Reference : Device-based assessment through a mobile application in the Luxembourg Parkinson Cohort
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Human health sciences : Neurology
Systems Biomedicine
http://hdl.handle.net/10993/37575
Device-based assessment through a mobile application in the Luxembourg Parkinson Cohort
English
Stallinger, Christian []
Satagopam, Venkata []
Banda, Peter mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Kolber, Pierre []
Suver, Christine []
Trister, Andrew []
Friend, Stephen []
Rejko, Krüger []
May-2017
Basal Ganglia
Elsevier
8
2
Yes
No
International
German Congress on Parkinson and Movement Disorders
2017
[en] Parkinson's disease
[en] Introduction: The project focuses on the integration device-based assessment (DBA) with a mobile application (mPower) into the longitudinal deeply-phenotyped HELP-PD (Health in the Elderly Luxembourgish Population with a focus on Parkinson’s disease) cohort for patients with Parkinsonism in Luxembourg and the Greater Region to monitor frequency and degree of variation in symptoms of Parkinsonism, to identify potential sources and modulators of variation and to evaluate how symptoms are correlated with these modulators across patients.

Methods: We integrate for the first time the mPower iOS app into a deeply phenotyped cohort. mPower is one of the first apps to use Apple’s Research Kit framework and combines a traditional survey-based approach with more granular and precise data gained from a person’s iPhone related to sensor- (e.g. step count, GPS-tracking) or task-based assessments (e.g. finger tapping, tremor detection, sustained phonation, simple gait analysis, memory test). Anonymized longitudinal data is sent to a repository, then retrieved, matched, and correlated with conventional HELP-PD data from a total of 47 screening instruments for motor and non-motor functions in Parkinsonism obtained from annual visits of study participants. 14 patients with clinically confirmed IPD are currently included in the pilot phase.

Results/Discussion: We modified the mPower app and successfully integrated it into HELP-PD’s novel database infrastructure, allowing for a wide variety of analyses. The reporting system is able to handle multiple DBAs, with the implementation of an in-depth gait analysis system currently pending. Considerable attention was given to data protection. The system is currently fully functional with the pilot phase having started in June 2016. First correlations with traditional clinical data are planned for early 2017.
http://hdl.handle.net/10993/37575
10.1016/j.baga.2017.02.006

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