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. Author manuscript; available in PMC: 2025 Feb 1.
Published in final edited form as: Stress Health. 2023 Jun 2;40(1):e3279. doi: 10.1002/smi.3279

Various Types of Stress and Greater Use of Disengagement Coping Are Associated with Worse Sleep Disturbance in Oncology Patients Undergoing Chemotherapy

Alejandra Calvo-Schimmel 1, Steven M Paul 1, Bruce A Cooper 1, Carolyn Harris 2, Joosun Shin 1, Kate Oppegaard 1, Marilyn J Hammer 3, Frances Cartwright 4, Yvette P Conley 2, Kord M Kober 1, Jon D Levine 5, Christine Miaskowski 1,5
PMCID: PMC10692307  NIHMSID: NIHMS1925297  PMID: 37265072

Abstract

Various types of stress and the choice of coping strategies may be risk factors for higher levels of sleep disturbance in oncology patients. Purposes were to evaluate for differences in global, cancer-specific, and cumulative life stress, as well as resilience and the use of coping strategies among three subgroups of patients with distinct sleep disturbance profiles (i.e., Low, High, Very High). Oncology outpatients (n=1331) completed measures of global (Perceived Stress Scale), cancer-specific (Impact of Event Scale-Revised), and cumulative life (Life Stressor Checklist-Revised) stress, resilience (Connor-Davidson Resilience Scale) and coping (Brief Cope) prior to their second or third cycle of chemotherapy. Sleep disturbance was assessed six times over two chemotherapy cycles. Differences were evaluated using parametric and non-parametric tests. All stress measures showed a dose response effect (i.e., as the sleep disturbance profile worsened, levels of all types of stress increased). Compared to Low class, the other two classes reported higher levels of global perceived stress and higher occurrence rates and effect from previous stressful life events. Impact of Event Scale-Revised scores for the Very High class indicated post-traumatic symptomatology. Patients in High and Very High classes had resilience scores below the normative score for the United States population and used a higher number of disengagement coping strategies. Our findings suggest that very high levels of sleep disturbance are associated with higher levels of various types of stress, lower levels of resilience, and higher use of disengagement coping strategies. Clinicians need to perform routine assessments and implement symptom management interventions to reduce stress and encourage the use of engagement coping strategies.

Keywords: cancer, chemotherapy, coping, resilience, sleep disturbance, stress

INTRODUCTION

Compared to the general adult population, prevalence rates for sleep disturbance in patients with cancer are nearly double and have more debilitating effects on health outcomes (Costa et al., 2014). In fact, clinically meaningful levels of sleep disturbance in oncology patients are associated with increases in mental health issues (Hoang et al., 2020; Sanford et al., 2013; Souza et al., 2020), decreases in immune responses (Souza et al., 2020), cardiotoxicity (Hoang et al., 2020), cognitive dysfunction (Chu et al., 2011), reductions in quality of life (QOL) (Hoang et al., 2020; Souza et al., 2020), and poorer prognosis (Sanford et al., 2013). In our recent longitudinal study that forms the basis for the current study (Tejada et al., 2019), three classes of patients with distinct sleep disturbance profiles (i.e., Low, High, Very High) were identified using the clinically meaningful cutoff score of ≥43 on the General Sleep Disturbance Scale (GSDS) (Lee, 1992). Of note, 75% of the 1331 patients met this clinically meaningful criteria. In addition, patients in the High and Very High classes reported significantly poorer quality of sleep and problems with sleep maintenance. These findings suggest that sleep disturbance is a significant problem for patients during chemotherapy.

While the risk factors for sleep disturbance are multifactorial, emerging evidence suggests that various types of stress are associated with an increased risk for sleep disturbance in both the general population (Alter et al., 2021; Lo Martire et al., 2020) and cancer patients (Chu et al., 2011; Xu Y., 2021). However, studies of stress in oncology patients encompass a broad range of concepts and a variety of operational definitions (Dai et al., 2020; Donovan et al., 2020; Mravec et al., 2020a, 2020b). In particular, confusion exists between the use of the terms “stress” and psychological distress”, the latter often encompassing anxiety and depression (National et al., 2022). In terms of stress, some studies focus on global stress (i.e., how patients evaluate stressful situations in general (Han et al., 2016)), while others focus on cancer-specific stress (Andersen et al., 2018). Other studies evaluate the number of and/or impact of cumulative life stress (Seib et al., 2017) or childhood adversity (Hu et al., 2021).

As noted in one review (Gosain et al., 2020) and two subsequent studies (Alter et al., 2021; Garvin et al., 2021), individuals’ perceptions of cancer-related stress (e.g., initial diagnosis, treatment-related symptoms), traumatic life events (e.g., divorce, childhood trauma) (Alter et al., 2021; Garvin et al., 2021)), and/or the use of various coping behaviors that develop in reaction to stressful experiences (Gosain et al., 2020), may contribute to the development or exacerbation of sleep disturbance. Equally important, while not studied in oncology patients, in a study that explored the temporal and bidirectional associations between stress and sleep in young adults (Yap et al., 2020), higher evening stress predicted shorter total sleep time and lower sleep efficiency predicted higher next day stress. These types of inter-relationships warrant additional investigation.

Only three longitudinal studies have investigated associations between sleep disturbance and various types of stress in oncology patients (Bean et al., 2021; Palesh et al., 2007; Shim et al., 2022). In the first study that described four distinct trajectory classes of insomnia symptoms (i.e., persistently very high, high, stable low, very low) in patients with breast cancer (Bean et al., 2021), higher levels of chronic life stress were associated with being in the very high insomnia class compared to the low class. In the second study that evaluated for associations between stressful life events (SLEs) and changes in sleep disturbance in women with metastatic breast cancer (Palesh et al., 2007), greater cumulative life stress was associated with more problems with sleep initiation and daytime sleepiness. In the third study that evaluated for associations between individual posttraumatic stress symptoms and trajectories of sleep disturbance in breast cancer patients receiving chemotherapy (Shim et al., 2022), higher hyperarousal scores at enrollment were associated with a higher initial and a slower increase in sleep disturbance.

These studies provide some insights into the associations between sleep disturbance and stress. However, all three studies included only women with breast cancer. While different instruments were used, none of these studies evaluated for three distinct types of stress (i.e., global, cancer-specific, and cumulative life stress). In addition, none of them assessed for associations between sleep disturbance and the use of various coping strategies.

Coping is defined as “constantly changing cognitive and behavioral efforts to manage specific external and/or internal demands that are appraised as taxing or exceeding the resources of a person” (Lazarus & Folkman, 1984). It is a multidimensional process that includes the use of behaviors or strategies to manage situations that are appraised as stressful in order to prevent harm, restore individual resources, and/or lessen associated distress (Johnson et al., 2022; Wright et al., 2020). Oncology patients use different types of coping strategies depending on their appraisal of the type of stress they are experiencing (Gosain et al., 2020). Common coping strategies are divided into engagement (e.g., active coping) or disengagement (e.g., denial) (Wright et al., 2020). Very limited data are available on associations between sleep disturbance and coping styles in patients with cancer. For example, in a longitudinal study that examined changes in sleep disturbance in breast and prostate cancer patients undergoing radiation (Thomas et al., 2010), the use of avoidance coping strategies (e.g., attempt not to think about the cancer) was associated with worse sleep in both groups. However, approach type coping strategies (e.g., actively focusing on the cancer) was associated with better sleep in the patients with prostate cancer. In another longitudinal study of patients with non-metastatic colorectal cancer (Hyphantis et al., 2016), a maladaptive action defense style (e.g., abandonment of personal interests) was associated with restless or disturbed sleep at enrollment and a self-sacrificing defense style was associated with problems with sleep maintenance. In another study of patients with prostate cancer (Hoyt et al., 2009), a higher use of avoidance coping at enrollment was associated with worse sleep quality six months after enrollment. While useful, these studies did not evaluate for differences in coping in patients with distinct sleep disturbance profiles.

Given the paucity of research on the relationships between sleep disturbance and stress and coping in oncology patients receiving chemotherapy, the purpose of this study, in a sample of oncology outpatients (n=1331), was to extend the findings from our previous study that describe differences in demographic, clinical, and sleep characteristics, and evaluate for differences in global, cancer-specific, and cumulative life stress, as well as resilience and coping among our previously identified latent classes of oncology patients with distinct sleep disturbance profiles (Tejada et al., 2019). We hypothesized that patients in the Very High sleep disturbance class would report higher levels of all three types of stress, increased occurrence rates and effects from life stressors, lower levels of resilience, and higher use of disengagement coping strategies.

METHODS

Patients and Settings

This study is part of a larger, longitudinal study of the symptom experience of oncology outpatients receiving chemotherapy whose details are reported elsewhere (Tejada et al., 2019). Briefly, patients were ≥18 years of age; had a diagnosis of breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding four weeks; were scheduled to receive at least two additional cycles of chemotherapy; were able to read, write, and understand English; and provided written informed consent. Patients were recruited from two Comprehensive Cancer Centers, one Veteran’s Affairs hospital, and four community-based oncology programs during their first or second cycle of chemotherapy.

Study Procedures

Study was approved by the Institutional Review Board at each of the study sites. Of the 2234 patients approached, 1343 consented to participate. The major reason for refusal was being too overwhelmed with their cancer treatments. Patients completed the GSDS a total of six times over two chemotherapy cycles (i.e., prior to chemotherapy administration, approximately 1 week after chemotherapy administration, and approximately 2 weeks after chemotherapy administration). The remaining measures were completed at enrollment (i.e., prior to the second or third cycle of chemotherapy). A total of 1331 patients who had complete data on the GSDS were included in this analysis (Tejada et al., 2019).

Instruments

Demographic and clinical measures

Patients completed a demographic questionnaire, Karnofsky Performance Status (KPS) scale (Karnofsky, 1977), Self-Administered Comorbidity Questionnaire (SCQ) (Sangha et al., 2003), Alcohol Use Disorders Identification Test (AUDIT) (Bohn et al., 1995), and a smoking history questionnaire. The toxicity of each patient’s chemotherapy regimen was rated using the MAX2 score (Extermann et al., 2004). Medical records were reviewed for disease and treatment information.

Sleep disturbance measure

The 21-item GSDS was designed to assess various aspects of sleep disturbance (i.e., quality, quantity, onset latency, mid and early awakenings, sleep medications, daytime sleepiness). Each item was rated on a 0 (never) to 7 (everyday) numeric rating scale (NRS). The GSDS total score ranges from 0 (no disturbance) to 147 (extreme sleep disturbance) (Lee, 1992). A GSDS total score of ≥43 indicates a significant level of sleep disturbance that warrants clinical evaluation and management (Fletcher et al., 2008). Cronbach’s alpha for the GSDS total score was 0.83.

Stress, resilience, and coping measures

The 14-item Perceived Stress Scale (PSS) was used as a measure of global perceived stress according to the degree that life circumstances are appraised as stressful over the course of the previous week (Cohen et al., 1983). Each item was rated on a 0 (never) to 4 (very often) Likert scale. Total PSS scores can range from 0 to 56. In this study, its Cronbach’s alpha was 0.85.

The 22-item Impact of Event Scale-Revised (IES-R) was used to measure cancer-related distress (Horowitz et al., 1979). Patients rated each item based on how distressing each potential difficulty was for them during the past week “with respect to their cancer and its treatment.” Three subscales evaluated levels of intrusion, avoidance, and hyperarousal perceived by the patient. Sum scores of ≥24 indicate clinically meaningful post-traumatic symptomatology and scores of ≥33 indicate probable post-traumatic stress disorder (PTSD) (Creamer et al., 2003). In this study, the Cronbach’s alpha for the IES-R total score was 0.92.

The 30-item Life Stressor Checklist-Revised (LSC-R) is an index of lifetime trauma exposure (e.g., being mugged, death of a loved one, sexual assault) (Wolfe & Kimmerling, 1997). The total LSC–R score is obtained by summing the total number of events endorsed. If patients endorsed an event, they were asked to indicate how much that stressor affected their life in the past year. These responses were summed to yield an “affected” sum score. In addition, a PTSD sum score was created based on the number of positively endorsed items (out of 21) that reflect the DSM-IV PTSD Criteria A for having experienced a traumatic event.

The 10-item Connor-Davidson Resilience Scale (CDRS) evaluates a patient’s personal ability to handle adversity (e.g., “I am able to adapt when changes occur”; “I tend to bounce back after illness, injury, or other hardships”) (Campbell-Sills & Stein, 2007). Total scores range from 0 to 40, with higher scores indicative of higher self-perceived resilience. The normative adult mean score in the United States is 31.8 (α5.4) (Campbell-Sills et al., 2009). In this study, its Cronbach’s alpha was 0.90.

The 28-item Brief Cope scale was designed to assess a broad range of coping responses among adults (Carver, 1997; Carver et al., 1989). Each item was rated on a 1 (I haven’t been doing this at all) to 4 (I have been doing this a lot) Likert scale. Higher scores indicate greater use of the various coping strategies. In total, 14 dimensions were evaluated using this instrument (with their respective Cronbach’s alphas), namely: self-distraction (0.46), active coping (0.75), denial (0.72), substance use (0.87), use of emotional support (0.77), use of instrumental support (0.77), behavioral disengagement (0.57), venting (0.65), positive reframing (0.79), planning (0.74), humor (0.83), acceptance (0.68), religion (0.92), and self-blame (0.73). Each dimension is evaluated using two items. The Brief Cope has well established validity and reliability in oncology patients (Scrignaro et al., 2011; Yusoff et al., 2010).

Data Analysis

As described previously (Tejada et al., 2019), latent profile analysis (LPA) was used to identify unobserved subgroups of patients (i.e., latent classes) with distinct sleep disturbance profiles over the six assessments, using the six total GSDS scores. In brief, LPA was performed using MPlus™ Version 8.4 (Muthen LK, 1998–2020). Estimation was carried out with full information maximum likelihood with standard errors and a Chi square test that are robust to non-normality and non-independence of observations (“estimator=MLR”). Model fit was evaluated to identify the solution that best characterized the observed latent class structure with the Bayesian Information Criterion (BIC), Vuong-Lo-Mendell-Rubin likelihood ratio test (VLRM), entropy, and latent class percentages that were large enough to be reliable (Muthen LK, 1998–2020; Nylund KL, 2007). Missing data were accommodated for with the use of the Expectation-Maximization (EM) algorithm (Muthen & Shedden, 1999).

Data were analyzed using SPSS version 28 (IBM Corporation, Armonk, NY). Differences among the sleep disturbance classes in stress, resilience, and coping at enrollment were evaluated using parametric and nonparametric tests. Bonferroni corrected p-value of <.017 was considered statistically significant for the pairwise contrasts (i.e., .05/3 possible pairwise contrasts).

RESULTS

Latent profile analysis

As described previously (Tejada et al., 2019), three sleep disturbance classes were identified using their GSDS scores over the six assessments and named Low (25.2%), High (50.8%), and Very High (24.0%). For both the Low and the Very High classes, sleep disturbance scores remained relatively constant across all six assessments. In contrast, patients in the High class had sleep disturbance scores slightly higher at the second and fifth assessments (i.e., following the administration of chemotherapy; Supplemental Figure 1).

Sample characteristics

In terms of differences in demographic and clinical characteristics among the latent classes (Tejada et al., 2019), in brief, the overall sample (n=1331) was predominantly female, White, and college educated. Compared to the Low class, the other two classes were significantly younger, more likely to be female, less likely to be employed, more likely to have childcare responsibilities, less likely to have gastrointestinal cancer, and had a lower KPS score and a higher SCQ score. (Supplemental Table 1).

Differences in stress and resilience

Significant differences were found among the three latent classes in PSS total, IES-R subscales (i.e., intrusion, avoidance, hyperarousal) and total, and LSC-R total, affected sum, and PTSD sum scores at enrollment (i.e., Low < High < Very High). In terms of resilience, significant differences were found among the three latent classes in the CDRS scores (i.e., Low > High > Very High; Table 1).

Table 1 –

Differences in Stress and Resilience Measures Among the Sleep Disturbance Latent Classes at Enrollment

Measuresa Low Sleep Disturbance (1)
25.2% (n=336)		 High Sleep Disturbance (2)
50.8% (n=676)		 Very High Sleep Disturbance (3)
24.0% (n=319)		 Statistics
Mean (SD) Mean (SD) Mean (SD)
PSS total score (range 0 to 56) 13.9 (6.5) 18.6 (7.5) 23.1 (8.5) F=118.17, p<.001
1 < 2 < 3
IES-R total score
  (≥24 – clinically meaningful PTSD symptomatology)
  (≥33 – probable PTSD)		 11.8 (8.9) 18.4 (11.2) 27.1 (15.8) F=128.93, p<.001
1 < 2 < 3
  IES-R intrusion 0.5 (0.5) 0.9 (0.6) 1.4 (0.8) F=129.52, p<.001
1 < 2 < 3
  IES-R avoidance 0.7 (0.6) 1.0 (0.7) 1.1 (0.7) F=31.37, p<.001
1 < 2 < 3
  IES-R hyperarousal 0.3 (0.4) 0.6 (0.5) 1.1 (0.8) F=174.60, p<.001
1 < 2 < 3
LSC-R total score (range 0–30) 5.1 (3.3) 6.0 (3.8) 7.4 (4.6) F=22.79, p<.001
1 < 2 < 3
LSC-R affected sum score (range 0–150) 8.8 (8.2) 11.4 (10.0) 16.5 (13.8) F=35.05, p<.001
1 < 2 < 3
LSC-R PTSD sum score (range 0–21) 2.3 (2.5) 3.1 (2.9) 4.1 (3.6) F=24.80, p<.001
1 < 2 < 3
CDRS total score (range 0–40)
  (31.8 (±5.4) – normative range for the United States population)		 31.8 (5.5) 30.0 (6.4) 28.4 (6.8) F=23.33, p<.001
1 > 2 > 3
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Abbreviations: CDRS = Connor Davidson Resilience Scale, IES-R = Impact of Event Scale – Revised, LSC-R = Life Stressor Checklist-Revised, PSS = Perceived Stress Scale, PTSD = post-traumatic stress disorder, SD = standard deviation

a

Clinically meaningful cutoff scores or range of scores

Differences in the occurrence of life stressors

As shown in Table 2, significant differences were found, among the latent classes, in the occurrence of 57.1% of the stressors listed on the LSC-R. Among the three latent classes, significant differences were found in the occurrence of emotional abuse and sexual harassment (i.e., Low < High < Very High). Compared to the Low class, the other two classes reported higher occurrence rates for physical abuse at ≥16 years, forced sex at <16 years, and having a family member jailed. Compared to the Low class, the Very High class reported higher occurrence rates for family violence in childhood, physical neglect, being forced to touch before and after 16 years, being separated or divorced, being separated from a child, and caring for someone with severe physical or mental handicap. Compared to the Low and High classes, the Very High class reported higher occurrence rates for forced sex at ≥16 years, having parents separated or divorced, having serious money problems, and having a serious physical or mental illness not related to cancer.

Table 2 –

Differences Among the Sleep Disturbance Latent Classes in the Percentage of Patients Exposed to Various Stressors on the Life Stressor Checklist-Revised at Enrollment

Characteristic Low Sleep Disturbance (1)
25.2% (n=336)		 High Sleep Disturbance (2)
50.8% (n=676)		 Very High Sleep Disturbance (3)
24.0% (n=319)		 Statistics
% (n) % (n) % (n)
Interpersonal Violence, Abuse, and Neglect Stressors
Family violence in childhood 19.3 (52) 23.3 (122) 30.0 (69) X2=8.04, p=.018
1 < 3
Emotional abuse 13.8 (38) 20.8 (110) 33.2 (76) X2=28.11, p<.001
1 < 2 < 3
Physical neglect 2.5 (7) 4.9 (26) 7.4 (17) X2=6.33, p=.042
1 < 3
Sexual harassment 10.8 (29) 17.6 (93) 28.3 (64) X2=25.63, p<.001
1 < 2 < 3
Physical abuse - <16 years 11.1 (30) 14.6 (77) 17.5 (40) X2=4.15, p=.126
Physical abuse - ≥16 years 7.0 (19) 15.0 (79) 17.7 (40) X2=14.06, p<.001
1 < 2 and 3
Forced to touch - <16 years 7.8 (21) 11.3 (59) 16.8 (39) X2=9.86, p=.007
1 < 3
Forced to touch - ≥16 years 3.4 (9) 5.5 (29) 10.3 (24) X2=11.16, p=.004
1 < 3
Forced sex - <16 years 1.1 (3) 4.8 (25) 7.3 (17) X2=11.77, p=.003
1 < 2 and 3
Forced sex - ≥16 years 3.7 (10) 5.7 (30) 11.2 (26) X2=12.47, p=.002
1 and 2 < 3
Other Stressors
Been in a serious disaster 40.0 (108) 39.5 (208) 45.1 (105) X2=2.21, p=.331
Seen serious accident 31.1 (84) 32.1 (170) 35.9 (84) X2=1.47, p=.479
Had serious accident or injury 21.8 (59) 23.1 (120) 29.7 (69) X2=5.11, p=.078
Jail (family member) 14.4 (39) 21.6 (114) 25.3 (59) X2=9.78, p=.008
1 < 2 and 3
Jail (self) 4.4 (12) 6.8 (36) 9.4 (22) X2=4.92, p=.085
Foster care or put up for adoption 2.9 (8) 1.7 (9) 3.4 (8) X2=2.49, p=.288
Separated/divorced (parents) 16.5 (45) 21.0 (111) 29.5 (69) X2=12.85, p=.002
1 and 2 < 3
Separated/divorced (self) 31.1 (85) 35.3 (188) 43.7 (101) X2=8.87, p=.012
1 < 3
Serious money problems 13.2 (36) 18.1 (96) 32.0 (74) X2=30.00, p<.001
1 and 2 < 3
Had serious physical or mental illness (not cancer) 13.5 (37) 18.5 (98) 26.5 (62) X2=14.05, p<.001
1 and 2 < 3
Abortion or miscarriage 42.9 (79) 45.8 (201) 41.9 (80) X2=0.98, p=.612
Separated from child 0.4 (1) 2.3 (12) 3.6 (8) X2= 6.26, p=.044
1 < 3
Care for child with handicap 3.1 (8) 4.0 (21) 4.5 (10) X2=0.66, p=.721
Care for someone with severe physical or mental handicap 19.5 (52) 24.4 (127) 30.3 (69) X2=7.76, p=.021
1 < 3
Death of someone close (sudden) 50.0 (135) 48.7 (253) 50.2 (115) X2=0.22, p=.898
Death of someone close (not sudden) 79.3 (207) 79.0 (413) 78.9 (179) X2=0.02, p=.991
Seen robbery/mugging 21.7 (59) 21.3 (112) 24.1 (56) X2=0.80, p=.669
Been robbed/mugged 25.5 (69) 26.2 (138) 28.9 (66) X2=0.86 p=.651
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Differences in the effect of life stressors

Compared to the Low class, the other two classes reported higher effect scores for the non-sudden death of someone close. Compared to the Low class, the Very High class reported higher effect scores for caring for someone with severe physical or mental handicap and the sudden death of someone close. Compared to the Low and High classes, Very High class reported higher effect scores for being forced to touch at <16 years, being in a serious disaster, being separated or divorced, having serious money problems, and having a miscarriage or abortion (Table 3).

Table 3 –

Differences Among the Sleep Disturbance Latent Classes at Enrollment in the Effect of Each of the Stressors on Life Over the Past Year

Characteristica Low Sleep Disturbance (1)
25.2% (n=336)		 High Sleep Disturbance (2)
50.8% (n=676)		 Very High Sleep Disturbance (3)
24.0% (n=319)		 Statistics
Mean (SD) Mean (SD) Mean (SD)
Interpersonal Violence, Abuse, and Neglect Stressors
Family violence in childhood 1.9 (1.1) 1.9 (1.2) 2.0 (1.1) KW=1.21, p=.547
Emotional abuse 2.6 (1.5) 2.4 (1.2) 3.0 (1.4) KW=8.09, p=.018
2 < 3
Physical neglect 2.7 (1.7) 2.7 (1.3) 3.0 (1.3) KW=0.69, p=.708
Sexual harassment 1.5 (1.1) 1.5 (1.0) 1.5 (0.9) KW=1.04, p=.595
Physical abuse - <16 years 1.9 (1.3) 1.8 (1.3) 2.2 (1.2) KW=4.13, p=.127
Physical abuse - ≥16 years 1.8 (1.3) 1.7 (1.1) 2.2 (1.4) KW=3.37, p=.185
Forced to touch - <16 years 1.6 (1.1) 1.8 (1.2) 2.6 (1.5) KW=12.38, p=.002
1 and 2 < 3
Forced to touch - ≥16 years 1.2 (0.4) 1.9 (1.2) 2.2 (1.4) KW=3.71, p=.157
Forced sex - <16 years 1.0 (0.0) 1.8 (1.0) 2.5 (1.6) KW=3.15, p=.207
Forced sex - ≥16 years 1.6 (1.3) 1.6 (1.0) 2.0 (1.3) KW=2.61, p=.271
Other Stressors
Been in a serious disaster 1.2 (0.7) 1.3 (0.8) 1.5 (0.9) KW=10.14, p=.006
1 and 2 < 3
Seen serious accident 1.4 (0.8) 1.4 (0.8) 1.7 (1.1) KW=4.38, p=.112
Had serious accident or injury 1.5 (0.9) 1.6 (1.1) 1.8 (1.1) KW=3.96, p=.138
Jail (family member) 1.7 (1.2) 1.9 (1.4) 2.0 (1.4) KW=1.83, p=.400
Jail (self) 1.8 (1.3) 1.7 (1.1) 1.8 (1.4) KW=0.25, p=.882
Foster care or put up for adoption 2.6 (1.7) 1.9 (1.4) 2.5 (1.4) KW=0.97, p=.616
Separated/divorced (parents) 1.5 (0.9) 1.7 (1.1) 2.0 (1.3) KW=4.93, p=.085
Separated/divorced (self) 1.7 (1.2) 2.0 (1.3) 2.5 (1.5) KW=13.54, p=.001
1 and 2 < 3
Serious money problems 2.1 (1.6) 2.5 (1.6) 3.2 (1.7) KW=13.09, p=.001
1 and 2 < 3
Had serious physical or mental illness (not cancer) 2.2 (1.3) 2.4 (1.4) 2.7 (1.4) KW=5.26, p=.072
Abortion or miscarriage 1.4 (0.9) 1.5 (0.9) 1.9 (1.3) KW=14.03, p<.001
1 and 2 < 3
Separated from child 1.0 (0.0) 2.6 (1.4) 3.4 (1.8) KW=2.43, p=.298
Care for child with handicap 3.4 (1.5) 3.1 (1.4) 3.4 (1.3) KW=0.40, p=.820
Care for someone with severe physical or mental handicap 2.1 (1.3) 2.6 (1.4) 2.9 (1.6) KW=6.21, p=.045
1 < 3
Death of someone close (sudden) 1.9 (1.3) 2.2 (1.3) 2.4 (1.5) KW=9.36, p=.009
1 < 3
Death of someone close (not sudden) 1.9 (1.2) 2.2 (1.3) 2.4 (1.4) KW=20.42, p<.001
1 < 2 and 3
Seen robbery/mugging 1.4 (0.8) 1.5 (1.0) 1.7 (1.3) KW=3.24, p=.198
Been robbed/mugged 1.6 (1.0) 1.6 (1.0) 1.8 (1.2) KW=0.59, p=.743
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Abbreviations: KW = Kruskal Wallis, SD = standard deviation

*

Range = 1 “not at all” to 5 “extremely”

a

These data are reported for those patients who reported the occurrence of the stressor (see Table 2)

Differences in coping strategies

As shown in Table 4, significant differences were found among the three latent classes in venting, behavioral disengagement, and self-blame (i.e., Low < High < Very High). Compared to the Low class, the other two classes reported higher scores for self-distraction. Compared to the Low class, the Very High class reported lower scores for active coping and higher scores for using instrumental support. Compared to the Low and High classes, the Very High class reported higher scores for denial and substance use.

Table 4 –

Differences in Brief COPE Subscale Scores Among the Sleep Disturbance Latent Classes at Enrollment

Subscale* Low Sleep Disturbance (1)
25.2% (n=336)		 High Sleep Disturbance (2)
50.8% (n=676)		 Very High Sleep Disturbance (3)
24.0% (n=319)		 Statistics
Mean (SD) Mean (SD) Mean (SD)
Engagement Coping Strategies
Active coping 6.2 (1.7) 6.0 (1.6) 5.8 (1.6) F=3.90, p=.020
1 > 3
Planning 5.1 (1.9) 5.3 (1.8) 5.4 (1.7) F=2.79, p=.062
Positive reframing 5.4 (2.0) 5.4 (2.0) 5.4 (1.9) F=0.06, p=.938
Acceptance 6.7 (1.3) 6.8 (1.3) 6.6 (1.4) F=2.53, p=.080
Humor 4.2 (2.0) 4.3 (1.9) 4.5 (2.0) F=2.78, p=.063
Religion 5.0 (2.3) 5.0 (2.3) 5.1 (2.4) F=0.48, p=.621
Using emotional support 6.3 (1.7) 6.4 (1.6) 6.2 (1.7) F=1.11, p=.330
Using instrumental support 5.1 (1.8) 5.4 (1.7) 5.5 (1.8) F=4.33, p=.013
1 < 3
Disengagement Coping Strategies
Self-distraction 5.2 (1.8) 5.6 (1.6) 5.5 (1.6) F=6.61, p<.001
1 < 2 and 3
Denial 2.4 (0.9) 2.5 (1.1) 2.7 (1.3) F=7.01, p<.001
1 and 2 < 3
Venting 3.5 (1.6) 4.0 (1.6) 4.4 (1.7) F=24.86, p<.001
1 < 2 < 3
Substance use 2.2 (0.6) 2.2 (0.7) 2.3 (0.9) F=5.68, p=.004
1 and 2 < 3
Behavioral disengagement 2.1 (0.4) 2.2 (0.7) 2.5 (1.1) F=22.75, p<.001
1 < 2 < 3
Self-blame 2.5 (1.0) 2.8 (1.1) 3.4 (1.6) F=51.41, p<.001
1 < 2 < 3
Open in a new tab

Abbreviation: SD = standard deviation

*

Each item was rate on a 4-point Likert scale that ranged from 1 (“I haven’t been doing this at all”) to 4 (“I have been doing this a lot”). Each coping strategy is evaluated using 2 items. Scores can range from 2 to 8 with higher scores indicating greater use of each of the coping strategies.

DISCUSSION

This study extends our prior work that identified differences in demographic, clinical, and sleep characteristics among three classes of oncology patients with distinct sleep disturbance profiles (Tejada et al., 2019) to include an evaluation of differences in global, cancer-specific, and cumulative life stress, as well as resilience and the use of engagement and disengagement coping behaviors. Consistent with previous albeit limited reports of associations between higher levels of sleep disturbance and higher levels of global (Xu Y., 2021), cancer-specific (Weng et al., 2021), and cumulative life (Garvin et al., 2021) stress in oncology patients, our findings support our a priori hypothesis that patients in the Very High class would report higher levels of all three types of stress; increased occurrence rates of and effects from a variety of life stressors; lower levels of resilience; as well as higher use of disengagement coping strategies.

Differences in stress and resilience

As shown in Table 1, all the stress measures exhibit a “dose response effect” in that as the sleep disturbance profile worsened the levels of all three types of stress increased significantly. Our findings are consistent with previous reports in adult survivors of sexual abuse (Steine et al., 2017) and healthy adults (Drake et al., 2014). For example, in a study of adult survivors of sexual abuse that examined the relationship between cumulative childhood maltreatment experiences (i.e., sexual abuse, physical/emotional abuse, neglect) and a number of symptoms (Steine et al., 2017), a statistically significant dose-response relationship was found between insomnia and cumulative childhood maltreatment scores. In another study that examined the relationships among stress, trait sleep reactivity (i.e., degree to which a given amount of stress disrupts sleep), and insomnia in healthy adults without a history of insomnia (Drake et al., 2014), sleep reactivity moderated the effects of stress. Specifically, sleep reactivity moderated the effects of stress, in that the risk for insomnia as a function of stress was significantly higher in individuals with higher sleep reactivity. Given that the current study is the first to describe this type of dose response effect in oncology patients and that a myriad of factors (e.g., chemotherapy (Liu & Ancoli-Israel, 2008), event appraisal (Pillai et al., 2014), stressor chronicity (Pillai et al., 2014), sleep reactivity (Drake et al., 2014; Kalmbach et al., 2018), substance misuse (Pillai et al., 2014)) may influence the relationships between sleep and stress, additional studies are warranted.

Global stress

Compared to the Low class, the other two classes reported higher levels of global perceived stress. While the PSS does not have a clinically meaningful cutoff score, our patients reported scores that were similar to those reported by oncology patients receiving radiation therapy (Ravindran et al., 2019) and cancer survivors who underwent chemotherapy within the previous year (Zhang et al., 2022). In addition, our findings are consistent with previous studies of patients with digestive (Lv, Zhao, Xie, et al., 2022) and breast (Ban Y, 2022) cancer that found significant positive associations between sleep disturbance and perceived global stress.

Cancer-specific stress

While the High class had an average IES-R total score that was below the clinically meaningful cutoff for posttraumatic symptomatology, 18.1% of these patients had an IES-R total score that suggests subsyndromal PTSD and 10.9% had scores indicative of probable PTSD. In the Very High class, the average IES-R total score indicates post-traumatic symptomatology. Among these patients, 31.4% had total scores of ≥33, suggestive of PTSD. Our findings are consistent with a study of women with breast cancer that found that patients who experienced worse sleep disturbance after cancer treatment were more likely to report higher IES-R scores (i.e., 21.5±14.4) (Weng et al., 2021). Cancer and its associated treatments are perceived by some patients as life-threatening and highly traumatic events (Seiler A, 2019). In addition, patients undergoing chemotherapy often experience higher levels of other symptoms (e.g., pain, fatigue, anxiety) which may contribute to increased levels of cancer-specific stress and disturbed sleep (Mystakidou et al., 2009).

Cumulative life stress

Compared to the other two latent classes, the Very High class reported the occurrence of 8 out of 30 SLEs. Specifically, some of the highest occurrence rates and effect scores, for the Very High class, were for: being separated/divorced (43.7%), emotional abuse (33.2%), having serious money problems (32.0%), and caring for someone with severe physical/emotional handicap (30.3%). In terms of adverse childhood experiences (ACEs), 30.0% of the patients in the Very High class reported family violence during childhood, 16.8% being forced to touch before the age of 16, and 7.3% being forced to have sex before the age of 16. Our findings are consistent with several studies of women with breast (Seib et al., 2017), ovarian (Garvin et al., 2021; Seib et al., 2017), and hematological (Seib et al., 2017) cancer that found that both recent and lifetime stressors were positively associated with higher levels of sleep disturbance. In addition, as noted in several systematic reviews (Hong-jie Yu, 2022; Kajeepeta et al., 2015), patients who experience one or several ACEs have a 14% to 33% increased risk for sleep disturbance during adulthood.

While the exact mechanism(s) for the association between sleep disturbance and stress are not fully understood, recent evidence suggests circadian dysregulation as a potential mechanism. For example, after the sensory systems collect information about a stressful event, these signals are transferred to the paraventricular nucleus of the hypothalamus to stimulate the secretion of corticotrophin releasing hormone (CRH) to initiate hypothalamus-pituitary-adrenal (HPA) axis activation (Kajeepeta et al., 2015; Koch et al., 2017). Higher levels of CRH lead to HPA axis hyperactivity, which in turn is associated with disturbed sleep. In addition, inflammation can disrupt circadian rhythms. In a recent meta-analysis (Hong-jie Yu, 2022), an association was reported between exposure to childhood trauma (e.g., sexual, emotional abuse) and increased circulating levels of interleukin-6, tumor necrosis factor-α, and C-reactive protein in adulthood.

An equally plausible explanation of the association between sleep disturbance in adulthood and cumulative life stress is a social mechanism. Individuals who are exposed to ACEs and/or grow up in stressful family environments (e.g., socioeconomically disadvantaged, abusive) have more difficulty learning healthy sleep habits and are at increased risk for higher levels of cumulative life stress (Garvin et al., 2021; Kajeepeta et al., 2015). Given that our data suggest that certain SLEs are more prevalent and have greater effects (e.g., being forced to touch before 16 years, separation/divorce) in oncology patients who experience higher levels of sleep disturbance, a careful evaluation of biological and social mechanisms is warranted.

Resilience

Of note, both the High and Very High classes had resilience scores that were below the normative score for the general population of the United States (Campbell-Sills et al., 2009). This finding is not surprising given that resilience levels in patients with cancer tend to decrease as the number and/or severity of symptoms and levels of stress increase (Ban Y, 2022). Specifically, in a study of patients with breast cancer (Lai et al., 2020), worse sleep quality was associated with lower levels of resilience. Given that in both the general population (Y. Cai et al., 2021) and cancer patients (Gao et al., 2019), higher levels of resilience were found to allow an individual to better adjust to stressful events and seek care to alleviate sleep disturbance, clinicians need to recommend interventions to oncology patients that decrease stress and facilitate resilience (e.g., exercise, psychological resilience training).

Differences in coping strategies

Oncology patients use numerous engagement and disengagement strategies to overcome the negative impact of stress (Kvillemo & Branstrom, 2014). Compared to the Low class, the other two classes reported higher use of most of the disengagement coping strategies (i.e., self-distraction, venting, behavioral disengagement, self-blame; Table 4) These types of behaviors aim to avoid or ignore stressors and their related emotional consequences (Kvillemo & Branstrom, 2014). This finding is not unexpected, because previous research found that the use of disengagement coping behaviors was associated with higher levels of sleep disturbance in patients with prostate (Hoyt et al., 2009; Thomas et al., 2010; Trudel-Fitzgerald et al., 2017), breast (Thomas et al., 2010; Trudel-Fitzgerald et al., 2017), gastrointestinal (Lv, Zhao, Li, et al., 2022; Trudel-Fitzgerald et al., 2017), and genitourinary (Trudel-Fitzgerald et al., 2017) cancers. For example, avoidance of stressors through self-distraction was associated with increased sleep disturbance in patients with various types of cancer two months after enrollment (Trudel-Fitzgerald et al., 2017).

While not evaluated in detail in oncology patients with heterogenous types of cancer, and since the mean age for our sample is 57.1 years, one plausible explanation for the association between higher levels of sleep disturbance and a higher use of disengagement coping strategies is age. Compared to older patients, younger patients tend to address symptoms (e.g., sleep disturbance) with more direct actions (e.g., stress reduction) because of their perceptions of a more limited future (Lv, Zhao, Li, et al., 2022). An equally plausible explanation for the increased use of disengagement coping strategies is that oncology patients receiving chemotherapy often experience multiple co-occurring symptoms. For example, in a study of women with breast cancer (Kenne Sarenmalm et al., 2007), a lower coping capacity was associated with the occurrence and greater severity of multiple symptoms (e.g., sleep disturbance, pain). Additional research is needed to elucidate the relationships between a variety of demographic and clinical characteristics and the use of disengagement coping strategies in oncology patients who experience high levels of sleep disturbance.

Compared to the Low class, the Very High class reported a higher use of the engagement strategy of instrumental support. Instrumental support refers to a type of support that involves help, advice, or comfort from other people (e.g., family members, friends) (T. Cai et al., 2021). Stronger support systems are known to improve not only oncology patients’ levels of resilience, but the use of positive coping strategies (Zhou et al., 2022). Furthermore, in patients with breast (Oh et al., 2020) and colorectal (Kang & Son, 2019) cancers, the buffering effect of structural support was associated with a better adaptation to stressful life events as well as less severe chemotherapy-related symptoms (e.g., insomnia). Given that oncology patients choose coping strategies based on a variety of factors (e.g., understanding of the illness, support system, personality, past lived stressful experiences, severity of symptoms) (Hagan et al., 2017), additional research that incorporates measures of social support, as well as other social determinants of health is needed to confirm and/or investigate more definitive associations between stress and coping in oncology patients who experience sleep disturbance.

Study Limitations

Several limitations must be acknowledged. First, while the use of subjective measures to evaluate sleep disturbance are valid and reliable, future studies should examine associations between stress and coping and objective measures of sleep disturbance. Second, this study evaluated for differences in stress and coping in patients undergoing chemotherapy. Therefore, these results may not generalize to patients who undergo other types of treatment. Third, the sample was relatively homogenous in terms of race/ethnicity, education, and socioeconomic status. These results warrant replication in more diverse samples. In addition, future studies need to evaluate for gender differences in the occurrence and effects of various life stressors and their associations with sleep disturbance. Fourth, given that other factors may moderate/mediate the relationships among sleep disturbance and various types of stress and coping strategies in oncology patients, additional factors that may influence these relationships (e.g., social determinants of health) warrant consideration. Finally, stress and coping were measured only at enrollment, so future studies should evaluate for changes in these relationships over time and the bidirectionality of the relationships between sleep disturbance and various types of stress.

Conclusions

Despite these limitations, this study is the first to evaluate for differences in global, cancer-specific, and cumulative life stress, as well as levels of resilience and use of various coping strategies, among oncology outpatients with distinct sleep disturbance undergoing chemotherapy. Taken together, our findings suggest that very high levels of sleep disturbance are associated with higher levels of all three types of stress, lower levels of resilience, and higher use of disengagement coping strategies. Given these relationships, clinicians need to conduct regular assessments on potential sources of cancer-specific and life stress, including prior traumatic events. To better manage both sleep disturbance and stress in patients undergoing chemotherapy, clinicians need to provide referrals to appropriate psychological services that can assist patients to implement targeted interventions (e.g., behavioral therapy, yoga, training on engagement coping). In addition, patients may warrant referrals to social services.

Additional research is warranted on the relationships between various sleep characteristics (e.g., sleep quality, sleep initiation, number of night time awakenings) and various types of stress. Future studies need to investigate the molecular mechanisms that contribute to both stress and sleep disturbance. Equally important, an evaluation of additional social factors (e.g., living environment, social support) that may influence the severity of stress and sleep disturbance in oncology patients as well as the choice of coping strategies is warranted.

Supplementary Material

Table S1
Fig S1

Acknowledgements -

This study was funded by a grant from the National Cancer Institute (CA134900). Dr. Calvo-Schimmel is supported by a grant from the National Institute of Nursing Research (T32NR016920). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dr. Miaskowski is an American Cancer Society Clinical Research Professor.

Footnotes

Conflict of interest statement - The authors have no conflicts of interest to declare.

Data accessibility statement –

Data is available from the corresponding author following the completion of a data sharing agreement with the University of California, San Francisco.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1
NIHMS1925297-supplement-Table_S1.docx (25KB, docx)
Fig S1
NIHMS1925297-supplement-Fig_S1.tif (2MB, tif)

Data Availability Statement

Data is available from the corresponding author following the completion of a data sharing agreement with the University of California, San Francisco.

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