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研究生:黃羽希
研究生(外文):Huang, Yu-Hsi
論文名稱:代謝當量與血液指標中之代謝、發炎與免疫因子之探討
論文名稱(外文):The study of Metabolic Equivalent for physical activity with blood metabolism, inflammation and immune response
指導教授:李佩珍李佩珍引用關係
指導教授(外文):Lee, Pei-Chen
口試委員:邱尚志朱大維
口試委員(外文):Chiou, Shang-JyhChu, Ta-Wei
口試日期:2022-01-20
學位類別:碩士
校院名稱:國立臺北護理健康大學
系所名稱:健康事業管理研究所
學門:商業及管理學門
學類:醫管學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:107
中文關鍵詞:代謝當量身體活動血液指標代謝因子發炎因子免疫因子
外文關鍵詞:metabolic equivalentsphysical activityblood biomarkersmetabolic factorsinflammatory factorsimmune factors
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研究背景:過去有關身體活動與健康相關性的研究中,其中身體活動的評估多半以強度區分,而常用強度的計算標準為代謝當量(Metabolic Equivalent,MET)。過去文獻探討代謝當量與健康之相關性時,多數以疾病為探討的結果,亦或是研究對象只針對運動員。然而,較少研究討論到運用代謝當量評估身體活動的強度不同是否會影響一般民眾血液指標。
研究目的:探討運用代謝當量評估身體活動強度與血液指標中之代謝、發炎及免疫因子之相關性。
研究方法:利用橫斷性研究設計,使用2014年1月至2017年12月間自行至美兆診所進行健康查20歲至65歲之受檢者,第一次之健康檢查和生活習慣調查問卷之數據。身體活動強度所使用的代謝當量之計算方式是使用MET-h公式 (MET-h=強度(MET) ×運動持續時間(h))。本研究利用邏輯斯迴歸模型來探討成年人代謝當量和血液指標三大類血液因子之相關性。
研究結果:本研究共納入66,297名成人,平均年齡為40.20歲,標準差為10.72歲。在多變量模型的代謝因子研究中,中度MET-h其空腹血糖異常之風險為低度MET-h者之0.95倍(95%CI=0.90至1.00),而高度MET-h其空腹血糖異常之風險為低度MET-h者之0.85倍(95%CI=0.81至0.90)。高度MET-h其糖化血色素異常之風險為低度MET-h者之0.84倍(95%CI=0.73至0.97)。中度MET-h其三酸甘油脂異常之風險為低度MET-h者之0.84倍(95%CI=0.79至0.90),而高度MET-h其三酸甘油脂異常之風險為低度MET-h者之0.69倍(95%CI=0.65至0.75)。中度MET-h其膽固醇異常之風險為低度MET-h者之0.95倍(95%CI=0.91至1.00),而高度MET-h其膽固醇異常之風險為低度MET-h者之0.89倍95%CI=0.85至0.94)。中度MET-h其高密度脂蛋白膽固醇異常之風險為低度MET-h者之0.89倍(95%CI=0.80至0.97,而高度MET-h其高密度脂蛋白膽固醇異常之風險為低度MET-h者之0.71倍(95%CI=0.63至0.79)。中度MET-h其低密度脂蛋白膽固醇異常之風險為低度MET-h者之0.87倍(95%CI=0.82至0.91),而高度MET-h其低密度脂蛋白膽固醇異常之風險為低度MET-h者之0.76倍(95%CI=0.71至0.80)。在多變量模型的發炎因子研究中,高度MET-h其高敏感C反應蛋白異常之風險為低度MET-h者之0.76倍(95%CI=0.66至0.88)。中度MET-h其纖維蛋白原異常之風險為低度MET-h者之1.25倍(95%CI=1.02至1.54)。在多變量模型的免疫因子中,高度MET-h其白血球異常之風險為低度MET-h者之1.18倍(95%CI=1.08至1.29)。中度MET-h其嗜中性球異常之風險為低度MET-h者之1.08倍(95%CI=1.00至1.18),而高度MET-h其嗜中性球異常之風險為低度MET-h者之1.24倍(95%CI=1.14至1.35)。中度MET-h其嗜鹼性球異常之風險為低度MET-h者之1.16倍(95%CI=1.01至1.34)。
研究結論:促進代謝當量(MET-h)與血液代謝因子、血液發炎因子、免疫因子的健康結果有相關。因此,若能促進身體活動進而改善血液指標,盡早提供預防或介入措施,來減緩疾病的發生以達到公共衛生預防之目的。
關鍵字:代謝當量、身體活動、血液指標、代謝因子、發炎因子、免疫因子
Background: In the past studies on the correlation between physical activity and health, physical activity was mostly evaluated by intensity of physical activity, and the commonly used standard for intensity calculation was Metabolic Equivalent (MET). In the past literature on the relationship between metabolic equivalence and health, most of the research results were based on disease, or the research object was only for athletes. Fewer studies have discussed whether differences in the intensity of physical activity using metabolic equivalent affect blood biomarker in the general population.
Objective: To explore the correlation between physical activity intensity using metabolic equivalent and blood biomarker of metabolism, inflammation, and immune factors.
Methodology: Using a cross-sectional study design, a total of 66,297 adults aged 20 to 65 with results for blood tests and lifestyle habit questionnaire including physical activity were drawn from the Taiwan MJ cohort from January 2014 to December 2017. The metabolic equivalent of physical activity intensity is calculated using the MET-h formula (MET-h = intensity (MET) × exercise duration (h)). In this study, logistic regression model was used to explore the correlation between metabolic equivalents and blood indexes of metabolism, inflammation, and immune factors.
Results: A total of 66,297 adults with a mean age of 40.20 years and a standard deviation of 10.72 years were included in this study. In the multivariate model study of the risk of fasting abnormal fasting glucose, moderate MET-h is associated with a 0.95-fold higher than low MET-h (95% CI=0.90 to 1.00), whereas in high MET-h is 0.85 times higher than that in low MET-h (95%CI=0.81 to 0.90). The risk of abnormal glycated hematocrit in high MET-h is 0.84 times higher than that in low MET-h(95% CI=0.73 to 0.97). The risk of triglyceride abnormalities in moderate MET-h is 0.84 times compared with low MET-h (95% CI = 0.79 to 0.90), while in high MET-h is 0.69 times that of low MET-h (95% CI = 0.65 to 0.75). The risk of cholesterol abnormality in moderate MET-h is 0.95 times compared with low MET-h (95% CI=0.91 to 1.00), while in high MET-h is 0.89 times that of lower MET-h (95% CI=0.85 to 0.94). The risk of HDL cholesterol abnormalities in moderate MET-h is 0.89 times that of low MET-h (95% CI = 0.80 to 0.97), while the risk of HDL cholesterol abnormalities in high MET-h is 0.71 times that of low MET-h (95% CI = 0.63 to 0.79). The risk of LDL cholesterol abnormalities in moderate MET-h is 0.87 times that of low MET-h (95% CI = 0.82 to 0.91), while the risk of LDL cholesterol abnormalities in high MET-h is 0.76 times that of low MET-h (95% CI = 0.71 to 0.80).In the multivariate model of the inflammatory factor study, the risk of high-sensitivity C-reactive protein abnormalities is 0.76 times higher than those with low MET-h (95% CI=0.66 to 0.88). The risk of fibrinogen abnormality in moderate MET-h is 1.25 times higher than that in low MET-h (95% CI=1.02 to 1.54). Among the immune factors in the multivariate model, the risk of white blood cell abnormalities is a 1.18-fold higher in high MET-h than low MET-h (95%CI=1.08 to 1.29). The risk of neutrophilic abnormalities is 1.08 times higher in moderate MET-h than in low MET-h(95% CI=1.00 to 1.18), whereas 1.24 times higher in high MET-h than in low MET-h (95% CI=1.14 to 1.35). The risk of basophilic abnormalities in moderate MET-h was 1.16 times higher than that in low MET-h (95%CI=1.01 to 1.34).
Conclusion: Promoting metabolic equivalent (MET-h) is associated with the health outcomes of blood metabolic factors, inflammation factors, and immune factors. Therefore, if physical activity can be promoted to improve blood indicators, provide preventive or interventional measures to slow down the occurrence of diseases as soon as possible and achieve the purpose of public health prevention.
Key words: metabolic equivalents, physical activity, blood biomarkers, metabolic factors, inflammatory factors, immune factors
中文摘要 i
Abstract iii
目錄 v
表列 vi
圖列 viii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的與研究假設 2
第三節本研究之重要性 3
第二章 文獻探討 5
第一節 代謝當量之定義 5
第二節 國內代謝當量與健康之相關研究 8
第三節 人口學變項與代謝當量之相關性 9
第四節 代謝當量與血液指標代謝因子之相關性 11
第五節 代謝當量與血液指標發炎因子之相關性 22
第六節 代謝當量與血液指標免疫因子之相關性 30
第三章 研究方法 34
第一節 研究架構與設計 34
第二節 資料來源與研究對象 36
第三節 研究變項與變項操作型定義 38
第四節 統計方法 48
第四章 研究結果 49
第一節 研究樣本人口統計學特徵 49
第二節 代謝當量(MET-h)與血液代謝因子分佈情形及相關性 53
第三節 代謝當量(MET-h)與血液發炎因子分佈情形及相關性 66
第四節 代謝當量(MET-h)與血液免疫因子分佈情形及相關性 74
第五章 討論與建議 85
第一節 研究結果總結與討論 85
第二節 本研究之優點、限制以及對未來研究建議 87
第三節 本研究結果實務之應用 87
參考文獻 88
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