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). L 1-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 (r 2 = 0.41, [Formula: see text] 0.05; MAE: 2.08 ± 0.34 (s)) than average dominant handed 9HPTs (r 2 = 0.39, [Formula: see text] 0.05; MAE: 2.32 ± 0.43 (s)), using simple linear IRLS ([Formula: see text] 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.
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). L 1-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 (r 2 = 0.41, [Formula: see text] 0.05; MAE: 2.08 ± 0.34 (s)) than average dominant handed 9HPTs (r 2 = 0.39, [Formula: see text] 0.05; MAE: 2.32 ± 0.43 (s)), using simple linear IRLS ([Formula: see text] 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.
Authors: Kaylene M Young; Bruce V Taylor; Kalina Makowiecki; Natasha Stevens; Carlie L Cullen; Amin Zarghami; Phuong Tram Nguyen; Lewis Johnson; Jennifer Rodger; Mark R Hinder; Michael Barnett Journal: Trials Date: 2022-08-03 Impact factor: 2.728
Authors: Xavier Montalban; Jennifer Graves; Luciana Midaglia; Patricia Mulero; Laura Julian; Michael Baker; Jan Schadrack; Christian Gossens; Marco Ganzetti; Alf Scotland; Florian Lipsmeier; Johan van Beek; Corrado Bernasconi; Shibeshih Belachew; Michael Lindemann; Stephen L Hauser Journal: Mult Scler Date: 2021-07-14 Impact factor: 6.312