Ignite Creation Date:
2025-12-24 @ 9:55 PM
Ignite Modification Date:
2026-02-23 @ 8:31 PM
Study NCT ID:
NCT05421832
Status:
COMPLETED
Last Update Posted:
2025-07-24
First Post:
2022-06-08
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
A Novel Measurement Concept to Objectively Quantify Severity of Vocal and Speech Related Symptoms Associated With Parkinson's Disease
Sponsor:
Northwestern University
Organization:
Study Overview
Official Title:
A Novel Measurement Concept to Objectively Quantify Severity of Vocal and Speech Related Symptoms Associated With Parkinson's Disease
Status:
COMPLETED
Status Verified Date:
2025-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
Voice-PD
Brief Summary:
The aim of this research program is to develop and validate a smartphone app-based digital measurement concept that:
* Objectively quantifies the severity of Parkinson's Disease (PD) related vocal and speech symptoms;
* Accurately and sensitively identifies vocal and speech abnormalities associated with the prodromal stage of PD.
* Objectively quantifies the severity of Parkinson's Disease (PD) related vocal and speech symptoms;
* Accurately and sensitively identifies vocal and speech abnormalities associated with the prodromal stage of PD.
Detailed Description:
Although multiple approaches to this problem have been proposed in addition to commercially available speech analytics platforms, there is currently no established measure which incorporates the disparate aspects of affected speech to fully characterize Parkinson's symptom progression, particularly in the prodromal phase.
The measurement concept being evaluated in the present study utilizes a custom smartphone-based speech assessment tool to extract multiple hypothesis-driven acoustic features from patient speech in a real-life environment. The resultant features will be used to train a pair of supervised machine learning models to predict clinical PD symptom severity scores, and to distinguish prodromal PD patients from both healthy matched controls and PD patients in more advanced phases of disease progression.
The measurement concept being evaluated in the present study utilizes a custom smartphone-based speech assessment tool to extract multiple hypothesis-driven acoustic features from patient speech in a real-life environment. The resultant features will be used to train a pair of supervised machine learning models to predict clinical PD symptom severity scores, and to distinguish prodromal PD patients from both healthy matched controls and PD patients in more advanced phases of disease progression.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?: