This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

Background: Pathological gambling is associated with several adverse outcomes, including depression, suicide, divorce, loss of employment, and debt. However, studies on the prevalence of pathological gambling are limited in South Korea. We assessed the prevalence of pathological gambling and its related factors. Methods: Data were obtained from 500 community-dwelling adults aged 20–59 years living in Jeju, Korea. This study assessed pathological/problem gambling using the Korean version of the Diagnostic Interview Schedule. We used the Patient Health Questionnaire-9 to obtain information about depression. Results: Lifetime prevalence rates of pathological gambling and problem gambling were 1.2% and 7.2%, respectively. The association between pathological/problem gambling and depression was highly significant (p<0.001). Multivariate analysis revealed significant relationships between men gender (odds ratio [OR], 2.62; 95% confidence interval [CI], 1.18–5.84; p=0.018) and depression (OR, 2.84; 95% CI, 1.42–5.68; p<0.001) and pathological/problem gambling. Conclusion Pathological/problem gambling is highly associated with depression, indicating that clinicians should carefully evaluate and treat depression among gamblers.

Background: Unemployment and the consequential mental health issues are emerging as a major social concern. This study aimed to evaluate factors associated with depression and suicidality among unemployed individuals in the Jeju area. Methods: Data from 624 unemployed adults who participated in a community survey were analyzed. Self-report questionnaires were completed by all participants; demographic variables and the Patient Health Questionnaire-9 were used to assess depression. The Mini International Neuropsychiatric Interview suicidality module was used to obtain information regarding suicidal ideation, suicide plans, and suicide attempts. Results: The prevalence of depressive symptoms among the unemployed population in Jeju area was 28.4%. Factors associated with suicidality included age, gender, unemployment period, job application period, and depression. Furthermore, unemployed participants who had an opportunity to receive vocational education had fewer symptoms of depression regardless of the number of vocational education sessions. A multivariate regression model demonstrated that depression (β = 0.51, p<0.001) was significantly correlated with suicidality. Conclusion The findings advance our understanding of the relationship between unemployment and psychological factors such as depression symptoms and suicidality. Further studies are required to determine the underlying mechanisms to aid in the development of preventive and treatment strategies.

Rapid developments in technology as part of the Fourth Industrial Revolution have created a demand for educational technology (EdTech) and a gradual transition from traditional teaching and learning to EdTech-assisted learning in medical education. EdTech is a portmanteau (blended word) combining the concepts of education and technology, and it refers to various attempts to solve education-related problems through information and communication technology. The aim of this study was to explore the use of key EdTech applications in medical education programs. A scoping review was conducted by searching three databases (PubMed, CINAHL, and Educational Sources) for articles published from 2000 to June 2021. Twenty-one studies were found that presented relevant descriptions of the effectiveness of EdTech in medical education programs. Studies on the application and effectiveness of EdTech were categorized as follows: (1) artificial intelligence with learner-adaptive evaluation and feedback, (2) augmented/virtual reality for improving learning participation and academic achievement through immersive learning, and (3) social media/social networking services with learner-directed knowledge generation, sharing, and dissemination in medical communities. Although this review reports the effectiveness of EdTech in various medical education programs, the number of studies and the validity of the identified research designs are insufficient to confirm the educational effects of EdTech. Future studies should utilize suitable research designs and examine the instructional objectives achievable by EdTech-based applications to strengthen the evidence base supporting the application of EdTech by medical educators and institutions.

Voiding cystourethrography (VCUG) demonstrates the anatomy of the urinary system and is used to detect the presence/absence of vesicoureteral reflux. It is the most important modality for urological fluoroscopic examination in children. For improved patient care, it is important to understand and perform VCUG appropriately. Therefore, an in-depth review of VCUG protocols and techniques has been presented herein. In addition, tips, tricks, and pitfalls associated with the technique have also been addressed.

Objectives: ：Childhood/adolescent-onset of bipolar disorder presents functional impairments on emotional, academic, and social aspects. These impairments could continue into adulthood. However, there are few studies comparing cognitive function between childhood/adolescent- and adult-onset using psychological test. This study aims to improve understanding of childhood/adolescent-onset of bipolar disorder by comparing differences in cognitive function, clinical and demographic features between the two groups. Methods: ：This study was conducted on 145 patients diagnosed with bipolar disorder type I, II, and other specified bipolar disorder by DSM 5 at the time of discharge from 2016 to 2019 at the Department of Psychiatry, Pusan National University Yangsan Hospital. Demographic information, clinical data, and results of psychological tests (K-WISC-IV, K-WAIS-IV) were collected and reviewed. Results: ：Childhood/adolescent-onset group was significantly low in total potential IQ and in language understanding than adult-onset group (p=0.008 and p=0.013). The childhood/adolescent group had significantly more psychiatric comorbidities than the adult group (p<0.001). The average number of prescribed antipsychotic agents was 1.18 (SD= ±0.64) in the childhood/adolescent group, and 1.78 (SD=±0.82) in the adult group. The difference was statistically significant (p<0.001). Conclusion ：Patients with childhood/adolescent-onset bipolar disorder have lower total potential IQ and language understanding comparing patients with adult-onset bipolar disorder. This highlights the importance of conducting a well-designed prospective study to find out more about the characteristics of childhood/adolescent-onset bipolar disorder.

BACKGROUND: In this report, we present the annual data of the intensive care unit (ICU) module of the Korean Nosocomial Infections Surveillance System (KONIS) from July 2013 through June 2014. METHODS: We performed a prospective surveillance of nosocomial urinary tract infections (UTIs), bloodstream infections (BSIs), and pneumonia (PNEU) in 166 ICUs of 94 hospitals using the KONIS. Nosocomial infection (NI) rate was defined as the number of infections per 1,000 patient-days or device-days. RESULTS: A total of 2,843 NIs were found during the study period: 861 UTIs (846 were urinary catheter-associated), 1,173 BSIs (1,021 were central line-associated), and 809 PNEUs (498 were ventilator-associated). The rate of urinary catheter-associated UTIs was 1.21 per 1,000 device-days (95% confidence interval [CI]=1.13-1.29), and the urinary catheter utilization ratio was 0.84 (95% CI=0.839-0.841). The rate of central line-associated BSIs was 2.33 per 1,000 device-days (95% CI=2.20-2.48), and the utilization ratio was 0.53 (95% CI=0.529-0.531). The rate of ventilatorassociated PNEUs (VAPs) was 1.46 per 1,000 device-days (95% CI=1.34-1.60), and the utilization ratio was 0.41 (95% CI=0.409-0.411). In hospitals with more than 900 beds, although the ventilator utilization ratio was highest, the rate of VAPs was lower than in hospitals with 300-699 or 700-899 beds. CONCLUSION: BSIs were the most commonly reported nosocomial infections. Although device utilization ratios had increased, nosocomial infection rates did not differ significantly from those during the previous period (July 2012 through June 2013).
Cross Infection* ; Intensive Care Units* ; Critical Care* ; Pneumonia ; Prospective Studies ; Urinary Catheters ; Urinary Tract Infections ; Ventilators, Mechanical