Objective: To evaluate the relationship between body mass index (BMI) and spatiotemporal, kinematic, and kinetic gait parameters in chronic hemiparetic stroke survivors.
Design: Secondary analysis of data collected in a randomized controlled trial comparing two 12-week ambulation training treatments.
Setting: Academic medical center.
Participants: Chronic hemiparetic stroke survivors (N = 108, >3 months poststroke)
Methods: Linear regression analyses were performed of BMI, and selected pretreatment gait parameters were recorded using quantitative gait analysis.
Main Outcome Measures: Spatiotemporal, kinematic, and kinetic gait parameters.
Results: A series of linear regression models that controlled for age, gender, stroke type (ischemic versus hemorrhagic), interval poststroke, level of motor impairment (Fugl-Meyer score), and walking speed found BMI to be positively associated with step width (m) (β = 0.364, P < .001), positively associated with peak hip abduction angle of the nonparetic limb during stance (deg) (β = 0.177, P = .040), negatively associated with ankle dorsiflexion angle at initial contact of the paretic limb (deg) (β = -0.222, P = .023), and negatively associated with peak ankle power at push-off (W/kg) of the paretic limb (W/kg)(β = -0.142, P = .026).
Conclusions: When walking at a similar speed, chronic hemiparetic stroke subjects with a higher BMI demonstrated greater step width, greater hip hiking of the paretic lower limb, less paretic limb dorsiflexion at initial contact, and less paretic ankle power at push-off as compared to stroke subjects with a lower BMI and similar level of motor impairment. Further studies are necessary to determine the clinical relevance of these findings with respect to rehabilitation strategies for gait dysfunction in hemiparetic patients with higher BMIs.
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http://dx.doi.org/10.1016/j.pmrj.2014.03.012 | DOI Listing |
J Neurophysiol
December 2024
Center for Movement Studies, Kennedy Krieger Institute, Baltimore, MD 21205.
Disabil Rehabil
October 2024
School of Allied Health, Loma Linda University, Loma Linda, CA, USA.
Purpose: The profession of physical therapy has historically relied on manual facilitation to improve motor control strategies and performance in persons rehabilitating from a stroke, yet there is insufficient evidence to support its use during functional task training. The purpose of this study was to determine the effects of integrated cueing (verbal and manual) and verbal cueing approaches during sit-to-stand training on midline alignment & muscle activation in chronic stroke survivors.
Methods: Twenty-one chronic right-brained stroke survivors with hemiplegia were randomly assigned to the Integrated Cueing or Verbal Only group and outcome measures were recorded using an 18-Camera Motion Capture System, force plates, and surface electromyography (EMG).
Neurorehabil Neural Repair
September 2024
Division of Neurotechnology, Department of Neurological Surgery, Washington University in St. Louis, St. Louis, MO, USA.
Background: Chronic hemiparetic stroke patients have very limited benefits from current therapies. Brain-computer interface (BCI) engaging the unaffected hemisphere has emerged as a promising novel therapeutic approach for chronic stroke rehabilitation.
Objectives: This study investigated the effectiveness of contralesionally-controlled BCI therapy in chronic stroke patients with impaired upper extremity motor function.
Top Stroke Rehabil
September 2024
Department of Rehabilitation Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Aims: The aim of this study was to evaluate the accuracy of screening tools for sarcopenia and to determine whether the same or different cutoff points should be applied in patients with chronic stroke.
Materials And Methods: Sixty-eight participants with residual hemiparetic deficit for over 6 months were enrolled. We evaluated the accuracy of calf circumference, SARC-F questionnaire, SARC-CalF, and Ishii's score chart using the Asia Working Group for Sarcopenia (AWGS) 2019 revised criteria as the gold standard.
Background: In individuals with chronic stroke and hemiparesis, noninvasive brain stimulation (NIBS) may be used as an adjunct to therapy for improving motor recovery. Specific states of movement during motor recovery are more responsive to brain stimulation than others, thus a system that could auto-detect movement state would be useful in correctly identifying the most effective stimulation periods. The aim of this study was to compare the performance of different machine learning models in classifying movement periods during EEG recordings of hemiparetic individuals receiving noninvasive brain stimulation.
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