Analytical and Cross-Sectional Clinical Validity of a Smartphone-Based U-Turn Test in Multiple Sclerosis
Marta P{\l}onka, Rafa{\l} Klimas, Dimitar Stanev, Lorenza Angelini, Natan Napi\'orkowski, Gabriela Gonz\'alez Chan, Lisa Bunn, Paul S Glazier, Richard Hosking, Jenny Freeman, Jeremy Hobart, Mattia Zanon, Jonathan Marsden, Licinio Craveiro, Mike D Rinderknecht

TL;DR
This study validates a smartphone-based U-Turn Test for assessing dynamic balance in multiple sclerosis patients, demonstrating high accuracy, reliability, and meaningful correlations with clinical measures.
Contribution
It provides the first comprehensive validation of a smartphone U-Turn Test for dynamic balance assessment in PwMS across supervised and unsupervised settings.
Findings
High accuracy of turn detection (>95%) across wear locations.
Strong agreement with motion capture (ICC > 0.87).
High test-retest reliability (ICC > 0.90) with multiple tests.
Abstract
Background: Gait and balance impairment can profoundly impact people with multiple sclerosis (PwMS). Objectives: To evaluate the analytical and clinical validity of the U-Turn Test (UTT), a smartphone-based assessment of dynamic balance in PwMS. Methods: The GaitLab study (ISRCTN15993728) enrolled adult PwMS (EDSS 0.0-6.5). PwMS performed the UTT in a gait laboratory (supervised) using 6 smartphones at different wear locations and daily during a two-week remote period (unsupervised) using one smartphone (belt front). Median turn speed was computed per UTT. In the supervised setting, turn detection accuracy of smartphones was compared to motion capture (mocap) via F1 scores. Agreement between smartphone- and mocap-derived turn speed was assessed by Bland-Altman and ICC(3,1). In the unsupervised setting, test-retest reliability (ICC[2,1]) and correlations with Timed 25-Foot Walk (T25FW),…
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