Abstract
Background:
Patients with gastrointestinal cancers experience moderate to high levels of sleep disturbance during chemotherapy that decreases their functional status and quality of life (QOL).
Objective:
Identify subgroups of patients with gastrointestinal cancers with distinct sleep disturbance profiles and evaluate for differences among these subgroups in demographic, clinical, and sleep characteristics, as well as co-occurring symptoms and QOL outcomes.
Methods:
Patients (n=405) completed questionnaires six times over two cycles of chemotherapy. Latent profile analysis (LPA) was used to identify subgroups of patients with distinct sleep disturbance profiles.
Results:
Three distinct sleep disturbance profiles (i.e., Low, High, Very High) were identified. Compared to the Low class, patients in the other two classes were significantly younger and less likely to be married and to exercise on a regular basis, and received a higher number of previous treatments. Compared to the Low Class, patients in the other two classes reported higher levels of anxiety, depressive symptoms, morning and evening fatigue, and pain, and lower levels of attentional function and QOL scores at enrollment.
Conclusions:
This study is the first to use LPA to identify subgroups of patients with gastrointestinal cancers with distinct sleep disturbance profiles. Findings provide new insights on the associations between sleep disturbance and multiple co-occurring symptoms in these patients.
Implications for Practice:
Clinicians can identify patients who are at the highest risk for sleep disturbance and recommend a variety of sleep hygiene interventions (e.g., establishment of a bedtime routine) as well as initiate interventions for other co-occurring symptoms.
Keywords: sleep disturbance, fatigue, depression, anxiety, pain, chemotherapy, gastrointestinal cancer, quality of life
INTRODUCTION
Sleep disturbance is a frequent and distressing symptom in patients receiving chemotherapy that has detrimental effects on their cognitive and functional status, quality of life (QOL), and disease progression.1 While sleep disturbance in patients with breast,2 lung,3 prostate,4 and gynecologic5 cancer has been documented, less is known about the occurrence of, risk factors for, and impact of this symptom in patients with gastrointestinal cancers. In previous studies of patients with gastrointestinal cancers, occurrence rates for sleep disturbance ranged from 38%6 to 63%7 and severity scores were in the moderate range.7, 8 In a longitudinal study of 361 patients with colorectal cancer,9 sleep disturbance was reported by 56% of patients prior to and by 52% during chemotherapy. These findings suggest that a significant number of patients with gastrointestinal cancers report moderate to high levels of sleep disturbance during chemotherapy.
In terms of risk factors, two studies of patients with colorectal cancer found that higher rates of sleep disturbance were associated with pain, anxiety, fatigue, retirement, and the existence of multiple comorbid conditions.10, 11 In another cross-sectional study of 434 patients with colorectal cancer,6 the occurrence of sleep disturbance was positively correlated with pain and anxiety. In addition, compared to patients with liver cancer who did not experience sleep disturbance,12 those who reported this symptom had higher rates and severity of co-occurring symptoms. In terms of patient outcomes, sleep disturbance was associated with a poor treatment response,9 worse QOL,13 higher risk of earlier death,9 and lower overall survival.9
While these studies provide important information on sleep disturbance in patients with gastrointestinal cancers, several limitations warrant consideration. First, only one study described changes over time in sleep disturbance in these patients.9 Second, only four studies examined risk factors for higher levels of sleep disturbance.6, 10–12 In addition, little information is available on specific sleep characteristics (e.g., sleep quality, sleep maintenance) in these patients. Finally, none of these studies used a person-centered analytic approach (e.g., latent variable modeling) to evaluate for distinct sleep disturbance profiles in patients with gastrointestinal cancers. Therefore, the purposes of this study were to identify subgroups of patients with gastrointestinal cancers with distinct sleep disturbance profiles and evaluate for differences among these subgroups in demographic, clinical, and sleep characteristics, as well as co-occurring symptoms and QOL outcomes.
METHODS
Patients and settings
This study is part of a prospective longitudinal study of symptom clusters in oncology outpatients receiving chemotherapy that used the Theory of Symptom Management as its theoretical framework.14 In this study, the symptom experience (i.e. severity of sleep disturbance) and outcome (i.e., quality of life) dimensions of the theory were explored. The methods for this study were described in detail in our previous publications.15, 16 In brief, eligible patients for the parent study: 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. A total of 2,234 patients were approached and 1,343 consented to participate (60.1% response rate) in the parent study. The major reason for refusal was being overwhelmed with their cancer treatment. For this study, only patients with gastrointestinal cancers who had complete data for sleep disturbance (n=405) were included.
Instruments
Patients completed a demographic questionnaire, the Karnofsky Performance Status (KPS) scale,17 and the Self-administered Comorbidity Questionnaire (SCQ).18 Patients medical records were reviewed for disease and treatment information.
Assessment of Sleep Disturbance
The 21-item General Sleep Disturbance Scale (GSDS) was designed to assess seven aspects of sleep disturbance (i.e., excessive daytime sleepiness, medications for sleep, sleep quality, sleep quantity, sleep onset latency, mid-sleep awakenings, early awakenings) in the past week. Each item was rated on a 0 (never) to 7 (everyday) numeric rating scale (NRS). The GSDS total score can range from 0 (no disturbance) to 147 (extreme disturbance). Each mean subscale score ranges from 0 to 7. A mean subscale score of ≥3 or a total GSDS score of ≥43 indicate a significant level of sleep disturbance that warrants clinical evaluation and management.19 The GSDS has well-established validity and reliability.20, 21 In our study, the Cronbach’s alpha for the GSDS total score was 0.83.
Assessment of Common Co-occurring Symptoms
All of the instruments that were used to assess common co-occurring symptoms are valid and reliable. The symptoms that were assessed included: state and trait anxiety (Spielberger State-Trait Anxiety Inventories (STAI-T and STAI-S)22); depressive symptoms (Center for Epidemiological Studies-Depression scale (CES-D)23); morning and evening fatigue and morning and evening energy (Lee Fatigue Scale (LFS)24); cognitive dysfunction (Attentional Function Index (AFI)25); and pain (Brief Pain Inventory (BPI)26).
Assessment of QOL
Quality of life was evaluated using disease-specific (i.e., Quality of Life Scale-Patient Version (QOL-PV)) and generic (i.e., Medical Outcomes Study-Short Form-12 (SF-12)) measures. The 41-item QOL-PV evaluated four dimensions of QOL (i.e., physical, psychological, social, and spiritual well-being) in oncology patients, as well as a total QOL score.27 The SF-12 was scored into two components (i.e., physical component summary (PCS) and mental component summary (MCS) scores). Higher PCS and MCS scores indicate a better QOL.28
Study procedures
The parent study was approved by the Committee on Human Research at the University of California, San Francisco, by the Institutional Review Board (IRB) at each of the study sites, and by the IRB of Duke University. Patients were approached by a research staff member in the infusion unit, during their first or second cycle of chemotherapy, to discuss participation in the study. Written informed consent was obtained from all patients. Depending on the length of their chemotherapy cycles, patients completed questionnaires in their home, a total of six times over two cycles of chemotherapy (i.e., recovery from previous chemotherapy cycle (i.e., assessments 1 and 4), approximately 1 week after chemotherapy administration (i.e., acute symptoms, assessments 2 and 5), and approximately 2 weeks after chemotherapy administration (i.e., potential nadir, assessments 3 and 6)).
Data analysis
Latent profile analysis (LPA) was used to identify subgroups of patients with distinct sleep disturbance profiles over the six assessments. 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. To determine the best fitting model to characterize the latent class structure, multiple information criteria were used. Lower values for the Akaike Information Criteria (AIC) and Bayesian Information Criterion (BIC) represent better fitting models. Entropy values classify the quality of the model, in which values close to 1 indicate good classification. When using the Vuong-Lo-Mendell-Rubin likelihood ratio test (VLMR) to compare the models, a significant p-value suggests that one estimated model fits the data better than another model with one fewer group.29, 30 Estimation of model fit was conducted with Mplus Version 8.0 with 800 to 2,000 random starts.31
Descriptive statistics and frequency distributions were calculated for demographic and clinical characteristics using SPSS, version 27 (IBM Corporation, Armonk, NY). Differences in demographic, clinical, and sleep characteristics, as well as co-occurring symptoms and QOL outcomes, among the latent classes, were evaluated using parametric and non-parametric tests. Post hoc contrasts were calculated using the Bonferroni procedure. A p-value of <0.05 was considered statistically significant.
RESULTS
Latent Classes for Sleep Disturbance
The three-class solution was selected for sleep disturbance because the BIC for that solution was lower than the BIC for the 2-class solution (Table 1). In addition, the VLMR was significant for the 3-class solution, indicating that three classes fit the data better than two classes. However, the VLMR was not significant for the 4-class solution, indicating that too many classes were extracted.
Table 1.
General Sleep Disturbance Scale Latent Profile Solutions and Fit Indices for One through Four Classes for Patients with Gastrointestinal Cancers
Model | LL | AIC | BIC | Entropy | VLMR |
---|---|---|---|---|---|
1 Class | −8539.59 | 17121.19 | 17205.27 | n/a | n/a |
2 Class | −8406.77 | 16869.54 | 16981.65 | 0.75 | 265.64c |
3 Classa | −8331.34 | 16732.68 | 16872.81 | 0.80 | 150.87b |
4 Class | −8290.61 | 16665.22 | 16833.38 | 0.798 | 81.46ns |
Baseline Entropy and VLMR are not applicable for the one-class solution
The three-class solution was selected because the BIC for that solution was lower than the BIC for the 2-class solution. In addition, the VLMR was significant for the 3-class solution, indicating that three classes fit the data better than two classes. However, the VLMR was not significant for the 4-class solution, indicating that too many classes were extracted. Although the BIC for the 4-class solution was smaller than the BIC for the 3-class solution, one predicted class in the 4-class solution was small (43 predicted cases; less than 11% of the sample), raising the concern that the solution would not generalize to other samples.
p < .05
p = .0004
Abbreviations: AIC, Akaike’s Information Criterion; BIC, Bayesian Information Criterion; LL, log-likelihood; n/a, not applicable; ns, not significant; VLMR, Vuong-Lo-Mendell-Rubin likelihood ratio test for the K vs. K-1 model.
The trajectories for sleep disturbance differed among the latent classes (Figure 1). Using the clinically meaningful total GSDS score of ≥43,19 the sleep disturbance classes were named Low (35.8%), High (48.6%), and Very High (15.6%). For the High and Very High classes, sleep disturbance scores increased slightly in the weeks following the administration of chemotherapy (i.e., assessment 2 and 4). In contrast, for the Low class, sleep disturbance scores increased slightly at assessment 2, decreased at assessment 3, and then remained relatively constant across assessments 4 through 6.
Figure 1.
Sleep disturbance trajectories for patients in each of the latent classes.
Differences in Demographic and Clinical Characteristics
As shown in Table 2, compared to the Low class, patients in the High class were more likely to be female. Compared to the Low class, patients in the Very High class were less likely to be employed and had a diagnosis of back pain. Compared to the Low class, patients in the High and Very High classes were significantly younger, had a higher number of comorbidities, had a higher number of prior cancer treatments, were less likely to be married/partnered, and were less likely to exercise on a regular basis. Compared to the Low and High classes, patients in the Very High class were more likely to report having childcare responsibilities. In addition, significant differences were found among the three classes for the KPS scores (i.e., Low > High > Very High), as well as SCQ scores and occurrence of self-reported depression (i.e., Low < High < Very High).
Table 2.
Differences in Demographic and Clinical Characteristics Among the Sleep Disturbance Classes
Characteristic | Low Sleep Disturbance (0) 35.8% (n=145) | High Sleep Disturbance (1) 48.6% (n=197) | Very High Sleep Disturbance (2) 15.6% (n=63) | Statistics |
---|---|---|---|---|
| ||||
Mean (SD) | Mean (SD) | Mean (SD) | ||
Age (years) | 60.4 (10.3) | 57.1 (12.3) | 55.0 (12.6) | F=5.79, p=.003 0 > 1 and 2 |
| ||||
Education (years) | 16.1 (2.9) | 16.0 (3.2) | 16.0 (3.1) | F=0.04, p=.958 |
| ||||
Body mass index (kg/m2) | 25.3 (4.5) | 25.6 (5.4) | 26.8 (5.4) | F=1.90, p=.151 |
| ||||
Karnofsky Performance Status score | 87.4 (9.4) | 78.6 (11.6) | 71.2 (12.4) | F=52.56, p<.001 0 > 1 > 2 |
| ||||
Number of comorbidities | 2.0 (1.1) | 2.5 (1.5) | 2.7 (1.4) | F=8.39, p<.001 0 < 1 and 2 |
| ||||
Self-administered Comorbidity Questionnaire score | 4.4 (2.3) | 5.6 (2.9) | 6.8 (3.7) | F=17.86, p<.001 0 < 1 < 2 |
| ||||
Time since cancer diagnosis (years) | 1.1 (1.9) | 1.8 (3.6) | 1.2 (2.1) | KW=5.57, p=.062 |
| ||||
Time since diagnosis (median; years) | 0.39 | 0.46 | 0.43 | |
| ||||
Number of prior cancer treatments | 1.1 (1.2) | 1.5 (1.3) | 1.8 (1.5) | F=7.92, p<.001 0 < 1 and 2 |
| ||||
Number of metastatic sites including lymph node involvement | 1.4 (1.1) | 1.5 (1.2) | 1.5 (1.0) | F=0.20, p=.816 |
| ||||
Number of metastatic sites excluding lymph node involvement | 1.0 (0.9) | 1.0 (1.0) | 0.9 (0.9) | F=0.34, p=.713 |
| ||||
AUDIT score | 3.6 (2.8) | 3.3 (3.1) | 3.0 (2.8) | F=0.84, p=.433 |
| ||||
Hemoglobin (gm/dl) | 11.9 (1.4) | 12.0 (1.6) | 11.9 (1.6) | F=0.28, p=.760 |
| ||||
Hematocrit (%) | 35.7 (3.7) | 35.9 (4.5) | 35.5 (4.3) | F=0.34, p=.715 |
| ||||
MAX-2 score | 0.14 (0.06) | 0.14 (0.06) | 0.13 (0.05) | F=0.46, p=.632 |
| ||||
% (n) | % (n) | % (n) | ||
| ||||
Gender (% female) | 34.5 (50) | 53.3 (105) | 49.2 (31) | Χ2=12.23, p=.002 0 < 1 |
| ||||
Ethnicity | Χ2=7.86, p=.248 | |||
White | 64.1 (91) | 71.1 (138) | 65.1 (41) | |
Asian or Pacific Islander | 14.8 (21) | 9.8 (19) | 12.7 (8) | |
Black | 12.0 (17) | 8.8 (17) | 4.8 (3) | |
Hispanic Mixed or Other | 9.2 (13) | 10.3 (20) | 17.5 (11) | |
| ||||
Married or partnered (% yes) | 78.9 (112) | 61.4 (121) | 58.7 (37) | Χ2=13.81, p=.001 0 > 1 and 2 |
| ||||
Lives alone (% yes) | 15.5 (22) | 20.3 (40) | 22.6 (14) | Χ2=1.87, p=.392 |
| ||||
Child care responsibilities (% yes) | 16.6 (24) | 18.5 (35) | 37.1 (23) | Χ2=12.22, p=.002 0 and 1 < 2 |
| ||||
Adult care responsibilities (% yes) | 7.2 (10) | 6.8 (12) | 8.6 (5) | Χ2=0.21, p=.900 |
| ||||
Currently employed (% yes) | 42.1 (59) | 32.1 (63) | 21.0 (13) | Χ2=9.14, p=.010 0 > 2 |
| ||||
Income | KW=4.18, p=.124 | |||
< $30,000+ | 13.9 (17) | 19.1 (34) | 35.5 (22) | |
$30,000 to <$70,000 | 20.5 (25) | 20.8 (37) | 14.5 (9) | |
$70,000 to < $100,000 | 22.1 (27) | 15.7 (28) | 11.3 (7) | |
> $100,000 | 43.4 (53) | 44.4 (79) | 38.7 (24) | |
| ||||
Specific comorbidities (% yes) | ||||
| ||||
Heart disease | 5.5 (8) | 5.1 (10) | 4.8 (3) | Χ2=0.06, p=.970 |
| ||||
High blood pressure | 33.8 (49) | 37.1 (73) | 22.2 (14) | Χ2=4.71, p=.095 |
| ||||
Lung disease | 2.8 (4) | 7.1 (14) | 9.5 (6) | Χ2=4.57, p=.102 |
| ||||
Diabetes | 9.7 (14) | 13.2 (26) | 20.6 (13) | Χ2=4.66, p=.097 |
| ||||
Ulcer or stomach disease | 3.4 (5) | 7.6 (15) | 4.8 (3) | Χ2=2.82, p=.244 |
| ||||
Kidney disease | 0.7 (1) | 1.5 (3) | 3.2 (2) | Χ2=1.86, p=.394 |
| ||||
Liver disease | 11.7 (17) | 13.2 (26) | 9.5 (6) | Χ2=0.64, p=.728 |
| ||||
Anemia or blood disease | 5.5 (8) | 10.2 (20) | 15.9 (10) | Χ2=5.81, p=.055 |
| ||||
Depression | 4.1 (6) | 16.2 (32) | 31.7 (20) | Χ2=28.44, p<.001 0 < 1 < 2 |
| ||||
Osteoarthritis | 4.8 (7) | 11.2 (22) | 12.7 (8) | Χ2=5.19, p=.075 |
| ||||
Back pain | 15.2 (22) | 22.8 (45) | 33.3 (21) | Χ2=8.80 p=.012 0 < 2 |
| ||||
Rheumatoid arthritis | 2.1 (3) | 2.0 (4) | 3.2 (2) | Χ2=0.31, p=.856 |
| ||||
Exercise on a regular basis (% yes) | 77.9 (113) | 60.9 (120) | 59.3 (35) | Χ2=12.67, p=.002 0 > 1 and 2 |
| ||||
Current or history of smoking (% yes) | 30.1 (43) | 31.1 (59) | 36.1 (22) | Χ2=0.74, p=.690 |
| ||||
Colon and rectal cancer (% yes) | 59.7 (86) | 64.1 (125) | 66.7 (42) | Χ2=1.13, p=.569 |
| ||||
Type of prior cancer treatment | Χ2=19.51, p=.003 0 > 1 and 2 |
|||
No prior treatment | 42.0 (58) | 23.3 (45) | 19.4 (12) | |
Only surgery, CTX, or RT | 31.9 (44) | 39.9 (77) | 41.9 (26) | NS |
Surgery & CTX, or Surgery & RT, or CTX & RT | 18.1 (25) | 25.4 (49) | 21.0 (13) | NS |
Surgery & CTX & RT | 8.0 (11) | 11.4 (22) | 17.7 (11) | NS |
| ||||
CTX regimen | Χ2=6.79, p=.341 | |||
FOLFIRI | 15.0 (21) | 10.8 (21) | 22.6 (14) | |
FOLFOX | 41.4 (58) | 44.3 (86) | 43.5 (27) | |
FOLFIRINOX | 12.1 (17) | 11.3 (22) | 6.5 (4) | |
Other | 31.4 (44) | 33.5 (65) | 27.4 (17) | |
| ||||
CTX cycle length | Χ2=3.84, p=.428 | |||
14 day | 80.7 (117) | 84.2 (165) | 84.1 (53) | |
21 day | 17.9 (26) | 12.2 (24) | 14.3 (9) | |
28 day | 1.4 (2) | 3.6 (7) | 1.6 (1) | |
| ||||
Emetogenicity of CTX | Χ2=3.62, p=.460 | |||
Minimal/low | 11.7 (17) | 17.9 (35) | 11.1 (7) | |
Moderate | 84.8 (123) | 79.0 (154) | 84.1 (53) | |
High | 3.4 (5) | 3.1 (6) | 4.8 (3) | |
| ||||
Antiemetic regimen | Χ2=4.78, p=.572 | |||
None | 3.5 (5) | 5.2 (10) | 9.8 (6) | |
Steroid alone or serotonin antagonist alone | 11.9 (17) | 11.5 (22) | 8.2 (5) | |
Serotonin receptor antagonist and steroid | 65.0 (93) | 61.3 (117) | 57.4 (35) | |
NK-1 receptor antagonist and two other antiemetics | 19.6 (28) | 22.0 (42) | 24.6 (15) |
Abbreviations: AUDIT, Alcohol Use Disorders Identification Test; CTX, chemotherapy; dl, deciliter; FOLFIRI, leucovorin/5-fluorouracil/irinotecan; FOLFIRINOX, leucovorin/5-fluorouracil/irinotecan/oxaliplatin; FOLFOX, leucovorin/5-fluorouracil/oxaliplatin; gm, grams; kg, kilograms; KW, Kruskal Wallis test; m2, meter squared; NK-1, neurokinin-1; NS, not significant; RT, radiation therapy; SD, standard deviation.
Differences in Symptom Scores
As shown in Table 3, significant differences were found among the three sleep disturbance classes for all of the GSDS subscale and total sleep disturbance scores at enrollment (i.e., Low < High < Very High).
Table 3.
Differences in the General Sleep Disturbance Subscale and Total Scores Among the Sleep Disturbance Classes at Enrollment
Subscales and Total GSDS Scoresa | Low Sleep Disturbance (0) 35.8% (n=145) | High Sleep Disturbance (1) 48.6% (n=197) | Very High Sleep Disturbance (2) 15.6% (n=63) | Statistics |
---|---|---|---|---|
Mean (SD) | Mean (SD) | Mean (SD) | ||
Excessive daytime sleepiness (≥3) | 1.4 (1.0) | 2.9 (1.2) | 4.1 (1.2) | F=139.20, p<.001 0 < 1 < 2 |
Medications for sleep (≥3) | 0.3 (0.6) | 0.6 (0.7) | 1.0 (1.1) | F=21.00, p<.001 0 < 1 < 2 |
Sleep quality (≥3) | 1.7 (1.4) | 3.5 (1.4) | 5.2 (1.4) | F=150.74, p<.001 0 < 1 < 2 |
Sleep quantity (≥3) | 3.9 (1.3) | 4.7 (1.5) | 5.7 (1.8) | F=32.47, p<.001 0 < 1 < 2 |
Sleep onset latency (≥3) | 1.2 (1.5) | 2.7 (1.9) | 4.6 (2.4) | F=73.48, p<.001 0 < 1 < 2 |
Mid-sleep awakenings (≥3) | 3.9 (2.4) | 5.0 (2.0) | 6.2 (1.3) | F=29.94, p<.001 0 < 1 < 2 |
Early awakenings (≥3) | 2.1 (2.0) | 3.9 (2.2) | 5.6 (1.8) | F=70.63, p<.001 0 < 1 < 2 |
Total GSDS scores (≥43) | 31.5 (11.6) | 54.5 (12.4) | 78.2 (14.6) | F=324.05, p<.001 0 < 1 < 2 |
Abbreviations: GSDS, General Sleep Disturbance Scale; SD, standard deviation.
Numbers in parenthesis indicate clinical meaningful cutoff scores.
As shown in Table 4, significant differences were found among the three classes in trait anxiety, state anxiety, depression, morning fatigue, and evening fatigue scores (i.e., Low < High < Very High). Differences in attentional function scores followed the expected pattern (i.e., Low > High > Very High). For morning and evening energy, compared to the Low class, patients in the High and Very High classes had lower scores. In terms of the occurrence of pain, compared to the Low class, a higher percentage of patients in the High and Very High classes reported both cancer and non-cancer pain. For patients who had pain, compared to the Low class, patients in the High and Very High classes had higher worst pain intensity and pain interference scores.
Table 4.
Differences in Co-occurring Symptoms Scores Among the Sleep Disturbance Classes at Enrollment
Symptomsa | Low Sleep Disturbance (0) 35.8% (n=145) | High Sleep Disturbance (1) 48.6% (n=197) | Very High Sleep Disturbance (2) 15.6% (n=63) | Statistics |
---|---|---|---|---|
| ||||
Mean (SD) | Mean (SD) | Mean (SD) | ||
Symptoms scores | ||||
| ||||
Trait anxiety (≥31.8) | 28.3 (6.4) | 35.2 (9.0) | 42.8 (11.6) | F=64.17, p<.001 0 < 1 < 2 |
| ||||
State anxiety (≥32.2) | 26.8 (7.6) | 35.1 (10.9) | 42.4 (14.4) | F=52.60, p<.001 0 < 1 < 2 |
| ||||
Depressive symptoms (≥16) | 6.3 (5.4) | 13.4 (7.7) | 19.5 (10.7) | F=74.21, p<.001 0 < 1 < 2 |
| ||||
Attentional function (≤7.5) | 7.7 (1.5) | 6.2 (1.6) | 5.2 (1.9) | F=59.68, p<.001 0 > 1 > 2 |
| ||||
Morning fatigue (≥3.2) | 1.2 (1.0) | 3.2 (2.1) | 5.3 (2.2) | F=120.20, p<.001 0 < 1 < 2 |
| ||||
Evening fatigue (≥5.6) | 3.6 (2.0) | 5.5 (2.1) | 6.7 (1.7) | F=58.71, p<.001 0 < 1 < 2 |
| ||||
Morning energy (≤6.2) | 5.2 (2.5) | 4.2 (2.3) | 3.7 (2.1) | F=10.74, p<.001 0 > 1 and 2 |
| ||||
Evening energy (≤3.5) | 4.2 (2.0) | 3.2 (1.9) | 2.9 (2.1) | F=12.74, p<.001 0 > 1 and 2 |
| ||||
% (n) | % (n) | % (n) | ||
| ||||
Pain type | Χ2=27.86, p<.001 0 > 1 and 2 |
|||
No pain | 45.1 (64) | 25.8 (50) | 19.0 (12) | |
Only non-cancer pain | 23.9 (34) | 27.3 (53) | 25.4 (16) | NS |
Only cancer pain | 15.5 (22) | 16.0 (31) | 12.7 (8) | NS |
Both cancer and non-cancer pain | 15.5 (22) | 30.9 (60) | 42.9 (27) | 0 < 1 and 2 |
| ||||
For patients with pain | Mean (SD) | Mean (SD) | Mean (SD) | |
| ||||
Worst pain intensity score | 4.7 (2.5) | 6.0 (2.4) | 7.3 (2.5) | F=12.89, p<.001 0 < 1 < 2 |
| ||||
Pain interference score | 1.8 (1.9) | 3.1 (2.2) | 4.9 (2.8) | F=26.35, p<.001 0 < 1 < 2 |
Abbreviations: NS, not significant; SD, standard deviation.
Numbers in parenthesis indicate clinical meaningful cutoff scores.
Differences in QOL Outcomes
As shown in Figure 2, except for the spiritual well-being subscale, significant differences were found among the three classes for the QOL-PV subscales and total scores (i.e., Low > High > Very High). In addition, significant differences were found among the three sleep disturbance classes for the PCS and the MCS scores (i.e., Low > High > Very High).
Figure 2.
A – Differences among the sleep disturbance latent classes in physical well-being, psychological well-being, social well-being, spiritual well-being and total quality of life (QOL) scores. Except for spiritual well-being (i.e., no significant differences among the classes), differences among the classes in the other QOL scores were in the expected direction (i.e., Low > Moderate > High; all p<.001).
B – Differences among the sleep disturbance latent classes in physical component summary (PCS) and mental component summary (MCS) scores from the Medical Outcomes Study- Short-form 12. differences among the classes in these two measures were in the expected direction (i.e., Low > Moderate > High; all p<.001).
DISCUSSION
This study is the first to identify subgroups of patients with gastrointestinal cancers with distinct sleep disturbance profiles. Consistent with previous studies of patients with gastrointestinal cancers,6, 7, 9 almost 65% of our patients reported high levels of sleep disturbance across all six assessments (i.e., the mean total GSDS scores were above the clinically meaningful cutoff score of ≥43). Similar to the findings from our previous report of the total sample of patients with heterogenous cancer diagnoses,32 the mean total GSDS score for the patients with gastrointestinal cancers (i.e., 50.2) suggests a high overall level of sleep disturbance. However, in the LPA analysis for the total sample that identified the same three sleep disturbance profiles, compared to the patients with the other types of cancer (i.e., breast, lung, gynecological), a higher percentage of patients with gastrointestinal cancers were classified in the Low class. Our current analysis of only patients with gastrointestinal cancers suggests that within cancer type analyses are warranted to identify the characteristics associated with membership in the higher classes even when a specific type of cancer may have a lower overall symptom burden compared to other cancer types. While the relative proportions of patients in the High and Very High classes were slightly higher in the total sample (i.e., 74.8%) compared to the patients with gastrointestinal cancers (i.e., 64.8%), for both samples, their total GSDS scores were almost identical. It should be noted that total GSDS scores of approximately 60 are indicative of significant sleep disturbance as reported in studies of shift workers33 and mothers and fathers of newborn infants.34
The GSDS subscale scores provide specific information about differences in a number of sleep characteristics among the patient subgroups. As shown in Table 3, for all of the GSDS subscales, scores increased in a stepwise fashion among the three classes. However, across the three classes, insufficient quantity of sleep and a high number of mid-sleep awakenings were the only two subscales that had scores above the clinically meaningful cutoff, which suggests that the problem occurs on three or more days per week.
In addition, the scores on the GSDS subscales allow for an evaluation of two common problems associated with sleep disturbance, namely: difficulty with the initiation of sleep (i.e., sleep onset latency) and difficulty with the maintenance of sleep (i.e., early awakenings and mid-sleep awakenings). The relatively high mid-sleep awakening (i.e., 3.9) score in the Low class as well as the relatively high mid-sleep awakening (i.e., 5.0) and early awakening (i.e., 3.9) scores in the High class suggest that both groups have problems with sleep maintenance. In contrast, patients in the Very High class had problems with both sleep initiation and maintenance. Consistent with previous studies of patients undergoing radiation therapy35 and treatment for breast cancer,36 the use of sleep medications was extremely low. Taken together, these findings suggest that sleep disturbance is a significant problem in patients with gastrointestinal cancers. Clinicians need to determine the underlying processes for these sleep disturbances and teach patients effective sleep hygiene interventions (e.g., increases in daytime activity, establishment of a regular bedtime).37, 38
One goal of this study was to identify common and distinct risk factors associated with higher levels of sleep disturbance within and across cancer types. To achieve this goal, as summarized in Table 5, we identified characteristics associated with membership in the Very High sleep disturbance class compared to the Low sleep disturbance class in both the current sample of patients with gastrointestinal cancers and the total sample with heterogeneous types of cancer.32 For both samples, the common risk factors associated with membership in the Very High class were: younger age, not being married/partnered, being unemployed, having childcare responsibilities, lack of regular exercise, having a lower performance status, and having a higher comorbidity burden. Findings regarding age differences in sleep disturbance are inconsistent with some finding associations with younger 39, 40 and others with older 41, 42 age. These inconsistencies may be related to specific types of cancer and/or the timing of the assessments.
Table 5.
Demographic, Clinical, and Symptom Characteristics Associated with Higher Levels of Sleep Disturbance
Characteristics (the comparisons done to the Low class) | Very High Sleep Disturbance Class GI Sample | Very High Sleep Disturbance Class Total Sample a |
---|---|---|
Demographic Characteristics | ||
Younger age | ♦ | ♦ |
Being female | ♦ | |
Being not married or partnered | ♦ | ♦ |
Living alone | ♦ | |
Being not currently employed | ♦ | ♦ |
Lower income | ♦ | |
Having childcare responsibilities | ♦ | ♦ |
Lack of exercise on a regular basis | ♦ | ♦ |
Clinical Characteristics | ||
Higher body mass index | ♦ | |
Lower KPS score | ♦ | ♦ |
Higher number of comorbidities | ♦ | NE |
Higher SCQ score | ♦ | ♦ |
Having a diagnosis of back pain | ♦ | NE |
Having a diagnosis of depression | ♦ | NE |
Having prior cancer treatments | ♦ | |
Higher number of prior cancer treatments | ♦ | |
Symptom Characteristics | ||
Higher trait anxiety | ♦ | ♦ |
Higher state anxiety | ♦ | ♦ |
Higher depressive symptoms | ♦ | ♦ |
Higher sleep disturbance | ♦ | ♦ |
Lower attentional function | ♦ | ♦ |
Higher morning fatigue | ♦ | ♦ |
Higher evening fatigue | ♦ | ♦ |
Lower morning energy | ♦ | ♦ |
Lower evening energy | ♦ | ♦ |
Having pain | ♦ | ♦ |
Having only non-cancer pain | ♦ | |
Having both cancer and non-cancer pain | ♦ | ♦ |
Abbreviations: KPS, Karnofsky Performance Status; NE, not evaluated; SCQ, Self-administered Comorbidity Questionnaire.
Reference: Tejada M, Viele C, Kober KM, et al. Identification of subgroups of chemotherapy patients with distinct sleep disturbance profiles and associated co-occurring symptoms. Sleep 2019;42(10) doi: 10.1093/sleep/zsz151
The added stress associated with some of the other demographic risk factors may explain their associations with very high levels of sleep disturbance. For example, patients who are not married or partnered may experience higher levels of loneliness which is associated with sleep disturbance.43 The added stress of caring for children while receiving chemotherapy may contribute to a lack of sleep or poorer quality of sleep.44 In addition, being unemployed may be associated with an increased financial burden and associated with sleep problems.45 While these characteristics are not modifiable, clinicians can provide referrals to social services and/or psychological counseling to help patients manage their stress.
Lack of regular exercise is the only modifiable risk factor associated with membership in the Very High sleep disturbance class. The benefits of exercise to decrease sleep problems are well documented.10, 46 For example, a meta-analysis found that patients who participated in an aerobic exercise program (i.e., 4 to 8 week, 80 to 149 minutes per week) experienced significant reductions in sleep disturbance (i.e., medium to large effects).47 Therefore, clinicians need to encourage patients to increase aerobic exercise during and following chemotherapy.
Consistent with prior studies,3, 36 higher levels of sleep disturbance were associated with a higher comorbidity burden and lower functional status in our patients with gastrointestinal cancer. Of note, the differences in SCQ and KPS scores between the patients in the Low and Very High classes represent not only statistically significant but clinically meaningful differences (i.e., d= 0.8 and 1.3, respectively). It is reasonable to hypothesize and is supported by studies of patients with other chronic conditions (e.g., hypertension, diabetes) that higher comorbidity burden contributes to sleep disturbance.48, 49 In addition, findings from this study suggest that a low functional status may contribute to a lack of regular exercise and associated sleep disturbance.
In terms of the distinct factors that were associated with membership in the Very High class, in the patients with gastrointestinal cancers, three factors were identified, namely: having received prior cancer treatments, having a higher total number of prior cancer treatments, and having higher number of comorbidities. While not assessed in the total sample, a higher percentage of patients in the Very High class self-reported diagnoses of back pain and depression. Compared to the 58% of patients in the Low class, 80.6% of the patients with gastrointestinal cancers in the Very High class had received at least one additional treatment prior to chemotherapy. While the timing between the previous and current treatments was not evaluated, this finding supports previous research that suggests that the effects of cancer treatments on sleep disturbance may be cumulative.7 The high occurrence rates for self-reported back pain (33.3%) and depression (31.7%) in the Very High class of patients with gastrointestinal cancers provide some insights into the associations between sleep disturbance and comorbidity burden. For example, in the general population both back pain50 and depression51 are associated with sleep disturbance.
In our previous studies,52, 53 we found that patients with gastrointestinal cancers experienced an average of 10 to 15 multiple co-occurring symptoms. Similar to the GSDS subscale scores, levels of trait and state anxiety, depressive symptoms, cognitive dysfunction, morning and evening fatigue, and pain intensity and interference were significantly different among the three sleep disturbance classes of patients with gastrointestinal cancers (Table 4). It is interesting to note that for all three sleep disturbance classes, morning energy scores were below the clinically meaningful cutoff score. However, only patients in the Very High class had scores that were well above the clinically meaningful cutoff for all of the other symptoms listed in Table 4.
When comparisons were done between the current sample and the total sample (Table 5), our findings suggest that very high levels of sleep disturbance are associated with multiple co-occurring symptoms. It should be noted that in both samples, the differences in all of the symptom severity scores, between the Low and Very High classes represent not only statistically significant but clinically meaningful differences (i.e., effect sizes ranged from 0.6 [morning energy] to 1.8 [morning fatigue]). While previous studies have reported on positive associations between sleep disturbance and fatigue,46 anxiety,54 depression,55 and pain56 in oncology patients, none of these studies assessed all of these co-occurring symptoms in the same sample of patients. The hypothesized mechanisms for the co-occurrence of sleep disturbance and other symptoms include: increases in proinflammatory cytokine activity; dysregulation of the hypothalamic-pituitary-adrenal axis; and misaligned circadian rhythms.55, 57 Future studies need to determine the common and distinct mechanisms for the co-occurrence of these symptoms.
In terms of generic and disease-specific QOL outcomes, except for the spiritual well-being subscale of the QOL-PV, significant differences were found among the three sleep disturbance classes. Compared to the Low class, all of the differences across the various domains of QOL in the Very High class represent not only statistically significant but clinically meaningful differences (d = 1.3 to 1.7). Of note, across all three classes, patients with gastrointestinal cancers reported PCS scores of <50 which is lower than the normative score for the general population.27 Consistent with previous studies in patients with lung3 and ovarian58 cancer, high levels of sleep disturbance were associated with poorer QOL outcomes.
Limitations
Several limitations warrant consideration. While a valid and reliable measure was used to evaluate sleep disturbance, future studies need to evaluate for similar associations using objective measures such as actigraphy and/or polysomnography. Because patients were not recruited prior to the initiation of chemotherapy, risk profiles for sleep disturbance from its initiation through completion were not evaluated. Given that the majority of the patients were White and well educated, our findings may not generalize to more diverse samples. In addition, given the heterogeneity in gastrointestinal cancers in this study, futures studies need to consider similar evaluations of patients with specific gastrointestinal cancers (e.g., pancreatic, gastric).
CONCLUSIONS
This study is the first to identify subgroups of patients with gastrointestinal cancers with distinct sleep disturbance profiles and identify risk factors associated with higher levels of sleep disturbance. Additional research is warranted to explore underlying molecular mechanisms that contribute to the development of sleep disturbance and the co-occurrence of other common symptoms during chemotherapy. Clinicians need to assess for the common risk factors and associated co-occurring symptoms, as well as initiate personalized symptom management interventions and referrals.
Acknowledgements:
This study was funded by a grant from the National Cancer Institute (CA134900). Dr. Miaskowski is an American Cancer Society Clinical Research Professor. Ms. Lin is supported by an American Cancer Society doctoral scholarship and an Oncology Nursing Foundation Research Doctoral Scholarship.
Footnotes
Conflicts of interest: The authors have no funding or conflicts of interest to disclose.
Contributor Information
Ms. Yufen Lin, School of Nursing, Duke University, Durham, NC, USA.
Drs. Donald E. Bailey, Jr., School of Nursing, Duke University, Durham, NC, USA.
Sharron L. Docherty, School of Nursing, Duke University, Durham, NC, USA.
Dr. Laura S. Porter, School of Medicine, Duke University, Durham, NC, USA.
Drs. Bruce A. Cooper, School of Nursing, University of California, San Francisco, CA, USA.
Steven M. Paul, School of Nursing, University of California, San Francisco, CA, USA.
Dr. Marilyn J. Hammer, Dana Farber Cancer Institute, Boston, MA, USA.
Yvette P. Conley, School of Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
Jon D. Levine, School of Medicine, University of California, San Francisco, CA, USA.
Drs. Christine Miaskowski, School of Nursing, University of California, San Francisco, CA, USA; School of Medicine, University of California, San Francisco, CA, USA.
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