Objective: We investigated the sleep parameters and clinical factors related to short sleep onset latency (SL) in cancer patients. Methods: We retrospectively reviewed the medical records of 235 cancer patients. Patient Health Questionnaire-9, State and Trait Anxiety Inventory (State subcategory), Insomnia Severity Index (ISI), Cancer-related Dysfunctional Beliefs about Sleep, and Fear of Progression scale scores and sleep related parameters including sleeping pill ingestion time, bedtime, sleep onset time, and wake-up time were collected. We also calculated the duration from sleeping pill ingestion to bedtime, sleep onset time, and wake-up time; duration from wake-up time to bedtime and sleep onset time; and time spent in bed over a 24 hours period. Results: Among patients not taking sleeping pills (n = 145), early wake-up time (adjusted odds ratio [OR]: 0.39, 95% confidence interval [CI] 0.19−0.78), early sleep onset time (OR: 0.50, 95% CI 0.27−0.93), and low ISI score (OR: 0.82, 95% CI 0.71−0.93) were identified as expecting variables for SL ≤ 30 minutes. Longer duration from wake-up time to bedtime (OR: 2.49, 95% CI 1.48−4.18) predicted SL ≤ 30 minutes. Among those taking sleeping pills (n = 90), early sleep onset time (OR: 0.54, 95% CI 0.39−0.76) and short duration from pill ingestion to sleep onset time (OR: 0.05, 95% CI 0.02−0.16) predicted SL ≤ 30 minutes. Conclusion Cancer patients who fell asleep quickly spent less time in bed during the day. Thus, before cancer patients with insomnia are prescribed sleeping pills, their sleep parameters should be examined to improve their SL.

Purpose: Cancer-related fatigue is a common and distressing symptom that occurs during cancer treatment. This study aimed to find factors that are related to cancer-related fatigue, and its effect on patients’ quality of life. Materials and Methods: This study included 159 patients who completed questionnaires and interviews during their initial examination at the sleep clinic for cancer patients, Asan Medical Center, between December 2018 and January 2020. Their medical reports were reviewed retrospectively. Questionnaire data about depression, anxiety, insomnia, fear of disease progression, and dysfunctional beliefs about sleep, pain, and quality of life, were reviewed. Additionally, patient sleep structure data were analyzed. Results: Factors such as depression (p < 0.001), anxiety (p < 0.001), fear of cancer progression (p < 0.001), fatigue (p=0.027), and time in bed during 24 hours (p=0.037) were significant expecting variables for low quality of life from logistic regression analysis. In pathway analysis, depression (p < 0.001), not cancer-related fatigue (p=0.537), act as a direct risk factor on quality of life. And also, depression was an overall risk factor for insomnia, fatigue, and daily activity of cancer patients. Conclusion Cancer-related fatigue did not show significant effect on patient’s quality of life in this study. However, the result of pathway analysis highlights the importance of assessing depression in the process of cancer treatment and providing appropriate interventions.

Purpose: Cancer-related fatigue is a common and distressing symptom that occurs during cancer treatment. This study aimed to find factors that are related to cancer-related fatigue, and its effect on patients’ quality of life. Materials and Methods: This study included 159 patients who completed questionnaires and interviews during their initial examination at the sleep clinic for cancer patients, Asan Medical Center, between December 2018 and January 2020. Their medical reports were reviewed retrospectively. Questionnaire data about depression, anxiety, insomnia, fear of disease progression, and dysfunctional beliefs about sleep, pain, and quality of life, were reviewed. Additionally, patient sleep structure data were analyzed. Results: Factors such as depression (p < 0.001), anxiety (p < 0.001), fear of cancer progression (p < 0.001), fatigue (p=0.027), and time in bed during 24 hours (p=0.037) were significant expecting variables for low quality of life from logistic regression analysis. In pathway analysis, depression (p < 0.001), not cancer-related fatigue (p=0.537), act as a direct risk factor on quality of life. And also, depression was an overall risk factor for insomnia, fatigue, and daily activity of cancer patients. Conclusion Cancer-related fatigue did not show significant effect on patient’s quality of life in this study. However, the result of pathway analysis highlights the importance of assessing depression in the process of cancer treatment and providing appropriate interventions.

Background: The coronavirus disease 2019, or COVID-19, has had a major psychological impact on healthcare workers. However, very few scales are available to specifically assess work-related stress and anxiety in healthcare workers responding to a viral epidemic. This study developed a new assessment tool, the Stress and Anxiety to Viral Epidemics-9 (SAVE-9) and aimed to validate it among healthcare workers directly affected by COVID-19 in Korea. Methods: A total of 1,019 healthcare workers responded through anonymous questionnaires during April 20–30, 2020. Exploratory factor analysis (EFA) was conducted to explore the construct validity, and the reliability was assessed using internal consistency measures of Cronbach's alpha coefficients. Receiver operating characteristic analysis was conducted to define the most appropriate cut-off point of SAVE-9 using the Generalized Anxiety Disorder-7 scale (GAD-7; ≥ 5). Second, Spearman's rank correlation coefficient was used to establish convergent validity for the SAVE-9 questionnaire with GAD-7 and the Patient Health Questionnaire-9. Results: The nine-item scale had satisfactory internal consistency (Cronbach's α = 0.795). It adopted a two-factor structure: 1) anxiety regarding viral epidemics and 2) work-related stress associated with viral epidemics. A cut-off score of 22 for the SAVE-9 ascertained levels of stress and anxiety in response to a viral epidemic in healthcare workers that warranted clinical attention. Correlations between the SAVE-9 and the other scales were statistically significant (P < 0.05). Conclusion The results suggest that the SAVE-9 is a useful, reliable, and valid tool to evaluate stress and anxiety responses in healthcare workers during viral epidemics.