Artificial Intelligence-based Parkinson's Disease Risk Assessment (AI-PRA) Study (AI-PRA)

July 15, 2026 updated by: Queen Mary University of London
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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Estimated)

60

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

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.

Description

Inclusion Criteria:

  1. Age ≥ 50 years.

  2. At least one of the following clinical markers for PD risk:

    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.
  3. Able and willing to give informed written consent.
  4. 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.

Exclusion Criteria:

  1. Clinical diagnosis of Parkinson's disease (PD) according to MDS clinical diagnostic criteria.
  2. 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).
  3. Dementia defined as deterioration of cognitive function severe enough to impair functioning on daily activities.
  4. 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.
  5. Pregnant women.
  6. Concomitant participation in interventional studies.
  7. Unwilling or unable to give informed written consent.
  8. Vulnerable individuals as defined by the HRA.
  9. Inability to use the smartwatch and/or the mAI-Health app for the purpose of the study as judged by the investigator.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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.
Device: Smartwatch and phone app
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Classification performance of the PD risk artificial intelligence-based model
Time Frame: From enrolment to 6 months
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.
From enrolment to 6 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Usability of study digital environment (mAI-Health phone app)
Time Frame: At 6 month visit
System Usability Scale (SUS) scores.
At 6 month visit

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Queen Mary University of London

Collaborators

Aristotle University Of Thessaloniki
University Hospital, Toulouse
Hospital Ruber Internacional

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

July 1, 2026

Primary Completion (Estimated)

September 30, 2027

Study Completion (Estimated)

September 30, 2027

Study Registration Dates

First Submitted

July 1, 2026

First Submitted That Met QC Criteria

July 15, 2026

First Posted (Actual)

July 16, 2026

Study Record Updates

Last Update Posted (Actual)

July 16, 2026

Last Update Submitted That Met QC Criteria

July 15, 2026

Last Verified

July 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 370634

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

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

IPD Sharing Time Frame

To be determined (after completion of the study and publication of the main study results).

IPD Sharing Access Criteria

Qualified investigators will be able to access fully anonymised dataset according to ethical permissions through public repository.

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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