Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Background: The existing literature has comprehensively examined the benefits of specialized wound-care services and multidisciplinary team care. However, information on the development and integration of wound-dressing teams for patients who do not require specialized wound care is scarce. Therefore, the present study aimed to elucidate the benefits of a wound-dressing team by reporting our experiences with the establishment of a wounddressing team. Methods: The wound-dressing team was established at Korea University Guro Hospital.Between July 2018 and June 2022, 180,872 cases were managed for wounds at the wounddressing team. The data were analyzed to assess the types of wounds and their outcomes.In addition, questionnaires assessing the satisfaction with the service were administered to patients, ward nurses, residents/internists, and team members. Results: Regarding the wound type, 80,297 (45.3%) were catheter-related, while 48,036 (27.1%), 26,056 (14.7%), and 20,739 (11.7%) were pressure ulcers, dirty wounds, and simple wounds, respectively. In the satisfaction survey, the scores of the patient, ward nurse, dressing team nurse, and physician groups were 8.9, 8.1, 8.2, and 9.1, respectively.Additionally, 136 dressing-related complications (0.08%) were reported. Conclusion The wound dressing team can enhance satisfaction among patients and healthcare providers with low complications. Our findings may provide a potential framework for establishing similar service models.

Background: This study focuses on the establishment and operation of a stroke patient hotline program to help patients and their caregivers determine when acute neurological changes require emergency attention.Method: The stroke hotline was established at the Gyeonggi Regional Cerebrovascular Center, Seoul National University Bundang Hospital, in June 2016. Patients diagnosed with stroke during admission or in outpatient clinics were registered and provided with stroke education. Consulting nurses managed hotline calls and made decisions about outpatient schedules or emergency room referrals, consulting physicians when necessary. The study analyzed consultation records from June 2016 to December 2020, assessing consultation volumes and types. Outcomes and hotline satisfaction were also evaluated. Results: Over this period, 6,851 patients were registered, with 1,173 patients (18%) undergoing 3,356 hotline consultations. The average monthly consultation volume increased from 29.2 cases in 2016 to 92.3 cases in 2020. Common consultation types included stroke symptoms (22.3%), blood pressure/glucose inquiries (12.8%), and surgery/procedure questions (12.6%). Unexpected outpatient visits decreased from 103 cases before the hotline to 81 cases after. Among the 2,244 consultations between January 2019 and December 2020, 9.6% were recommended hospital visits, with two cases requiring intra-arterial thrombectomy. Patient satisfaction ratings of 9–10 points increased from 64% in 2019 to 69% in 2020. Conclusion The stroke hotline program effectively reduced unexpected outpatient visits and achieved high patient satisfaction.Expanding the program could enhance the management of stroke-related neurological symptoms and minimize unnecessary healthcare resource utilization.

Background/Aims: Sarcopenia negatively affects the prognosis of cirrhotic patients, but clinical implications of changes in muscle mass remain unclear. We aimed to elucidate its role in the prognosis of outpatients with cirrhosis. Methods: Patients with cirrhosis who underwent annual abdominal computed tomography (CT) for hepatocellular carcinoma surveillance were included in the prospective cohort. The L3 skeletal muscle index (SMI) was adopted as a proxy for the amount of skeletal muscle, and the rate of SMI change between inclusion and after 1 year (ΔSMI/yr%) was calculated. Results: In total, 595 patients underwent a second CT after 1 year. Among them, 109 and 64 patients had sarcopenia and Child-Pugh class B/C decompensation at inclusion, which changed to 103 and 45 at the 1-year follow-up, respectively. During a median follow-up of 30.1 months after 1 year, 86 patients had at least one cirrhosis complication, and 18 died or received liver transplantation. In the development of cirrhosis complications, ΔSMI/yr% was independently associated, even after adjusting for the Child-Pugh and model for end stage liver disease (MELD)-Na scores. In addition, ΔSMI/yr% showed a good predictive performance for the development of cirrhosis complications within 6 months after 1-year follow-up in all subgroups, with a cut-off of -2.62 (sensitivity, 83.9%; specificity, 74.5%) in the overall population. SMI at 1-year and Child-Pugh score were independent factors associated with survival. In addition, changes in sarcopenia status significantly stratified survival. Conclusion ΔSMI/yr% was a good predictor of the development of cirrhosis complications in outpatients with cirrhosis, independent of Child-Pugh and MELD scores.