Gait characteristics of post-stroke hemiparetic patients with different walking speeds

Yiji Wang, Masahiko Mukaino, Kei Ohtsuka, Yohei Otaka, Hiroki Tanikawa, Fumihiro Matsuda, Kazuhiro Tsuchiyama, Junya Yamada, Eiichi Saitoh, Yiji Wang, Masahiko Mukaino, Kei Ohtsuka, Yohei Otaka, Hiroki Tanikawa, Fumihiro Matsuda, Kazuhiro Tsuchiyama, Junya Yamada, Eiichi Saitoh

Abstract

Hemiparesis resulting from stroke presents characteristic spatiotemporal gait patterns. This study aimed to clarify the spatiotemporal gait characteristics of hemiparetic patients by comparing them with height-, speed-, and age-matched controls while walking at various speeds. The data on spatiotemporal gait parameters of stroke patients and that of matched controls were extracted from a hospital gait analysis database. In total, 130 pairs of data were selected for analysis. Patients and controls were compared for spatiotemporal gait parameters and the raw value (RSI) and absolute value (ASI) of symmetry index and coefficient of variation (CV) of these parameters. Stroke patients presented with prolonged nonparetic stance (patients vs. controls: 1.01 ± 0.41 vs. 0.83 ± 0.25) and paretic swing time (0.45 ± 0.12 vs. 0.39 ± 0.07), shortened nonparetic swing phase (0.35 ± 0.07 vs. 0.39 ± 0.07), and prolonged paretic and nonparetic double stance phases [0.27 ± 0.13 (paretic)/0.27 ± 0.17 (nonparetic) vs. 0.22 ± 0.10]. These changes are especially seen in low-gait speed groups (<3.4 km/h). High RSIs of stance and swing times were also observed (-9.62 ± 10.32 vs. -0.79 ± 2.93, 24.24 ± 25.75 vs. 1.76 ± 6.43, respectively). High ASIs and CVs were more generally observed, including the groups with gait speed of ≥3.5 km/h. ASI increase of the swing phase (25.79 ± 22.69 vs. 4.83 ± 4.88) and CV of the step length [7.7 ± 4.9 (paretic)/7.6 ± 5.0 (nonparetic) vs. 5.3 ± 3.0] were observed in all gait speed groups. Our data suggest that abnormalities in the spatiotemporal parameters of hemiparetic gait should be interpreted in relation to gait speed. ASIs and CVs could be highly sensitive indices for detecting gait abnormalities.

Figures

Fig. 1
Fig. 1
Flow diagram for data extraction and matching.

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Source: PubMed

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