Institute of Biomedical Engineering, University of Oxford, Oxford United Kingdom. F. Hoffmann-La Roche Ltd Basel, Switzerland.
Physiol Meas. 2020 Jun 19;41(5):054002. doi: 10.1088/1361-6579/ab8771.
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.
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 -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.
It was observed that average non-dominant handed 9HPT times were more accurately predicted by DaS features (r = 0.41, [Formula: see text] 0.05; MAE: 2.08 ± 0.34 (s)) than average dominant handed 9HPTs (r = 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.
This study demonstrates the potential of the smartphone-based DaS Test to reliably predict 9HPT times and remotely monitor UE function in PwMS.
智能手机设备可以实现慢性神经系统疾病的门诊外评估。我们描述了 Draw a Shape(DaS)测试,这是一种基于智能手机和远程管理的上肢(UE)功能测试,专为多发性硬化症(PwMS)患者开发。本研究介绍了与 DaS 相关的特征,这些特征可用于描述 UE 功能和损伤,并旨在证明对这些指标进行多元建模如何能够可靠地预测 9 孔钉测试(9HPT),这是一种针对 PwMS 患者的临床医生管理的 UE 评估。
DaS 测试指示 PwMS 和健康对照组(HC)在智能手机屏幕上追踪预定义的形状。共有 93 名受试者(HC,n = 22;PwMS,n = 71)分别贡献了惯用手和非惯用手的 DaS 测试。根据他们的平均 9HPT 时间(分别为≤或> 22.7(s)),将 PwMS 患者定义为 UE 功能正常(nPwMS,n = 50)和异常(aPwMS,n = 21)。研究了 L-正则化技术,结合线性最小二乘法(OLS,IRLS)、非线性支持向量(SVR)或随机森林(RFR)回归,作为将相关 DaS 特征映射到 9HPT 时间的函数。
观察到非惯用手的平均 9HPT 时间比惯用手的平均 9HPT 时间更能被 DaS 特征准确预测(r = 0.41,[公式:见正文] 0.05;MAE:2.08 ± 0.34(s)),使用简单的线性 IRLS([公式:见正文] 0.01)。此外,研究发现,与 HC、nPwMS 和 aPwMS 组内实际 9HPT 时间的变异性相比,预测的 9HPT 的平均绝对误差(MAE)是相当的。然而,9HPT 表现出较大的异方差性,导致较长 9HPT 时间的预测不太稳定。
本研究证明了基于智能手机的 DaS 测试具有可靠地预测 9HPT 时间和远程监测 PwMS 患者 UE 功能的潜力。