Contributions
Abstract: P382
Type: Poster
Abstract Category: Clinical aspects of MS - 8 Clinical assessment tools
Background: Gait abnormalities are common in multiple sclerosis (MS) and contribute to disability but may not be easily detected in the early stages of the disease.
Objective: We evaluated reliability and validity of a new, sensor-based system for gait analysis in a large group of patients with MS (PwMS) with different disability stages, and healthy controls (HC).
Methods: PwMS admitted to the University outpatient clinic or to inpatient rehabilitation, age > 18 years, EDSS ≤ 7.0 and given informed consent were included in the study. Healthy volunteers (hospital staff, relatives and medical students) served as controls. The automatic gait analysis system eGaIT (“embedded gait analysis using intelligent technology”) consists of a small sensor device (accelerometer and gyroscope) laterally attached to both shoes and enables data capturing and analysis, wireless data transfer, feature extraction and gait parameter calculation by pattern recognition algorithms. PwMS and HC were asked to perform the 25-foot walking test (25FWT) two times in a self-selected, comfortable speed (25FWT_slow), followed by two times in a speed as fast as possible (25FWT_fast). Reliability was assessed by correlation analysis between results of the two 25FWT recordings (in slow and fast speed) for different gait parameters (stride length, gait speed, swing time, cadence). Validity was estimated by
1) comparing PwMS with HC, and
2) PwMS subgroups with different disability levels (EDSS ≤ 3.5 and EDSS ≥ 4.0).
Results: Between January 2016 and August 2016, 102 PwMS (68% female, mean age 43.0 ± 11.6 years, median EDSS 4.0), and 22 HC (45% female, mean age 34.3 ± 15.6 years) were investigated. Upon comparison of datasets from the first and second measurement, data highly correlated in both, PwMS (i.e. stride length 25FWT_slow, r=0.90, p< 0.001; 25FWT_fast, r=0.91, p< 0.001) and HC (25FWT_slow, r=0.75, p< 0.001; 25FWT_fast, r=0.54, p= 0.03). For all parameters investigated, we found statistically significant differences between HC and PwMS as well as between PwMS subgroups with lower (n=44) versus higher disability (n=58). These differences were more pronounced for 25FWT_fast (i.e. cadence, p< 0.001) than 25FWT_slow (cadence, p=0.033).
Conclusion: The eGaIT system is a reliable and valid tool for instrumented gait analysis in PwMS that may easily be administered and objectively supports the clinical workup by detection of gait abnormalities even in the early stages of MS.
Disclosure:
Felix Flachenecker: Nothing to disclose.
Heiko Gaßner: Nothing to disclose.
De-Hyung Lee received travel support and/or compensation for activities with Biogen, Genzyme, Merck, Novartis, Roche and TEVA as well as research support from Novartis.
Björn Eskofier reports grants from Emerging Fields Initiative (EFI), University Erlangen-Nuernberg, during the conduct of the study; grants from adidas AG, outside the submitted work; In addition, Dr. Eskofier has a patent related to gait assessments pending.
Jochen Klucken received compensation and honoraria from serving on scientific advisory boards for LicherMT GmbH, Abbvie GmbH, UCB Pharma GmbH and GlaxoSmithKline GmbH & Co. KG, Athenion GmbH, Thomashilfen GmbH; and lecturing from UCB Pharma GmbH, TEVA Pharma GmbH, Licher MT GmbH, Desitin GmbH, Abbvie GmbH, Solvay Pharmaceuticals, Ever Neuro Pharma GmbH. Dr. Klucken has a patent related to gait assessments pending and holds shares from Portabiles GmbH and Portabiles HCT GmbH
Peter Flachenecker has received speaker´s fees and honoraria for advisory boards from Bayer, Biogen, Genzyme, Merck-Serono, Novartis, Roche and Teva. None resulted in a conflict of interest.
Ralf Linker received compensation for activities with Allmirall, Bayer Health Care, Biogen, Fresenius, Genzyme, Merck, Novartis Pharma, Roche, TEVA as well as research support from Biogen, Merck and Novartis Pharma.
Abstract: P382
Type: Poster
Abstract Category: Clinical aspects of MS - 8 Clinical assessment tools
Background: Gait abnormalities are common in multiple sclerosis (MS) and contribute to disability but may not be easily detected in the early stages of the disease.
Objective: We evaluated reliability and validity of a new, sensor-based system for gait analysis in a large group of patients with MS (PwMS) with different disability stages, and healthy controls (HC).
Methods: PwMS admitted to the University outpatient clinic or to inpatient rehabilitation, age > 18 years, EDSS ≤ 7.0 and given informed consent were included in the study. Healthy volunteers (hospital staff, relatives and medical students) served as controls. The automatic gait analysis system eGaIT (“embedded gait analysis using intelligent technology”) consists of a small sensor device (accelerometer and gyroscope) laterally attached to both shoes and enables data capturing and analysis, wireless data transfer, feature extraction and gait parameter calculation by pattern recognition algorithms. PwMS and HC were asked to perform the 25-foot walking test (25FWT) two times in a self-selected, comfortable speed (25FWT_slow), followed by two times in a speed as fast as possible (25FWT_fast). Reliability was assessed by correlation analysis between results of the two 25FWT recordings (in slow and fast speed) for different gait parameters (stride length, gait speed, swing time, cadence). Validity was estimated by
1) comparing PwMS with HC, and
2) PwMS subgroups with different disability levels (EDSS ≤ 3.5 and EDSS ≥ 4.0).
Results: Between January 2016 and August 2016, 102 PwMS (68% female, mean age 43.0 ± 11.6 years, median EDSS 4.0), and 22 HC (45% female, mean age 34.3 ± 15.6 years) were investigated. Upon comparison of datasets from the first and second measurement, data highly correlated in both, PwMS (i.e. stride length 25FWT_slow, r=0.90, p< 0.001; 25FWT_fast, r=0.91, p< 0.001) and HC (25FWT_slow, r=0.75, p< 0.001; 25FWT_fast, r=0.54, p= 0.03). For all parameters investigated, we found statistically significant differences between HC and PwMS as well as between PwMS subgroups with lower (n=44) versus higher disability (n=58). These differences were more pronounced for 25FWT_fast (i.e. cadence, p< 0.001) than 25FWT_slow (cadence, p=0.033).
Conclusion: The eGaIT system is a reliable and valid tool for instrumented gait analysis in PwMS that may easily be administered and objectively supports the clinical workup by detection of gait abnormalities even in the early stages of MS.
Disclosure:
Felix Flachenecker: Nothing to disclose.
Heiko Gaßner: Nothing to disclose.
De-Hyung Lee received travel support and/or compensation for activities with Biogen, Genzyme, Merck, Novartis, Roche and TEVA as well as research support from Novartis.
Björn Eskofier reports grants from Emerging Fields Initiative (EFI), University Erlangen-Nuernberg, during the conduct of the study; grants from adidas AG, outside the submitted work; In addition, Dr. Eskofier has a patent related to gait assessments pending.
Jochen Klucken received compensation and honoraria from serving on scientific advisory boards for LicherMT GmbH, Abbvie GmbH, UCB Pharma GmbH and GlaxoSmithKline GmbH & Co. KG, Athenion GmbH, Thomashilfen GmbH; and lecturing from UCB Pharma GmbH, TEVA Pharma GmbH, Licher MT GmbH, Desitin GmbH, Abbvie GmbH, Solvay Pharmaceuticals, Ever Neuro Pharma GmbH. Dr. Klucken has a patent related to gait assessments pending and holds shares from Portabiles GmbH and Portabiles HCT GmbH
Peter Flachenecker has received speaker´s fees and honoraria for advisory boards from Bayer, Biogen, Genzyme, Merck-Serono, Novartis, Roche and Teva. None resulted in a conflict of interest.
Ralf Linker received compensation for activities with Allmirall, Bayer Health Care, Biogen, Fresenius, Genzyme, Merck, Novartis Pharma, Roche, TEVA as well as research support from Biogen, Merck and Novartis Pharma.