Background This study aimed to explore clinical correlates of fear of progression (FoP) among patients with cancer during the coronavirus disease 2019 (COVID-19) pandemic and examine the mediation effect of cancer-related dysfunctional beliefs about sleep (C-DBS). Methods Medical charts of patients with cancer who visited a psycho-oncology clinic between July and November 2021 were reviewed. Baseline socio-demographic and cancer-related variables were collected. Patients’ self-report questionnaires, regarding FoP, depression (Patient Health Questionnaire-9 items; PHQ-9), viral anxiety (Stress and Anxiety to Viral Epidemics-6 items; SAVE-6), C-DBS, and other distress, were investigated. Pearson’s correlation and linear regression were performed to examine the risk factors of FoP. Mediation effect analysis with the bootstrap method with 2,000 resamples was implemented. Results A total of 231 patients were included in the analysis. Linear regression revealed that FoP was predicted by age (β = −0.14, P = 0.003), PHQ-9 (β = 0.48, P < 0.001), SAVE-6 (β = 0.34, P < 0.001), and C-DBS (β = 0.15, P = 0.005). FoP was directly influenced by SAVE-6 and mediated by C-DBS, while it was directly influenced by PHQ-9 with no mediation effect. Conclusion During the COVID-19 pandemic, the FoP of patients with cancer was associated with younger age, depression, viral anxiety, and C-DBS. Depression and viral anxiety directly influenced FoP, while C-DBS mediated the association between viral anxiety and FoP. Therefore, oncology healthcare professionals are recommended to assess C-DBS of their patients when they are highly distressed from FoP.

Objective The aim of this study was to explore the factors that can influence the severity of insomnia in the general population. We also aimed to examine whether sleep effort mediates the association between dysfunctional beliefs about sleep or the discrepancy between desired time in bed and desired total sleep time (DBST) and insomnia severity in individuals.Methods A total of 387 participants enrolled in this e-survey study. The symptoms were rated using the insomnia severity index (ISI), Patients Health Questionnaire-9 items, Dysfunctional Beliefs about Sleep-2 items, Glasgow Sleep Effort Scale, and Stress and Anxiety to Viral Epidemics-6 items. In addition, we defined a new sleep index named the DBST index. A linear regression analysis was performed to explore the factors predicting ISI scores, and mediation analysis was implemented to explore whether persistent preoccupation with sleep mediated the influence of dysfunctional beliefs about sleep and DBST on insomnia severity.Results A linear regression analysis investigated depression (β=0.17, p<0.001), sleep effort (β=0.50, p<0.001), dysfunctional beliefs about sleep (β=0.13, p=0.001), and DBST (β=0.09, p=0.014) (adjusted R<i>2</i>=0.50, F=65.7, p<0.001). Additionally, we observed that persistent preoccupation with sleep partially mediated the influence of dysfunctional beliefs about sleep and DBST on insomnia severity. Conclusion Depression, preoccupation with sleep, dysfunctional beliefs about sleep, and DBST influenced the insomnia severity of the general population. We also observed that a persistent preoccupation with sleep partially mediated the influence of dysfunctional beliefs about sleep and the DBST index on insomnia severity.Conclusion Depression, preoccupation with sleep, dysfunctional beliefs about sleep, and DBST influenced the insomnia severity of the general population. We also observed that a persistent preoccupation with sleep partially mediated the influence of dysfunctional beliefs about sleep and the DBST index on insomnia severity.

Objective: This study aimed to compare the adaptability of the adapted version of Stress and Anxiety to Viral Epidemics-9 (SAVE-9) for public workers and the SAVE-6 scale and to validate them among public workers who are on the frontline of the coronavirus disease 2019 pandemic.Methods: A total of 300 public workers responded to the anonymous online survey during April 1–12, 2021. Principal component analysis was conducted with varimax rotation to explore the factor structure of this scale. Confirmatory factor analysis was also used to explore construct validity. Spearman correlation analysis of the scale with the Generalized Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9) was performed to explore the convergent validity. The cut-off score in accordance with the mild degree of generalized anxiety symptoms (GAD-7 score of 5) was defined using the receiver operating characteristic (ROC) analysis.Findings: The single-structure model of each scale (the adapted version of SAVE-9 and SAVE-6) was adopted based on the results of the parallel analysis. Because SAVE-6 showed good construct validity, but the adapted version of SAVE-9 did not, we adopted to apply the SAVE-6 scale to assess the anxiety response of public workers in response to the viral epidemic. SAVE-6 showed good internal consistency (Cronbach's alpha = 0.817; McDonald's Omega = 0.818) and good convergent validity with GAD-7 (rho = 0.417, p &lt; 0.001) and PHQ-9 (rho = 0.317, p &lt; 0.001) scale scores. The appropriate cut-off score for SAVE-6 was determined to be ≥ 16.Conclusion: The SAVE-6 scale, as compared to the public workers' version of SAVE-9, is a reliable and valid rating scale to assess the work-related stress and anxiety of public workers due to the viral epidemic.

Background The epidemiology of pharmaceutically treated depression (PTD) and treatment resistant depression (TRD) is largely unknown in South Korea. The aim of this study was to develop a greater understanding of the characteristics of PTD and TRD in nearly the entire adult population in South Korea using the Health Insurance Review and Assessment Service (HIRA). Method Diagnostic codes and prescription data for South Korean adults were extracted from the HIRA. Subjects were included in the PTD cohort if they received at least one prescription for antidepressants and were diagnosed with depression. TRD was defined as PTD having two or more regimen failures of antidepressants or antipsychotics. Results In 2012, there were 41,256,396 adults in South Korea with 834,694 meeting the criteria for PTD (2.0%). Among subjects with PTD, 57% stopped treatment in less than 28 days of antidepressant supply. Tricyclic and tetracyclic antidepressants were the most frequently used antidepressants as a first-line regimen for PTD (44.3% of PTD) followed by selective serotonin reuptake inhibitors (32.1% of PTD). Results also indicated that 34,812 subjects developed TRD (4.2% of PTD). Median PTD and TRD durations were 28 and 623 days respectively. Proportions of psychiatric and non-psychiatric comorbidities were higher in TRD cases than in PTD cases that were not treatment resistant. Conclusions Despite a small proportion of patients with TRD, the prolonged duration of illness and higher comorbidity implies the need for better treatment.

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Both minor and major depression have high prevalence and are important causes of social burden worldwide; however, there is still no objective indicator to detect minor depression. This study aimed to examine if voice could be used as a biomarker to detect minor and major depression. Ninety-three subjects were classified into three groups: the not depressed group (n = 33), the minor depressive episode group (n = 26), and the major depressive episode group (n = 34), based on current depressive status as a dimension. Twenty-one voice features were extracted from semi-structured interview recordings. A three-group comparison was performed through analysis of variance. Seven voice indicators showed differences between the three groups, even after adjusting for age, BMI, and drugs taken for non-psychiatric disorders. Among the machine learning methods, the best performance was obtained using the multi-layer processing method, and an AUC of 65.9%, sensitivity of 65.6%, and specificity of 66.2% were shown. This study further revealed voice differences in depressive episodes and confirmed that not depressed groups and participants with minor and major depression could be accurately distinguished through machine learning. Although this study is limited by a small sample size, it is the first study on voice change in minor depression and suggests the possibility of detecting minor depression through voice.