Single- and dual-task gait parameters in determination of cerebral small vessel disease

Single- and dual-task gait parameters in determination of cerebral small vessel disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Single- and dual-task gait parameters in determination of cerebral small vessel disease Xianghua He, Jinshan Huang, Caiyou Hu, Mei Liang, Xuemin Cheng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3952547/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and objective: Gait disorder is one of the primary symptoms of cerebral small vessel disease (CSVD) and its potential diagnostic value was not known. We aimed to investigate the gait performance in CSVD and to determine the diagnostic value of gait parameters for CSVD under single-task and dual-task walking conditions. Methods: We prospectively recruited consecutive patients with CSVD from January 1, 2022 to August 31, 2023. A total of 129 CSVD patients and 71 healthy controls were enrolled. Direct gait parameters in the patient group and the control group were compared under single-task and dual-task conditions, controlling for covariates. Gait parameters were compared between the two groups, using the receiver operating characteristic curve. Results: Compared to controls, participants with CSVD had shorter stride length, slower stride speed, shorter cadence, longer stance time/phase, longer stride time, shorter swing phase, smaller average toe-off angle and smaller heel stride angle either in single-task walking test or in dual-task walking test (all P<0.05). Average heel strike angle could distinguish CSVD from healthy controls both in single-task (AUC = 0.858, P < 0.001, sensitivity, 83.1%; specificity, 76.7%) and dual-task walking tests (AUC = 0.865, P < 0.001, sensitivity, 91.5%; specificity, 70.5%) with moderate accuracy, independent of covariates. Conclusion: Gait patterns changed in patients with CSVD. Our findings suggest that average heel strike angle was one of the most valuable gait parameters of altered gait in CSVD and that could serve as a diagnostic marker of CSVD. Cerebral small vessel disease gait diagnostic marker dual-task single task Figures Figure 1 Figure 2 Figure 3 1. Introduction Cerebral small vessel disease (CSVD) is one of the most common subtypes of vascular diseases with the aging global population [ 1 ]. The disorder affects arterioles, capillaries and small veins causing stroke incidents, cognitive impairment, dementia, late-life depression and gait disturbances [ 2 ]. The two most common pathologies implicating CSVD are arteriolosclerosis (related to aging and other vascular risk factors) and cerebral amyloid angiopathy (associated with the deposition of β-amyloid) [ 3 ]. Recent study demonstrated high prevalence and economic burden of CSVD [ 4 ]. Therefore, it is significant to diagnose CSVD as soon as possible to improve its clinical management. Recently, the diagnosis of CSVD is primarily relied on magnetic resonance imaging (MRI) features, which include white matter hyperintensities (WMH), cerebral microbleeds (CMB), lacunar infarcts, enlarged perivascular spaces, and brain atrophy[ 3 ]. However, MRI is not always available especially for patients who had metal foreign objects in the body that cannot be removed. Due to technological factors or lesions, diagnosing CSVD only by using computed tomography (CT) has low sensitivity [ 5 ]. In the past years, other potential candidate biomarkers for the diagnosis of CSVD have been found but most of them are restricted in clinical practice due to the accuracy [ 6 ]. Consequently, it is crucial to provide available, reproducible, and reliable clinical diagnostic markers of CSVD. Gait disturbance is one of the common symptoms of CSVD and it will deteriorate as the disease progresses, sometimes leading to future falls [ 7 ]. A study from China investigated 127 symptomatic CSVD patients and implied that cerebral microbleeds in basal ganglia and brainstem will be conducive to gait impairment in participants with CSVD [ 8 ]. Study from America including 701 participants showed that white matter hyperintensities (WMH) were associated with slowing of gait over time[ 9 ]. This was consistent with a prospective study from the Netherlands. In this study, white matter atrophy as well as loss of white matter integrity was reported to be related to gait deterioration in older patients with CSVD after 5 years of follow-up [ 10 ]. However, similar prospective study did not find CSVD progression related to gait decline [ 11 ]. So far, most studies about gait and CSVD were focused on the characteristic imaging features and gait impairment [ 2 , 8 , 12 – 14 ], whereas few studies investigated the associations between CSVD and quantitative gait with instrumented walkways [ 15 ]. Recent study from China investigated 46 patients with CSVD and 22 controls and demonstrated that gait asymmetry, gait variability, and phase coordination index are biomarkers for gait disturbance in participants with CSVD [ 15 ]. They also found that patients with CVSD had altered gait features under both single-task (ST) walking and Dual-task (DT) walking conditions [ 15 ]. Due to the small sample size, the result of this study requires further replication. Consequently, this study aims to compare the performance of gait parameters between CSVD participants and healthy controls under both ST walking and DT walking conditions and to assess the diagnostic value of gait parameters in CSVD. Our findings may be significant to demonstrate the gait pattern of CSVD and to develop a reproducible, low-cost, available and reliable method for CSVD, accordingly allowing for a new diagnostic biomarker. 2. Methods Participants From January 1, 2022 to August 31, 2023, clinical information of 140 patients with CSVD who were admitted to the department of neurology in our hospital and 75 healthy, age- and sex-matched controls from the outpatient department were collected. From 215 participants initially recruited, we excluded fifteen cases without gait data (Fig. 1 ). All patients fulfilled the CSVD diagnostic criteria [ 16 ]. In this study, CSVD patients who had Parkinson’s disease, symptomatic stroke, traumatic brain injury, antipsychotic medication, brain tumor, encephalitis, and traffic hydrocephalus were excluded. Patients who were inability to ambulate independently were excluded. Patients with other systemic diseases that affect walking ability were also excluded, such as arthritis, joint injury, cervical and lumbar spine disease. All control participants underwent brain MRI examinations and were excluded from the diagnosis of CSVD. Controls with history of dementia were also excluded. General physical and nervous system examinations were carried out for all participants. Participant characteristics of age, sex, height, weight, shoe size, history of stroke, hypertension, type 2 diabetes (T2DM), and cardiovascular disorder were recorded. Gait evaluation The gait data of the study was collected by the JiBuEn® gait analysis system[17]. The system is comprised of wearable shoes and modules with the inertial Micro Electro-Mechanical Systems (MEMS) sensors. The modules collected motion signals and transmitted them to a computer. The MEMS sensors were fixed under the shoe heel bottom, behind the upper and lower limbs, and wrist. In data preprocessing, we used the high-order low-pass filter and hexahedral calibration technique, which can reduce high-frequency noise interference and installation errors produced by sensor devices. Based on the zero-correction algorithm, the accumulative errors were corrected. Then, the final gait parameters were obtained. Using the quaternary complementary filtering technique, the fusing acceleration data and posture was calculated. All participants were arranged to carry out two walking tests: (1) ST walking test: All participants were instructed to walk in a straight line on a 10 m footpath at their usual/normal gait speed. At the same time, gait parameters were collected during natural walking. (2) DT walking test: While walking, all participants perform serial subtraction of 7 beginning with 100. They walked in a straight line on the same 10 m footpath as in the ST walking test. During DT walking, they were required to focus on both walking and performing subtraction. In order to measure steady-state walking, all participants were instructed to perform one practice trial before ST and DT walking test. In this trial, the walking data will not be collected and processed by the JiBuEn® gait analysis system. Gait parameters including stride length, stride speed, cadence, stance phase, swing phase, stride time, stance time, swing time, average toe-off angle and average heel strike angle were collected. Statistical analysis. The normality test on demographic profiles and gait parameters were performed. For those with continuous variables, comparisons of between groups were performed using the Independent Samples T-Test or Mann–Whitney test; for dichotomous variables, the chi square test was used. Baseline variables that were considered clinically relevant or that showed a univariate relationship with outcome were entered into logistic regression model (Forward: LR). Variables for inclusion were carefully chosen, given the number of events available, to ensure parsimony of the final model. To evaluate the association between gait pattern and diagnosis of CSVD, multivariate binary logistic regression models were conducted. Then, Spearman’s correlation was used to investigate the relationship between clinical variables associated with CSVD in multivariate logistic regression models and gait parameters and diagnosis of CSVD. To classify healthy controls and CSVD, we drew the ROC curves. The AUC values were calculated to measure the parameter’s overall accuracy. SPSS 26.0 software (IBM, Armonk, NY, USA) was used to analyze the data. 3. Results Participants characteristics In total, 200 participants were included in the study, i.e., 129 patients diagnosed with CSVD. Clinical data are presented in Table 1—no significant group differences in age, sex, ethic, marriage, BMI and shoe size. There was significance difference between CSVD group and controls group in education, drinking, hypertension, type 2 diabetes mellitus (T2DM), history of hypoglycemia and stroke, Other neurological disorders and cardiovascular disorder. Compared to controls, participants with CSVD had shorter stride length, slower stride speed, shorter cadence, longer stance time/phase, longer stride time, shorter swing phase, and smaller average toe-off/heel stride angle either in ST or in DT walking conditions (all P<0.05). Multivariable logistic regression of potential diagnostic parameters for CSVD. The potential diagnostic parameters for CSVD were age, T2DM and average heel strike angle both in single-task walking test [age: P = 0.010, OR (odds ratio) = 0.858, 95% CI (confidence interval) = 0.764-0.963; T2DM: P = 0.033, OR = 15.096, 95%CI = 1.242-183.418; average heel strike angle: P<0.001, OR = 0.608, 95%CI = 0.480-0.771]and in dual-task walking test [age: P = 0.020, OR = 0.881, 95% CI = 0.791-0.980; T2DM: P = 0.023, OR = 17.253, 95%CI = 1.476-201.632; average heel strike angle: P = 0.015, OR = 0.731, 95%CI = 0.568-0.941]; drinking in single-task walking test [P = 0.017, OR = 0.031, 95%CI = 0.002-0.532] and stance phase in dual-task walking test[P = 0.041, OR = 1.505, 95% CI = 1.017-2.228] (Table 2). Correlations analysis for potential diagnostic clinical parameters and gait variables for CSVD. A correlation analysis was conducted for potential diagnostic clinical parameters and gait variables for CSVD. The results indicated that age, T2DM, stance phase, stride time, stance time were positively associated with CSVD (all p < 0.05) and drinking, stride length, stride speed, cadence, swing phase, average toe-off angle and average heel strike angle negatively correlated with CSVD (all p < 0.05) (Table 3). Diagnostic accuracy of gait parameters for CSVD Receiver operating characteristic curves were employed to demonstrate how gait tests differentiated CSVD from healthy controls (Figure 2 and Figure 3). Factors of stride length, stride speed, cadence, stance phase, swing phase, stride time, stance time, toe-off angle and heel strike angle (AUC = 0.858, P < 0.001, 95%CI: 0.807-0.909, sensitivity, 83.1%; specificity, 76.7%;) showed moderate ability to separate CSVD from healthy controls (Table 4) in single-task walking condition and stride length, stride speed, stance phase, swing phase, toe-off angle and heel strike angle (AUC = 0.865, P<0.001, 95%CI: 0.817-0.914, sensitivity: 91.5%, specificity: 70.5%) in dual-task walking condition (Table 5). Among them, heel strike angle in dual-task walking condition was the best one to differentiated CSVD from healthy participants. Table 1. Characteristics of the study participants. all(n=200) CSVD(n=129) control(n=71) P-value Demographic characteristics age,y 70.27±9.48 71.06±9.82 68.83±8.71 0.112 Sex (male),% 120(60.0%) 83(64.3%) 37(52.1%) 0.091 ethic the Han nationality 148(74.0%) 99(76.7%) 49(69.0%) 0.464 the Zhuang nationality 48(24.0%) 28(21.7%) 20(28.2%) others 4(2.0%) 2(1.6%) 2(2.8%) marriage married 185(92.5%) 120(93.0%) 65(91.5%) 0.705 bereave 15(7.5%) 9(7.0%) 6(8.5%) BMI,Kg/m2 23.2(2.5) 23.3(4.2) 22.9(5.2) 0.342* shoe size 40.0(2.0) 40.0(4.0) 39.0(5.0) 0.230* education illiteracy 8(4.0%) 8(6.2%) 0(0%) 0.020 primary school 37(18.5%) 28(21.7%) 9(12.7%) secondary school and above 155(77.5%) 93(72.1%) 62(87.3%) fall,yes,n% 28(14.0%) 19(14.7%) 9(12.7%) 0.689 smoke,yes,n% 48(24.0%) 32(24.8%) 16(22.5%) 0.719 drinking,yes,n% 45(22.5%) 22(17.1%) 23(32.3%) 0.013 hypertension,yes,n% 133(66.5%) 99(76.7%) 34(47.9%) <0.001 T2DM,yes,n% 41(20.5%) 33(25.6%) 8(11.2%) 0.016 hypoglycemia,yes,n% 10(5.0%) 0(0%) 10(14.1%) <0.001 syncope,yes,n% 14(7.0%) 7(5.4%) 7(9.9%) 0.240 dementia,yes,n% 13(6.5%) 13(10.1%) 0(0%) 0.006 stroke,yes,n% 114(57.0%) 108(83.7%) 6(8.5%) <0.001 Other neurological disorders,yes,n% 101(50.5%) 100(77.5%) 1(1.4%) <0.001 cardiovascular disorders,yes,n% 137(68.5%) 102(79.1%) 35(49.3%) <0.001 visual system diseases,yes,n% 2(1.0%) 2(1.6%) 0(0%) 0.292 musculoskeletal system diseases,yes,n% 2(1.0%) 2(1.6%) 0(0%) 0.292 incontinence,yes,n% 5(2.5%) 5(3.9%) 0(0%) 0.093 single-task Left (STL) STL-stride length(m) 0.99±0.25 0.90±0.25 1.16±0.13 <0.001 STL-stride speed(m/s) 0.85±0.26 0.75±0.26 1.03±0.15 <0.001 STL-cadence (steps/min) 101.51±13.39 98.31±14.48 107.32±8.54 <0.001 STL-stance phase (%) 66.09±3.73 67.49±3.65 63.54±2.23 <0.001 STL-swing phase (%) 33.91±3.73 32.51±3.65 36.46±2.23 <0.001 STL-stride time(s) 1.17(0.20) 1.23(0.24) 1.13(0.13) <0.001* STL-stance time(s) 0.76(0.18) 0.83(0.21) 0.71(0.10) <0.001* STL-swing time(s) 0.40(0.03) 0.40(0.04) 0.41(0.04) 0.066* STL-average toe-off angle (°) 39.78±9.02 36.48±9.17 45.77±4.57 <0.001 STL-Average heel strike angle(°) 28.75(13.2) 23.9(13.1) 34.8(6.9) <0.001* single-task Right (STR) STR-stride length(m) 1.00±0.27 0.91±0.29 1.15±0.14 <0.001 STR-stride speed(m/s) 0.85±0.26 0.75±0.26 1.03±0.15 <0.001 STR-cadence (steps/min) 101.51±13.39 98.31±14.48 107.32±8.54 <0.001 STR-stance phase(%) 66.13±4.06 67.61±4.10 63.45±2.17 <0.001 STR-swing phase(%) 33.87±4.06 32.39±4.10 36.55±2.17 <0.001 STR-stride time(s) 1.17(0.19) 1.23(0.23) 1.12(0.13) <0.001* STR-stance time(s) 0.76(0.18) 0.82(0.21) 0.71(0.10) <0.001* STR-swing time(s) 0.41(0.05) 0.40(0.05) 0.41(0.03) 0.051* STR-average toe-off angle(°) 40.09±8.83 36.99±9.17 45.72±4.25 <0.001 STR-Average heel strike angle(°) 26.95±9.16 23.11±8.62 33.93±5.11 <0.001 dual-task Left (DTL) DTL-stride length (m) 0.94±0.26 0.84±0.25 1.11±0.14 <0.001 DTL-stride speed (m/s) 0.78±0.27 0.69±0.26 0.95±0.18 <0.001 DTL-cadence (steps/min) 98.99±14.95 96.47±16.11 103.57±11.31 <0.001 DTL-stance phase (%) 67.00±3.88 68.41±3.79 64.44±2.49 <0.001 DTL-swing phase (%) 33.00±3.88 31.59±3.79 35.57±2.49 <0.001 DTL-stride time (s) 1.20(0.24) 1.23(0.27) 1.16(0.19) <0.001* DTL-stance time (s) 0.80(0.21) 0.83(0.22) 0.74(0.15) <0.001* DTL-swing time (s) 0.41(0.05) 0.40(0.05) 0.41(0.05) 0.009* DTL-average toe-off ground angle(°) 37.90±9.49 34.27±9.56 44.50±4.50 <0.001 DTL-Average heel strike angle(°) 25.66±9.10 21.82±8.52 32.65±5.09 <0.001 dual-task Right(DTR) DTR-stride length(m) 0.93±0.28 0.85±0.30 1.08±0.14 <0.001 DTR-stride speed(m/s) 0.78±0.27 0.69±0.26 0.95±0.18 <0.001 DTR-cadence (steps/min) 98.99±14.95 96.47±16.11 103.57±11.31 <0.001 DTR-stance phase (%) 67.08±4.15 68.56±4.15 64.40±2.49 <0.001 DTR-swing phase (%) 32.92±4.15 31.44±4.15 35.60±2.49 <0.001 DTR-stride time(s) 1.18(0.24) 1.21(0.27) 1.16(0.19) 0.002* DTR-stance time(s) 0.79(0.22) 0.82(0.24) 0.73(0.13) <0.001* DTR-swing time(s) 0.40(0.05) 0.39(0.05) 0.40(0.04) 0.001* DTR-average toe-off angle (°) 40.40(12.50) 35.90(14.70) 44.30(6.0) <0.001* DTR-Average heel strike angle(°) 25.16±9.03 21.32±8.33 32.15±5.32 <0.001 STL: single task walking for the left foot; STR: single task walking for the Right foot; DTL: dual-task walking for the left foot; DTR: dual-task walking for the right foot; T2DM: type 2 diabetes mellitus. *: Wilcoxon rank sum test. Table 2. Multivariate binary logistic regression analysis for diagnosis of CSVD and controls. Single-task walking test Dual-task walking test Variables P Exp(B) β(95%CI) P Exp(B) β(95%CI) age 0.010 0.858 0.764-0.963 0.020 0.881 0.791-0.980 drinking (yes) 0.017 0.031 0.002-0.532 0.059 0.071 0.005-1.106 T2DM (yes) 0.033 15.096 1.242-183.418 0.023 17.253 1.476-201.632 average heel strike angle <0.001 0.608 0.480-0.771 0.015 0.731 0.568-0.941 stance phase - - - 0.041 1.505 1.017-2.228 T2DM: type 2 diabetes mellitus. Tabel 3. Correlation analysis for variables associated with diagnosis of CSVD and healthy controls. Variables diagnosis (single-task walking test) diagnosis (dual-task walking test) age rho .143 * .143 * P 0.044 0.044 drinking rho -.176 * -.176 * P 0.013 0.013 T2DM rho .170 * .170 * P 0.016 0.016 stride length(m) rho -.483 ** -.491 ** P 0.000 0.000 stride speed(m/s) rho -.523 ** -.482 ** P 0.000 0.000 Cadence (steps/min) rho -.335** -.232** P 0.000 0.001 stance phase (%) rho .546 ** .521 ** P 0.000 0.000 swing phase (%) rho -.546 ** -.521 ** P 0.000 0.000 stride time(s) rho .335 ** .232 ** P 0.000 0.001 stance time (s) rho .415 ** .327 ** P 0.000 0.000 average toe-off angle (°) rho -.540 ** -.530 ** P 0.000 0.000 average heel strike angle (°) rho -.593 ** -.606 ** P 0.000 0.000 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Table 4. Variables of ROC analysis for gait parameters distinguishing the individuals with CSVD and healthy controls in single-task walking tests. Test Result Variable(s) Area Std. Error a Asymptotic Sig. b 95%CI sensitivity specificity Youden’s index ST-stride length 0.792 0.031 <0.001 0.731-0.852 0.887 0.620 0.507 ST-stride speed 0.815 0.029 <0.001 0.758-0.873 0.845 0.690 0.535 ST-cadence 0.702 0.036 <0.001 0.631-0.772 0.887 0.519 0.407 ST-stance phase 0.171 0.029 <0.001 0.115-0.227 0.859 0.713 -0.572 ST-swing phase 0.829 0.029 <0.001 0.773-0.885 0.859 0.713 0.572 ST-stride time 0.298 0.036 <0.001 0.228-0.369 0.859 0.535 -0.394 ST-stance time 0.250 0.034 <0.001 0.183-0.316 0.958 0.496 -0.454 ST-toe-off angle 0.826 0.029 <0.001 0.769-0.882 0.746 0.798 0.545 ST-heel strike angle 0.858 0.026 <0.001 0.807-0.909 0.831 0.767 0.598 ST: single-task; 95%CI：95% Confidence Interval. a.Under the nonparametric assumption; b.Null hypothesis: true area = 0.5. Table 5. Variables of ROC analysis for gait parameters distinguishing the individuals with CSVD and healthy controls. Test Result Variable(s) Area Std. Error a Asymptotic Sig. b 95%CI sensitivity specificity Youden’s index DT-stride length 0.796 0.031 <0.001 0.736-0.856 0.901 0.651 0.553 DT-stride speed 0.791 0.031 <0.001 0.730-0.851 0.901 0.574 0.475 DT-stance phase 0.186 0.030 <0.001 0.128-0.244 0.831 0.682 -0.513 DT-swing phase 0.814 0.030 <0.001 0.756-0.872 0.831 0.682 0.513 DT-toe-off angle 0.820 0.029 <0.001 0.764-0.876 0.915 0.620 0.536 DT-heel strike angle 0.865 0.025 <0.001 0.817-0.914 0.915 0.705 0.621 DT: dual-task; 95%CI：95% Confidence Interval. a. Under the nonparametric assumption; b. Null hypothesis: true area = 0.5. 4. Discussion This is a cross-sectional study. In this study, we aimed to demonstrate gait parameters for diagnosis of CSVD and to identify the gait pattern of patients with CSVD in the single-task and dual-task walking tests. To the best of our knowledge, this is the first study aimed to distinguish CSVD patients from healthy controls using gait biomarkers. Our results showed that: (1) Compared to controls, CSVD group demonstrated impaired stride length, stride speed, cadence, stance time/phase, stride time, swing phase, and average toe-off/heel stride angle both in single-task and dual-task walking tests; (2) Average heel strike angle could distinguish CSVD from healthy controls both in single-task and dual-task walking tests; (3) Several demographic covariates and gait parameters correlated with CSVD. Among them, average heel strike angle was the most robust diagnostic markers of CSVD. Study from Yang S et.al investigated 99 CSVD patients with enlarged perivascular spaces in basal ganglia and found that the stride length, cadence was significantly shorter than those of controls [ 18 ]. other study under single-task and dual-task walking conditions also verified that CSVD patients had short stride length and slow stride speed [ 15 ]. Furthermore, a prospective cohort study showed that stride length decline was independent of sex, age, height, follow-up duration and baseline stride length [ 10 ]. In line with the previous study, our study also found that patients with CSVD had an impaired stride length. Similar to stride speed, researchers found decreased cadence in CSVD patients[ 15 , 18 – 20 ]. Interestingly, a cross-sectional study with large samples showed that patients with CSVD had step length, step speed, swing time and stance time impaired and age was associated with step length, step speed, swing time and stance time [ 21 ]. All these studies suggesting progression of CSVD might play an important role in gait deterioration. Patients with Parkinson’s disease had impaired foot strike angle [ 22 ]. The foot strike maintains forward stability and the toe-off angle means the ground clearance ability of the foot [ 23 , 24 ], implicating an association with fall risk. Seemingly, researchers found decreased foot strike angle and toe-off angle in patients with CSVD [ 25 ], indicating a trend with fall risk. In our study, we found a significant difference of average heel strike angle between CSVD patients and healthy controls, which were independent of sex, age, clinical comorbidities of T2DM, hypertension and cardiovascular disorder. Since CSVD patients may develop Parkinsonism and similar gait disorder, more studies about stride angle of CSVD patients should be conducted. Consistent with the previous studies, we demonstrated that clinical factors including age, and T2DM associated with CSVD [ 26 – 28 ], implicating a replication of the robust diagnostic value for these variables. In our study, drinking in single-task walking test was associated with CSVD, while drinking was not a risk factor in other study [ 29 ]. More research about drinking and gait performance in CSVD was needed to clarify this area. Furthermore, several gait parameters correlated with CSVD both in ST and DT walking test. Among them, average heel strike angle was the one with highest sensitivity and specificity for diagnosing of CSVD. This study was particularly insightful due to the strengths of its methodology. Firstly, using wearable sensors, we performed an analysis of the gait impairment between CSVD patients and healthy controls under both ST and DT walking tests. Secondly, we investigate the diagnostic value of gait parameters and use these gait parameters to distinguish CSVD from healthy controls under ST and DT walking conditions for the first time. This study also has some limitations. Firstly, the participants recruited from a single center may lead to potential selection biases. However, the consecutive recruitment of this study decreased the biases. Future large-sample, multi-center studies should be designed for validation. Secondly, the cross-sectional study limited the study of pathological gait features of CSVD. So longitudinal studies are needed to replicate the diagnostic value of gait parameters for CSVD individuals. In conclusion, the gait pattern changed in patients with CSVD, especially for the elderly ones with a history of T2DM. Compared to controls, CSVD group demonstrated impaired stride length, stride speed, cadence, stance time/phase, stride time, swing phase, and average toe-off/heel stride angle both in ST and DT walking tests. Gait parameters could distinguish CSVD individuals from healthy controls. Among these, average heel strike angle was one of the most valuable gait parameters of altered gait and had moderate predictive values for CSVD. Further large-sample, multi-center, and longitudinal studies are needed to clarify the development of gait performance in CSVD individuals and to replicate the diagnostic value of gait parameters for CSVD. Declarations Acknowledgements We thank the Cerebral small vessel disease patients for their participation in our study. Ethics statement. We confirm that we have read the Editorial Policy pages. This study was conducted with approval from the Ethics Committee of Jiangbin Hospital, Guangxi Zhuang Autonomous Region (No.KY-GXZR-2020-01). This study was conducted in accordance with the declaration of Helsinki. Written informed consent was obtained from all participants. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Guangxi Natural Science Foundation (2020GXNSFAA297189), the Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project (S2018012), the Guangxi Science and Technology Base and Talent Special Project (GUIKE AD20238075), the National Key Research and Development Program of China (2018YFC2000400), the National Natural Science Foundation of China (91849118, 31760299, and 82260289) and the Guangxi Key Research and Development Plan(2023AB22024). Author contributions Xianghua He and Jinshan Huang conceptualization, data curation, formal analysis, methodology, software, validation, visualization, writing-original draft, and writing-review and editing. Caiyou Hu funding acquisition. Caiyou Hu, Mei Liang, Xuemin Cheng and Dongdong Jiang data curation, formal analysis, methodology, and writing-original draft. Wei Zhang conceptualization, funding acquisition, project administration, supervision and writing-review & editing. References Gorelick P, Sorond F: Advancing our knowledge about cerebral small vessel diseases . The Lancet Neurology 2023, 22 (11):972-973. Blumen HM, Jayakody O, Verghese J: Gait in cerebral small vessel disease, pre-dementia, and dementia: A systematic review . International journal of stroke : official journal of the International Stroke Society 2023, 18 (1):53-61. Markus HS, de Leeuw FE: Cerebral small vessel disease: Recent advances and future directions . International journal of stroke : official journal of the International Stroke Society 2023, 18 (1):4-14. Lam BYK, Cai Y, Akinyemi R, Biessels GJ, van den Brink H, Chen C, Cheung CW, Chow KN, Chung HKH, Duering M et al : The global burden of cerebral small vessel disease in low- and middle-income countries: A systematic review and meta-analysis . International journal of stroke : official journal of the International Stroke Society 2023, 18 (1):15-27. Schwarz G, Banerjee G, Hostettler IC, Ambler G, Seiffge DJ, Ozkan H, Browning S, Simister R, Wilson D, Cohen H et al : MRI and CT imaging biomarkers of cerebral amyloid angiopathy in lobar intracerebral hemorrhage . International journal of stroke : official journal of the International Stroke Society 2023, 18 (1):85-94. Wan S, Dandu C, Han G, Guo Y, Ding Y, Song H, Meng R: Plasma inflammatory biomarkers in cerebral small vessel disease: A review . CNS neuroscience & therapeutics 2023, 29 (2):498-515. Sharma B, Wang M, McCreary CR, Camicioli R, Smith EE: Gait and falls in cerebral small vessel disease: a systematic review and meta-analysis . Age and ageing 2023, 52 (3). Mao HJ, Zhang JX, Zhu WC, Zhang H, Fan XM, Han F, Ni J, Zhou LX, Yao M, Tian F et al : Basal Ganglia and Brainstem Located Cerebral Microbleeds Contributed to Gait Impairment in Patients with Cerebral Small Vessel Disease . Journal of Alzheimer's disease : JAD 2023, 94 (3):1005-1012. Willey JZ, Scarmeas N, Provenzano FA, Luchsinger JA, Mayeux R, Brickman AM: White matter hyperintensity volume and impaired mobility among older adults . Journal of neurology 2013, 260 (3):884-890. van der Holst HM, Tuladhar AM, Zerbi V, van Uden IWM, de Laat KF, van Leijsen EMC, Ghafoorian M, Platel B, Bergkamp MI, van Norden AGW et al : White matter changes and gait decline in cerebral small vessel disease . NeuroImage Clinical 2018, 17 :731-738. van der Holst HM, van Uden IWM, de Laat KF, van Leijsen EMC, van Norden AGW, Norris DG, van Dijk EJ, Tuladhar AM, de Leeuw FE: Baseline Cerebral Small Vessel Disease Is Not Associated with Gait Decline After Five Years . Movement disorders clinical practice 2017, 4 (3):374-382. Tripathi S, Verghese J, Blumen HM: Gray matter volume covariance networks associated with dual-task cost during walking-while-talking . Human brain mapping 2019, 40 (7):2229-2240. Yuan J, Blumen HM, Verghese J, Holtzer R: Functional connectivity associated with gait velocity during walking and walking-while-talking in aging: a resting-state fMRI study . Human brain mapping 2015, 36 (4):1484-1493. M H, Y T, A U, T Y, H Y: Dual task walking reveals cognitive dysfunction in community-dwelling elderly subjects: the Sefuri brain MRI study . Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association 2014, 23 (7):1770-1775. Ma R, Zhào H, Wei W, Liu Y, Huang Y: Gait characteristics under single-/dual-task walking conditions in elderly patients with cerebral small vessel disease: Analysis of gait variability, gait asymmetry and bilateral coordination of gait . Gait & posture 2022, 92 :65-70. Pantoni L: Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges . The Lancet Neurology 2010, 9 (7):689-701. Gao Q, Lv Z, Zhang X, Hou Y, Liu H, Gao W, Chang M, Tao S: Validation of the JiBuEn® System in Measuring Gait Parameters . In : 2021; Cham . Springer International Publishing: 526-531. Yang S, Li X, Qin W, Yang L, Hu W: Association Between Large Numbers of Enlarged Perivascular Spaces in Basal Ganglia and Motor Performance in Elderly Individuals: A Cross-Sectional Study . Clinical interventions in aging 2022, 17 :903-913. Chen K, Jin Z, Fang J, Qi L, Liu C, Wang R, Su Y, Yan H, Liu A, Xi J et al : The impact of cerebral small vessel disease burden and its imaging markers on gait, postural control, and cognition in Parkinson's disease . Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 2023, 44 (4):1223-1233. de Laat K, Reid A, Grim D, Evans A, Kötter R, van Norden A, de Leeuw F: Cortical thickness is associated with gait disturbances in cerebral small vessel disease . NeuroImage 2012, 59 (2):1478-1484. Jiang M, Wu S, Zhang Y, Li Y, Lin B, Pan Q, Tian S, Ni R, Liu Q, Zhu Y: Impact of White Matter Hyperintensity and Age on Gait Parameters in Patients With Cerebral Small Vessel Disease . Journal of the American Medical Directors Association 2023, 24 (5):672-678. Vitorio R, Hasegawa N, Carlson-Kuhta P, Nutt JG, Horak FB, Mancini M, Shah VV: Dual-Task Costs of Quantitative Gait Parameters While Walking and Turning in People with Parkinson's Disease: Beyond Gait Speed . Journal of Parkinson's disease 2021, 11 (2):653-664. Moore SR, Martinez A, Kröll J, Strutzenberger G, Schwameder H: Simple foot strike angle calculation from three-dimensional kinematics: A methodological comparison . Journal of sports sciences 2022, 40 (12):1343-1350. Anderson FC, Goldberg SR, Pandy MG, Delp SL: Contributions of muscle forces and toe-off kinematics to peak knee flexion during the swing phase of normal gait: an induced position analysis . Journal of biomechanics 2004, 37 (5):731-737. Wang Y, Li Y, Liu S, Liu P, Zhu Z, Wu J: Gait characteristics related to fall risk in patients with cerebral small vessel disease . Frontiers in neurology 2023, 14 :1166151. Fang F, Cao R, Luo Q, Ge R, Lai M, Yang J, Ma M, Kang M, Zhang L, Wang Y et al : The silent occurrence of cerebral small vessel disease in nonelderly patients with type 2 diabetes mellitus . Journal of diabetes 2021, 13 (9):735-743. Yu L, Yang L, Zhang X, Yuan J, Li Y, Yang S, Gu H, Hu W, Gao S: Age and recurrent stroke are related to the severity of white matter hyperintensities in lacunar infarction patients with diabetes . Clinical interventions in aging 2018, 13 :2487-2494. Liu J, Rutten-Jacobs L, Liu M, Markus HS, Traylor M: Causal Impact of Type 2 Diabetes Mellitus on Cerebral Small Vessel Disease: A Mendelian Randomization Analysis . Stroke 2018, 49 (6):1325-1331. Wang Z, Chen Q, Chen J, Yang N, Zheng K: Risk factors of cerebral small vessel disease: A systematic review and meta-analysis . Medicine 2021, 100 (51):e28229. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3952547","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274145909,"identity":"aa16adb8-1a8b-429f-ad37-ce2c6652ae08","order_by":0,"name":"Xianghua He","email":"","orcid":"","institution":"Jiangbin Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xianghua","middleName":"","lastName":"He","suffix":""},{"id":274145910,"identity":"8ad6e6e4-98bc-474b-a05c-5fb0fdc99f46","order_by":1,"name":"Jinshan Huang","email":"","orcid":"","institution":"Jiangbin Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinshan","middleName":"","lastName":"Huang","suffix":""},{"id":274145911,"identity":"f0c54ebd-0938-4177-b590-edb1288482f1","order_by":2,"name":"Caiyou Hu","email":"","orcid":"","institution":"Jiangbin Hospital","correspondingAuthor":false,"prefix":"","firstName":"Caiyou","middleName":"","lastName":"Hu","suffix":""},{"id":274145912,"identity":"ab9b6ac6-23db-4d77-aa8b-2f57f33ef21b","order_by":3,"name":"Mei Liang","email":"","orcid":"","institution":"Jiangbin Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Liang","suffix":""},{"id":274145913,"identity":"1abc82ce-74d0-4abc-9cd8-cba0e2917083","order_by":4,"name":"Xuemin Cheng","email":"","orcid":"","institution":"Jiangbin Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuemin","middleName":"","lastName":"Cheng","suffix":""},{"id":274145914,"identity":"a197ef80-a815-40c4-a947-f08fb17a76bf","order_by":5,"name":"Dongdong Jiang","email":"","orcid":"","institution":"Jiangbin Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongdong","middleName":"","lastName":"Jiang","suffix":""},{"id":274145915,"identity":"bad78771-963e-493c-8c5e-b5dd42da4be2","order_by":6,"name":"Wei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3RsWrDMBCA4TMHl0WNV4UW5xUEhUx5GJmAJhc6agg0JUUZSpNHyCt07Cgj0HTZMzpPUEKXDh3aPSVytgz65vu5EwLIsitE5aYLP3aKNFi2nbbzdDKUXHjBZjAUcaY6jumkggb9jQtlJZvJ6PCCPQ6DnfeSzS2Bnth6QVCuXvX5BNfaKzu9J/BmX3/cgeTde2KLV16zmVGxiPuaCZR8SCVa+daFJ4eFe6wd9kka1T67gERI0C+RUQdggyQIpeYokm8Zb5bhC/6+crz9PB6/7bwqV2/nkxPisvEsy7LsX791QEuS8RvA8AAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangbin Hospital","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-02-13 03:32:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3952547/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3952547/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51562655,"identity":"02df0e74-95b2-4a9d-a93e-6c874cde6fff","added_by":"auto","created_at":"2024-02-23 18:32:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96921,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow diagram of participants.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3952547/v1/0bd6dc01f15f2b94b361ac0c.png"},{"id":51562654,"identity":"6fd9fef3-202e-48a9-b66c-b5d7ed3c55bc","added_by":"auto","created_at":"2024-02-23 18:32:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51994,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristics (ROC) analysis plots for gait parameters distinguishing the individuals with CSVD and healthy controls in single--task walking test.\u003c/p\u003e\n\u003cp\u003eST: single-task.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3952547/v1/f795a5caa930020d4724d912.png"},{"id":51562653,"identity":"d7173aa5-74c8-4a93-b8af-d314c3c80d6c","added_by":"auto","created_at":"2024-02-23 18:32:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38247,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristics (ROC) analysis plots for gait parameters distinguishing the individuals with CSVD and healthy controls in dual--task walking test.\u003c/p\u003e\n\u003cp\u003eDT: dual-task.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3952547/v1/8da0cb30ea0f6e4bbebf9cce.png"},{"id":59990991,"identity":"06c5711b-f827-4284-9c60-3811998443f0","added_by":"auto","created_at":"2024-07-10 08:29:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1711648,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3952547/v1/52ab7d82-f24b-4382-9fe6-0f62a226b8b3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single- and dual-task gait parameters in determination of cerebral small vessel disease","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCerebral small vessel disease (CSVD) is one of the most common subtypes of vascular diseases with the aging global population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The disorder affects arterioles, capillaries and small veins causing stroke incidents, cognitive impairment, dementia, late-life depression and gait disturbances [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The two most common pathologies implicating CSVD are arteriolosclerosis (related to aging and other vascular risk factors) and cerebral amyloid angiopathy (associated with the deposition of β-amyloid) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent study demonstrated high prevalence and economic burden of CSVD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, it is significant to diagnose CSVD as soon as possible to improve its clinical management. Recently, the diagnosis of CSVD is primarily relied on magnetic resonance imaging (MRI) features, which include white matter hyperintensities (WMH), cerebral microbleeds (CMB), lacunar infarcts, enlarged perivascular spaces, and brain atrophy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, MRI is not always available especially for patients who had metal foreign objects in the body that cannot be removed. Due to technological factors or lesions, diagnosing CSVD only by using computed tomography (CT) has low sensitivity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the past years, other potential candidate biomarkers for the diagnosis of CSVD have been found but most of them are restricted in clinical practice due to the accuracy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, it is crucial to provide available, reproducible, and reliable clinical diagnostic markers of CSVD.\u003c/p\u003e \u003cp\u003eGait disturbance is one of the common symptoms of CSVD and it will deteriorate as the disease progresses, sometimes leading to future falls [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A study from China investigated 127 symptomatic CSVD patients and implied that cerebral microbleeds in basal ganglia and brainstem will be conducive to gait impairment in participants with CSVD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Study from America including 701 participants showed that white matter hyperintensities (WMH) were associated with slowing of gait over time[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This was consistent with a prospective study from the Netherlands. In this study, white matter atrophy as well as loss of white matter integrity was reported to be related to gait deterioration in older patients with CSVD after 5 years of follow-up [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, similar prospective study did not find CSVD progression related to gait decline [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. So far, most studies about gait and CSVD were focused on the characteristic imaging features and gait impairment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], whereas few studies investigated the associations between CSVD and quantitative gait with instrumented walkways [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recent study from China investigated 46 patients with CSVD and 22 controls and demonstrated that gait asymmetry, gait variability, and phase coordination index are biomarkers for gait disturbance in participants with CSVD [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. They also found that patients with CVSD had altered gait features under both single-task (ST) walking and Dual-task (DT) walking conditions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Due to the small sample size, the result of this study requires further replication.\u003c/p\u003e \u003cp\u003eConsequently, this study aims to compare the performance of gait parameters between CSVD participants and healthy controls under both ST walking and DT walking conditions and to assess the diagnostic value of gait parameters in CSVD. Our findings may be significant to demonstrate the gait pattern of CSVD and to develop a reproducible, low-cost, available and reliable method for CSVD, accordingly allowing for a new diagnostic biomarker.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e \u003cb\u003eParticipants\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFrom January 1, 2022 to August 31, 2023, clinical information of 140 patients with CSVD who were admitted to the department of neurology in our hospital and 75 healthy, age- and sex-matched controls from the outpatient department were collected. From 215 participants initially recruited, we excluded fifteen cases without gait data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All patients fulfilled the CSVD diagnostic criteria [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this study, CSVD patients who had Parkinson\u0026rsquo;s disease, symptomatic stroke, traumatic brain injury, antipsychotic medication, brain tumor, encephalitis, and traffic hydrocephalus were excluded. Patients who were inability to ambulate independently were excluded. Patients with other systemic diseases that affect walking ability were also excluded, such as arthritis, joint injury, cervical and lumbar spine disease. All control participants underwent brain MRI examinations and were excluded from the diagnosis of CSVD. Controls with history of dementia were also excluded. General physical and nervous system examinations were carried out for all participants. Participant characteristics of age, sex, height, weight, shoe size, history of stroke, hypertension, type 2 diabetes (T2DM), and cardiovascular disorder were recorded.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGait evaluation\u003c/b\u003eThe gait data of the study was collected by the JiBuEn\u0026reg; gait analysis system[17]. The system is comprised of wearable shoes and modules with the inertial Micro Electro-Mechanical Systems (MEMS) sensors. The modules collected motion signals and transmitted them to a computer. The MEMS sensors were fixed under the shoe heel bottom, behind the upper and lower limbs, and wrist. In data preprocessing, we used the high-order low-pass filter and hexahedral calibration technique, which can reduce high-frequency noise interference and installation errors produced by sensor devices. Based on the zero-correction algorithm, the accumulative errors were corrected. Then, the final gait parameters were obtained. Using the quaternary complementary filtering technique, the fusing acceleration data and posture was calculated.\u003c/p\u003e \u003cp\u003eAll participants were arranged to carry out two walking tests: (1) ST walking test: All participants were instructed to walk in a straight line on a 10 m footpath at their usual/normal gait speed. At the same time, gait parameters were collected during natural walking. (2) DT walking test: While walking, all participants perform serial subtraction of 7 beginning with 100. They walked in a straight line on the same 10 m footpath as in the ST walking test. During DT walking, they were required to focus on both walking and performing subtraction. In order to measure steady-state walking, all participants were instructed to perform one practice trial before ST and DT walking test. In this trial, the walking data will not be collected and processed by the JiBuEn\u0026reg; gait analysis system. Gait parameters including stride length, stride speed, cadence, stance phase, swing phase, stride time, stance time, swing time, average toe-off angle and average heel strike angle were collected.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe normality test on demographic profiles and gait parameters were performed. For those with continuous variables, comparisons of between groups were performed using the Independent Samples T-Test or Mann\u0026ndash;Whitney test; for dichotomous variables, the chi square test was used. Baseline variables that were considered clinically relevant or that showed a univariate relationship with outcome were entered into logistic regression model (Forward: LR). Variables for inclusion were carefully chosen, given the number of events available, to ensure parsimony of the final model. To evaluate the association between gait pattern and diagnosis of CSVD, multivariate binary logistic regression models were conducted. Then, Spearman\u0026rsquo;s correlation was used to investigate the relationship between clinical variables associated with CSVD in multivariate logistic regression models and gait parameters and diagnosis of CSVD. To classify healthy controls and CSVD, we drew the ROC curves. The AUC values were calculated to measure the parameter\u0026rsquo;s overall accuracy. SPSS 26.0 software (IBM, Armonk, NY, USA) was used to analyze the data.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003eParticipants characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 200 participants were included in the study, i.e., 129 patients diagnosed with CSVD. Clinical data are presented in Table 1\u0026mdash;no significant group differences in age, sex, ethic, marriage, BMI and shoe size. There was significance difference between CSVD group and controls group in education, drinking, hypertension, type 2 diabetes mellitus (T2DM), history of hypoglycemia and stroke, Other neurological disorders and cardiovascular disorder. Compared to controls, participants with CSVD had shorter stride length, slower stride speed, shorter cadence, longer stance time/phase, longer stride time, shorter swing phase, and smaller average toe-off/heel stride angle either in ST or in DT walking conditions (all P\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariable logistic regression of potential diagnostic parameters for CSVD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe potential diagnostic parameters for CSVD were age, T2DM and average heel strike angle both in single-task walking test [age: P = 0.010, OR (odds ratio) = 0.858, 95% CI (confidence interval) = 0.764-0.963; T2DM: P = 0.033, OR = 15.096, 95%CI = 1.242-183.418; average heel strike angle: P\u0026lt;0.001, OR = 0.608, 95%CI = 0.480-0.771]and in dual-task walking test [age: P = 0.020, OR = 0.881, 95% CI = 0.791-0.980; T2DM: P = 0.023, OR = 17.253, 95%CI = 1.476-201.632; average heel strike angle: P = 0.015, OR = 0.731, 95%CI = 0.568-0.941]; drinking in single-task walking test [P = 0.017, OR = 0.031, 95%CI = 0.002-0.532] and stance phase in dual-task walking test[P = 0.041, OR = 1.505, 95% CI = 1.017-2.228] (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations analysis for potential diagnostic clinical parameters and gait variables for CSVD.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA correlation analysis was conducted for potential diagnostic clinical parameters and gait variables for CSVD. The results indicated that age, T2DM, stance phase, stride time, stance time were positively associated with CSVD (all p \u0026lt; 0.05) and drinking, stride length, stride speed, cadence, swing phase, average toe-off angle and average heel strike angle negatively correlated with CSVD (all p \u0026lt; 0.05) (Table 3). \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic accuracy of gait parameters for CSVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic curves were employed to demonstrate how gait tests differentiated CSVD from healthy controls (Figure 2 and Figure 3). Factors of stride length, stride speed, cadence, stance phase, swing phase, stride time, stance time, toe-off angle and heel strike angle (AUC = 0.858, P \u0026lt; 0.001, 95%CI: 0.807-0.909, sensitivity, 83.1%; specificity, 76.7%;) showed moderate ability to separate CSVD from healthy controls (Table 4) in single-task walking condition and stride length, stride speed, stance phase, swing phase, toe-off angle and heel strike angle (AUC = 0.865, P\u0026lt;0.001, \u0026nbsp;95%CI: 0.817-0.914, sensitivity: 91.5%, specificity: 70.5%) in dual-task walking condition (Table 5). Among them, heel strike angle in dual-task walking condition was the best one to differentiated CSVD from healthy participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Characteristics of the study participants.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003eall(n=200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\"\u003e\n \u003cp\u003eCSVD(n=129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003econtrol(n=71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.09747292418773%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eage,y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e70.27\u0026plusmn;9.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e71.06\u0026plusmn;9.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e68.83\u0026plusmn;8.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSex (male),%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e120(60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\"\u003e\n \u003cp\u003e83(64.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e37(52.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eethic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003ethe Han nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e148(74.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e99(76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e49(69.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" rowspan=\"3\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.196687370600415%\" valign=\"bottom\"\u003e\n \u003cp\u003ethe Zhuang nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.11801242236025%\" valign=\"bottom\"\u003e\n \u003cp\u003e48(24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.946169772256727%\" valign=\"bottom\"\u003e\n \u003cp\u003e28(21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.73913043478261%\" valign=\"bottom\"\u003e\n \u003cp\u003e20(28.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.196687370600415%\" valign=\"bottom\"\u003e\n \u003cp\u003eothers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.11801242236025%\" valign=\"bottom\"\u003e\n \u003cp\u003e4(2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.946169772256727%\" valign=\"bottom\"\u003e\n \u003cp\u003e2(1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.73913043478261%\" valign=\"bottom\"\u003e\n \u003cp\u003e2(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003emarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e185(92.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e120(93.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e65(91.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.196687370600415%\" valign=\"bottom\"\u003e\n \u003cp\u003ebereave\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.11801242236025%\" valign=\"bottom\"\u003e\n \u003cp\u003e15(7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.946169772256727%\" valign=\"bottom\"\u003e\n \u003cp\u003e9(7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.73913043478261%\" valign=\"bottom\"\u003e\n \u003cp\u003e6(8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eBMI,Kg/m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.2(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.3(4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.9(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\"\u003e\n \u003cp\u003e0.342*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eshoe size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.0(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.0(4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e39.0(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\"\u003e\n \u003cp\u003e0.230*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eeducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eilliteracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e8(4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e8(6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" rowspan=\"3\"\u003e\n \u003cp\u003e0.020\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.196687370600415%\" valign=\"bottom\"\u003e\n \u003cp\u003eprimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.11801242236025%\" valign=\"bottom\"\u003e\n \u003cp\u003e37(18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.946169772256727%\" valign=\"bottom\"\u003e\n \u003cp\u003e28(21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.73913043478261%\" valign=\"bottom\"\u003e\n \u003cp\u003e9(12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.196687370600415%\" valign=\"bottom\"\u003e\n \u003cp\u003esecondary school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.11801242236025%\" valign=\"bottom\"\u003e\n \u003cp\u003e155(77.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.946169772256727%\" valign=\"bottom\"\u003e\n \u003cp\u003e93(72.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.73913043478261%\" valign=\"bottom\"\u003e\n \u003cp\u003e62(87.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003efall,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e28(14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e19(14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e9(12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003esmoke,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e48(24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e32(24.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e16(22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003edrinking,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e45(22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e22(17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e23(32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003ehypertension,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e133(66.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e99(76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e34(47.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eT2DM,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e41(20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e33(25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e8(11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003ehypoglycemia,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e10(5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e10(14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003esyncope,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e14(7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e7(5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e7(9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.240\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003edementia,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e13(6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e13(10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003estroke,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e114(57.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e108(83.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e6(8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eOther neurological disorders,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e101(50.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e100(77.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e1(1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003ecardiovascular disorders,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e137(68.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e102(79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e35(49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003evisual system diseases,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e2(1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e2(1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003emusculoskeletal system diseases,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e2(1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e2(1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eincontinence,yes,n%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e5(2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e5(3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.09747292418773%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003esingle-task Left (STL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTL-stride length(m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.90\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.16\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTL-stride speed(m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.85\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.75\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.03\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTL-cadence (steps/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e101.51\u0026plusmn;13.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e98.31\u0026plusmn;14.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e107.32\u0026plusmn;8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTL-stance phase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e66.09\u0026plusmn;3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.49\u0026plusmn;3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e63.54\u0026plusmn;2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTL-swing phase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.91\u0026plusmn;3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.51\u0026plusmn;3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e36.46\u0026plusmn;2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTL-stride time(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.17(0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.23(0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.13(0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTL-stance time(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.76(0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.83(0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.71(0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTL-swing time(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.40(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\"\u003e\n \u003cp\u003e0.40(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.41(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.066*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTL-average toe-off angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e39.78\u0026plusmn;9.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\"\u003e\n \u003cp\u003e36.48\u0026plusmn;9.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.77\u0026plusmn;4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTL-Average heel strike angle(\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.75(13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.9(13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e34.8(6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.09747292418773%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003esingle-task Right (STR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTR-stride length(m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.00\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.91\u0026plusmn;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.15\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTR-stride speed(m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.85\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.75\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.03\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTR-cadence (steps/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e101.51\u0026plusmn;13.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e98.31\u0026plusmn;14.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e107.32\u0026plusmn;8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTR-stance phase(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e66.13\u0026plusmn;4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.61\u0026plusmn;4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e63.45\u0026plusmn;2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTR-swing phase(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.87\u0026plusmn;4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.39\u0026plusmn;4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e36.55\u0026plusmn;2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTR-stride time(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.17(0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.23(0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.12(0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTR-stance time(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.76(0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.82(0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.71(0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTR-swing time(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.41(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.40(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.41(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.051*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTR-average toe-off angle(\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.09\u0026plusmn;8.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e36.99\u0026plusmn;9.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.72\u0026plusmn;4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eSTR-Average heel strike angle(\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.95\u0026plusmn;9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.11\u0026plusmn;8.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.93\u0026plusmn;5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.09747292418773%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003edual-task Left (DTL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTL-stride length (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.94\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.84\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.11\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTL-stride speed (m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.78\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.69\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.95\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTL-cadence (steps/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e98.99\u0026plusmn;14.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e96.47\u0026plusmn;16.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e103.57\u0026plusmn;11.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTL-stance phase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.00\u0026plusmn;3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e68.41\u0026plusmn;3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e64.44\u0026plusmn;2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTL-swing phase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.00\u0026plusmn;3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.59\u0026plusmn;3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.57\u0026plusmn;2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTL-stride time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.20(0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.23(0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.16(0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTL-stance time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.80(0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.83(0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.74(0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTL-swing time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.41(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.40(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.41(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTL-average toe-off ground angle(\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e37.90\u0026plusmn;9.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e34.27\u0026plusmn;9.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.50\u0026plusmn;4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTL-Average heel strike angle(\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.66\u0026plusmn;9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.82\u0026plusmn;8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.65\u0026plusmn;5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.09747292418773%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003edual-task Right(DTR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTR-stride length(m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.93\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.85\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.08\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTR-stride speed(m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.78\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.69\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.95\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTR-cadence (steps/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e98.99\u0026plusmn;14.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e96.47\u0026plusmn;16.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e103.57\u0026plusmn;11.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTR-stance phase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.08\u0026plusmn;4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e68.56\u0026plusmn;4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e64.40\u0026plusmn;2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTR-swing phase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.92\u0026plusmn;4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.44\u0026plusmn;4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.60\u0026plusmn;2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTR-stride time(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.18(0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.21(0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.16(0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTR-stance time(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.79(0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.82(0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.73(0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTR-swing time(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.40(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.39(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.40(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTR-average toe-off angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.40(12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.90(14.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e44.30(6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"bottom\"\u003e\n \u003cp\u003eDTR-Average heel strike angle(\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.411552346570396%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.16\u0026plusmn;9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.133574007220215%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.32\u0026plusmn;8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95306859205776%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.15\u0026plusmn;5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.815884476534295%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSTL: single task walking for the left foot; STR: single task walking for the Right foot; DTL: dual-task walking for the left foot; DTR: dual-task walking for the right foot; T2DM: type 2 diabetes mellitus.\u003c/p\u003e\n\u003cp\u003e*: Wilcoxon rank sum test. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Multivariate binary logistic regression analysis for diagnosis of CSVD and controls.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.875776397515526%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"38.35403726708075%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eSingle-task walking test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.77018633540373%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eDual-task walking test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.906687402799378%\" valign=\"bottom\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709175738724728%\" valign=\"bottom\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.331259720062208%\" valign=\"bottom\"\u003e\n \u003cp\u003eExp(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.21772939346812%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026beta;(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.553654743390357%\" valign=\"bottom\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003eExp(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.86158631415241%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026beta;(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.906687402799378%\" valign=\"bottom\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709175738724728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.331259720062208%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.21772939346812%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.764-0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.553654743390357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.020\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.86158631415241%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.791-0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.906687402799378%\" valign=\"bottom\"\u003e\n \u003cp\u003edrinking (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709175738724728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.331259720062208%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.21772939346812%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002-0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.553654743390357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.86158631415241%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.005-1.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.906687402799378%\" valign=\"bottom\"\u003e\n \u003cp\u003eT2DM (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709175738724728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.331259720062208%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.21772939346812%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.242-183.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.553654743390357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.86158631415241%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.476-201.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.906687402799378%\" valign=\"bottom\"\u003e\n \u003cp\u003eaverage heel strike angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709175738724728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.331259720062208%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.21772939346812%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.480-0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.553654743390357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.86158631415241%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.568-0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.906687402799378%\" valign=\"bottom\"\u003e\n \u003cp\u003estance phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.709175738724728%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.331259720062208%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.21772939346812%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.553654743390357%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.419906687402799%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.86158631415241%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.017-2.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eT2DM: type 2 diabetes mellitus.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTabel 3. Correlation analysis for variables associated with diagnosis of CSVD and healthy controls.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"560\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"bottom\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003ediagnosis (single-task walking test)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003ediagnosis (dual-task walking test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"bottom\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e.143\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e.143\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.044\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.044\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"bottom\"\u003e\n \u003cp\u003edrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e-.176\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e-.176\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"bottom\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e.170\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e.170\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003estride length(m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e-.483\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e-.491\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003estride speed(m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e-.523\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e-.482\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003eCadence (steps/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e-.335**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e-.232**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003estance phase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e.546\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e.521\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003eswing phase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e-.546\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e-.521\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003estride time(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e.335\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e.232\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003estance time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e.415\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e.327\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003eaverage toe-off angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e-.540\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e-.530\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003eaverage heel strike angle (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003erho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e-.593\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e-.606\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.25%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.071428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*. Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e\n\u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n\u003cp\u003eTable 4. Variables of ROC analysis for gait parameters distinguishing the individuals with CSVD and healthy controls in single-task walking tests.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"639\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.683881064162755%\" valign=\"bottom\"\u003e\n \u003cp\u003eTest Result Variable(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.007824726134586%\" valign=\"bottom\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.92018779342723%\" valign=\"bottom\"\u003e\n \u003cp\u003eStd. Error\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98904538341158%\" valign=\"bottom\"\u003e\n \u003cp\u003eAsymptotic Sig.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"bottom\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"bottom\"\u003e\n \u003cp\u003esensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"bottom\"\u003e\n \u003cp\u003especificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.05790297339593%\" valign=\"bottom\"\u003e\n \u003cp\u003eYouden\u0026rsquo;s index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.683881064162755%\" valign=\"bottom\"\u003e\n \u003cp\u003eST-stride length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.007824726134586%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.792\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.92018779342723%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98904538341158%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.731-0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.887\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.620\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.05790297339593%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.507\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.683881064162755%\" valign=\"bottom\"\u003e\n \u003cp\u003eST-stride speed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.007824726134586%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.815\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.92018779342723%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.029\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98904538341158%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.758-0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.845\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.690\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.05790297339593%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.535\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.683881064162755%\" valign=\"bottom\"\u003e\n \u003cp\u003eST-cadence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.007824726134586%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.702\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.92018779342723%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.036\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98904538341158%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.631-0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.887\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.519\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.05790297339593%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.407\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.683881064162755%\" valign=\"bottom\"\u003e\n \u003cp\u003eST-stance phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.007824726134586%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.171\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.92018779342723%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.029\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98904538341158%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.115-0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.859\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.713\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.05790297339593%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.572\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.683881064162755%\" valign=\"bottom\"\u003e\n \u003cp\u003eST-swing phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.007824726134586%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.829\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.92018779342723%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.029\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98904538341158%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.773-0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.859\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.713\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.05790297339593%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.572\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.683881064162755%\" valign=\"bottom\"\u003e\n \u003cp\u003eST-stride time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.007824726134586%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.298\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.92018779342723%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.036\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98904538341158%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.228-0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.859\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.535\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.05790297339593%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.394\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.683881064162755%\" valign=\"bottom\"\u003e\n \u003cp\u003eST-stance time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.007824726134586%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.250\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.92018779342723%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.034\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98904538341158%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.183-0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.958\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.496\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.05790297339593%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.454\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.683881064162755%\" valign=\"bottom\"\u003e\n \u003cp\u003eST-toe-off angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.007824726134586%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.826\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.92018779342723%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.029\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98904538341158%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.769-0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.746\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.798\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.05790297339593%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.545\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.683881064162755%\" valign=\"bottom\"\u003e\n \u003cp\u003eST-heel strike angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.007824726134586%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.858\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.92018779342723%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.026\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.98904538341158%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.807-0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.831\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.050078247261347%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.767\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.05790297339593%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.598\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eST: single-task; 95%CI：95% Confidence Interval.\u003c/p\u003e\n\u003cp\u003ea.Under the nonparametric assumption; b.Null hypothesis: true area = 0.5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. Variables of ROC analysis for gait parameters distinguishing the individuals with CSVD and healthy controls. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"659\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.450682852807283%\" valign=\"bottom\"\u003e\n \u003cp\u003eTest Result Variable(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.132018209408194%\" valign=\"bottom\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.587253414264036%\" valign=\"bottom\"\u003e\n \u003cp\u003eStd. Error\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5948406676783%\" valign=\"bottom\"\u003e\n \u003cp\u003eAsymptotic Sig.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56752655538695%\" valign=\"bottom\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.380880121396055%\" valign=\"bottom\"\u003e\n \u003cp\u003esensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.773899848254931%\" valign=\"bottom\"\u003e\n \u003cp\u003especificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.512898330804248%\" valign=\"bottom\"\u003e\n \u003cp\u003eYouden\u0026rsquo;s index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.450682852807283%\" valign=\"bottom\"\u003e\n \u003cp\u003eDT-stride length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.132018209408194%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.796\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.587253414264036%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5948406676783%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56752655538695%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.736-0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.380880121396055%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.901\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.773899848254931%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.651\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.512898330804248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.553\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.450682852807283%\" valign=\"bottom\"\u003e\n \u003cp\u003eDT-stride speed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.132018209408194%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.791\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.587253414264036%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5948406676783%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56752655538695%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.730-0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.380880121396055%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.901\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.773899848254931%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.574\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.512898330804248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.475\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.450682852807283%\" valign=\"bottom\"\u003e\n \u003cp\u003eDT-stance phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.132018209408194%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.186\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.587253414264036%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.030\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5948406676783%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56752655538695%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.128-0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.380880121396055%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.831\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.773899848254931%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.682\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.512898330804248%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.513\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.450682852807283%\" valign=\"bottom\"\u003e\n \u003cp\u003eDT-swing phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.132018209408194%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.814\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.587253414264036%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.030\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5948406676783%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56752655538695%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.756-0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.380880121396055%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.831\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.773899848254931%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.682\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.512898330804248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.513\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.450682852807283%\" valign=\"bottom\"\u003e\n \u003cp\u003eDT-toe-off angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.132018209408194%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.820\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.587253414264036%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.029\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5948406676783%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56752655538695%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.764-0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.380880121396055%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.915\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.773899848254931%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.620\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.512898330804248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.536\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.450682852807283%\" valign=\"bottom\"\u003e\n \u003cp\u003eDT-heel strike angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.132018209408194%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.865\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.587253414264036%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.025\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5948406676783%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56752655538695%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.817-0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.380880121396055%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.915\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.773899848254931%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.705\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.512898330804248%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.621\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDT: dual-task; 95%CI：95% Confidence Interval.\u003c/p\u003e\n\u003cp\u003ea. Under the nonparametric assumption; b. Null hypothesis: true area = 0.5.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis is a cross-sectional study. In this study, we aimed to demonstrate gait parameters for diagnosis of CSVD and to identify the gait pattern of patients with CSVD in the single-task and dual-task walking tests. To the best of our knowledge, this is the first study aimed to distinguish CSVD patients from healthy controls using gait biomarkers. Our results showed that: (1) Compared to controls, CSVD group demonstrated impaired stride length, stride speed, cadence, stance time/phase, stride time, swing phase, and average toe-off/heel stride angle both in single-task and dual-task walking tests; (2) Average heel strike angle could distinguish CSVD from healthy controls both in single-task and dual-task walking tests; (3) Several demographic covariates and gait parameters correlated with CSVD. Among them, average heel strike angle was the most robust diagnostic markers of CSVD.\u003c/p\u003e \u003cp\u003eStudy from Yang S et.al investigated 99 CSVD patients with enlarged perivascular spaces in basal ganglia and found that the stride length, cadence was significantly shorter than those of controls [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. other study under single-task and dual-task walking conditions also verified that CSVD patients had short stride length and slow stride speed [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, a prospective cohort study showed that stride length decline was independent of sex, age, height, follow-up duration and baseline stride length [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In line with the previous study, our study also found that patients with CSVD had an impaired stride length. Similar to stride speed, researchers found decreased cadence in CSVD patients[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Interestingly, a cross-sectional study with large samples showed that patients with CSVD had step length, step speed, swing time and stance time impaired and age was associated with step length, step speed, swing time and stance time [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. All these studies suggesting progression of CSVD might play an important role in gait deterioration.\u003c/p\u003e \u003cp\u003ePatients with Parkinson\u0026rsquo;s disease had impaired foot strike angle [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The foot strike maintains forward stability and the toe-off angle means the ground clearance ability of the foot [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], implicating an association with fall risk. Seemingly, researchers found decreased foot strike angle and toe-off angle in patients with CSVD [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], indicating a trend with fall risk. In our study, we found a significant difference of average heel strike angle between CSVD patients and healthy controls, which were independent of sex, age, clinical comorbidities of T2DM, hypertension and cardiovascular disorder. Since CSVD patients may develop Parkinsonism and similar gait disorder, more studies about stride angle of CSVD patients should be conducted.\u003c/p\u003e \u003cp\u003eConsistent with the previous studies, we demonstrated that clinical factors including age, and T2DM associated with CSVD [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], implicating a replication of the robust diagnostic value for these variables. In our study, drinking in single-task walking test was associated with CSVD, while drinking was not a risk factor in other study [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. More research about drinking and gait performance in CSVD was needed to clarify this area. Furthermore, several gait parameters correlated with CSVD both in ST and DT walking test. Among them, average heel strike angle was the one with highest sensitivity and specificity for diagnosing of CSVD.\u003c/p\u003e \u003cp\u003eThis study was particularly insightful due to the strengths of its methodology. Firstly, using wearable sensors, we performed an analysis of the gait impairment between CSVD patients and healthy controls under both ST and DT walking tests. Secondly, we investigate the diagnostic value of gait parameters and use these gait parameters to distinguish CSVD from healthy controls under ST and DT walking conditions for the first time.\u003c/p\u003e \u003cp\u003eThis study also has some limitations. Firstly, the participants recruited from a single center may lead to potential selection biases. However, the consecutive recruitment of this study decreased the biases. Future large-sample, multi-center studies should be designed for validation. Secondly, the cross-sectional study limited the study of pathological gait features of CSVD. So longitudinal studies are needed to replicate the diagnostic value of gait parameters for CSVD individuals.\u003c/p\u003e \u003cp\u003eIn conclusion, the gait pattern changed in patients with CSVD, especially for the elderly ones with a history of T2DM. Compared to controls, CSVD group demonstrated impaired stride length, stride speed, cadence, stance time/phase, stride time, swing phase, and average toe-off/heel stride angle both in ST and DT walking tests. Gait parameters could distinguish CSVD individuals from healthy controls. Among these, average heel strike angle was one of the most valuable gait parameters of altered gait and had moderate predictive values for CSVD. Further large-sample, multi-center, and longitudinal studies are needed to clarify the development of gait performance in CSVD individuals and to replicate the diagnostic value of gait parameters for CSVD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Cerebral small vessel disease patients for their participation in our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe confirm that we have read the Editorial Policy pages. This study was conducted with approval from the Ethics Committee of Jiangbin Hospital, Guangxi Zhuang Autonomous Region (No.KY-GXZR-2020-01). This study was conducted in accordance with the declaration of Helsinki. Written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Guangxi Natural Science Foundation (2020GXNSFAA297189), the Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project (S2018012), the Guangxi Science and Technology Base and Talent Special Project (GUIKE AD20238075), the National Key Research and Development Program of China (2018YFC2000400), the National Natural Science Foundation of China (91849118, 31760299, and 82260289) and the Guangxi Key Research and Development Plan(2023AB22024). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXianghua He and Jinshan Huang conceptualization, data curation, formal analysis, methodology, software, validation, visualization, writing-original draft, and writing-review and editing. Caiyou Hu funding acquisition. Caiyou Hu, Mei Liang, Xuemin Cheng and Dongdong Jiang data curation, formal analysis, methodology, and writing-original draft. Wei Zhang conceptualization, funding acquisition, project administration, supervision and writing-review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGorelick P, Sorond F: \u003cstrong\u003eAdvancing our knowledge about cerebral small vessel diseases\u003c/strong\u003e. \u003cem\u003eThe Lancet Neurology \u003c/em\u003e2023, \u003cstrong\u003e22\u003c/strong\u003e(11):972-973.\u003c/li\u003e\n\u003cli\u003eBlumen HM, Jayakody O, Verghese J: \u003cstrong\u003eGait in cerebral small vessel disease, pre-dementia, and dementia: A systematic review\u003c/strong\u003e. \u003cem\u003eInternational journal of stroke : official journal of the International Stroke Society \u003c/em\u003e2023, \u003cstrong\u003e18\u003c/strong\u003e(1):53-61.\u003c/li\u003e\n\u003cli\u003eMarkus HS, de Leeuw FE: \u003cstrong\u003eCerebral small vessel disease: Recent advances and future directions\u003c/strong\u003e. \u003cem\u003eInternational journal of stroke : official journal of the International Stroke Society \u003c/em\u003e2023, \u003cstrong\u003e18\u003c/strong\u003e(1):4-14.\u003c/li\u003e\n\u003cli\u003eLam BYK, Cai Y, Akinyemi R, Biessels GJ, van den Brink H, Chen C, Cheung CW, Chow KN, Chung HKH, Duering M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe global burden of cerebral small vessel disease in low- and middle-income countries: A systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eInternational journal of stroke : official journal of the International Stroke Society \u003c/em\u003e2023, \u003cstrong\u003e18\u003c/strong\u003e(1):15-27.\u003c/li\u003e\n\u003cli\u003eSchwarz G, Banerjee G, Hostettler IC, Ambler G, Seiffge DJ, Ozkan H, Browning S, Simister R, Wilson D, Cohen H\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMRI and CT imaging biomarkers of cerebral amyloid angiopathy in lobar intracerebral hemorrhage\u003c/strong\u003e. \u003cem\u003eInternational journal of stroke : official journal of the International Stroke Society \u003c/em\u003e2023, \u003cstrong\u003e18\u003c/strong\u003e(1):85-94.\u003c/li\u003e\n\u003cli\u003eWan S, Dandu C, Han G, Guo Y, Ding Y, Song H, Meng R: \u003cstrong\u003ePlasma inflammatory biomarkers in cerebral small vessel disease: A review\u003c/strong\u003e. \u003cem\u003eCNS neuroscience \u0026amp; 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\u003cstrong\u003e100\u003c/strong\u003e(51):e28229.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cerebral small vessel disease, gait, diagnostic marker, dual-task, single task","lastPublishedDoi":"10.21203/rs.3.rs-3952547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3952547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and objective: \u003c/strong\u003eGait disorder is one of the primary symptoms of cerebral small vessel disease (CSVD) and its potential diagnostic value was not known. We aimed to investigate the gait performance in CSVD and to determine the diagnostic value of gait parameters for CSVD under single-task and dual-task walking conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe prospectively recruited consecutive patients with CSVD from January 1, 2022 to August 31, 2023. A total of 129 CSVD patients and 71 healthy controls were enrolled. Direct gait parameters in the patient group and the control group were compared under single-task and dual-task conditions, controlling for covariates. Gait parameters were compared between the two groups, using the receiver operating characteristic curve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCompared to controls, participants with CSVD had shorter stride length, slower stride speed, shorter cadence, longer stance time/phase, longer stride time, shorter swing phase, smaller average toe-off angle and smaller heel stride angle either in single-task walking test or in dual-task walking test (all P\u0026lt;0.05). Average heel strike angle could distinguish CSVD from healthy controls both in single-task (AUC = 0.858, P \u0026lt; 0.001, sensitivity, 83.1%; specificity, 76.7%) and dual-task walking tests (AUC = 0.865, P \u0026lt; 0.001, sensitivity, 91.5%; specificity, 70.5%) with moderate accuracy, independent of covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eGait patterns changed in patients with CSVD. Our findings suggest that average heel strike angle was one of the most valuable gait parameters of altered gait in CSVD and that could serve as a diagnostic marker of CSVD.\u003c/p\u003e","manuscriptTitle":"Single- and dual-task gait parameters in determination of cerebral small vessel disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-23 18:32:29","doi":"10.21203/rs.3.rs-3952547/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aa38dc94-6155-499f-a175-1b903bc50ceb","owner":[],"postedDate":"February 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-10T08:21:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-23 18:32:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3952547","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3952547","identity":"rs-3952547","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}