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Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.

ABSTRACT: Technological advances in passive digital phenotyping present the opportunity to quantify neurological diseases using new approaches that may complement clinical assessments. Here, we studied multiple sclerosis (MS) as a model neurological disease for investigating physiometric and environmental signals. The objective of this study was to assess the feasibility and correlation of wearable biosensors with traditional clinical measures of disability both in clinic and in free-living in MS patients. This is a single site observational cohort study conducted at an academic neurological center specializing in MS. A cohort of 25 MS patients with varying disability scores were recruited. Patients were monitored in clinic while wearing biosensors at nine body locations at three separate visits. Biosensor-derived features including aspects of gait (stance time, turn angle, mean turn velocity) and balance were collected, along with standardized disability scores assessed by a neurologist. Participants also wore up to three sensors on the wrist, ankle, and sternum for 8 weeks as they went about their daily lives. The primary outcomes were feasibility, adherence, as well as correlation of biosensor-derived metrics with traditional neurologist-assessed clinical measures of disability. We used machine-learning algorithms to extract multiple features of motion and dexterity and correlated these measures with more traditional measures of neurological disability, including the expanded disability status scale (EDSS) and the MS functional composite-4 (MSFC-4). In free-living, sleep measures were additionally collected. Twenty-three subjects completed the first two of three in-clinic study visits and the 8-week free-living biosensor period. Several biosensor-derived features significantly correlated with EDSS and MSFC-4 scores derived at visit two, including mobility stance time with MSFC-4 z-score (Spearman correlation -0.546; p?=?0.0070), several aspects of turning including turn angle (0.437; p?=?0.0372), and maximum angular velocity (0.653; p?=?0.0007). Similar correlations were observed at subsequent clinic visits, and in the free-living setting. We also found other passively collected signals, including measures of sleep, that correlated with disease severity. These findings demonstrate the feasibility of applying passive biosensor measurement techniques to monitor disability in MS patients both in clinic and in the free-living setting.

SUBMITTER: Chitnis T

PROVIDER: S-EPMC6906296 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.

Chitnis Tanuja T Glanz Bonnie I BI Gonzalez Cindy C Healy Brian C BC Saraceno Taylor J TJ Sattarnezhad Neda N Diaz-Cruz Camilo C Polgar-Turcsanyi Mariann M Tummala Subhash S Bakshi Rohit R Bajaj Vikram S VS Ben-Shimol David D Bikhchandani Nikhil N Blocker Alexander W AW Burkart Joshua J Cendrillon Raphael R Cusack Michael P MP Demiralp Emre E Jooste Sarel Kobus SK Kharbouch Alaa A Lee Amy A AA Lehár Joseph J Liu Manway M Mahadevan Swaminathan S Murphy Mark M Norton Linda C LC Parlikar Tushar A TA Pathak Anupam A Shoeb Ali A Soderberg Erin E Stephens Philip P Stoertz Aaron H AH Thng Florence F Tumkur Kashyap K Wang Hongsheng H Rhodes Jane J Rudick Richard A RA Ransohoff Richard M RM Phillips Glenn A GA Bruzik Effie E Marks William J WJ Weiner Howard L HL Snyder Thomas M TM

NPJ digital medicine 20191211

Technological advances in passive digital phenotyping present the opportunity to quantify neurological diseases using new approaches that may complement clinical assessments. Here, we studied multiple sclerosis (MS) as a model neurological disease for investigating physiometric and environmental signals. The objective of this study was to assess the feasibility and correlation of wearable biosensors with traditional clinical measures of disability both in clinic and in free-living in MS patients ...[more]

PMID: 31840094

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Project description:BackgroundWearable sensors offer the potential to bring new knowledge to inform interventions in patients affected by multiple sclerosis (MS) by thoroughly quantifying gait characteristics and gait deficits from prolonged daily living measurements. The aim of this study was to characterise gait in both laboratory and daily life conditions for a group of patients with moderate to severe ambulatory impairment due to MS. To this purpose, algorithms to detect and characterise gait from wearable inertial sensors data were also validated.MethodsFourteen patients with MS were divided into two groups according to their disability level (EDSS 6.5-6.0 and EDSS 5.5-5.0, respectively). They performed both intermittent and continuous walking bouts (WBs) in a gait laboratory wearing waist and shank mounted inertial sensors. An algorithm (W-CWT) to estimate gait events and temporal parameters (mean and variability values) using data recorded from the waist mounted sensor (Dynaport, Mc Roberts) was tested against a reference algorithm (S-REF) based on the shank-worn sensors (OPAL, APDM). Subsequently, the accuracy of another algorithm (W-PAM) to detect and classify WBs was also tested. The validated algorithms were then used to quantify gait characteristics during short (sWB, 5-50 steps), intermediate (iWB, 51-100 steps) and long (lWB, >100 steps) daily living WBs and laboratory walking. Group means were compared using a two-way ANOVA.ResultsW-CWT compared to S-REF showed good gait event accuracy (0.05-0.10 s absolute error) and was not influenced by disability level. It slightly overestimated stride time in intermittent walking (0.012 s) and overestimated highly variability of temporal parameters in both intermittent (17.5%-58.2%) and continuous walking (11.2%-76.7%). The accuracy of W-PAM was speed-dependent and decreased with increasing disability. The ANOVA analysis showed that patients walked at a slower pace in daily living than in the laboratory. In daily living gait, all mean temporal parameters decreased as the WB duration increased. In the sWB, the patients with a lower disability score showed, on average, lower values of the temporal parameters. Variability decreased as the WB duration increased.ConclusionsThis study validated a method to quantify walking in real life in people with MS and showed how gait characteristics estimated from short walking bouts during daily living may be the most informative to quantify level of disability and effects of interventions in patients moderately affected by MS. The study provides a robust approach for the quantification of recognised clinically relevant outcomes and an innovative perspective in the study of real life walking.

Dataset Information

Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.

Publications

Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.

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