Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Shah, Vrutangkumar V.a | McNames, Jamesb; c | Mancini, Martinaa | Carlson-Kuhta, Patriciaa | Nutt, John G.a | El-Gohary, Mahmoudc | Lapidus, Jodi A.d | Horak, Fay B.a; c; * | Curtze, Carolina; e
Affiliations: [a] Department of Neurology, Oregon Health & Science University, Portland, OR, USA | [b] Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA | [c] APDM, Inc., Portland, OR, USA | [d] School of Public Health, Oregon Health & Science University–Portland State University, Portland, OR, USA | [e] Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, USA
Correspondence: [*] Correspondence to: Fay B. Horak, PhD, Department of Neurology, Oregon Health & Science University, 3181 Sam Jackson Park Road, Portland, OR 97239-3098, USA. Tel.: +1 503 704 3988; E-mail: horakf@ohsu.edu.
Abstract: Background:Identifying digital biomarkers of mobility is important for clinical trials in Parkinson’s disease (PD). Objective:To determine which digital outcome measures of mobility discriminate mobility in people with PD from healthy control (HC) subjects over a week of continuous monitoring. Methods:We recruited 29 people with PD, and 27 age-matched HC subjects. Subjects were asked to wear three inertial sensors (Opal by APDM) attached to both feet and to the lumbar region, and a subset of subjects also wore two wrist sensors, for a week of continuous monitoring. We derived 43 digital outcome measures of mobility grouped into five domains. An Area Under Curve (AUC) was calculated for each digital outcome measures of mobility, and logistic regression employing a ‘best subsets selection strategy’ was used to find combinations of measures that discriminated mobility in PD from HC. Results:Duration of recordings was 66±14 hours in the PD and 59±16 hours in the HC. Out of a total of 43 digital outcome measures of mobility, we found six digital outcome measures of mobility with AUC > 0.80. Turn angle (AUC = 0.89, 95% CI: 0.79–0.97) and swing time variability (AUC = 0.87, 95% CI: 0.75–0.96) were the most discriminative individual measures. Turning measures were most consistently selected via the best subsets strategy to discriminate people with PD from HC, followed by gait variability measures. Conclusion:Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures.
Keywords: Parkinson’s disease, digital outcome measures of mobility, inertial sensors, biomarkers, continuous monitoring
DOI: 10.3233/JPD-201914
Journal: Journal of Parkinson's Disease, vol. 10, no. 3, pp. 1099-1111, 2020
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl