DementiaNCT06450418
Digital App for Speech & Health Monitoring
An observational study of Dementia and Motor Neuron Disease, run by University of Edinburgh at 1 site in United Kingdom.
Trial summary
Many people living with neurodegenerative conditions like dementia, motor neuron disease (MND), multiple sclerosis (MS), and Parkinson's disease (PD), suffer from speech problems. Read more about this study
- Who is it for?
- Ages 16 years and olderHealthy volunteers can take part
- Conditions the study is for
- Dementia, Motor Neuron Disease, Multiple Sclerosis
- How many people
- 150 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 12 Jul 2024to Jun 2029
- Phase
- Observational
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 years and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Participants with Neurodegenerative Disorders and their relatives or carers without a diagnosis of a Neurodegenerative disorder.
Who may be able to join
- 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.
Who cannot join
- 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).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United Kingdom1 site, 1 recruiting
- Recruiting
NHS Lothian
Edinburgh
Suvankar Pal
About this study
Official title: Digital App for Speech & Health Monitoring in Neurodegenerative Disorders
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.
Read the 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), 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.
- Conditions
- Dementia, Motor Neuron Disease, Multiple Sclerosis, Parkinson Disease
- Medical subject headings
- Dementia, Motor Neuron Disease, Multiple Sclerosis, Parkinson Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Carers/Realtives/ Healthy - No Diagnosis of a Neurodegenerative Disorder | Not described | |
| Motor Neuron Disease | Not described | |
| Multiple Sclerosis | Not described | |
| Dementia | Not described | |
| Parkinson's Disease | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Primary outcome measures
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.
Measured: 24 months
Secondary outcomes (2)
Secondary outcome measure
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.
Measured: 24 months
Secondary outcome measure
Mean squared error of 4 regression models making predictions of condition-specific clinical rating scores (ACE-III, ALSFRS-R, EDSS, MDS-UPDRS)
Measured: 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jun 2024
- Study start12 Jul 2024
- Primary completion30 Jan 2029 (estimated)
- Study completionJun 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for DementiaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 150 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Edinburgh
- Sponsor type
- University, hospital or other
- Collaborators
- NHS Lothian
- Responsible party
- Sponsor
- Investigators
- Suvankar Pal, Prof, Principal investigator, University of Edinburgh
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06450418
- Sponsor's protocol ID
- AC24003
- Status last verified
- Sep 2026
- First submitted
- 9 Feb 2024
- Last updated
- 10 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Tam J, Weaver C, Ihenacho A, Newton J, Virgo B, Barrett S, Neale J, Perry D, Smith A, Chandran S, Watts O, Pal S; DASH Consortium. Digital App for Speech and Health Monitoring Study (DASH): protocol for a prospective longitudinal case-control observational study for developing speech datasets in neurodegenerative disorders and dementia. BMJ Open. 2025 Dec 5;15(12):e100222. doi: 10.1136/bmjopen-2025-100222.
PubMed 41360452