Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease

Abstract

Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.

Mobile App Interventions for Parkinson’s Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review

“…The present review highlights the importance of appropriate study design for the evaluation of apps against the standard of care [ 60 , 61 , 62 ]. While all non-randomized clinical studies demonstrated improved outcomes for patients using mobile apps, studies with an RCT design and high methodological quality indicated mixed findings.…”

Telemedicine in neurology: advances and possibilities

“…In the near future, a set of quantitative measures derived from wearables or apps could be considered digital biomarkers of a disease, contributing to early diagnosis, stratification of subgroups and prediction of treatment outcomes 45 .…”

Digital Health Applications in der Neurologie und Psychiatrie

“…B. Parkinson hinweisen [27]. Touch-Displays können für klinische Tests wie den Tapping-Test für Parkinsonpatienten verwendet werden [27,28].…”

“…Technische Möglichkeiten von Mobiltelefonen und tragbaren Sensoren in Kombination mit KI-Algorithmen werden noch kaum genutzt. Hier hinkt das DiGA-Verzeichnis den technischen Möglichkeiten, die in der wissenschaftlichen Literatur beschrieben sind [13,14,[26][27][28], hinterher. Ein Grund hierfür kann die teils fehlende Evidenz für klinische Nutzen der technischen Möglichkeiten sein.…”

“…sich verändernde Stimm- oder Sprachmuster können auf psychische Erkrankungen, aber auch neurologische Erkrankungen, z. B. Parkinson hinweisen 27 . Touch-Displays können für klinische Tests wie den Tapping-Test für Parkinsonpatienten verwendet werden 27 , 28 .…”