Identification of distinct subgroups of breast cancer patients based on self-reported changes in sleep disturbance.
TL;DR: A high percentage of women has significant sleep disturbance prior to surgery that persists during subsequent treatments (i.e., radiation therapy and chemotherapy).
Abstract: The purposes of this study were to identify distinct subgroups of patients based on self-reported sleep disturbance prior to through 6 months after breast cancer surgery and evaluate for differences in demographic, clinical, and symptom characteristics among these latent classes. Women (n = 398) who underwent unilateral breast cancer surgery were enrolled prior to surgery. Patients completed measures of functional status, sleep disturbance (i.e., General Sleep Disturbance Scale (GSDS); higher scores indicate higher levels of sleep disturbance), fatigue, attentional fatigue, depressive symptoms, and anxiety prior to surgery and monthly for 6 months. Three distinct classes of sleep disturbance trajectories were identified using growth mixture modeling. The high sustained class (55.0%) had high and the low sustained class (39.7%) had low GSDS scores prior to surgery that persisted for 6 months. The decreasing class (5.3%) had high GSDS score prior to surgery that decreased over time. Women in the high sustained class were significantly younger, had more comorbidity and poorer function, and were more likely to report hot flashes compared to the low sustained class. More women who underwent mastectomy or breast reconstruction were in the decreasing class. Decreasing and high sustained classes reported higher levels of physical fatigue, attentional fatigue, depressive symptoms, and anxiety compared to the low sustained class. A high percentage of women has significant sleep disturbance prior to surgery that persists during subsequent treatments (i.e., radiation therapy and chemotherapy). Clinicians need to perform routine assessments and initiate appropriate interventions to improve sleep prior to and following surgery.
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TL;DR: Seventeen studies that compared yoga versus no therapy provided moderate-quality evidence showing that yoga improved health-related quality of life, and one study did not appear to reduce depression, but hints at overall low risk of publication bias.
Abstract: Background
Breast cancer is the cancer most frequently diagnosed in women worldwide. Even though survival rates are continually increasing, breast cancer is often associated with long-term psychological distress, chronic pain, fatigue and impaired quality of life. Yoga comprises advice for an ethical lifestyle, spiritual practice, physical activity, breathing exercises and meditation. It is a complementary therapy that is commonly recommended for breast cancer-related impairments and has been shown to improve physical and mental health in people with different cancer types.
Objectives
To assess effects of yoga on health-related quality of life, mental health and cancer-related symptoms among women with a diagnosis of breast cancer who are receiving active treatment or have completed treatment.
Search methods
We searched the Cochrane Breast Cancer Specialised Register, MEDLINE (via PubMed), Embase, the Cochrane Central Register of Controlled Trials (CENTRAL; 2016, Issue 1), Indexing of Indian Medical Journals (IndMED), the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) search portal and Clinicaltrials.gov on 29 January 2016. We also searched reference lists of identified relevant trials or reviews, as well as conference proceedings of the International Congress on Complementary Medicine Research (ICCMR), the European Congress for Integrative Medicine (ECIM) and the American Society of Clinical Oncology (ASCO). We applied no language restrictions.
Selection criteria
Randomised controlled trials were eligible when they (1) compared yoga interventions versus no therapy or versus any other active therapy in women with a diagnosis of non-metastatic or metastatic breast cancer, and (2) assessed at least one of the primary outcomes on patient-reported instruments, including health-related quality of life, depression, anxiety, fatigue or sleep disturbances.
Data collection and analysis
Two review authors independently collected data on methods and results. We expressed outcomes as standardised mean differences (SMDs) with 95% confidence intervals (CIs) and conducted random-effects model meta-analyses. We assessed potential risk of publication bias through visual analysis of funnel plot symmetry and heterogeneity between studies by using the Chi2 test and the I2 statistic. We conducted subgroup analyses for current treatment status, time since diagnosis, stage of cancer and type of yoga intervention.
Main results
We included 24 studies with a total of 2166 participants, 23 of which provided data for meta-analysis. Thirteen studies had low risk of selection bias, five studies reported adequate blinding of outcome assessment and 15 studies had low risk of attrition bias.
Seventeen studies that compared yoga versus no therapy provided moderate-quality evidence showing that yoga improved health-related quality of life (pooled SMD 0.22, 95% CI 0.04 to 0.40; 10 studies, 675 participants), reduced fatigue (pooled SMD -0.48, 95% CI -0.75 to -0.20; 11 studies, 883 participants) and reduced sleep disturbances in the short term (pooled SMD -0.25, 95% CI -0.40 to -0.09; six studies, 657 participants). The funnel plot for health-related quality of life was asymmetrical, favouring no therapy, and the funnel plot for fatigue was roughly symmetrical. This hints at overall low risk of publication bias. Yoga did not appear to reduce depression (pooled SMD -0.13, 95% CI -0.31 to 0.05; seven studies, 496 participants; low-quality evidence) or anxiety (pooled SMD -0.53, 95% CI -1.10 to 0.04; six studies, 346 participants; very low-quality evidence) in the short term and had no medium-term effects on health-related quality of life (pooled SMD 0.10, 95% CI -0.23 to 0.42; two studies, 146 participants; low-quality evidence) or fatigue (pooled SMD -0.04, 95% CI -0.36 to 0.29; two studies, 146 participants; low-quality evidence). Investigators reported no serious adverse events.
Four studies that compared yoga versus psychosocial/educational interventions provided moderate-quality evidence indicating that yoga can reduce depression (pooled SMD -2.29, 95% CI -3.97 to -0.61; four studies, 226 participants), anxiety (pooled SMD -2.21, 95% CI -3.90 to -0.52; three studies, 195 participants) and fatigue (pooled SMD -0.90, 95% CI -1.31 to -0.50; two studies, 106 participants) in the short term. Very low-quality evidence showed no short-term effects on health-related quality of life (pooled SMD 0.81, 95% CI -0.50 to 2.12; two studies, 153 participants) or sleep disturbances (pooled SMD -0.21, 95% CI -0.76 to 0.34; two studies, 119 participants). No trial adequately reported safety-related data.
Three studies that compared yoga versus exercise presented very low-quality evidence showing no short-term effects on health-related quality of life (pooled SMD -0.04, 95% CI -0.30 to 0.23; three studies, 233 participants) or fatigue (pooled SMD -0.21, 95% CI -0.66 to 0.25; three studies, 233 participants); no trial provided safety-related data.
Authors' conclusions
Moderate-quality evidence supports the recommendation of yoga as a supportive intervention for improving health-related quality of life and reducing fatigue and sleep disturbances when compared with no therapy, as well as for reducing depression, anxiety and fatigue, when compared with psychosocial/educational interventions. Very low-quality evidence suggests that yoga might be as effective as other exercise interventions and might be used as an alternative to other exercise programmes.
241 citations
TL;DR: Results suggest that CBT-I is associated with statistically and clinically significant improvements in subjective sleep outcomes in patients with cancer and can be successfully delivered through a variety of treatment modalities, making it possible to reach a broader range of patients who may not have access to more traditional programs.
Abstract: Individuals with cancer are disproportionately affected by sleep disturbance and insomnia relative to the general population. These problems can be a consequence of the psychological, behavioral, and physical effects of a cancer diagnosis and treatment. Insomnia often persists for years and, when combined with already high levels of cancer-related distress, may place cancer survivors at a higher risk of future physical and mental health problems and poorer quality of life. The recommended first-line treatment for insomnia is cognitive behavioral therapy for insomnia (CBT-I), a non-pharmacological treatment that incorporates cognitive and behavior-change techniques and targets dysfunctional attitudes, beliefs, and habits involving sleep. This article presents a comprehensive review of the literature examining the efficacy of CBT-I on sleep and psychological outcomes in cancer patients and survivors. The search revealed 12 studies (four uncontrolled, eight controlled) that evaluated the effects of CBT-I in cancer patients or survivors. Results suggest that CBT-I is associated with statistically and clinically significant improvements in subjective sleep outcomes in patients with cancer. CBT-I may also improve mood, fatigue, and overall quality of life, and can be successfully delivered through a variety of treatment modalities, making it possible to reach a broader range of patients who may not have access to more traditional programs. Future research in this area should focus on the translation of evidence into clinical practice in order to increase awareness and access to effective insomnia treatment in cancer care.
185 citations
TL;DR: It is suggested that approximately 25% of women experience significant and persistent levels of breast pain in the first 6 months following breast cancer surgery, and severe breast pain is associated with clinically meaningful decrements in functional status and quality of life.
114 citations
TL;DR: It is suggested that approximately 35% of women experience persistent levels of moderate arm/shoulder pain in the first six months following breast cancer surgery, which is associated with clinically meaningful decrements in functional status and quality of life.
89 citations
TL;DR: Evidence for the importance of psychosocial factors such as catastrophizing, anxiety, depression, somatization and sleep quality play an important role in shaping an individual's risk of developing persistent pain after breast cancer surgery is presented.
Abstract: SUMMARY Persistent pain after breast cancer surgery (PPBCS) is increasingly recognized as a potential problem facing a sizeable subset of the millions of women who undergo surgery as part of their treatment of breast cancer. Importantly, an increasing number of studies suggest that individual variation in psychosocial factors such as catastrophizing, anxiety, depression, somatization and sleep quality play an important role in shaping an individual’s risk of developing PPBCS. This review presents evidence for the importance of these factors and puts them within the context of other surgical, medical, psychophysical and demographic factors, which may also influence PPBCS risk, as well as discusses potential perioperative therapies to prevent PPBCS.
84 citations
References
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TL;DR: The CES-D scale as discussed by the authors is a short self-report scale designed to measure depressive symptomatology in the general population, which has been used in household interview surveys and in psychiatric settings.
Abstract: The CES-D scale is a short self-report scale designed to measure depressive symptomatology in the general population. The items of the scale are symptoms associated with depression which have been used in previously validated longer scales. The new scale was tested in household interview surveys and in psychiatric settings. It was found to have very high internal consistency and adequate test- retest repeatability. Validity was established by pat terns of correlations with other self-report measures, by correlations with clinical ratings of depression, and by relationships with other variables which support its construct validity. Reliability, validity, and factor structure were similar across a wide variety of demographic characteristics in the general population samples tested. The scale should be a useful tool for epidemiologic studies of de pression.
48,339 citations
"Identification of distinct subgroup..." refers background in this paper
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TL;DR: 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.
Abstract: Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
10,568 citations
"Identification of distinct subgroup..." refers methods in this paper
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TL;DR: Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test proved to be a very consistent indicator of classes across all of the models considered.
Abstract: Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study population. This article presents the results of a simulation study that examines the performance of likelihood-based tests and the traditionally used Information Criterion (ICs) used for determining the number of classes in mixture modeling. We look at the performance of these tests and indexes for 3 types of mixture models: latent class analysis (LCA), a factor mixture model (FMA), and a growth mixture models (GMM). We evaluate the ability of the tests and indexes to correctly identify the number of classes at three different sample sizes (n = 200, 500, 1,000). Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test ...
7,716 citations
"Identification of distinct subgroup..." refers methods in this paper
...Then, the number of latent growth classes that best fit the data was identified using guidelines recommended by a number of experts [27, 40, 54]....
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TL;DR: The authors provide an overview of latent class and growth mixture modeling techniques for applications in the social and psychological sciences, discuss current debates and issues, and provide readers with a practical guide for conducting LCGA and GMM using the Mplus software.
Abstract: In recent years, there has been a growing interest among researchers in the use of latent class and growth mixture modeling techniques for applications in the social and psychological sciences, in part due to advances in and availability of computer software designed for this purpose (e.g., Mplus and SAS Proc Traj). Latent growth modeling approaches, such as latent class growth analysis (LCGA) and growth mixture modeling (GMM), have been increasingly recognized for their usefulness for identifying homogeneous subpopulations within the larger heterogeneous population and for the identification of meaningful groups or classes of individuals. The purpose of this paper is to provide an overview of LCGA and GMM, compare the different techniques of latent growth modeling, discuss current debates and issues, and provide readers with a practical guide for conducting LCGA and GMM using the Mplus software. Researchers in the fields of social and psychological sciences are often interested in modeling the longitudinal developmental trajectories of individuals, whether for the study of personality development or for better understanding how social behaviors unfold over time (whether it be days, months, or years). This usually requires an extensive dataset consisting of longitudinal, repeated measures of variables, sometimes including multiple cohorts, and analyzing this data using various longitudinal latent variable modeling techniques such as latent growth curve models (cf. MacCallum & Austin, 2000). The objective of these approaches is to capture information about interindividual differences in intraindividual change over time (Nesselroade, 1991). However, conventional growth modeling approaches assume that individuals come from a single population and that a single growth trajectory can adequately approximate an entire population. Also, it is assumed that covariates that affect the growth factors influence each individual in the same way. Yet, theoretical frameworks and existing studies often categorize individuals into distinct subpopulations (e.g., socioeconomic classes, age groups, at-risk populations). For example, in the field of alcohol research, theoretical literature suggests different classes
2,273 citations
2,056 citations
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