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: To validate the treatment target of low-density lipoprotein cholesterol (LDL-C) level according to the cardiovascular disease (CVD) risk which was recommended by Korean dyslipidemia guideline. Methods: We used the Korean National Health Insurance Service database which included 3,958,048 people aged 20 to 89 years who underwent regular health screening. The primary outcome was incident CVD, defined as a composite of myocardial infarction and stroke during the follow-up period from 2009 to 2018. Results: The risk of CVD increased from LDL-C level of 70 mg/dL in very high-risk and high-risk groups and from 130 mg/dL in moderate-risk and low-risk groups. Adjusted hazard ratios (HRs) of LDL-C ranges 70–99, 100–129, 130–159, 160–189, and ≥190 mg/dL were 1.20 (95% confidence interval [CI], 1.08–1.33), 1.27 (1.15–1.42), 1.39 (1.23–1.56), 1.69 (1.45–1.96), and 1.84 (1.49– 2.27) in very high-risk group, and 1.07 (1.02–1.13), 1.16 (1.10–1.21), 1.29 (1.22–1.36), 1.45 (1.36–1.55), and 1.73 (1.58–1.90) in high-risk group. Adjusted HRs (95% CI) of LDL-C ranges 130–159, 160–189, and ≥190 mg/dL were 1.15 (1.11–1.20), 1.28 (1.22– 1.34), and 1.45 (1.36–1.54) in moderate-risk group and 1.07 (1.02–1.13), 1.20 (1.13–1.26), and 1.47 (1.37–1.57) in low-risk group. Conclusion We confirmed the incidence of CVD was increased in higher LDL-C range. The risk of CVD increased from ≥70 mg/dL of LDL-C in very high-risk and high-risk groups, and from ≥130 mg/dL of LDL-C in moderate-risk and low-risk groups in Korean adults.

Background: There are no clear data to support the cardiovascular (CV) risk categories and low-density lipoprotein cholesterol (LDL-C) treatment goals in Korean people with type 2 diabetes mellitus (T2DM). We evaluated the incidence of cardiovascular disease (CVD) according to comorbidities and suggested LDL-C treatment goals in Korean people with T2DM in nationwide cohort data. Methods: Using the Korean National Health Insurance Service database, 248,002 people aged 30 to 90 years with T2DM who underwent routine health check-ups during 2009 were included. Subjects with previous CVD were excluded from the study. The primary outcome was incident CVD, defined as a composite of myocardial infarction and ischemic stroke during the follow-up period from 2009 to 2018. Results: The mean age of the study participants was 59.6±10.9 years, and median follow-up period was 9.3 years. CVD incidence increased in the order of DM duration of 5 years or more (12.04/1,000 person-years), hypertension (HT) (12.27/1,000 personyears), three or more CV risk factors (14.10/1,000 person-years), and chronic kidney disease (18.28/1,000 person-years). The risk of incident CVD increased linearly from an LDL-C level of ≥70 mg/dL in most patients with T2DM. In T2DM patients without HT or with a DM duration of less than 5 years, the CVD incidence increased from LDL-C level of ≥100 mg/dL. Conclusion For primary prevention of CVD in Korean adults with T2DM, it can be helpful to lower LDL-C targets when there are chronic kidney disease, HT, a long duration of diabetes mellitus, or three or more CV risk factors.

Diabetes mellitus is a major risk factor for the development of heart failure. Furthermore, the prognosis of heart failure is worse in patients with diabetes mellitus than in those without it. Therefore, early diagnosis and proper management of heart failure in patients with diabetes mellitus are important. This review discusses the current criteria for diagnosis and screening tools for heart failure and the currently recommended pharmacological therapies for heart failure. We also highlight the effects of anti-diabetic medications on heart failure.

In May 2023, the Committee of Clinical Practice Guidelines of the Korean Diabetes Association published the revised clinical practice guidelines for Korean adults with diabetes and prediabetes. We incorporated the latest clinical research findings through a comprehensive systematic literature review and applied them in a manner suitable for the Korean population. These guidelines are designed for all healthcare providers nationwide, including physicians, diabetes experts, and certified diabetes educators who manage patients with diabetes or individuals at risk of developing diabetes. Based on recent changes in international guidelines and the results of a Korean epidemiological study, the recommended age for diabetes screening has been lowered. In collaboration with the relevant Korean medical societies, recently revised guidelines for managing hypertension and dyslipidemia in patients with diabetes have been incorporated into this guideline. An abridgment containing practical information on patient education and systematic management in the clinic was published separately.