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. Author manuscript; available in PMC: 2022 Feb 5.
Published in final edited form as: J Am Assoc Nurse Pract. 2021 Jul 2;34(2):284–291. doi: 10.1097/JXX.0000000000000625

Sleep and quality of life in lung cancer patients and survivors

Rachel E Martin 1, Dianne M Loomis 1, Grace E Dean 1
PMCID: PMC8720315  NIHMSID: NIHMS1706986  PMID: 34225324

Abstract

Background:

Lung cancer patients and survivors are vulnerable to disturbed sleep and impaired quality of life (QOL) across the continuum of illness. Few studies have sought to identify predictors of QOL using well-validated measures of both sleep quality and QOL in this population.

Purpose:

The purpose of this study was to examine factors associated with lung cancer that are predictive of QOL in adult lung cancer patients and survivors in the outpatient setting.

Method:

Cross-sectional data collected exclusively in the outpatient setting from three lung cancer clinics in the Northeastern United States were pooled and analyzed. The pooled sample (N = 103) data included cancer type and stage, body mass index, Pittsburgh Sleep Quality Index, and Functional Assessment of Cancer Treatment-Lung information.

Results:

Significant correlations between sleep quality, lung cancer symptom severity, and QOL were observed. Sleep quality and lung cancer symptoms were found to be statistically significant predictors of QOL. No significant differences in QOL were found based on cancer type or recruitment source. Demographic factors and cancer stage were also not predictive of overall QOL.

Conclusions:

Lung cancer symptoms and sleep quality were important determinants of QOL in this pooled sample of lung cancer patients and survivors.

Implications for practice:

Patients and survivors of lung cancer require routine screening for sleep disturbance, lung cancer symptoms, and QOL needs. Nurse practitioners can help improve QOL in this population by screening for and treating sleep disturbance and lung cancer symptoms.

Keywords: Lung cancer, quality of life, sleep disturbance, sleep quality

There were 228, 820 new cases of lung cancer projected to be diagnosed in 2020, along with an additional 135,720 deaths (Siegel et al., 2020). Lung cancer is the leading cause of cancer-related death in the United States and resulted in more deaths than breast, prostate, colorectal, and brain cancer combined for the year 2017 (Siegel et al., 2020). Patients and families facing a diagnosis of lung cancer may experience a myriad of disease and treatment-related symptoms, as well as financial difficulties and changes to global health status (Nishiura et al., 2015). Between 2010 and 2016, 57% of all lung cancer cases were diagnosed at an advanced stage with distant metastatic disease, for which the 5-year survival rate was 5.8% (Howlander et al., 2020). Although new targeted therapies and immunotherapies are improving the outlook for many patients, metastatic lung cancer remains an incurable disease with a poor prognosis and frequently high symptom burden (Ambroggi et al., 2018; Ramirez et al., 2018). As such, optimizing a patient’s quality of life (QOL) and controlling symptoms continue to be crucially important objectives of lung cancer treatment in addition to prolonging survival (Ambroggi et al., 2018; Ramirez et al., 2018).

Quality of life is a broad-ranging concept that has been described as an individual’s subjective appraisal of their status and function in life relative to a perceived ideal or expected standard (WHOQOL Group, 1997). It is affected not only by physical health but also by psychological and emotional well-being (EWB), social relationships, functional independence, and behaviors (WHOQOL Group, 1997). Previous research has demonstrated that an increased symptom burden in lung cancer patients is associated with decreased QOL (Lin et al., 2013; Lou et al., 2017). Symptoms commonly experienced by lung cancer patients include pain, fatigue, disturbed sleep, distress, and respiratory problems (Lou et al., 2017; Mendoza et al., 2019). Poor sleep quality is especially troubling for lung cancer patients and is independently associated with decreases in QOL (Gooneratne et al., 2007; Mendoza et al., 2019).

Background

Given the high prevalence and significant morbidity and mortality of lung cancer, it is critically important for health care providers to understand and address the QOL needs of this patient population. Patients diagnosed with lung cancer have been found to have poorer self-reported QOL and sleep quality compared with healthy controls (Gooneratne et al., 2007; Vena et al., 2006). Furthermore, QOL and sleep appear to be impacted across the continuum of illness from the time of diagnosis through survivorship (Dean et al., 2015; Gooneratne et al., 2007; Mendoza et al., 2019). A study of long-term survivors of lung cancer found that 56.6% of survivors experienced poor sleep compared with 29.5% of noncancer controls and poor sleep correlated with decreased QOL (Gooneratne et al., 2007).

Sleep disturbance is a common symptom among cancer patients and survivors, but lung cancer appears to confer increased vulnerability (Davidson et al., 2002; Palesh et al., 2010). In a seminal study conducted by Davidson et al. (2002), the prevalence and nature of sleep problems were examined in 982 cancer patients in six clinics at a regional cancer center in Canada. Patients in the lung cancer clinic reported the highest prevalence of excessive fatigue and sleepiness, sleeping more than usual, and using tranquilizers/sleeping pills compared with breast, gastrointestinal, genitourinary, gynecologic, and nonmelanoma skin cancer patients (Davidson et al., 2002). Lung cancer patients also reported the second highest prevalence of insomnia and sleep interruptions due to breathing difficulties (Davidson et al., 2002). Sleep quality in lung cancer patients may be associated with disease-specific symptoms in particular (Dean et al., 2015; Vena et al., 2006).

Despite advances in treatment that are changing the course of the disease, many lung cancer patients continue to face significant disease-related or treatment-related symptoms to the detriment of their QOL. A greater understanding of the determinants of QOL in this patient population is needed so that targeted interventions may be developed. Although several studies have explored the association between lung cancer symptoms, sleep, and QOL, few have examined predictive factors with respect to the QOL of these patients using well-validated measures of both sleep and QOL. The purpose of this pooled secondary analysis was to examine factors associated with lung cancer that are predictive of QOL using data from three previously published studies of adult lung cancer patients and survivors (Dean et al., 2013, 2015, 2020). The specific aim was to identify predictors of QOL, which may serve as future targets for interventions to improve QOL in this population.

Conceptual framework

The revised Wilson and Cleary model for health-related QOL was adapted for this study (Ferrans et al., 2005). The model provides a useful framework for linking clinical variables, symptom status, and health-related QOL using the following five measures of patient outcomes: biological function, symptoms, functional status, general health perceptions, and overall QOL (Ferrans et al., 2005). The Wilson and Cleary model was previously adapted to serve as a conceptual framework in a study of predictors of QOL in adult patients with obstructive sleep apnea (OSA) (Ye et al., 2008). Study authors used the framework to hypothesize causal links between demographic and physiological factors, symptoms, and overall QOL in OSA patients (Ye et al., 2008). Based on the revised Wilson and Cleary model, we hypothesized links between individual characteristics such as age, gender, and body mass index (BMI); biological variables, such as lung cancer type and stage, sleep, and lung cancer-related symptom status with overall QOL.

Methods

Study design

The three studies from which the pooled data originated used varying study designs and methods. A cross-sectional, descriptive, correlational design was used by Dean et al. (2013) to evaluate sleep, mood, and QOL in lung cancer patients undergoing treatment (N = 35). A longitudinal design with four-time repeated measures was implemented by Dean et al. (2015) to describe and compare QOL and sleep in lung cancer patients before, during, and after chemotherapy (N = 29). In Dean et al. (2020), authors conducted a pilot feasibility study to evaluate the preliminary effects of a nurse-delivered brief behavioral treatment for insomnia (BBTI) compared with an attention control in a sample of lung cancer survivors (N = 40).

All data used were obtained from participants exclusively in the outpatient setting at one of the three lung cancer clinics in the Northeastern United States over the course of the three studies. Informed consent was obtained in each study, and participants received instructions on how to complete survey instruments including the Pittsburgh Sleep Quality Index (PSQI) and Functional Assessment of Cancer Treatment-Lung (FACT-L). The surveys were then completed by patients either in clinic or at home. Additional data, including demographic, clinical, and medical information, were extracted from each participant’s medical record. Institutional review board approval was obtained for the three previous studies at the University at Buffalo, the University of Pennsylvania, Roswell Park Comprehensive Cancer Center, and Veterans Administration hospitals.

Participants

Participants from the previous three studies were adults diagnosed with either small cell lung cancer (SCLC) or non–small cell lung cancer (NSCLC) at an advanced or early stage of disease (Dean et al., 2013, 2015, 2020). Participants diagnosed at an advanced stage were either pretreatment or undergoing treatment, while the majority of participants diagnosed at an early stage had finished treatment by the time of data collection. Participants in all three studies were recruited exclusively from an ambulatory oncology clinic setting. While inclusion and exclusion criteria varied slightly between the three studies, all participants were at least 21 years or older and diagnosed with SCLC or NSCLC. Participants unable to complete self-report surveys were excluded, along with those with known brain metastasis or untreated preexisting sleep disorders.

Measures

Data, including cancer type and stage, BMI, and self-reported sleep quality and QOL, were reviewed and analyzed. Subjective sleep quality was measured using the PSQI, a 19-item self-rated questionnaire, which assesses sleep quality and disturbances over a one-month period. The 19 individual items produce the following seven component scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction (Buysse et al., 1989). The component scores are then totaled to yield a single global score, which differentiates good sleepers (global score 5 or less) from poor sleepers (global score greater than 5) (Buysse et al., 1989). The PSQI is one of the most commonly used measures of sleep quality in clinical and research settings and has strong reliability and validity (Mollayeva et al., 2016). Good measures of internal consistency (Cronbach alpha 0.77 to 0.83), test–retest reliability (r = .87), and construct validity have been observed (Backhaus et al., 2002; Beck et al., 2004; Buysse et al., 1989; Carpenter & Andrykowski, 1998).

Quality of life was measured using the FACT-L, a disease-specific questionnaire that consists of 27 items from the FACT-General (FACT-G) combined with a nine-item lung cancer–specific subscale (LCS). The FACT-L measures self-reported QOL by eliciting patient responses to statements based on a five-point Likert scale (Butt et al., 2005). The total FACT-G score is calculated by adding the following subscales together: physical well-being (PWB), social/family well-being (SWB), emotional well-being (EWB), and functional well-being (FWB) (Butt et al., 2005). The LCS, which evaluates the severity of lung cancer–specific symptoms, is then added to generate the total FACT-L score (Butt et al., 2005). A higher total score corresponds with better QOL (Butt et al., 2005). The FACT-L has been used extensively in clinical and research settings, with demonstrated validity and reliability in lung cancer patients receiving chemotherapy, radiotherapy, end-of-life patients, and also disease-free survivors (Butt et al., 2005). Internal consistency of the five FACT-L subscales (PWB, FWB, SWB, EWB, and LCS) has been found to range from 0.56 to 0.89 (Butt et al., 2005).

Data analysis

IBM SPSS Statistics (version 27) software was used for statistical analysis. Frequencies and descriptive statistics were completed to describe demographics, cancer type and stage, BMI, disease-specific symptoms/concerns, sleep quality, and QOL. An independent group t-test was used to assess for differences between participants based on recruitment source and cancer type. The associations between lung cancer symptoms, sleep quality, and QOL were examined via Pearson correlations. Multiple linear regression analyses were then performed with the FACT-G total score as the dependent variable and demographics (age, gender, BMI), cancer stage, LCS score, and global PSQI score as predictors. The PSQI subscales and individual items of the LCS were then examined to assess if any components of the statistically significant predictors were more important contributors to QOL.

Results

Pooled sample characteristics

The pooled sample included 103 participants. Table 1 provides descriptive data for participants; of whom, 81 (78.6%) were recruited from a comprehensive cancer center and 22 (21.4%) were recruited from Veterans Administration hospitals. The mean age of the pooled sample was 65 years (±8.8; range, 48–86 years) with a mean BMI of 27.4 kg/m2(±6.2; range, 16.8–54.8 kg/m2). The majority of the sample was male (65%), White (78.6%), and married (47.6%), with a diagnosis NSCLC (86.4%).

Table 1.

Pooled sample characteristics

Characteristic n %
Gender
 Female 36 35
 Male 67 65
Race
 White 81 78.6
 Black 20 19.4
 Other 2 1.9
Marital status
 Married 49 47.6
 Single 11 10.7
 Separated/divorced 23 22.3
 Widowed 18 17.5
 Missing 2 1.9
Cancer stage
 I 31 30.1
 II 12 11.7
 III 32 31.1
 IV 22 21.4
 Missing 6 5.8
Lung cancer type
 Small cell 12 11.7
 Non–small cell 89 86.4
 Missing 2 1.9
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Note: N = 103.

Sleep quality

Table 2 represents the means, standard deviations, and ranges of sleep, lung cancer symptoms, and QOL scores. Results of PSQI identified 86.4% (n = 89) of participants as poor sleepers, defined by a global score greater than 5. Subscale scores revealed that sleep quality was rated as good or fairly good by 64.1% (n = 66) of participants, whereas 73.8% reported sleep duration of 7 hours or less. Additionally, sleep quality was better (had a lower score) than the sleep efficiency, sleep duration, sleep latency, and sleep disturbance subscales. Only the daytime dysfunction and sleep medication subscales were lower than sleep quality, indicating better daytime functioning and little medication use among participants.

Table 2.

Descriptive statistics of the PSQI, FACT-L, FACT-G, and LCS

Variable Mean SD Range
PSQI global score 9 3.77 0–17
 Sleep quality subscale 1.31 0.89 0–3
 Sleep latency subscale 1.52 1.12 0–3
 Sleep duration subscale 1.37 1.07 0–3
 Sleep efficiency subscale 1.32 1.23 0–3
 Sleep disturbance subscale 1.67 0.63 0–3
 Sleep medication use subscale 1.05 1.04 0–3
 Daytime dysfunction subscale 0.77 0.80 0–3
FACT-L 93.39 18.82 42–132
FACT-G 74.8 16.80 24–105
 Physical well-being subscale 19.95 5.39 2–28
 Social well-being subscale 20.79 6.14 1–30
 Emotional well-being subscale 17.5 4.64 1–24
 Functional well-being subscale 16.52 6.23 4–24
LCS 18.42 4.38 8–28
 Shortness of breath 2.35 1.29 0–4
 Weight loss 2.97 1.17 0–4
 Clear thinking 3.02 1.11 0–4
 Cough 2.52 1.33 0–4
 Appetite 2.46 1.37 0–4
 Chest tightness 3.06 1.23 0–4
 Breathing difficulty 2.02 1.28 0–4
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Note: N = 103. FACT-G = Functional Assessment of Cancer Therapy-General; FACT-L = Functional Assessment of Cancer Therapy-Lung; LCS = Lung Cancer Subscale; PSQI = Pittsburgh Sleep Quality Index.

Lung cancer symptoms

Of the LCS scores, breathing difficulty was the most severe symptom (had the lowest score), followed by shortness of breath, appetite, cough, and weight loss. Thinking clear and chest tightness were better among participants, with 71.9% (n = 74) reporting clear thinking “very much” or “quite a bit” and 72.9% (n = 75) reporting chest tightness “a little bit”’ or “not at all.”

Quality of life

The associations between lung cancer symptoms, sleep quality, and overall QOL were evaluated via Pearson correlations. Scores of LCS demonstrated a statistically significant relationship to FACT-G scores (r = .468; p < .01), indicating that improved lung cancer symptoms were associated with better QOL. A statistically significant correlation was also observed between FACT-G and PSQI global scores (r = −.406; p < .01), suggesting that worse sleep is associated with poorer QOL.

Predictors

Simultaneous multiple linear regressions were used to examine the predictive value of factors affecting QOL. Demographic factors (age, gender, and BMI) were found to have no statistically significant impact on QOL (Table 3). Regression of LCS scores, cancer stage, and PSQI global scores as predictors explained 29% of the variance in FACT-G total score, but only LCS and PSQI scores were found to be statistically significant predictors (Table 3).

Table 3.

Multiple Regression Coefficients of Quality of Life Predictors

Variable B SE β t p R 2 Adj. R2
Demographics as predictors .09 .06
 Constant 37.93 13.31 2.85 .005
 Age .37 .19 .20 1.97 .052
 Gender 5.51 3.43 .18 1.61 .112
 BMI .19 .27 .07 .70 .485
Lung cancer symptoms, cancer stage, and sleep as predictors .31 .29
 Constant 56.42 9.13 6.18 <.001
 Lung cancer subscale 1.60 .34 .42 4.67 <.001
 Cancer stage −.29 1.25 −.02 −.23 .817
 PSQI global score −1.19 .39 −.28 −3.02 .003
Lung cancer subscale items as predictors .30 .25
 Constant 35.98 7.20 4.99 <.001
 Shortness of breath 1.63 1.45 .12 1.12 .264
 Weight loss .96 1.42 .07 .67 .503
 Clear thinking 4.01 1.48 .26 2.72 .008
 Cough .84 1.23 .06 .69 .495
 Appetite 1.22 1.28 .10 .95 .344
 Chest tightness 4.03 1.28 .30 3.15 .002
 Breathing difficulty 1.27 1.37 .10 .92 .359
PSQI subscales as predictors .31 .26
 Constant 94.68 4.56 4.99 <.001
 Sleep quality subscale −4.83 1.83 .12 1.12 .010
 Sleep latency subscale −2.28 1.48 .07 .67 .127
 Sleep duration subscale −.92 1.60 .26 2.72 .568
 Sleep efficiency subscale 1.88 1.39 .06 .69 .181
 Sleep disturbance subscale −1.52 2.56 .10 .95 .537
 Sleep medication use subscale −4.02 1.59 .30 3.15 .011
 Daytime dysfunction subscale −6.07 1.84 .10 .92 .001
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Note: N = 103. BMI = body mass index; PSQI = Pittsburgh Sleep Quality Index.

Of the seven individual LCS items, only clear thinking and chest tightness were found to be statistically significant predictors (Table 3). Overall, the LCS items explained 25% of the variance in FACT-G total score. Regression of the PSQI subscales together accounted for 26% of the variance in FACT-G total score (Table 3). The sleep quality, sleep medication use, and daytime dysfunction subscales were found to have statistically significant predictive value.

Discussion

Lung cancer patients and survivors may experience a myriad of disease and treatment-related symptoms across the continuum of illness to the detriment of their QOL. An increased understanding of the determinants of QOL in this population is necessary to improve patient care and outcomes. This pooled secondary analysis examined predictors of QOL in adult lung cancer patients and survivors in the outpatient setting. The findings from this study demonstrate significant correlations between sleep, lung cancer symptoms, and QOL in this population. Sleep quality and lung cancer symptom severity were found to be statistically significant predictors of QOL. No significant differences in QOL were found based on cancer type, stage, or recruitment source. Additionally, demographic factors (age, gender, and BMI) were not predictive of overall QOL.

Research in lung cancer patients and survivors has shown that poor sleep is a common and persistent symptom across the disease and treatment trajectory (Dean et al., 2019; Halle et al., 2017). In this pooled sample, 86.4% of participants were noted to be poor sleepers (based on the PSQI global score of >5), and the mean PSQI global score found was 9. Both the mean score and percentage of poor sleepers in this study were slightly higher than those reported in previous lung cancer research (Gooneratne et al., 2007; Lou et al., 2017). The PSQI sleep disturbance and sleep latency subscales had the highest mean scores, indicating that trouble falling asleep and staying asleep were the more severe symptoms among participants—findings that are consistent with previous studies (Gu et al., 2018; Vena et al., 2006). Poor sleep in lung cancer patients may be related to disease-specific or treatment-specific variables, including respiratory symptoms, psychological distress, or chemotherapy status (Nishiura et al., 2015; Vena et al., 2006).

Of interest, more than half of all participants rated their sleep quality as “fairly” or “very good” on a single item question, which may indicate a tendency to overestimate sleep on such assessments. Gooneratne et al. (2007) reported in a study of lung cancer survivors that a single-item question about sleep quality was ineffective in detecting sleep disturbance or insomnia. In a longitudinal study of adults with lung cancer, Dean et al. (2019) found that participants did not recognize that the quality of their sleep was worse than the general population and often accepted insomnia as an expected outcome of treatment. These findings underscore the importance of using validated instruments to screen for sleep disturbance in lung cancer patients and survivors.

The mean total FACT-L, FACT-G, and LCS subscale scores found in this study were comparable to findings from previous lung cancer research and were indicative of poorer QOL (Lou et al., 2017; Temel et al., 2010). The most severe lung cancer symptoms reported were related to respiratory function and included breathing difficulty and shortness of breath. Respiratory symptoms in lung cancer patients and survivors may be worsened by disease-related or treatment-related factors, as well as smoking status (Gu et al., 2018; Vena et al., 2006). As expected, more severe lung cancer symptoms and poorer sleep quality were both found to have statistically significant associations with lower overall QOL. These findings are supported by previous sleep and QOL research in lung cancer patients and survivors (Gu et al., 2018; Hung et al., 2018; Lou et al., 2017; Nishiura et al., 2015).

The findings of the present study identified lung cancer symptoms and sleep quality as important predictors of QOL for lung cancer patients and survivors. A previous study by Lou et al. (2017) on 128 advanced lung cancer patients also found sleep disturbance and respiratory symptoms, specifically, to be determinants of QOL. Surprisingly, of the individual LCS items examined in this study, only clear thinking and chest tightness were found to be statistically significant predictors. Although the use of sleep medications was found to be low in the pooled sample, it was still identified as a statistically significant predictor of QOL, along with the daytime dysfunction and sleep quality PSQI subscales. Future research is needed to confirm these findings in larger samples. Additional unexpected findings from this study include that cancer stage was not found to be a statistically significant predictor. One possible contributing factor may be that the advanced-stage participants in the pooled sample were either pretreatment or had recently started treatment at the time of data collection, while the early stage survivors had completed treatment. Prior research has demonstrated that treatment status can significantly impact symptoms and QOL (Mendoza et al., 2019; Temel et al., 2010).

Given the significant impact of poor sleep and lung cancer symptoms on QOL, routine screening and treatment are imperative in the clinical setting. Cognitive behavioral therapy for insomnia (CBT-I) represents an important treatment modality for improving sleep quality and insomnia in both cancer patients and survivors (Roscoe et al., 2015; Zhou et al., 2017). It is a non-pharmacological approach to insomnia treatment that uses basic behavioral principles to help patients resolve factors that perpetuate insomnia and relearn sleep behaviors more conducive with quality sleep (Roscoe et al., 2015). Research suggests that the CBT-I is associated with statistically and clinically significant improvements in sleep in cancer patients and survivors and may also improve fatigue, mood, and overall QOL (Garland et al., 2014). However, barriers exist, which limit the accessibility of CBT-I, including the critical shortage of specialty-trained psychologists, the duration of treatment, and high associated costs (Garland et al., 2014; Troxel et al., 2012). One promising alternative is BBTI—a concise version of CBT-I developed to be disseminated by nurses and other trained health care professionals—but continued research is needed (Troxel et al., 2012). Based on the pilot study results of Dean et al. (2020), a randomized control trial is underway to investigate the efficacy and durability of BBTI in lung, breast, prostate, and colorectal cancer patients.

Additionally, the early integration of palliative care (PC) into standard lung cancer care represents an important treatment strategy for improving QOL through comprehensive symptom management. Research indicates that in patients with advanced cancer, early PC is associated with decreased symptom intensity, increased QOL, and clinically significant improvements in survival (Haun et al., 2017; Temel et al., 2010). Furthermore, in two studies of lung cancer patients, early integrated PC was associated with both improved mood and QOL (Temel et al., 2010, 2017).

As the landscape of lung cancer treatment continues to evolve, another important point to consider is that while new targeted treatments and immunotherapies are changing the course of disease for many through improved survival and reduced therapy-related toxicities, continued research is needed to fully understand the impact on QOL (Ramirez et ah, 2018). Research indicates that targeted therapies and immunotherapies may be associated with better QOL relative to chemotherapy, but studies examining patient-reported QOL are limited (Ramirez et al., 2018; Van Der Weijst et al., 2019). Development of new QOL instruments to capture adverse effects associated specifically with targeted therapies and immunotherapies may also be useful because existing QOL measures were created for use with chemotherapy (Ramirez et al., 2018).

This study has several limitations. The small sample size limits generalizability. The pooled data were also cross-sectional; further examination of predictors of QOL in longitudinal studies is warranted. Additionally, sleep was assessed using only a self-reported measure. Objective measures of sleep, such as actigraphy, may help strengthen future results.

Conclusion and implications for nurse practitioners

Lung cancer symptoms and sleep quality were statistically significant determinants of QOL in this pooled sample of adult lung cancer patients and survivors. Furthermore, results demonstrated that two of the seven items on the LCS and three of the seven PSQI subscales were found to have predictive value, which may assist in future development of abbreviated screening tools for sleep and lung cancer symptoms. Future investigation is needed to confirm these findings, however.

This secondary analysis of pooled data contributes to the existing body of lung cancer research and nursing science. Study results may help researchers, providers, and care team members to better understand and treat the complex QOL needs in this population. Sleep and lung cancer symptom screening may be integrated into routine care through the use of validated instruments, such as the PSQI and FACT-L. Nurse practitioners can significantly improve the QOL for lung cancer patients and survivors by providing comprehensive care through evidence-based treatments, awareness of available resources, and education on the importance of good sleep and other factors affecting QOL.

Acknowledgments:

The authors thank Christopher Barrick, PhD, for his assistance with statistical analysis and data interpretation for this project. The authors also thank Pamela Paplham, DNP, AOCNP, FNP-BC, FAANP, and Loralee Sessanna, DNS, RN, AHN-BC, for their assistance with project development and review of earlier versions of this manuscript. The authors gratefully acknowledge the support by a) 5T32HL007953 Postdoctoral Fellowship (Dean), University at Buffalo School of Nursing Garman Fund (Dean), and Veterans Affairs Competitive Pilot Project Fund, VISN 4 (Gooneratne) for the financial support of the study. This work was conducted at, and supported by the Philadelphia VAMC, Philadelphia, PA, and VA Western New York Health care System, Buffalo, NY. The contents of this article do not represent the views of the Department of Veterans Affairs or the United States Government. b) This research was supported by a grant from the Oncology Nursing Society Foundation (REO1) and the National Lung Cancer Partnership Lung Cancer Nursing Research Grant (Dean and Dickerson). c) This study was funded with grant #R15NR013779, ClinicalTrials.gov Identifier: NCT02121652. d) This work was funded with grant #R01NR018215.

Footnotes

Competing interests: The authors report no conflicts of interest.

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