• 우울조울병
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• 제10권1호
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• pp.13-21
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• 2012

자살은 다양한 신경생물학적, 심리사회적 요인들이 모두 관여하고 있는 복합적인 행동으로, 유전적 요인과 가족력, 신경생물학적인 특성, 발달 단계에서의 요인들과 성격적 특성 등의 취약한 체질적 소인을 가진 사람들에게서 환경적인 스트레스와 물질남용, 정신질환, 신체질환 등의 영향을 통해 최종적으로 발생하게 된다. 신경생물학의 발달로 인해 자살에는 정신질환과 무관하게 독립적으로 작용하는 유전적 소인이 있으며, 이와 관련된 신경전달물질, 신경호르몬, 신경영양인자, cytokine, 지질 대사의 이상 등이 신경회로의 신호 전달을 교란시켜 자살에 취약하도록 만든다는 사실이 밝혀졌다. 또한 그 중 가장 주된 역할을 하는 인자는 5-HT와 HPA 축의 기능부전인 것으로 생각할 수 있다. 이러한 소견들은 향후 자살의 생물학적 표지자를 설정하고, 임상적으로 자살 고위험군에 대한 조기 개입을 하는 한편 새로운 치료 방법을 개발하는데 많은 도움을 줄 수 있을 것이다.

• 정신신체의학
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• 제31권2호
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• pp.72-78
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• 2023

연구목적 코로나19 감염으로 인한 다양한 정신과적 증상, 정신신체증상을 파악하고 장기적 영향을 조사하였다. 방 법 체계적 문헌고찰을 통해 국내외 데이터베이스에서 논문을 선정하고, "COVID-19", "psychosomatic" 등의 검색어를 활용하였다. 정신신체증상에 대한 구조화된 측정도구를 사용한 연구를 포함하여, 총 16편의 논문이 최종 분석 대상으로 포함되었다. 결 과 코로나19 급성 감염과 관련된 정신증상으로는 불안, 우울, 신체증상 등이 보고되고 있다. 장기간 지속되는 포스트 코로나증후군의 증상으로는 흉통, 피로 등이 보고되었고, 이와 관련된 정신신체증상의 발생 빈도는 10%-20%로 파악되었다. 감염병으로 인한 심리사회적 스트레스, 여성, 노인, 정신과적 기왕력이나 동반 정신질환 등의 요인이 관련을 미친다. 전신염증, 자가면역, 자율신경계의 이상반응 등이 관련이 있을 것으로 생각되고 있다. 결 론 코로나19 감염 이후 발생하는 정신신체증상은 삶의 질과 심리사회적 기능에 부정적인 영향을 미칠 뿐 아니라 증상에 대한 정신과적 이해와 접근은 예방과 치료에도 중요하다.

• 대한인간공학회지
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• 제36권3호
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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.