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. Author manuscript; available in PMC: 2025 Oct 24.
Published in final edited form as: Behav Sleep Med. 2025 Aug 6;23(6):834–849. doi: 10.1080/15402002.2025.2542296

Insomnia and Related Symptom Severity in Women with Breast Cancer and Pain Receiving Treatment in Medically Underserved Areas

Hannah M Fisher a, Joseph G Winger a, Natalie A Chou a, Shifa S Banani b, Sarah A Kelleher a, Kelly A Hyland c, Catherine M Majestic a, Rebecca A Shelby a, Linda M Sutton d, Gloria Broadwater e, Yueqi Gu e, Francis J Keefe a, Tamara J Somers a
PMCID: PMC12547985  NIHMSID: NIHMS2117562  PMID: 40766990

Abstract

Objectives:

Insomnia is common for women with breast cancer, and related to fatigue, depression, and pain. Research exploring these symptoms among breast cancer patients in medically underserved areas is lacking. This study aimed to characterize symptom severity, and examine how fatigue, depression, and pain vary based on categories of insomnia severity.

Methods:

Women (N=127) with Stage 0-IV breast cancer receiving care at clinics in mostly rural, medically underserved areas completed self-report measures of insomnia (Insomnia Severity Index), fatigue (PROMIS-Fatigue), depression (Center for Epidemiological Studies Depression Scale), and pain (Brief Pain Inventory). ANOVA or Kruskal-Wallis tests compared differences in fatigue, depression, and pain across insomnia severity categories. Post-hoc tests determined pairwise significant differences. Analyses were conducted using SAS software.

Results:

Median [IQR] insomnia symptom severity fell within the Subthreshold/Mild range (12.00 [6.00, 16.00]). Thirty-four percent of women endorsed insomnia symptoms in the Moderate range or higher. Median fatigue was moderate (60.80 [55.60, 64.85]), and median depressive symptoms (17.00 [10.50, 23.50]) indicated risk for clinical depression. Mean pain severity (4.59 [1.85]) and median pain interference (4.29 [2.57, 6.46]) were moderate. Women endorsing Subthreshold/Mild and Moderate/Severe insomnia symptoms exhibited significantly worse fatigue, depressive symptoms, and pain.

Conclusions:

Results highlight a multi-symptom burden for women with breast cancer receiving care at clinics in medically underserved areas with a largely rural population. Behavioral symptom management is critically needed. Intervening on insomnia may, in turn, improve fatigue, depression, and pain. Behavioral interventions targeting insomnia and related symptoms should be adapted for, and tested, in this population.

Keywords: insomnia, medically underserved, breast cancer symptoms, rural

Introduction

Insomnia symptoms (i.e., difficulty falling asleep, staying asleep, waking up too early) are prevalent and challenging for women with breast cancer, and related to other common breast cancer symptoms such as fatigue, depression, and pain (Bean et al., 2021; Fleming et al., 2019; Kwak et al., 2020; Leysen et al., 2019; Mandelblatt et al., 2020; Schreier et al., 2019; Whisenant et al., 2022). Associations between insomnia and fatigue, depression, and pain symptoms are likely bidirectional, with sleep disturbance worsening fatigue, depression, and pain, and these symptoms in turn interfering with sleep quality (Bean et al., 2021; Jim et al., 2013; Leysen et al., 2019; Loh et al., 2018). Notably, symptoms of insomnia, fatigue, depression, and pain are often worse for women receiving breast cancer treatment at clinics in medically underserved areas (Ozcan et al., 2024; Ratcliff et al., 2021; Wells et al., 2022).

Women receiving breast cancer care in medically underserved areas are more likely to identify as a racial minority and report lower education and socioeconomic levels compared to those being treated a larger, well-resourced academic medical centers (Miller et al., 2017). A meta-analysis showed that women with breast cancer who identify as a racial minority are 2.31 times more likely to develop sleep disturbances compared to their White counterparts (Leysen et al., 2019). Women of minority race, and women reporting lower education and socioeconomic levels, can experience delays in breast cancer diagnosis and treatment (Husain et al., 2019; Lund et al., 2008; Ozcan et al., 2024; Sprague et al., 2021). More advanced cancer (e.g., Stage III and above) results in a considerable symptom burden, with some women reporting higher levels of sleep disturbances, fatigue, depression, and pain at higher cancer stage (Hamer et al., 2017; Hamood et al., 2018; Irvin et al., 2011; Soucise et al., 2017). Pain symptoms, such as bone, abdominal, and headache pain, can be elevated in those with advanced breast cancer due to metastatic load and site (Irvin et al., 2011; von Moos et al., 2017). Later stage disease may also necessitate aggressive treatments (i.e., mastectomy, chemotherapy, radiation) that have been linked to worse fatigue and pain (e.g., surgical, lymphedema, neuropathy), as well as worse sleep quality and depressive symptoms (Booth et al., 2007; Castel et al., 2008; Ell et al., 2005; Gonzalez et al., 2021; Hamood et al., 2018; Mandelblatt et al., 2020). Additionally, women with breast cancer in medically underserved areas are more likely to have comorbid health problems (e.g., obesity, arthritis) which further increase overall symptom burden (Aziz & Rowland, 2002; Bubis et al., 2018; Ratcliff et al., 2021).

Much of the literature examining insomnia and symptoms of fatigue, depression, and pain in women with breast cancer has been conducted in predominately White, well-resourced populations (Ratcliff et al., 2021; Whisenant et al., 2022). Existing work that does explore this symptom burden among underserved, minority race women with breast cancer is often qualitative and/or uses small samples (Geiss et al., 2022; Schreier et al., 2019; Wells et al., 2022). Additionally, extant research typically assesses insomnia symptom severity on a continuous scale despite categories of insomnia (i.e., on the Insomnia Severity Index) being widely used in clinical practice (Morin et al., 2011; Ratcliff et al., 2021; Schreier et al., 2019; Whisenant et al., 2022). Indeed, there is very little research exploring associations between established clinical categories of insomnia severity and symptoms of fatigue, depression, and pain among women receiving breast cancer treatment in medically underserved areas (Ratcliff et al., 2021; Schreier et al., 2019). In a rural cancer clinic setting, where adjunctive supportive services may be limited, it could be useful to know who might experience a worse symptom burden based on a quickly assessed clinical category of insomnia severity using a brief measure such as the Insomnia Severity Index. More research is also needed to elucidate a broader scope of demographic characteristics (e.g., partner status, education, income) that might influence insomnia and related symptom severity in this at-risk population since the literature often focuses primarily on race (Schreier et al., 2019; Whisenant et al., 2022). Results from such work might improve development and testing of efficient behavioral interventions that target not just one, but multiple related breast cancer symptoms, as well as highlight groups of women who are most in need of such behavioral insomnia and symptom management intervention.

To address these gaps, we conducted a secondary analysis of data from a behavioral pain intervention trial for women with breast cancer and pain treated at cancer clinics in medically underserved areas with a largely rural patient population. First, we aimed to characterize the severity of insomnia, fatigue, depressive symptoms, and pain, and examine how symptoms of fatigue, depression, and pain vary based on clinical categories of insomnia severity. We hypothesized that higher clinical categories of insomnia severity would correspond with higher levels of fatigue, depressive symptoms, and pain. Second, in an exploratory aim, we sought to assess how symptom severity differs based on relevant demographic (i.e., age, race, education, partner status, income) and clinical (i.e., cancer stage) variables seen in the literature (Bjerkeset et al., 2020; Hamer et al., 2017; Hamood et al., 2018; Leysen et al., 2019; Schreier et al., 2019).

Methods

Participants

Participants were women diagnosed with stage 0-IV breast cancer within the past three years. Additional inclusion criteria were: 1) ≥18 years of age, 2) self-reported worst pain severity rating in the past week ≥4/10, and 2) self-reported pain at any level on ≥10 days in the past month. Pain could be related to breast cancer and its treatment (e.g., pain at the surgical site, lymphedema, neuropathy) and/or other pain conditions (e.g., arthritis). Exclusion criteria included: 1) cognitive impairment, 2) severe psychiatric condition (e.g., psychosis, suicidal intent) that would contraindicate safe participation, 3) brain metastases and/or life expectancy <12 months, and/or 4) current or past (<6 months) Pain Coping Skills Training (PCST) for cancer pain. Participants were recruited from eight cancer clinics in the Duke Cancer Network (DCN). Clinics are in mostly rural, medically underserved areas of North Carolina, South Carolina, and Virginia, as evidenced by <62 Index of Medical Underservice from Health Resources and Services Administration. The parent study was a randomized trial of PCST (N=180) approved by the Duke University Institutional Review Board (IRB #: Pro00103527) and registered on ClinicalTrials.gov (NCT04175639). The parent study protocol has been previously published (Kelleher et al., 2021).

Procedures

Recruitment procedures complied with HIPAA guidelines. Participants were recruited between October 2021 and May 2024. Electronic medical records were used to assess initial eligibility. Following oncologist approval, study staff mailed recruitment letters to inform women they may qualify for participation. Women (N=449) were then contacted by staff to complete a brief screening interview via telephone. Of those 449 women verbally screened, 160 did not meet the eligibility criteria. Reasons for ineligibility include (1) pain <4 (n=119), (2) pain <10 days in the past month (n=29), and (3) other (n=12). Around 24% (n=109/449) screened eligible but either declined consent (n=64) or could not be reached for consent (n=45). Eligible and interested women provided informed consent in-person or via telephone using Research Electronic Data Capture (REDCap) (Harris et al., 2019). Participants completed an electronic baseline assessment consisting of self-report questionnaires measuring insomnia, fatigue, depressive symptoms and pain (severity, interference). The present study is a secondary analysis of these baseline data for women that completed the insomnia measure, which was added to the assessment after the study began (N=127).

Measures

Insomnia.

Insomnia severity was assessed with the 7-item Insomnia Severity Index (ISI; (Bastien et al., 2001). The ISI consists of three items assessing difficulty falling asleep, difficulty staying asleep, and problems waking up too early in the past two weeks. Responses range from 0 (no difficulty) to 4 (very severe difficulty). The ISI also includes four items assessing satisfaction with sleep, how noticeably insomnia impairs quality of life, distress regarding current sleep disturbance, and interference to daily functioning. Items are summed to yield a total score, with higher scores indicating worse insomnia symptoms (0-7: No Clinical Insomnia; 8-14: Subthreshold/Mild; 15-21: Moderate; 22-28: Severe; Cronbach’s α=.90). The ISI is routinely used in clinical practice given its brevity and clinical severity categories.

Fatigue.

Fatigue was assessed with the 7-item Patient-Reported Outcome Measurement Information System-Fatigue (PROMIS-Fatigue) scale. Participants were asked to identify the number of times during the past week they experienced tiredness, extreme exhaustion, lack of energy, and limitations in performing work/housework due to fatigue, as well as how many times during the past week they felt too tired to think clearly, bathe/shower, and whether they had enough energy to exercise strenuously. Response options ranged from 1 (never) to 5 (always). Items were summed and then converted to T-scores. The T-score distribution has a mean of 50 and a standard deviation of 10, and higher T-scores indicated higher levels of fatigue (<55: Within Normal Limits; 55-60: Mild; 60-70: Moderate; >70: Severe [Cella et al., 2010]). The PROMIS-Fatigue scale is commonly used in cancer samples (Cella et al., 2016) and demonstrated adequate reliability (Cronbach’s α=.79).

Depressive Symptoms.

Depressive symptoms were assessed using the 20-item Center for Epidemiological Studies Depression Scale (CES-D) (Carleton et al., 2013). Participants rated the number of times during the previous week they experienced depressive symptoms (e.g., low mood, anhedonia). Response options ranged from 0 (rarely or none of the time) to 3 (all of the time). These items were summed to obtain a total score with higher scores indicating higher levels of depressive symptoms. A score of ≥16 indicates risk for clinical symptoms of depression (Lewinsohn et al., 1997). The CES-D is frequently used to assess depressive symptoms among survivors of breast cancer (Cronbach’s α=.90).

Pain.

Pain severity and interference were assessed with the 11-item Brief Pain Inventory-Short Form (BPI-SF; [Cleeland & Ryan, 1994]). The BPI-SF consists of four items assessing pain severity (worst, least, average, current pain) and seven items assessing pain interference (e.g., general activity, mood, walking ability) in the past week. The BPI-SF uses an 11-point response scale with options ranging from 0 (no pain or no interference) to 10 (pain as bad as you can imagine or completely interferes). Separate composite scores are computed for pain severity (Cronbach’s α = .84) and pain interference (Cronbach’s α = .94) by averaging items, with higher scores indicating greater pain severity (1-4: Mild; 5-6: Moderate; 7-10: Severe [Serlin et al., 1995]) and pain interference (<2: Mild; 2-6: Moderate: >6: Severe [Shi et al., 2017]). The BPI-SF has demonstrated good reliability and validity in prior studies with women with breast cancer.

Demographic and Clinical Variables.

Demographic (e.g., age, race, ethnicity, partner status, education, income) and clinical (e.g., cancer stage, surgeries and treatments received, use of antidepressant and/or pain medication) characteristics were collected by participant self-report and confirmed with electronic medical record review.

Analytic Strategy

Continuous variables were summarized using mean and standard deviation (SD) or median and interquartile range (IQR) if data was not distributed normally. Categorical descriptors were summarized using frequencies and percentages. Spearman correlation coefficients assessed multicollinearity and demonstrated the pairwise relationships among main study variables (i.e., insomnia, fatigue, depressive symptoms, pain severity, pain interference). Absolute value of Spearman correlation coefficient (r) indicates strength of the correlation (i.e., 0.10 – 0.39 = weak correlation; 0.40 - 0.69 = moderate correlation; 0.70 - 0.89 = strong correlation; and 0.90 - 1.0 = very strong).

Independent t-tests or ANOVA compared differences in the means between or among groups when the data distribution was normal. If the distribution was non-parametric, Wilcoxon rank sum or Kruskal-Wallis tests were used to compare differences in central tendencies between or among groups. Effect size for the Kruskal-Wallis test is η2 and indicates the proportion of variance in the dependent variable that is explained by the independent variable (i.e., η2 ≈ 0.01, small effect; η2 ≈ 0.06, moderate effect; η2 ≥ 0.14, large effect [Cohen, 1988]). Grouping for age (≤60 years vs. >60 years), race (White vs. Non-white), partner status (Partnered vs. Unpartnered), education (High School Diploma or less vs. Some College or more), income (<$20K vs. $20-60K vs. >$60K), and cancer stage (Stage I vs. Stage II vs. Stage III/IV) variables was based on adequate sample size across groups. When overall p-values of the ANOVA or Kruskal-Wallis test were significant, Tukey’s HSD tests or Dwass, Steel, Critchlow-Fligner methods were applied to determine which pairs differed significantly. Analyses were conducted using SAS software (Version 9.4; SAS Institute Inc., Cary, NC) and plots were created in the R language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria).

Results

Participant Characteristics

Mean age was 60.5 (SD=12.5) years old. Approximately 60% of participants identified as Caucasian/White and 28% identified as Black/African-American. For most women (91%), this was their first breast cancer diagnosis. Median time since diagnosis was 1.51 years (IQR [0.32, 2.23]). Demographic and clinical characteristics are reported in Tables 1 and 2, respectively.

Table 1.

Demographic Characteristics (N=127)

N (%) M (SD)
Age (years) 60.5 (12.5)
Race
White/Caucasian 76 (59.8%)
Black/African American 36 (28.3%)
American Indian or Alaskan Native 9 (7.1%)
Asian 1 (0.8%)
Two or More Races 4 (3.1%)
Other 1 (0.8%)
Ethnicity
Non-Hispanic 121 (99.2%)
Hispanic or Latino 1 (0.8%)
Partner Status
Single, Never Married 20 (15.7%)
Married 66 (52.0%)
Divorced 17 (13.4%)
Separated 3 (2.4%)
Widowed 17 (13.4%)
Life-/Long-term Partner 4 (3.1%)
Education
9th Grade or Less 3 (2.4%)
Less than High School Diploma 4 (3.2%)
High School Diploma 25 (19.8%)
Some College 52 (41.3%)
Bachelor’s Degree 26 (20.6%)
Graduate Degree 16 (12.7%)
Income (Pre-Tax)
Less than $10,000 15 (13.2%)
$10,000 - $19,000 11 (9.6%)
$20,000 - $39,000 24 (21.1%)
$40,000 - $59,000 28 (24.6%)
$60,000 - $100,000 20 (17.5%)
More than $100,000 16 (14.0%)
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Note. M=mean; SD=standard deviation.

Table 2.

Clinical Characteristics (N=127)

N (%) Median (IQR)
Cancer Diagnosis
First/Initial 115 (90.6%)
Recurrence 12 (9.4%)
Years Since Diagnosis 1.51 (0.32, 2.23)
Stage
I 80 (63.0%)
II 27 (21.3%)
III 9 (7.1%)
IV 11 (8.7%)
Breast Surgery History (% Yes)
Mastectomy – one breast 18 (14.9%)
Mastectomy – two breasts 31 (25.4%)
Breast Conserving Surgery 75 (60.5%)
Lymph Node Removal 99 (78.6%)
Reconstruction 18 (14.3%)
Treatment Received (% Yes)
Chemotherapy 21 (16.8%)
Radiation 8 (6.5%)
Surgery 3 (2.5%)
Endocrine Therapy 43 (35.5%)
Immunotherapy 9 (7.3%)
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Note. IQR=first quartile, third quartile; Breast Conserving Surgery includes lumpectomy, quadrantectomy, partial mastectomy, segmental mastectomy; Receipt of chemotherapy, radiation, surgery, endocrine therapy, and immunotherapy is for 7 day period before baseline assessment.

Severity of Insomnia, Fatigue, Depressive Symptoms, and Pain

Median insomnia symptom severity fell within the Subthreshold/Mild range (12.00 [6.00, 16.00]). Prevalence across clinical categories of insomnia symptom severity was: 32% None (N=41), 34% Subthreshold/Mild (N=43), 28% Moderate (N=36), and 6% Severe (N=7). Median fatigue was moderate (60.80 [55.60, 64.85]), and median depressive symptoms (17.00 [10.50, 23.50]) was above the cut-off (≥16) that indicates risk for clinical symptoms of depression. Mean pain severity (4.59 [1.85]) and median pain interference (4.29 [2.57, 6.46]) were moderate. Descriptive statistics and Spearman correlation coefficients for main study variables are shown in Table 3. Higher insomnia severity was associated with worse fatigue (r=0.47), depressive symptoms (r=0.64), and pain severity (r=0.23) and interference (r=0.49).

Table 3.

Ranges, Descriptive Statistics, and Correlation Matrix for Main Study Variables

Variable Insomnia Symptoms Fatigue Depressive Symptoms Pain Severity Pain Interference
N 127 127 127 127 124
Range 0 – 28 29.4 – 80.3 0 – 60 0 – 10 0 – 10
M (SD) or Median [IQR] 12.00 [6.00, 16.00] 60.80 [55.60, 64.85]) 17.00 [10.50, 23.50] 4.59 (1.85) 4.29 [2.57, 6.46]
Insomnia Symptoms 1 - - - -
Fatigue 0.47** 1 - - -
Depressive Symptoms 0.64** 0.63** 1 - -
Pain Severity 0.23** 0.20* 0.34** 1 -
Pain Interference 0.49** 0.50** 0.55** 0.61** 1
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Note. N for pain interference is 124 due to 3 missing observations; M=mean; SD=standard deviation; IQR=first quartile, third quartile; Fatigue scores shown as T-score with a distribution M=50 and SD=10; Spearman correlation coefficients (r) shown; r=.10 - .39 indicates Weak correlation; r=.40 - .69 indicates Moderate correlation; r=.70 - .89 indicates Strong correlation; r=.90 - 1.0 indicates Very Strong correlation.

**

p<.01.

*

p<.05.

Comparison of Fatigue, Depressive Symptoms, and Pain by Insomnia Severity Categories

Participants were categorized into three groups based on their insomnia severity: None, Subthreshold/Mild, and Moderate/Severe. Moderate and Severe categories were combined due to small sample size observed for the Severe group (7 participants). Participants in Subthreshold/ Mild (60.00 [57.70, 63.70]) and Moderate/Severe (63.70 [59.80, 69.30]) categories exhibited significantly higher fatigue scores than those in the None category (55.70 [52.10, 62.30], p<.0001). Participants in Subthreshold/Mild [18.00 (13.00, 22.00)] and Moderate/Severe (25.00 [19.00, 32.00]) categories exhibited significantly worse depressive symptoms than those in the None category (9.00 [6.00, 13.00], p<.0001); those in the Moderate/Severe category also demonstrated significantly worse depressive symptoms than those in the Subthreshold/Mild category. Participants in the Moderate/Severe (5.25 [4.25, 6.50]) category reported significantly higher pain severity than those in the None category (3.75 [3.00, 4.75], p=.004). Finally, participants in Subthreshold/Mild (4.86 [3.29, 7.00]) and Moderate/Severe (5.93 [3.86, 7.29]) categories exhibited significantly higher levels of pain interference than those in the None category (2.57 [1.57, 4.29], p<.0001). These data are summarized in Table 4 and Figure 1.

Table 4.

Comparison of Fatigue, Depressive Symptoms, and Pain by Insomnia Severity Categories

Insomnia Severity Category
(1)
None		 (2)
Subthreshold/Mild		 (3)
Moderate/Severe		 p Kruskal-Wallis H η2 Groups That Differ
Median [IQR] Median [IQR] Median [IQR]
Fatigue 55.70 [52.10, 62.30] 60.00 [57.70, 63.70] 63.70 [59.80, 69.30] <.0001 21.93 .16 (1,2)
(1,3)
(2,3)
Depressive Symptoms 9.00 [6.00, 13.00] 18.00 [13.00, 22.00] 25.00 [19.00, 32.00] <.0001 50.03 .39 (1,2)
(1,3)
(2,3)
Pain Severity 3.75 [3.00, 4.75] 4.50 [2.75, 5.67] 5.25 [4.25, 6.50] .0035 11.33 .08 (1,3)
Pain Interference 2.57 [1.57, 4.29] 4.86 [3.29, 7.00] 5.93 [3.86, 7.29] <.0001 27.10 .20 (1,2)
(1,3)
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Note. IQR=first quartile, third quartile; p-value shown is for ANOVA for normally distributed variables and Kruskal-Wallis for non-parametric variables. Pairwise comparison tests conducted for normally distributed variables; Dwass, Steel, Critchlow-Fligner multiple comparison analysis conducted for nonparametric variables. η2 is an effect size from the Kruskal-Wallis test and indicates the proportion of the variance in the dependent variable that is explained by insomnia severity category; η2≈0.01, small effect; η2≈0.06, moderate effect; η2≥0.14, large effect.

Figure 1.

Figure 1.

Figure 1.

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Box Plots of Fatigue, Depressive Symptoms, and Pain by Insomnia Severity Categories

Note. ISI=Insomnia Severity Index. Box plot shows median, IQR (first quartile, third quartile), and minimum and maximum values. Yellow diamond indicates the mean value. Black dot indicates an outlier.

Post-hoc linear regression analyses were conducted to examine the relationships between fatigue, depressive symptoms, and pain (severity, interference) with insomnia, when assessed as a continuous variable. Insomnia symptoms significantly predicted fatigue (βˆ=0.51, 95% CI: 0.34-0.68, p<.0001), depressive symptoms (βˆ=1.05, 95% CI: 0.83-1.27, p<.0001), pain severity (βˆ=0.07, 95% CI: 0.02-0.12, p=.008), and pain interference (βˆ=0.19, 95% CI: 0.13-0.25, p<.0001), closely aligning with the categorical results. Scatterplots in Figure 2 visualize these relationships. An analysis of the residuals from the linear regression models predicting depressive symptoms revealed that the relationship between depressive symptoms and insomnia severity is approximately linear. In contrast, the associations between fatigue, pain severity, and pain interference with insomnia severity appear to be non-linear.

Figure 2.

Figure 2.

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Scatterplots of Fatigue, Depressive symptoms, and Pain by Insomnia Severity

Note. ISI=Insomnia Severity Index. R2 = indicates the proportion of variance in the dependent variable (i.e., fatigue, depressive symptoms, pain) that is explained by insomnia severity.

Comparison of Main Study Variables by Demographic and Clinical Variables

Age.

Younger (≤60 years) participants had significantly higher insomnia severity (12.00 [8.00, 17.00]) compared to older (>60 years) participants (10.00 [4.00, 15.00], p=.048, η2=.02). Similarly, younger participants had significantly worse fatigue (61.30 [57.27, 66.45] vs. 59.70 [53.90, 63.70], p=.03, η2=.03), depressive symptoms (20.00 [13.00, 26.00] vs. 13.00 [7.00, 20.00], p=.0005, η2=.09), and pain interference (5.86 [3.43, 7.29] vs. 3.50 [2.00, 5.57], p=.0003, η2=.10). Pain severity did not differ by age group (Table 5).

Table 5.

Comparison of Main Study Variables by Demographic and Clinical Variables

Age Age ≤60 (n=64) Age > 60 (n=63) p η2
Insomnia 12.00 [8.00, 17.00] 10.00 [4.00, 15.00] .0485 .015
Depressive Symptoms 20.00 [13.00, 26.00] 13.00 [7.00, 20.00] .0005 .091
Fatigue 61.30 [57.27, 66.45] 59.70 [53.90, 63.70] .0279 .030
Pain Severity 4.77 (1.88) 4.40 (1.81) .2551
Pain Interference 5.86 [3.43, 7.29] 3.50 [2.00, 5.57] .0003 .097
Race White (n=76) Non-White (n=51) p η2
Insomnia 12.00 [6.00, 17.00] 10.00 [5.00, 15.00] .3661
Depressive Symptoms 17.00 [10.00, 23.50] 19.00 [12.00, 25.00] .6811
Fatigue 60.60 [54.80, 64.50] 60.80 [56.70, 65.00] .5534
Pain Severity 4.03 (1.52) 5.41 (2.00) <.0001 .166
Pain Interference 4.00 [2.36, 6.00] 5.36 [3.00, 7.43] .0742
Partner Status Unpartnered (n=57) Partnered (n=70) p η2
Insomnia 10.00 [5.00, 16.00] 12.00 [6.00, 16.00] .4576
Depressive Symptoms 20.00 [12.00, 26.00] 15.50 [10.00, 20.00] .0446 .024
Fatigue 62.00 [58.00, 66.10] 59.90 [54.40, 63.70] .1146
Pain Severity 4.80 (2.01) 4.42 (1.71) .2519
Pain Interference 4.43 [2.00, 6.43] 4.00 [2.57, 6.57] .9940
Education High School Diploma or Less (n=32) Some College or More (n=94) p η2
Insomnia 10.50 [7.00, 16.00] 12.00 [6.00, 16.00] .7919
Depressive Symptoms 17.00 [12.00, 24.50] 17.00 [10.00, 24.00] .7748
Fatigue 60.60 [57.30, 65.35] 60.95 [55.00, 64.70] .4343
Pain Severity 5.34 (2.20) 4.28 (1.59) .0040 .065
Pain Interference 4.36 [3.00, 7.29] 4.29 [2.43, 6.29] .3930
Income <20K (n=26) 20-60K (n=52) >60K (n=36) p η2
Insomnia 13.00 [10.00, 17.00] 11.50 [5.00, 15.00] 12.00 [7.00, 14.50] .2750
Depressive Symptoms 22.00 [13.00, 32.00] 17.50 [10.50, 24.00] 16.0 [11.50, 22.00] .2072
Fatigue 62.50 [58.00, 66.40] 61.05 [56.45, 64.95] 59.95 [55.15, 62.65] .1956
Pain Severity 5.69 (1.98) 4.65 (1.86) 3.81 (1.41) .0003 .134
Pain Interference 5.86 [4.29, 7.00] 4.21 [2.29, 6.29] 4.14 [2.64, 6.50] .0639
Cancer Stage I (n=80) II (n=27) III/IV (n=20) p η2
Insomnia 12.00 [6.00, 17.00] 10.00 [5.00, 15.00] 12.50 [9.50, 17.50] .2714
Depressive Symptoms 18.00 [10.00, 23.00] 13.00 [9.00, 25.00] 19.00 [13.50, 26.00] .3210
Fatigue 61.55 [56.25, 65.55] 59.20 [54.60, 63.70] 60.90 [57.30, 62.95] .3878
Pain Severity 4.39 (1.82) 4.91 (1.73) 4.93 (2.12) .3105
Pain Interference 4.36 [2.57, 6.29] 4.29 [2.43, 7.00] 4.71 [2.29, 6.71] .9366
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Note. Mean (standard deviation); Median [first quartile, third quartile]; Bolded indicates a significant result; p-values are from two sample t-tests or Wilcoxon rank sum tests; η2 is an effect size from the Kruskal-Wallis test and indicates the proportion of the variance in the dependent variable that is explained by demographic or clinical variable category; η2≈0.01, small effect; η2≈0.06, moderate effect; η2≥0.14, large effect.

Race.

Participants identifying as Non-White reported significantly higher pain severity (5.41 [2.00]) than those identifying as White (4.03 [1.52], p<.0001, η2=.17). Insomnia severity, fatigue, depressive symptoms, and pain interference did not differ between White and Non-White participants (Table 5).

Partner Status.

Unpartnered participants endorsed significantly higher depressive symptoms (20.00 [12.00, 26.00] vs. 15.50 [10.00, 20.00], p=.04, η2=.02) compared to Partnered participants. Insomnia severity, fatigue, pain severity, and pain interference were not different between Unpartnered and Partnered participants (Table 5).

Education.

Pain severity was significantly higher for participants with a High School Diploma or less (5.34 [2.20]) compared to those reporting Some College/Bachelor’s Degree or more (4.28 [1.59], p=.004, η2=.07). Insomnia severity, fatigue, depressive symptoms, and pain interference did not differ between participants with a High School Diploma or less compared to those with Some College/Bachelor’s Degree or more (Table 5).

Income.

Pain severity was significantly higher for participants reporting an income of <$20K (5.69 [1.98]) compared to those reporting an income of $20-60K (4.65 [1.86]) and >$60K (3.81 [1.41], p=.0003, η2=.13). Insomnia severity, fatigue, depressive symptoms, and pain interference did not differ across income groups (Table 5).

Cancer Stage.

There was no difference in insomnia severity, fatigue, depressive symptoms, pain severity, and pain interference across cancer stages (Table 5).

Discussion

This was a cross-sectional analysis of data from a trial testing a behavioral pain intervention for women with breast cancer and pain receiving treatment at clinics in medically underserved, mostly rural areas across the Southeast. We sought to characterize severity of insomnia, fatigue, depressive symptoms, and pain, and examine how fatigue, depressive symptoms, and pain differ based on established clinical categories of insomnia severity. Further, we aimed to assess how symptom severity might vary based on relevant demographic (e.g., age, race, partner status, education, income) and clinical (e.g., cancer stage) variables.

Strikingly, 68% of women reported some complaint of sleep disturbance (e.g., Subthreshold/Mild, Moderate, or Severe) overall, while 34% of women endorsed insomnia symptoms in the Moderate range or higher. These estimates are in line with some previous reports (e.g., Kwak et al., 2020; Rehman et al., 2022), however are higher than other estimates seen during the first 12 months after diagnosis (13.7% [Bean et al., 2021]; 18.5% [Fleming et al., 2019]). Our observation that women in this sample still report insomnia symptoms approximately 1.5 years post-diagnosis underscores the persistence of sleep problems for breast cancer survivors. Fatigue severity was above average and median depressive symptoms exceeded the cut-off for clinical symptoms of depression (Cella et al., 2010; Lewinsohn et al., 1997). Both pain severity and interference were moderate (Serlin et al., 1995; Shi et al., 2017). Together, these findings highlight several distinct symptoms (i.e., insomnia, fatigue, depression, pain) that comprise a high symptom burden continuing past initial diagnosis and primary treatment for unique sample of women with breast cancer from rural, medically underserved areas (de Rooij et al., 2021; So et al., 2021).

Supporting our hypothesis, we found that women with Moderate/Severe insomnia symptoms endorsed fatigue levels nearly one standard deviation higher than those with no insomnia symptoms. Similar results were observed for depressive symptoms, wherein women with Subthreshold/Mild and Moderate/Severe insomnia symptoms reported depressive symptoms double and nearly triple those of women with no insomnia symptoms, respectively. Pain severity and interference levels escalated from the mild to moderate range when comparing women with no insomnia symptoms to those with Moderate/Severe insomnia symptoms. These results are notable and correspond with previous work suggesting that when breast cancer survivors are not sleeping well, other common symptoms are significantly worse (Jim et al., 2013). Likewise, when fatigue, depressive symptoms, and pain levels are higher, sleep quality is poor. The increase in symptom level across insomnia categories is impressive, especially for depression, wherein a woman experiencing Moderate/Severe insomnia could report depressive symptoms approximately three times that of a woman reporting no insomnia. To our knowledge, this is the first study to examine how established clinical categories of insomnia severity used routinely in clinical practice are associated with related symptom (i.e., fatigue, depression, pain) severity in an understudied sample of women with breast cancer and pain from medically underserved, primarily rural regions. It appears that women endorsing Subthreshold/Mild insomnia symptoms, and especially those reporting Moderate/Severe insomnia symptoms, report significantly worse symptom burden overall. Results from post-hoc linear regressions closely aligned with these categorical analyses.

Younger participants (≤60 years) reported higher insomnia symptom severity and worse fatigue, depressive symptoms, and pain interference. Our results correspond with prior research showing younger women with breast cancer fair worse than their older counterparts (Bjerkeset et al., 2020; Davis et al., 2018; Naik et al., 2020). This may be due, in part, to aggressive treatment regimens for more advanced disease diagnosed at younger age, and competing work and family demands that complicate efforts to manage symptoms (Kim et al., 2022; Naik et al., 2020). Severity of insomnia symptoms did not vary on any other demographic or clinical factors. This diverges from prior work suggesting minority race and lower education and income levels are associated with higher insomnia symptom severity (Gonzalez et al., 2021; Leysen et al., 2019). However, like our results, others have also found that insomnia symptoms may not vary based on demographic and/or clinical factors such as race, ethnicity, partner status and cancer stage (Beverly Hery et al., 2023; Daldoul et al., 2023). Black/African-American participants and those with lower education and income levels reported significantly higher pain severity. Notably, the parent study screened for pain at enrollment, yielding a unique sample with at least moderate pain severity. It is possible that if insomnia severity had been used as an inclusion criterion (e.g., at least Moderate symptoms of clinical insomnia), more robust associations between insomnia and demographic and clinical variables may have been observed. It is also possible that insomnia is a relevant concern across demographic and clinical groups. Results presented here can help target supportive care at clinics serving mostly rural patient populations, in underserved areas where such adjunctive supportive care services are sparse, yet the need for behavioral sleep and symptom management is high for many women (i.e., younger, Black/African-American, lower education and income).

This study is limited in its cross-sectional design. Conclusions cannot be made regarding the directionality of the associations or longitudinal relationships between symptoms. Moreover, symptoms were assessed on different timeframes; fatigue, depression, and pain symptoms were measured “across the past week,” while insomnia symptoms were measured “across the last two weeks.” The percentage of women identifying as Black/African-American (28%) in this study was higher than that observed among all patients in the Duke Cancer Institute catchment area being treated at Duke Cancer Network clinics (24%). Still, the current sample was relatively modest in size and mostly White, potentially limiting generalizability to those identifying as a minority race. Though not utilized here, future work could leverage qualitative methods to query participants on factors that may contribute to insomnia. Women receiving cancer care in rural, medically underserved areas might cite varying and unique explanations for insomnia based on particular life circumstances (e.g., work and financial demands, child- and/or elder-care responsibilities) and home environment. Likewise, more thorough assessment of comorbid physical and mental health conditions (e.g., obesity, arthritis, depression, anxiety) and sleep disorders (e.g., sleep apnea, restless leg syndrome) is warranted and could be achieved in future work via a diagnostic clinical interview and/or additional self-report questionnaires at screening. Collecting these data could further explicate reasons for insomnia symptoms and targets for intervention, as well as confounding variables that might explain both insomnia and related fatigue, depression, and pain symptoms. Finally, time since diagnosis ranged from a few months to over two years. It is likely that the sleep and related symptom experience differs depending on phase of cancer care; for example, during surgical, chemotherapy, or radiation treatment versus endocrine treatment during survivorship (Ferreira et al., 2019; So et al., 2021).

Clinical Implications

An increasing number of women with breast cancer living some distance from an academic medical center are now receiving their care at smaller, rural network clinics with less resources for adjunctive symptom management. There is a growing but still limited literature exploring the symptom experience of this cancer population. Our findings add to existing research by first, highlighting a significant multi-symptom burden reported by this unique sample of women with breast cancer receiving treatment at network clinics in rural, medically underserved areas. Second, we show that with increasing category of insomnia severity, women experience a noteworthy increase in fatigue, depression, and pain symptoms. As such, the brief Insomnia Severity Index could be used in the cancer clinic setting to quickly identify women that might experience a higher symptom burden.

Behavioral interventions for insomnia, fatigue, depression, and pain are recommended in survivorship guidelines from the National Comprehensive Cancer Network. Yet, despite a need for and support of behavioral insomnia and symptom management interventions, there may be barriers that limit access to such supportive care services for women with breast cancer being treated in rural, medically underserved areas. Primary barriers include a lack of behavioral providers and symptom management interventions. Services that are available may only be offered in-person, and patients may experience time constraints due to competing responsibilities (e.g., work, child- and/or elder-care responsibilities), transportation difficulties (e.g., availability, parking), and burdensome distance from the medical center (Carlson et al., 2004; Greenberg, 2004; Keefe et al., 2005). Telehealth approaches and interventions automated through the Internet and/or mobile applications are being increasingly used to overcome resource and time constraints and extend the reach of critical supportive care services (Morris, Rossi, & Fuemmeler, 2021; Shaffer et al., 2023).

Additionally, behavioral insomnia and symptom management interventions are seldom adapted for rural, medically underserved populations, where minority race and low income and education level may necessitate adaptations to intervention format and content. An excellent example of this type of work comes from Zhou and colleagues (2022). In a large randomized trial, Zhou et al. (2022) found that although reductions to insomnia severity were equivalent, intervention completion rates were higher for a culturally tailored, automated and Internet-delivered Cognitive-Behavioral Therapy for Insomnia (CBT-I) protocol compared to standard CBT-I in a sample of Black/African-American women with insomnia (Zhou et al., 2022). Key adaptations were: 1) use of visual and audio content with only Black men and women; and 2) inclusion of didactic content on the social and cultural contexts in which insomnia occurs for Black women.

Another innovative solution to improve the availability and applicability of supportive care services for breast cancer survivors in rural, less resourced areas is exploring efficient, telehealth behavioral interventions that simultaneously target sleep concerns and related symptoms of fatigue, depression, and pain. Given the interrelationships among symptoms found here, it is possible that intervening on insomnia, for example, may in turn improve fatigue, depressive symptoms, and pain. Behavioral interventions that target multiple symptoms might help mitigate access barriers, such as time constraints, and financial and physical costs of traveling to a distant medical center for numerous separate appointments with specialized clinicians (e.g., behavioral sleep and pain psychologists). Promising transdiagnostic (e.g., Transdiagnostic Intervention for Sleep and Circadian Dysfunction [Harvey & Sarfan, 2024], Unified Protocol [Cassiello-Robbins et al., 2020]) and multi-symptom management interventions exist (Fisher et al., 2024), yet need to be carefully adapted for and tested in women with breast cancer from rural, medically underserved areas to ensure feasibility and acceptability in this population.

Acknowledgements

The parent study was funded through an NIH/NCI grant (R01CA237892) awarded to senior author, Tamara J. Somers. The work of first author, Hannah M. Fisher, was supported by the NIH/NCI under a Mentored Clinical Scientist Research Career Development Award (K08CA283026).

Footnotes

Conflicts of Interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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