Zhu Zihao, Xu Feng, Li Qiujie, Wan Xianglin
School of Sport Science, Beijing Sport University, Beijing, 100084, China.
Key Laboratory for Performance Training & Recovery of General Administration of Sport of China, Beijing, 100084, China.
BMC Neurol. 2025 Mar 18;25(1):111. doi: 10.1186/s12883-025-04131-6.
Spatio-temporal parameters provide reference information for the gait variations of stroke patients during obstacle crossing. Analyzing these gait spatio-temporal characteristics of patients during obstacle crossing can assist in assessing the risk of falls. The aim of this study was to analyze the variances in gait spatio-temporal characteristics during obstacle crossing between stroke patients with and without a history of falls, to explore spatio-temporal parameters for assessing fall risk, and to construct a regression model for predicting patients' fall risk.
Thirty-three patients with unilateral brain injury from stroke who were discharged from rehabilitation were included. These patients were categorized into a falls group (with a history of falls) and a non-falls group (without a history of falls) based on whether they had experienced a fall in the previous six months. A Qualisys motion capture system was used to record the marker positions when crossing an obstacle 4 cm in height with the affected leg as the leading limb, and gait spatio-temporal parameters were calculated and obtained. Univariate analysis and logistic regression models were used to compare the gait spatio-temporal parameters of the two groups.
17 participants were categorised into the falls group and 16 into the non-falls group. The single support phase of leading limb, post-obstacle swing phase of trailing limb, obstacle-heel distance of leading limb, and obstacle-heel distance of trailing limb were significantly smaller in the fall group compared to the non-fall group (P < 0.05). The gait spatio-temporal parameter ultimately included in the fall risk prediction model was the obstacle-heel distance of leading limb (OR = 0.819, 95%CI = 0.688-0.973, P = 0.023). The overall correct classification rate from this model was 69.7%, and the area under the curve (AUC) was 0.750 (P = 0.014).
Abnormalities in gait spatio-temporal parameters during obstacle crossing in stroke patients can contribute to an increased risk of falls. The fall risk prediction model developed in this study demonstrated excellent predictive performance, indicating its potential utility in clinical settings.
时空参数为中风患者在跨越障碍物时的步态变化提供了参考信息。分析患者在跨越障碍物时的这些步态时空特征有助于评估跌倒风险。本研究的目的是分析有跌倒史和无跌倒史的中风患者在跨越障碍物时步态时空特征的差异，探索用于评估跌倒风险的时空参数，并构建预测患者跌倒风险的回归模型。
纳入33例从中风康复出院的单侧脑损伤患者。根据患者在过去6个月内是否有跌倒经历，将这些患者分为跌倒组（有跌倒史）和非跌倒组（无跌倒史）。使用Qualisys运动捕捉系统记录以患侧腿为前导肢体跨越4厘米高障碍物时的标记位置，并计算和获取步态时空参数。采用单因素分析和逻辑回归模型比较两组的步态时空参数。
17名参与者被归入跌倒组，16名被归入非跌倒组。与非跌倒组相比，跌倒组前导肢体的单支撑期、后随肢体的障碍物后摆动期、前导肢体的障碍物-足跟距离和后随肢体的障碍物-足跟距离显著更小（P < 0.05）。最终纳入跌倒风险预测模型的步态时空参数是前导肢体的障碍物-足跟距离（OR = 0.819，95%CI = 0.688 - 0.973，P = 0.023）。该模型的总体正确分类率为69.7%，曲线下面积（AUC）为0.750（P = 0.014）。
中风患者在跨越障碍物时步态时空参数异常会导致跌倒风险增加。本研究开发的跌倒风险预测模型显示出优异的预测性能，表明其在临床环境中的潜在应用价值。
