Objective: To validate the smartphone sensor-based Draw a Shape Test - a part of the Floodlight Proof-of-Concept app for remotely assessing multiple sclerosis-related upper extremity impairment by tracing six different shapes.

Methods: People with multiple sclerosis, classified functionally normal/abnormal via their Nine-Hole Peg Test time, and healthy controls participated in a 24-week, nonrandomized study. Spatial (trace accuracy), temporal (mean and variability in linear, angular, and radial drawing velocities, and dwell time ratio), and spatiotemporal features (trace celerity) were cross-sectionally analyzed for correlation with standard clinical and brain magnetic resonance imaging (normalized brain volume and total lesion volume) disease burden measures, and for capacity to differentiate people with multiple sclerosis from healthy controls.

Results: Data from 69 people with multiple sclerosis and 18 healthy controls were analyzed. Trace accuracy (all shapes), linear velocity variability (circle, figure-of-8, spiral shapes), and radial velocity variability (spiral shape) had a mostly fair/moderate-to-good correlation (|r| = 0.14-0.66) with all disease burden measures. Trace celerity also had mostly fair/moderate-to-good correlation (|r| = 0.18-0.41) with Nine-Hole Peg Test performance, cerebellar functional system score, and brain magnetic resonance imaging. Furthermore, partial correlation analysis related these results to motor impairment. People with multiple sclerosis showed greater drawing velocity variability, though slower mean velocity, than healthy controls. Linear velocity (spiral shape) and angular velocity (circle shape) potentially differentiate functionally normal people with multiple sclerosis from healthy controls.

Interpretation: The Draw a Shape Test objectively assesses upper extremity impairment and correlates with all disease burden measures, thus aiding multiple sclerosis-related upper extremity impairment characterization.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9930424PMC
http://dx.doi.org/10.1002/acn3.51705DOI Listing

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