Sleep disturbance and cancer-related fatigue symptom cluster in breast cancer patients undergoing chemotherapy

医学 乳腺癌 睡眠障碍 癌症 内科学 匹兹堡睡眠质量指数 逻辑回归 物理疗法 失眠症 精神科 睡眠质量
作者
Rina S. Fox,Sonia Ancoli‐Israel,Scott C. Roesch,Erin L. Merz,Sarah D. Mills,Kristen J. Wells,Georgia Robins Sadler,Vanessa L. Malcarne
出处
期刊:Supportive Care in Cancer [Springer Science+Business Media]
卷期号:28 (2): 845-855 被引量:162
标识
DOI:10.1007/s00520-019-04834-w
摘要

Sleep disturbance and cancer-related fatigue (CRF) are among the most commonly reported symptoms associated with breast cancer and its treatment. This study identified symptom cluster groups of breast cancer patients based on multidimensional assessment of sleep disturbance and CRF prior to and during chemotherapy. Participants were 152 women with stage I–IIIA breast cancer. Data were collected before chemotherapy (T1) and during the final week of the fourth chemotherapy cycle (T2). Latent profile analysis was used to derive groups of patients at each timepoint who scored similarly on percent of the day/night asleep per actigraphy, the Pittsburgh Sleep Quality Index global score, and the five subscales of the Multidimensional Fatigue Symptom Inventory-Short Form. Bivariate logistic regression evaluated if sociodemographic/medical characteristics at T1 were associated with group membership at each timepoint. Three groups (Fatigued with sleep complaints, Average, Minimal symptoms) were identified at T1, and five groups (Severely fatigued with poor sleep, Emotionally fatigued with average sleep, Physically fatigued with average sleep, Average, Minimal symptoms) at T2. The majority of individuals in a group characterized by more severe symptoms at T1 were also in a more severe symptom group at T2. Sociodemographic/medical variables at T1 were significantly associated with group membership at T1 and T2. This study identified groups of breast cancer patients with differentially severe sleep disturbance and CRF symptom profiles prior to and during chemotherapy. Identifying groups with different symptom management needs and distinguishing groups by baseline sociodemographic/medical variables can identify patients at risk for greater symptom burden.

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