Do the gait domains change in PD patients with freezing of gait during their ‘interictal’ period?

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Jiahao Zhao, Chen Liu, Ying Wan, Xiaobo Zhu, Lu Song, Zhenguo Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4154081/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2025 Read the published version in BMC Geriatrics → Version 1 posted 11 You are reading this latest preprint version Abstract Objectives Freezing of Gait (FOG) is one of the disabling symptoms in patients with Parkinson's Disease (PD). While it is difficult to early detect because of the sporadic occurrence of initial freezing events. Whether the characteristic of gait impairments in PD patients with FOG during the ‘interictal’ period is different from that in non-FOG patients is still unclear. Methods The gait parameters were measured by wearable inertial sensors. Exploratory factor analysis was used to investigate the inherent structure of diverse univariate gait parameters, with the aim of identifying shared characteristics among the gait variables. Results This cross-sectional study involved 68 controls and 245 PD patients (167 without FOG and 78 with FOG). The analysis yielded six distinct gait domains which were utilized to describe the impaired gait observed during the “interictal” period of FOG. Both PD-nFOG and PD-FOG groups exhibited significant impairments in the pace domain, kinematic domain, gait phase domain, and turning process domain compared to the healthy control. The gait phase domain was different in the PD-FOG group compared to the PD-nFOG group (p corrected = 0.004, Cohen's d = -0.46). And it was identified as independent risk factor for FOG (OR = 1.64, 95% CI = 1.05–2.55, p = 0.030), as well as other risk factors: gender (OR = 2.67, 95% CI = 1.19–5.99, p = 0.017), MDS-UPDRS IV score (OR = 1.23, 95% CI = 1.10–1.37, p < 0.001), and PIGD subscore (OR = 1.50, 95% CI = 1.30–1.73, p < 0.001). The model demonstrated a correct discrimination rate of 0.78 between PD-FOG and PD-nFOG, with an area under the receiver operating characteristic curve (AUC) of 0.87. Conclusions FOG was found to be associated with abnormal alterations in the gait phase domain during the interictal period. Models constructed using gait phase domain, PIGD subscore, gender, and severity of motor complications can better differentiate freezers from no-freezers during ‘interictal’ period. Parkinson's disease freezing of gait wearable inertial sensor exploratory factor analysis gait domain Figures Figure 1 Figure 2 Introduction Walking safely and comfortably is critical to patients with Parkinson's disease (PD). As we know, freezing of gait (FOG) is one of the main risk factors for falls in PD [ 1 ] . The tendency of the trunk to move forward while the feet are relatively “frozen” in place during walking can easily lead to balance dysfunction and falls, which reduce the ability to move independently and impair the quality of life of PD patients. It is not evident to observe the onset of FOG in outpatient visits due to its “episodic” nature [ 2 ] . During the outpatient visit, the gait performance of Parkinsonians with FOG may be improved by increased attention and alertness, especially in patients with mild or less frequent FOG episodes, which are difficult to elicit in the clinic [ 2 – 4 ] . It is important to detect the gait characteristic changes for PD patients with FOG, even if FOG is not observed by the clinician. With the development of technology-based gait analysis in PD, subtle changes in gait characteristics can be captured using wearable sensors [ 5 – 7 ] , and these quantified gait parameters can provide more information on how PD gait converts to FOG gait. Several studies showed the gait characteristics during FOG episodes. There is a decrease in gait frequency, variability of gait speed, stride length, and mean lateral displacement amplitude during FOG [ 8 ] . A typical FOG event occurs with a progressive decrease in step length and eventually a freeze, also known as the sequence effect [ 9 ] . The sequence effect prior to a FOG episode is a direct description of how gait impairment develops prior to a FOG episode [ 10 , 11 ] . Our hypothesis is that gait impairments in PD-FOG patients may be preserved in some form and to some extent during the “interictal” period. These “interictal” changes in gait characteristics predict that these patients are prone to develop FOG, and even further exacerbation of these gait impairments may lead to the onset of FOG. We suggest that these continuity impairments hidden in the gait pattern may be a risk factor for developing FOG. To test our hypothesis, we analyzed the gait parameters of PD patients with FOG during their “interictal” episodes using wearable inertial measurement sensors. We extracted several gait domains to better describe gait impairment by grouping numerous univariate gait parameters with the exploratory factor analysis (EFA). Our objective is: (1) to find the distinct abnormal gait domains in FOG patients from non-FOG patients during interictal period; (2) to analyze the relevant influencing factors of FOG by integrating clinical information and abnormal gait domain characteristics. Material and methods Participants Two hundred forty-five patients with idiopathic PD who visited the Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University, School of Medicine were enrolled in this study from November 2019 to December 2021. The inclusion criteria for the PD group were: (1) the diagnosis of PD was based on International Movement Disorders Society (MDS) PD diagnostic criteria 2015 [ 12 ] ; (2) Hoehn and Yahr (H-Y) stages ≤ 3; (3) walking independently for at least 10 m without any assistive device; (4) Mini-Mental State Examination (MMSE) > 24 points. The exclusion criteria were: (1) were diagnosed with parkinsonism-plus syndromes or other diseases that may affect gait performance (e.g., stroke, trauma, orthopedic disease, abnormal vision and serious cardio-pulmonary diseases); (2) Severe psychiatric symptoms, dementia, and inability to cooperate with the completion of the examination. The age-matched healthy controls (HC group) were partners of patients with PD or volunteers of the nearby community during the same period. They were excluded if they reported previous neurological, orthopedic, abnormal vision or musculoskeletal disorders that could impact gait. Clinical assessments A detailed medical history (including age, sex, height, weight, education, time of disease onset, first symptoms, antiparkinsonian drugs, etc.) was collected. The time of disease onset was defined as the onset of subjective perceived motor symptoms of PD. The motor assessment scales included the MDS-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) and the H-Y stage. The motor subscores were calculated as the bradykinesia subscore (sum of items 3.4–3.8 and 3.14), rigidity subscore (sum of item 3.3), tremor subscore (sum of items 3.15–3.18), and postural instability and gait difficulty (PIGD) subscore (sum of items 3.9–3.13) based on the MDS-UPDRS part III. Information on pharmacological treatment was collected and calculated in total daily levodopa equivalent dose (LED) [ 13 ] . The New Freezing of Gait Questionnaire (NFOG-Q) was used to determine the presence and severity of the freezing of gait. The Mini Balance Evaluation Systems Test (mini-BESTest) was used to evaluate the balance function. The cognitive and emotional assessment scales included the MMSE, the Montreal Cognitive Assessment (MoCA), the Hamilton Anxiety Scale (HAMA), the Hamilton Depression Scale (HAMD), and the Frontal Assessment Battery (FAB). The 8-item Parkinson's Disease Questionnaire (PDQ-8) was used to assess the quality of life. All assessments were performed during the "ON" period. The study was a cross-sectional study, approved by the Research Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (XHEC-C-2015-019-2), and all subjects were fully informed of the purpose and content of the study and provided written informed consent. Kinematic analysis of gait Gait testing was performed by a Wearable Movement and Gait Quantitative Assessment System. The inertial measurement units (IMU) (GYENNO Science, Shenzhen, China) were applied to collect kinematic parameters in real time [ 7 , 14 ] . Participants wore ten inertial sensors fixed to the lower back (L5), anterior chest (sternum), bilateral thighs, ankles, feet, and wrists by elastic bands. After the sensors were placed correctly, the participants were asked to perform the Timed Up and Go Test (TUG): (1) stand up from the chair; (2) walk straight for a 5-m distance at their regular pace; (3) turn and walk back to the starting point; and (4) sit down. Prior to commencing the test, the subjects were initially instructed on the process by the researcher and subsequently allowed to practice it once. Subjects in the PD group completed the test during their "ON" period. Gait parameters are transmitted in real-time via Bluetooth to the computer for 3D motion posture reconstruction to assess gait, arm swing, whole-body coordination, and other indicators. If the investigator observed or the device automatically identified a FOG episode during walking, the patient was excluded from the final analysis. All parameters which we can obtain were calculated automatically during the motor test using built-in algorithms (Appendix 1). Statistical analysis The Shapiro-Wilk test combined with Q-Q plots was used to determine the distribution of continuous variables. Normally distributed measures were expressed using the mean ± standard deviation (SD), and non-normally distributed measures were expressed using the median (quartiles). The independent t-test/Mann-Whitney U test was used for comparison of measures between independent groups, and X 2 test/Fisher exact probability method was used for comparison of numerical data. Differences in baseline clinical characteristics and gait parameters among controls, PD-nFOG patients, and PD-FOG patients were assessed using analysis of covariance (ANCOVA) and Bonferroni post hoc tests, and homogeneity of variance was determined by plotting scatter plots and performing Levene's tests. Forward stepwise binomial logistic regression was used to analyze factors associated with FOG, and the degree of influence was evaluated using the odds ratio (OR) and 95% confidence interval (CI). The Box-Tidwell method is used to test whether there is a linear relationship between the logit transformed values of the continuous independent and dependent variables. Tolerance and variance inflation factor (VIF) was calculated to diagnose the presence of multicollinearity between the independent variables. Exploratory factor analysis (EFA) was used to explore the intrinsic structure of the 22 gait variables and to identify the common features behind the gait variables to categorize and extract several major gait domains that represent different gait characteristics [ 15 , 16 ] . Each gait variable was first transformed separately by normalization. Each value was subtracted from the mean value of the parameter in the whole sample (including the PD and HC groups), respectively, and the difference was divided by the standard deviation of the whole sample. The Kaiser-Meyer-Olkin (KMO) sampling fitness test and Bartlett's sphericity test were used to clarify whether the 22 variables were suitable for factor analysis among themselves. Horn's parallel analysis [ 17 ] was then used to determine the appropriate number of factors, namely, the number of gait domains. The maximum likelihood method was used for factor extraction. Further, the oblimin oblique rotation method was used to improve the interpretability of their loadings to avoid possible correlations between potential factors. Variables with loadings up to 0.5 were considered significant. After EFA analysis, the Thurstone method [ 18 ] was used to summarize each parameter's standard score coefficients based on EFA's results and calculate the factor scores for each gait domain separately. The obtained factor scores were converted to a Z-score with HC as the reference value [e.g., factor 1 Z-score = (factor 1 - mean of factor 1 in HC group)/standard deviation of factor 1 in HC group] to draw radar plots for comparing the different levels of impairment in the gait domain between the PD-nFOG group and PD-FOG group relative to the subjects in the HC group. Statistical analyses were performed with R (version number: 4.1.2) (R Foundation for Statistical Computing, Vienna, Austria). The threshold for statistically significant differences was set at a two-tailed p < 0.05. Results The differences in clinical characteristics among the HC group, PD-nFOG group and PD-FOG group A total of 245 patients with PD were enrolled (124 males and 121 females) in our study. The mean age was 67.07 ± 7.80 years, height was 164.92 ± 7.80 cm, the mean disease duration was 5.45 ± 4.52 years, the H-Y stage was 2.13 ± 0.75, and the MDS-UPDRS III score was 25.55 ± 14.26. Sixty-eight healthy controls (27 males and 41 females) were enrolled in the HC group with a mean age of 66.44 ± 8.76 years and a mean height of 163.22 ± 8.19 cm. According to the scale of the NFOG, the patients with PD were classified into the PD-FOG group and PD-nFOG group (Table 1 ). Seventy-eight (31.84%) of the 245 PD patients had FOG, of which 55 were levodopa responsive, and 19 were levodopa unresponsive. No difference in gender, age, height, and education was found among the HC group, PD-nFOG group, and PD-FOG group (p > 0.05). Compared with PD-nFOG patients, PD-FOG patients had a longer disease duration (7.65 ± 4.95 vs. 4.43 ± 3.91, t=-5.07, p < 0.001), a higher H-Y stage (2.51 ± 0.64 vs. 1.95 ± 0.73, t=-5.84, p < 0.001), and higher doses of LED (632.29 ± 352.10 vs. 398.82 ± 333.90, t=-4.96, p < 0.001). The scores of MDS-UPDRS were significantly higher in the PD-FOG group than those in the PD-nFOG group with part I (10.97 ± 5.55 vs. 9.13 ± 5.23, t=-2.30, p = 0.022), part II (16.89 ± 7.75 vs. 9.98 ± 6.23, t=-6.87, p < 0.001), part III (31.31 ± 29.50 vs. 22.86 ± 13.43, t=-4.48, p < 0.001) and part IV (Mann-Whitney U = 2838.50, p < 0.001). Moreover, compared to the PD-nFOG group, the PD-FOG group obtained higher bradykinesia subscore (16.17 ± 7.37 vs. 11.42 ± 7.02, t=-4.83, p < 0.001) and PIGD subscore (6.23 ± 3.25 vs. 3.41 ± 2.42, t=-6.79, p 0.05). The PD-FOG group performed worse balance based on the mini-BESTest (t = 4.39, p < 0.001), and in terms of non-motor symptoms of PD, the PD-FOG group presented a more elevated HAMD score (10.45 ± 6.88 vs. 8.10 ± 6.85, t=-2.30, p = 0.023) and lower FAB score (Mann-Whitney U = 1383.50, p 0.05). PD patients with FOG had significantly lower quality of life (1.70 ± 1.45 vs. 0.56 ± 0.91, t=-5.11, p < 0.001). Table 1 Clinical characteristics of control, PD-nFOG group and PD-FOG group HC PD-nFOG PD-FOG t/U/χ 2 /F p-value No. 68 167 78 Gender (male/female) 27/41 78/89 46/32 5.74 0.057 Age (years) 66.44 ± 8.76 66.90 ± 8.27 67.42 ± 6.72 0.30 0.738 Height (cm) 163.22 ± 8.19 165.70 ± 7.84 163.15 ± 7.54 1.39 0.240 Education illiteracy or primary school graduates 11 32 6 7.60 0.668 middle school graduates 46 110 55 high school graduates or above 11 23 14 Disease duration (years) / 4.43 ± 3.91 7.65 ± 4.95 -5.07 < 0.001 H-Y stage / 1.95 ± 0.73 2.51 ± 0.64 -5.84 < 0.001 LED (mg/d) / 398.82 ± 333.90 632.29 ± 352.10 -4.96 < 0.001 MDS-UPDRS I / 9.13 ± 5.23 10.97 ± 5.55 -2.30 0.022 MDS-UPDRS II / 9.98 ± 6.23 16.89 ± 7.75 -6.87 < 0.001 MDS-UPDRS III / 22.86 ± 13.43 31.31 ± 29.50 -4.48 < 0.001 MDS-UPDRS IV / 0(0, 2) 4(0, 7.5) 2838.50 < 0.001 Moto symptom / bradykinesia subscore / 11.42 ± 7.02 16.17 ± 7.37 -4.83 < 0.001 rigidity subscore / 2.64 ± 3.10 3.52 ± 3.91 -1.73 0.087 tremor subscore / 3.77 ± 3.85 3.34 ± 4.08 0.79 0.430 PIGD subscore / 3.41 ± 2.42 6.23 ± 3.25 -6.79 < 0.001 NFOG-Q / / 18.31 ± 6.81 mini-BESTest / 22.95 ± 4.88 17.96 ± 5.10 4.39 < 0.001 Levodopa response of FOG / levodopa responsive / / 55 levodopa unresponsive / / 19 other / / 4 Non-motor symptom MMSE / 28(27, 30) 28(26, 29) 5209.50 0.097 MoCA / 25(21, 27) 24(19, 26) 4218.50 0.193 HAMA / 7.62 ± 5.43 9.18 ± 6.17 -1.97 0.050 HAMD / 8.10 ± 6.85 10.45 ± 6.88 -2.30 0.023 FAB / 16(15, 18) 15(13, 17) 1383.50 < 0.001 PDQ-8 / 0.56 ± 0.91 1.70 ± 1.45 -5.11 < 0.001 MDS-UPDRS, MDS-Unified Parkinson’s Disease Rating Scale; H-Y stage, Hoehn & Yahr stage; LED, levodopa equivalent dose; PIGD, postural instability and gait difficulty; NFOG-Q, New Freezing of Gait Questionnaire; mini-BESTest, Mini Balance Evaluation Systems Test; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; HAMA, Hamilton Anxiety Scale; HAMD, Hamilton Depression Scale; FAB, Frontal Assessment Battery; PDQ-8, 8-item Parkinson's Disease Questionnaire. Differences in gait parameters among the HC group, PD-nFOG group and PD-FOG group Table 2 showed the gait parameters during walking, turning, and sit-stand shift tasks among the HC group, PD-nFOG group, and PD-FOG group. Table 2 Differences in gait parameters among the PD-nFOG group, PD-FOG group, and HC group post hoc tests Gait parameters HC PD-nFOG PD-FOG Corrected p-value (age、gender、height) HC vs PD-nFOG&PD-FOG PD-nFOG vs PD-FOG Sit-to-stand SiSt - Average Duration (s) 1.43 ± 0.49 1.80 ± 0.78 1.95 ± 0.97 < 0.001 HC < < PD; HC<<<FOG — SiSt - Trunk Sagittal Peak Velocity (degree/s) 85.73 ± 23.94 65.55 ± 18.76 63.78 ± 18.95 >>PD, FOG — SiSt - Trunk Sagittal RoM (degree) 37.44 ± 10.56 32.8 ± 7.78 32.49 ± 7.97 > PD, FOG — Walking Step Length (cm) 55.11 ± 9.82 49.24 ± 10.18 46.55 ± 10.36 >>PD, FOG — Stride Velocity (m/s) 1.01 ± 0.22 0.90 ± 0.22 0.86 ± 0.20 >>PD, FOG — Stride Length (cm) 109.19 ± 19.70 97.57 ± 20.10 92.22 ± 20.27 >>PD, FOG — Cadence (step/min) 111.87 ± 13.97 110.18 ± 11.99 113.61 ± 12.88 0.243 — — Gait Cycle (s) 1.11 ± 0.14 1.11 ± 0.13 1.08 ± 0.12 0.302 — — Double Support (%) 21.85 ± 5.34 23.19 ± 7.18 20.56 ± 5.13 0.009 — PD > > FOG Swing Phase (%) 39.62 ± 2.90 38.82 ± 3.81 40.19 ± 2.78 0.010 — PD < FOG Swing Phase CV (%) 5.93 ± 3.13 6.74 ± 2.74 6.61 ± 2.70 0.165 — — Double Support CV (%) 14.76 ± 5.82 16.22 ± 7.41 16.96 ± 7.15 0.182 — — Cadence CV (%) 7.50 ± 8.10 8.32 ± 10.86 8.16 ± 4.45 0.773 — — Gait Cycle CV (%) 4.15 ± 1.86 4.66 ± 2.45 5.13 ± 3.73 0.121 — — Stride Velocity CV (%) 8.81 ± 4.95 9.94 ± 5.23 10.89 ± 5.90 0.071 — — Step Length CV (%) 6.01 ± 3.05 7.66 ± 5.69 8.87 ± 7.16 0.007 HC < < FOG — Shank RoM (degree) 70.43 ± 10.72 59.87 ± 11.78 60.77 ± 11.08 >>PD, FOG — Peak Shank Angular Velocity (degree/s) 357.38 ± 60.09 314.97 ± 57.76 311.52 ± 57.74 >>PD, FOG — Trunk Coronal Peak Velocity (degree/s) 24.70 ± 6.55 21.62 ± 6.82 21.83 ± 7.46 0.005 HC > > PD; HC > FOG — Trunk Coronal RoM (degree) 5.02 ± 2.20 3.31 ± 1.70 3.49 ± 1.59 >>PD, FOG — Trunk Sagittal Peak Velocity (degree/s) 36.42 ± 12.28 31.16 ± 11.31 31.58 ± 9.03 0.009 HC > PD, FOG — Trunk Sagittal RoM (degree) 4.85 ± 1.47 3.99 ± 1.67 4.29 ± 1.28 0.001 HC>>>PD — Trunk Transverse Peak Velocity (degree/s) 45.29 ± 12.5 38.01 ± 11.77 37.71 ± 11.16 >>PD, FOG — Trunk Transverse RoM (degree) 10.32 ± 3.36 8.05 ± 3.72 8.70 ± 3.45 >>PD; HC > FOG — Arm Peak Velocity (degree/s) 182.11 ± 59.48 151.07 ± 66.08 159.91 ± 79.91 0.005 HC > > PD — Arm RoM (degree) 34.41 ± 14.07 24.52 ± 12.28 25.97 ± 14.05 >>PD, FOG — Stride Length Asymmetry (%) 3.62 ± 2.62 3.63 ± 3.00 4.49 ± 2.68 0.052 — — Swing Asymmetry (%) 6.37 ± 4.42 8.28 ± 3.93 8.10 ± 4.25 0.007 HC < < PD — Shank RoM Asymmetry (%) 6.96 ± 5.30 9.67 ± 8.38 7.95 ± 5.31 0.013 HC < PD — Shank Symbolic Symmetry Index (%) 11.72 ± 3.30 11.73 ± 2.83 11.20 ± 2.48 0.433 — — Phase Coordination Index (%) 7.63 ± 4.54 9.00 ± 5.38 8.82 ± 5.67 0.220 — — Arm Symbolic Symmetry Index (%) 34.95 ± 5.54 37.31 ± 5.29 37.10 ± 5.25 0.004 HC < < PD; HC < FOG — Turning Turning - Average Duration (s) 1.51 ± 0.25 2.09 ± 1.26 2.09 ± 1.07 < 0.001 HC<<<PD; HC < < FOG — Turning - Average Steps 2.10 ± 0.74 2.53 ± 1.74 3.27 ± 2.30 < 0.001 HC<<<FOG PD < < FOG Turning - Peak Velocity (degree/s) 166.83 ± 31.99 129.15 ± 31.51 126.56 ± 30.08 >>PD, FOG — Turning - Average Angular Velocity (degree/s) 122.29 ± 13.21 99.77 ± 26.46 100.71 ± 27.14 >>PD, FOG — Stand-to-sit StSi - Average Duration (s) 1.99 ± 0.53 2.30 ± 0.89 2.17 ± 0.71 0.024 HC < PD — StSi - Trunk Sagittal Peak Velocity (degree/s) 86.47 ± 27.00 64.28 ± 22.18 62.41 ± 19.11 >>PD, FOG — StSi - Trunk Sagittal RoM (degree) 40.76 ± 11.80 33.95 ± 9.38 33.67 ± 10.17 >>PD, FOG — Statistically significant differences between groups after Bonferroni correction were expressed as follows: (> or <), p > or <<), p >>> or <<<), p < 0.001. All results were adjusted for age, gender, and height by analysis of covariance. During the sit-to-stand task, there were statistical differences in mean duration, trunk sagittal peak velocity, and trunk sagittal ROM among these three groups (F = 8.15, p < 0.001; F = 26.49, p < 0.001; F = 7.73, p < 0.001, respectively), after adjusting for age, gender, and height. The HC group had a shorter mean duration, faster trunk sagittal peak velocity, and larger trunk sagittal RoM than PD-FOG or PD-nFOG patients. At the same time, there was no difference between PD-FOG and PD-nFOG group after post hoc tests with Bonferroni correction. There were similar differences in these three parameters above during the stand-to-sit task among the three groups. During the walking process, there were significant differences in step length (F = 17.42, p < 0.001), stride velocity (F = 10.38, p < 0.001), and stride length (F = 17.41, p < 0.001) among these three groups. However, no difference was found in step frequency (F = 1.42, p = 0.243) and gait cycle (F = 1.20, p = 0.302). The differences between the PD-FOG and PD-nFOG groups were mainly reflected in the decrease in the proportion of the double support phase (p corrected = 0.007, Cohen's d = 0.43) and the increase in the proportion of the swing phase (p corrected = 0.010, Cohen's d = -0.42) during walking. Regarding gait variability parameters: step length CV was significantly greater in the PD-FOG group compared to the HC group (p corrected = 0.005, Cohen's d = -0.53), and there were no statistical differences in swing phase CV, double support phase CV, cadence CV, and stride velocity CV among the three groups (p > 0.05). Regarding kinematic gait parameters: shank RoM (F = 23.97, p < 0.001) and peak shank angular velocity (F = 14.21, p < 0.001) were significantly reduced in both PD-nFOG and PD-FOG groups compared to the HC group. Trunk coronal peak velocity (F = 5.36, p = 0.005), trunk coronal RoM (F = 23.08, p < 0.001), trunk sagittal peak velocity (F = 4.76, p = 0.009), trunk sagittal RoM (F = 6.79, p = 0.001), trunk transverse peak velocity (F = 9.25, p < 0.001), trunk transverse RoM (F = 8.84, p < 0.001) and arm RoM (F = 14.93, p < 0.001) were reduced both in the PD-nFOG and PD-FOG groups when compared to the HC group. The arm peak velocity (F = 5.31, p = 0.005) was decreased in the PD-nFOG group compared to the HC group, while there was no difference between the PD-FOG group and the HC group. Regarding the parameters of gait asymmetry: the swing asymmetry (F = 5.08, p = 0.007) and shank RoM asymmetry (F = 4.37, p = 0.013) increased in the PD-nFOG group compared to the HC group, while the PD-FOG group did not differ from the HC group. The arm symmetry index increased both in the PD-nFOG and PD-FOG groups compared to the HC group (F = 5.73, p = 0.004). There were no statistical differences in the stride length asymmetry (F = 2.98, p = 0.052), shank symmetry index (F = 0.84, p = 0.433), and phase coordination index (F = 1.52, p = 0.220) among the three groups. During turning, the mean duration of turning was longer both in the PD-FOG and PD-nFOG groups compared to the HC group (F = 7.85, p < 0.001). The mean number of steps in the turning process increased in the PD-FOG group compared to the PD-nFOG and HC groups (F = 8.79, p < 0.001). The peak angular velocity of the turning process decreased both in the PD-FOG and PD-nFOG groups compared to the HC group (F = 41.93, p < 0.001). Obtain the gait domains and factor scores The EFA approach was used to extract the gait domains and to reduce the dimensionality of gait parameters. We first performed KMO sampling fitness tests on 22 representative gait variables, and the results are shown in Appendix 2. The total KMO was 0.78, and each variable individually had KMO > 0.5. Bartlett's test of sphericity X 2 = 8552.20, p < 0.001, indicating that the correlation between the variables was appropriate and suitable for further factor analysis. The results of the parallel analysis method suggest that six factors/domains are the optimal number of factors to explain the data distribution. These six factors explained a total of 74.23% of the variance in the data set, with 16.83% of the variance explained by factor 1, 14.77% by factor 2, 11.52% by factor 3, 12.76% by factor 4, 9.37% by factor 5, and 8.98% by factor 6. Based on the loadings of the gait variables in each factor, we grouped them into six gait domains: the pace factor (including stride length, step length, stride velocity, shank RoM, and stride velocity CV), the kinematic factor (including trunk transverse RoM, trunk coronal RoM, trunk sagittal RoM, trunk transverse peak velocity, trunk sagittal peak velocity, and trunk coronal peak velocity), gait phase factor (including double support phase, swing phase and double support phase CV), turning process factor (including turning process average duration, turning process average steps, turning process average angular velocity and turning process peak velocity), rhythm factor (including gait cycle and cadence) and asymmetry factor (including swing phase CV and swing asymmetry) (Table 3). We further summarize the standard score coefficients of each parameter based on the results of EFA and use the Thurstone method to calculate the factor scores of each gait domain separately. Differences in gait domains impairment in the PD-nFOG group and PD-FOG group in comparison to HC group Each group's gait domain factor scores were separately transformed to a Z-score with HC as the reference value. The mean value of each variable in the HC group was 0, and the standard deviation was 1 after the transformation. Radar plots were used to indicate the degree of impairment and the direction of change in the gait domain in the PD-nFOG and PD-FOG groups relative to the HC group (Fig. 1 ). The results showed that the factor scores of pace and kinematic domain were reduced in both two groups compared to the HC group. The gait phase domain factor score was significantly higher in the PD-FOG group compared to the PD-nFOG group (p corrected = 0.004, Cohen's d = -0.46). The turning process domain factor score was greater in both the PD-nFOG and PD-FOG groups compared to the HC group by approximately two standard deviations (F = 16.72, p 0.05). The related factors of FOG through the combination of clinical and gait domain characteristics We constructed a binomial logistic regression model with the presence or absence of FOG as the dependent variable, and independent variables with p-values < 0.1 were included in the model based on the results of the univariate analysis. After eliminating the independent variables with excessive covariance, the final independent variables included in the multinomial model were: gender, height, disease duration, H-Y stage, MDS-UPDRS II score, MDS-UPDRS IV score, bradykinesia subscore, rigidity subscore, PIGD subscore, HAMD score, MMSE score, gait phase domain factor score, and LEDD. The results showed that gender (OR = 2.67, 95% CI = 1.19–5.99, p = 0.017), MDS-UPDRS IV score (OR = 1.23, 95% CI = 1.10–1.37, p < 0.001), gait phase domain (OR = 1.64, 95% CI = 1.05–2.55, p = 0.030) and PIGD subscore (OR = 1.50, 95% CI = 1.30–1.73, p < 0.001) were independent risk factors for FOG after forward stepwise (likelihood ratio) selection (Table 4 ). The Hosmer-Lemeshow test showed a good model fit (X 2 = 1.09, degrees of freedom = 8, p = 0.998). The model had a sensitivity of 0.78 and a specificity of 0.77 for differentiating FOG and PD patients at a cut-off value of 0.28, with an accuracy of 0.78 and an area under the receiver operating characteristic curve (AUC) was 0.87 (Fig. 2 ). Discussion In this study, we found that some characteristics of gait impairments in PD patients with FOG were different from those of non-FOG patients when FOG episode was not present. Common gait characteristics contained in twenty-two gait variables were identified, and six gait domains were categorized and extracted to represent a synthetic description of gait. Among these gait domains, the impairment of the gait phase domain was the critical abnormality in identifying potential PD-FOG from PD-nFOG patients during an interictal period of freezing. Combined with the clinical information, males, with higher MDS-UPDRS IV score, higher PIGD subscore, and higher gait phase domain factor score were independent risk factors for FOG. We used wearable sensors for gait detection, which has the advantages of being portable and less restricted by the testing environment compared to a 3D optical motion analysis system [ 7 , 19 , 20 ] . Some studies previously focused on the changes in gait parameters during FOG episodes [ 8 ] . The decrease in gait frequency during FOG episodes can be explained by the tendency of the trunk to walk continuously forward. Still, the inability of the feet to produce an effective stride length makes the walking movement collapse and reduces the number of steps. FOG may result from dysfunctional stride control. In addition, FOG events are often preceded by postural instability when FOG patients compensate by increasing the duration of the double support phase [ 21 ] . It has also been found that an anterior tilt of the pelvis occurs prior to the onset of a FOG event, suggesting an impaired anticipatory postural adjustment, leading to an increased risk of falling forward [ 22 , 23 ] . However, few studies have examined the gait characteristics of FOG patients during the “interictal” period. Our results found that patients with FOG had a reduced double-support phase time, a relatively increased swing phase during the interictal period compared to PD patients without FOG, and a significant increase in the number of steps during turning. Some hypotheses on the pathophysiological mechanisms of FOG point to an impairment in the coordination of the gait cycle in FOG patients, as evidenced by increased step frequency variability, gait asymmetry, and double support phase [ 24 , 25 ] . The double-support phase represents the period of the gait cycle when both feet are in contact with the ground at the same time, during which the body has better control over the movement of the center of gravity. An increase in the percentage of the double support phase suggests impaired dynamic balance and postural control [ 26 ] . It has been suggested that the percentage of the double support phase significantly increased during FOG events, reflecting that patients are in the double support phase to correct the interference of FOG on balance postural control [ 21 ] . The percentage of the double support phase is also related to the walking speed, which may increase as the walking speed decreases [ 26 ] . However, in the present study, the double support phase percentage was reduced, and the swing phase was relatively increased during the interictal period in PD-FOG patients, and there was no significant difference between the two groups regarding gait speed. This suggests that the typical gait cycle control impairment pattern of FOG is not present in the interictal period and is even slightly better than in the average PD-nFOG patient. Consistent with the results of other studies, gait characteristics such as gait speed, stride length, stride variability, and left-right asymmetry were impaired to varying degrees in PD patients relative to healthy controls [ 27 ] . With the aid of gait analysis systems, we can obtain multi-variables. However, these parameters are too many to highlight PD's gait impairment characteristic and to be unsatisfactory in descript or distinguishing FOG. Previous studies have attempted to describe gait in PD with different approaches and analyzed numerous spatiotemporal and kinematic parameters. However, the extracted variables are not readily interpretable from a clinical standpoint and are frequently analyzed in isolation from clinical correlations, thus disregarding the comprehensive features of gait. Moreover, many gait parameters are often widely correlated with each other, and the problem of collinearity among variables limits the multinominal analysis of gait [ 28 , 29 ] . Exploratory factor analysis (EFA) has been widely used in social science and medical research and is less commonly used in the field of chronic non-communicable diseases. Some recent studies have attempted to use EFA methods to reveal complex correlations among variables, identify potential common factors among variables, and further explain the practical significance of each factor [ 15 ] . For example, one study obtained four gait domains (variability, asymmetry, postural control, and pace/cadence factors) after factor analysis of gait parameters for assessing the gait characteristics of elderly patients with hip fractures [ 16 ] . Few studies are using EFA to characterize the gait of PD patients, especially the gait characteristics of FOG patients in the ‘interictal’ period relative to PD patients without FOG symptoms are uncertain. Therefore, in this study, we used EFA to analyze the differences in the changes in gait characteristics between PD-FOG patients and PD-nFOG patients compared to healthy controls in six gait dimensions: pace, kinematics, gait phase, turning process, rhythm and asymmetry, and to describe the degree of impairment in different gait domains. Our results showed that the pace, kinematic, gait phase, and turning process domains were impaired in both PD-nFOG and PD-FOG groups compared to healthy controls. Among them, the difference between the PD and FOG groups in the interictal period was mainly in the domain of the gait phase. This suggested that gait phase parameters were important indicators to distinguish PD-FOG patients from PD-nFOG patients in the interictal period. The present study also showed that patients with FOG had a longer disease duration, more progressive disease, poorer balance, and more severe motor and non-motor symptoms, with more prominent aspects of bradykinesia and PIGD. These results are also consistent with the findings of other previous studies [ 30 ] . Notably, motor complications were more severe in patients with FOG, which may also be related to the longer disease duration and higher doses of antiparkinsonian medication in patients with FOG. The results of the multinominal analysis showed that gender, motor complications, PIGD, and impaired gait phase domain are associated with FOG and that these indicators could effectively distinguish PD patients with FOG in the interictal period from those without FOG. There were some limitations of this study. Firstly, the gait test was completed during the “ON” period, and this might inaccurately reflect the degree of gait impairment of PD patients because of the differences in drug effect on motor symptoms and gait control among individuals. Secondly, visual judgment is currently the gold standard for FOG identification. Still, the standard definitions of the beginning and end of FOG events are inconsistent or not even clearly defined in many studies [ 31 , 32 ] . This also reduces the comparability between different studies. Our study focused on people with interictal periods of FOG episodes, and the presence or absence of FOG events was determined by visual observation. There may be a problem of low identification accuracy, which in turn may introduce some patients with FOG events but mild symptoms into the population. Finally, FOG is a highly heterogeneous symptom, and there may be differences in the gait characteristics of patients with different types of FOG (complete blocking, shuffling forward with small steps, and trembling on the spot). Therefore, future prospective studies with larger sample sizes and less heterogeneous candidates are needed to further clarify these changes in FOG patients. Conclusions This study focused on analyzing the characteristic differences of gait parameters in FOG patients during the ‘interictal’ period. We focused not only on a single gait variable but also on revealing the common features behind numerous gait parameters and extracting independent gait domains. Abnormal change in the gait phase domain was associated with FOG during the interictal period. Models constructed using gait phase domain factor score, PIGD subscore, gender, and severity of motor complications can better differentiate patients with interictal FOG. The timely recognition by clinicians of individuals who may present with FOG, yet do not manifest freezing events during clinical evaluation, can aid in effectively managing FOG. Declarations Authors' contributions ZJH contributed to data collection, data collection, methodology, writing the original draft. LC contributed to data curation and formal analysis. WY and ZXB contributed to data collection, data curation. SL contributed to study conceptualization, funding acquisition, investigation. LZG contributed to study conceptualization, funding acquisition, investigation, project administration, supervision. GJ contributed to study conceptualization, funding acquisition, project administration, and review and editing of the paper. All authors have read and approved the manuscript. Funding This study was funded by grants from the Shanghai Committee of Science and Technology (22Y11904100), the National Natural Science Foundation of China (82271274, 82171242), Shanghai Pujiang Program (21PJD046). Availability of data and materials The data-set generated and analyzed during the current study is also available now from the corresponding author on a reasonable request. Ethics approval and consent to participate All participants have given their written informed consent. The current research was approved by the Research Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (XHEC-C-2015-019-2). All methods were performed in accordance with the relevant guidelines and regulations. Consent for publication Not Applicable. Competing interests None. References Camicioli R, Morris ME, Pieruccini-Faria F, Montero-Odasso M, Son S, Buzaglo D, Hausdorff JM, Nieuwboer A. Prevention of Falls in Parkinson's Disease: Guidelines and Gaps. Mov Disord Clin Pract.2023; 10(10):1459-69. Mancini M, Bloem BR, Horak FB, Lewis SJG, Nieuwboer A, Nonnekes J. Clinical and methodological challenges for assessing freezing of gait: Future perspectives. Mov Disord.2019; 34(6):783-90. Barthel C, Mallia E, Debû B, Bloem BR, Ferraye MU. The Practicalities of Assessing Freezing of Gait. J Parkinsons Dis.2016; 6(4):667-74. van Dijsseldonk K, Wang Y, van Wezel R, Bloem BR, Nonnekes J. Provoking Freezing of Gait in Clinical Practice: Turning in Place is More Effective than Stepping in Place. J Parkinsons Dis.2018; 8(2):363-65. Bouça-Machado R, Jalles C, Guerreiro D, Pona-Ferreira F, Branco D, Guerreiro T, Matias R, Ferreira JJ. Gait Kinematic Parameters in Parkinson's Disease: A Systematic Review. J Parkinsons Dis.2020; 10(3):843-53. Sotirakis C, Conway N, Su Z, Villarroel M, Tarassenko L, FitzGerald JJ, Antoniades CA. Longitudinal Monitoring of Progressive Supranuclear Palsy using Body-Worn Movement Sensors. Mov Disord.2022; 37(11):2263-71. Gan J, Wu X, Wan Y, Zhao J, Song L, Wu N, Wang H, Yin Y, Liu Z. Evolution characteristics of dynamic balance disorder over the course of PD and relationship with dopamine depletion. Front Aging Neurosci.2022; 14:1075572. Mitchell T, Conradsson D, Paquette C. Gait and trunk kinematics during prolonged turning in Parkinson's disease with freezing of gait. Parkinsonism Relat Disord.2019; 64:188-93. Chee R, Murphy A, Danoudis M, Georgiou-Karistianis N, Iansek R. Gait freezing in Parkinson's disease and the stride length sequence effect interaction. Brain.2009; 132(Pt 8):2151-60. Iansek R, Huxham F, McGinley J. The sequence effect and gait festination in Parkinson disease: contributors to freezing of gait? Mov Disord.2006; 21(9):1419-24. Cao SS, Yuan XZ, Wang SH, Taximaimaiti R, Wang XP. Transverse Strips Instead of Wearable Laser Lights Alleviate the Sequence Effect Toward a Destination in Parkinson's Disease Patients With Freezing of Gait. Front Neurol.2020; 11:838. Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, Obeso J, Marek K, Litvan I, Lang AE et al . MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord.2015; 30(12):1591-601. Tomlinson CL, Stowe R, Patel S, Rick C, Gray R, Clarke CE. Systematic review of levodopa dose equivalency reporting in Parkinson's disease. Mov Disord.2010; 25(15):2649-53. Lin S, Gao C, Li H, Huang P, Ling Y, Chen Z, Ren K, Chen S. Wearable sensor-based gait analysis to discriminate early Parkinson's disease from essential tremor. J Neurol.2023; 270(4):2283-301. Horak FB, Mancini M, Carlson-Kuhta P, Nutt JG, Salarian A. Balance and Gait Represent Independent Domains of Mobility in Parkinson Disease. Phys Ther.2016; 96(9):1364-71. Thingstad P, Egerton T, Ihlen EF, Taraldsen K, Moe-Nilssen R, Helbostad JL. Identification of gait domains and key gait variables following hip fracture. BMC Geriatr.2015; 15:150. Horn JL. A RATIONALE AND TEST FOR THE NUMBER OF FACTORS IN FACTOR ANALYSIS. Psychometrika.1965; 30:179-85. Tukey JW, Thurstone LLJTAMM. Multiple Factor Analysis. 1947; 54(10):613-. Prasanth H, Caban M, Keller U, Courtine G, Ijspeert A, Vallery H, von Zitzewitz J. Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review. Sensors (Basel).2021; 21(8). Ossig C, Antonini A, Buhmann C, Classen J, Csoti I, Falkenburger B, Schwarz M, Winkler J, Storch A. Wearable sensor-based objective assessment of motor symptoms in Parkinson's disease. J Neural Transm (Vienna).2016; 123(1):57-64. Nieuwboer A, Dom R, De Weerdt W, Desloovere K, Fieuws S, Broens-Kaucsik E. Abnormalities of the spatiotemporal characteristics of gait at the onset of freezing in Parkinson's disease. Mov Disord.2001; 16(6):1066-75. Alice N, Fabienne C, Anne-Marie W, Kaat D. Does freezing in Parkinson's disease change limb coordination? A kinematic analysis. J Neurol.2007; 254(9):1268-77. Schlenstedt C, Mancini M, Nutt J, Hiller AP, Maetzler W, Deuschl G, Horak F. Are Hypometric Anticipatory Postural Adjustments Contributing to Freezing of Gait in Parkinson's Disease? Front Aging Neurosci.2018; 10:36. Okuma Y. Practical approach to freezing of gait in Parkinson's disease. Pract Neurol.2014; 14(4):222-30. Okuma Y. Freezing of gait in Parkinson's disease. J Neurol.2006; 253 Suppl 7:Vii27-32. Williams DS, Martin AE. Gait modification when decreasing double support percentage. J Biomech.2019; 92:76-83. Rehman RZU, Del Din S, Guan Y, Yarnall AJ, Shi JQ, Rochester L. Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson's Disease: A Comprehensive Machine Learning Approach. Sci Rep.2019; 9(1):17269. Godi M, Arcolin I, Giardini M, Corna S, Schieppati M. A pathophysiological model of gait captures the details of the impairment of pace/rhythm, variability and asymmetry in Parkinsonian patients at distinct stages of the disease. Sci Rep.2021; 11(1):21143. Arcolin I, Corna S, Giardini M, Giordano A, Nardone A, Godi M. Proposal of a new conceptual gait model for patients with Parkinson's disease based on factor analysis. Biomed Eng Online.2019; 18(1):70. Sawada M, Wada-Isoe K, Hanajima R, Nakashima K. Clinical features of freezing of gait in Parkinson's disease patients. Brain Behav.2019; 9(4):e01244. Palmerini L, Rocchi L, Mazilu S, Gazit E, Hausdorff JM, Chiari L. Identification of Characteristic Motor Patterns Preceding Freezing of Gait in Parkinson's Disease Using Wearable Sensors. Front Neurol.2017; 8:394. Mazilu S, Blanke U, Hardegger M, Troster G, Hausdorff JM: GaitAssist: A wearable assistant for gait training and rehabilitation in Parkinson's disease. In: 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS): 2014. Table 3 Table 3 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files 20240323Table3.docx Appendix1.docx Appendix2.docx Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2025 Read the published version in BMC Geriatrics → Version 1 posted Editorial decision: Revision requested 18 Nov, 2024 Reviews received at journal 16 Nov, 2024 Reviews received at journal 05 Oct, 2024 Reviewers agreed at journal 03 Oct, 2024 Reviewers agreed at journal 27 Sep, 2024 Reviewers agreed at journal 27 Sep, 2024 Reviewers invited by journal 05 Jul, 2024 Editor assigned by journal 18 Jun, 2024 Editor invited by journal 11 Apr, 2024 Submission checks completed at journal 11 Apr, 2024 First submitted to journal 23 Mar, 2024 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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18:38:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":254724,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis and cut-off plot for prediction of FOG.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4154081/v1/77de48849d886abf6bfe5a1d.png"},{"id":73694768,"identity":"3ca4ecd9-531b-4476-874f-6863241657e4","added_by":"auto","created_at":"2025-01-13 16:14:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1454218,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4154081/v1/bc46029d-2cdb-433e-8a1c-e5af503ba208.pdf"},{"id":55001541,"identity":"89dc8c01-4256-4761-90e6-076c7001526e","added_by":"auto","created_at":"2024-04-19 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18:38:04","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":14442,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4154081/v1/a64d2c1d24b0626f621230f1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Do the gait domains change in PD patients with freezing of gait during their ‘interictal’ period?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWalking safely and comfortably is critical to patients with Parkinson's disease (PD). As we know, freezing of gait (FOG) is one of the main risk factors for falls in PD\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The tendency of the trunk to move forward while the feet are relatively \u0026ldquo;frozen\u0026rdquo; in place during walking can easily lead to balance dysfunction and falls, which reduce the ability to move independently and impair the quality of life of PD patients. It is not evident to observe the onset of FOG in outpatient visits due to its \u0026ldquo;episodic\u0026rdquo; nature\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. During the outpatient visit, the gait performance of Parkinsonians with FOG may be improved by increased attention and alertness, especially in patients with mild or less frequent FOG episodes, which are difficult to elicit in the clinic\u003csup\u003e[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. It is important to detect the gait characteristic changes for PD patients with FOG, even if FOG is not observed by the clinician.\u003c/p\u003e \u003cp\u003eWith the development of technology-based gait analysis in PD, subtle changes in gait characteristics can be captured using wearable sensors\u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, and these quantified gait parameters can provide more information on how PD gait converts to FOG gait. Several studies showed the gait characteristics during FOG episodes. There is a decrease in gait frequency, variability of gait speed, stride length, and mean lateral displacement amplitude during FOG\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. A typical FOG event occurs with a progressive decrease in step length and eventually a freeze, also known as the sequence effect\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The sequence effect prior to a FOG episode is a direct description of how gait impairment develops prior to a FOG episode\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur hypothesis is that gait impairments in PD-FOG patients may be preserved in some form and to some extent during the \u0026ldquo;interictal\u0026rdquo; period. These \u0026ldquo;interictal\u0026rdquo; changes in gait characteristics predict that these patients are prone to develop FOG, and even further exacerbation of these gait impairments may lead to the onset of FOG. We suggest that these continuity impairments hidden in the gait pattern may be a risk factor for developing FOG.\u003c/p\u003e \u003cp\u003eTo test our hypothesis, we analyzed the gait parameters of PD patients with FOG during their \u0026ldquo;interictal\u0026rdquo; episodes using wearable inertial measurement sensors. We extracted several gait domains to better describe gait impairment by grouping numerous univariate gait parameters with the exploratory factor analysis (EFA). Our objective is: (1) to find the distinct abnormal gait domains in FOG patients from non-FOG patients during interictal period; (2) to analyze the relevant influencing factors of FOG by integrating clinical information and abnormal gait domain characteristics.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eTwo hundred forty-five patients with idiopathic PD who visited the Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University, School of Medicine were enrolled in this study from November 2019 to December 2021. The inclusion criteria for the PD group were: (1) the diagnosis of PD was based on International Movement Disorders Society (MDS) PD diagnostic criteria 2015\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e; (2) Hoehn and Yahr (H-Y) stages\u0026thinsp;\u0026le;\u0026thinsp;3; (3) walking independently for at least 10 m without any assistive device; (4) Mini-Mental State Examination (MMSE)\u0026thinsp;\u0026gt;\u0026thinsp;24 points. The exclusion criteria were: (1) were diagnosed with parkinsonism-plus syndromes or other diseases that may affect gait performance (e.g., stroke, trauma, orthopedic disease, abnormal vision and serious cardio-pulmonary diseases); (2) Severe psychiatric symptoms, dementia, and inability to cooperate with the completion of the examination.\u003c/p\u003e \u003cp\u003eThe age-matched healthy controls (HC group) were partners of patients with PD or volunteers of the nearby community during the same period. They were excluded if they reported previous neurological, orthopedic, abnormal vision or musculoskeletal disorders that could impact gait.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eClinical assessments\u003c/h2\u003e \u003cp\u003eA detailed medical history (including age, sex, height, weight, education, time of disease onset, first symptoms, antiparkinsonian drugs, etc.) was collected. The time of disease onset was defined as the onset of subjective perceived motor symptoms of PD. The motor assessment scales included the MDS-Unified Parkinson\u0026rsquo;s Disease Rating Scale (MDS-UPDRS) and the H-Y stage. The motor subscores were calculated as the bradykinesia subscore (sum of items 3.4\u0026ndash;3.8 and 3.14), rigidity subscore (sum of item 3.3), tremor subscore (sum of items 3.15\u0026ndash;3.18), and postural instability and gait difficulty (PIGD) subscore (sum of items 3.9\u0026ndash;3.13) based on the MDS-UPDRS part III. Information on pharmacological treatment was collected and calculated in total daily levodopa equivalent dose (LED)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The New Freezing of Gait Questionnaire (NFOG-Q) was used to determine the presence and severity of the freezing of gait. The Mini Balance Evaluation Systems Test (mini-BESTest) was used to evaluate the balance function. The cognitive and emotional assessment scales included the MMSE, the Montreal Cognitive Assessment (MoCA), the Hamilton Anxiety Scale (HAMA), the Hamilton Depression Scale (HAMD), and the Frontal Assessment Battery (FAB). The 8-item Parkinson's Disease Questionnaire (PDQ-8) was used to assess the quality of life. All assessments were performed during the \"ON\" period.\u003c/p\u003e \u003cp\u003e The study was a cross-sectional study, approved by the Research Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (XHEC-C-2015-019-2), and all subjects were fully informed of the purpose and content of the study and provided written informed consent.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eKinematic analysis of gait\u003c/h2\u003e \u003cp\u003eGait testing was performed by a Wearable Movement and Gait Quantitative Assessment System. The inertial measurement units (IMU) (GYENNO Science, Shenzhen, China) were applied to collect kinematic parameters in real time\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Participants wore ten inertial sensors fixed to the lower back (L5), anterior chest (sternum), bilateral thighs, ankles, feet, and wrists by elastic bands. After the sensors were placed correctly, the participants were asked to perform the Timed Up and Go Test (TUG): (1) stand up from the chair; (2) walk straight for a 5-m distance at their regular pace; (3) turn and walk back to the starting point; and (4) sit down. Prior to commencing the test, the subjects were initially instructed on the process by the researcher and subsequently allowed to practice it once. Subjects in the PD group completed the test during their \"ON\" period.\u003c/p\u003e \u003cp\u003eGait parameters are transmitted in real-time via Bluetooth to the computer for 3D motion posture reconstruction to assess gait, arm swing, whole-body coordination, and other indicators. If the investigator observed or the device automatically identified a FOG episode during walking, the patient was excluded from the final analysis. All parameters which we can obtain were calculated automatically during the motor test using built-in algorithms (Appendix 1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe Shapiro-Wilk test combined with Q-Q plots was used to determine the distribution of continuous variables. Normally distributed measures were expressed using the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and non-normally distributed measures were expressed using the median (quartiles). The independent t-test/Mann-Whitney U test was used for comparison of measures between independent groups, and X\u003csup\u003e2\u003c/sup\u003e test/Fisher exact probability method was used for comparison of numerical data. Differences in baseline clinical characteristics and gait parameters among controls, PD-nFOG patients, and PD-FOG patients were assessed using analysis of covariance (ANCOVA) and Bonferroni post hoc tests, and homogeneity of variance was determined by plotting scatter plots and performing Levene's tests. Forward stepwise binomial logistic regression was used to analyze factors associated with FOG, and the degree of influence was evaluated using the odds ratio (OR) and 95% confidence interval (CI). The Box-Tidwell method is used to test whether there is a linear relationship between the logit transformed values of the continuous independent and dependent variables. Tolerance and variance inflation factor (VIF) was calculated to diagnose the presence of multicollinearity between the independent variables.\u003c/p\u003e \u003cp\u003eExploratory factor analysis (EFA) was used to explore the intrinsic structure of the 22 gait variables and to identify the common features behind the gait variables to categorize and extract several major gait domains that represent different gait characteristics\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Each gait variable was first transformed separately by normalization. Each value was subtracted from the mean value of the parameter in the whole sample (including the PD and HC groups), respectively, and the difference was divided by the standard deviation of the whole sample. The Kaiser-Meyer-Olkin (KMO) sampling fitness test and Bartlett's sphericity test were used to clarify whether the 22 variables were suitable for factor analysis among themselves. Horn's parallel analysis\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e was then used to determine the appropriate number of factors, namely, the number of gait domains. The maximum likelihood method was used for factor extraction. Further, the oblimin oblique rotation method was used to improve the interpretability of their loadings to avoid possible correlations between potential factors. Variables with loadings up to 0.5 were considered significant. After EFA analysis, the Thurstone method\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e was used to summarize each parameter's standard score coefficients based on EFA's results and calculate the factor scores for each gait domain separately. The obtained factor scores were converted to a Z-score with HC as the reference value [e.g., factor 1 Z-score = (factor 1 - mean of factor 1 in HC group)/standard deviation of factor 1 in HC group] to draw radar plots for comparing the different levels of impairment in the gait domain between the PD-nFOG group and PD-FOG group relative to the subjects in the HC group.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed with R (version number: 4.1.2) (R Foundation for Statistical Computing, Vienna, Austria). The threshold for statistically significant differences was set at a two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eThe differences in clinical characteristics among the HC group, PD-nFOG group and PD-FOG group\u003c/h2\u003e\n \u003cp\u003eA total of 245 patients with PD were enrolled (124 males and 121 females) in our study. The mean age was 67.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.80 years, height was 164.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.80 cm, the mean disease duration was 5.45\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52 years, the H-Y stage was 2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75, and the MDS-UPDRS III score was 25.55\u0026thinsp;\u0026plusmn;\u0026thinsp;14.26. Sixty-eight healthy controls (27 males and 41 females) were enrolled in the HC group with a mean age of 66.44\u0026thinsp;\u0026plusmn;\u0026thinsp;8.76 years and a mean height of 163.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.19 cm.\u003c/p\u003e\n \u003cp\u003eAccording to the scale of the NFOG, the patients with PD were classified into the PD-FOG group and PD-nFOG group (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). Seventy-eight (31.84%) of the 245 PD patients had FOG, of which 55 were levodopa responsive, and 19 were levodopa unresponsive. No difference in gender, age, height, and education was found among the HC group, PD-nFOG group, and PD-FOG group (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Compared with PD-nFOG patients, PD-FOG patients had a longer disease duration (7.65\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95 vs. 4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91, t=-5.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a higher H-Y stage (2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64 vs. 1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73, t=-5.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher doses of LED (632.29\u0026thinsp;\u0026plusmn;\u0026thinsp;352.10 vs. 398.82\u0026thinsp;\u0026plusmn;\u0026thinsp;333.90, t=-4.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The scores of MDS-UPDRS were significantly higher in the PD-FOG group than those in the PD-nFOG group with part I (10.97\u0026thinsp;\u0026plusmn;\u0026thinsp;5.55 vs. 9.13\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23, t=-2.30, p\u0026thinsp;=\u0026thinsp;0.022), part II (16.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.75 vs. 9.98\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23, t=-6.87, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), part III (31.31\u0026thinsp;\u0026plusmn;\u0026thinsp;29.50 vs. 22.86\u0026thinsp;\u0026plusmn;\u0026thinsp;13.43, t=-4.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and part IV (Mann-Whitney U\u0026thinsp;=\u0026thinsp;2838.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, compared to the PD-nFOG group, the PD-FOG group obtained higher bradykinesia subscore (16.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.37 vs. 11.42\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02, t=-4.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PIGD subscore (6.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25 vs. 3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42, t=-6.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At the same time, there was no difference in rigidity subscore and tremor subscore between these two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The PD-FOG group performed worse balance based on the mini-BESTest (t\u0026thinsp;=\u0026thinsp;4.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and in terms of non-motor symptoms of PD, the PD-FOG group presented a more elevated HAMD score (10.45\u0026thinsp;\u0026plusmn;\u0026thinsp;6.88 vs. 8.10\u0026thinsp;\u0026plusmn;\u0026thinsp;6.85, t=-2.30, p\u0026thinsp;=\u0026thinsp;0.023) and lower FAB score (Mann-Whitney U\u0026thinsp;=\u0026thinsp;1383.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than those in PD-nFOG group. No difference in MMSE and HAMA scores was found between the two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). PD patients with FOG had significantly lower quality of life (1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45 vs. 0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91, t=-5.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eClinical characteristics of control, PD-nFOG group and PD-FOG group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePD-nFOG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePD-FOG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et/U/\u0026chi;\u003csup\u003e2\u003c/sup\u003e/F\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (male/female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27/41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78/89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46/32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.44\u0026thinsp;\u0026plusmn;\u0026thinsp;8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.90\u0026thinsp;\u0026plusmn;\u0026thinsp;8.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.42\u0026thinsp;\u0026plusmn;\u0026thinsp;6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165.70\u0026thinsp;\u0026plusmn;\u0026thinsp;7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163.15\u0026thinsp;\u0026plusmn;\u0026thinsp;7.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eilliteracy or primary school graduates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiddle school graduates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh school graduates or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.65\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH-Y stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLED (mg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e398.82\u0026thinsp;\u0026plusmn;\u0026thinsp;333.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e632.29\u0026thinsp;\u0026plusmn;\u0026thinsp;352.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDS-UPDRS I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.13\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.97\u0026thinsp;\u0026plusmn;\u0026thinsp;5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDS-UPDRS II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.98\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDS-UPDRS III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.86\u0026thinsp;\u0026plusmn;\u0026thinsp;13.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.31\u0026thinsp;\u0026plusmn;\u0026thinsp;29.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDS-UPDRS IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0, 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(0, 7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2838.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoto symptom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebradykinesia subscore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.42\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erigidity subscore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etremor subscore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIGD subscore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNFOG-Q\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.31\u0026thinsp;\u0026plusmn;\u0026thinsp;6.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emini-BESTest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.95\u0026thinsp;\u0026plusmn;\u0026thinsp;4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.96\u0026thinsp;\u0026plusmn;\u0026thinsp;5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevodopa response of FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elevodopa responsive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elevodopa unresponsive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-motor symptom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(27, 30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(26, 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5209.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(21, 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(19, 26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4218.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.62\u0026thinsp;\u0026plusmn;\u0026thinsp;5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.18\u0026thinsp;\u0026plusmn;\u0026thinsp;6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.10\u0026thinsp;\u0026plusmn;\u0026thinsp;6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.45\u0026thinsp;\u0026plusmn;\u0026thinsp;6.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(15, 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(13, 17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1383.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDQ-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eMDS-UPDRS, MDS-Unified Parkinson\u0026rsquo;s Disease Rating Scale; H-Y stage, Hoehn \u0026amp; Yahr stage; LED, levodopa equivalent dose; PIGD, postural instability and gait difficulty; NFOG-Q, New Freezing of Gait Questionnaire; mini-BESTest, Mini Balance Evaluation Systems Test; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; HAMA, Hamilton Anxiety Scale; HAMD, Hamilton Depression Scale; FAB, Frontal Assessment Battery; PDQ-8, 8-item Parkinson\u0026apos;s Disease Questionnaire.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eDifferences in gait parameters among the HC group, PD-nFOG group and PD-FOG group\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e showed the gait parameters during walking, turning, and sit-stand shift tasks among the HC group, PD-nFOG group, and PD-FOG group.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDifferences in gait parameters among the PD-nFOG group, PD-FOG group, and HC group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"12\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003epost hoc tests\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGait parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePD-nFOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePD-FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCorrected p-value (age、gender、height)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC vs PD-nFOG\u0026amp;PD-FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD-nFOG vs PD-FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSit-to-stand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSiSt - Average Duration (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;PD; HC\u0026lt;\u0026lt;\u0026lt;FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSiSt - Trunk Sagittal Peak Velocity (degree/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e85.73\u0026thinsp;\u0026plusmn;\u0026thinsp;23.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e65.55\u0026thinsp;\u0026plusmn;\u0026thinsp;18.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e63.78\u0026thinsp;\u0026plusmn;\u0026thinsp;18.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSiSt - Trunk Sagittal RoM (degree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37.44\u0026thinsp;\u0026plusmn;\u0026thinsp;10.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e32.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e32.49\u0026thinsp;\u0026plusmn;\u0026thinsp;7.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWalking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStep Length (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e55.11\u0026thinsp;\u0026plusmn;\u0026thinsp;9.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e49.24\u0026thinsp;\u0026plusmn;\u0026thinsp;10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e46.55\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStride Velocity (m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStride Length (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e109.19\u0026thinsp;\u0026plusmn;\u0026thinsp;19.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e97.57\u0026thinsp;\u0026plusmn;\u0026thinsp;20.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e92.22\u0026thinsp;\u0026plusmn;\u0026thinsp;20.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCadence (step/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e111.87\u0026thinsp;\u0026plusmn;\u0026thinsp;13.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e110.18\u0026thinsp;\u0026plusmn;\u0026thinsp;11.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e113.61\u0026thinsp;\u0026plusmn;\u0026thinsp;12.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGait Cycle (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDouble Support (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21.85\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20.56\u0026thinsp;\u0026plusmn;\u0026thinsp;5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSwing Phase (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e39.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e38.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e40.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD\u0026thinsp;\u0026lt;\u0026thinsp;FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSwing Phase CV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDouble Support CV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14.76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e16.22\u0026thinsp;\u0026plusmn;\u0026thinsp;7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e16.96\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCadence CV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.50\u0026thinsp;\u0026plusmn;\u0026thinsp;8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.32\u0026thinsp;\u0026plusmn;\u0026thinsp;10.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGait Cycle CV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStride Velocity CV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10.89\u0026thinsp;\u0026plusmn;\u0026thinsp;5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStep Length CV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.87\u0026thinsp;\u0026plusmn;\u0026thinsp;7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShank RoM (degree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e70.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e59.87\u0026thinsp;\u0026plusmn;\u0026thinsp;11.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e60.77\u0026thinsp;\u0026plusmn;\u0026thinsp;11.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeak Shank Angular Velocity (degree/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e357.38\u0026thinsp;\u0026plusmn;\u0026thinsp;60.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e314.97\u0026thinsp;\u0026plusmn;\u0026thinsp;57.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e311.52\u0026thinsp;\u0026plusmn;\u0026thinsp;57.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrunk Coronal Peak Velocity (degree/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e24.70\u0026thinsp;\u0026plusmn;\u0026thinsp;6.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21.62\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;PD; HC\u0026thinsp;\u0026gt;\u0026thinsp;FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrunk Coronal RoM (degree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrunk Sagittal Peak Velocity (degree/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e36.42\u0026thinsp;\u0026plusmn;\u0026thinsp;12.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e31.16\u0026thinsp;\u0026plusmn;\u0026thinsp;11.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e31.58\u0026thinsp;\u0026plusmn;\u0026thinsp;9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026thinsp;\u0026gt;\u0026thinsp;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrunk Sagittal RoM (degree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrunk Transverse Peak Velocity (degree/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e45.29\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e38.01\u0026thinsp;\u0026plusmn;\u0026thinsp;11.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37.71\u0026thinsp;\u0026plusmn;\u0026thinsp;11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrunk Transverse RoM (degree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD; HC\u0026thinsp;\u0026gt;\u0026thinsp;FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArm Peak Velocity (degree/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e182.11\u0026thinsp;\u0026plusmn;\u0026thinsp;59.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e151.07\u0026thinsp;\u0026plusmn;\u0026thinsp;66.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e159.91\u0026thinsp;\u0026plusmn;\u0026thinsp;79.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArm RoM (degree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e34.41\u0026thinsp;\u0026plusmn;\u0026thinsp;14.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e24.52\u0026thinsp;\u0026plusmn;\u0026thinsp;12.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e25.97\u0026thinsp;\u0026plusmn;\u0026thinsp;14.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStride Length Asymmetry (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSwing Asymmetry (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.37\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.10\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShank RoM Asymmetry (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.96\u0026thinsp;\u0026plusmn;\u0026thinsp;5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.95\u0026thinsp;\u0026plusmn;\u0026thinsp;5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026thinsp;\u0026lt;\u0026thinsp;PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShank Symbolic Symmetry Index (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhase Coordination Index (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.82\u0026thinsp;\u0026plusmn;\u0026thinsp;5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArm Symbolic Symmetry Index (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e34.95\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37.31\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;PD; HC\u0026thinsp;\u0026lt;\u0026thinsp;FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTurning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurning - Average Duration (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026lt;\u0026lt;\u0026lt;PD; HC\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurning - Average Steps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026lt;\u0026lt;\u0026lt;FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD\u0026thinsp;\u0026lt;\u0026thinsp;\u0026lt;\u0026thinsp;FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurning - Peak Velocity (degree/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e166.83\u0026thinsp;\u0026plusmn;\u0026thinsp;31.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e129.15\u0026thinsp;\u0026plusmn;\u0026thinsp;31.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e126.56\u0026thinsp;\u0026plusmn;\u0026thinsp;30.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurning - Average Angular Velocity (degree/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e122.29\u0026thinsp;\u0026plusmn;\u0026thinsp;13.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e99.77\u0026thinsp;\u0026plusmn;\u0026thinsp;26.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100.71\u0026thinsp;\u0026plusmn;\u0026thinsp;27.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStand-to-sit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStSi - Average Duration (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026thinsp;\u0026lt;\u0026thinsp;PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStSi - Trunk Sagittal Peak Velocity (degree/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e86.47\u0026thinsp;\u0026plusmn;\u0026thinsp;27.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e64.28\u0026thinsp;\u0026plusmn;\u0026thinsp;22.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e62.41\u0026thinsp;\u0026plusmn;\u0026thinsp;19.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStSi - Trunk Sagittal RoM (degree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e40.76\u0026thinsp;\u0026plusmn;\u0026thinsp;11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e33.95\u0026thinsp;\u0026plusmn;\u0026thinsp;9.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e33.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHC\u0026gt;\u0026gt;\u0026gt;PD, FOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eStatistically significant differences between groups after Bonferroni correction were expressed as follows: (\u0026gt;\u0026thinsp;or \u0026lt;), p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; (\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;or \u0026lt;\u0026lt;), p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; (\u0026gt;\u0026gt;\u0026gt;\u0026gt; or \u0026lt;\u0026lt;\u0026lt;), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. All results were adjusted for age, gender, and height by analysis of covariance.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eDuring the sit-to-stand task, there were statistical differences in mean duration, trunk sagittal peak velocity, and trunk sagittal ROM among these three groups (F\u0026thinsp;=\u0026thinsp;8.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; F\u0026thinsp;=\u0026thinsp;26.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; F\u0026thinsp;=\u0026thinsp;7.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively), after adjusting for age, gender, and height. The HC group had a shorter mean duration, faster trunk sagittal peak velocity, and larger trunk sagittal RoM than PD-FOG or PD-nFOG patients. At the same time, there was no difference between PD-FOG and PD-nFOG group after post hoc tests with Bonferroni correction. There were similar differences in these three parameters above during the stand-to-sit task among the three groups.\u003c/p\u003e\n \u003cp\u003eDuring the walking process, there were significant differences in step length (F\u0026thinsp;=\u0026thinsp;17.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), stride velocity (F\u0026thinsp;=\u0026thinsp;10.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and stride length (F\u0026thinsp;=\u0026thinsp;17.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among these three groups. However, no difference was found in step frequency (F\u0026thinsp;=\u0026thinsp;1.42, p\u0026thinsp;=\u0026thinsp;0.243) and gait cycle (F\u0026thinsp;=\u0026thinsp;1.20, p\u0026thinsp;=\u0026thinsp;0.302).\u003c/p\u003e\n \u003cp\u003eThe differences between the PD-FOG and PD-nFOG groups were mainly reflected in the decrease in the proportion of the double support phase (p corrected\u0026thinsp;=\u0026thinsp;0.007, Cohen\u0026apos;s d\u0026thinsp;=\u0026thinsp;0.43) and the increase in the proportion of the swing phase (p corrected\u0026thinsp;=\u0026thinsp;0.010, Cohen\u0026apos;s d = -0.42) during walking. Regarding gait variability parameters: step length CV was significantly greater in the PD-FOG group compared to the HC group (p corrected\u0026thinsp;=\u0026thinsp;0.005, Cohen\u0026apos;s d = -0.53), and there were no statistical differences in swing phase CV, double support phase CV, cadence CV, and stride velocity CV among the three groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Regarding kinematic gait parameters: shank RoM (F\u0026thinsp;=\u0026thinsp;23.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and peak shank angular velocity (F\u0026thinsp;=\u0026thinsp;14.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly reduced in both PD-nFOG and PD-FOG groups compared to the HC group. Trunk coronal peak velocity (F\u0026thinsp;=\u0026thinsp;5.36, p\u0026thinsp;=\u0026thinsp;0.005), trunk coronal RoM (F\u0026thinsp;=\u0026thinsp;23.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), trunk sagittal peak velocity (F\u0026thinsp;=\u0026thinsp;4.76, p\u0026thinsp;=\u0026thinsp;0.009), trunk sagittal RoM (F\u0026thinsp;=\u0026thinsp;6.79, p\u0026thinsp;=\u0026thinsp;0.001), trunk transverse peak velocity (F\u0026thinsp;=\u0026thinsp;9.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), trunk transverse RoM (F\u0026thinsp;=\u0026thinsp;8.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and arm RoM (F\u0026thinsp;=\u0026thinsp;14.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were reduced both in the PD-nFOG and PD-FOG groups when compared to the HC group. The arm peak velocity (F\u0026thinsp;=\u0026thinsp;5.31, p\u0026thinsp;=\u0026thinsp;0.005) was decreased in the PD-nFOG group compared to the HC group, while there was no difference between the PD-FOG group and the HC group. Regarding the parameters of gait asymmetry: the swing asymmetry (F\u0026thinsp;=\u0026thinsp;5.08, p\u0026thinsp;=\u0026thinsp;0.007) and shank RoM asymmetry (F\u0026thinsp;=\u0026thinsp;4.37, p\u0026thinsp;=\u0026thinsp;0.013) increased in the PD-nFOG group compared to the HC group, while the PD-FOG group did not differ from the HC group. The arm symmetry index increased both in the PD-nFOG and PD-FOG groups compared to the HC group (F\u0026thinsp;=\u0026thinsp;5.73, p\u0026thinsp;=\u0026thinsp;0.004). There were no statistical differences in the stride length asymmetry (F\u0026thinsp;=\u0026thinsp;2.98, p\u0026thinsp;=\u0026thinsp;0.052), shank symmetry index (F\u0026thinsp;=\u0026thinsp;0.84, p\u0026thinsp;=\u0026thinsp;0.433), and phase coordination index (F\u0026thinsp;=\u0026thinsp;1.52, p\u0026thinsp;=\u0026thinsp;0.220) among the three groups.\u003c/p\u003e\n \u003cp\u003eDuring turning, the mean duration of turning was longer both in the PD-FOG and PD-nFOG groups compared to the HC group (F\u0026thinsp;=\u0026thinsp;7.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean number of steps in the turning process increased in the PD-FOG group compared to the PD-nFOG and HC groups (F\u0026thinsp;=\u0026thinsp;8.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The peak angular velocity of the turning process decreased both in the PD-FOG and PD-nFOG groups compared to the HC group (F\u0026thinsp;=\u0026thinsp;41.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eObtain the gait domains and factor scores\u003c/h2\u003e\n \u003cp\u003eThe EFA approach was used to extract the gait domains and to reduce the dimensionality of gait parameters. We first performed KMO sampling fitness tests on 22 representative gait variables, and the results are shown in Appendix 2. The total KMO was 0.78, and each variable individually had KMO\u0026thinsp;\u0026gt;\u0026thinsp;0.5. Bartlett\u0026apos;s test of sphericity X\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8552.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating that the correlation between the variables was appropriate and suitable for further factor analysis.\u003c/p\u003e\n \u003cp\u003eThe results of the parallel analysis method suggest that six factors/domains are the optimal number of factors to explain the data distribution. These six factors explained a total of 74.23% of the variance in the data set, with 16.83% of the variance explained by factor 1, 14.77% by factor 2, 11.52% by factor 3, 12.76% by factor 4, 9.37% by factor 5, and 8.98% by factor 6. Based on the loadings of the gait variables in each factor, we grouped them into six gait domains: the pace factor (including stride length, step length, stride velocity, shank RoM, and stride velocity CV), the kinematic factor (including trunk transverse RoM, trunk coronal RoM, trunk sagittal RoM, trunk transverse peak velocity, trunk sagittal peak velocity, and trunk coronal peak velocity), gait phase factor (including double support phase, swing phase and double support phase CV), turning process factor (including turning process average duration, turning process average steps, turning process average angular velocity and turning process peak velocity), rhythm factor (including gait cycle and cadence) and asymmetry factor (including swing phase CV and swing asymmetry) (Table\u0026nbsp;3). We further summarize the standard score coefficients of each parameter based on the results of EFA and use the Thurstone method to calculate the factor scores of each gait domain separately.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDifferences in gait domains impairment in the PD-nFOG group and PD-FOG group in comparison to HC group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eEach group\u0026apos;s gait domain factor scores were separately transformed to a Z-score with HC as the reference value. The mean value of each variable in the HC group was 0, and the standard deviation was 1 after the transformation. Radar plots were used to indicate the degree of impairment and the direction of change in the gait domain in the PD-nFOG and PD-FOG groups relative to the HC group (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). The results showed that the factor scores of pace and kinematic domain were reduced in both two groups compared to the HC group. The gait phase domain factor score was significantly higher in the PD-FOG group compared to the PD-nFOG group (p corrected\u0026thinsp;=\u0026thinsp;0.004, Cohen\u0026apos;s d = -0.46). The turning process domain factor score was greater in both the PD-nFOG and PD-FOG groups compared to the HC group by approximately two standard deviations (F\u0026thinsp;=\u0026thinsp;16.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The differences in asymmetry and rhythm factors were not statistically significant among the three groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eThe related factors of FOG through the combination of clinical and gait domain characteristics\u003c/h2\u003e\n \u003cp\u003eWe constructed a binomial logistic regression model with the presence or absence of FOG as the dependent variable, and independent variables with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were included in the model based on the results of the univariate analysis. After eliminating the independent variables with excessive covariance, the final independent variables included in the multinomial model were: gender, height, disease duration, H-Y stage, MDS-UPDRS II score, MDS-UPDRS IV score, bradykinesia subscore, rigidity subscore, PIGD subscore, HAMD score, MMSE score, gait phase domain factor score, and LEDD. The results showed that gender (OR\u0026thinsp;=\u0026thinsp;2.67, 95% CI\u0026thinsp;=\u0026thinsp;1.19\u0026ndash;5.99, p\u0026thinsp;=\u0026thinsp;0.017), MDS-UPDRS IV score (OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI\u0026thinsp;=\u0026thinsp;1.10\u0026ndash;1.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), gait phase domain (OR\u0026thinsp;=\u0026thinsp;1.64, 95% CI\u0026thinsp;=\u0026thinsp;1.05\u0026ndash;2.55, p\u0026thinsp;=\u0026thinsp;0.030) and PIGD subscore (OR\u0026thinsp;=\u0026thinsp;1.50, 95% CI\u0026thinsp;=\u0026thinsp;1.30\u0026ndash;1.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent risk factors for FOG after forward stepwise (likelihood ratio) selection (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e). The Hosmer-Lemeshow test showed a good model fit (X\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.09, degrees of freedom\u0026thinsp;=\u0026thinsp;8, p\u0026thinsp;=\u0026thinsp;0.998). The model had a sensitivity of 0.78 and a specificity of 0.77 for differentiating FOG and PD patients at a cut-off value of 0.28, with an accuracy of 0.78 and an area under the receiver operating characteristic curve (AUC) was 0.87 (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found that some characteristics of gait impairments in PD patients with FOG were different from those of non-FOG patients when FOG episode was not present. Common gait characteristics contained in twenty-two gait variables were identified, and six gait domains were categorized and extracted to represent a synthetic description of gait. Among these gait domains, the impairment of the gait phase domain was the critical abnormality in identifying potential PD-FOG from PD-nFOG patients during an interictal period of freezing. Combined with the clinical information, males, with higher MDS-UPDRS IV score, higher PIGD subscore, and higher gait phase domain factor score were independent risk factors for FOG.\u003c/p\u003e \u003cp\u003eWe used wearable sensors for gait detection, which has the advantages of being portable and less restricted by the testing environment compared to a 3D optical motion analysis system\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Some studies previously focused on the changes in gait parameters during FOG episodes\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The decrease in gait frequency during FOG episodes can be explained by the tendency of the trunk to walk continuously forward. Still, the inability of the feet to produce an effective stride length makes the walking movement collapse and reduces the number of steps. FOG may result from dysfunctional stride control. In addition, FOG events are often preceded by postural instability when FOG patients compensate by increasing the duration of the double support phase\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. It has also been found that an anterior tilt of the pelvis occurs prior to the onset of a FOG event, suggesting an impaired anticipatory postural adjustment, leading to an increased risk of falling forward\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. However, few studies have examined the gait characteristics of FOG patients during the \u0026ldquo;interictal\u0026rdquo; period.\u003c/p\u003e \u003cp\u003eOur results found that patients with FOG had a reduced double-support phase time, a relatively increased swing phase during the interictal period compared to PD patients without FOG, and a significant increase in the number of steps during turning. Some hypotheses on the pathophysiological mechanisms of FOG point to an impairment in the coordination of the gait cycle in FOG patients, as evidenced by increased step frequency variability, gait asymmetry, and double support phase\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The double-support phase represents the period of the gait cycle when both feet are in contact with the ground at the same time, during which the body has better control over the movement of the center of gravity. An increase in the percentage of the double support phase suggests impaired dynamic balance and postural control\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. It has been suggested that the percentage of the double support phase significantly increased during FOG events, reflecting that patients are in the double support phase to correct the interference of FOG on balance postural control\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The percentage of the double support phase is also related to the walking speed, which may increase as the walking speed decreases\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. However, in the present study, the double support phase percentage was reduced, and the swing phase was relatively increased during the interictal period in PD-FOG patients, and there was no significant difference between the two groups regarding gait speed. This suggests that the typical gait cycle control impairment pattern of FOG is not present in the interictal period and is even slightly better than in the average PD-nFOG patient. Consistent with the results of other studies, gait characteristics such as gait speed, stride length, stride variability, and left-right asymmetry were impaired to varying degrees in PD patients relative to healthy controls\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith the aid of gait analysis systems, we can obtain multi-variables. However, these parameters are too many to highlight PD's gait impairment characteristic and to be unsatisfactory in descript or distinguishing FOG. Previous studies have attempted to describe gait in PD with different approaches and analyzed numerous spatiotemporal and kinematic parameters. However, the extracted variables are not readily interpretable from a clinical standpoint and are frequently analyzed in isolation from clinical correlations, thus disregarding the comprehensive features of gait. Moreover, many gait parameters are often widely correlated with each other, and the problem of collinearity among variables limits the multinominal analysis of gait\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Exploratory factor analysis (EFA) has been widely used in social science and medical research and is less commonly used in the field of chronic non-communicable diseases. Some recent studies have attempted to use EFA methods to reveal complex correlations among variables, identify potential common factors among variables, and further explain the practical significance of each factor\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. For example, one study obtained four gait domains (variability, asymmetry, postural control, and pace/cadence factors) after factor analysis of gait parameters for assessing the gait characteristics of elderly patients with hip fractures\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Few studies are using EFA to characterize the gait of PD patients, especially the gait characteristics of FOG patients in the \u0026lsquo;interictal\u0026rsquo; period relative to PD patients without FOG symptoms are uncertain. Therefore, in this study, we used EFA to analyze the differences in the changes in gait characteristics between PD-FOG patients and PD-nFOG patients compared to healthy controls in six gait dimensions: pace, kinematics, gait phase, turning process, rhythm and asymmetry, and to describe the degree of impairment in different gait domains. Our results showed that the pace, kinematic, gait phase, and turning process domains were impaired in both PD-nFOG and PD-FOG groups compared to healthy controls. Among them, the difference between the PD and FOG groups in the interictal period was mainly in the domain of the gait phase. This suggested that gait phase parameters were important indicators to distinguish PD-FOG patients from PD-nFOG patients in the interictal period.\u003c/p\u003e \u003cp\u003eThe present study also showed that patients with FOG had a longer disease duration, more progressive disease, poorer balance, and more severe motor and non-motor symptoms, with more prominent aspects of bradykinesia and PIGD. These results are also consistent with the findings of other previous studies\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Notably, motor complications were more severe in patients with FOG, which may also be related to the longer disease duration and higher doses of antiparkinsonian medication in patients with FOG. The results of the multinominal analysis showed that gender, motor complications, PIGD, and impaired gait phase domain are associated with FOG and that these indicators could effectively distinguish PD patients with FOG in the interictal period from those without FOG.\u003c/p\u003e \u003cp\u003eThere were some limitations of this study. Firstly, the gait test was completed during the \u0026ldquo;ON\u0026rdquo; period, and this might inaccurately reflect the degree of gait impairment of PD patients because of the differences in drug effect on motor symptoms and gait control among individuals. Secondly, visual judgment is currently the gold standard for FOG identification. Still, the standard definitions of the beginning and end of FOG events are inconsistent or not even clearly defined in many studies\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. This also reduces the comparability between different studies. Our study focused on people with interictal periods of FOG episodes, and the presence or absence of FOG events was determined by visual observation. There may be a problem of low identification accuracy, which in turn may introduce some patients with FOG events but mild symptoms into the population. Finally, FOG is a highly heterogeneous symptom, and there may be differences in the gait characteristics of patients with different types of FOG (complete blocking, shuffling forward with small steps, and trembling on the spot). Therefore, future prospective studies with larger sample sizes and less heterogeneous candidates are needed to further clarify these changes in FOG patients.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study focused on analyzing the characteristic differences of gait parameters in FOG patients during the \u0026lsquo;interictal\u0026rsquo; period. We focused not only on a single gait variable but also on revealing the common features behind numerous gait parameters and extracting independent gait domains. Abnormal change in the gait phase domain was associated with FOG during the interictal period. Models constructed using gait phase domain factor score, PIGD subscore, gender, and severity of motor complications can better differentiate patients with interictal FOG. The timely recognition by clinicians of individuals who may present with FOG, yet do not manifest freezing events during clinical evaluation, can aid in effectively managing FOG.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZJH contributed to data collection, data collection, methodology, writing the original draft. LC contributed to data curation and formal analysis. WY and ZXB contributed to data collection, data curation. SL contributed to study conceptualization, funding acquisition, investigation. LZG contributed to study conceptualization, funding acquisition, investigation, project administration, supervision. GJ contributed to study conceptualization, funding acquisition, project administration, and review and editing of the paper. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by grants from the Shanghai Committee of Science and Technology (22Y11904100), the National Natural Science Foundation of China (82271274, 82171242), Shanghai Pujiang Program (21PJD046).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data-set generated and analyzed during the current study is also available now from the corresponding author on a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants have given their written informed consent. The current research was approved by the Research Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (XHEC-C-2015-019-2). All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u0026nbsp;\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eCamicioli R, Morris ME, Pieruccini-Faria F, Montero-Odasso M, Son S, Buzaglo D, Hausdorff JM, Nieuwboer A. Prevention of Falls in Parkinson\u0026apos;s Disease: Guidelines and Gaps. Mov Disord Clin Pract.2023; 10(10):1459-69.\u003c/li\u003e\n\u003cli\u003eMancini M, Bloem BR, Horak FB, Lewis SJG, Nieuwboer A, Nonnekes J. 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Front Aging Neurosci.2018; 10:36.\u003c/li\u003e\n\u003cli\u003eOkuma Y. Practical approach to freezing of gait in Parkinson\u0026apos;s disease. Pract Neurol.2014; 14(4):222-30.\u003c/li\u003e\n\u003cli\u003eOkuma Y. Freezing of gait in Parkinson\u0026apos;s disease. J Neurol.2006; 253 Suppl 7:Vii27-32.\u003c/li\u003e\n\u003cli\u003eWilliams DS, Martin AE. Gait modification when decreasing double support percentage. J Biomech.2019; 92:76-83.\u003c/li\u003e\n\u003cli\u003eRehman RZU, Del Din S, Guan Y, Yarnall AJ, Shi JQ, Rochester L. Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson\u0026apos;s Disease: A Comprehensive Machine Learning Approach. Sci Rep.2019; 9(1):17269.\u003c/li\u003e\n\u003cli\u003eGodi M, Arcolin I, Giardini M, Corna S, Schieppati M. A pathophysiological model of gait captures the details of the impairment of pace/rhythm, variability and asymmetry in Parkinsonian patients at distinct stages of the disease. Sci Rep.2021; 11(1):21143.\u003c/li\u003e\n\u003cli\u003eArcolin I, Corna S, Giardini M, Giordano A, Nardone A, Godi M. Proposal of a new conceptual gait model for patients with Parkinson\u0026apos;s disease based on factor analysis. Biomed Eng Online.2019; 18(1):70.\u003c/li\u003e\n\u003cli\u003eSawada M, Wada-Isoe K, Hanajima R, Nakashima K. Clinical features of freezing of gait in Parkinson\u0026apos;s disease patients. Brain Behav.2019; 9(4):e01244.\u003c/li\u003e\n\u003cli\u003ePalmerini L, Rocchi L, Mazilu S, Gazit E, Hausdorff JM, Chiari L. Identification of Characteristic Motor Patterns Preceding Freezing of Gait in Parkinson\u0026apos;s Disease Using Wearable Sensors. Front Neurol.2017; 8:394.\u003c/li\u003e\n\u003cli\u003eMazilu S, Blanke U, Hardegger M, Troster G, Hausdorff JM: GaitAssist: A wearable assistant for gait training and rehabilitation in Parkinson\u0026apos;s disease. In: 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS): 2014.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 3","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson's disease, freezing of gait, wearable inertial sensor, exploratory factor analysis, gait domain","lastPublishedDoi":"10.21203/rs.3.rs-4154081/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4154081/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eFreezing of Gait (FOG) is one of the disabling symptoms in patients with Parkinson's Disease (PD). While it is difficult to early detect because of the sporadic occurrence of initial freezing events. Whether the characteristic of gait impairments in PD patients with FOG during the \u0026lsquo;interictal\u0026rsquo; period is different from that in non-FOG patients is still unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe gait parameters were measured by wearable inertial sensors. Exploratory factor analysis was used to investigate the inherent structure of diverse univariate gait parameters, with the aim of identifying shared characteristics among the gait variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis cross-sectional study involved 68 controls and 245 PD patients (167 without FOG and 78 with FOG). The analysis yielded six distinct gait domains which were utilized to describe the impaired gait observed during the \u0026ldquo;interictal\u0026rdquo; period of FOG. Both PD-nFOG and PD-FOG groups exhibited significant impairments in the pace domain, kinematic domain, gait phase domain, and turning process domain compared to the healthy control. The gait phase domain was different in the PD-FOG group compared to the PD-nFOG group (p corrected\u0026thinsp;=\u0026thinsp;0.004, Cohen's d = -0.46). And it was identified as independent risk factor for FOG (OR\u0026thinsp;=\u0026thinsp;1.64, 95% CI\u0026thinsp;=\u0026thinsp;1.05\u0026ndash;2.55, p\u0026thinsp;=\u0026thinsp;0.030), as well as other risk factors: gender (OR\u0026thinsp;=\u0026thinsp;2.67, 95% CI\u0026thinsp;=\u0026thinsp;1.19\u0026ndash;5.99, p\u0026thinsp;=\u0026thinsp;0.017), MDS-UPDRS IV score (OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI\u0026thinsp;=\u0026thinsp;1.10\u0026ndash;1.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and PIGD subscore (OR\u0026thinsp;=\u0026thinsp;1.50, 95% CI\u0026thinsp;=\u0026thinsp;1.30\u0026ndash;1.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The model demonstrated a correct discrimination rate of 0.78 between PD-FOG and PD-nFOG, with an area under the receiver operating characteristic curve (AUC) of 0.87.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFOG was found to be associated with abnormal alterations in the gait phase domain during the interictal period. Models constructed using gait phase domain, PIGD subscore, gender, and severity of motor complications can better differentiate freezers from no-freezers during \u0026lsquo;interictal\u0026rsquo; period.\u003c/p\u003e","manuscriptTitle":"Do the gait domains change in PD patients with freezing of gait during their ‘interictal’ period?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:37:59","doi":"10.21203/rs.3.rs-4154081/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-18T08:10:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-16T09:48:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-05T22:01:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194422509395302552764421161023811967168","date":"2024-10-03T09:22:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191172317345447474314695474437938739255","date":"2024-09-27T16:11:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289146141067585728572804845381355972224","date":"2024-09-27T11:04:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-05T07:34:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-18T13:53:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-11T05:03:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-11T05:01:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2024-03-23T11:13:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d978f49-686a-428d-9328-90a7db7990d4","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T16:10:52+00:00","versionOfRecord":{"articleIdentity":"rs-4154081","link":"https://doi.org/10.1186/s12877-024-05673-z","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2025-01-09 15:58:01","publishedOnDateReadable":"January 9th, 2025"},"versionCreatedAt":"2024-04-19 18:37:59","video":"","vorDoi":"10.1186/s12877-024-05673-z","vorDoiUrl":"https://doi.org/10.1186/s12877-024-05673-z","workflowStages":[]},"version":"v1","identity":"rs-4154081","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4154081","identity":"rs-4154081","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}