Martin Regensburger1,2, Imke Tabea Spatz3, Malte Ollenschläger3,4, Christine F Martindale4, Philipp Lindeburg5, Zacharias Kohl3,2, Björn Eskofier4, Jochen Klucken3, Rebecca Schüle6,7, Stephan Klebe5, Jürgen Winkler3,2, Heiko Gaßner3,2,8. 1. Department of Molecular Neurology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany martin.regensburger@uk-erlangen.de. 2. Center for Rare Diseases Erlangen (ZSEER), Universitätsklinikum Erlangen, Erlangen, Germany. 3. Department of Molecular Neurology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany. 4. Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany. 5. Department of Neurology, University Hospital Essen, Essen, Germany. 6. Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany. 7. German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany. 8. Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, 91058 Erlangen, Germany.
Abstract
BACKGROUND AND OBJECTIVES: Hereditary spastic paraplegia (HSP) causes progressive spasticity and weakness of the lower limbs. As neurological examination and the clinical Spastic Paraplegia Rating Scale (SPRS) are subject to potential patient- and clinician-dependent bias, instrumented gait analysis bears the potential to objectively quantify impaired gait. The aim of the present study was to investigate gait cyclicity parameters by application of a mobile gait analysis system in a cross sectional cohort of HSP patients and a longitudinal fast progressing subcohort. METHODS: Using wearable sensors attached to the shoes, HSP patients and controls performed a 4x10 meters walking test during regular visits in three 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 employed to extract stride parameters and respective coefficients of variation. RESULTS: Mobile gait analysis was performed in a total of 112 ambulatory HSP patients and 112 age and gender matched controls. While swing time was unchanged compared to 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. While stride parameters did not correlate to age, weight or height of the patients, there were significant associations of absolute stride parameters to single SPRS items reflecting impaired mobility (|r| > 0.50), to patients' quality of life (|r| > 0.44), and notably to disease duration (|r| > 0.27). Sensor-derived coefficients of variation, on the other hand, were associated with patient-reported fear of falling (|r| > 0.41) and cognitive impairment (|r| > 0.40). In a small 1-year follow-up analysis of patients with complicated HSP and fast progression, absolute values of mobile gait parameters had significantly worsened compared to baseline. DISCUSSION: The presented wearable sensor system provides parameters of stride characteristics which appear clinically valid to reflect gait impairment in HSP. Due to the feasibility with regard to time, space and costs, the present study forms the basis for larger scale longitudinal and interventional studies in HSP.
BACKGROUND AND OBJECTIVES: Hereditary spastic paraplegia (HSP) causes progressive spasticity and weakness of the lower limbs. As neurological examination and the clinical Spastic Paraplegia Rating Scale (SPRS) are subject to potential patient- and clinician-dependent bias, instrumented gait analysis bears the potential to objectively quantify impaired gait. The aim of the present study was to investigate gait cyclicity parameters by application of a mobile gait analysis system in a cross sectional cohort of HSP patients and a longitudinal fast progressing subcohort. METHODS: Using wearable sensors attached to the shoes, HSP patients and controls performed a 4x10 meters walking test during regular visits in three 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 employed to extract stride parameters and respective coefficients of variation. RESULTS: Mobile gait analysis was performed in a total of 112 ambulatory HSP patients and 112 age and gender matched controls. While swing time was unchanged compared to 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. While stride parameters did not correlate to age, weight or height of the patients, there were significant associations of absolute stride parameters to single SPRS items reflecting impaired mobility (|r| > 0.50), to patients' quality of life (|r| > 0.44), and notably to disease duration (|r| > 0.27). Sensor-derived coefficients of variation, on the other hand, were associated with patient-reported fear of falling (|r| > 0.41) and cognitive impairment (|r| > 0.40). In a small 1-year follow-up analysis of patients with complicated HSP and fast progression, absolute values of mobile gait parameters had significantly worsened compared to baseline. DISCUSSION: The presented wearable sensor system provides parameters of stride characteristics which appear clinically valid to reflect gait impairment in HSP. Due to the feasibility with regard to time, space and costs, the present study forms the basis for larger scale longitudinal and interventional studies in HSP.
Authors: S Klimpe; R Schüle; J Kassubek; S Otto; Z Kohl; S Klebe; T Klopstock; S Ratzka; K Karle; L Schöls Journal: Eur J Neurol Date: 2011-06-01 Impact factor: 6.089
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Authors: Christine F Martindale; Nils Roth; Heiko Gasner; Julia List; Martin Regensburger; Bjoern M Eskofier; Zacharias Kohl Journal: IEEE J Biomed Health Inform Date: 2019-08-26 Impact factor: 5.772
Authors: Rebecca Schüle; Sarah Wiethoff; Peter Martus; Kathrin N Karle; Susanne Otto; Stephan Klebe; Sven Klimpe; Constanze Gallenmüller; Delia Kurzwelly; Dorothea Henkel; Florian Rimmele; Henning Stolze; Zacharias Kohl; Jan Kassubek; Thomas Klockgether; Stefan Vielhaber; Christoph Kamm; Thomas Klopstock; Peter Bauer; Stephan Züchner; Inga Liepelt-Scarfone; Ludger Schöls Journal: Ann Neurol Date: 2016-03-11 Impact factor: 10.422
Authors: Irene Pulido-Valdeolivas; David Gómez-Andrés; Juan Andrés Martín-Gonzalo; Irene Rodríguez-Andonaegui; Javier López-López; Samuel Ignacio Pascual-Pascual; Estrella Rausell Journal: PLoS One Date: 2018-03-08 Impact factor: 3.240