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Medical Science Monitor Basic Research


eISSN: 1643-3750

A Novel Multiple-Cue Observational Clinical Scale for Functional Evaluation of Gait After Stroke – The Stroke Mobility Score (SMS)

Dominik Raab, Brigitta Diószeghy-Léránt, Meret Wünnemann, Christina Zumfelde, Elena Cramer, Alina Rühlemann, Johanna Wagener, Silke Gegenbauer, Francisco Geu Flores, Marcus Jäger, Dörte Zietz, Harald Hefter, Andres Kecskemethy, Mario Siebler

Chair of Mechanics and Robotics, University of Duisburg-Essen, Duisburg, Germany

Med Sci Monit 2020; 26:e923147

DOI: 10.12659/MSM.923147

Available online: 2020-07-23

Published: 2020-09-15

BACKGROUND: For future development of machine learning tools for gait impairment assessment after stroke, simple observational whole-body clinical scales are required. Current observational scales regard either only leg movement or discrete overall parameters, neglecting dysfunctions in the trunk and arms. The purpose of this study was to introduce a new multiple-cue observational scale, called the stroke mobility score (SMS).
MATERIAL AND METHODS: In a group of 131 patients, we developed a 1-page manual involving 6 subscores by Delphi method using the video-based SMS: trunk posture, leg movement of the most affected side, arm movement of the most affected side, walking speed, gait fluency and stability/risk of falling. Six medical raters then validated the SMS on a sample of 60 additional stroke patients. Conventional scales (NIHSS, Timed-Up-And-Go-Test, 10-Meter-Walk-Test, Berg Balance Scale, FIM-Item L, Barthel Index) were also applied.
RESULTS: (1) High consistency and excellent inter-rater reliability of the SMS were verified (Cronbach’s alpha >0.9). (2) The SMS subscores are non-redundant and reveal much more nuanced whole-body dysfunction details than conventional scores, although evident correlations as e.g. between 10-Meter-Walk-Test and subscore “gait speed” are verified. (3) The analysis of cross-correlations between SMS subscores unveils new functional interrelationships for stroke profiling.
CONCLUSIONS: The SMS proves to be an easy-to-use, tele-applicable, robust, consistent, reliable, and nuanced functional scale of gait impairments after stroke. Due to its sensitivity to whole-body motion criteria, it is ideally suited for machine learning algorithms and for development of new therapy strategies based on instrumented gait analysis.

Keywords: Gait Disorders, Neurologic, Stroke, Symptom Assessment, Treatment Outcome