• Mood & Emotion
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• v.10 no.1
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• pp.13-21
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• 2012

Suicide is a complex behavior associated with various neurobiological and psychosocial factors. It is considered that genetic polymorphism combined with environmental stress such as child-adolescent trauma make differences in neurobiological systems, which cause psychiatric disorders or pessimistic personality, impulse-aggressive behaviors, lack of judgment, and finally result in suicidal behavior. Much progress in the neurobiology of suicide has been made over the several decades. There seems to be a hereditary disposition to suicide independent of psychiatric disorder. The changes in neurotransmitters, neurohormones, neurotrophic factors, cytokines, lipid metabolisms related with their genetic polymorphism can contribute to disturbance of signal transductions and neuronal circuits vulnerable to suicide. It is likely that the main factors are dysfunctions of serotonin (5-HT) and hypothalamus-pituitary-adrenal (HPA) axis. Our understanding about the neurobiology of suicide is still limited. However, clinical practice could be assisted by neurobiological findings capable of making the detection of risk populations with higher sensitivity and the development of new treatment interventions. The settlement of biological markers in suicidal behaviors and their relationships is required.

• Korean Journal of Psychosomatic Medicine
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• v.31 no.2
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• pp.72-78
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• 2023

Objectives : This study aims to identify various psychiatric symptoms and psychosomatic symptoms caused by COVID-19 infection and investigate their long-term impact. Methods : A systematic literature review was conducted, selecting papers from domestic and international databases using keywords such as "COVID-19" and "psychosomatic." A total of 16 papers, including those using structured measurement tools for psychosomatic symptoms, were included in the final analysis. Results : Psychiatric symptoms such as anxiety, depression, and somatic symptoms have been reported in acute COVID-19 infection, while long-term post-COVID symptoms include chest pain and fatigue. The frequency of long-term psychosomatic symptoms has been estimated to be 10%-20%. Factors contributing to these symptoms include psychological and social stress related to infectious diseases, gender, elderly age, a history of psychiatric disorders, and comorbid mental illnesses. It is suggested that systemic inflammation, autoimmune responses, and dysregulation of the autonomic nervous system may be involved. Conclusions : Psychosomatic symptoms arising after COVID-19 infection have a negative impact on quality of life and psychosocial functioning. Understanding and addressing psychiatric aspects are crucial for symptom prevention and treatment.

• Journal of the Ergonomics Society of Korea
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• v.36 no.3
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• pp.183-196
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• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.