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NCT Number: NCT06450418

Digital App for Speech & Health Monitoring

Many people living with neurodegenerative conditions like dementia, motor neuron disease (MND), multiple sclerosis (MS), and Parkinson's 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, Parkinson's 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.

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Key information

About this study

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), Parkinson's disease (PD), and multiple sclerosis(MS). 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.

2.2 Objectives Primary Objectives

  • 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, Parkinson's disease), and a healthy control cohort (comprising relatives/carers and volunteers without a neurological diagnosis), linked to our highly curated clinical registries at the Anne Rowling Regenerative Neurology Clinic.
  • To collect a large body of acoustic speech data from well characterised cohorts of people living with neurodegenerative disorders (dementia, MND/ALS, multiple sclerosis, Parkinson's disease), and a healthy control cohort (comprising relatives/carers and volunteers without a neurological diagnosis), linked to highly curated clinical registries.
  • To apply machine learning approaches directly to acoustic and linguistic signals from voices from people with dementia, MND, MS, Parkinson's, and healthy controls (comprising relatives/carers 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.
  • Compare speech based digital outcome measures to current clinical standards to characterise and validate their clinimetric properties.

Secondary Objectives

  • Assess the feasibility and acceptability of a digital outcome measure platform in people living with neurodegenerative conditions, for use in clinical care and research.
  • To create a repository of well characterised acoustic voice samples for open access sharing/collaboration with research and industry partners.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

- Any one of the following:

  • A person with a diagnosis of Motor Neuron Disease, Dementia, Multiple Sclerosis, or Parkinson's Disease.
  • A relative or carer of the above who does not report to have a neurological condition.
  • A healthy volunteer who does not report to have a neurological condition.

Exclusion criteria

  • Age <16 years
  • Significant and uncorrected visual or hearing impairment (precluding use of the App).
  • Lack capacity to consent to project due to cognitive impairment (precluding understanding of the study and use of the App).

Treatment and study plan

Primary outcomes

  1. Primary outcome measures

    Time frame: 24 months

    Area under the curve (AUC) of the receiver operating characteristic (ROC) curve for each of the 4 binary classifiers distinguishing between a disease-positive group and a healthy control group.

Secondary outcomes

  1. Secondary outcome measure

    Time frame: 24 months

    Sensitivity, specificity, positive and negative predictive values for each of the 4 binary classifiers distinguishing between a disease-positive group and a healthy control group.

  2. Secondary outcome measure

    Time frame: 24 months

    Mean squared error of 4 regression models making predictions of condition-specific clinical rating scores (ACE-III, ALSFRS-R, EDSS, MDS-UPDRS)

Study contacts

Contact information is provided by the study sponsor or research team.

Christine R Weaver, MSc

CONTACT

[email protected]

01314659512

Sponsors and collaborators

Lead sponsor

University of Edinburgh

Other

Collaborators

  • NHS Lothian

Registry information

Official study title

Digital App for Speech & Health Monitoring in Neurodegenerative Disorders

Important dates

Study start
2024
Primary completion
2026
Study completion
2026
First posted
Jun 10, 2024
Registry last updated
Sep 18, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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