Parkinson Disease (PD)AI-PRANCT07706829
Artificial Intelligence-based Parkinson's Disease Risk Assessment (AI-PRA) Study
An observational study of Parkinson Disease (PD) and REM Sleep Behavior Disorder (iRBD), run by Queen Mary University of London at 3 sites in 3 countries.
Trial summary
The study aims to provide initial proof-of-concept validation data of an artificial intelligence-based model to estimate individual Parkinson's disease risk using demographic, clinical, genetic information and digital biomarker data collected via a smartwatch and a mobile application. Read more about this study
- Who is it for?
- Ages 50 years and older
- Conditions the study is for
- Parkinson Disease (PD), REM Sleep Behavior Disorder (iRBD), Neurogenic Orthostatic Hypotension
- What is being tested?
- Smartwatch and phone app
- Type of treatment
- Medical device
- How many people
- 60 participants
- Where
- 3 sitesin 3 countries
- Study dates
- 1 Oct 2026to 30 Sep 2027
- 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 50 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: People at risk of PD due to the presence of clinical markers associated with prodromal PD including one of the following: 1. REM sleep behaviour disorder (RBD) confirmed with polysomnography. 2. Neurogenic orthostatic hypotension (nOH) defined as a drop in systolic / diastolic blood pressure ≥ 20/10mmHg within 3 minutes of active standing or tilt-table test, and with a blunted heart rate response (ΔHeart rate/ΔSBP ratio < 0.5 bpm/mmHg). 3. Objective hyposmia defined as University of Pennsylvania Smell Identification Test (UPSIT) score ≤ 15th percentile for age and sex.
Who may be able to join
- Age ≥ 50 years.
- At least one of the following clinical markers for PD risk:
- REM sleep behaviour disorder (RBD) confirmed with polysomnography.
- Neurogenic orthostatic hypotension (nOH) defined as a drop in systolic / diastolic blood pressure ≥ 20/10mmHg within 3 minutes of active standing or tilt-table test, and with a blunted heart rate response (ΔHeart rate/ΔSBP ratio < 0.5 bpm/mmHg).
- Objective hyposmia defined as University of Pennsylvania Smell Identification Test (UPSIT) score ≤ 15th percentile for age and sex.
- Able and willing to give informed written consent.
- Use of compatible smartphone (mobile operating system Android version 11 or newer). A smartwatch will be provided to each participant for the duration of the study.
Who cannot join
- Clinical diagnosis of Parkinson's disease (PD) according to MDS clinical diagnostic criteria.
- Currently taking levodopa, dopamine agonists, MAO-B inhibitors, amantadine or another PD medication, except for low-dose treatment of restless leg syndrome (with permission of investigator).
- Dementia defined as deterioration of cognitive function severe enough to impair functioning on daily activities.
- Active treatment with neuroleptics, reserpine or metoclopramide (these drugs should be discontinued for at least 6 months before screening visit) due to their interference with dopamine transporter SPECT imaging acquisition and interpretation.
- Pregnant women.
- Concomitant participation in interventional studies.
Show all 9Show fewer
- Unwilling or unable to give informed written consent.
- Vulnerable individuals as defined by the HRA.
- Inability to use the smartwatch and/or the mAI-Health app for the purpose of the study as judged by the investigator.
Trial locations and contacts
3 sites in 3 countries. Contact the study team or a site near you to ask about taking part.
3 sites in 3 countries
France1 site
Centre Hospitalier Universitaire de Toulouse
Toulouse
Margherita Fabbri, +33 5 61 77 22 33, fabbri.m@chu-toulouse.fr
Spain1 site
Fundación Iniciativa para las Neurociencias. Hospital Ruber Internacional.
Madrid
Monica Kurtis, +34913875000, mkurtis@ruberinternacional.es
United Kingdom1 site
Queen Mary University of London
London
Eduardo de Pablo Fernandez, +44 20 7882 8693, e.depablofernandez@qmul.ac.uk
About this study
The study aims to provide initial proof-of-concept validation data of an artificial intelligence-based model to estimate individual Parkinson's disease risk using demographic, clinical, genetic information and digital biomarker data collected via a smartwatch and a mobile application.
Read the detailed description
Background: Everyday electronic devices may detect subtle motor and non-motor abnormalities years before the clinical diagnosis of Parkinson's disease (PD) providing opportunities for early detection.
Study aim and impact: This study aims to validate an artificial intelligence based model that provides an individualised risk of PD based on demographic, clinical, genetic and digital biomarker data (smartwatch and a phone app). An early diagnosis will allow timely interventions to manage symptoms and risk stratification of participants for early clinical trials.
Methods: 60 people at risk of PD (either with polysomnography confirmed REM sleep behaviour disorder; OR neurogenic orthostatic hypotension; OR objective hyposmia on smell test) will be recruited.
Participants will complete study assessments to provide PD risk estimation using current research clinical criteria and the artificial intelligence model. Study assessments will include:
- In-person visits (baseline and 6 months) to complete validated questionnaires and a neurological examination (including cognitive and motor assessments).
- Brain dopamine (DAT) scan (baseline only).
- blood tests for PD polygenic risk score (baseline only) and plasma urate (in males only at baseline and 6 months).
- Smartwatch and phone app: a smartwatch linked to the participants' smartphone will provide digital biomarker and additional clinical information through questionnaires via study phone app.
An artificial intelligence based model (AI-PROGNOSIS model) will use these digital data in combination with demographics, clinical and genetic information to provide an individualised PD risk estimation.
Accuracy measures of the risk estimates from the current research diagnostic criteria and artificial intelligence model using the presence of abnormal dopamine DAT scan as the ground truth for PD diagnosis will be provided.
- Conditions
- Parkinson Disease (PD), REM Sleep Behavior Disorder (iRBD), Neurogenic Orthostatic Hypotension, Hyposmia
- Being tested
- Smartwatch and phone app
- Keywords
- prodromal Parkinson's disease, neurogenic orthostatic hypotension, REM sleep behaviour disorder, hyposmia, Digital biomarkers, AI-PROGNOSIS, Smartwatch, Wearable electronic devices
- Medical subject headings
- Parkinson Disease, REM Sleep Behavior Disorder, Anosmia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Smartwatch and phone appDevice
Wearing a smartwatch and using a mobile phone application for 6 months in order to provide digital biomarker data and additional self reported clinical information.
| Study group | Role | What they receive |
|---|---|---|
| Cohort of people at risk of Parkinson's disease | People at risk of PD defined by the presence of either polysomnography-confirmed REM sleep behaviour disorder, neurogenic orthostatic hypotension or objective hyposmia documented with smell test. Receives: Device: Smartwatch and phone app |
What is being measured
The primary outcome is the main question the study is designed to answer.
Classification performance of the PD risk artificial intelligence-based model
Classification performance of the model in predicting dopaminergic degeneration defined as a binary outcome: a participant will be considered to have dopaminergic degeneration if putamen specific binding ratio (SBR) on the most affected side is below 2 standard deviations of age-matched normative data or shows abnormal visual inspection by a qualified nuclear medicine specialist on dopamine transporter SPECT imaging.
Measured: From enrolment to 6 months
Secondary outcomes (1)
Usability of study digital environment (mAI-Health phone app)
System Usability Scale (SUS) scores. The SUS includes 10 statement items regarding the usability of the study phone application that will be rated on a scale of 1 - 5 (strongly disagree - strongly agree). Range 10-50 with higher scores meaning a better outcome.
Measured: At 6 month visit
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jul 2026
- Study start1 Oct 2026 (estimated)
- Primary completion30 Sep 2027 (estimated)
- Study completion30 Sep 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Parkinson Disease (PD)Studies 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
- 60 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Queen Mary University of London
- Sponsor type
- University, hospital or other
- Collaborators
- Hospital Ruber Internacional, University Hospital, Toulouse, Aristotle University Of Thessaloniki
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07706829
- Sponsor's protocol ID
- 370634
- Acronym
- AI-PRA
- Status last verified
- Oct 2026
- First submitted
- 1 Jul 2026
- Last updated
- 2 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: After completion of the study and publication of results, study data for participants that specifically consented for this, will be made accessible in a public repository, after full anonymisation, to the research community following FAIR principles for non-commercial research purposes
Publications
Hastings A, Cullinane P, Wrigley S, Revesz T, Morris HR, Dickson JC, Jaunmuktane Z, Warner TT, De Pablo-Fernandez E. Neuropathologic Validation and Diagnostic Accuracy of Presynaptic Dopaminergic Imaging in the Diagnosis of Parkinsonism. Neurology. 2024 Jun 11;102(11):e209453. doi: 10.1212/WNL.0000000000209453. Epub 2024 May 17.
PubMed 38759132Heinzel S, Berg D, Gasser T, Chen H, Yao C, Postuma RB; MDS Task Force on the Definition of Parkinson's Disease. Update of the MDS research criteria for prodromal Parkinson's disease. Mov Disord. 2019 Oct;34(10):1464-1470. doi: 10.1002/mds.27802. Epub 2019 Aug 14.
PubMed 31412427Berg D, Postuma RB, Adler CH, Bloem BR, Chan P, Dubois B, Gasser T, Goetz CG, Halliday G, Joseph L, Lang AE, Liepelt-Scarfone I, Litvan I, Marek K, Obeso J, Oertel W, Olanow CW, Poewe W, Stern M, Deuschl G. MDS research criteria for prodromal Parkinson's disease. Mov Disord. 2015 Oct;30(12):1600-11. doi: 10.1002/mds.26431.
PubMed 26474317