Development of Digital Diagnostic Devices for Parkinson's Disease

NCT06663826 | Recruiting

• 2 other identifiers: HRO-002_BIP-PD109.813 INT-LS
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

In this project, ocular motor, pupil and gait data in people with Parkinson's disease (PD) will be collected in order to develop machine learning models for the diagnosis and monitoring of PD. With this, the investigators aim to advance the state of the art in PD diagnosis and monitoring. By integrating the principles of machine learning with high-quality sensor data, more accurate and earlier diagnosis could potentially be achieved. Ocular motor and pupil data will be collected with the standard clinical examination and with neos, a medical device approved for objective ocular motor and pupil measurement. Gait will be collected using an IMU sensor and GaitQ senti, a consumer device that allows for an objective and continuous remote gait monitoring.

Conditions

Parkinson Disease

Keywords

Parkinson's disease; oculomotor; ocular motor; gait; eye movements; pupil; virtual reality headset; eye tracking

Outcome Measures

Primary Outcomes (1)

• Development of machine learning models for diagnosing and monitoring of PD

  The machine learning algorithms will be trained based on a clinical dataset of 50 PD patients, healthy individuals (data from another study), and 12 additional patients with other parkinsonian disorders. This dataset consists of ocular motor and pupil data provided by neos, ocular motor and pupil assessment provided by the standard clinical examination, gait data provided by GaitQ senti (placed on the patient's leg), gait data provided by an IMU sensor placed on the patient's back, demographic information (age, sex, ethnicity, eye colour), clinical information (disease stage, disease duration, age of onset of disease, medication, MDS-UPDRS score).

  1 year

Secondary Outcomes (1)

• Correlation with clinical parameters

  1 year

Study Arms (2)

people with Parkinson's disease

Diagnostic Test: oculomotor and pupillary function assessment

Atypical parkinsonism

Diagnostic Test: oculomotor and pupillary function assessment

Interventions

oculomotor and pupillary function assessment DIAGNOSTIC_TEST

This is an exploratory open-label single-centre research project intended to collect data of PD patients in order to develop machine learning models for helping in the diagnosis and monitoring of PD. Each patient will have an initial visit and then a second visit after two weeks. The estimated duration of the study visit 1 is 3 hours, not taking into account the time for patient information and informed consent. The estimated duration of the study visit 2 is 2 hours. During these two visits, patients will undergo an MDS-UPDRS assessment, a neos examination, a standard manual ocular motor and pupil function examination, and gait assessment simultaneously with GaitQ senti and an IMU sensor placed on their back. In the two weeks separating the two visits, patient's gait will be monitored daily at home with GaitQ senti, where they will perform a daily TUG test comprising of 15 m walk, 5 sit to stand, and 5-minute walk. Healthy individuals data will be included from a previous study.

Atypical parkinsonism; people with Parkinson's disease

Eligibility Criteria

• Sex: all
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

At least 100 patients with typical PD and at least 12 patients with atypical Parkinson's.

You may qualify if:

• Diagnosis of Parkinson's disease or of another parkinsonian syndrome (atypical Parkinson's)
• Refractive error between -6 and +4 diopters, on both eyes
• Informed consent by participant documented per signature
• Able to self-report history of daily gait freezing and/or festination
• Able to walk unsupported or using an aid for at least 5 minutes and if over 69 used to carrying out this level of exercise

You may not qualify if:

• Other known neurological diseases
• Current medication/drugs that could potentially influence performance in ocular motor tasks and/or compliance in the judgement of the investigator (e.g. benzodiazepines, alcohol, stimulants, or recreational drugs) - except Parkinson's medications
• Incapacity to understand and comply with the examination (e.g. due to advanced cognitive decline, failure to comply with easy experimental instructions and tasks)
• Any injury or disorder that may affect eye movement measurements or balance (other than Parkinson's or referring primary condition)
• Any skin conditions or broken skin in the calf and behind the knee area
• Lack of access or limited connectivity to WiFi in home setting

Sponsors & Collaborators

• machineMD AG: lead
• University of Zurich: collaborator
• University Hospital, Zürich: collaborator
• University of Exeter: collaborator
• gaitQ Limited: collaborator

Study Sites (1)

University Hospital of Zurich

Zurich, Canton of Zurich, 8091, Switzerland

RECRUITING

MeSH Terms

Conditions

Parkinson Disease

Condition Hierarchy (Ancestors)

Parkinsonian Disorders; Basal Ganglia Diseases; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Movement Disorders; Synucleinopathies; Neurodegenerative Diseases

Study Officials

• Konrad Weber, Prof. Dr. med.

  University of Zurich

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Ana Coito, Ph.D.

CONTACT

+41 (0)31 589 67 92; ana.coito@machinemd.com

Pia Massatsch, Ph.D.

CONTACT

pia.massatsch@machinemd.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Target Duration: 15 Days
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 24, 2024
• First Posted: October 29, 2024
• Study Start: October 1, 2024
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): June 30, 2026
• Last Updated: October 7, 2025

Record last verified: 2025-10

Locations

CH (1)