BACKGROUND AND PURPOSE: Vocabulary knowledge is used as a representative index of general intelligence, and is regarded as a marker for cognitive reserve in elderly people. However, vocabulary knowledge mainly depends on formal education, hence, it may not fully represent cognitive functioning in elderly people with poor educational backgrounds. Herein, we investigated whether vocabulary knowledge is a measure of general cognitive ability among normal elderly people with few years of formal education. METHODS: The association between vocabulary knowledge and general cognitive functioning was compared between 35 elderly females with very low educational attainment and 68 elderly females with higher education. RESULTS: The vocabulary knowledge was a significant predictor of general cognitive functioning in elderly individuals with more than primary education, even after controlling the effects of age and years of education. However, it was not a significant predictor of general cognitive functioning in elderly individuals with very low educational attainment. CONCLUSIONS: Vocabulary assessment was effective in estimating general cognitive functioning in elderly individuals who received ≥6 years of education. Our findings suggested that vocabulary knowledge may not be an effective proxy of premorbid intelligence or cognitive reserve in people who have not completed elementary schooling.
Aged* ; Cognitive Aging ; Cognitive Reserve ; Education ; Female ; Humans ; Intelligence ; Proxy ; Vocabulary*

OBJECTIVE: The cognitive reserve theory explicates individual differences observed in the clinical manifestation of dementia despite similar brain pathology. Education, a popular proxy of the cognitive reserve, has been shown to have protective effects delaying the onset of clinical symptoms including memory. This study was conducted to test whether education can moderate the negative effect of depressive mood on memory performance in elderly women residing in the community. METHODS: 29 elderly “unschooled” female (less than 6 years of formal education) and 49 “schooled” female (6 or more years) people were compared with regard to association between depressive mood and verbal memory functioning, which were measured by the Geriatric Depression Scale and the Elderly Verbal Learning Test, respectively. RESULTS: The results showed that completing or receiving more than primary school education significantly reduced the negative association between depressive mood and memory performance. Participants who did not complete primary schooling showed a decline in memory test scores depending on the level of depressive mood; whereas participants who have completed or received more than primary education displayed relatively stable memory function despite varying level of depressive mood. CONCLUSION: Our findings imply that education in early life may have protective effects against memory impairment related to elderly depression.
Aged ; Brain ; Cognitive Aging ; Cognitive Reserve ; Dementia ; Depression ; Education ; Female ; Humans ; Individuality ; Memory ; Pathology ; Protective Factors ; Proxy ; Verbal Learning

Objective: This study aimed to investigate the prevalence, clinical characteristics, and the correlates of nonsuicidal self-injury (NSSI) in firefighters. We also investigated the mediating role of NSSI frequency in the association between posttraumatic stress disorder (PTSD), depression, and suicidal behavior. Methods: A total of 51,505 Korean firefighters completed a web-based self-reported survey, including demographic and occupational characteristics, NSSI, PTSD, depression, and suicidal behavior. Multivariable logistic regression analyses and serial mediation analyses were performed. Results: The 1-year prevalence of NSSI was 4.67% in Korean firefighters. Female gender, the presence of recent traumatic experience, and PTSD and depression symptoms were correlated with NSSI. Serial mediation analyses revealed that NSSI frequency mediated the association between PTSD, depression, and suicidal behavior; it indicates more severe PTSD was sequentially associated with more severe depression symptoms and more frequent NSSI, leading to higher risk of suicidal behavior. Conclusion NSSI is prevalent and may play a significant mediating role when PTSD is associated with suicidal behavior in firefighters. Our results imply the need for screening and early intervention of NSSI in firefighters.

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.