Prediction of Sleep Disturbances in Korean Rural Elderly through Longitudinal Follow Up.
10.14401/KASMED.2017.24.1.38
- Author:
Kyung Mee PARK
1
;
Woo Jung KIM
;
Eun Chae CHOI
;
Suk Kyoon AN
;
Kee NAMKOONG
;
Yoosik YOUM
;
Hyeon Chang KIM
;
Eun LEE
Author Information
1. Department of Psychiatry and Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Korea. leeeun@yuhs.ac
- Publication Type:Original Article
- Keywords:
Aged;
Comorbidity;
Depression;
Insomnia;
Sleep;
Stress
- MeSH:
Aged*;
Aging;
Anger;
Angina Pectoris;
Arthritis;
Asthma;
Cataract;
Cognition;
Cohort Studies;
Comorbidity;
Dementia;
Depression;
Education;
Epidemiologic Studies;
Follow-Up Studies*;
Glaucoma;
Hepatitis B;
Humans;
Hyperlipidemias;
Hypertension;
Hypertrophy;
Korea;
Logistic Models;
Mass Screening;
Myocardial Infarction;
Osteoporosis;
Prevalence;
Prostate;
Sleep Initiation and Maintenance Disorders;
Stroke;
Tuberculosis, Pulmonary;
Urinary Incontinence
- From:Sleep Medicine and Psychophysiology
2017;24(1):38-45
- CountryRepublic of Korea
- Language:Korean
-
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.
