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      Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study

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          Abstract

          Background

          Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments.

          Objective

          This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD.

          Methods

          The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation.

          Results

          From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor.

          Conclusions

          We present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.

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          Most cited references45

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          The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms

          P. Welch (1967)
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            Why We (Usually) Don't Have to Worry About Multiple Comparisons

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              Levodopa Is a Double-Edged Sword for Balance and Gait in People With Parkinson's Disease.

              The effects of levodopa on balance and gait function in people with Parkinson's disease (PD) is controversial. This study compared the relative responsiveness to l-dopa on six domains of balance and gait: postural sway in stance; gait pace; dynamic stability; gait initiation; arm swing; and turning in people with mild and severe PD, with and without dyskinesia.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                October 2020
                9 October 2020
                : 22
                : 10
                : e19068
                Affiliations
                [1 ] Center of Expertise for Parkinson and Movement Disorders, department of Neurology Donders Institute for Brain, Cognition and Behaviour Radboud University Medical Center Nijmegen Netherlands
                [2 ] Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands
                [3 ] Department of Mathematics School of Engineering and Applied Sciences Aston University Birmingham United Kingdom
                [4 ] School of Computer Science University of Birmingham Birmingham United Kingdom
                [5 ] UCB Pharma Brussels Belgium
                [6 ] Scientific Center for Quality of Healthcare (IQ healthcare) Radboud Institute for Health Sciences Radboud University Medical Center Nijmegen Netherlands
                Author notes
                Corresponding Author: Luc JW Evers luc.evers@ 123456radboudumc.nl
                Author information
                https://orcid.org/0000-0002-8241-5087
                https://orcid.org/0000-0003-0753-717X
                https://orcid.org/0000-0003-3435-6358
                https://orcid.org/0000-0002-0948-9290
                https://orcid.org/0000-0002-9291-7240
                https://orcid.org/0000-0003-1674-9940
                https://orcid.org/0000-0002-3398-5235
                https://orcid.org/0000-0002-1507-3822
                https://orcid.org/0000-0001-6491-7035
                https://orcid.org/0000-0002-6371-3337
                Article
                v22i10e19068
                10.2196/19068
                7584982
                33034562
                79168c74-c36a-4d66-bab7-665064141775
                ©Luc JW Evers, Yordan P Raykov, Jesse H Krijthe, Ana Lígia Silva de Lima, Reham Badawy, Kasper Claes, Tom M Heskes, Max A Little, Marjan J Meinders, Bastiaan R Bloem. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 09.10.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 2 April 2020
                : 22 June 2020
                : 10 August 2020
                : 21 August 2020
                Categories
                Original Paper
                Original Paper

                Medicine
                digital biomarkers,remote patient monitoring,wearable sensors,real-life gait,parkinson disease,biomarker,patient monitoring,wearables,gait

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