Utilizing Smart Devices to Identify New Phenotypical Characteristics in Movement Disorders

June 10, 2022 updated by: Julian Varghese, Westfälische Wilhelms-Universität Münster
This observational and experimental study seeks to establish a Smart Device System (SDS) to monitor high-resolution handtremor-based data using Smartphones, SmartWatches and Tablets. By doing this, movement data will be analyzed in depth with advanced statistical and Deep-Learning algorithms to identify new clinical phenotypical characteristics Parkinson's Disease and Essential Tremor.

Study Overview

Detailed Description

Current smart devices as smartphones and smartwatches have reached a level of technical sophistication that enables high-resolution monitoring of movements not only for everyday sports activities but also for movement disorders. Tremor-related diseases as Parkinson's Disease (PD) and Essential Tremor (ET) are two of the most common movement disorders. Disease classification is primarily based on clinical criteria and remains challenging. The primary goal of this study is to identify new phenotypical characteristics based on the captured movement data by the tremor-capturing smartwatches and tablets and smartphone-based questionnaires.

The system will be applied and analyzed within an experimental and observational setting and only captures from patients, which have received informed consent. Within the study period, the SDS is not intended as clinical diagnostic support for physicians and will be not be used as medical device.

Study Type

Observational

Enrollment (Actual)

513

Contacts and Locations

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

Study Locations

      • Münster, Germany, 48149
        • Institute of Medical Informatics, University of Münster

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

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Participants who attend the ambulatory clinicic for movement disorders at the University Hospital Münster will be asked for inclusion.

Description

Inclusion Criteria:

  • Diagnosed with Parkinson's Disease (ICD-10-GM G20.-) or Essential Tremor (G25.0)
  • Comparison group: Other movement disorders including atypical Parkinsonian disorders and healthy participants

Exclusion Criteria:

  • Unable to obtain informed consent
  • Skin-related conditions at one of the wrists or any other medical conditions that could harm the participant's health by wearing the smartwatch at both wrists.

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

  • Observational Models: Case-Control
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Parkinson's Disease
Participant's diagnosed with Parkinson's Disease
This is no intervention. Participants of all groups will receive data Capture with smartphones, smartwatches and tablets.
Essential Tremor
Participant's diagnosed with Essential Tremor or other Movement Disorders
This is no intervention. Participants of all groups will receive data Capture with smartphones, smartwatches and tablets.
No Parkinson's Disease and No Essential Tremor
Participant's with no diagnosis of PD, ET or other Movement Disorders
This is no intervention. Participants of all groups will receive data Capture with smartphones, smartwatches and tablets.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Acceleration data in all three axes (x,y,z) measured at both wrists via Smartwatches during 10 minutes of neurological examination. Aggregated data: Mean Frequency and Amplitude of Tremor.
Time Frame: 2018-2020
The raw time series data (acceleration data) and the aggregated data will be analyzed to train a neural network to classify the participant's movement disorder.
2018-2020

Collaborators and Investigators

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

Investigators

  • Principal Investigator: Julian Varghese, MD, WWU Münster, Institut für Medizinische Informatik

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.

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 (Actual)

January 8, 2019

Primary Completion (Actual)

December 31, 2021

Study Completion (Actual)

December 31, 2021

Study Registration Dates

First Submitted

August 13, 2018

First Submitted That Met QC Criteria

August 15, 2018

First Posted (Actual)

August 20, 2018

Study Record Updates

Last Update Posted (Actual)

June 13, 2022

Last Update Submitted That Met QC Criteria

June 10, 2022

Last Verified

June 1, 2022

More Information

Terms related to this study

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

No

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

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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