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From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany.
Published: September 2022
AI Article Synopsis
Article Abstract
Background And Objectives: Hereditary spastic paraplegia (HSP) causes progressive spasticity and weakness of the lower limbs. As neurologic examination and the clinical Spastic Paraplegia Rating Scale (SPRS) are subject to potential patient-dependent and clinician-dependent bias, instrumented gait analysis bears the potential to objectively quantify impaired gait. The aim of this study was to investigate gait cyclicity parameters by application of a mobile gait analysis system in a cross-sectional cohort of patients with HSP and a longitudinal fast progressing subcohort.
Methods: Using wearable sensors attached to the shoes, patients with HSP and controls performed a 4 × 10 m walking test during regular visits in 3 outpatient centers. Patients were also rated according to the SPRS, and in a subset, questionnaires on quality of life and fear of falling were obtained. An unsupervised segmentation algorithm was used to extract stride parameters and respective coefficients of variation.
Results: Mobile gait analysis was performed in a total of 112 ambulatory patients with HSP and 112 age-matched and sex-matched controls. Although swing time was unchanged compared with controls, there were significant increases in the duration of the total stride phase and the duration of the stance phase, both regarding absolute values and coefficients of variation values. Although stride parameters did not correlate with age, weight, or height of the patients, there were significant associations of absolute stride parameters with single SPRS items reflecting impaired mobility (|| > 0.50), with patients' quality of life (|| > 0.44), and notably with disease duration (|| > 0.27). Sensor-derived coefficients of variation, on the other hand, were associated with patient-reported fear of falling (|| > 0.41) and cognitive impairment (|| > 0.40). In a small 1-year follow-up analysis of patients with complicated HSP and fast progression, the absolute values of mobile gait parameters had significantly worsened compared with baseline.
Discussion: The presented wearable sensor system provides parameters of stride characteristics which seem clinically valid to reflect gait impairment in HSP. Owing to the feasibility regarding time, space, and costs, this study forms the basis for larger scale longitudinal and interventional studies in HSP.
