Effects of robot-assisted gait training on the balance and gait of chronic stroke patients: focus on dependent ambulators

Duk Youn Cho, Si-Woon Park, Min Jin Lee, Dae Sung Park, Eun Joo Kim, Duk Youn Cho, Si-Woon Park, Min Jin Lee, Dae Sung Park, Eun Joo Kim

Abstract

[Purpose] The purpose of this study was to confirm the effect of robot-assisted gait training on the balance and gait ability of stroke patients who were dependent ambulators. [Subjects and Methods] Twenty stroke patients participated in this study. The participants were allocated to either group 1, which received robot-assisted gait training for 4 weeks followed by conventional physical therapy for 4 weeks, or group 2, which received the same treatments in the reverse order. Robot-assisted gait training was conducted for 30 min, 3 times a week for 4 weeks. The Berg Balance Scale, Modified Functional Reach Test, Functional Ambulation Category, Modified Ashworth Scale, Fugl-Meyer Assessment, Motricity Index, and Modified Barthel Index were assessed before and after treatment. To confirm the characteristics of patients who showed a significant increase in Berg Balance Scale after robot-assisted gait training as compared with physical therapy, subgroup analysis was conducted. [Results] Only lateral reaching and the Functional Ambulation Category were significantly increased following robot-assisted gait training. Subscale analyses identified 3 patient subgroups that responded well to robot-assisted gait training: a subgroup with hemiplegia, a subgroup in which the guidance force needed to be decreased to needed to be decreased to ≤45%, and a subgroup in which weight bearing was decreased to ≤21%. [Conclusion] The present study showed that robot-assisted gait training is not only effective in improving balance and gait performance but also improves trunk balance and motor skills required by high-severity stroke patients to perform activities daily living. Moreover, subscale analyses identified subgroups that responded well to robot-assisted gait training.

Keywords: Gait; Robotics; Stroke.

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Source: PubMed

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