We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Clinical Evaluation in Parkinson's Disease: Is the Golden Standard Shiny Enough?
- Authors
Kanellos, Foivos S.; Tsamis, Konstantinos I.; Rigas, Georgios; Simos, Yannis V.; Katsenos, Andreas P.; Kartsakalis, Gerasimos; Fotiadis, Dimitrios I.; Vezyraki, Patra; Peschos, Dimitrios; Konitsiotis, Spyridon
- Abstract
Parkinson's disease (PD) has become the second most common neurodegenerative condition following Alzheimer's disease (AD), exhibiting high prevalence and incident rates. Current care strategies for PD patients include brief appointments, which are sparsely allocated, at outpatient clinics, where, in the best case scenario, expert neurologists evaluate disease progression using established rating scales and patient-reported questionnaires, which have interpretability issues and are subject to recall bias. In this context, artificial-intelligence-driven telehealth solutions, such as wearable devices, have the potential to improve patient care and support physicians to manage PD more effectively by monitoring patients in their familiar environment in an objective manner. In this study, we evaluate the validity of in-office clinical assessment using the MDS-UPDRS rating scale compared to home monitoring. Elaborating the results for 20 patients with Parkinson's disease, we observed moderate to strong correlations for most symptoms (bradykinesia, rest tremor, gait impairment, and freezing of gait), as well as for fluctuating conditions (dyskinesia and OFF). In addition, we identified for the first time the existence of an index capable of remotely measuring patients' quality of life. In summary, an in-office examination is only partially representative of most PD symptoms and cannot accurately capture daytime fluctuations and patients' quality of life.
- Subjects
PARKINSON'S disease; ALZHEIMER'S disease; DEEP brain stimulation; GAIT disorders; MEMORY bias; DROWSINESS; PATIENT monitoring
- Publication
Sensors (14248220), 2023, Vol 23, Issue 8, p3807
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s23083807