Biometric Digital Health Technology for Measuring Motor Function in Parkinson’s Disease: Results from a Feasibility and Patient Satisfaction Study

Abstract

ObjectivesTo assess the feasibility, predictive value, and user satisfaction of objectively quantifying motor function in Parkinson’s disease (PD) through a tablet-based application (iMotor) using self-administered tests.MethodsPD and healthy controls (HCs) performed finger tapping, hand pronation–supination and reaction time tasks using the iMotor application.ResultsThirty-eight participants (19 with PD and 17 HCs) were recruited in the study. PD subjects were 53% male, with a mean age of 67.8 years (±8.8), mean disease duration of 6.5 years (±4.6), Movement Disorders Society version of the Unified Parkinson Disease Rating Scale III score 26.3 (±6.7), and Hoehn & Yahr stage 2. In the univariate analysis, most tapping variables were significantly different in PD compared to HC. Tap interval provided the highest predictive ability (90%). In the multivariable logistic regression model reaction time (reaction time test) (p = 0.021) and total taps (two-target test) (p = 0.026) were associated with PD. A combined model with two-target (total taps and accuracy) and reaction time produced maximum discriminatory performance between HC and PD. The overall accuracy of the combined model was 0.98 (95% confidence interval: 0.93–1). iMotor use achieved high rates of patients’ satisfaction as evaluated by a patient satisfaction survey.ConclusioniMotor differentiated PD subjects from HCs using simple alternating tasks of motor function. Results of this feasibility study should be replicated in larger, longitudinal, appropriately designed, controlled studies. The impact on patient care of at-home iMotor-assisted remote monitoring also deserves further evaluation.

Tablet-Based Application for Objective Measurement of Motor Fluctuations in Parkinson Disease

“…It was estimated that a sample size of 11 was needed to detect between-group differences in motor performance with 80% power and a 1% level of significance, adjusted due to multiple tests, assuming at least a 20% mean difference in each test with a 15% standard deviation (SD), estimated based on a previous study using tapping tests that compared PD patients with controls [21], using a paired t test. Data are reported as means ± SD, where applicable.…”

“…Finger tapping data have been successfully used to evaluate motor function in PD [19-21]. Individuals with PD often have motor fluctuations in response to commonly used medications for the condition.…”

Application of Machine Learning in a Parkinson's Disease Digital Biomarker Dataset Using Neural Network Construction (NNC) Methodology Discriminates Patient Motor Status

“…Tapping tests conducted through the interface of fingers and/or other areas of the hand and a touch sensing screen has been shown to measure motor function in PD (Djuric-Jovicic et al, 2016;Giancardo et al, 2016;Mitsi et al, 2017). PD patients frequently develop fluctuations in motor function as a side effect of commonly used anti-parkinsonian medications.…”

“…iMotor is a clinically validated digital platform (iMotor; Apptomics Inc., Wellesley Hills, MA, USA) that utilizes the smart-tablet's screen sensing capabilities during upper limb function tests (similar to ones conducted in the neurologists' office) to objectively collect data that allow detection and quantification of neuromotor function. It has been tested in various movement disorders including PD and more recently Essential tremor (Mitsi et al, 2017). A series of screenshots from the application are presented in Figure 1.…”

“…We demonstrated that by using a variety of upper limb functional tests iMotor differentiated PD subjects from healthy volunteers (HV) (Mitsi et al, 2017;Wissel et al, 2017). The objective of this paper is to provide preliminary evidence that artificial intelligence systems may allow one to discriminate PD patients from HV further and determine different features of the disease within a cohort of PD subjects.…”