Abstract
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, 0.05; MAE: 2.08 ± 0.34 (s)) than average dominant handed 9HPTs (r2 = 0.39, 0.05; MAE: 2.32 ± 0.43 (s)), using simple linear IRLS ( 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.
Highlights
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system, affecting more than 2 million people worldwide [1]
Features were selected using LASSO and those features each presented to linear models where this study investigated the performance of ordinary least squares (OLS), and iteratively re-weighted least squares (IRLS), which minimizes the weighted sum of square using a ‘bisquare weighting’ function [40, 43]
The present study examines upper extremity (UE) function in people with MS’ (PwMS) with mild-to-moderate disability in comparison with healthy controls (HC) using Draw a Shape (DaS), a self-administered digital drawing test captured on a smartphone, and demonstrates how modelling of DaS features from a test can reliably predict the average time of the clinician-administered 9-Hole Peg Test (9HPT)
Summary
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system, affecting more than 2 million people worldwide [1]. While various performance tests and patient-reported outcome measures are available [6], the 9-Hole Peg Test (9HPT) is the most frequently used measure of manual dexterity in MS research, clinical trials and clinical practice [7, 8]. Along with the Expanded Disability Status Scale (EDSS) and timed 25-foot walk (T25FW), the 9HPT is typically used as a standardized upper extremity outcome measure in MS and is integrated in the so-called Multiple Sclerosis Functional Composite (MSFC) [8,9,10,11]
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