ABSTRACT
Objective: To describe changes in daily activity measured by wearable sensors in participants with Parkinson's disease (PD) following the COVID-19 pandemic. Background: Digital tools provide objective, frequent and sensitive data collection in real-world settings. In a natural history study of PD, participants used wearable sensors before and after COVID-19 shutdowns. Design/Methods: At research visits throughout this two-year study at the University of Rochester Medical Center, participants wore sensors with accelerometer and gyroscopic capabilities and completed questionnaires. Following each visit, participants wore sensors remotely for 7 days during waking hours. Participant position and activity from days 1-6 of wear was classified from sensor data. Results: Prior to March 14 2020, when COVID-19 shutdowns began in Monroe County, NY, 17 participants with PD (70.4 (6.3) years) and 13 controls (61.1 (13.5) years) completed a baseline visit. All 30 later completed a month 12 visit after COVID-19 shutdowns. Sensor wear was comparable at baseline (13.9 (1.4) hours/day) and month 12 (13.74 (2.1) hours/day). At month 12, PD participants walked an average of 1709 (1457) steps/day, approximately 17% less than at baseline (2048 (1416) steps/day), with considerable individual variation. PD participants spent approximately 20% more time lying while awake at month 12 (112.7 (149.9) min) than at baseline (93.6 (103.1) min). Daytime sleep did not increase from baseline (39.6 (39.3) min) to month 12 (39.2 (32.8) min). PD and control participants reported greater anxiety and depression at month 12. From baseline to month 12, controls had similar activity trends as participants with PD, but walked more, spent less time lying, had less daytime sleep, and reported less depression and anxiety at both time points. Conclusions: Following the emergence of COVID-19, participants with PD walked less and spent more time resting. These data provide an objective measure of the pandemic's impact on a small cohort of individuals with PD.
ABSTRACT
Personalized pharmacotherapy has become a new paradigm of safe and efficient treatment and its role is specifically seen nowadays in the COVID-19 era. The therapies explored for SARS-CoV-2 are based on repurposed marketed antiviral drugs that have known cardiac safety issues. It gives a perfect example of the need to develop a tool that helps to optimize dosing strategies. The below-described project aims to propose a general concept and to develop an advanced prototype of the pharmacotherapy optimization system based on mathematical models and clinical data. A hybrid approach is proposed, merging various sources of data and techniques. The system is planned to be algorithm naive and clinicians are actively included in its development and verification. A combination of the PBPK and pharmacodynamic models, including artificial intelligence and Internet-based data-sharing technologies, has been used. Antazoline has been chosen as an exemplary drug due to the challenges connected with the optimal dosing for individual patients during the atrial fibrillation conversion. The developed PBPK model allows for precise exposure assessment for individual patients, while the QSP model predicts the ECG modification triggered by the drug. Further, plans cover thorough verification of the system and expansion towards mobile application. © 2020 Praise Worthy Prize S.r.l.-All rights reserved.