Assessing Tele-Health Outcomes in Multiyear Extensions of Parkinson's Disease Trials-2 (AT-HOME PD-2) - The COVID-19 pandemic has disrupted clinical research and highlighted the value of patient centered research methods that enable participation from the home and collection of data directly from participants. Such decentralized research studies that harness video visits, digital tools and participant reporting, can reach a large, geographically dispersed population of participants, increase the frequency and scope of evaluation, and reduce the burden of participation. Parkinson’s disease, a clinically heterogeneous neurodegenerative disorder that causes progressive disability, is well suited to such a model. Traditional assessments are typically subjective, insensitive to change, and limited to episodic administration and therefore fail to capture the complexity of Parkinson’s disease. AT-HOME PD, the largest on-going decentralized longitudinal observational Parkinson’s disease study with digital tools, is remotely characterizing ~225 participants with Parkinson’s disease from two NINDS-funded, phase 3 clinical trials, STEADY-PD III and SURE-PD3. These studies yielded cohorts with comprehensive clinical phenotyping, whole genome sequencing, and serial plasma collection. AT-HOME PD participants are being characterized through video visits, smartphone-based assessments, and an online survey platform. The cohort is now approaching mid-stage Parkinson’s disease, presenting an opportunity to advance our understanding of this under-studied population, improve the prediction of clinically relevant disease milestones like falls and cognitive impairment, quantify physical activity, and identify sensitive remote disease measures. This project will extend the follow-up of this cohort by 3 years and expand digital phenotyping of participants, using smartphone-based assessments and two wrist-worn sensors. The aims of this project are to 1) evaluate the extent to which digital tools and remote participant reporting can improve the prediction of clinically relevant disease milestones compared with traditional measures, 2) quantify longitudinal change in physical activity, steps taken, and gait in mid-stage Parkinson’s disease in the real-world, and 3) explore the relationship between physical activity and clinical outcomes in mid-stage Parkinson’s disease. We will generate a dataset with approximately 10 continuous years of data on PD progression that begins prior to use of dopaminergic medications and progresses to midstage Parkinson’s disease and beyond. This rich dataset will accelerate therapeutic development by filling knowledge gaps in the mid-stage Parkinson’s disease population, helping to optimize models for conducting patient-centered remote research, evaluating new methods for predicting disease outcomes, and evaluating remote outcome measures.
