Abstract
Significance:
Poor sleep quality is a common and persistent problem reported by women with breast cancer (BC). Empirical evidence identifies many risk factors for self-reported sleep deficiency but inconsistencies limit translation to practice.
Purpose:
To increase understanding of risk factors predicting self-reported poor sleep quality in women with BC who completed the Breast Cancer Collaborative Registry (BCCR) questionnaire.
Methods:
This cross-sectional study recruited women with a first diagnosis of BC (n=1302) at five sites in Nebraska and South Dakota. Women completed the BCCR that includes numerous variables as well as the Pittsburgh Sleep Quality Index (PSQI) and SF-36v2 (n=1260). Descriptive statistics and non-parametric correlations were used to determine associations and create predictive models of sleep quality with BCCR variables and SF36v2 subscales.
Results:
Most women were white (93.7%) and married (71.5%); mean age was 60.1 (21–90) years. Poor sleep was self-reported by 53% of women. Seven variables were highly associated with sleep quality (p≤0.001). The first model found younger age, lower physical activity, and higher fatigue were the strongest combined and independent variables predicting poor sleep quality (F=23.0 (p<.001), R2 =.103). Participants self-reported lower health status on most SF-36v2 subscales [Z=44.9(11.6) to 49.1 (10.1)]. A second model found all subscales were predictors of poor sleep; vitality, mental health, bodily pain, and general health were the strongest predictors (F=101.3 (p<.001), R2 =.26).
Conclusions:
Results confirm previously identified risk factors and reveal inconsistencies in other variables. Clinicians need to routinely screen for the identified risk factors of self-reported poor sleep quality.
Keywords: sleep quality, sleep deficiency, symptoms, physical health, mental health, quality of life
Introduction
Despite receiving considerable attention during the last decade, breast cancer (BC) survivors are at increased risk for sleep deficiency [1, 2], defined as the discrepancy in sleep duration and/or quality obtained compared to the amount needed for optimal health [3]. Women who report poor sleep quality prior to chemotherapy often remain poor sleepers afterwards, and a percentage of good sleepers become poor sleepers, with 30–60% of patients affected [4]. Different BC treatments increase the risk of impaired sleep quality, thus contributing to the global disability associated with cancer treatments [5]. When poor sleep occurs with depression in patients with cancer, epidemiological observations link these symptoms to cancer morbidity and mortality risk [6]. In addition, until there is routine screening, assessment, and treatment of sleep deficiency, an increasing number of BC survivors are likely to experience lower quality of life (QOL) [5, 7].
Several measurements are available to screen and assess sleep quality/deficiency in the general population, in patients with chronic insomnia without comorbid disease(s), and in patients with cancer. The Pittsburgh Sleep Quality Index (PSQI) is the gold standard and most widely used measurement for assessment of sleep quality in various populations in research studies. The PSQI was designed to assess sleep quality, sleep duration, and frequency and severity of sleep disturbances [8]. A global score >5 indicates poor sleep quality and a score of ≤5 indicates good sleep [8, 9].
Clinicians and researchers rely on data from healthy controls to determine if a sample/population meets criteria for good or poor sleep. In initial research by Buysse and team, individuals identified as healthy controls had a mean PSQI global score of 2.67 with a standard deviation (SD) of 1.7 [8]. Buysse next reported on sleep in both healthy young and older adults. The mean PSQI global score in young adults was 3.1 (1.6) for males and 1.9 (1.4) for females. In older adults, the mean score was 4.4 (2.8) for males and 5.1 (3.2) for females [9]. Ten years later, a study examining PSQI in patients with primary insomnia reported the mean global score for healthy controls was 3.3 (1.8) and 12.5 (3.8) for patients with primary insomnia [10]. Bush et al. examined older primary care patients referred for treatment of worry or anxiety. At baseline, mean PSQI global score of individuals who were diagnosed was 8.74 (4.05) compared to 6.65 (3.7) in those not diagnosed with generalized anxiety disorder [11].
Beck et al. was the first to examine PSQI scores in patients with cancer and reported a high mean global score of 8.15 (4.7) in several types of cancer and 7.31 (4.03) in BC patients [12]. A second study also reported a high mean score of 7.98 (4.12) in multiple types of cancer and 6.80 (3.64) in BC patients [13]. These results indicate poor sleep is often associated with age, anxiety disorder, and cancer diagnosis.
The literature identifies several risk factors that are associated with poor sleep quality among patients with BC: age, higher body mass index (BMI), hot flashes, pain, and cancer-related fatigue; as well as lower physical functioning and mental health (depression and anxiety). However, empirical data are limited and inconsistencies in findings limit translation to practice. Both younger age [1] and older age [14] have been associated with poor sleep measured by the PSQI in patients with BC; whereas, another study found no significant correlations between age and sleep in patients with BC five years after completion of treatment [15]. Higher BMI has been associated with poor sleep quality in BC during chemotherapy [16] and in survivors [17] but was not associated with either short sleep (<6 h) or excessive daytime sleepiness in another large sample (N=861) of survivors [18]. Hot flashes/vasomotor symptoms [2, 15] and pain [2] have been associated with poor sleep using PSQI.
The relationship between cancer-related fatigue and poor sleep in women with BC often is considered reciprocal. Higher cancer-related fatigue was associated with poor sleep quality in a large, mixed sample (N=1,938) of Chinese cancer patients [19]; in patients with BC [20–22]; and in a mixed sample during chemotherapy [16]. However, reviewed studies were inconsistent in reporting cancer-related fatigue as a risk factor for poor sleep.
Poor physical functioning has been a consistent predictor of poor sleep among patients with BC [14–17]. Empirical evidence recently has established that adult patients with cancer who engage in physical activity/exercise report better sleep quality [23, 24]. A recent systematic review concluded that aerobic exercise positively affects sleep outcomes in women with BC [25]. Lower mental health is the most consistent risk factor for poor sleep on PSQI. Depressive symptoms and anxiety significantly influence both the baseline and the trajectory of subjective sleep disturbances [5, 26].
Based on this knowledge, we sought to increase understanding of risk factors predicting self-reported poor sleep quality in patients with BC who completed the Breast Cancer Collaborative Registry (BCCR) questionnaire. Specific aims were to analyze associations between sleep quality with demographic, medical, tumor, lifestyle, and environmental variables and quality of life subscales, and create predictive models using these variables and subscales to identify factors that predict self-reported sleep quality. We hypothesized that sleep quality would be associated with demographic, medical, tumor, lifestyle, and environmental variables and quality of life subscales and that our models would confirm factors previously identified and explicate the inconsistencies among predictors.
Methods
Study Design
A cross-sectional study design used data from women who participated in the BCCR.
Sample and Setting
The BCCR database was used to locate all cases in five registries from January 2008 to January 2017. Registries included UNMC/Nebraska Medicine, Omaha, NE; Saint Francis Medical Center, Grand Island, NE; Good Samaritan Hospital, Kearney, NE; Avera, Aberdeen SD; and Avera Cancer Institute, Sioux Falls, SD. Inclusion criteria were: 1) women with a first BC diagnosis; and 2) at any phase of the cancer trajectory. Exclusion criteria were: 1) diagnosed with recurrent BC, and 2) males.
Procedures
The Institutional Review Board (IRB) of the University of Nebraska Medical Center approved the study. At enrollment, patients provided informed consent for use of the data in clinical studies. All participants completed the BCCR questionnaire either at a clinic appointment or at home and returned it by United States Postal Service.
BCCR Questionnaire/Study Variables
The BCCR was developed in collaboration with BC experts and research questions were standardized to satisfy the needs of all the centers [27]. The questionnaire contains standard data to provide a comprehensive review of the patient’s demographic, medical, tumor, lifestyle, environmental, quality of life, and sleep quality that could influence BC cancer diagnosis and survivorship. Demographic data include variables such as participant’s age, race/ethnicity, marital status, and educational status. Medical data include height/weight/BMI and a list of chronic conditions but no comorbidity index; gynecologic data such as menstrual status, pregnancy, breast-feeding, and birth control; and BC data such as therapies received, functional changes, and symptoms since surgery or completing therapy. Tumor data include stage and receptor status. Lifestyle data include history of smoking, alcohol consumption, and physical activity. Environmental factors include annual household income and history of night or rotating shiftwork. Measures of physical and mental health status and subjective sleep quality complete the questionnaire.
Physical and mental health status were measured using the SF-36 v2 Health Survey [28]. The tool includes 36 items measuring eight aspects of physical and mental health in the last 4 weeks (standard form); including physical function, role function, role emotional, social functioning, bodily pain, mental health, vitality, and general health. Higher scores indicate higher functioning. Physical and mental health component scores were calculated according to standard procedures. Reliability and validity of the SF-36 are well established. Reliability scores for the eight aspects/subscales ranged from .80-.96 in this sample.
Subjective sleep quality during the previous month was measured using the PSQI, a 19-item tool [8, 9]. A global score includes seven components: sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, sleeping medication use, and daytime dysfunction. Components are scored on a 0 – 3 scale and combined with equal weights to yield a global score (0–21). Higher scores indicate more severe complaints and poorer sleep quality. The PSQI takes 5 – 10 minutes to complete. The psychometrics of the PSQI have been established in cancer patients; Cronbach’s alpha for the global PSQI was reported as .80 and was .71 in this study. A global PSQI score >5 has a sensitivity of 89.6% and a specificity of 86.5% in identifying poor sleepers. Optional questions 10 – 11 were not included in the BCCR questionnaire.
Data Analysis
All BCCR data were entered and verified by trained research assistants. Descriptive statistics (frequency distributions, means and SD were used to analyze BCCR questionnaire data. Scores were calculated according to established procedures for the PSQI total score and the eight SF-36 subscales as well as physical and mental component scores. Non-parametric correlations were calculated to determine associations between BCCR variables, SF-36 scores, and PSQI global score. Stepwise multiple regression was used to build predictive models of sleep quality. Due to positive skew in PSQI scores, a natural log transformation was applied prior to performing the regression models, which normalized the distribution. Variables were considered for inclusion using the common practice of a significance level of less than .20 on the bivariate correlations with PSQI scores. Two separate stepwise regression models were performed. One was for significant demographic, medical, tumor, lifestyle, and environmental variables, and the second one for physical and mental health status subscales. Pairwise deletion was used, as opposed to listwise deletion, so all available data were utilized in building the models. IBM SPSS version 24 was used for all analyses.
Results
Table 1 presents demographic data using the number and percent of the sample who completed the BCCR questionnaire. Only questionnaires with the PSQI data were included in this analysis (n=1260). An additional 42 women’s data were not included because they did not complete the PSQI. No records were kept of the number of patients approached.
Table 1.
Descriptive Data of the Sample (n=1,260)
| Variable | Yes (% of sample | No (% of sample) |
|---|---|---|
| Race: Caucasian/White | 1181 (93.7%) | 79 (6.3%) |
| Ethnicity: Hispanic/Non-Hispanic | 17 (1.3%) | 1243 (98.7%) |
| Married/Partnered* | 893 (71.5%) | 356 (28.5%) |
| Education: College degree or higher* | 434 (36.4%) | 758 (63.6%) |
| Income 75k or higher* | 383 (34.9%) | 715 (63.6%) |
| Post-Menopausal status* | 490 (49.4%) | 502 (50.6%) |
| Chemotherapy in past 30 days* | 398 (31.6%) | 821 (65.2%) |
| Chemotherapy | 612 (48.6%) | 648 (51.4%) |
| Radiation Therapy | 505 (40.1%) | 755 (59.9%) |
| Estrogen-blocking Therapy | 458 (36.3%) | 802 (63.7%) |
| Stage I-II* | 721 (78.6%) | 198 (21.4%) |
| Estrogen Receptor (ER) positive* | 972 (77.9%) | 275 (22.1%) |
| Progesterone Receptor (PR) positive* | 793 (63.8%) | 450 (36.2%) |
| HER2 positive* | 218 (18.3%) | 972 (81.7%) |
| Smoking: Current smoker* | 87 (7.0%) | 1148 (93.0%) |
| Alcohol: Currently drinks* | 672 (55.2%) | 545 (44.8%) |
| Employment: Full-time* | 599 (47.9%) | 652 (52.1%) |
| Ever worked the night shift* | 307 (36.5%) | 534 (63.5%) |
Note:
indicates missing data
HER-2 = human epidermal growth factor receptor 2
The sample was predominately Caucasian, non-Hispanic, and married. The mean age was 60.1 years (range: 21–90; SD=12.1) and the average length of time since BC diagnosis was 3.1 (0 to 33) years. Almost 80% of participants were diagnosed as Stage I or II. Almost 50% received chemotherapy (30% in the last month), about 40% received radiation therapy, and 36% took estrogen-blocking therapy.
The sample’s mean PSQI global scores are shown in Figure 1. There was a full range of scores (0–19) with a mean of 6.51 (3.64), with 47.1% of scores reflecting good sleepers and 52.9% of scores indicating poor sleepers in the last month.
Figure 1.
Distribution of global Pittsburgh Sleep Quality Index (PSQI) scores (n=1,260) [8]
The range, mean, and SD of the eight SF-36 subscale scores appear on Table 2. Mean scores were generally higher for the mental than physical subscales.
Table 2.
Descriptive statistics for Quality of Life Indexa
| Tumor | N | Minimum | Maximum | Mean | SD |
|---|---|---|---|---|---|
| SF36 - Physical Functioning | 1229 | 14.94 | 57.03 | 45.01 | 11.39 |
| SF36 - Role Physical | 1230 | 17.67 | 56.85 | 44.39 | 11.59 |
| SF36 - Bodily Pain | 1251 | 19.86 | 62.12 | 49.06 | 10.14 |
| SF36 - General Health | 1237 | 16.23 | 63.90 | 47.50 | 10.16 |
| SF36 - Vitality | 1248 | 20.87 | 70.82 | 48.64 | 10.58 |
| SF36 - Social Functioning | 1217 | 13.22 | 56.85 | 46.90 | 10.99 |
| SF36 - Role Emotional | 1219 | 9.23 | 55.88 | 47.27 | 11.84 |
| SF36 - Mental Health | 1245 | 10.58 | 64.09 | 50.29 | 9.59 |
| SF36 - Physical Component Score | 1147 | 15.85 | 67.73 | 45.78 | 10.24 |
| SF36 - Mental Component Score | 1147 | 6.70 | 73.60 | 49.84 | 10.48 |
Note:
Short Form-36v2 T-Scores [28]
Table 3 displays non-parametric BCCR questionnaire variable correlates with global sleep quality. Many demographic, medical, tumor, lifestyle, and environmental variables and QOL subscales correlated with sleep quality. Several variables had a correlation of .10 or higher and p < .001, including age, race, cognitive problems, hot flashes, and urinary problems; BMI did not meet this criteria. The only variables with stronger correlations of .20 or higher and p <.001 were fatigue and physical activity. Poor sleep quality was strongly related to all QOL subscales at .25 and higher, especially lower vitality and mental health. All variables with a superscript met inclusion criteria and were entered in the models.
Table 3.
Non-parametric Correlates* of Study Variables with Sleep Quality (PSQI)
| Factor | BCCR Variable | correlation | p-value | N |
|---|---|---|---|---|
| Demographic | Age | −.146* | <.001 | 1260 |
| White v Non-White | .102* | <.001 | 1260 | |
| Marital Status | −.031 | .276 | 1249 | |
| Education | −.007 | .796 | 1192 | |
| Medical | Arm Swelling | .063* | .026 | 1260 |
| Arthritis | .092* | .001 | 1260 | |
| BMI | .087* | .002 | 1244 | |
| Chemotherapy | .068* | .015 | 1260 | |
| Cognitive Problems | .163* | <.001 | 1260 | |
| Fatigue | .229* | <.001 | 1260 | |
| Heart Problems | .031 | .265 | 1260 | |
| Hot Flashes | .104* | <.001 | 1260 | |
| Hypothyroidism | .054* | .057 | 1260 | |
| Infertility | .038* | .180 | 1260 | |
| Osteoporosis | −.013 | .642 | 1260 | |
| Peripheral Neuropathy | .071* | .012 | 1260 | |
| Post-Menopausal | −.094* | .003 | 992 | |
| Scleroderma | .011 | .700 | 1260 | |
| Urinary Problem | .116* | <.001 | 1260 | |
| Tumor | Estrogen Receptor (ER) + | −.038* | .184 | 1247 |
| Progesterone Receptor (PR) + | −.017 | .560 | 1243 | |
| HER2 | −.008 | .779 | 1190 | |
| Stage | .101* | .002 | 924 | |
| Environment | Income | −.081* | .007 | 1098 |
| Employment | .041* | .143 | 1251 | |
| Ever Worked Night Shift | .052* | .135 | 841 | |
| Lifestyle | Current Smoker | .073* | .126 | 1235 |
| Smoking - Pack Years | .099* | .112 | 259 | |
| Current Drinker | −.033 | .246 | 1217 | |
| Drinks per Week | −.008 | .778 | 1213 | |
| Physical Activity | −.228* | <.001 | 1256 | |
| Quality of Life | SF36 - Physical Functioning (PF)- T-score | −.257* | <.001 | 1229 |
| SF36 - Role Physical (RP)- T-score | −.321* | <.001 | 1230 | |
| SF36 - Bodily Pain (BP)- T-score | −.362* | <.001 | 1251 | |
| SF36 - General Health (GH)- T-score | −.351* | <.001 | 1237 | |
| SF36 - Vitality (VT)- T-score | −.450* | <.001 | 1248 | |
| SF36 - Social Functioning (SF)- T-score | −.384* | <.001 | 1217 | |
| SF36 - Role Emotional (RE)- T-score | −.385* | <.001 | 1219 | |
| SF36 - Mental Health (MH)- T-score | −.450* | <.001 | 1245 | |
| SF36 - Physical Component Score** | −.281 | <.001 | 1147 | |
| SF36 - Mental Component Score** | −.449 | <.001 | 1147 |
included in regression model
Physical Component score calculated from PF, RP, BP, GH; Mental Component score calculated from VT, SF, RE, MH
A stepwise method entered all included variables in the regression model displayed in Table 4. Demographic, medical, tumor, lifestyle, and environmental variables were represented. A model that included age, fatigue, and physical activity was the strongest in predicting risk factors influencing sleep quality and all were significant independent variables as well with moderate bivariate correlations. The model accounted for approximately 10% of the overall variance in sleep quality (Overall F = 23.005 (p < .001); R2=.103, Adjusted R2=.099).
Table 4.
Regression coefficients for Sleep Qualitya regressed on influencing factors in women who completed the BCCR
| Variables | ||||
|---|---|---|---|---|
| Beta | t | p | Simple r | |
| Fatigue | .190 | 4.83 | <.001 | .229 |
| Physical Activity | −.199 | −5.09 | <.001 | −.228 |
| Age | −.125 | −3.20 | .001 | −.146 |
Overall F = 23.005 (p < .001); R2=.103, Adjusted R2=.099.
Pittsburgh Sleep Quality Index [8]
A second regression model shown in Table 5 entered all eight physical and mental health subscales. All were significant bivariate predictors of sleep quality. A model that included subscales of vitality, mental health, bodily pain, and general health was the strongest in predicting risk factors influencing sleep quality. The quality of life model accounted for approximately 26% of the overall variance in sleep quality (Overall F = 101.33 (p < .001); R2=.256, Adjusted R2=.253).
Table 5.
Regression coefficients for Sleep Qualitya regressed on Physical and Mental Healthb factors in women who completed the BCCR
| Variables | ||||
|---|---|---|---|---|
| Beta | t | p | Simple r | |
| Vitality | −.184 | −4.805 | <.001 | −.450 |
| Mental Health | −.237 | −7.152 | <.001 | −.450 |
| Bodily Pain | −.124 | −4.062 | <.001 | −.362 |
| General Health | −.071 | −2.210 | .027 | −.351 |
Discussion
Our purpose was to increase understanding of risk factors predicting self-reported poor sleep quality (deficiency) in a large sample of women with BC who completed the BCCR questionnaire. Our examination increases understanding of associations between self-reported sleep quality with demographic, medical, tumor, lifestyle, and environmental variables and quality of life subscales. The main findings were that younger age, self-reported co-occurring symptoms of higher cancer-related fatigue (and its’ reverse expression as lower vitality), lower mental health (anxiety and depression), higher pain, lower general health and lower physical activity were the risk factors for poor sleep quality. Our results confirm several previously identified risk factors and explicate inconsistencies among predictors of self-reported poor sleep quality in women with BC. We now discuss these findings.
More than 50% of our large sample of women with a mean time of over 3 years since BC diagnosis self-reported poor sleep within the past month. This finding is consistent with extant literature reports of 30–60% of patients being affected [1, 4]. One explanation is that about 30% of the women in our sample received chemotherapy within the last month [4, 13]. These figures confirm reports that sleep deficiency is a common and persistent symptom after BC treatment [29].
Most of the demographic, medical, tumor, lifestyle, and environment variables and quality of life subscales associated with sleep quality in this study are identified in the literature. Variables of younger age, BMI, race, cognitive problems, and urinary problems are inconsistent in prior literature. White women reported poorer sleep than a small percentage (1.3%) of minority women, a finding inconsistent from a prior report [30]. Cognitive problems were supported by one recent study [31]. Urinary problems/symptoms is an understudied area in women with BC. A systematic review of urinary problems/symptoms reported this symptom has been included as one of several menopausal symptoms in women with BC [32]. Although urinary symptoms were suggested as having a negative effect on quality of life, the effect on sleep was not included in the review.
Both SF-36 physical and mental component scores indicate functioning lower than U.S. population norms. Mental health was the only subscale score at the U.S. population norm of 50.0 or higher with the remaining seven subscale scores below population norms [28]. Several subscales were predictors of poor sleep. These findings are consistent with reports from BC survivors [33, 34]. Patients with BC who reported significant anxiety and depression at baseline were more likely to have a trajectory of poor sleep quality during the first three years after BC diagnosis [29]. A national cohort of Danish women (n=3344) identified seven predictors of poor sleep three to four months post BC surgery [14]. In order of strength, they were depressive symptoms, poor physical functioning, older age, higher levels of trait anxiety, cigarette smoking, post-lumpectomy, and lower levels of physical activity. The strongest of these predictors were depressive symptoms and poor physical functioning. The current study’s results agree with several of the above findings; however, we found younger (not older) age, no relation with cigarette smoking, and were unable to examine if type of surgery was a predictor.
Our first model supports younger age [1, 35], higher cancer-related fatigue [19–21], and lower physical activity [14, 23] as the strongest combined and independent variables in predicting sleep quality. The second model supports lower vitality and mental health as the strongest mental health predictors, and higher bodily pain and lower general health as the strongest physical health predictors of sleep quality. Sustained sleep duration changes over time have been related to higher fatigue and lower vitality in women with BC, but self-reported sleep quality was not measured [36]. Lower mental health, reported as depressive symptoms and trait anxiety, has been identified as a predictor of poor sleep previously in patients with cancer [14, 29, 37, 38] and in healthy populations.
Early identification and treatment of anxiety and depression may lead to reduced rates of sleep deficiency. However, one study reported only 29% of the patients with clinically significant anxiety and depression sought help [38]. Poor sleep in patients with BC also has been linked to pain during adjuvant treatment [2] and in advanced cancer [39]. Literature of healthy adults links good general health with good sleep quality. BC survivors who return to good general health are more likely to regain higher vitality and mental health and lower pain, fatigue, and sleep deficiency.
Strengths of the study were the sample size and the finding that mean physical and mental component scores from SF-36v2 indicated that many, but not all, of the women had QOL similar to healthy U.S. women. Limitations include the cross-sectional design, the quality of some questionnaire items, the wide range of time since diagnosis, missing data, measurement of mental health but no specific measurements of anxiety and depression, and use of a single item to measure physical activity level and cancer-related fatigue. Variables that may explain poor sleep quality in this sample represent the breadth of the data collected but lack a depth of details.
Implications for research include the need to develop methods to identify and analyze individual symptoms, symptom clusters, and mental and physical health status. Interventions are needed that are designed to improve symptom clusters with an emphasis on a “driving” symptom [40]. Researchers can use current knowledge to test evidence-based interventions to change lifestyle behaviors and modify medical and environmental factors identified in this and other studies to reduce risk factors of poor sleep.
Implications for practice include the urgent need to screen and assess for risk factors for poor sleep using measures that rate several symptoms and their impact on functioning. Screening and assessment needs to begin at BC diagnosis, a time when a risk-based assessment for distress is performed routinely [41]. Policy regulators currently mandate regular screening and assessment of pain, emotional distress, performance status, and QOL in BC survivors, but not sleep deficiency and/or fatigue. Clinicians can implement routine screening measures such as PROMIS-29 [42] or the M.D. Anderson Symptom Inventory (MDASI) [43] to identify patients with poor sleep and co-occurring symptoms.
Interventions to improve poor sleep in BC patients have been developed and tested for use when sleep is the primary or only symptom [25]. Meta-analysis results provide a strong quality of evidence and large effect sizes for the use of cognitive behavior therapy (CBT) in cancer survivors [44]. Evidence also suggests that physical activity/exercise interventions favorably affect both fatigue and sleep quality. The National Comprehensive Cancer Network (NCCN) Guidelines for Cancer-Related Fatigue [45] and Survivorship [46] recommend a personalized physical activity/exercise program for patients during and after treatment. Likewise, the Oncology Nursing Society Putting Evidence into Practice (ONS-PEP) resource rates CBT as “Recommended for Practice” and physical activity/exercise as “Likely to be Effective” to reduce poor sleep [25].
When the screen is positive for moderate to severe co-occurring symptoms, clinicians need to focus their assessment on modifiable risk factors identified as predictors of poor sleep in BC survivors. Treatment to reduce co-occurring symptoms is paramount to improving sleep quality. Treatment is needed for distress, worries, anxiety, and depression. Evidence-based strategies can be initiated for cancer-related fatigue and pain. Strategies can be taught to increase physical activity and vitality. These treatments are likely to result in improved sleep quality, lower fatigue, enhanced daytime vitality, and gradual improvement in physical, mental, and general health.
Acknowledgement:
This project was supported by a pilot project award from the Fred & Pamela Buffett Cancer Center which is funded by a National Cancer Institute Cancer Center Support Grant under award number P30 CA036727. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We also acknowledge Alice Kueh, MS, Oleg Shats, PhD, and Simon Sherman, PhD for their assistance on this project.
Footnotes
Conflict of Interest
The authors declare they have no conflict of interest. The corresponding author has full control of primary data and review of data can be arranged as requested.
Contributor Information
Ann M. Berger, University of Nebraska Medical Center, College of Nursing, 985330 NE MED CENTER, Omaha, NE 68198-5330.
Kevin A. Kupzyk, University of Nebraska Medical Center, College of Nursing.
Dilorom M. Djalilova, University of Nebraska Medical Center.
Kenneth H. Cowan, Eppley Institute, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center.
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