Although, attempts to apply virtual reality (VR) in mental healthcare are rapidly increasing, it is still unclear whether VR relaxation can reduce stress more than conventional biofeedback.

Participants consisted of 83 healthy adult volunteers with high stress, which was defined as a score of 20 or more on the Perceived Stress Scale-10 (PSS-10). This study used an open, randomized, crossover design with baseline, stress, and relaxation phases. During the stress phase, participants experienced an intentionally generated shaking VR and serial-7 subtraction. For the relaxation phase, participants underwent a randomly assigned relaxation session on day 1 among VR relaxation and biofeedack, and the other type of relaxation session was applied on day 2. We compared the StateTrait Anxiety Inventory-X1 (STAI-X1), STAI-X2, the Numeric Rating Scale (NRS), and physiological parameters including heart rate variability (HRV) indexes in the stress and relaxation phases.

A total of 74 participants were included in the analyses. The median age of participants was 39 years, STAI-X1 was 47.27 (SD = 9.92), and NRS was 55.51 (SD = 24.48) at baseline. VR and biofeedback significantly decreased STAI-X1 and NRS from the stress phase to the relaxation phase, while the difference of effect between VR and biofeedback was not significant. However, there was a significant difference in electromyography, LF/HF ratio, LF total, and NN50 between VR relaxation and biofeedback

VR relaxation was effective in reducing subjectively reported stress in individuals with high stress.

Predicting the course of depression is necessary for personalized treatment. Impaired glucose metabolism (IGM) was introduced as a promising depression biomarker, but no consensus was made. This study aimed to predict IGM at the time of depression diagnosis and examine the relationship between long-term prognosis and predicted results.

Clinical data were extracted from four electronic health records in South Korea. The study population included patients with depression, and the outcome was IGM within 1 year. One database was used to develop the model using three algorithms. External validation was performed using the best algorithm across the three databases. The area under the curve (AUC) was calculated to determine the model’s performance. Kaplan–Meier and Cox survival analyses of the risk of hospitalization for depression as the long-term outcome were performed. A meta-analysis of the long-term outcome was performed across the four databases.

A prediction model was developed using the data of 3,668 people, with an AUC of 0.781 with least absolute shrinkage and selection operator (LASSO) logistic regression. In the external validation, the AUCs were 0.643, 0.610, and 0.515. Through the predicted results, survival analysis and meta-analysis were performed; the hazard ratios of risk of hospitalization for depression in patients predicted to have IGM was 1.20 (95% confidence interval [CI] 1.02–1.41, p = 0.027) at a 3-year follow-up.

We developed prediction models for IGM occurrence within a year. The predicted results were related to the long-term prognosis of depression, presenting as a promising IGM biomarker related to the prognosis of depression.

Mood disorders require consistent management of symptoms to prevent recurrences of mood episodes. Circadian rhythm (CR) disruption is a key symptom of mood disorders to be proactively managed to prevent mood episode recurrences. This study aims to predict impending mood episodes recurrences using digital phenotypes related to CR obtained from wearable devices and smartphones.

The study is a multicenter, nationwide, prospective, observational study with major depressive disorder, bipolar disorder I, and bipolar II disorder. A total of 495 patients were recruited from eight hospitals in South Korea. Patients were followed up for an average of 279.7 days (a total sample of 75 506 days) with wearable devices and smartphones and with clinical interviews conducted every 3 months. Algorithms predicting impending mood episodes were developed with machine learning. Algorithm-predicted mood episodes were then compared to those identified through face-to-face clinical interviews incorporating ecological momentary assessments of daily mood and energy.

Two hundred seventy mood episodes recurred in 135 subjects during the follow-up period. The prediction accuracies for impending major depressive episodes, manic episodes, and hypomanic episodes for the next 3 days were 90.1, 92.6, and 93.0%, with the area under the curve values of 0.937, 0.957, and 0.963, respectively.

We predicted the onset of mood episode recurrences exclusively using digital phenotypes. Specifically, phenotypes indicating CR misalignment contributed the most to the prediction of episodes recurrences. Our findings suggest that monitoring of CR using digital devices can be useful in preventing and treating mood disorders.

There are growing concerns about the impact of the COVID-19 pandemic on the mental health of older adults. We examined the effect of the pandemic on the risk of depression in older adults.

We analyzed data from the prospective cohort study of Korean older adults, which has been followed every 2 years. Among the 2308 participants who completed both the third and the fourth follow-up assessments, 58.4% completed their fourth follow-up before the outbreak of COVID-19 and the rest completed it during the pandemic. We conducted face-to-face diagnostic interviews using Mini International Neuropsychiatric Interview and used Geriatric Depression Scale. We performed generalized estimating equations and logistic regression analyses.

The COVID-19 pandemic was associated with increased depressive symptoms in older adults [b (standard error) = 0.42 (0.20), p = 0.040] and a doubling of the risk for incident depressive disorder even in euthymic older adults without a history of depression (odds ratio = 2.44, 95% confidence interval 1.18–5.02, p = 0.016). Less social activities, which was associated with the risk of depressive disorder before the pandemic, was not associated with the risk of depressive disorder during the pandemic. However, less family gatherings, which was not associated with the risk of depressive disorder before the pandemic, was associated with the doubled risk of depressive disorder during the pandemic.

The COVID-19 pandemic significantly influences the risk of late-life depression in the community. Older adults with a lack of family gatherings may be particularly vulnerable.

Early replacement of a new central venous catheter (CVC) may pose a risk of persistent or recurrent infection in patients with a catheter-related bloodstream infection (CRBSI). We evaluated the clinical impact of early CVC reinsertion after catheter removal in patients with CRBSIs.

We conducted a retrospective chart review of adult patients with confirmed CRBSIs in 2 tertiary-care hospitals over a 7-year period.

To treat their infections, 316 patients with CRBSIs underwent CVC removal. Among them, 130 (41.1%) underwent early CVC reinsertion (≤3 days after CVC removal), 39 (12.4%) underwent delayed reinsertion (>3 days), and 147 (46.5%) did not undergo CVC reinsertion. There were no differences in baseline characteristics among the 3 groups, except for nontunneled CVC, presence of septic shock, and reason for CVC reinsertion. The rate of persistent CRBSI in the early CVC reinsertion group (22.3%) was higher than that in the no CVC reinsertion group (7.5%; P = .002) but was similar to that in the delayed CVC reinsertion group (17.9%; P > .99). The other clinical outcomes did not differ among the 3 groups, including rates of 30-day mortality, complicated infection, and recurrence. After controlling for several confounding factors, early CVC reinsertion was not significantly associated with persistent CRBSI (OR, 1.59; P = .35) or 30-day mortality compared with delayed CVC reinsertion (OR, 0.81; P = .68).

Early CVC reinsertion in the setting of CRBSI may be safe. Replacement of a new CVC should not be delayed in patients who still require a CVC for ongoing management.

To evaluate the bidirectional relationship between blood pressure (BP) and depressive symptoms using a large prospective cohort study.

Prospective cohort study was performed in 276 244 adults who participated in a regular health check-up and were followed annually or biennially for up to 5.9 years. BP levels were categorised according to the 2017 American College of Cardiology and American Heart Association hypertension guidelines. Depressive symptoms were assessed using Centre for Epidemiologic Studies-Depression (CESD) questionnaire and a cut-off score of ≥25 was regarded as case-level depressive symptoms.

During 672 603.3 person-years of follow-up, 5222 participants developed case-level depressive symptoms. The multivariable-adjusted hazard ratios (HRs) [95% confidence interval (CI)] for incident case-level depressive symptoms comparing hypotension, elevated BP, hypertension stage 1 and hypertension stage 2 to normal BP were 1.07 (0.99–1.16), 0.93 (0.82–1.05), 0.89 (0.81–0.97) and 0.81 (0.62–1.06), respectively (p for trend <0.001). During 583 615.3 person-years of follow-up, 27 787 participants developed hypertension. The multivariable-adjusted HRs (95% CI) for incident hypertension comparing CESD 16–24 and ⩾25 to CESD < 16 were 1.05 (1.01–1.11) and 1.12 (1.03–1.20), respectively (p for trend <0.001) and in the time-dependent models, corresponding HRs (95% CI) were 1.12 (1.02–1.24) and 1.29 (1.10–1.50), respectively (p for trend <0.001).

In this large cohort study of young and middle-aged individuals, higher BP levels were independently associated with a decreased risk for developing case-level depressive symptoms and depressive symptoms were also associated with incident hypertension. Further studies are required to elucidate the mechanisms underlying the bidirectional association between BP levels and incident depression.

This study investigates the relationship between insomnia and cognitive dysfunctions including, subjective memory impairment (SMI), mild cognitive impairment (MCI), and dementia, by considering depression in a community sample of elderly individuals.

Data for 1,740 elderly individuals aged 65 years and over were obtained from a nationwide dementia epidemiological study conducted in South Korea. Cognitive functional status was assessed by the Mini-Mental State Examination and the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet Clinical Assessment Battery. Insomnia was defined as the presence of at least one of the four sleep complaints (difficulty in initiating sleep, difficulty in maintaining sleep, early morning awakening, and non-restorative sleep), accompanied by moderate to severe daytime consequences. Depression was evaluated using the Geriatric Depression Scale.

The prevalence of insomnia in the patients with SMI, MCI, and dementia was found to be 23.2%, 19.6%, and 31.0%, respectively. The patients with SMI, MCI, and dementia were significantly more likely to have insomnia and the four sleep complaints than the normal comparison patients. After adjusting for sociodemographic factors, the significant relationships between cognitive dysfunctional status and insomnia remained. However, after adjusting for sociodemographic factors and depression, no significant relationships with any of the sleep complaints or insomnia remained.

Insomnia is a very common complaint in the elderly with SMI, MCI, and dementia. Depression might play an important factor in the relationship between insomnia and cognitive dysfunctional status in the elderly.

Social support programs for dementia caregivers were widely used in order to reduce care burden. We investigated which types of social supports can reduce psychological and non-psychological burdens of dementia caregivers, and explored the mechanism of those social supports.

We evaluated 731 community-dwelling dementia patients and their caregivers from the National Survey of Dementia Care in South Korea. We investigated the five types of social supports (emotional support, informational support, tangible support, positive social interaction, affectionate support) using the Medical Outcomes Study Social Support Survey in each caregiver. The mechanisms of specific types of social support on psychological/non-psychological burden were examined using path analysis.

Positive social interaction and affectionate support reduced psychological burden via direct and indirect paths. Tangible support reduced the non-psychological burden via direct and indirect paths. Informational support and emotional support were not helpful for reducing psychological or non-psychological burden. A maximum of 20% of psychological burden could be relieved by positive social interaction and 10.3% of that could be reduced by affectionate support. Tangible support was associated with a 15.1% maximal improvement in non-psychological burden.

In order to reduce caregiver burden in dementia effectively, psychosocial interventions should be tailored to target type of caregiver burden.