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研究生:廖慧儀
研究生(外文):Hui-Yi Liao
論文名稱:睡眠呼吸中止症與高血壓之相關性研究-以駕駛為例
論文名稱(外文):Association of Sleep Apnea Syndrome and Hypertension-A study of drivers
指導教授:劉紹興劉紹興引用關係
指導教授(外文):Saou-Hsing Liou
學位類別:碩士
校院名稱:國防醫學院
系所名稱:公共衛生學研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:115
中文關鍵詞:睡眠呼吸中止症血氧儀高血壓
外文關鍵詞:Sleep apnea syndromeoximetryhypertension
相關次數:
  • 被引用被引用:3
  • 點閱點閱:542
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
睡眠呼吸中止症會使患者於睡眠時期產生組織缺氧情形,不僅造成不良的睡眠品質、專注力下降增加交通意外事故的發生,也會促使交感神經活化刺激兒茶酚胺(catecholamines)的分泌增加高血壓之發生率,然而在台灣,雖然有文獻認為駕駛之高血壓盛行率高於一般民眾,可能是由於工作特質所造成,但是針對駕駛之睡眠呼吸中止症與高血壓之相關性研究卻相當稀少,因此本研究以台灣某客運公司駕駛為研究對象,希望能了解駕駛之睡眠呼吸中止症、高血壓之盛行率與其相關因子,並且進一步探討睡眠呼吸中止症與高血壓之相關性。
在此,本研究以橫斷式研究法(Cross-sectional study),利用攜帶式血氧儀了解駕駛睡眠時期之血氧分佈情形,並且使用結構式問卷收集駕駛之高血壓狀況以及個人基本特質、健康行為、工作狀況與其他疾病和不適症狀資料。在統計方法上除了描述型分析以外,以多變項邏輯斯回歸(Multiple logistic regression)分析了解睡眠呼吸中止症、高血壓之相關因子,此外,控制睡眠呼吸中止症與高血壓之干擾因子以獲得睡眠呼吸中止症與高血壓之相關性。
結果顯示,駕駛之睡眠呼吸中止症盛行率為11~12%,其相關因子有年齡、BMI(kg/m2)、抽菸習慣、輪班方式、打鼾情形以及近一個月服用睡眠相關藥物;駕駛之高血壓盛行率為11.8%,其相關因子為年齡、BMI(kg/m2)、喝酒習慣以及腎臟疾病。最後在控制睡眠呼吸中止症與高血壓之干擾因子後,可以發現不管在何種血氧判定準則下(ODI4、ODI3以及「ODI4與ODI3判定基準」),睡眠呼吸中止症與高血壓達到統計上顯著相關(p<0.05),且隨著血氧飽和度下降發生次數越多的組別,其罹患高血壓之風險越高,在趨勢分析上達到統計意義(p<0.01),意即睡眠呼吸中止症與高血壓具有劑量反應效應(Dose-response)。
因此,本研究建議駕駛應控制BMI於正常範圍之內以保持健康狀態,政府相關單位也應將睡眠呼吸中止症納入職業駕駛之例行身體檢查項目中,以避免睡眠呼吸中止症所帶來之傷害。
The sleep apnea syndrome causes the patients hypoxia during sleeping, not only make low sleep quality, adding of traffic accident from decreasing of attention and also cause sympathetic activation to stimulate secrete of catecholamines to add the incidence of hypertension. However, there are evidences show the prevalence of hypertension in drivers is higher then public humans in Taiwan. Maybe it is caused by special working type, but little is known about the sleep apnea syndrome and hypertension among drivers. Therefore, this study selected the drivers of some bus company in Taiwan to be our study population and we hope to realize the prevalence, related factors of sleep apnea syndrome and hypersention, and further more understanding the relationship between sleep apnea syndrome and hypertension.
This research conducted a Cross-sectional study, we use pulse oximetry to understand a variation of blood oxygen of sleeping and collected the hypertension situation, personal based-information about demography, healthy habits, working conditions and history of disease by questionnaires. On the statistical analysis, in addition the describable analysis, we also use the Multiple logistic regression to understand the related factors of sleep apnea syndrome and hypertension, and controling the confounding factors of sleep apnea syndrome and hypertension to get the association of sleep apnea syndrome and hypertension among drivers.
The results of sleep apnea syndrome prevalence of drivers is 11-12% and the related factors are age, BMI(kg/m2), smoking habit, the type of working shifts, snore and taking sleep-related medicine during recently month; the hypertension prevalence of drivers is 11.8%, and the related factors are age, BMI(kg/m2), drinking habits and Kidney disease. Finally when we controling the confounding factors of sleep apnea syndrome and hypertension, we can find that which criterion of oxygen desaturation index, the sleep apnea syndrome and hypertension are significance association, and when we do the analysis for trend, we can find that the risk of hypertension increasing when the oxygen desaturation index raise (p<0.01). That means that Sleep apnea syndrome and hypertension have dose response.
Therefore, this research suggest that drivers should be control the BMI in the correct range to maintain healthy situation, and the government need to add the testing item of sleep apnea syndrome into the routine health examine among occupational drivers to avoid the hurm from sleep apnea syndrome.
正文目錄
正文目錄 I
表目錄 II
中文摘要 IV
Abstract VI
符號與縮寫 VIII
第一章 緒論 1
第一節 研究背景與重要性 1
第二節 研究目的 4
第二章 文獻探討 5
第一節 睡眠呼吸中止症之定義與其相關研究 5
第二節 高血壓之定義與其相關研究 12
第三節 睡眠呼吸中止症與高血壓之相關研究 17
第三章 材料與方法 20
第一節 研究設計與研究架構 20
第二節 研究假設 22
第三節 研究工具 23
第四節 研究變項操作型定義 25
第五節 研究對象 26
第六節 資料整理與統計分析 27
第四章 結果 31
第一節 客運駕駛之基本資料及其睡眠呼吸中止症與高血壓之盛行率調查 31
第二節 客運駕駛之睡眠呼吸中止症與高血壓之相關性 35
第三節 客運駕駛之睡眠呼吸中止症相關因子 47
第四節 客運駕駛之高血壓相關因子 49
第五章 討論 50
第一節 客運駕駛之睡眠呼吸中止症與高血壓之盛行率調查 50
第二節 客運駕駛之睡眠呼吸中止症與高血壓相關性 53
第三節 睡眠呼吸中止症之相關因子 56
第四節 高血壓之相關因子 59
第五節 研究限制 61
第六章 結論與建議 62
第一節 結論 62
第二節 建議 64
參考文獻 65

表目錄
表1 客運駕駛之基本人口學特性與健康行為分布情形 70
表2 客運駕駛之工作狀況 71
表3 客運駕駛之血氧濃度分布情形 73
表4 客運駕駛之疾病與不適症狀分布情形 74
表5 年齡、BMI與駕駛年資在不同血氧濃度分組(ODI4)之分布情形 75
表6 客運駕駛之基本人口學特性在不同血氧濃度分組(ODI4)之分布情形 76
表7 客運駕駛之健康行為在不同血氧濃度分組(ODI4)之分布情形 77
表8 客運駕駛之工作狀況在不同血氧濃度(ODI4)下之分布情形 78
表8 客運駕駛之工作狀況在不同血氧濃度(ODI4)下之分布情形 79
表9 客運駕駛之疾病與不適症狀在不同血氧濃度分組(ODI4)之分布情形 80
表10 年齡、BMI與駕駛年資在不同血氧濃度分組(ODI3)之分布情形 81
表11 客運駕駛之基本人口學特性在不同血氧濃度分組(ODI3)之分布情形 82
表12 客運駕駛之健康行為在不同血氧濃度分組(ODI3)之分布情形 83
表13 客運駕駛之工作狀況在不同血氧濃度(ODI3)下之分布情形 84
表14 客運駕駛之疾病與不適症狀在不同血氧濃度分組(ODI3)之分布情形 86
表15 年齡、BMI與駕駛年資在不同血氧濃度分組(「ODI4與ODI3判定基準」)之分布情形 87
表16 客運駕駛之基本人口學特性在不同血氧濃度分組(「ODI4與ODI3判定基準」)
之分布情形 88
表17 客運駕駛之健康行為在不同血氧濃度分組(「ODI4與ODI3判定基準」)之分布情
形 89
表18 客運駕駛之工作狀況在不同血氧濃度(「ODI4與ODI3判定基準」)下之分布情形
90
表19 客運駕駛之疾病與不適症狀在不同血氧濃度分組(「ODI4與ODI3判定準」)
之分布情形 92
表20 年齡、BMI與駕駛年資在有無高血壓之分布情形 93
表21 客運駕駛之基本人口學特性在有無高血壓之分布情形 94
表22 客運駕駛之健康行為在有無高血壓之分布情形 95
表23 客運駕駛之工作狀況在有無高血壓之分布情形 96
表24 客運駕駛之疾病與不適症狀在有無高血壓之分布情形 98
表25 睡眠呼吸中止症與高血壓之影響因子總整理 99
表26以多變項邏輯斯回歸校正其他干擾因子後睡眠呼吸中止症(ODI4≧10次/時)與高
血壓之相關性 100
表27以多變項邏輯斯回歸校正其他干擾因子後睡眠呼吸中止症(ODI3≧15次/時)與高
血壓之相關性 101

表28以多變項邏輯斯回歸校正其他干擾因子後睡眠呼吸中止症(ODI4≧10次/時或ODI3
≧15次/時)與高血壓之相關性 102
表29 在BMI分層分析下,以多變項邏輯斯回歸校正其他干擾因子後睡眠呼吸中止症
(ODI4≧10次/時)與高血壓之相關性 103
表30在BMI分層分析下,以多變項邏輯斯回歸校正其他干擾因子後睡眠呼吸中止症
(ODI3≧15次/時)與高血壓之相關性 104
表31在BMI分層分析下,以多變項邏輯斯回歸校正其他干擾因子後睡眠呼吸中止症
(ODI4≧10次/時或ODI3≧15次/時)與高血壓之相關性 105
表32 睡眠呼吸中止症與心血管疾病之影響因子整理 106
表33以多變項邏輯斯回歸校正其他干擾因子後睡眠呼吸中止症(ODI4≦10次/時)與心血
管疾病之相關性 107
表34以多變項邏輯斯回歸校正其他干擾因子後睡眠呼吸中止症(ODI3≦15次/時)與心血
管疾病之相關性 108
表35以多變項邏輯斯回歸校正其他干擾因子後睡眠呼吸中止症(ODI4≧10次/時或ODI3
≧15次/時)與心血管疾病之相關性 109
表36以逐步邏輯斯回歸分析睡眠呼吸中止症(ODI4≧10次/時)之相關因子 110
表37以逐步邏輯斯回歸分析睡眠呼吸中止症(ODI3≧15次/時)之相關因子 111
表38以逐步邏輯斯回歸分析睡眠呼吸中止症(ODI4≧10次/時或ODI3≧15次/時)
之相關因子 112
表39以逐步邏輯斯回歸分析高血壓之相關因子 113
表40各國睡眠呼吸中止症之盛行率調查 114
表41各國高血壓之盛行率調查 115
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