Gait analysis may distinguish progressive supranuclear palsy and Parkinson disease since the earliest stages

Author:

Amboni Marianna,Ricciardi Carlo,Picillo Marina,De Santis Chiara,Ricciardelli Gianluca,Abate Filomena,Tepedino Maria Francesca,D’Addio Giovanni,Cesarelli Giuseppe,Volpe Giampiero,Calabrese Maria Consiglia,Cesarelli Mario,Barone Paolo

Abstract

AbstractProgressive supranuclear palsy (PSP) is a rare and rapidly progressing atypical parkinsonism. Albeit existing clinical criteria for PSP have good specificity and sensitivity, there is a need for biomarkers able to capture early objective disease-specific abnormalities. This study aimed to identify gait patterns specifically associated with early PSP. The study population comprised 104 consecutively enrolled participants (83 PD and 21 PSP patients). Gait was investigated using a gait analysis system during normal gait and a cognitive dual task. Univariate statistical analysis and binary logistic regression were used to compare all PD patients and all PSP patients, as well as newly diagnosed PD and early PSP patients. Gait pattern was poorer in PSP patients than in PD patients, even from early stages. PSP patients exhibited reduced velocity and increased measures of dynamic instability when compared to PD patients. Application of predictive models to gait data revealed that PD gait pattern was typified by increased cadence and longer cycle length, whereas a longer stance phase characterized PSP patients in both mid and early disease stages. The present study demonstrates that quantitative gait evaluation clearly distinguishes PSP patients from PD patients since the earliest stages of disease. First, this might candidate gait analysis as a reliable biomarker in both clinical and research setting. Furthermore, our results may offer speculative clues for conceiving early disease-specific rehabilitation strategies.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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1. The Role of Deep Learning and Gait Analysis in Parkinson’s Disease: A Systematic Review;Sensors;2024-09-13

2. Kinematic and Kinetic Gait Features Associated With Mild Cognitive Impairment in Parkinson’s Disease;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024

3. Multiple System Atrophy (MSA);Movement Disorders Phenomenology;2024

4. A Novel Two-Stage Data-mining Model Combining Gait Recognition and Temporal Sequence Mining;Proceedings of the 2023 7th International Conference on Video and Image Processing;2023-12-14

5. Using Wearable Sensors and Motion Parameters for Recognizing Progressive Supranuclear Palsy Phenotypes;2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE);2023-10-25

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