Journal Article

FZJ-2021-05156

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png JTrack: A Digital Biomarker Platform for Remote Monitoring of Daily-Life Behaviour in Health and Disease

Sahandi Far, M.FZJ* ; Stolz, M. ; Fischer, J. M.FZJ* ; Eickhoff, S. B.FZJ* ; Dukart, J. (Corresponding author)FZJ*

2021
Frontiers Media Lausanne

This record in other databases:      

Please use a persistent id in citations: http://hdl.handle.net/2128/29479  doi:10.3389/fpubh.2021.763621

Abstract: Background: Digital biomarkers (DB), as captured using sensors embedded in modern smart devices, are a promising technology for home-based sign and symptom monitoring in Parkinson disease (PD).Objective: Despite extensive application in recent studies, test-retest reliability and longitudinal stability of DB have not been well addressed in this context. We utilized the large-scale m-Power data set to establish the test-retest reliability and longitudinal stability of gait, balance, voice, and tapping tasks in an unsupervised and self-administered daily life setting in patients with PD and healthy controls (HC).Methods: Intraclass correlation coefficients were computed to estimate the test-retest reliability of features that also differentiate between patients with PD and healthy volunteers. In addition, we tested for longitudinal stability of DB measures in PD and HC, as well as for their sensitivity to PD medication effects.Results: Among the features differing between PD and HC, only a few tapping and voice features had good to excellent test-retest reliabilities and medium to large effect sizes. All other features performed poorly in this respect. Only a few features were sensitive to medication effects. The longitudinal analyses revealed significant alterations over time across a variety of features and in particular for the tapping task.Conclusions: These results indicate the need for further development of more standardized, sensitive, and reliable DB for application in self-administered remote studies in patients with PD. Motivational, learning, and other confounders may cause variations in performance that need to be considered in DB longitudinal applications.

---

Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5254 - Neuroscientific Data Analytics and AI (POF4-525) (POF4-525)

Appears in the scientific report 2021

---

Database coverage:
; ; ; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Social and Behavioral Sciences ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Social Sciences Citation Index ; Web of Science Core Collection

---