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

Bean, Helena Rose; Diggens, Justine; Ftanou, Maria; Weihs, Karen L.; Stanton, Annette L.; Wiley, Joshua F.

doi:10.1080/15402002.2020.1869005

Cited by 31 publications

(32 citation statements)

References 38 publications

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“…Notably, levels of perinatal fatigue were significantly higher in women classified as “persistently high” than in women classified as “persistently low” at all six time points from late pregnancy to 6 months after delivery, which was consistent with prior study in other patients ( Willems et al, 2017 ; Li et al, 2020 ; Bean et al, 2021 ). Additionally, the general level of prenatal fatigue grew dramatically with time in the “persistently high” group, whereas it decreased significantly in the “persistently low” group.…”

Section: Discussionsupporting

confidence: 90%

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

Zhu¹,

Xia²

2022

PeerJ

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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.

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Section: Discussionsupporting

confidence: 90%

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

Zhu¹,

Xia²

2022

PeerJ

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“…[4][5][6][7][8][9]26 Some previous studies have suggested that there are clusters of patients who experience distinct trajectories of fatigue. 27 For example, Bower et al examined post-treatment fatigue trajectories in 191 women with early-stage BC. This cohort 15 had a follow-up time of 4.3 years after treatment and identified four distinct patterns of fatigue after BC: (1) low fatigue group (44%), where patients presented low fatigue levels throughout the duration of the observation; (2) recovery group (28%), where patients were fatigued at the initial post-treatment assessment but then recovered their energy levels; (3) late oup (17%) where patients presented with low post-treatment levels of fatigue but then gradually started to report increased fatigue levels over time; and (4) high fatigue group (11%), where fatigue was persistently elevated across the study period.…”

Section: Discussionmentioning

confidence: 99%

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

Vaz-Luís

Meglio

Havas

et al. 2022

JCO

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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.

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“…These variables were selected in both the logistic regression model and the XGBoost model, so the results were consistent. The impact of insomnia on QOL is widely recognized and the relationship between insomnia, fatigue and resilience has also been reported ( 33 , 34 ).…”

Section: Discussionmentioning

confidence: 99%

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

Ueno

Ichikawa

Shimizu

et al. 2021

Japanese Journal of Clinical Oncology

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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.

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

Cited by 31 publications

References 38 publications

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

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

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

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

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