Frailty is a condition marked by a progressive decline in physiological functioning due to aging, leading to increased disease morbidity and rising healthcare costs. Therefore, it is important to prevent frailty through proper management. Not only does a lack of physical activity contribute to frailty, but inadequate dietary intake, resulting from decreased appetite and malabsorption, is also a significant risk factor. This study aimed to develop a 16-week algorithm to assess the nutritional risks associated with frailty, provide personalized nutritional solutions, and manage frailty risk factors through continuous monitoring and periodic reassessment. A total of 20 indicators were selected to create a nutritional health score. The selected indicators encompassed nutritional risk factors for frailty, disease history, and hematological factors. Reassessments were designed to occur at four-week intervals to revise personalized management goals and adjust solutions. Metrics were prioritized to provide a personalized solution. A user-friendly monitoring system was developed that leveraged voice recognition technology to determine compliance. It is anticipated that the algorithm will serve several purposes. First, this outcome will allow us to help prevent and delay the onset of frailty by utilizing a mobile app. Second, it will reduce the time and economic costs associated with nutritional management. Finally, it will facilitate future professional counseling and monitoring.

Frailty is a condition marked by a progressive decline in physiological functioning due to aging, leading to increased disease morbidity and rising healthcare costs. Therefore, it is important to prevent frailty through proper management. Not only does a lack of physical activity contribute to frailty, but inadequate dietary intake, resulting from decreased appetite and malabsorption, is also a significant risk factor. This study aimed to develop a 16-week algorithm to assess the nutritional risks associated with frailty, provide personalized nutritional solutions, and manage frailty risk factors through continuous monitoring and periodic reassessment. A total of 20 indicators were selected to create a nutritional health score. The selected indicators encompassed nutritional risk factors for frailty, disease history, and hematological factors. Reassessments were designed to occur at four-week intervals to revise personalized management goals and adjust solutions. Metrics were prioritized to provide a personalized solution. A user-friendly monitoring system was developed that leveraged voice recognition technology to determine compliance. It is anticipated that the algorithm will serve several purposes. First, this outcome will allow us to help prevent and delay the onset of frailty by utilizing a mobile app. Second, it will reduce the time and economic costs associated with nutritional management. Finally, it will facilitate future professional counseling and monitoring.

Frailty is a condition marked by a progressive decline in physiological functioning due to aging, leading to increased disease morbidity and rising healthcare costs. Therefore, it is important to prevent frailty through proper management. Not only does a lack of physical activity contribute to frailty, but inadequate dietary intake, resulting from decreased appetite and malabsorption, is also a significant risk factor. This study aimed to develop a 16-week algorithm to assess the nutritional risks associated with frailty, provide personalized nutritional solutions, and manage frailty risk factors through continuous monitoring and periodic reassessment. A total of 20 indicators were selected to create a nutritional health score. The selected indicators encompassed nutritional risk factors for frailty, disease history, and hematological factors. Reassessments were designed to occur at four-week intervals to revise personalized management goals and adjust solutions. Metrics were prioritized to provide a personalized solution. A user-friendly monitoring system was developed that leveraged voice recognition technology to determine compliance. It is anticipated that the algorithm will serve several purposes. First, this outcome will allow us to help prevent and delay the onset of frailty by utilizing a mobile app. Second, it will reduce the time and economic costs associated with nutritional management. Finally, it will facilitate future professional counseling and monitoring.

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 Pandemic Grief Risk Factors (PGRFs) was developed as a self-report tool to compile a comprehensive list of unique risk factors related to grief when experiencing a coronavirus disease 2019 (COVID-19) loss. We explored the reliability and validity of the PGRF among healthcare workers who witnessed their patients’ deaths during the COVID-19 pandemic. Further, we examined whether the general severity of PGRF may have been associated with work-related stress and pandemic grief reactions. Methods: An online survey was conducted among tertiary hospital healthcare workers (doctors and nursing professionals) who had witnessed the deaths of patients they cared for.Pandemic Grief Scale for healthcare workers, the Stress and Anxiety to Viral Epidemics-3 items, the Patient Health Questionnaire-9, and the Generalized Anxiety Disorder-7 responses were collected. Results: In total, 267 responses were analyzed. The single-factor structure of the Korean version of the PGRF showed a good fit for the model. The scale demonstrated good internal consistency and convergent validity with other depression and anxiety rating scales. The mediation analysis revealed that work-related stress directly influenced pandemic grief reactions positively, and depression, anxiety, and general severity of grief risk factors partially mediated the association positively. Conclusion Among healthcare workers who witnessed the deaths of their patients due to COVID-19, the Korean version of the PGRF was valid and reliable for measuring the overall severity of PGRF. The PGRF can be used to identify individuals at risk for dysfunctional grief.

Objective: The rising prevalence of mild cognitive impairment (MCI) has spurred interest in innovative cognitive rehabilitation approaches, including serious games. This review summarizes randomized clinical trials (RCTs) exploring the impact of serious games on MCI patients. Methods: We conducted a comprehensive data search using key terms such as “gamification,” “digital therapy,” “cognition,” “mild cognitive impairment,” and “Alzheimer’s disease.” We exclusively considered published RCTs, excluding animal studies and basic research. Results: We identified eight RCTs. Four RCTs examined the effects of serious games using cognitive training for MCI patients. Notably, one study found that non-specific training (Nintendo Wii) significantly enhanced cognitive function and quality of life compared to cognition-specific computer training (CoTras). Among the remaining three RCTs, one specifically demonstrated that personalized serious game-based cognitive training yielded superior cognitive outcomes and reduced depressive symptoms. One RCT focused on serious games incorporating physical exercise, highlighting the effectiveness of kinetic-based exergaming in enhancing overall cognition. Three RCT focused on combined cognitive training and physical exercise. A double-blind RCT revealed that progressive resistance training or standalone physical exercise outperformed the combined approach in improving executive function and global cognition. Two additional RCTs reported positive outcomes, including improvements in cognitive function and electroencephalogram patterns associated with game-based interventions. Conclusion Serious games, whether focusing on cognitive training, physical exercise, or a combination of both, have potential to improve cognitive and functional outcomes in individuals with MCI. Further research and standardization of protocols are needed to better understand the full potential of serious games in MCI.