001014708 001__ 1014708
001014708 005__ 20231005201920.0
001014708 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-03403
001014708 037__ $$aFZJ-2023-03403
001014708 1001_ $$0P:(DE-Juel1)177922$$aSahandi Far, Mehran$$b0$$eCorresponding author
001014708 245__ $$aSensor-based Assessments as a Disease Progression and Treatment Biomarker for Neuropsychiatric Diseases$$f - 2023-08-22
001014708 260__ $$c2023
001014708 300__ $$a157
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001014708 502__ $$aDissertation, HHU Düsseldorf, 2023$$bDissertation$$cHHU Düsseldorf$$d2023$$o2023-05-26
001014708 520__ $$aThe growing trend of personalised health care and remote monitoring has led to increased interest in using embedded sensors in portable smart devices (smartphones and smartwatches) in clinical studies. Health-related data collected from such devices are referredtoasDigitalBiomarkers(DBs). Unliketraditionalin-clinicassessmentmethods, DBs provide cost-effective, objective, and ecologically valid data. DBs enable clinical studies to recruit a larger and more diverse population. Furthermore, DBs provide high temporal and spatial resolution data, which increase the chance of gaining a comprehensive understanding of disease progression.Neurodegenerative diseases, due to their lack of accessible and objective assessment tools, have been a primary focus for the DBs research community. Parkinson's disease (PD) is particularly well-suited for studying DBs due to its heterogeneous onset age, symptom prevalence, severity progression rate, and multiple aspects of the disease. Therefore, there is a need to integrate DBs and remote assessment into the routine clinical evaluation of PD. However, using DBs for PD in non-controlled, at-home settings poses practical challenges that have hindered this goal. Additionally, the longitudinal stability of DBs collected in such settings has not yet been thoroughly investigated, with previous studies limited to in-lab settings. Thus, this thesis aims to provide insight into how remote monitoring in an at-home environment alongside the data collection methods can be leveraged to improve the way PD is assessed.The first section of this dissertation focuses on introducing a platform named "JTrack", designed for remote disease monitoring and to address technical aspects such as security, privacy, modularity, and reusability. This platform aims to provide a comprehensive solution for clinical studies involving multiple aspects of various diseases. In addition, this section assesses the agreement between features collected through "JTrack" with two widely used stationary systems for analysing gait and balance, demonstrating the potential of using smartphones and particularly the "JTrack" platform in future clinical studies. The second part of this thesis investigates the potential of using various commonly reported features in PD studies as biomarkers. To do this, we first investigate these features' test-retest reliability and longitudinal stability, considering how the timescale may affect their stability. Next, we use various machine learning algorithms to assess the ability of these features to differentiate between PD and HC. Also, we evaluated the influence of different confounding factors such as comorbidities, age, and sex on the prediction performance of the machine learning algorithms. For this, the various tasks (gait, balance, voice, and tapping) of the m-Power database, collected remotely and in a self-managed setting, were investigated.Overall, this thesis discusses the potential and limitations of using smartphones for remote assessment of PD. It examines the possible sources of confounding factors related to DBs in remote and self-managed collection methods. It also highlights the need to develop more controlled, standardised, sensitive, and reliable DBs before taking them into any clinical application. This thesis also introduces a new DBs platform for remote assessment, which can be leveraged for various types of disease.
001014708 536__ $$0G:(DE-HGF)POF4-5252$$a5252 - Brain Dysfunction and Plasticity (POF4-525)$$cPOF4-525$$fPOF IV$$x0
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001014708 9141_ $$y2023
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