Jiska C E Kempen1, Caroline A M Doorenbosch2, Dirk L Knol3, Vincent de Groot4, Heleen Beckerman5. 1. J.C.E. Kempen, PhD, Department of Rehabilitation Medicine, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, the Netherlands; EMGO Institute for Health and Care Research, VU University and VU University Medical Center; and MS Center, Amsterdam, the Netherlands. jiskakempen@hotmail.com. 2. C.A.M. Doorenbosch, PhD, Department of Rehabilitation Medicine, VU University Medical Center; Research Institute MOVE, VU University and VU University Medical Center; and Academy of Human Kinetic Technology, The Hague University of Applied Sciences, The Hague, the Netherlands. 3. D.L. Knol, PhD, Department of Epidemiology and Biostatistics, VU University Medical Center. 4. V. de Groot, MD, PhD, Department of Rehabilitation Medicine, VU University Medical Center; EMGO Institute for Health and Care Research, VU University and VU University Medical Center; and MS Center. 5. H. Beckerman, PhD, Department of Rehabilitation Medicine, VU University Medical Center; EMGO Institute for Health and Care Research, VU University and VU University Medical Center; and MS Center.
Abstract
BACKGROUND: Limited walking ability is an important problem for patients with multiple sclerosis. A better understanding of how gait impairments lead to limited walking ability may help to develop more targeted interventions. Although gait classifications are available in cerebral palsy and stroke, relevant knowledge in MS is scarce. OBJECTIVE: The aims of this study were: (1) to identify distinctive gait patterns in patients with MS based on a combined evaluation of kinematics, gait features, and muscle activity during walking and (2) to determine the clinical relevance of these gait patterns. DESIGN: This was a cross-sectional study of 81 patients with MS of mild-to-moderate severity (Expanded Disability Status Scale [EDSS] median score=3.0, range=1.0-7.0) and an age range of 28 to 69 years. METHOD: The patients participated in 2-dimensional video gait analysis, with concurrent measurement of surface electromyography and ground reaction forces. A score chart of 73 gait items was used to rate each gait analysis. A single rater performed the scoring. Latent class analysis was used to identify gait classes. RESULTS: Analysis of the 73 gait variables revealed that 9 variables could distinguish 3 clinically meaningful gait classes. The 9 variables were: (1) heel-rise in terminal stance, (2) push-off, (3) clearance in initial swing, (4) plantar-flexion position in mid-swing, (5) pelvic rotation, (6) arm-trunk movement, (7) activity of the gastrocnemius muscle in pre-swing, (8) M-wave, and (9) propulsive force. The EDSS score and gait speed worsened in ascending classes. LIMITATIONS: Most participants had mild-to-moderate limitations in walking ability based on their EDSS scores, and the number of walkers who were severely limited was small. CONCLUSIONS: Based on a small set of 9 variables measured with 2-dimensional clinical gait analysis, patients with MS could be divided into 3 different gait classes. The gait variables are suggestive of insufficient ankle push-off.
BACKGROUND: Limited walking ability is an important problem for patients with multiple sclerosis. A better understanding of how gait impairments lead to limited walking ability may help to develop more targeted interventions. Although gait classifications are available in cerebral palsy and stroke, relevant knowledge in MS is scarce. OBJECTIVE: The aims of this study were: (1) to identify distinctive gait patterns in patients with MS based on a combined evaluation of kinematics, gait features, and muscle activity during walking and (2) to determine the clinical relevance of these gait patterns. DESIGN: This was a cross-sectional study of 81 patients with MS of mild-to-moderate severity (Expanded Disability Status Scale [EDSS] median score=3.0, range=1.0-7.0) and an age range of 28 to 69 years. METHOD: The patients participated in 2-dimensional video gait analysis, with concurrent measurement of surface electromyography and ground reaction forces. A score chart of 73 gait items was used to rate each gait analysis. A single rater performed the scoring. Latent class analysis was used to identify gait classes. RESULTS: Analysis of the 73 gait variables revealed that 9 variables could distinguish 3 clinically meaningful gait classes. The 9 variables were: (1) heel-rise in terminal stance, (2) push-off, (3) clearance in initial swing, (4) plantar-flexion position in mid-swing, (5) pelvic rotation, (6) arm-trunk movement, (7) activity of the gastrocnemius muscle in pre-swing, (8) M-wave, and (9) propulsive force. The EDSS score and gait speed worsened in ascending classes. LIMITATIONS: Most participants had mild-to-moderate limitations in walking ability based on their EDSS scores, and the number of walkers who were severely limited was small. CONCLUSIONS: Based on a small set of 9 variables measured with 2-dimensional clinical gait analysis, patients with MS could be divided into 3 different gait classes. The gait variables are suggestive of insufficient ankle push-off.