 1.02, 95% CI 1.01-1.04).

CONCLUSIONS

Long-term trajectories with high level of increasing cancer-related fatigue and the associated modifiable factors were identified after oesophageal cancer treatment. The results may facilitate early identification and targeted intervention for such high-risk patients.

Various types of stress and greater use of disengagement coping are associated with worse sleep disturbance in oncology patients undergoing chemotherapy

Various types of stress and the choice of coping strategies may be risk factors for higher levels of sleep disturbance in oncology patients. Purposes were to evaluate for differences in global, cancer-specific, and cumulative life stress, as well as resilience and the use of coping strategies among three subgroups of patients with distinct sleep disturbance profiles (i.e., Low, High, Very High). Oncology outpatients (n = 1331) completed measures of global (Perceived Stress Scale), cancer-specific (Impact of Event Scale-Revised), and cumulative life (Life Stressor Checklist-Revised) stress, resilience (Connor-Davidson Resilience Scale) and coping (Brief Cope) prior to their second or third cycle of chemotherapy. Sleep disturbance was assessed six times over two chemotherapy cycles. Differences were evaluated using parametric and non-parametric tests. All stress measures showed a dose response effect (i.e., as the sleep disturbance profile worsened, levels of all types of stress increased). Compared to Low class, the other two classes reported higher levels of global perceived stress and higher occurrence rates and effect from previous stressful life events. Impact of Event Scale-Revised scores for the Very High class indicated post-traumatic symptomatology. Patients in High and Very High classes had resilience scores below the normative score for the United States population and used a higher number of disengagement coping strategies. Our findings suggest that very high levels of sleep disturbance are associated with higher levels of various types of stress, lower levels of resilience, and higher use of disengagement coping strategies. Clinicians need to perform routine assessments and implement symptom management interventions to reduce stress and encourage the use of engagement coping strategies.

Neurological mechanism and efficacy of acupuncture for breast cancer-related insomnia: a study protocol for randomized clinical trial

Background

Breast cancer survivors (BCSs) are at a higher risk of developing insomnia. The negative effects of cancer-related insomnia (CRI) include depression, anxiety, fatigue, aggressive pain, impaired immune functioning, decreased quality of life, and even increased cancer mortality. Although preliminary progress has been made in the treatment of CRI with acupuncture, the evidence is insufficient and the neurological mechanism underlying the effect of acupuncture is still unclear.

Methods

The study employs a single-blinded, randomized, controlled trial design. A total of 80 participants will be randomly allocated in a 1:1 ratio to either the treatment group (n = 40) or the control group (n = 40). The former will receive acupuncture treatment, while the latter will receive sham acupuncture treatment. Both groups will receive 12 sessions over a 4-week period, three times per week (every other day), and each session will last for 30 min. Follow-up assessments will be conducted in week 8. The primary outcome will be the treatment response rate. Secondary outcomes include the change in Insomnia Severity Index (ISI), the treatment remission rate, actigraphy sleep assessment, Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Quality of Life Core Scale (QLQ-C30), the weekly usage of remedial drugs, and functional magnetic resonance imaging (fMRI) analysis. Data for the outcomes will be collected at week 0 (the baseline), week 1 (the intervention period), week 4 (the post-treatment period), and week 8 (the follow-up period).

Discussion

The objective of this study is to assess the efficacy of acupuncture for patients with CRI in comparison with sham acupuncture. Additionally, the research aims to explore the neuropathological mechanisms of CRI and provide the first evidence on the characteristics of acupuncture treatment using fMRI. We expect that the results of this study will provide valuable scientific evidence of acupuncture treatment for CRI.Clinical trial registration: Chinese Clinical Trial Registry, identifier ChiCTR2300070349: https://www.chictr.org.cn/showproj.html?proj=188677.

(Pre)treatment risk factors for late fatigue and fatigue trajectories following radiotherapy for breast cancer

Fatigue is common in breast-cancer survivors. Our study assessed fatigue longitudinally in breast cancer patients receiving adjuvant radiotherapy (RT) and aimed to identify risk factors associated with long-term fatigue and underlying fatigue trajectories. Fatigue was measured in a prospective multicenter cohort (REQUITE) using the Multidimensional Fatigue Inventory (MFI-20) and analyzed using mixed models. Multivariable logistic models identified factors associated with fatigue dimensions at 2 years post-RT and latent class growth analysis identified individual fatigue trajectories. A total of 1443, 1302, 1203 and 1098 patients completed the MFI-20 at baseline, end of RT, after 1 and 2 years. Overall, levels of fatigue significantly increased from baseline to end of RT for all fatigue dimensions (P < .05) and returned to baseline levels after 2 years. A quarter of patients were assigned to latent trajectory high (23.7%) and moderate (24.8%) fatigue classes, while 46.3% and 5.2% to the low and decreasing fatigue classes, respectively. Factors associated with multiple fatigue dimensions at 2 years include age, BMI, global health status, insomnia, pain, dyspnea and depression. Fatigue present at baseline was consistently associated with all five MFI-20 fatigue dimensions (ORGeneralFatigue  = 3.81, P < .001). From latent trajectory analysis, patients with a combination of factors such as pain, insomnia, depression, younger age and endocrine therapy had a particularly high risk of developing early and persistent high fatigue years after treatment. Our results confirmed the multidimensional nature of fatigue and will help clinicians identify breast cancer patients at higher risk of having persistent/late fatigue so that tailored interventions can be delivered.

Factors associated with insomnia symptoms over three years among premenopausal women with breast cancer

PURPOSE

We examined longitudinal trends and factors associated with insomnia over 3 years in a cohort of young breast cancer patients.

METHODS

Women with stage I-III breast cancer at ≤ 45 years were recruited at five institutions from New York, Texas, and North Carolina, within 8 months of diagnosis (n = 836). Participants completed questionnaires every 6 months for 3 years. Linear mixed-effects models were used to examine insomnia over time, using the Women's Health Initiative Insomnia Rating Scale (WHIIRS). We evaluated the relations of insomnia with demographic (age, race, education, income, employment, marital status), clinical (cancer stage, histologic grade, chemotherapy, radiation, hormone therapy, surgery, tumor size, body mass index, hot flashes), and social/behavioral variables (smoking status, social support, physical activity, depressive symptoms).

RESULTS

At baseline, 57% of participants met or exceeded the cut-off for clinical insomnia (WHIIRS score ≥ 9). Insomnia symptoms were most prevalent at baseline (p < 0.0001), but decreased significantly throughout follow-up (p < 0.001). However, 42% of participants still experienced insomnia symptoms 3 years after diagnosis. In multivariable models, older age (p = 0.02), hot flashes (p < 0.0001), and depressive symptoms (p < 0.0001) remained significantly associated with insomnia over time.

CONCLUSIONS

Insomnia symptoms were most frequent closer to breast cancer diagnosis and treatment, but persisted for some women who were older and those reporting higher hot flashes and depressive symptoms. Survivorship care should include assessing insomnia symptoms, particularly during and immediately after primary treatment. Implementing early interventions for sleep problems may benefit young breast cancer survivors and improve their quality of life.

Associations between insomnia symptom trajectories with depression and self-harm behaviors in Chinese college students before and during the COVID-19 pandemic: A five-wave longitudinal investigation

BACKGROUND

Sleep problems are highly prevalent during COVID-19 pandemic. However, only very limited studies have examined the changing patterns of insomnia symptom before and during the COVID-19 pandemic, and most of these studies were limited to two-wave designs and the variable-centered approach.

METHODS

The data was taken from a large-scale health-related cohort among Chinese college students. This cohort was a five-wave design and 3834 participants who completed at least two waves were included in the present study. Growth mixture modeling (GMM) was used to estimate trajectory classes for insomnia symptoms, followed by binary logistic regression to explore the association between trajectory classes and subsequent mental health problems.

RESULTS

GMM analyses extracted four distinct trajectories of insomnia symptoms: stable-low pattern (n = 2897, 75.6 %), increasing pattern (n = 405, 10.6 %), decreasing pattern (n = 182, 4.7 %), and stable-high pattern (n = 350, 9.1 %). Additionally, we found that individuals in stable-high and increasing patterns were more likely to experience mental health problems after the COVID-19 pandemic even after adjusting significant covariates and outcomes at baseline.

CONCLUSIONS

Appreciable heterogeneity in insomnia symptoms among college students was revealed before and during the COVID-19 pandemic. About 20 % of college students were classified as high-risk patterns of insomnia symptoms. Psychological interventions should target such vulnerable groups to reduce the rates of mental health problems.

Supporting Return to Work after Breast Cancer: A Mixed Method Study

Breast cancer (BC) is the most common invasive cancer in the world. Most BC survivors (BCSs) continue working while dealing with cancer-related disabilities. BCSs' return-to-work (RTW) after cancer treatment is an important stage of their recovery and is associated with a higher survival rate. In this study, we addressed the RTW of BCSs with the intention of facilitating this process through direct action in the workplace. Thirty-two women who requested assistance from January to December 2022 were enrolled in the study. Semi-structured interviews and medical examinations were conducted by a team of three physicians. Interviews were analyzed using Thematic Analysis. Moreover, a quantitative cross-sectional study was conducted to compare the health status of BCSs with that of a control group of 160 working women, using standardized questionnaires on work ability, fatigue, sleep problems, anxiety, depression, and happiness. BCSs were also asked to rate the level of organizational justice they perceived at work prior to their illness. From the qualitative analysis emerged three facilitating/hindering themes: (1) person-related factors, (2) company-related factors, and (3) society-related factors. In the quantitative analysis, BCSs had significantly higher scores for anxiety, depression, sleep problems and fatigue, and lower levels of happiness than controls. The RTW of BCSs entails adapting working conditions and providing adequate support. The work-related analysis of each case made it possible to highlight the measures that need to be taken in the workplace to promote RTW. The treatment of cancer should be paired with advice on the best way to regain the ability to work.

Trajectories of sleep disturbance in cancer survivors during the first 2 years post-treatment

STUDY OBJECTIVES

To examine the trajectories of sleep disturbance in cancer survivors during the first 2 years post-treatment and to investigate whether psychological, cognitive, and physical factors differentiate trajectories.

METHODS

A total of 623 Chinese cancer survivors of diverse cancer types participated in a 2-year-long prospective study after the completion of cancer treatment. Sleep disturbance was measured using Pittsburgh Sleep Quality Index at 3 (T2), 6 (T3), 12 (T4), 18 (T5), and 24 (T6) months after baseline (within 6-months post-treatment; T1). Latent growth mixture modeling identified distinctive sleep disturbance trajectories and tested if these longitudinal patterns were predicted by baseline psychological distress, attentional control, attentional bias and physical symptom distress and T2 cancer-related distress. Fully adjusted multinomial logistic regression then identified whether these factors differentiated trajectories.

RESULTS

Two distinct sleep disturbance trajectories were identified, namely stable good sleepers (69.7%) and persistent high sleep disturbance (30.3%). Compared to those in the stable good sleep group, patients in the persistent high sleep disturbance group were less likely to report avoidant (OR=0.49, 95% CI = 0.26-0.90), while more likely to report intrusive thoughts (OR = 1.76, 95% CI = 1.06-2.92) and cancer-related hyperarousal (OR = 3.37, 95% CI = 1.78-6.38). Higher depression scores also predicted persistent high sleep disturbance group membership (OR = 1.13, 95% CI = 1.03-1.25). Attentional bias, attentional control, anxiety, and physical symptom distress did not predict sleep trajectory membership.

CONCLUSIONS

One in three cancer survivors experienced persistent high sleep disturbance. Screening and managing depressive symptoms and cancer-related distress in early cancer rehabilitation may reduce risk of persistent sleep disturbance among cancer survivors.

Sleep problems during and after paediatric brain tumours

Brain tumours are among the most common cancer diagnoses in paediatrics. Children with brain tumours are at risk of developing sleep problems because of direct and indirect effects of the tumour and its treatment, in addition to psychosocial and environmental factors. Sleep has an important role in physical and psychological wellbeing, and sleep problems are associated with many adverse outcomes. In this Review, we describe the state of the evidence regarding sleep in people with paediatric brain tumours, prevalence and types of sleep problems, risk factors, and effectiveness of interventions. Evidence shows that sleep problems, particularly excessive daytime sleepiness, are common in people with paediatric brain tumours, with high BMI emerging as a consistent predictor of sleep disruption. Further intervention studies are needed, and clinical evaluation of sleep is warranted for people with paediatric brain tumours.

Poor Subjective Sleep Quality Among Patients with Cancer and Comorbid Depression: An Opportunity to Inform Screening and Intervention

OBJECTIVE

Sleep disturbances are under-identified and under-treated in oncology settings, especially for underserved populations and those with psychiatric comorbidities. This study examined the prevalence and correlates of poor subjective sleep quality as well as clinical sleep recommendations among a socioeconomically and ethnically diverse population of patients with cancer referred for depression management.

METHODS

Participants were 140 adults with cancer who screened positive for depression through routine, practice-based assessment with the Patient Health Questionnaire (PHQ-9 ≥ 8) and were referred to a study of collaborative care for depression. Demographics, clinical characteristics, subjective sleep quality, and sleep recommendations received were self-reported by patients prior to intervention. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), general health status was measured using the Patient-Reported Outcomes Measurement Information System (PROMIS) Global-10, and depressive symptoms were measured using the PHQ-9.

RESULTS

Of 138 patients with complete data, 123 (89.1%) reported poor sleep quality, and 87 (63%) met the threshold for possible insomnia. The strongest correlates of poor subjective sleep were female gender (β = 0.19, p = .02), greater depressive symptom severity (β = 0.28, p = .001), and worse physical health (β = -0.19, p = .04). Of 118 patients reporting problems with sleep since their cancer diagnosis, 95 discussed the issue with a medical provider; medications were recommended most often (37; 38.9%); only 9 (9.5%) received recommendations for cognitive-behavioral therapy for insomnia (CBT-I) or other CBT.

CONCLUSIONS

Patients with cancer seeking treatment for depression report very high rates of poor subjective sleep quality and insomnia, underscoring the importance of providing and referring to guideline-concordant sleep interventions in oncology supportive care contexts.

Compas-Y: A mixed methods pilot evaluation of a mobile self-compassion training for people with newly diagnosed cancer

Objective

Compas-Y is a compassionate mind training app that was co-designed to be fully adapted to mobile technology and to people with newly diagnosed cancer. This study aimed to evaluate the use, appreciation and impact of the app.

Methods

Seventy-one people with cancer who created an app account were included (38% breast cancer, 72% diagnosed <4 months ago, 76% received chemotherapy). Participants had very high baseline scores of self-compassion. In a convergent mixed methods design, back-end log-data (n = 71), pre-post surveys (n = 34) and semi-structured interviews (n = 23) collected for >8 weeks and were concurrently analysed using joint displays.

Results

About half of the participants (45%) used 4 of the 6 modules. Compas-Y was highly appreciated, with all content considered relevant and a source of support. Experienced benefits related to improved mental health. Particularly, we found significant changes in anxiety, but not in depression or well-being. In the interviews, people reported experiencing more rest and more positive emotions due to using the app. Process benefits included significant reductions in self-criticism (inadequate self and self-blame), but not self-compassion. In the interviews, people reported improved self-compassion and less self-criticism, more self-awareness, recognition and support, and improved emotion regulation and coping. The surveys did not capture the full range of outcomes that participants reported in the interviews.

Conclusions

Compas-Y is a highly appreciated mobile intervention that supported users in aspects of their mental health. Findings are discussed in terms of reach and adherence, app functionalities, co-design and tailoring of cancer-related and compassion-based eHealth.

Trajectories of symptom severity predicts quality of life change in newly diagnosis lymphoma survivors: An initial study

OBJECTIVE

This study aimed to explore the subgroups of symptom severity and impact of their trajectories on quality of life in lymphoma survivors.

METHODS

Secondary data were analysed from a prospective study with four-time measures: before treatment (T1), during treatment (T2), treatment completion (T3) and 10 weeks after treatment (T4). Data were analysed using descriptive statistics, group-based trajectory model and generalised estimation equation.

RESULTS

Fifty nine of 61 participants completed three-time measure (mean age = 60.43 years, male-predominant). The changes in symptom severity over time were divided into two subgroups: slight-stable group (n = 54, 89%) and mild-fickle group (n = 7, 11%). Pain, tiredness and sleeping trouble were the predominant symptoms. The quality of life change in the slight-stable group was significantly better than that of the mild-fickle group (B = 13.35, SE = 3.53, p < 0.001). The overall quality of life at T2, T3 and T4 was better than it was at T1.

CONCLUSION

The different trajectories of symptom severity significantly influenced quality of life changes in lymphoma survivors. Healthcare providers must be aware that there is a group of lymphoma survivors with relatively severe symptoms when newly diagnosed, compared to the opposite. More attention must be paid to this group, in addition to providing in-time symptom management.

Trajectory patterns and factors influencing perinatal fatigue among Chinese women from late pregnancy to 6 months after delivery

Background

Perinatal fatigue among women is related to the clinical outcomes of mothers and infants. Perinatal fatigue changes over time, and the trajectory varies according to the predictors and outcomes of the mothers. This, however, has not been documented in any study.

Objectives

This study aimed to identify and characterize the trajectory patterns of perinatal fatigue among women from late pregnancy to 6 months after delivery.

Methods

We used growth mixture modeling to estimate the trajectory of perinatal fatigue at 28 gestational weeks (T0), 37 gestational weeks (T1), 3 days (T2), 1 week (T3), 6 weeks (T4), and 6 months (T5) after delivery with (n = 1,030). The Mann-Whitney U test and binary logistic regression were used to tie the selected trajectory classes to predictors and outcomes.

Results

There were two distinct patterns of perinatal fatigue in women: "persistently high" (11.1%, n = 114) and "persistently low" (88.9%, n = 916). Levels of perinatal fatigue among women in the "persistently high" group were higher than those in the "persistently low" group across the six measurements. Complications, fatigue at T0, and employment status in late pregnancy were all significant predictors of trajectories. Additionally, the "persistently high" group had a greater prevalence of difficult baby care and weight retention and a lower prevalence of exclusive breastfeeding.

Conclusions

Our study proved the heterogeneity and characteristics of perinatal fatigue among women. Future research should concentrate on developing intervention packages targeted at specific individuals in order to alleviate perinatal fatigue in women.

Long-Term Longitudinal Patterns of Patient-Reported Fatigue After Breast Cancer: A Group-Based Trajectory Analysis

PURPOSE

Fatigue is recognized as one of the most burdensome and long-lasting adverse effects of cancer and cancer treatment. We aimed to characterize long-term fatigue trajectories among breast cancer survivors.

METHODS

We performed a detailed longitudinal analysis of fatigue using a large ongoing national prospective clinical study (CANcer TOxicity, ClinicalTrials.gov identifier: NCT01993498) of patients with stage I-III breast cancer treated from 2012 to 2015. Fatigue was assessed at diagnosis and year 1, 2, and 4 postdiagnosis. Baseline clinical, sociodemographic, behavioral, tumor-related, and treatment-related characteristics were available. Trajectories of fatigue and risk factors of trajectory-group membership were identified by iterative estimates of group-based trajectory models.

RESULTS

Three trajectory groups were identified for severe global fatigue (n = 4,173). Twenty-one percent of patients were in the high-risk group, having risk estimates of severe global fatigue of 94.8% (95% CI, 86.6 to 100.0) at diagnosis and 64.6% (95% CI, 59.2 to 70.1) at year 4; 19% of patients clustered in the deteriorating group with risk estimates of severe global fatigue of 13.8% (95% CI, 6.7 to 20.9) at diagnosis and 64.5% (95% CI, 57.3 to 71.8) at year 4; 60% were in the low-risk group with risk estimates of 3.6% (95% CI, 2.5 to 4.7) at diagnosis and 9.6% (95% CI, 7.5 to 11.7) at year 4. The distinct dimensions of fatigue clustered in different trajectory groups than those identified by severe global fatigue, being differentially affected by sociodemographic, clinical, and treatment-related factors.

CONCLUSION

Our findings highlight the multidimensional nature of cancer-related fatigue and the complexity of its risk factors. This study helps to identify patients with increased risk of severe fatigue and to inform personalized interventions to ameliorate this problem.

Psychometric Properties of the Insomnia Severity Index Among Arabic Chronic Diseases Patients

Introduction

The Insomnia Severity Index (ISI) is a self-administrated questionnaire most frequently used to assess insomnia in clinical and non-clinical populations.

Objective

To evaluate the psychometric properties of the Arabic ISI among patients diagnosed with chronic diseases.

Methods

A cross-sectional and descriptive correlational design was used. A total of 1,005 patients with chronic diseases completed the seven items of the Arabic ISI version. The scale was assessed in terms of acceptability, internal consistency, and validity. Construct validity was explored with the use of principal factor analysis and confirmatory factor analysis, to examine the dimensional structure of the ISI.

Results

The Cronbach's alpha coefficient for the Arabic ISI was 0.82, which shows good reliability. The total ISI score did not have floor or ceiling effects. There was evidence of discriminate validity. The Principal Component Analysis (PCA) indicated two factors (four items loading on Factor I and three items loading on Factor II). The construct validity of PCA in terms of two factors was explored by confirmatory factor analysis to examine the dimensional structure of the ISI. The confirmatory factor analysis showed an absolute fit for the two-factor model.

Conclusion

The results support the two-factor structure of ISI. The Arabic version of the ISI demonstrated good reliability and validity for assessing insomnia in patients diagnosed with chronic diseases.

Comorbid insomnia among breast cancer survivors and its prediction using machine learning: a nationwide study in Japan

OBJECTIVE

Insomnia is an increasingly recognized major symptom of breast cancer which can seriously disrupt the quality of life during and many years after treatment. Sleep problems have also been linked with survival in women with breast cancer. The aims of this study were to estimate the prevalence of insomnia in breast cancers survivors, clarify the clinical characteristics of their sleep difficulties and use machine learning techniques to explore clinical insights.

METHODS

Our analysis of data, obtained in a nationwide questionnaire survey of breast cancer survivors in Japan, revealed a prevalence of suspected insomnia of 37.5%. With the clinical data obtained, we then used machine learning algorithms to develop a classifier that predicts comorbid insomnia. The performance of the prediction model was evaluated using 8-fold cross-validation.

RESULTS

When using optimal hyperparameters, the L2 penalized logistic regression model and the XGBoost model provided predictive accuracy of 71.5 and 70.6% for the presence of suspected insomnia, with areas under the curve of 0.76 and 0.75, respectively. Population segments with high risk of insomnia were also extracted using the RuleFit algorithm. We found that cancer-related fatigue is a predictor of insomnia in breast cancer survivors.

CONCLUSIONS

The high prevalence of sleep problems and its link with mortality warrants routine screening. Our novel predictive model using a machine learning approach offers clinically important insights for the early detection of comorbid insomnia and intervention in breast cancer survivors.

Light Enhanced Cognitive Behavioral Therapy (CBT-I+Light) for Insomnia and Fatigue During Chemotherapy for Breast Cancer: A Randomized Controlled Trial

STUDY OBJECTIVES

Sleep problems are common during chemotherapy for breast cancer (BC). We evaluated whether combined brief cognitive behavioral and bright light therapy (CBT-I+Light) is superior to treatment as usual with relaxation audio (TAU+) for insomnia symptoms and sleep efficiency (primary outcomes).

METHODS

We randomized women receiving intravenous chemotherapy, stratified by tumor stage and insomnia severity index (ISI), to 6-weeks CBT-I+Light or TAU+. CBT-I+Light included one in-person session, one telephone call, seven emails, and 20 minutes bright light each morning. TAU+ comprised usual treatment and two emails with relaxation audio tracks. Patient-reported outcomes were assessed at baseline, midpoint (week 3), post (week 6) and 3-month follow-up.

RESULTS

Women (N = 101) were randomly assigned to CBT-I+Light or TAU+. The CBT-I+Light group showed significantly greater improvement in insomnia symptoms than the TAU+ group (-5.06 vs -1.93, P = .009; between-group effect size [ES] = .69). At 3-month follow-up, both groups were lower than baseline but did not differ from each other (between-group ES = .18, P = .56). CBT-I+Light had higher patient-reported sleep efficiency than TAU+ immediately after the start of intervention (P = .05) and significantly greater improvement in fatigue (between-group ES = .59, P = .013) and daytime sleep-related impairment (between-group ES = .61, P = .009) than the TAU+ group.

CONCLUSION

CBT-I+Light had a clinically significant impact on insomnia and fatigue with moderate effect sizes. Results support offering cognitive behavioral therapy for insomnia and bright light therapy during chemotherapy for breast cancer to help manage sleep and fatigue.

Feasibility of a video-based cognitive behavioral therapy for insomnia in French adult cancer outpatients: results from the Sleep-4-All-1 study

BACKGROUND

Insomnia affects up to 63% of patients with cancer. Cognitive behavioral therapy for insomnia (CBT-I) is considered to be the non-pharmacological gold standard treatment, but it remains underutilized in France. Self-administered interventions offer new ways to overcome some of the barriers that restrict access to efficacious supportive care.

OBJECTIVE

To assess the feasibility, among French adult cancer outpatients, of a validated Quebec video-based, self-administered, cognitive behavioral therapy for insomnia (VCBT-I).

METHODS

A pre-post design with quantitative measures (Insomnia Severity Index, Edmonton Symptom Assessment System, Treatment Perception Questionnaire) and qualitative measures (semi-structured interviews) was used.

RESULTS

One hundred and seventy-three cancer outpatients were self-screened for insomnia, and 57% (n=99) reported significant symptoms. Among them, 80% (n=79) agreed to participate in the VCBT-I. The download rate of the VCBT-I was 78% (n=62/79). Several technical and contextual barriers to the delivery and the applicability of the VCBT-I emerged. However, participants reported a high level of satisfaction, and some valuable benefits at post-immediate intervention (increased knowledge about sleep, better quality of sleep, and higher acceptance of the burden of insomnia), regardless of whether or not they still had insomnia.

DISCUSSION

This study confirms that there is a demand for a VCBT-I, which was perceived as appropriate by a sample of French cancer outpatients with insomnia, but it also highlights some limitations in terms of implementation and practicality. Remote professional support appears to be a core need in order to address these issues and personalize the guidance process.