TY - JOUR
T1 - Objective sensor-based gait measures reflect motor impairment in multiple sclerosis patients
T2 - Reliability and clinical validation of a wearable sensor device
AU - Felix, Flachenecker
AU - Heiko, Gaßner
AU - Julius, Hannik
AU - De-Hyung, Lee
AU - Peter, Flachenecker
AU - Jürgen, Winkler
AU - Bjoern, Eskofier
AU - Linker, Ralf A.
AU - Klucken, Jochen
N1 - Funding Information:
The study was supported by the Emerging Fields Initiative of the Friedrich-Alexander- University Erlangen-Nürnberg , Germany (EFI Moves, 2 Med 03), the Bavarian State Ministry for Education, Science and the Arts, Munich, Germany (MotionLab@Home, E|Home-Center), the Bavarian Ministry of Economic Affairs and Media, Energy and Technology (Medical Valley Award 2016, Risk-e-Gait) MoveIT, an EIT Health innovation project, and Mobilise-D (IMI H2020 project). Study sponsors were not involved in the study design, the collection, analysis, and interpretation of data, in the writing of the manuscripts, or decision to submit the manuscript for publication.
Funding Information:
HG, JH, BE, JK and JW have received institutional research grants from the Emerging Field Initiative of the Friedrich Alexander-University Erlangen-Nürnberg (EFI Moves, 2 Med 03).
Funding Information:
JK reports institutional research grants from Bavarian Research Foundation; Emerging Field Initiative, FAU; EIT-Health; EIT-Digital; EU (H2020), German Research Foundation (DFG); BMBF. Industry sponsored institutional IITs and grants from Teva GmbH; Licher MT GmbH; Astrum IT GmbH; Alpha-Telemed AG. He holds shares of Portabiles HealthCare Technologies GmbH, Portabiles GmbH, Alpha-Telemed AG, and received compensation and honoraria from serving on scientific advisory boards for LicherMT GmbH, Abbvie GmbH, UCB Pharma GmbH, Athenion GmbH, and Thomashilfen GmbH; as well as lecturing from UCB Pharma GmbH, TEVA Pharma GmbH, Licher MT GmbH, Desitin GmbH, Abbvie GmbH, Solvay Pharmaceuticals, and Ever Neuro Pharma GmbH; Dr. Klucken has a patent related to gait assessments pending.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - 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.
AB - 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.
KW - Ambulatory sensing
KW - EDSS
KW - Gait analysis
KW - Gait impairment
KW - Mobility disability, 25 foot walk
KW - Wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85125651324&partnerID=8YFLogxK
U2 - 10.1016/j.msard.2019.101903
DO - 10.1016/j.msard.2019.101903
M3 - Article
C2 - 31927199
AN - SCOPUS:85077648672
SN - 2211-0348
VL - 39
JO - Multiple Sclerosis and Related Disorders
JF - Multiple Sclerosis and Related Disorders
M1 - 101903
ER -