The association between gait variability with the energy cost of walking depends on the fall status in people with multiple sclerosis without mobility aids
Kalron A, Frid L, Menascu S, Givon U. Gait Posture 2019; 74: 231-235.
Multiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel; Department of Pediatric Orthopedics, Sheba Medical Center, Tel Hashomer, Israel; Sackler Faculty of Medicine, Tel Aviv University, Israel. Electronic address: email@example.com.
(Copyright © 2019, Elsevier Publishing)
DOI 10.1016/j.gaitpost.2019.09.021 PMID 31563824
BACKGROUND: Falls, gait variability and increased energy cost of walking are common in people with multiple sclerosis (PwMS). However, no studies have as yet examined this triple association in PwMS or in other neurological populations. RESEARCH QUESTION: Does a relationship exist between gait variability, falls and the energy cost of gait in PwMS? METHODS: This cross sectional study included 88 PwMS (50 women), mean age 39.8 (S.D = 13.0) and mean disease duration of 6.2 (SD = 8.2) years since diagnosis. Energy expenditure during walking was collected via a portable metabolic device (COSMED K5, COSMED Srl, Roma, Italy). Gait variability was measured by an electronic walkway (GAITRite™). Participants were divided into groups based on fall history (fallers and non-fallers). Differences between groups in terms of energy expenditure measures and gait variability metrics were determined by the analysis of variance test. The relationship between gait variability and energy cost of walking was examined by the Pearson’s correlation coefficient test.
RESULTS: Thirty-three PwMS were classified as fallers and 55 as non-fallers. Non-significant differences between groups were observed in the energy expenditure measures, including cost of walking. Fallers demonstrated higher step length variability compared with non-fallers (4.58 (S.D. = 2.42 vs. 3.40 (S.D. = 1.40); p-value = 0.005). According to the Pearson’s correlation coefficient analysis, a significant relationship was found between step length variability and energy cost of walking in the non-fallers group (Rho = 0.372, P-value = 0.006) and the total group (Rho = 0.296, p-value = 0.005), but not in those PwMS with a history of falls. SIGNIFICANCE: We demonstrated a significant relationship between increased gait variability and energy expenditure while walking only in MS patients without a history of falls. This is important as there is evidence of the clinical relevance of increased gait variability, poor fitness level and high risk of falling in the MS population.
Keywords Energy cost of walking; Falls; Gait variability; Multiple sclerosis
Critically appraised paper: Task-oriented gait training that focuses on the safe and correct use of a walking aid may reduce falls in people with multiple sclerosis [commentary]
Karpatkin H. J. Physiother. 2019; ePub(ePub): ePub.
Department of Physical Therapy, Hunter College, New York, USA.
(Copyright © 2019, Australian Physiotherapy Association)
DOI 10.1016/j.jphys.2019.10.006 PMID 31718961
Falls and falls-related injuries are commonly seen by clinicians who treat multiple sclerosis. Therefore, developing intervention strategies specifically targeting falls in this population should be a priority. Approaches specifically targeting people with greater multiple sclerosis-related disability and who are still walking should receive particular attention, as this population is more likely to fall or stop walking due to fear of falls.1 The authors describe an intervention program combining assistive device training with task-oriented gait training with assistive devices, finding that participants who received this training had fewer falls and spent less time sitting than controls. However, there was no between-group difference in mobility scores.
Two clinically important messages emerged from this study. First, using task-specific programs, as opposed to more generalised programs (eg, aerobic fitness, resistance training), indicates that clinicians working with patients with multiple sclerosis should focus on tailoring treatments to the specific gait problems of the patient. Multiple sclerosis has a very specific clinical presentation, and clinicians who work with this population should be mindful that interventions are targeted to their patient’s specific impairments and functional limitations. Second, despite the fact that the participants in this study were fairly disabled and required constant use of an assistive device, improvements in mobility were still evident after intervention, indicating that even patients with greater disability may improve mobility in meaningful ways with appropriate intervention.
Fall risk prediction in multiple sclerosis using postural sway measures: a machine learning approach
Sun R, Hsieh KL, Sosnoff JJ. Sci. Rep. 2019; 9(1): e16154.
Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, USA.
(Copyright © 2019, Nature Publishing Group)
DOI 10.1038/s41598-019-52697-2 PMID 31695127
Numerous postural sway metrics have been shown to be sensitive to balance impairment and fall risk in individuals with MS. Yet, there are no guidelines concerning the most appropriate postural sway metrics to monitor impairment. This investigation implemented a machine learning approach to assess the accuracy and feature importance of various postural sway metrics to differentiate individuals with MS from healthy controls as a function of physiological fall risk. 153 participants (50 controls and 103 individuals with MS) underwent a static posturography assessment and a physiological fall risk assessment. Participants were further classified into four subgroups based on fall risk: controls, low-risk MS (n = 34), moderate-risk MS (n = 27), high-risk MS (n = 42). Twenty common sway metrics were derived following standard procedures and subsequently used to train a machine learning algorithm (random forest – RF, with 10-fold cross validation) to predict individuals’ fall risk grouping. The sway-metric based RF classifier had high accuracy in discriminating controls from MS individuals (>86%). Sway sample entropy was identified as the strongest feature for classification of low-risk MS individuals from healthy controls. Whereas for all other comparisons, mediolateral sway amplitude was identified as the strongest predictor for fall risk groupings.