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).

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. 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, 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. 2. 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. 3. 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. 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 sharing/collaboration with research and industry partners.

Arms

Interventions