Abstract
Background:
Breast cancer survivors (BCS) often report poor sleep quality and wakefulness throughout the night as the greatest challenges experienced during and posttreatment.
Objectives:
This study aimed to elucidate characteristics of sleep disturbances and determine potential predictors that affect sleep disturbances in BCS for 2 years postchemotherapy.
Methods:
This is a secondary analysis of data from the EPIGEN study, which longitudinally examined sociodemographic and cancer-related factors, lifestyle, symptom characteristics, and epigenetic factors at baseline prior to chemotherapy (T1), the midpoint (T2), 6-month (T3), 1-year (T4), and 2-year (T5) time points postchemotherapy. Temporal lifestyle changes, symptom characteristics, and epigenetic factors were explored using linear mixed-effects models with a random intercept. A linear regression model was fitted to identify significant predictors of sleep disturbances at each time point.
Results:
In 74 BCS with an average age of 51 years and 70% non-Hispanic White, BCS experienced severe sleep disturbances at T2, which gradually improved over time. Significant temporal changes in midsleep awakenings, early awakenings, and fatigue at work were observed, with disturbances being elevated at T2. Anxiety (T1, T2, T4), fatigue (T3, T4), and perceived stress (T3) were significant predictors after adjusting for radiation therapy, surgery, and adjuvant endocrine therapy.
Discussion:
This study highlights predictors of sleep disturbances change over time, with anxiety being a factor earlier in the treatment trajectory (prechemotherapy) and continuing over time with fatigue and perceived stress being involved later in the treatment trajectory. Our results indicate that symptom management strategies to address sleep disturbances should be tailored to the temporal factors that may change in severity during active treatment and early survivorship period. Findings gained from this study on sleep disturbance patterns and the potential risk factors can be incorporated into clinical practice in planning education and developing interventions.
Keywords: breast cancer, chemotherapy, longitudinal study, sleep disturbance
Breast cancer is the foremost cause of cancer death (42,170 estimated deaths in 2020) after lung cancer in women in the U.S. (Siegel et al., 2020). With the advances in cancer treatment and early detection, the 5-year relative survival rate for breast cancer is 90%, which is much higher than esophagus, lung, liver, and pancreas cancers (9%–20%; Siegel et al., 2020). Extended survivorship has brought challenges to breast cancer survivors (BCS); they tend to live with unremitting symptoms, such as sleep disturbances, fatigue, pain, depression, anxiety, and/or cognitive impairment during and after cancer treatments (Bodai & Tuso, 2015).
BCS often report that poor quality of sleep and staying awake throughout the night are among the greatest challenges experienced (Lowery-Allison et al., 2018), with more than 50% of BCS experiencing sleep disturbances in the first 3 months following chemotherapy and radiation treatment (Lester et al., 2015; Schreier et al., 2019). Sleep disturbances have been found to begin or worsen throughout cancer treatment and continue for as long as 10 years following the cessation of cancer treatment (Lowery-Allison et al., 2018). Sleep disturbances among BCS have been described as multifactorial, with numerous risk factors and environmental conditions affecting their prevalence.
The physical and emotional effect of cancer diagnosis and treatment often stems from heightened anxiety, depression, fear of reoccurrence, and fatigue, which directly affect sleep quality (Strollo et al., 2020). These symptoms may share common underlying mechanisms, such as inflammatory signaling processes or posttreatment circadian misalignment (Ancoli-Israel et al., 2014; Liu et al., 2012; Whisenant et al., 2017). Notably, cancer treatments, such as chemotherapy and steroids, are associated with impaired sleep, and the increased prevalence of vasomotor symptoms can have a profound effect on sleep quality (Carreira et al., 2018). Abrupt onset of vasomotor symptoms, such as hot flashes and night sweats, correlates with increased incidence of depression and sleep disturbances in BCS (Accortt et al., 2015). Furthermore, prior studies found that Black women and women of minority status are at increased risk for experiencing sleep disturbances, which may signal the role race play in our understanding of sleep disturbances among BCS (Budhrani et al., 2015; Schreier et al., 2019).
BCS may be at additional risk for sleep disorders due to epigenetic factors, including telomere shortening. Telomere length shortening is a normal occurrence associated with aging; it is accelerated with inflammation and oxidative stress (Alhareeri et al., 2020; Garland et al., 2014). Telomere shortening is associated with breast cancer-specific mortality (Duggan et al., 2014). There is increased DNA damage and a reduction in telomerase activity in BCS following treatment (Scuric et al., 2017). Garland et al. (2014) reported that telomere lengths of BCS with insomnia symptoms were shorter, but not significantly, compared to those without insomnia symptoms. Evidence supports that poor sleep quality is associated with telomere shortening in the wider population (Cribbet et al., 2014; Tempaku et al., 2015).
Long-term sleep disturbances affect the overall health of BCS in a multitude of ways. Poor sleep has been shown to have a long-lasting negative effect on quality of life and is predictive of adverse health outcomes, such as poor healing, cognitive impairment, and increased economic distress (Henneghan et al., 2018; Strollo et al., 2020). While the significance of long-term sleep disturbances on overall health of BCS warrants increased clinical awareness and further investigation, only a few studies have examined long-term changes of sleep disturbances and their relationships with multiple potential risk factors, such as demographics, cancer, and treatment-related factors, lifestyle, symptom characteristics, and biological factors in a well-defined cohort of BCS. Despite other research with similar aims to this study, a long-term examination of salient factors that may affect sleep quality in BCS is greatly warranted to optimize symptom management strategies over the active treatment and early survivorship. Thus, this study aimed to elucidate characteristics of sleep disturbances and determine possible predictors that may contribute to sleep disturbances in BCS through 2 years postchemotherapy.
Methods
Study Design and Participants
The parent study (EPIGEN) of this secondary analysis was a longitudinal, prospective study to examine interplays among sociodemographic variables, psychoneurological symptoms (i.e., anxiety, depression, fatigue, cognitive dysfunction, pain, and sleep disturbances), and epigenetic alterations in adult women diagnosed with early-stage breast cancer over 2 years. The data collection time points were (a) baseline before starting the chemotherapy (T1), (b) the midpoint of chemotherapy (T2), (c) 6-month postchemotherapy (T3), (d) 1-year postchemotherapy (T4), and (e) 2-year postchemotherapy (T5). Recruitment occurred at a university-affiliated National Cancer Center and four regional collaborative sites located in central Virginia. Participants were eligible if they were diagnosed with Stage I–III breast cancer, scheduled to initiate chemotherapy treatment, and aged 21 years or older. Participants were ineligible if they had a history of chemotherapy for cancer, severe mental health problems including dementia or active psychosis, or immune diseases. An institutional review board (IRB) approved the study (Virginia Commonwealth University IRB#HM 13194), and written informed consent was provided before the initiation of the study. The ethical standards for human subjects were met according to the 1964 Helsinki Declaration through all procedures in this study.
Measures
Sleep disturbance conditions (a total score and seven subscale scores) were considered dependent variables, and sociodemographic, cancer-related factors, health behaviors, symptom characteristics, and biological factors were included as independent variables.
General Sleep Disturbance
Sleep quality was evaluated using the General Sleep Disturbance Scale (GSDS), which consists of 21 items on an 8-point rating scale and includes seven subscales, including quality of sleep, quantity of sleep, sleep onset latency, midsleep awakenings, early awakenings, use of medications for sleep, and fatigue and alertness at work (excessive daytime sleepiness). Negatively worded items were reversed scored to make higher scores correspond to more sleep disturbance symptoms. A total score of 43 or greater and subscale scores of 3 or greater indicate a significant presence of sleep disturbance symptoms (Lee, 1992).
Sociodemographic and Cancer-Related Factors
Age, race, educational level, income, employment, marital status, human epidermal growth factor receptor 2 (HER2+), estrogen receptor (ER), reproductive stage, cancer stage (I–III), surgery (biopsy/simple vs. lumpectomy/segmental), and treatment regimen (e.g., adjuvant endocrine therapy) were collected from interviews/surveys with study participants and their medical records.
Lifestyle
Information on alcohol drinking, smoking status, body mass index (BMI), antihypertension medication, antipsychotropic medication (e.g., depression, anxiety), and leisure activity was collected. The Godin leisure-exercise questionnaire measured leisure activity to evaluate the frequency and intensity of weekly leisure activity score during free time, in which the Godin scale score is divided into three levels: active (24 units or more), moderately active (14–23 units), and insufficiently active/sedentary (less than 14 units) out of a total of 99 units (Godin, 2011).
Symptom Characteristics
Symptoms were measured with reliable and valid tools; the tools used have been frequently used in studies with BCS.
Depressive Symptoms.
Anxiety and depression were assessed with the Hospital and Anxiety and Depression Scale (HADS), which includes 14 items on a scale of 0 to 3 and comprises two subscales for respective depression and anxiety symptom severity (Snaith, 2003). A participant was considered normal, borderline, or clinical case for an obtained score of 0–7, 8–10, or 11 or greater, respectively (Snaith, 2003). Scores of negatively worded items were reversed to indicate that higher scores mean higher levels of depression and anxiety.
Fatigue.
Fatigue severity and interference were measured with the Brief Fatigue Inventory (BFI), which includes nine items on a scale of 11 points, with 0 indicating “no fatigue” and 10 indicating “as bad as you can imagine” (Mendoza et al., 1999). Scores of 1–3, 4–7, and 8–10 correspond to mild, moderate, and severe fatigue, respectively.
Pain.
Pain was assessed with the 9-item Brief Pain Inventory (BPI) short form on a scale of 0 to 10 (Cleeland, 1989). This measure consists of the severity of pain and its interference with daily function. A higher average score for each category indicates greater pain severity or effects on interference on daily function (Cleeland, 1989).
Cognitive Function.
Neurocognitive function was measured by the noninvasive performance-based computerized test Central Nervous System Vital Signs (CNSVS; https://www.cnsvs.com; Gualtieri & Johnson, 2006). A neurocognitive index score was obtained by calculating an average of five representative domains, including composite memory, psychomotor speed, reaction time, complex attention, and cognitive flexibility, as a proxy of global neurocognitive function, higher scores indicating better neurocognitive function.
Perceived Stress.
The 10-item Perceived Stress Scale (PSS) was employed to evaluate the stress level appraised by various situations in daily life during the last month (Cohen et al., 1983). Scores ranging from 0 to 13, 14 to 26, and 27 to 40 were considered low, moderate, and high perceived stress, respectively (Cohen et al., 1983).
Epigenetic Factors
Telomere Length.
Genomic DNA was extracted from whole blood using a Puregene DNA Isolation Kit (Qiagen, Valencia, CA). The telomere (T) and single copy housekeeping gene (S) values were obtained by performing monochrome multiplex quantitative polymerase chain reaction (MMqPCR) assay, which used a set of target and reference primers in a single reaction and was run on a BioRad CFX96 (Dahlgren et al., 2018). If more than 10% variance was observed in the T/S ratio values between all three measures, the assay was repeated to meet quality control standards (Alhareeri et al., 2020).
DNA Methylation Age Analysis.
Genomic DNA was extracted from whole blood using the Puregene DNA isolation kit (Qiagen, Valencia, CA); an aliquot of 500 μg of DNA was bisulfite converted using an EZ DNA Methylation Kit (Zymo Research, Irvine, CA) and then hybridized into a genome-wide Infinium HumanMethylation450K BeadChip (HM450K; Illumina, San Diego, CA) at HudsonAlpha Institute for Biotechnology (Yang et al., 2020). DNA methylation age was estimated using R version 3.6.1. To eliminate systematic bias and reduce technical variability, the quantile normalization plus beta-mixture quantile normalization method was used to preprocess the raw data (Wang et al., 2015). Afterward, the methylation age was calculated from the preprocessed data by the Horvath method using the watermelon package (Pidsley et al., 2013).
Statistical Analysis
We computed descriptive statistics to report means, standard deviation (SD), frequencies, and percentages for all applicable variables. The temporal changes across five time points in telomere length (relative telomere to single copy gene ratio [T/S ratio]), DNA methylation age, leisure activity, psychoneurological symptoms (anxiety, depression, fatigue, pain, and neurocognitive function), perceived stress, and sleep disturbances (total and subscale scores) were obtained by the F-tests included in linear mixed-effects models with a random intercept to account for the dependency in repeated measures over multiple time points. To identify significant predictors of sleep disturbances at each time point, bivariate and linear regression models were fitted with GSDS total score as the outcome and other variables as the predictors. We tested multicollinearity of variables included in the current analysis and excluded variables showing high variance inflation factor (VIF) scores (VIF > 10). Potential predictors were chosen from the bivariate analyses if the p-value was < .25 and then moved to the multivariable linear regression model (Grant et al., 2019). Statistical significance was considered at a p-value of ≤ .05. SPSS version 25 was used to conduct the analysis.
Results
Participant Characteristics
Baseline characteristics of women with early-stage breast cancer in the EPIGEN study are shown in Table 1. Among 77 participants enrolled, a total of 74 women were included for the analysis (three were dropped due to missing data). The average age was 51.3 (SD = 10.3) years old, and 70.3% (n = 52) were non-Hispanic White. Most participants attained high educational level (any education beyond high school [78.4%]), were employed (62.2%), were nonsmokers (79.7%), and received radiation therapy (78.4%). Nearly half reported drinking alcohol (55.4%), earned an income of less than $60,000 (45.9%), received biopsy/simple surgery (50.7%), and received adjuvant endocrine therapy (52.9%). The average BMI was 29.9 kg/m2 (SD = 7.5), indicating overweight. Approximately nineteen percent of participants were HER2 receptor-positive, and 28.4% were triple-negative (estrogen receptor [ER], progesterone receptor [PR], and HER2). Nearly one third (35.1%) took antihypertensive medication, and 37.0% used antipsychotropic medication for depression and anxiety.
Table 1.
Baseline Demographic and Cancer-Related Characteristics of Women with Breast Cancer (N = 74)
| Characteristic | n (%) |
|---|---|
| Age in year (minimum-maximum) | M = 51.3, SD = 10.3 (23, 71) |
| Race Non-Hispanic White African American |
52 (70.3%) 22 (29.7%) |
| Educational level Did not finish high school Any education beyond high school |
16 (21.6%) 58 (78.4%) |
| Employment Employed (Part-time/Full-time) Unemployed/Disabled/Retired/Student |
46 (62.2%) 28 (37.8%) |
| Marital status Married/Partner Single/Divorced/Separated |
46 (62.2%) 28 (37.8%) |
| Income < $60,000 ≥ $60,000 |
34 (45.9%) 40 (54.1%) |
| Alcohol drinking Yes No |
41 (55.4%) 33 (44.6%) |
| Smoking status Yes No |
15 (20.3%) 59 (79.7%) |
|
| |
| BMI (kg/m2)(minimum-maximum) | M = 29.9, SD = 7.5 (19.1, 54.3) |
|
| |
| Reproductive stage Pre- and peri-menopause Post-menopause |
32 (43.2%) 42 (56.8%) |
|
| |
| HER2 Yes No |
14 (18.9%) 60 (81.1%) |
| Triple negative Yes No |
21 (28.4%) 53 (71.6%) |
| Antihypertensive medication Yes No |
26 (35.1%) 48 (64.9%) |
| Psychotropic medication (e.g., depression, anxiety) Yes No |
27 (37.0%) 46 (63.0%) |
| Cancer stage I IIA IIB IIIA |
20 (27.0%) 31 (41.9%) 15 (20.3%) 8 (10.8%) |
| Surgery Biopsy/Simple Lumpectomy/Segmental |
37 (50.7%) 36 (49.3%) |
| Radiation therapy Yes No |
58 (78.4%) 16 (21.6%) |
| Adjuvant endocrine therapy Yes No |
36 (52.9%) 32 (47.1%) |
Note. BMI = Body mass index; M =Mean; SD = Standard Deviation.
Temporal Changes in Potential Predictor Variables
As shown in Table 2, participants were assessed on epigenetic factors, lifestyle, and symptom characteristics over 2 years. The analysis included telomere length (T/S ratio) and DNA methylation age for epigenetic factors as biological predictors. DNA methylation age was decreased during chemotherapy treatment (T2–T1), persisting until 6-month follow-up time point, and gradually improved over time. DNA methylation age was significantly changed over 2 years (F = 6.2, p < .001), whereas no significant change in telomere length was observed (F = 0.6, p = .683). For lifestyle factors, it appeared that leisure activities decreased during chemotherapy and at 6-month follow-up time point compared to baseline; however, they increased over time at 1- and 2-year follow-up time points after the initiation of chemotherapy. There was a difference in the leisure activity score across time points (F = 5.1, p < .001). Participants experienced temporal changes of symptom characteristics, including anxiety (F = 9.0, p = .007), depression (F = 4.9, p < .001), fatigue (F = 6.6, p < .001), pain (F = 2.7, p = .032), neurocognitive function (F = 2.8, p =.024), and perceived stress (F = 2.9, p = .022). In general, these symptoms worsened at T2 and T3; however, the neurocognitive index gradually increased over time regardless of chemotherapy treatment.
Table 2.
Temporal Changes of Potential Predictors: Leisure Activity, Symptom Characteristics, and Biological (epigenetic) Factors over Two Years
| Variables | Baseline (T1) M (SD) |
4th chemotherapy (T2) M (SD) |
6-month follow-up (T3) M (SD) |
1-year follow-up (T4) M (SD) |
2-year follow-up (T5) M (SD) |
F (p-value) |
|---|---|---|---|---|---|---|
| Leisure activity (Godin) | 29.2 (32.4) | 20.6 (20.3) | 25.4 (21.1) | 33.3 (26.1) | 33.3 (36.5) | 5.1 (.001) |
| Anxiety (HADS-A) | 8.5 (3.5) | 6.8 (3.3) | 7.0 (3.9) | 6.9 (3.5) | 6.5 (3.5) | 9.0 (< .001) |
| Depression (HADS-D) | 3.7 (3.3) | 4.7 (3.1) | 4.1 (3.6) | 3.5 (3.1) | 3.2 (3.0) | 4.9 (.001) |
| Fatigue (BFI) | 2.0 (2.6) | 3.3 (2.7) | 2.9 (2.8) | 2.3 (2.4) | 2.3 (2.5) | 6.6 (< .001) |
| Pain (BPI) | 1.7 (2.4) | 1.9 (2.4) | 2.3 (2.6) | 1.6 (2.4) | 1.9 (2.4) | 2.7 (.032) |
| Neurocognitive function (CNSVS neurocognitive index) | 99.8 (14.4) | 102.9 (15.1) | 105.3 (14.8) | 105.9 (15.7) | 107.4 (14.6) | 2.8 (.024) |
| Perceived stress (PSS) | 16.8 (8.0) | 14.7 (7.6) | 14.2 (7.9) | 13.5 (7.3) | 12.8 (7.6) | 2.9 (.022) |
| Telomere length (T/S ratio) | 1.5 (0.5) | 1.5 (0.6) | 1.5 (0.6) | 1.6 (0.6) | 1.6 (0.6) | 0.6 (.683) |
| DNA methylation age | 52.3 (12.5) | 50.3 (11.7) | 50.2 (12.0) | 52.2 (11.5) | 54.4 (10.2) | 6.2 (< .001) |
Note. BFI = Brief Fatigue Inventory; BPI = Brief Pain Inventory; CNSVS = Central Nervous System Vital Signs (computerized neurocognitive assessment software program); HADS-A = Hospital Anxiety and Depression Scale-Anxiety; HADS-D = Hospital Anxiety and Depression Scale-Depression; PSS = Perceived Stress Scale; SD = Standard deviation; T/S ratio = Relative telomere to single copy gene ratio.
General Sleep Disturbances
The summary of total and subscale scores of GSDS is shown in Table 3. Participants showed significant temporal changes of sleep disturbances over 2 years (F = 7.4, p < .001). The total score of GSDS was highest at T2 (mean = 48.9, SD = 22.9) and decreased over time (T3: mean = 44.6, SD = 21.6; T4: mean = 42.9, SD = 21.7; T5: mean = 37.4, SD = 20.4), indicating that participants experienced a significant level of sleep disturbances (a total score ≥ 43) during chemotherapy treatment. They reported clinically meaningful sleep disturbances in sleep onset latency, midsleep awakenings, early awakenings, and quality of sleep at T2. Midsleep awakenings (F = 3.4, p = .010), early awakenings (F = 2.9, p = .024), and fatigue at work (excessive daytime sleepiness; F = 9.3, p < .001) showed significant temporal changes.
Table 3.
Temporal Changes of General Sleep Disturbances over Time
| Visit | Baseline (T1) M (SD) |
4th chemotherapy (T2) M (SD) |
6-month follow-up (T3) M (SD) |
1-year follow-up (T4) M (SD) |
2-year follow-up (T5) M (SD) |
F (p-value) |
|---|---|---|---|---|---|---|
| N | 74 | 73 | 74 | 71 | 68 | |
| GSDS total | 39.1 (22.9) | 48.9 (22.9) | 44.6 (21.6) | 42.9 (21.7) | 37.4 (20.4) | 7.4 (<.001)*** |
| 1. Sleep onset latency | 2.8 (2.4) | 3.1 (2.7) | 3.0 (2.5) | 2.8 (2.4) | 2.4 (2.5) | 1.1 (.357) |
| 2. Mid-sleep awakenings | 4.6 (2.7) | 5.2 (2.4) | 4.5 (2.4) | 4.5 (2.4) | 4.1 (2.3) | 3.4 (.010)* |
| 3. Early awakenings | 3.3 (2.5) | 3.9 (2.6) | 3.4 (2.6) | 3.2 (2.3) | 2.9 (2.3) | 2.9 (.024)* |
| 4. Quality of sleep | 3.1 (2.1) | 3.5 (2.2) | 3.4 (2.1) | 3.5 (2.0) | 2.9 (1.9) | 2.2 (.070) |
| 5. Quantity of sleep | 1.8 (1.2) | 2.0 (1.3) | 1.8 (1.2) | 1.8 (1.1) | 1.8 (1.1) | 0.5 (.704) |
| 6. Fatigue and alertness at work | 1.9 (1.4) | 2.8 (1.6) | 2.4 (1.5) | 2.2 (1.4) | 1.9 (1.3) | 9.3 (<.001)*** |
| 7. Use of sleep medication | 0.4 (0.6) | 0.5 (0.7) | 0.5 (0.8) | 0.5 (0.8) | 0.4 (0.6) | 0.8 (.558) |
Note. GSDS = General sleep disturbance scale; M = Mean; SD = Standard deviation.
p < .05,
p < .01,
p < .001
Bivariate and Multivariable Regression Analyses at Each Time Point
Individual demographic and cancer-related factors, lifestyle, symptom characteristics, and epigenetic factors were added as potential predictors into the bivariate and multivariable regression analyses to determine their associations with sleep disturbances at each time point. Results from bivariate analyses and multivariable analyses are shown in Table 4. All bivariate and multivariable analyses were adjusted for radiation therapy, surgery, and adjuvant endocrine therapy. At T1, the bivariate association of perceived stress, anxiety, depression, pain, and fatigue with sleep disturbances was supported. In the multivariable analysis, anxiety (β = .36, p = .005) was identified as a significant predictor for sleep disturbances whereas smoking status (β = −.10, p = .325), perceived stress (β = .01, p = .973), depression (β = −.02, p = .870), pain (β = .27, p = .096), and fatigue (β = .32, p = .080) were not associated with sleep disturbances (model summary: adjusted R2 = .513, F = 8.7, p < .001). At T2, HER2+ status, taking antipsychotropic medication, perceived stress, anxiety, depression, pain, and fatigue showed bivariate associations with sleep disturbances. In the multivariable analysis, anxiety was a significant predictor (β = .28, p = .013); though, other variables were not associated with sleep disturbances (model summary: adjusted R2 = .562, F = 9.1, p < .001). At T3, the bivariate associations between all symptom characteristics, income, employment, taking antipsychotropic medication, and sleep disturbances were supported. In the multivariable analysis, perceived stress (β = 0.33, p = .028) and fatigue (β = .61, p < .001) were significant predictors for developing sleep disturbances (model summary: adjusted R2 = .723, F = 16.0, p < .001). At T4, taking antipsychotropic medication and all symptom characteristics showed bivariate associations with sleep disturbances. In the multivariable analysis, anxiety (β = .41, p = .005) and fatigue (β = .72, p < .001) were significant predictors for sleep disturbances (model summary: adjusted R2 = .605, F = 10.4, p < .001). At T5, according to bivariate analyses, education level, income, cancer stage, leisure activity, and all symptom characteristics were associated with sleep disturbances; still, no significant association was found in the multivariable analysis.
Table 4.
Bivariate and Multivariable Analyses at Baseline Prior to Chemotherapy, the Mid-Point of Chemotherapy, 6-month, 1-year, and 2-year Follow-Up Time Points Post-Chemotherapy
| Time point | Variables | Bivariate analysis (Controlling for radiation therapy, surgery, and endocrine therapy) |
Multivariable analysis (Controlling for radiation therapy, surgery, and endocrine therapy; variables of p < .25 from the bivariate analysis) |
|---|---|---|---|
|
|
|||
| β (p-value) | β (p-value) | ||
| T1 | Demographic characteristics | ||
| Age | −.03 (.816) | ||
| Race | .04 (.748) | ||
| Age x Race | .05 (.692) | ||
| Educational level | −.14 (.288) | ||
| Income | −.07 (.570) | ||
| Employment | −.03 (.804) | ||
| Marital status | .05 (.675) | ||
| Disease characteristics | |||
| HER2+ | .12 (.341) | ||
| Triple negative | .05 (.771) | ||
| Reproductive stage | .10 (.446) | ||
| Stage | Stage III: −.06 (.630) Stage II: .06 (.670) Stage I: - |
||
| Lifestyle | |||
| Alcohol drinking | −.06 (.635) | ||
| Smoking status | .19 (.137) | −.10 (.325) | |
| Leisure activity | −.12 (.329) | ||
| BMI | .04 (.746) | ||
| Antihypertension medication | −.02 (.847) | ||
| Antipsychotropic medication | .14 (.268) | ||
| Biological factors | |||
| Telomere length | .03 (.803) | ||
| DNA methylation age | .02 (.905) | ||
| Symptom characteristics | |||
| Perceived stress | .52 (< .001) *** | .01 (.973) | |
| Anxiety | .53 (< .001) *** | .36 (.005) ** | |
| Depression | .52 (< .001) *** | −.02 (.870) | |
| Pain | −.58 (< .001) *** | .27 (.096) | |
| Fatigue | .66 (< .001) *** | .32 (.080) | |
| Neurocognition index | −.15 (.255) | ||
| Model summary | adjusted R2 = .513, F (9, 57) =8.7, p < .001 |
||
| T2 | Demographic characteristics | ||
| Age | −.04 (.713) | ||
| Race | −.05 (.678) | ||
| Age x Race | −.06 (.662) | ||
| Educational level | .13 (.280) | ||
| Income | −.04 (.753) | ||
| Employment | −.06 (.612) | ||
| Marital status | .07 (.579) | ||
| Disease characteristics | |||
| HER2+ | .15 (.235) | .16 (.104) | |
| Triple negative | −.03 (.845) | ||
| Stage | Stage III: .004 (.972) Stage II: −.05 (.717) Stage I: - |
||
| Lifestyle | |||
| Leisure activity | −.06 (.615) | ||
| BMI | .05 (.897) | ||
| Antihypertension medication | −.09 (.482) | ||
| Antipsychotropic medication | .25 (.041) * | .08 (.387) | |
| Biological factors | |||
| Telomere length | −.19 (.331) | ||
| DNA methylation age | −.05 (.697) | ||
| Symptom characteristics | |||
| Perceived stress | .55 (<.001) *** | .21 (.090) | |
| Anxiety | .53 (<.001) *** | .28 (.013) * | |
| Depression | .64 (<.001) *** | .20 (.136) | |
| Pain | .31 (.012) * | −.08 (.455) | |
| Fatigue | .54 (<.001) *** | .20 (.120) | |
| Neurocognition index | −.13 (.340) | ||
| Model summary | adjusted R2 = .562, F (10, 53) = 9.1, p < .001 |
||
| T3 | Demographic characteristics | ||
| Age | −.06 (.621) | ||
| Race | .08 (.534) | ||
| Age x Race | .07 (.617) | ||
| Educational level | −.05 (.719) | ||
| Income | −.22 (.078) | .10 (.236) | |
| Employment | −.16 (.210) | −.08 (.325) | |
| Marital status | .12 (.369) | ||
| Disease characteristics | |||
| HER2+ | .03 (.793) | ||
| Triple negative | .09 (.608) | ||
| Stage | Stage III: −.15 (.258) Stage II: .11 (.391) Stage I: - |
||
| Lifestyle | |||
| Leisure activity | −.004 (.973) | ||
| BMI | .09 (.497) | ||
| Antihypertension medication | −.10 (.416) | ||
| Antipsychotropic medication | .18 (.150) | −.04 (.581) | |
| Biological factors | |||
| Telomere length | −.10 (.411) | ||
| DNA methylation age | .04 (.759) | ||
| Symptom characteristics | |||
| Perceived stress | .67 (<.001) *** | .33 (.028) * | |
| Anxiety | .51 (<.001) *** | .17 (.186) | |
| Depression | .51 (<.001) *** | −.17 (.136) | |
| Pain | .61 (<.001) *** | .08 (.485) | |
| Fatigue | .77 (<.001) *** | .61 (<.001) *** | |
| Neurocognition index | −.10 (.453) | ||
| Model summary | adjusted R2 = .723, F (11, 52) = 16.0, p < .001 |
||
| T4 | Demographic characteristics | ||
| Age | −.09 (.482) | ||
| Race | .07 (.557) | ||
| Age x Race | .07 (.605) | ||
| Educational level | −.14 (.266) | ||
| Income | −.12 (.342) | ||
| Employment | −.06 (.617) | ||
| Marital status | .05 (.675) | ||
| Disease characteristics | |||
| HER2+ | .03 (.837) | ||
| Triple negative | .02 (.917) | ||
| Stage | Stage III: .09 (.466) Stage II: −.02 (.894) Stage I: - |
||
| Lifestyle | |||
| Leisure activity | −.10 (.451) | ||
| BMI | .01 (.935) | ||
| Antihypertension medication | −.06 (.627) | ||
| Antipsychotropic medication | .25 (.044) * | −.01 (.884) | |
| Biological factors | |||
| Telomere length | .10 (.426) | ||
| DNA methylation age | −.05 (.702) | ||
| Symptom characteristics | |||
| Perceived stress | .65 (<.001) *** | .12 (.453) | |
| Anxiety | .59 (<.001) *** | .41 (.005) ** | |
| Depression | .52 (<.001) *** | −.22 (.131) | |
| Pain | .45 (<.001) *** | −.23 (.127) | |
| Fatigue | .63 (<.001) *** | .72 (<.001) *** | |
| Neurocognition index | −.17 (.192) | −.002 (.981) | |
| Model summary | adjusted R2 = .605, F (10, 51) = 10.4, p < .001 |
||
| T5 | Demographic characteristics | ||
| Age | −.08 (.499) | ||
| Race | .13 (.291) | ||
| Age x Race | .12 (.351) | ||
| Educational level | −.21 (.095) | −.08 (.549) | |
| Income | −.23 (.065) | .08 (.497) | |
| Employment | −.06 (.642) | ||
| Marital status | .26 (.045) * | .14 (.229) | |
| Disease characteristics | |||
| HER2+ | .03 (.801) | ||
| Triple negative | .05 (.794) | ||
| Stage | Stage III: −.06 (.669) Stage II: .24 (.057) Stage I: - |
Stage III: −.05 (.655) Stage II: .08 (.501) Stage I: - |
|
| Lifestyle | |||
| Leisure activity | −.33 (.006) ** | −.13 (.268) | |
| BMI | −.01 (.954) | ||
| Antihypertension medication | .04 (.748) | ||
| Antipsychotropic medication | .03 (.822) | ||
| Biological factors | |||
| Telomere length | .13 (.299) | ||
| DNA methylation age | −.07 (.628) | ||
| Symptom characteristics | |||
| Perceived stress | .66 (<.001) *** | .21 (.221) | |
| Anxiety | .51 (<.001) *** | .24 (.109) | |
| Depression | .54 (<.001) *** | −.03 (.877) | |
| Pain | .56 (<.001) *** | .16 (.363) | |
| Fatigue | .64 (<.001) *** | .29 (.140) | |
| Neurocognition index | −.16 (.205) | .15 (.214) | |
| Model summary | adjusted R2 = .570, F (15, 42) = 6.1, p < .001 |
||
Note. Covariate references include African American (race), “did not finish high school” (education), <$60,000 (income), unemployed/disabled/retired/student (employment), single/divorced/separated (marriage), no HER2+, no triple negative, no reproductive stage, cancer stage I, no alcohol drinking, no smoking, no antihypertension medication, and no antipsychotropic medication. BMI = Body mass index; SE = Standard error; T/S ratio = Relative telomere to single copy gene ratio. T1 = Baseline prior to chemotherapy; T2 = 4th chemotherapy; T3 = 6-month follow-up post-chemotherapy; T4 = 1-year follow-up post-chemotherapy; T5 = 2-year follow-up post chemotherapy. Reproductive stage, alcohol drinking, and smoking status were included in only T1.
p < .05
p < .01
p < .001
Discussion
This study addressed temporal changes of sleep disturbances and potential predictors that may be associated with sleep disturbances in BCS from baseline through 2-year survivorship after initiating chemotherapy. Our results demonstrated that BCS experienced poor sleep quality during the period of chemotherapy treatment and gradually improved over time. Specifically, significant temporal changes were observed in midsleep awakenings, early awakenings, and fatigue at work (excessive daytime sleepiness), with disturbances being elevated at T2. Symptom characteristics were associated with higher levels of sleep disturbances across all time points in the bivariate analyses. Anxiety (T1, T2, and T4), fatigue (T3 and T4), and perceived stress (T3) were significant predictors of sleep disturbances across treatment and survivorship trajectories in the multivariable analyses.
Sleep disturbances in BCS were found to be most prevalent during active treatment (T2) and steadily decreased following treatment through the 2-year follow-up time point. Ancoli-Israel et al. (2014) reported similar sleep symptom trajectories; peak sleep disturbance and fatigue levels were observed following four cycles of chemotherapy and then improved by 1-year postchemotherapy (Ancoli-Israel et al., 2014). The most common sleep disturbance characteristics reported in this study consisted of sleep onset latency, midsleep awakenings, early awakenings, and overall sleep quality (Ancoli-Israel et al., 2014). In a sample of BCS up to 10 years posttreatment, Lowery-Allison et al. (2018) found that greater than one third of the population took twice as long to fall asleep, had twice as many awakenings during the night, and slept for 2 hours less on average per night (Lowery-Allison et al., 2018). In an integrative review including 12 studies on sleep disturbances, findings detailed that at 1-year posttreatment, sleep quality remained poor in BCS, indicating a continued negative effect of chemotherapy on sleep, and the most common symptoms experienced consisted of increased nocturnal awakening and waking after sleep onset (Budhrani et al., 2015). Liu et al. (2012) also found that there were overall positive relationships between changes in interleukin-6 (IL-6) and IL-1 receptor antagonist (IL-1RA) and changes in sleep disturbances scores as well as between C-reactive protein (CRP) and total wake time during the night in BCS who received at least four cycles of chemotherapy. This may implicate that inflammation may mediate chemotherapy-related sleep disturbances (Liu et al., 2012). Similarly, the meta-analysis on sleep deprivation that includes a total of 72 studies showed the positive association of shorter sleep duration and extremely long sleep duration with higher levels of CRP (Irwin et al., 2016). Our finding on sleep disturbance patterns may be informative for an educational intervention for this population.
As previously discussed, epigenetics factors are thought to play a role in sleep disturbances in BCS. Current literature supports that telomere length may be associated with poor sleep quality in BCS (Cribbet et al., 2014; Tempaku et al., 2015); however, we showed nonsignificant results in telomere length and DNA methylation age in association with sleep disturbances partially due to a small sample size. Given that telomere length and DNA methylation age are age-associated biological measures, this nonsignificant result could be expected because age was not a significant factor for developing sleep disturbances at all time points in this study. Further, the deviation of age in this cohort is slight, which would make it difficult to determine the effect of age or age-related biological factors on sleep disturbances. Further research is needed to investigate the association between these variables and sleep disturbances in a large sample size.
In this study, sleep disturbances in BCS were closely associated with anxiety, fatigue, and perceived stress, although the influence of these factors was shown to fluctuate at different time points. Specifically, both anxiety and fatigue frequently appeared to be associated with sleep disturbances. Multiple studies suggested that anxiety of cancer reoccurrence is associated with sleep disturbances in cancer patients (Hall et al., 2014; Lowery-Allison et al., 2018; Strollo et al., 2020). BCS reported considerable anxiety when diagnosed with cancer (Jones et al., 2014). We identified that fatigue was significantly associated with sleep disturbances at 6-month and 1-year follow-up time points. In contrast, a recent study showed that fatigue co-occurs with severe sleep disturbances before and during chemotherapy (Fox et al., 2020). Further, increased levels of pain were associated with sleep disturbances prior to chemotherapy. This correlation is consistent with results from several other studies investigating sleep disturbances in BCS (Lowery-Allison et al., 2018; Syrjala et al., 2014).
Clusters of both physical and emotional symptoms can contribute to perceived distress in BCS. Physical and emotional symptoms causing distress in this study included fatigue, anxiety, and pain, all of which affected sleep disturbances. These findings are supported by current literature, which has reported that perceived distress extends beyond treatment into early survivorship and is related to clusters of distressing symptoms (Lester et al., 2015; Schreier et al., 2019; Strollo et al., 2020). Additionally, Strollo et al. (2020) reported that at 9 years posttreatment, sleep disturbances of cancer survivors were significantly affected by physical, emotional, and economic distress (Strollo et al., 2020).
Our results demonstrated that the association between pain and sleep disturbances was supported in the bivariate analyses at all time points. Current literature suggests that circadian rhythms are frequently disrupted in cancer patients, worsening symptoms experienced in this population (Ancoli-Israel et al., 2014; Roveda et al., 2019). It is known that sleep and pain interact bidirectionally and that circadian rhythms play a role in this relationship (Palada et al., 2020). For example, circadian genes, such as CLOCK and BMAL1, regulate the expression of pain-related genes, such as TAC1, which encodes for substance P (Zhang et al., 2012). Additionally, circadian clock rhythmicity may regulate the expression of nociceptive neurons (e.g., voltage-gated calcium channel, NMDA glutamate receptor) in the dorsal root ganglions as well as the expression of μ-opioid receptors in part of the brainstem that is involved in descending pain inhibition (Takada et al., 2013). Through these mechanisms, disruption of the circadian rhythm in BCS negatively affects sleep quality and can also affect their experience of pain.
Potential limitations should be noted. This study had a relatively small sample size with no control group, affecting the generalizability of findings. However, due to the nature of longitudinal study design, intrinsic and extrinsic factors could be controlled when comparing variables between the baseline and other time points. In addition, we included only composite or total scores of questionnaires as predictor variables in the regression models; although, more detailed information can be obtained if subscale scores are used. Lastly, sleep disturbances were self-reported; future studies could consider a combination of subjective and objective measurement tools, such as actigraphy, for more robust results.
Conclusion
This study indicates that the severity of sleep disturbances changes over time, with the most severe sleep disturbances during the chemotherapy phase of treatment. In addition, the predictors of sleep disturbances changed over time, with anxiety being a factor earlier in the treatment trajectory (prechemotherapy) and continuing over time with fatigue and perceived stress being involved later in the treatment trajectory. These findings show that symptom management strategies to address sleep disturbances should be tailored to address the temporal factors that may change in severity over the active treatment and early survivorship period. Notably, sleep improved after the cessation of active treatment for most participants. Findings gained from this study on sleep disturbance patterns and the potential risk factors can be incorporated into clinical practice in planning education and developing interventions.
Acknowledgement:
Research reported in this publication was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number R01NR012667 to DEL. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The institutional review board at Virginia Commonwealth University (IRB #HM 13194) approved this study.
Footnotes
Ethical Conduct of Research: The institutional review board at Virginia Commonwealth University (IRB#HM 13194) approved this study.
Clinical Trial Registration: None applicable
The authors have no conflicts of interest to report.
Contributor Information
Gee Su Yang, University of Connecticut School of Nursing Storrs, CT.
Angela R. Starkweather, University of Connecticut School of Nursing Storrs, CT.
Debra Lynch Kelly, University of Florida College of Nursing Gainesville, FL.
Taylor Meegan, University of Connecticut School of Nursing Storrs, CT.
Ha Do Byon, University of Virginia School of Nursing Charlottesville, VA.
Debra E. Lyon, University of Florida College of Nursing Gainesville, FL.
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