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研究生：	曹書涵 Tsao, Shu-Han
論文名稱：	老人跌倒者與非跌倒者雙重任務步態與臨床測試表現之比較 Comparing the performance of dual task gait and clinical tests between old fallers and non-fallers
指導教授：	卓瓊鈺 Cho, Chiung-Yu
學位類別：	碩士 Master
系所名稱：	醫學院 - 物理治療學系 Department of Physical Therapy
論文出版年：	2020
畢業學年度：	108
語文別：	英文
論文頁數：	100
中文關鍵詞：	跌倒、老人、雙重任務、步態、肌力、平衡、臨床測試
外文關鍵詞：	Fall, Older adults, Dual task, Gait, Muscle strength, Balance, Clinical test
相關次數：	點閱：90 下載：3
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摘要

背景與目的: 跌倒及其相關的後遺症對老人而言是一大健康問題。根據世界衛生組織的統計，每年約有28-35%的老年人會發生跌倒。跌倒會造成骨折、創傷性腦損傷、甚至死亡。因此找到一個理想的跌倒風險評估來篩檢出老年人中潛在的跌倒者，以利於及早進行介入，進而預防跌倒是很重要的。目前的文獻顯示平衡、步態與肌力等因子或評估測試，是和跌倒最相關的。最近的研究也指出，雙重任務情況下測量到的步態參數適合用來預測跌倒風險。然而，對於哪一種因子或評估測試最適合評估老年人的跌倒風險，目前並沒有共識。由於跌倒是多種因素造成的結果，結合多個不同領域的身體評估測試或許會比針對單一領域的評估測試更能偵測到老人的跌倒風險。因此本篇研究的目的為: (1)比較老人跌倒者與非跌倒者的步態與認知任務表現是否不同、(2)比較使用多個不同面向的因子與僅使用單一因子來區分老人跌倒者與非跌倒者的效果是否有差異。此外，本篇研究亦會探討不同雙重任務對老人步態的影響。
方法: 本研究招募了20位跌倒者與20位非跌倒者，皆為65歲以上的老年人。受試者在實驗中會在單一任務與雙重任務下測量步態與認知任務表現與肌力，並接受功能性表現測試。在雙重任務步態測試中，有四種認知任務(兩種Stroop test、連續減一與連續減三)會和走路一起執行。本研究使用二因子變異數重複測量分析(重複因子為情況)比較兩組受試者步態與認知任務表現的測量結果。若發現主效果或交互作用，會使用獨立t檢定來比較組間或情況間的差異。獨立t檢定也會用來比較兩組受試者的肌力與功能性表現測試的測量結果。此外，單變量羅吉斯迴歸與多變量羅吉斯迴歸則會用來比較使用單一因子或多個因子來區分老人跌倒者與非跌倒者的效果。
結果: 在所有的步態參數與認知任務表現結果中都沒有發現組別與情況的交互作用。研究者只有在步長上發現組別的主效果(p=.028)，進一步分析，發現有三個情況下(單一任務行走、Stroop test-1s、連續減一)，跌倒者的步長顯著比非跌倒者的步長短(p皆<.05)。此外，研究者在所有的步態參數與認知任務表現上都有發現情況的主效果(p皆<.05)，其中在雙重任務情況下的步態參數表現幾乎都明顯比單一任務情況下來的差(p皆<.05)。另外，在Stroop test-2s和Stoop test-1s情況下的步長與標準化步長比在連續減一和減三的情況短(p皆<.05)。在連續減三情況下的跨步時間、站立時間、擺盪時間與雙腳站立時間皆比其它情況來的長(p皆<.05)。在其他臨床測試結果中，標準化右側腳趾屈曲肌力(p=.049)與TUG測試(p=.043)組間也有顯著差異。在單變量羅吉斯迴歸中，三個情況下的步長(單一任務行走、Stroop test-1s、連續減一)皆為能顯著區分跌倒者的變數(p皆<.05)，尤其以連續減一情況下的步長達到最高的顯著水準(p=.016)。在多變量羅吉斯迴歸中，在與標準化右側腳趾屈曲肌力的搭配下，只有連續減一情況下的步長為能顯著區分出跌倒者的變數(p<.05)。
結論: 相較於非跌倒者而言，跌倒者步長較短、TUG測試時間較長、標準化右側腳趾屈曲肌力較小。雙重作業連續減一情況下的步長可能是最能區分老人跌倒者的步態參數。此外，將多個分別來自平衡、步態與肌力領域的因子結合並沒有比單一因子更能區辨老人跌倒者。而雙重任務的確會影響到受試者的步態，其中Stroop test-2s與Stroop test-1s對步長影響最大，而連續減三則對大部分與時間相關的步態參數造成最大的影響。然而此篇研究為橫斷性研究，其結果是否能應用於預測老人未來跌倒仍需要更多研究繼續探討。

Abstract

Background and purposes: Among older adults, falls and their consequences are a major health problem. According to World Health Organization, 28-35% of people over 65 years old fall every year. Falls could lead to fractures, traumatic brain injury and even death. Therefore, an optimal screening assessment for older adults is needed to identify potential fallers so that interventions can be applied in the early stage of frailty. According to previous studies, the factors and assessments from the domains of balance, gait, and muscle strength were the most common and consistent factors related falls. Recently, gait parameters assessed in the dual task condition were also shown to be promising predictors of fall risk. However, there is no consensus on which assessment of fall risk is the most optimal for the elderly. Since the causes of fall are multifactorial, combining different tests that assess different aspects of the elderly’s health may be more sensitive than a single test to detect fall risk in older adults. The purposes of this study were: (1) to compare the performance of gait and clinical tests between old fallers and non-fallers; (2) to determine whether using a combination of variables from more than one domain of health was better than using single variable to discriminate old fallers from non-fallers. Furthermore, the dual task effect on gait was investigated.
Methods: Twenty fallers and twenty non-fallers aged 65 or above were recruited. The participants need to encounter assessments of gait and cognitive task performance in both single task and dual task conditions, muscle strength tests, and functional performance-based tests. Four cognitive tasks (Stroop test-2s, Stroop test-1s, counting backward by 1, and counting backward by 3) were combined with walking as the dual task condition. Two-way ANOVA with repeated measure was used to analyze the main effect of group or condition and the interaction of both factors on gait parameters and cognitive task performance. Independent t test was used for further analysis to compare the difference between groups or conditions of gait parameters and cognitive task performance. Independent t test was also used to analyze the group difference on muscle strength and functional performance-based tests. Besides, univariate and multivariate logistic regression were used to analyze the effect of using single variable or multiple variables to discriminate fallers from non-fallers.
Results: For the gait parameters and cognitive task performance, no interaction of group and condition was found. The main effect of group was only found on step length (p=.028). Step length measurements in the conditions of walking, Stroop test-1s, and counting backward by 1 were significantly shorter in fallers than non-fallers (all p<.05). The main effects of condition were found on all gait parameters and cognitive task performance (all p<.05). The performance of most gait parameters was significantly worse in dual task than single task condition (all p<.05). Regarding the gait performance in different dual task conditions, the step length and normalized step length in the conditions of Stoop test-2s and Stoop test-1s were shorter than those in the conditions of counting backward by 1 and by 3 (all p<.05). The stride time, stance time, swing time and double support time in the condition of counting backward by 3 were significantly longer than those in other conditions (all p<.05). Significant group differences were also found on normalized right toe flexor strength (p=.049) and Timed-Up-and-Go (TUG) test (p=.043). In the univariate logistic regression, step length in the conditions of walking (p=.032), Stroop test-1s (p=.047), and counting backward by 1 (p=.016) were significant variables to discriminate fallers from non-fallers. The step length in the condition of counting backward by 1 has reached the highest significant level. In the multivariate logistic regression, step length in the condition of counting backward by 1 was the only significant variable to discriminate fallers from non-fallers while being combined with normalized right toe flexor strength (p<.05).
Conclusion: Fallers had shorter step length, longer TUG time, and smaller normalized right toe flexor strength than non-fallers. Step length in the condition of counting backward by 1 seems to be the most sensitive parameter to discriminate fallers from non-fallers. The combination of the variables from the domains of balance, gait, and muscle strength had no superior benefit compared with single variable in discriminating fallers. In addition, dual task indeed affected gait performance of the participants. Among the cognitive tasks, Stroop test-2s and Stroop test-1s had the greatest impact on step length, while counting backward by 3 had the greatest impact on temporal gait parameters. Since this is a cross-sectional study, whether these results could be used to predict future falls in older adults needs further investigation.

Table of contents

Abstract	I
摘要	III
誌謝	V
Table of contents	VI
List of tables	IX
List of figures	X
Chapter 1 Introduction	1
1.1 Background	1
1.2 General insight about fall risk assessments	2
1.3 Clinical tests and fall risk	3
1.3.1 Functional performance-based tests of fall risk	3
1.3.2 Muscle strength assessment	6
1.4 Laboratory gait assessment	8
1.5 Motivation and purposes	14
Chapter 2 Methods	16
2.1 Participants	16
2.2 Experimental procedure	17
2.3 Instrumentation	18
2.3.1 Motion capture system	18
2.3.2 Dynamometer	18
2.3.3 Video recorder	18
2.4 Clinical evaluation	19
2.4.1 Mini-Mental State Examination (MMSE)	19
2.4.2 Fall Efficacy Scale (FES)	19
2.4.3 Geriatric Depression Scale 15-item version (GDS-15)	20
2.4.4 Berg Balance Scale (BBS)	20
2.4.5 Timed Up and Go test (TUG)	20
2.4.6 Five-Time Sit-to-Stand test (FTSTS)	21
2.4.7 Muscle strength	21
2.5 Dual task paradigm	24
2.6 Data analysis	25
2.6.1 Kinematic data from motion capture system	25
2.6.2 Performance of secondary task	26
2.6.3 Muscle strength	27
2.7 Statistical analysis	27
Chapter 3 Results	29
3.1 Participants’ characteristic	29
3.2 Gait parameters and performance of cognitive task	29
3.3 Muscle strength	33
3.4 Functional performance-based tests	33
3.5 The accuracy of using single variable to discriminate fallers	34
3.6 The best regression model consisted of more than one variable to discriminate fallers	35
Chapter 4 Discussion	37
4.1 Step length is the most significant variable to discriminate old fallers from non-fallers in both single and dual task conditions	38
4.2 Why main effects of group were not found on other gait variables and cognitive task	42
4.3 Significant main effects of condition were found on all gait parameters and cognitive task	44
4.4 The toe flexor strength is the best variable than other muscle strength measurement to discriminate fallers from non-fallers	47
4.5 TUG is more sensitive assessment than BBS and 5TSTS for discriminating fallers in the high function elderly people	49
4.6 Combinations of variables from the domain of balance, gait, and muscle strength did not show superior benefit than single variable	52
4.7 Limitation and future study	54
Chapter 5 Conclusion	57
References	59
Appendix	92
Appendix 1	92
Appendix 2	93
Appendix 3	94
Appendix 4	95
 
List of tables

Table 3.1 Demographic data of the participants	74
Table 3.2 Causes of falls for the participants in faller group (N=20)	75
Table 3.3 Brief summary of two-way ANOVA and further analysis for gait parameters	76
Table 3.4 ANOVA results and further analysis of spatial gait parameters	77
Table 3.5 ANOVA results and further analysis of temporal gait parameters	78
Table 3.6 ANOVA results and further analysis of cognitive task performance – arithmetic tasks	79
Table 3.7 ANOVA results and further analysis of cognitive task performance – Stroop tests	80
Table 3.8 Normalized muscle strength of the participants (%)	81
Table 3.9 Mean muscle strength of participants (kg)	82
Table 3.10 Balance performance of the participants	82
Table 3.11 Univariate logistic regression including TUG, toe flexor strength, and step length as independent variables	83
Table 3.12 Pearson correlation coefficient between the variables that were significantly different between groups	84
Table 3.13 Multivariate logistic regression including two independent variables	84

 
List of figures

Fig. 2.1 Procedure of the experiment	85
Fig. 2.2 Marker set placement on foot	85
Fig. 3.1 Step length: A main effect on group (p=.028) and a main effect on condition (p<.001) were found	86
Fig. 3.2 Normalized step length: A main effect on condition (p<.001) was found	86
Fig. 3.3 Step width: A main effect on condition (p=.003) was found	87
Fig. 3.4 Toe clearance: A main effect on condition (p=.001) was found	87
Fig. 3.5 Gait speed: A main effect on condition (p<.001) was found	88
Fig. 3.6 Stride time: A main effect on condition (p<.001) was found	88
Fig. 3.7 Stance time: A main effect on condition (p<.001) was found	89
Fig. 3.8 Normalized stance time: A main effect on condition (p=.001) was found	89
Fig. 3.9 Swing time: A main effect on condition (p<.001) was found	90
Fig. 3.10 Normalized swing time: A main effect on condition (p=.001) was found	90
Fig. 3.11 Double support time: A main effect on condition (p=.005) was found	91
Fig. 3.12 Normalized double support time: A main effect on condition (p=.001) was found	91
                                    

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