Ignite Creation Date:
2024-06-16 @ 11:51 AM
Last Modification Date:
2024-10-26 @ 3:31 PM
Study NCT ID:
NCT06450418
Status:
RECRUITING
Last Update Posted:
2024-06-10
First Post:
2024-02-09
Brief Title:
Digital App for Speech Health Monitoring
Sponsor:
University of Edinburgh
Organization:
University of Edinburgh
Study Overview
Official Title:
Digital App for Speech Health Monitoring in Neurodegenerative Disorders
Status:
RECRUITING
Status Verified Date:
2024-09
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:
None
Brief Summary:
Many people living with neurodegenerative conditions like dementia motor neuron disease MND multiple sclerosis MS and Parkinsons disease PD suffer from speech problems Using common digital technologies such as smartphone apps the investigators can record and analyse speech in detail to provide new information for people living with these conditions researchers and healthcare professionals This study will investigate the use of these digital speech recordings to help diagnose and monitor these conditions
To take part participants will have either a diagnosis of dementia motor neuron disease Parkinsons disease or Multiple Sclerosis OR they will have no diagnosis of a neurological condition Researchers will compare people with a diagnosis of a Neurological condition to those without
To take part participants will have either a diagnosis of dementia motor neuron disease Parkinsons disease or Multiple Sclerosis OR they will have no diagnosis of a neurological condition Researchers will compare people with a diagnosis of a Neurological condition to those without
Detailed Description:
This project aims to create novel speech-based solutions for 1 Early detection 2 Monitoring and 3 Stratification of neurodegenerative disorders including dementia motor neuron disease MND Parkinsons disease PD and multiple sclerosisMS The investigators will develop and validate proof of concept and early-stage algorithms derived from acoustic data which will be scaled and tested in deeply-phenotyped population
22 Objectives Primary Objectives
1 To deploy and iterate a digital platform co-produced with people living with neurodegenerative disorders for acquisition of speech data from well characterised cohorts of people living with neurodegenerative disorders dementia motor neuron disease multiple sclerosis Parkinsons disease and a healthy control cohort comprising relativescarers and volunteers without a neurological diagnosis linked to our highly curated clinical registries at the Anne Rowling Regenerative Neurology Clinic
2 To collect a large body of acoustic speech data from well characterised cohorts of people living with neurodegenerative disorders dementia MNDALS multiple sclerosis Parkinsons disease and a healthy control cohort comprising relativescarers and volunteers without a neurological diagnosis linked to highly curated clinical registries
3 To apply machine learning approaches directly to acoustic and linguistic signals from voices from people with dementia MND MS Parkinsons and healthy controls comprising relativescarers and volunteers without a neurological diagnosis and to characterise prosodic patterns rhythm intonation and fluency without explicit reference to the text which is spoken providing powerful cues about the health of the speaker
4 Compare speech based digital outcome measures to current clinical standards to characterise and validate their clinimetric properties
Secondary Objectives
1 Assess the feasibility and acceptability of a digital outcome measure platform in people living with neurodegenerative conditions for use in clinical care and research
2 To create a repository of well characterised acoustic voice samples for open access sharingcollaboration with research and industry partners
22 Objectives Primary Objectives
1 To deploy and iterate a digital platform co-produced with people living with neurodegenerative disorders for acquisition of speech data from well characterised cohorts of people living with neurodegenerative disorders dementia motor neuron disease multiple sclerosis Parkinsons disease and a healthy control cohort comprising relativescarers and volunteers without a neurological diagnosis linked to our highly curated clinical registries at the Anne Rowling Regenerative Neurology Clinic
2 To collect a large body of acoustic speech data from well characterised cohorts of people living with neurodegenerative disorders dementia MNDALS multiple sclerosis Parkinsons disease and a healthy control cohort comprising relativescarers and volunteers without a neurological diagnosis linked to highly curated clinical registries
3 To apply machine learning approaches directly to acoustic and linguistic signals from voices from people with dementia MND MS Parkinsons and healthy controls comprising relativescarers and volunteers without a neurological diagnosis and to characterise prosodic patterns rhythm intonation and fluency without explicit reference to the text which is spoken providing powerful cues about the health of the speaker
4 Compare speech based digital outcome measures to current clinical standards to characterise and validate their clinimetric properties
Secondary Objectives
1 Assess the feasibility and acceptability of a digital outcome measure platform in people living with neurodegenerative conditions for use in clinical care and research
2 To create a repository of well characterised acoustic voice samples for open access sharingcollaboration with research and industry partners
Study Oversight
Has Oversight DMC:
None
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?:
None
Is an FDA AA801 Violation?:
None