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研究生:鍾國洲
研究生(外文):Chung, Kuo-Chou
論文名稱:智慧慢性精神病房跌倒預測風險系統開發與驗證
論文名稱(外文):Development and Validation of the Fall Risk Prediction System for a Chronic Psychiatric Smart Ward
指導教授:楊靜修楊靜修引用關係
指導教授(外文):Yang, Cheryl C.H.
口試委員:楊靜修郭冠良李嘉宜
口試委員(外文):Yang, Cheryl C.H.Kuo, Kuan-LiangLi, Jia-Yi
口試日期:2023-03-13
學位類別:碩士
校院名稱:國立陽明交通大學
系所名稱:腦科學研究所
學門:醫藥衛生學門
學類:醫學學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:中文
論文頁數:65
中文關鍵詞:慢性精神疾病患者跌倒評估量表跌倒預測物聯網穿戴式裝置
外文關鍵詞:patients with chronic mental illnessfall risk assessment scalefall detectionfall predictionInternet of Things (IoT)wearable devices
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背景:慢性精神疾病患者因受疾病與藥物影響,導致日間活動量不足,其身體平衡能力較差,因此跌倒的風險也相對較高,跌倒事件對於慢性精神病患者而言可能導致嚴重的身體傷害,甚至可能造成死亡。目前仍少有可有效評估之系統改善罹病者的生活品質。目標與假說:研究開發之系統可有效檢測慢性精神病患者活動量低下以及跌倒風險值等問題。比較「衛生福利部桃園療養院現行NIS(Nursing Information System, NIS)護理系統跌倒評估及防範表」與「Wilson-Sims跌倒風險評估量表」(WSFRAT)之準確度。並透過穿戴智慧型感測器(藍牙三軸感測帽)觀察身體活動與步態變化,輔助預測精神科住院受試者跌倒危險因素之效能。藉由驗證出準確度高的跌倒風險評估量表及智慧型穿戴式感測器,及早篩選高危險性受試者,加以防範,提升受試者安全。資料與方法:研究採用評估量表:NIS護理系統跌倒評估及防範措施表以及WSFRAT跌倒評估量表,同時研究者協助精神疾病住院受試者穿戴智慧型感測器(藍牙三軸感測帽),以評估TUG(Timed Up and Go Test)其對於篩選高危險性跌倒受試者的準確性、成效差異。研究對象為年滿20歲以上的精神疾病住院受試者。排除重複住院的病人。結果:本研究經驗證現行的NIS護理系統跌倒評估及防範措施表和WSFRAT跌倒評估量表之間有顯著相關性,其準確度可達85.1%。跌倒評估量表顯示,年齡、性別及骨骼肌肉疾病可能增加跌倒風險。透過藍牙三軸感測帽的輔助,可以觀測受試者步態再RMS(Root mean square)數據上有顯著性的差異,其準確度可達69.7%,並透過跌倒預測系統提早發現受試者出現步態施力不平衡的情況。結論:配戴藍牙三軸感測帽的研究可以證實步態及平衡不佳的受試者與衛生福利部桃園療養院現行NIS護理系統跌倒評估及防範表篩選出的高風險受試者有顯著相關。而對於WSFRAT跌倒評估量表則無顯著差異。本研究有助於提高慢性精神疾病患者跌倒風險的認識,並提供了一種有效的評估和監測方法。
Background: Patients with chronic mental illness (CMI) have reduced daytime physical activity and poor balance due to the effects of illness and medication, resulting in a higher risk of falls. Previous studies have shown that falls can lead to serious physical injuries and even death in patients with chronic mental illness. Currently, there are few effective systems available for assessing and improving the quality of life of these patients. Hypotheses: The developed system in this research can effectively detect the issues of low physical activity and fall risk in patients with chronic mental illness. Aims: Compare the accuracy of the "Nursing Information System (NIS) Fall Assessment and Prevention Form" and the "Wilson-Sims Fall Risk Assessment Tool" (WSFRAT). Use a wearable smart sensor (Bluetooth G sensor cap) to observe body activity and gait changes and assist in predicting the risk factors for falls in psychiatric inpatients. By verifying high accuracy fall risk assessment tools and intelligent wearable sensors, high-risk cases can be screened early, prevented, and patient safety can be improved. Materials and Methods: The study used two assessment scales: the NIS fall assessment and prevention form and the WSFRAT and assisted in the wearing of a Bluetooth G sensor cap to evaluate the accuracy and effectiveness of using the Timed Up and Go Test (TUG) to screen for high-risk fall cases. The study population consisted of hospitalized patients with mental illness over 20 years old, with repeat hospitalizations excluded from the study. Results: This study validated the current NIS nursing system fall assessment and prevention measures form and found a significant correlation between this form and the WSFRAT fall assessment scale, with an accuracy of 85.1%. The fall assessment scales showed that age, gender, and musculoskeletal disorders may increase fall risk. By using a Bluetooth three-axis sensor cap, significant differences in RMS (Root mean square) data in the gait of participants were observed, with an accuracy of 69.7%, and through a fall prediction system, early detection of gait force imbalance in participants was possible. Conclusions: The study using a Bluetooth three-axis sensor cap confirmed a significant correlation between cases with poor gait and balance and high-risk cases identified by the NIS fall assessment tool. There was no significant difference found in the WSFRAT fall assessment tool. This study helps to increase awareness of the risk of falls in patients with chronic mental illness and provides an effective method for assessment and monitoring.
中文摘要 i
英文摘要 ii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 研究背景介紹 1
第一節 跌倒定義 1
第二節 跌倒風險評估 1
第三節 身體活動量的評估 2
第四節 活動量計 2
第五節 頭部穩定性 3
第六節 物聯網技術應用於醫療及慢性精神病房 4
第二章 研究假說與目的 5
第一節 假說 5
第二節 目的 5
第三節 重要性 5
第三章 研究材料與方法 6
第一節 研究對象 6
第二節 研究流程 7
第三節 研究工具 8
第四節 藍牙三軸感測帽校正與驗證方法 9
第五節 演算法建立 10
第六節 統計分析 12
第四章 研究結果 14
第一節 藍牙三軸感測帽的開發 14
第二節 慢性精神病患者之人口學特徵 14
第三節 兩種跌倒風險評估量表的總分相關性檢定 15
第四節 受試者之描述性統計資料 15
第五節 兩種跌倒風險評估量表的診斷工具效能評估 16
第六節 配戴智慧型感測器(藍牙三軸感測帽)與兩種跌倒風險評估量表評估研究分析 18
第七節 兩種跌倒風險評估量表與藍牙三軸感測帽的診斷工具效能評估19
第八節藍牙三軸感測帽的診斷工具效能評估 20
第五章 討論 21
第一節 本研究之重要發現 21
第二節 兩種跌倒風險評估量表與跌倒風險 21
第三節 本研究跌倒預測系統 21
第四節 研究限制 22
結論 23
參考文獻 24
附表 27
附圖 34
附件 58
附件一、衛生福利部桃園療養院倫理委員會 58
附件二、衛生福利部桃園療養院NIS護理系統跌倒評估及防範表 59
附件三、Wilson-Sims跌倒風險評估量表 62
附件四、Wilson-Sims跌倒危險評估表英文版授權信件證明 65
附件五、Wilson-Sims跌倒危險評估表中文版授權信件證明 65
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