Felix Flachenecker1, Heiko Gaßner2, Julius Hannik3, De-Hyung Lee4, Peter Flachenecker5, Jürgen Winkler2, Bjoern Eskofier6, Ralf A Linker4, Jochen Klucken7. 1. Department of Neurology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen 91054, Germany. 2. Department of Molecular Neurology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen 91054, Germany. 3. Portabiles HealthCare Technologies GmbH, Erlangen 91054, Germany. 4. Department of Neurology, University of Regensburg, Regensburg 93053, Germany. 5. Neurological Rehabilitation Center Quellenhof, Bad Wildbad, Germany. 6. Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen 91054, Germany. 7. Department of Molecular Neurology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen 91054, Germany; Fraunhofer Institut für Integrierte Schaltungen, Erlangen, Germany. Electronic address: jochen.klucken@uk-erlangen.de.
Abstract
BACKGROUND: Gait deficits are common in multiple sclerosis (MS) and contribute to disability but may not be easily detected in the early stages of the disease. OBJECTIVES: We investigated whether sensor-based gait analysis is able to detect gait impairments in patients with MS (PwMS). METHODS: A foot-worn sensor-based gait analysis system was used in 102 PwMS and 22 healthy controls (HC) that were asked to perform the 25-foot walking test (25FWT) two times in a self-selected speed (25FWT_pref), followed by two times in a speed as fast as possible (25FWT_fast). The Multiple Sclerosis Walking Scale (MSWS-12) was used as a subjective measure of patient mobility. Patients were divided into EDSS and functional system subgroups. RESULTS: Datasets between two consecutive measurements (test-retest-reliability) were highly correlated in all analysed mean gait parameters (e.g. 25FWT_fast: stride length r = 0.955, gait speed r = 0.969) Subgroup analysis between HC and PwMS with lower (EDSS≤3.5) and higher (EDSS 4.0-7.0) disability showed significant differences in mean stride length, gait speed, toe off angle, stance time and swing time (e.g. stride length of EDSS subgroups 25FWT_fast p ≤ 0.001, 25FWT_pref p = 0.003). The differences between EDSS subgroups were more pronounced in fast than in self-selected gait speed (e.g. stride length 25FWT_fast 33.6 cm vs. 25FWT_pref 16.3 cm). Stride length (25FWT_fast) highly correlated to EDSS (r=-0.583) and MSWS-12 (r=-0.668). We observed significant differences between HC and PwMS with (FS 0-1) and without (FS≥2) pyramidal or cerebellar disability (e.g. gait speed of FS subgroups p ≤ 0.001). CONCLUSION: Sensor-based gait analysis objectively supports the clinical assessment of gait abnormalities even in the lower stages of MS, especially when walking with fast speed. Stride length and gait speed where identified as the most clinically relevant gait measures. Thus, it may be used to support the assessment of PwMS with gait impairment in the future, e.g. for more objective classification of disability. Its role in home-monitoring scenarios need to be evaluated in further studies.
BACKGROUND: Gait deficits are common in multiple sclerosis (MS) and contribute to disability but may not be easily detected in the early stages of the disease. OBJECTIVES: We investigated whether sensor-based gait analysis is able to detect gait impairments in patients with MS (PwMS). METHODS: A foot-worn sensor-based gait analysis system was used in 102 PwMS and 22 healthy controls (HC) that were asked to perform the 25-foot walking test (25FWT) two times in a self-selected speed (25FWT_pref), followed by two times in a speed as fast as possible (25FWT_fast). The Multiple Sclerosis Walking Scale (MSWS-12) was used as a subjective measure of patient mobility. Patients were divided into EDSS and functional system subgroups. RESULTS: Datasets between two consecutive measurements (test-retest-reliability) were highly correlated in all analysed mean gait parameters (e.g. 25FWT_fast: stride length r = 0.955, gait speed r = 0.969) Subgroup analysis between HC and PwMS with lower (EDSS≤3.5) and higher (EDSS 4.0-7.0) disability showed significant differences in mean stride length, gait speed, toe off angle, stance time and swing time (e.g. stride length of EDSS subgroups 25FWT_fast p ≤ 0.001, 25FWT_pref p = 0.003). The differences between EDSS subgroups were more pronounced in fast than in self-selected gait speed (e.g. stride length 25FWT_fast 33.6 cm vs. 25FWT_pref 16.3 cm). Stride length (25FWT_fast) highly correlated to EDSS (r=-0.583) and MSWS-12 (r=-0.668). We observed significant differences between HC and PwMS with (FS 0-1) and without (FS≥2) pyramidal or cerebellar disability (e.g. gait speed of FS subgroups p ≤ 0.001). CONCLUSION: Sensor-based gait analysis objectively supports the clinical assessment of gait abnormalities even in the lower stages of MS, especially when walking with fast speed. Stride length and gait speed where identified as the most clinically relevant gait measures. Thus, it may be used to support the assessment of PwMS with gait impairment in the future, e.g. for more objective classification of disability. Its role in home-monitoring scenarios need to be evaluated in further studies.
Authors: Casey Little; Connor Moore; Emily Bean; Denise M Peters; Ryan S McGinnis; Susan L Kasser Journal: Gait Posture Date: 2022-03-03 Impact factor: 2.746