Category: Clinical Practice (assessment, diagnosis, treatment, knowledge translation/EBP, implementation science, program development); Stroke; Neurodegenerative Disease (e.g. MS, Parkinson's disease)
To identify basic gait features and abnormal gait patterns that are common to different neurological or musculoskeletal conditions, such as cerebral stroke, Parkinsonian disorders, radiculopathy, and musculoskeletal pain.
Retrospective cross sectional study
Temporal-spatial, kinematic, and kinetic gait parameters were analyzed in 424 patients with hemiplegia after stroke, 205 patients with Parkinsonian disorders, 216 patients with radiculopathy, 167 patients with musculoskeletal pain, and 316 normal controls (total, 1,328 subjects).
Participants (or Animals, Specimens, Cadavers) :
By randomized selection, 424 patients with hemiplegia after stroke, 205 patients with Parkinsonian disorders, 216 patients with radiculopathy, 167 patients with musculoskeletal pain, and 316 normal controls (total, 1,328 subjects) were included our study.
Interventions: Not applicable.
Main Outcome Measure(s) :
We assessed differences according to the condition and used a community detection algorithm to identify subgroups within each condition. Additionally, we developed a prediction model for subgroup classification according to gait speed and maximal hip extension in the stance phase.
The main findings can be summarized as follows. First, there was an asymmetric decrease of the knee/ankle flexion angles in hemiplegia and a marked reduction of the hip/knee range of motion with increased moment in Parkinsonian disorders. Second, three abnormal gait patterns, including fast gait speed with adequate maximal hip extension, fast gait speed with inadequate maximal hip extension, and slow gait speed, were found throughout the conditions examined. Third, our simple prediction model based on gait speed and maximal hip extension angle was characterized by a high degree of accuracy in predicting subgroups within a condition.
Conclusions : Our findings suggest the existence of specific gait patterns within and across conditions. Our novel subgrouping algorithm can be employed in routine clinical settings to classify abnormal gait patterns in various neurological disorders and guide the therapeutic approach and monitoring.