收集血壓波以及皮膚電阻資料並進行衰弱以及老化進程之分析

Abstract

老化是一種過程，老化不但會導致生理功能障礙，也會提高健康風 險，而老化到一定程度就會呈現衰弱症的臨床症狀，也會造成更進一步失 能狀態，然而衰弱症的診斷仍有待優化。目前用於診斷衰弱症的主流方式 是使用 Fried 的表徵模型，其量表主要包含五個部分:體重減輕、疲勞感、 步行速度􏰀、低活動量以及手握力不足。這些數值中，體重變化與活動量 因涉及過往資訊，往往需要仰賴照顧者的協助才能順利完成，進而造成老 年人填答上的困難。此外，該量表僅能診斷個體目前的衰弱狀態，而無法 預測衰弱症的發生。因此，本研究旨在透過收集不同年齡層於經絡能量分 析儀(皮膚電阻)和脈診儀(血壓波)等兩種非侵入性的科學脈診數據來 分析老化的進程，同時比對老年人族群之脈診數據與 Fried 量表的判斷結果 來預測衰弱症的發生。本研究是一項橫斷性臨床試驗，共招募了 330 名不 同年齡的受試者，並測量了他們的血壓波、皮膚電阻和衰弱症問卷。通過 簡單和多元線性回歸分析，我們初步確定了衰老的相關因素為皮膚電阻的 第 7(腎經)、9(脾經)和 11(膽經)穴位，以及血壓波上的第 0(心包 經)、2(腎經)、3(脾經)、4(肺經)、6(膽經)和 9(三焦經)個諧 波。結合中醫的理論基礎，我們從食療的角度針對經絡以及脈診儀所量測 出的經絡問題提出改善方法。此外，我們亦使用邏輯回歸以及支持向量機 (SVM)來進行衰弱症族群的分類，並可達到 85%的準確性。本研究發現 皮膚電阻和血壓波可能是衰老的潛在指標，可用以提供診斷衰弱和確定衰 老相關因素的資訊，具有識別高風險衰弱人群之潛力。

Aging is a natural process that leads to physiological impairments and increases health risks. Clinical symptoms of frailty may manifest at a certain point in aging, further contributing to disability. However, the diagnosis of frailty still requires optimization. Generally, diagnosing frailty relies on Fried's phenotype model, which can be challenging for elderly individuals due to the need to recall physical activity and weight changes from the past, often requiring assistance from a caregiver. Moreover, it can only diagnose an individual's current frailty status and cannot predict the syndrome’s onset. Therefore, this study aims to analyze the aging process by collecting non-invasive scientific data using a meridian energy analysis device and pulse sensor across different age groups. Furthermore, it seeks to predict the occurrence of frailty by comparing the pulse data of the elderly population with the results of the Fried scale. We adopted a cross-sectional clinical trial that recruited 330 participants of different ages and measured their pulse wave, skin resistance, and frailty questionnaire. Using simple and multiple linear regression analysis, we identified the contributing factors of aging: the 7th (kidney meridian), 9th (spleen meridian), and 11th (gallbladder meridian) acupuncture points of skin electric resistance and the 0 (pericardium meridian), 2nd (kidney meridian), 3rd (spleen meridian), 4th (lung meridian), 6th (gallbladder meridian) and 9th (sanjiao meridian) harmonics on the pulse wave. We also used logistic regression and support vector machine (SVM) to classify frailty populations, which has the potential to use these devices to diagnose frailty with an accuracy of 85%. Our study provides a method to diagnose frailty and identify the contributing factors of aging. This study suggests that skin electric resistance and pulse wave may serve as potential indicators of aging, offering information for diagnosing frailty and determining aging-related factors, showing the potential to identify high-risk populations for frailty.