Hyposmia, Neurogenic Orthostatic Hypotension, Parkinson Disease (PD), REM Sleep Behavior Disorder (iRBD)
Conditions
Keywords
prodromal Parkinson's disease, neurogenic orthostatic hypotension, REM sleep behaviour disorder, hyposmia, Digital biomarkers, AI-PROGNOSIS, Smartwatch, Wearable electronic devices
Brief 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.
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.
Interventions
DEVICESmartwatch 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.
Sponsors
Queen Mary University of London
Hospital Ruber Internacional
University Hospital, Toulouse
Aristotle University Of Thessaloniki
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
50 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Classification performance of the PD risk artificial intelligence-based model | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Usability of study digital environment (mAI-Health phone app) | At 6 month visit | 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. |
Countries
France, Spain, United Kingdom
Contacts
CONTACTEduardo de Pablo Fernández
Outcome results
None listed