ArticleFrontiers in neurology2025
Quantitative analysis of gait parameters in Parkinson's disease and the clinical significance.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of different types of deep brain stimulation on gait disorders in patients with Parkinson's disease: a network meta-analysis of randomized controlled trials.Frontiers in aging neuroscience · 2025Pooled it
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- Mediation analysis for integrating motor symptoms of Parkinson's disease, cognitive function, and gait characteristics.Frontiers in neurology · 2025Article
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Authors and funding
8 authors.
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Abstract
Background: Gait disorder is one of the clinical manifestations of Parkinson's disease (PD). Investigating the characteristics of gait disorder in patients with PD and the changes in gait before and after taking levodopa is crucial for the recognition, diagnosis and treatment of gait disorders in PD patients. Methods: In this study, we measured the gait parameters of 20 patients with PD and 17 healthy controls and analyzed the changes of gait parameters of these patients before and after taking levodopa. We also used gait parameters as input features and MDS-UPDRS III score (which was further subdivided into tremor and non-tremor part score) as output labels to train machine learning regression models. Results: We found that except for cadence and stride time, most gait parameters of PD patients, including plantar dorsiflexion angle, plantar flexion angle, stride length, velocity were all smaller than those of the healthy controls. Moreover, the severity of gait disorders correlated with the severity of motor symptoms. After taking levodopa, the stride length, velocity and cadence were increased, but stride time was decreased. We also found that the trained machine learning model could explain and predict the MDS-UPDRS III score and non-tremor part score, and the non-tremor part score was better than the MDS-UPDRS III score. Conclusion: Our gait assessment work can help clinicians recognize gait disorder in PD patients and predict the severity of clinical symptoms.
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