Parkinson's Progression Marker Initiative (PPMI) Online identifies people with features of prodromal Parkinson's Disease through online questionnaires

Ethan Brown; Lana Chahine; David Alonso; Christopher S. Coffey; Roseanne Dobkin; Tatiana Foroud; Monica Korell; Ana Lorenzo; Connie Marras; Werner Poewe; Todd Sherer; Andrew Siderowf; Tanya Simuni; Eduardo Tolosa; Julien Valverde Twiggs; Daniel Weintraub; Sohini Chowdhury; Kenneth Marek; Caroline M. Tanner

doi:10.5281/zenodo.7015717

Poster

Parkinson's Progression Marker Initiative (PPMI) Online identifies people with features of prodromal Parkinson's Disease through online questionnaires

Ethan Brown, Lana Chahine, David Alonso, Christopher S. Coffey, Roseanne Dobkin, Tatiana Foroud, Monica Korell, Ana Lorenzo, Connie Marras, Werner Poewe, …

Zenodo

09/17/2022

DOI: 10.5281/zenodo.7015717

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https://doi.org/10.5281/zenodo.7015717View

Open Access

Abstract

This work describes the use of PPMI Online as a broad-based and cost-effective method to identify people at risk of developing PD through a staged screening process. This process begins with questionnaires focused on symptoms relevant to the prodromal stages of PD, then invites eligible participants to complete a DATScan and, if still eligible, participate further in PPMI Clinic. This poster describes preliminary characteristics of participants at various stages of this pipeline.

Parkinson's Disease

Prodromal Symptoms

Patient Reported Outcomes Measures

Details

Title: Subtitle: Parkinson's Progression Marker Initiative (PPMI) Online identifies people with features of prodromal Parkinson's Disease through online questionnaires
Creators: Ethan Brown
Lana Chahine
David Alonso
Christopher S. Coffey
Roseanne Dobkin
Tatiana Foroud
Monica Korell
Ana Lorenzo
Connie Marras
Werner Poewe
Todd Sherer
Andrew Siderowf
Tanya Simuni
Eduardo Tolosa
Julien Valverde Twiggs
Daniel Weintraub
Sohini Chowdhury
Kenneth Marek
Caroline M. Tanner
Resource Type: Poster
DOI: 10.5281/zenodo.7015717
Publisher: Zenodo
Language: English
Date published: 09/17/2022
Academic Unit: Biostatistics
Record Identifier: 9984530380002771

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