추적 관찰을 통한 한국 농촌 노인의 수면 장애 예측
Prediction of Sleep Disturbances in Korean Rural Elderly through Longitudinal Follow Up
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목 적：불면 증상을 나타내는 수면 장애는 고령화 현상과더불어 매우 급격히 증가중인 질환이다. 3년간 추적 관찰한우리나라 농촌의 60세 이상 노인 코호트를 대상으로 수면 장애의 유병률과 이를 예측하는 요인을 알아보고자 하였다. 방 법：한국인의 사회적 삶, 건강과 노화에 대한 2012년과 2014년의 조사를 통해 두 번 데이터를 얻었다. 뇌졸중, 심근경색증, 협심증, 관절염, 폐결핵, 천식, 백내장, 녹내장, B형 간염, 요실금, 전립선 비대, 암, 골다공증, 고혈압, 당뇨, 고지혈증, 대사증후군에 대한 진단 여부를 물었다. 치매선별용 간이정신상태검사를 이용하여 인지 기능을 평가하였고역학연구 우울척도를 이용하여 우울 증상을 평가하였다. 2015년에는 이 중 235명에게 DSM-IV의 제1축 장애의 구조화된 임상적 면담을 시행하였고 피츠버그 수면 질 척도로 불면증의 정도, 즉 수면 장애를 평가하였다. 또한, 지각된 스트레스 척도와 상태-특성 분노표현 척도를 시행하였다. 수면장애를 예측하기 위하여 성별, 나이, 교육, 첫 번째와 세 번째주기의 우울 점수, 공존 질환의 개수, 그리고 현재의 분노 억제 점수와 지각된 스트레스 정도를 설명 변인으로 삼아 로지스틱 회귀분석을 시행하였다. 결 과：조사 대상의 27%가 수면 장애를 가지고 있었다. 회귀 분석 결과, 3년 전의 공존 질환 수, 1년 전의 우울 점수및 현재 지각된 스트레스 정도가 수면 장애를 유의하게 예측하였다. 결 론：3년 전 측정한 공존 질환과 1년 전 평가한 우울증상이 현재의 수면 장애를 예측할 수 있었다. 공존 질환 및우울 증상의 치료가 수면 장애를 호전시킬 수 있는지 추가적 연구가 필요하다.
Abstract
Objectives: Sleep disturbance is a very rapidly growing disease with aging. The purpose of this study was to investigate the prevalence of sleep disturbances and its predictive factors in a three-year cohort study of people aged 60 years and over in Korea. Methods: In 2012 and 2014, we obtained data from a survey of the Korean Social Life, Health, and Aging Project. We asked participants if they had been diagnosed with stroke, myocardial infarction, angina pectoris, arthritis, pulmonary tuberculosis, asthma, cataract, glaucoma, hepatitis B, urinary incontinence, prostate hypertrophy, cancer, osteoporosis, hypertension, diabetes, hyperlipidemia, or metabolic syndrome. Cognitive function was assessed using the Mini-Mental State Examination for dementia screening in 2012, and depression was assessed using the Center for Epidemiologic Studies Depression Scale in 2012 and 2014. In 2015, a structured clinical interview for Axis I psychiatric disorders was administered to 235 people, and sleep disturbance was assessed using the Pittsburgh Sleep Quality Index. The perceived stress scale and the State-trait Anger Expression Inventory were also administered. Logistic regression analysis was used to predict sleep disturbance by gender, age, education, depression score, number of coexisting diseases in 2012 and 2014, current anger score, and perceived stress score. Results: Twenty-seven percent of the participants had sleep disturbances. Logistic regression analysis showed that the number of medical diseases three years ago, the depression score one year ago, and the current perceived stress significantly predicted sleep disturbances. Conclusion: Comorbid medical disease three years previous and depressive symptoms evaluated one year previous were predictive of current sleep disturbances. Further studies are needed to determine whether treatment of medical disease and depressive symptoms can improve sleep disturbances.
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