Effect of dual task walking on spatiotemporal gait in patients with cerebral small vessel disease
10.3760/cma.j.cn113694-20230112-00025
- VernacularTitle:双重任务行走对脑小血管病患者时空步态参数的影响
- Author:
Hongyang XIE
1
;
Nan ZHANG
;
Cuiqiao XIA
;
Yu DING
;
Hongyi ZHAO
;
Yonghua HUANG
Author Information
1. 解放军总医院第七医学中心神经内科，北京100700
- Keywords:
Cerebrovascular disorders;
Locomotion;
Gait;
Dual-task walking
- From:
Chinese Journal of Neurology
2023;56(6):646-653
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To compare the gait characteristics of cognitive and motor dual task walking (DTW) in patients with cerebral small vessel disease (CSVD), and determine the best gait parameters to diagnose CSVD and judge the severity of the disease.Methods:A total of 106 patients with CSVD and 21 healthy individuals were included from September 1, 2020 to July 1, 2021 in the Seventh Medical Center of Chinese People′s Liberation Army General Hospital. According to the Fazekas scores, the subjects were divided into mild ( n=34, 1 point), moderate ( n=34, 2 points), severe ( n=38,3 points) groups and control group ( n=21). Participants were recorded parameters under single task walking (STW) and DTW conditions, and calculated dual task effect (DTC) through the difference between single task and dual task. The differences in gait variances and their DTC were shown by generalized estimation equations when performed in STW and DTW and 4 groups of the severity of disease. Post-hoc comparisons were corrected using Bonferroni′s method. Spearman analyses were applied to explore the correlations between gait parameters and their DTC during STW or DTW and severity of disease. Based on the Logistic model, combining predictors or probabilities were gained and applied to establish receiver operating characteristic curve in order to calculate sensitivity, specificity, and the area under the curve. Results:In the control group, there was no statistically significant difference in gait parameters between STW and DTW. In the CSVD group, the gait parameters of STW were significantly better than cognitive or motor DTW (all P<0.05). In the control group, there was no statistically significant difference in basic gait parameters under different tasks (all P>0.05). In cognitive DTW, temporal gait parameters (stride frequency and stride time) deteriorated significantly only in moderate and severe groups [stride frequency:moderate group 100.220±1.795/min,severe group 94.525±2.139/min;stride time:moderate group (1.227±0.024) s, severe group (1.299±0.031) s], but spatial parameters [stride length: control group (1.050±0.021) m, mild group (0.974±0.022) m, moderate group (0.903±0.025) m, severe group (0.793±0.026) m; stride speed: control group (0.944±0.028) m/s, mild group (0.866±0.030) m/s, moderate group (0.751±0.027) m/s, severe group (0.606±0.022) m/s] were significantly different among all groups (except the control group and mild group;all P<0.05). The DTC of all gait parameters during cognitive DTW was higher than that during motor DTW (all P<0.05) for CSVD patients. While no any difference was found between cognitive DTW and motor DTW in the control group (all P>0.05). Similarly, the temporal parameters′ DTC of cognitive DTW was abnormal only in the late stage of disease, while the spatial parameters′ DTC showed statistically significant difference among all the groups (including the control group and the mild group;all P<0.05). Correlation coefficients of the spatial parameters and their DTC in condition of cognitive DTW were significantly higher than temporal parameters and their DTC (0.50< r<0.64 vs 0.15< r<0.39). The area under curve of the combined predictor was significantly higher than that of any single index. Conclusions:Cognitive DTW can better reflect the abnormal gait of CSVD patients. The spatial parameters and DTC of cognitive DTW could effectively diagnose CSVD and distinguish the disease of severity. And DTC might be better indicators. For diagnosis of CSVD, there was no significant discrepancy between the spatial parameters and DTC, but the combined predictor could significantly improve the sensitivity and reduce the false negative rate.
