Introduction: The effect of early initiation of gait training using hybrid assistive limb (HAL) remains unclear. This observational study aimed to investigate whether early initiation of gait training using HAL improves functional outcomes in patients with stroke.
Methods: We retrospectively analyzed patients with acute stroke admitted to our facility. HAL was used for exoskeletal robotic gait training. Study participants were median split into an early group and a late group based on the days from stroke onset to initiation of gait training using HAL. The functional outcomes, defined by the Brunnstrom recovery stage (BRS), modified Rankin Scale (mRS), and Functional Independence Measure (FIM) at discharge, were compared using propensity score-matched analysis.
Results: We performed a propensity score-matched analysis in 63 patients with stroke (31 from the early group and 32 from the late group), and 17 pairs were matched. There were no significant differences in discharge in the BRS of the upper limb and finger in the post-matched cohort. On the other hand, the BRS of the lower limb in the early group was significantly higher than that in the late group. In addition, the mRS, but not FIM scores, was significantly better in the early group than that in the late group.
Conclusions: In conclusion, early initiation of gait training using HAL might improve the motor function of the paralyzed lower limb and disability in patients with stroke.
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http://dx.doi.org/10.1159/000527710 | DOI Listing |
Eur Geriatr Med
January 2025
Institute for Health Development, Medical School of Nantong University, Affiliated Hospital of Nantong University, 20 Xisi Road, Nantong, 226001, China.
The aim of this study is to investigate the association between four phenotypes of sarcopenia/obesity in older individuals and functional disability, malnutrition, and all-cause mortality. This study is a cross-sectional study, survival is 3 years. A total of 487 Chinese older adults were included with 283 (58.
View Article and Find Full Text PDFGait Posture
January 2025
Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan Tung Road, Chungli District, Taoyuan, Taiwan. Electronic address:
Background: The use of inertial measurement units (IMUs) in assessing fall risk is often limited by subject discomfort and challenges in data interpretation. Additionally, there is a scarcity of research on attitude estimation features. To address these issues, we explored novel features and representation methods in the context of sit-to-stand transitions.
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December 2024
IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy.
Background: Wearable powered exoskeletons could be used to provide robotic-assisted gait training (RAGT) in people with stroke (PwST) and walking disability. The study aims to compare the differences in cardiac function, fatigue, and workload during activities of daily living (ADLs), while wearing an exoskeleton.
Methods: Five PwST were recruited in this pilot cross-sectional study.
Sensors (Basel)
December 2024
Department of Neurology, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
Freezing of gait (FOG) is a debilitating symptom of Parkinson disease (PD). It is episodic and variable in nature, making assessment difficult. Wearable sensors used in conjunction with specialized algorithms, such as our group's pFOG algorithm, provide objective data to better understand this phenomenon.
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December 2024
Faculty of Mechanical Engineering, Poznan University of Technology, Piotrowo 3 Street, 61-138 Poznan, Poland.
This paper is dedicated to the analysis of a foot prosthesis optimization process, with a particular focus on the application of optimization algorithms and unconventional materials, such as auxetic materials. The study aims to enhance prosthesis performance by minimizing the difference between the ground reaction force generated by the prosthetic foot and that of a natural limb. In the initial part of the study, the basic topics concerning the parameterization of the foot prosthesis geometry and the preparation of a finite element model for human gait are discussed.
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