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Leveraging consumer technology such as smartphone and smartwatch devices to objectively assess people with multiple sclerosis (PwMS) remotely could capture unique aspects of disease progression. This study explores the feasibility of assessing PwMS and Healthy Control's (HC) physical function by characterising gait-related features, which can be modelled using machine learning (ML) techniques to correctly distinguish subgroups of PwMS from healthy controls. A total of 97 subjects (24 HC subjects, 52 mildly disabled (PwMSmild, EDSS [0-3]) and 21 moderately disabled (PwMSmod, EDSS [3.5-5.5]) contributed data which was recorded from a Two-Minute Walk Test (2MWT) performed out-of-clinic and daily over a 24-week period. Signal-based features relating to movement were extracted from sensors in smartphone and smartwatch devices. A large number of features (n = 156) showed fair-to-strong (R 0.3) correlations with clinical outcomes. LASSO feature selection was applied to select and rank subsets of features used for dichotomous classification between subject groups, which were compared using Logistic Regression (LR), Support Vector Machines (SVM) and Random Forest (RF) models. Classifications of subject types were compared using data obtained from smartphone, smartwatch and the fusion of features from both devices. Models built on smartphone features alone achieved the highest classification performance, indicating that accurate and remote measurement of the ambulatory characteristics of HC and PwMS can be achieved with only one device. It was observed however that smartphone-based performance was affected by inconsistent placement location (running belt versus pocket). Results show that PwMSmod could be distinguished from HC subjects (Acc. 82.2 ± 2.9%, Sen. 80.1 ± 3.9%, Spec. 87.2 ± 4.2%, F 1 84.3 ± 3.8), and PwMSmild (Acc. 82.3 ± 1.9%, Sen. 71.6 ± 4.2%, Spec. 87.0 ± 3.2%, F 1 75.1 ± 2.2) using an SVM classifier with a Radial Basis Function (RBF). PwMSmild were shown to exhibit HC-like behaviour and were thus less distinguishable from HC (Acc. 66.4 ± 4.5%, Sen. 67.5 ± 5.7%, Spec. 60.3 ± 6.7%, F 1 58.6 ± 5.8). Finally, it was observed that subjects in this study demonstrated low intra- and high inter-subject variability which was representative of subject-specific gait characteristics. ispartof: Ieee Journal Of Biomedical And Health Informatics vol:25 issue:3 pages:838-849 ispartof: location:United States status: published

BACKGROUND With the advent of smart sensing technology, mobile and wearable devices can provide continuous and objective monitoring and assessment of motor function outcomes. OBJECTIVE We aimed to describe the existing scientific literature on wearable and mobile technologies that are being used or tested for assessing motor functions in mobility-impaired and healthy adults, and to evaluate the degree to which these devices provide clinically valid measures of motor function in these populations. METHODS A systematic literature review was conducted by searching Embase, MEDLINE®, CENTRAL (January 1, 2015 – June 24, 2020), the United States and European Union clinical trial registries, and the United States Food and Drug Administration website using predefined study selection criteria. Study selection, data extraction, and quality assessment were performed by two independent reviewers. RESULTS Ninety-one publications representing 87 unique studies were included. The most represented clinical conditions were Parkinson’s disease (k = 51 studies), followed by stroke (k = 5), Huntington’s disease (k = 5), and multiple sclerosis (k = 2). A total of 42 motion-detecting devices were identified, and the majority (k = 27; 64%) were created for the purpose of healthcare-related data collection, though approximately 25% were personal electronic devices (e.g., smartphones, watches) and 11% were entertainment consoles (e.g., Microsoft Kinect/Xbox™, Nintendo Wii™). The primary motion outcomes were related to gait (k = 30), gross motor movements (k = 25), and fine motor movements (k = 23). As a group, sensor-derived motion data showed a mean sensitivity of .83 (SD = 7.27), mean specificity of .84 (SD = 15.40), mean accuracy of .90 (SD = 5.87) in discriminating between diseased individuals and healthy controls, and mean Pearson’s r validity coefficient of .52 (SD = 0.22) relative to clinical measures. We did not find significant differences in the degree of validity between in-lab and at-home sensor-based assessments, nor between device class (i.e., healthcare-related device, personal electronic devices, entertainment consoles). CONCLUSIONS Sensor-derived motion data can be leveraged to classify and quantify disease status for a variety of neurological conditions. However, most of recent research on digital clinical measures is derived from proof-of-concept studies with considerable variation in methodological approaches, and much of the reviewed literature has focused on clinical validation, with less than a quarter of the studies performing analytical validation. Overall, future research is crucially needed to further consolidate that sensor-derived motion data may lead to the development of robust and transformative digital measurements intended to predict, diagnose, and quantify neurological disease state and its longitudinal change.

Background: Sensor-based monitoring tools fill a critical gap in multiple sclerosis (MS) research and clinical care. Objective: The aim of this study is to assess performance characteristics of the Floodlight Proof-of-Concept (PoC) app. Methods: In a 24-week study (clinicaltrials.gov: NCT02952911), smartphone-based active tests and passive monitoring assessed cognition (electronic Symbol Digit Modalities Test), upper extremity function (Pinching Test, Draw a Shape Test), and gait and balance (Static Balance Test, U-Turn Test, Walk Test, Passive Monitoring). Intraclass correlation coefficients (ICCs) and age- or sex-adjusted Spearman’s rank correlation determined test–retest reliability and correlations with clinical and magnetic resonance imaging (MRI) outcome measures, respectively. Results: Seventy-six people with MS (PwMS) and 25 healthy controls were enrolled. In PwMS, ICCs were moderate-to-good (ICC(2,1) = 0.61–0.85) across tests. Correlations with domain-specific standard clinical disability measures were significant for all tests in the cognitive ( r = 0.82, p < 0.001), upper extremity function (|r|= 0.40–0.64, all p < 0.001), and gait and balance domains ( r = −0.25 to −0.52, all p < 0.05; except for Static Balance Test: r = −0.20, p > 0.05). Most tests also correlated with Expanded Disability Status Scale, 29-item Multiple Sclerosis Impact Scale items or subscales, and/or normalized brain volume. Conclusion: The Floodlight PoC app captures reliable and clinically relevant measures of functional impairment in MS, supporting its potential use in clinical research and practice.

The measurement of gait characteristics during a self-administered 2-minute walk test (2MWT), in persons with multiple sclerosis (PwMS), using a single body-worn device, has the potential to provide high-density longitudinal information on disease progression, beyond what is currently measured in the clinician-administered 2MWT. The purpose of this study is to determine the test-retest reliability, standard error of measurement (SEM) and minimum detectable change (MDC) of features calculated on gait characteristics, harvested during a self-administered 2MWT in a home environment, in 51 PwMS and 11 healthy control (HC) subjects over 24 weeks, using a single waist-worn inertial sensor-based smartphone. Excellent, or good to excellent test-retest reliability were observed in 58 of the 92 temporal, spatial and spatiotemporal gait features in PwMS. However, these were less reliable for HCs. Low SEM% and MDC% values were observed for most of the distribution measures for all gait characteristics for PwMS and HCs. This study demonstrates the inter-session test-retest reliability and provides an indication of clinically important change estimates, for interpreting the outcomes of gait characteristics measured using a body-worn smartphone, during a self-administered 2MWT. This system thus provides a reliable measure of gait characteristics in PwMS, supporting its application for the longitudinal assessment of gait deficits in this population.

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, P < 0.05; MAE: 2.08 ± 0.34 (s)) than average dominant handed 9HPTs (r 2 = 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.

Background: Ubiquitous digital technologies such as smartphone sensors promise to fundamentally change biomedical research and treatment monitoring in neurological diseases such as PD, creating a new domain of digital biomarkers. Objectives: The present study assessed the feasibility, reliability, and validity of smartphone‐based digital biomarkers of PD in a clinical trial setting. Methods: During a 6‐month, phase 1b clinical trial with 44 Parkinson participants, and an independent, 45‐day study in 35 age‐matched healthy controls, participants completed six daily motor active tests (sustained phonation, rest tremor, postural tremor, finger‐tapping, balance, and gait), then carried the smartphone during the day (passive monitoring), enabling assessment of, for example, time spent walking and sit‐to‐stand transitions by gyroscopic and accelerometer data. Results: Adherence was acceptable: Patients completed active testing on average 3.5 of 7 times/week. Sensor‐based features showed moderate‐to‐excellent test‐retest reliability (average intraclass correlation coefficient = 0.84). All active and passive features significantly differentiated PD from controls with P

Background Current clinical assessments of people with multiple sclerosis are episodic and may miss critical features of functional fluctuations between visits. Objective The goal of the research was to assess the feasibility of remote active testing and passive monitoring using smartphones and smartwatch technology in people with multiple sclerosis with respect to adherence and satisfaction with the FLOODLIGHT test battery. Methods People with multiple sclerosis (aged 20 to 57 years; Expanded Disability Status Scale 0-5.5; n=76) and healthy controls (n=25) performed the FLOODLIGHT test battery, comprising active tests (daily, weekly, every two weeks, or on demand) and passive monitoring (sensor-based gait and mobility) for 24 weeks using a smartphone and smartwatch. The primary analysis assessed adherence (proportion of weeks with at least 3 days of completed testing and 4 hours per day passive monitoring) and questionnaire-based satisfaction. In-clinic assessments (clinical and magnetic resonance imaging) were performed. Results People with multiple sclerosis showed 70% (16.68/24 weeks) adherence to active tests and 79% (18.89/24 weeks) to passive monitoring; satisfaction score was on average 73.7 out of 100. Neither adherence nor satisfaction was associated with specific population characteristics. Test-battery assessments had an at least acceptable impact on daily activities in over 80% (61/72) of people with multiple sclerosis. Conclusions People with multiple sclerosis were engaged and satisfied with the FLOODLIGHT test battery. FLOODLIGHT sensor-based measures may enable continuous assessment of multiple sclerosis disease in clinical trials and real-world settings. Trial Registration ClinicalTrials.gov: NCT02952911; https://clinicaltrials.gov/ct2/show/NCT02952911

Smartphone-based assessments have been considered a potential solution for continuously monitoring gait and mobility in mild to moderate Parkinson’s disease (PD) patients. Forty-four PD patients from cohorts 4 to 6 of the Multiple Ascending Dose (MAD) study of PRX002/RG7935 and thirty-five age- and gender-matched healthy individuals (i.e. healthy controls - HC) in a separate study performed smartphone-based assessments for up to 24 weeks and up to 6 weeks, respectively. The assessments included “active gait tests”, where all participants were asked to walk for 30 s with at least one 180\(^\circ \) turn, and “passive monitoring”, in which subjects carried the smartphone in a pocket or fanny pack as part of their daily routine. In total, over 6,600 active gait tests and over 30,000 h of passive monitoring data were collected. A mobility analysis indicates that patients with PD are less mobile than HCs, as manifested in time spent in gait-related activities, number of turns and sit-to-stand transitions, and power per step. It supports the potential use of smartphones for continuous mobility monitoring in future clinical practice and drug development.

Smartphone-based remote monitoring is a potential solution for providing long-term, objective assessment of gait and mobility in patients with Parkinsons disease (PD). In the Multiple Ascending Dose study of PRX002/RG7935, forty-four mild to moderate PD patients from cohorts 4 to 6 were included in a smartphone-based assessment for up to 24 weeks, while in a separate control study, thirty-five age-and gender-matched healthy individuals performed the same assessment up to 6 weeks. In total, over 30,000 hours of sensor data from subjects' daily activities were collected. A convolutional recurrent neural network was used for human activity recognition and extracted gait-related activities, followed by a mobility analysis on extracted mobility features during ambulatory bouts and turns. The analysis revealed that PD patients showed significantly lower mobility in terms of average ambulatory bout length — length of time of one continuous ambulatory segment, average per-step power, turn speed, and number of turns per ambulatory minute. In addition, bout-length stratified analysis shows the between-group difference of multiple features is associated with bout lengths. These study results support the potential use of smartphones for long-term mobility monitoring in future clinical practice, and also shed lights on previously inaccessible relationships between bout length and gait features under free-living condition.

Smartphone-based assessments have been considered a potential solution to passively monitor gait and mobility in early-stage Parkinson's disease (PD) patients. In the Multiple Ascending Dose clinical trial of PRX002/RG7935, 44 PD patients and 35 age- and gender-matched healthy individuals performed smartphone-based assessments for up to 24 weeks and up to 6 weeks respectively. For "passive monitoring", subjects carried the smartphone with them as part of their daily routine, while sensors in the smartphone recording movement data continuously. In total, over 30,000 hours of passive monitoring data were collected. To classify the sensor signal into activity profiles, we built a Human Activity Recognition (HAR) model using Deep Neural Networks (DNN) trained on previously published data. The activity profiles of the participants determined by the HAR model showed significant differences between PD patients and healthy controls in the percentage of time walking and frequency in which subjects changed positions (sitting and standing). This combination of sensor data and machine learning-based activity profiling was shown to hold great promise for use in future clinical practice and drug development.