Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape Test

• Published in: Physiological measurement Vol. 41; no. 5; pp. 54002 - 54016
• Main Authors: Creagh, A P, Simillion, C, Scotland, A, Lipsmeier, F, Bernasconi, C, Belachew, S, van Beek, J, Baker, M, Gossens, C, Lindemann, M, De Vos, M
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.05.2020
• Subjects: Adult; Diagnostic Techniques and Procedures - instrumentation; digital biomarkers, multiple sclerosis, hand and upper extremity function, smartphone; Female; Humans; Male; Multiple Sclerosis - diagnosis; Multiple Sclerosis - physiopathology; Regression Analysis; Smartphone; Upper Extremity - physiopathology
• ISSN: 0967-3334; 1361-6579; 1361-6579
• DOI: 10.1088/1361-6579/ab8771

Objective: Smartphone devices may enable out-of-clinic assessments in chronic neurological diseases. We describe the Draw a Shape (DaS) Test, a smartphone-based and remotely administered test of Upper Extremity (UE) function developed for people with multiple sclerosis (PwMS). This work introduces DaS-related features that characterise UE function and impairment, and aims to demonstrate how multivariate modelling of these metrics can reliably predict the 9-Hole Peg Test (9HPT), a clinician-administered UE assessment in PwMS. Approach: The DaS Test instructed PwMS and healthy controls (HC) to trace predefined shapes on a smartphone screen. A total of 93 subjects (HC, n = 22; PwMS, n = 71) contributed both dominant and non-dominant handed DaS tests. PwMS subjects were characterised as those with normal (nPwMS, n = 50) and abnormal UE function (aPwMS, n = 21) with respect to their average 9HPT time (≤ or > 22.7 (s), respectively). L1-regularization techniques, combined with linear least squares (OLS, IRLS), or non-linear support vector (SVR) or random forest (RFR) regression were investigated as functions to map relevant DaS features to 9HPT times. Main results: It was observed that average non-dominant handed 9HPT times were more accurately predicted by DaS features (r2 = 0.41, P< 0.05; MAE: 2.08 ± 0.34 (s)) than average dominant handed 9HPTs (r2 = 0.39, P< 0.05; MAE: 2.32 ± 0.43 (s)), using simple linear IRLS ( P< 0.01). Moreover, it was found that the Mean absolute error (MAE) in predicted 9HPTs was comparable to the variability of actual 9HPT times within HC, nPwMS and aPwMS groups respectively. The 9HPT however exhibited large heteroscedasticity resulting in less stable predictions of longer 9HPT times. Significance: This study demonstrates the potential of the smartphone-based DaS Test to reliably predict 9HPT times and remotely monitor UE function in PwMS.