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北京地區(qū)亞健康人群的睡眠狀況分析(已修改)

2024-09-03 08:43 本頁面
 

【正文】 8北京地區(qū)亞健康人群的睡眠狀況分析何麗云1 劉保延 1李霞2 匡宏波2 謝雁鳴1 易丹輝21中國中醫(yī)研究院 2中國人民大學(xué)統(tǒng)計學(xué)院 【摘要】目的 應(yīng)用流行病學(xué)調(diào)查的方法,研究亞健康人群睡眠異常的特點及其影響因素。方法 建立亞健康狀態(tài)判斷標(biāo)準(zhǔn),采用整群抽樣的方法在北京城區(qū)選擇被調(diào)查對象,應(yīng)用亞健康人群中醫(yī)基本證候調(diào)查問卷,進(jìn)行現(xiàn)場流行病學(xué)調(diào)查,發(fā)放問卷4000份,%。經(jīng)過對健康和亞健康兩人群的分類,得到亞健康問卷1828份,針對其中與睡眠相關(guān)的9個癥狀(睡眠滿意度、入睡困難、睡眠不實、早醒、多夢、睡眠質(zhì)量、白天困倦、思睡、精力不集中等),應(yīng)用描述性統(tǒng)計、相關(guān)系數(shù)法及l(fā)ogistic回歸模型,探討亞健康狀態(tài)睡眠異常的特點及其影響因素。結(jié)果 1828份調(diào)查數(shù)據(jù)顯示:睡眠明顯異常的共630人,%,以睡眠質(zhì)量差和入睡困難最嚴(yán)重,其次是早醒、睡眠不實和飯后困倦。相關(guān)性最強(qiáng)的因素是疲勞程度和食欲(p), 其次是頭腦昏沉(p)和心情(p)。結(jié)論 睡眠異常是亞健康狀態(tài)的重要表現(xiàn)之一,其特點是睡眠質(zhì)量差和入睡困難,影響睡眠的主要因素有疲勞程度、食欲和心情。【關(guān)鍵詞】亞健康狀態(tài) 睡眠 影響因素The analysis of sleeping status of the subhealth He Liyun1 Liu Baoyan1 Li Xia2 Kuang Hongbo2 Xie Yanming1 Yi Danhui2 Academy of Traditional Chinese Medicine, Beijing 100700,China of Statistics Renmin University of China,Beijing 100872,China[Abstract] Objective: The aim of this study is to observe the characteristic and factors affect the dormant of subhealth status by using epidemiological investigation methods. Method: We establish the criterion of subhealth, select investigation objects in Beijing district by using cluster sampling, apply questionnaire of Traditional Chinese Medicine (TCM) syndrome of subhealth population and conduct site epidemiological survey. We distribute four thousand questionnaire and get back 3768 of them with the recovery rate is %. After the classification of health and subhealth population, we get 1828 subhealth questionnaire. We extract 9 items which is associated with sleeping (satisfaction degree of sleeping, difficulty in falling asleep, not sound sleep, early wake, many dreams, sleep quality, day drowsiness, longing for sleep, cannot focus one’s mind). We analyze characteristic and affecting factors of sleeping abnormity of subhealth by using descriptives statistics, coefficient correlation method and Logistic regression model. Results: In 1882 quest
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