Literature DB >> 27919488

Clinical assessment of gait in individuals with multiple sclerosis using wearable inertial sensors: Comparison with patient-based measure.

Massimiliano Pau1, Silvia Caggiari2, Alessandro Mura2, Federica Corona2, Bruno Leban2, Giancarlo Coghe3, Lorena Lorefice3, Maria Giovanna Marrosu3, Eleonora Cocco3.   

Abstract

BACKGROUND: This study aims to verify the feasibility of use of wearable accelerometers in an ambulatory environment to assess spatiotemporal parameters of gait in people with Multiple Sclerosis (pwMS), as well as the correlation of objective data with patient-reported outcomes.
METHODS: One hundred and five pwMS (Expanded Disability Status Scale, EDSS in the range 0-6.5) classified in three sub-groups (EDSS 0-1.5, EDSS 2-4, EDSS 4.5-6.5) and 47 healthy controls (HC) participated in the study. All the subjects were evaluated with the timed 25-foot walking test (T25FW) while wearing a commercially available accelerometer. PwMS also rated the impact of the disease on their walking abilities using the 12-item MS walking scale (MSWS-12).
RESULTS: All parameters objectively measured, except stride length, were significantly modified in pwMS with higher EDSS, with respect to HC and lower disability participants. Moderate to high correlations (r =0.57-0.79) were observed between gait parameters and MSWS-12 for pwMS of higher EDSS. The correlation was found moderate for the intermediate EDSS category (r =0.42-0.62).
CONCLUSION: Wearable accelerometers are a useful tool for assessing gait performance for pwMS in a clinical setting, especially in cases of mild to moderate disability. Compared with other quantitative techniques, these devices allow patient testing under realistic conditions (i.e., fully dressed, with their usual shoes) using a simple procedure with immediate availability of data. Copyright Â
© 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  12-item Multiple Sclerosis Walking Scale (MSWS-12); Accelerometer; Expanded Disability Status Scale (EDSS); Gait; Multiple Sclerosis (MS); Spatio-temporal parameters; Timed 25-Foot Walk Test (T25FWT)

Mesh:

Year:  2016        PMID: 27919488     DOI: 10.1016/j.msard.2016.10.007

Source DB:  PubMed          Journal:  Mult Scler Relat Disord        ISSN: 2211-0348            Impact factor:   4.339


  15 in total

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Authors:  E Ray Dorsey; Alistair M Glidden; Melissa R Holloway; Gretchen L Birbeck; Lee H Schwamm
Journal:  Nat Rev Neurol       Date:  2018-04-06       Impact factor: 42.937

2.  What the Tech? The Management of Neurological Dysfunction Through the Use of Digital Technology.

Authors:  Caitlin Carswell; Paul M Rea
Journal:  Adv Exp Med Biol       Date:  2021       Impact factor: 2.622

Review 3.  Next Steps in Wearable Technology and Community Ambulation in Multiple Sclerosis.

Authors:  Mikaela L Frechette; Brett M Meyer; Lindsey J Tulipani; Reed D Gurchiek; Ryan S McGinnis; Jacob J Sosnoff
Journal:  Curr Neurol Neurosci Rep       Date:  2019-09-04       Impact factor: 5.081

4.  Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis.

Authors:  Sriram Raju Dandu; Matthew M Engelhard; Asma Qureshi; Jiaqi Gong; John C Lach; Maite Brandt-Pearce; Myla D Goldman
Journal:  IEEE J Biomed Health Inform       Date:  2018-01       Impact factor: 5.772

Review 5.  Gait metrics analysis utilizing single-point inertial measurement units: a systematic review.

Authors:  Ralph Jasper Mobbs; Jordan Perring; Suresh Mahendra Raj; Monish Maharaj; Nicole Kah Mun Yoong; Luke Wicent Sy; Rannulu Dineth Fonseka; Pragadesh Natarajan; Wen Jie Choy
Journal:  Mhealth       Date:  2022-01-20

6.  Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach.

Authors:  Nicola Marotta; Alessandro de Sire; Cinzia Marinaro; Lucrezia Moggio; Maria Teresa Inzitari; Ilaria Russo; Anna Tasselli; Teresa Paolucci; Paola Valentino; Antonio Ammendolia
Journal:  J Clin Med       Date:  2022-06-17       Impact factor: 4.964

7.  Normative database of spatiotemporal gait parameters using inertial sensors in typically developing children and young adults.

Authors:  Stephanie Voss; Jessica Joyce; Alexandras Biskis; Medha Parulekar; Nicholas Armijo; Cris Zampieri; Rachel Tracy; Alexandra Sasha Palmer; Marie Fefferman; Bichun Ouyang; Yuanqing Liu; Elizabeth Berry-Kravis; Joan A O'Keefe
Journal:  Gait Posture       Date:  2020-05-21       Impact factor: 2.840

8.  Using Body-Worn Sensors to Detect Changes in Balance and Mobility After Acute Aerobic Exercise in Adults with Multiple Sclerosis.

Authors:  Susan L Kasser; Jesse V Jacobs; Jeremy Sibold; Avery Marcus; Laurel Cole
Journal:  Int J MS Care       Date:  2020 Jan-Feb

9.  Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach.

Authors:  Dylan Kobsar; Reed Ferber
Journal:  Sensors (Basel)       Date:  2018-08-27       Impact factor: 3.576

10.  Movement measurements at home for multiple sclerosis: walking speed measured by a novel ambient measurement system.

Authors:  Victoria Mj Smith; Jonathan S Varsanik; Rachel A Walker; Andrew W Russo; Kevin R Patel; Wendy Gabel; Glenn A Phillips; Zebadiah M Kimmel; Eric C Klawiter
Journal:  Mult Scler J Exp Transl Clin       Date:  2018-01-23
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