臺灣博碩士論文加值系統

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

[1] Jansen, S., Schoe, J., van Rijn, M., Abu-Hanna, A., Moll van Charante, E. P., van der Velde, N., & de Rooij, S. E. J. B. g. Factors associated with recognition and prioritization for falling, and the effect on fall incidence in community dwelling older adults. 15, 1-10 (2015).
[2] Wu, T. Y., Chie, W. C., Yang, R. S., Liu, J. P., Kuo, K. L., Wong, W. K., & Liaw, C. K. J. A. A. M. S.. Factors associated with falls among community-dwelling older people in Taiwan. 42(7), 320-327 (2013).
[3] Gerges, S., Haddad, C., Daoud, T., Tarabay, C., Kossaify, M., Haddad, G., & Hallit, S. J. B. p. A cross-sectional study of current and lifetime sexual hallucinations and delusions in Lebanese patients with schizophrenia: frequency, characterization, and association with childhood traumatic experiences and disease severity. 22(1), 360 (2022).
[4] 莊佳雯, 曹芳馨, 楊斐茹, 王郁婷, 吳雅婷, & 長庚護理, 邱. J.。運用跨團隊共同照護網降低住院病人傷害性跌倒發生率, 28（4）,頁 640-653。 （2017）
[5] 林美惠, 陳淑如, 廖美南, 陳勇志, & 護理雜誌, 張. J.。住院病人跌倒事件之原因分析及醫療成本, 64（4）,頁 44-52 （2017）.
[6] Chiu, M. H., Lee, H. D., Hwang, H. F., Wang, S. C., Lin, M. R. J. G., & international, g.. Medication use and fall‐risk assessment for falls in an acute care hospital. 15(7), 856-863 (2015).
[7] 邱旅揚, 馮已榕, 陳綉琴, 鐘麗芬, & 醫療品質雜誌, 柯. J.（2020）。跌倒危險評估量表準確度研究-次級資料分析, 14（5）,頁 48-55。
[8] 蔡益堅, & 台灣老年醫學暨老年學會雜誌, 孫. J.探討社區老人自宅內與自宅外的跌倒相關因子, 15（1）,頁 11-26 （2020）.
[9] Hong, H.-J., Kim, N.-c., Jin, Y., Piao, J., & Lee, S.-M. J. C. N. R. Trigger factors and outcomes of falls among Korean hospitalized patients: analysis of electronic medical records. 24(1), 51-72 (2015).
[10] Callis, N. J. A. n. r. Falls prevention: Identification of predictive fall risk factors. 29, 53-58 (2016).
[11] Quigley, P. A. J. R. n. Evidence levels: applied to select fall and fall injury prevention practices. 41(1), 5-15. (2016).
[12] 郭素真, 陳尹甄, 陳盈妘, 林郁淇, & 榮總護理, 許. J.降低住院病人跌倒發生率改善方案, 36（4）,頁 408-416 （2019）
[13] Wen, C. P., Wai, J. P. M., Tsai, M. K., Yang, Y. C., Cheng, T. Y. D., Lee, M.-C., . . . Wu, X. J. T. l.. Minimum amount of physical activity for reduced mortality and extended life expectancy: a prospective cohort study. 378(9798), 1244-1253. (2011)
[14] Kuo, T. B., Li, J.-Y., Chen, C.-Y., Lin, Y.-C., Tsai, M.-W., Lin, S.-P., & Yang, C. C. J. J. o. m. b. Influence of accelerometer placement and/or heart rate on energy expenditure prediction during uphill exercise. 50(2), 127-133 (2018).
[15] Bourke, A., Van de Ven, P., Gamble, M., O’connor, R., Murphy, K., Bogan, E., . . . Nelson, J. J. J. o. b. Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. 43(15), 3051-3057 (2010).
[16] Özdemir, A. T., & Barshan, B. J. S. Detecting falls with wearable sensors using machine learning techniques. 14(6), 10691-10708 (2014).
[17] Tong, L., Song, Q., Ge, Y., & Liu, M. J. I. S. J. HMM-based human fall detection and prediction method using tri-axial accelerometer. 13(5), 1849-1856 (2013).
[18] Ravi, D., Wong, C., Lo, B., Yang, G.-Z. J. I. j. o. b., & informatics, h. A deep learning approach to on-node sensor data analytics for mobile or wearable devices. 21(1), 56-64 (2016).
[19] Freedson, P. S., Melanson, E., Sirard, J. J. M., sports, s. i., & exercise. Calibration of the computer science and applications, inc. accelerometer. 30(5), 777-781 (1998).
[20] Pate, R. R., Stevens, J., Webber, L. S., Dowda, M., Murray, D. M., Young, D. R., & Going, S. J. J. o. A. H. Age-related change in physical activity in adolescent girls. 44(3), 275-282 (2009).
[21] Chen, K. Y., David R Bassett, J. J. M., Sports, S. i., & Exercise. The technology of accelerometry-based activity monitors: current and future. 37(11), S490-S500 (2005).
[22] Elgendi, M., & Menon, C. J. B. s. Assessing anxiety disorders using wearable devices: Challenges and future directions. 9(3), 50 (2019).
[23] Hickey, B. A., Chalmers, T., Newton, P., Lin, C.-T., Sibbritt, D., McLachlan, C. S., . . . Lal, S. J. S. Smart devices and wearable technologies to detect and monitor mental health conditions and stress: A systematic review. 21(10), 3461 (2021).
[24] Sheikh, M., Qassem, M., & Kyriacou, P. A. J. F. i. D. H. Wearable, environmental, and smartphone-based passive sensing for mental health monitoring. 3, 662811 (2021).
[25] Shah, R. V., Grennan, G., Zafar-Khan, M., Alim, F., Dey, S., Ramanathan, D., & Mishra, J. J. T. p. Personalized machine learning of depressed mood using wearables. 11(1), 1-18 (2021).
[26] Gaggioli, A., & Riva, G. J. M. M. V. R. From mobile mental health to mobile wellbeing: opportunities and challenges. 141-147 (2013).
[27] Seppälä, J., De Vita, I., Jämsä, T., Miettunen, J., Isohanni, M., Rubinstein, K., . . . Berdun, J. J. J. m. h. Mobile phone and wearable sensor-based mHealth approaches for psychiatric disorders and symptoms: systematic review. 6(2) (2019).
[28] Hatano, M., Kamei, H., Inagaki, R., Matsuzaki, H., Hanya, M., Yamada, S., . . . Neuroscience. Assessment of switching to suvorexant versus the use of add-on suvorexant in combination with benzodiazepine receptor agonists in insomnia patients: a retrospective study. 16(2), 184 (2018).
[29] Tsai, M.-T., Lee, S.-M., Chen, H.-K., & Wu, B.-J. J. S. R. Association between frailty and its individual components with the risk of falls in patients with schizophrenia spectrum disorders. 197, 138-143 (2018).
[30] Stubbs, B., Koyanagi, A., Schuch, F., Firth, J., Rosenbaum, S., Gaughran, F., . . . Vancampfort, D. J. S. b. Physical activity levels and psychosis: a mediation analysis of factors influencing physical activity target achievement among 204 186 people across 46 low-and middle-income countries. 43(3), 536-545 (2017).
[31] Wong, M. M., Pang, P., Chan, C., Lau, M., Tse, W., Lam, L. C.-W., . . . Yan, C. T. J. E. A. a. o. p. Wilson sims fall risk assessment tool versus Morse Fall Scale in psychogeriatric inpatients: A multicentre study. 31(3), 67-70 (2021).
[32] Bayat, A., Pomplun, M., & Tran, D. A. J. P. C. S. A study on human activity recognition using accelerometer data from smartphones. 34, 450-457 (2014).
[33] Del Din, S., Hickey, A., Ladha, C., Stuart, S., Bourke, A. K., Esser, P., . . . Godfrey, A. J. F. Instrumented gait assessment with a single wearable: an introductory tutorial. 5(2323),2323. (2016).
[34] Grindstaff, G. A., & Whitaker, S. G. Real-time image stabilization: Google Patents (2013).
[35] Freeman, D., Garety, P. A., Kuipers, E., Fowler, D., & Bebbington, P. E. J. B. J. o. C. P. A cognitive model of persecutory delusions. 41(4), 331-347 (2002).
[36] Pickup, G. J. J. P.. Relationship between theory of mind and executive function in schizophrenia: a systematic review. 41(4), 206-213 (2008).
[37] Nierat, M.-C., Demiri, S., Dupuis-Lozeron, E., Allali, G., Morélot-Panzini, C., Similowski, T., & Adler, D. J. P. o. When breathing interferes with cognition: experimental inspiratory loading alters timed up-and-go test in normal humans. 11(3), e0151625 (2016).
[38] Podsiadlo, D., & Richardson, S. J. J. o. t. A. g. S. The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. 39(2), 142-148 (1991).
[39] Shumway-Cook, A., Brauer, S., & Woollacott, M. J. P. t. Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. 80(9), 896-903 (2000).