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The aim of this research program is to develop and validate a smartphone app-based digital measurement concept that:
Full 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.
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Inclusion criteria
PD:
Male or female age 30 years or older at Screening Visit.
Diagnosis of PD as defined by MDS PD diagnostic criteria [1]
PD severity at Screening Visit of either:
Able and willing to complete all aspects of the study, including at home smartphone app and Zoom telehealth assessments.
Able to provide informed consent.
Prodromal PD:
Confirmation that participant is eligible based on clinician determined predictive criteria of known risk of PD including
Male or female age 30 or older at Screening Visit.
Able and willing to complete all aspects of the study, including at home smartphone app and Zoom telehealth assessments
Able to provide informed consent.
Age & Sex Matched Healthy Control:
Exclusion criteria
PD:
Prodromal PD:
Age & Sex Matched Healthy Control:
120 participants in 4 patient groups
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Central trial contact
Max Galarce; Cynthia Poon, PhD
Data sourced from clinicaltrials.gov
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