Identification of Motor Symptoms Related to Parkinson's Disease Using Motion Tracking Sensors at Home

NCT ID: NCT03366558

Last Updated: 2020-11-09

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

97 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-03-27

Study Completion Date

2019-12-31

Brief Summary

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Parkinson's disease (PD) is a chronic and progressive neurological movement disorder, meaning that symptoms continue and worsen over time. Nearly 10 million people worldwide are living with Parkinson's disease. Finding cost-effective non-invasive monitoring techniques for detecting motor symptoms caused by Parkinson's disease are potentially of significant value for improving care. Of the PD symptoms, the motor symptoms are the most common and detectable signs that can be assessed unobtrusively for both diagnosis and for evaluating the effectiveness of the treatments.

The goal of our study is to find methods for identifying and classifying the motor symptoms caused by Parkinson's disease. Focus of the study is on long-term motion tracking measurements conducted at home during normal everyday life. Both accelerometers connected to arm and leg and mobile phone inbuilt sensors carried in the belt are utilized in the study. The research has two main objectives / hypotheses:

1. Can the motor symptoms related to different levels of Parkinson's disease be identified using motion tracking sensors? The first objective includes extracting and screening the motion differences of patients in early stages of the diseases in comparison with the patients in developed stages (patients having hypokinesia, dyskinesia and state changes) of the diseases and their differences with healthy control elderly adults using advanced signal and data analytics. Data from questionnaires and walking test conducted in the hospital environment are utilized as comparison points. Goal is to test the hypothesis that the amount of motor symptoms can be detected and the three groups can be reliably separated using sensor data.
2. Can the time when the Parkinson medicine is taken be detected from the movement signals?

A sample of 50 volunteer PD patients with early stage of the disease (no dyskinesia and state changes), plus 50 volunteer PD patients in the later stage of the disease (having dyskinesia and state changes), plus 50 volunteers who do not have Parkinson's disease will be recruited for the research.

Study starts with a telephone screening and visit to the hospital. Background characteristics and stage of the Parkinson's disease is evaluated in the hospital using a UPDRS questionnaires (Unified Parkinson's Disease Rating Scale; Finnish version) and a standardized 20-step walking test. Before the walking test, accelerometer sensors are attached to the shank and on the nondominant wrist. In addition, the participant wears a smart mobile phone with embedded accelerometer and gyroscope sensors. Based on the questionnaires and walking test study physiotherapist classifies the participant into one of the three study groups.

The major part of the study involves a 3-day motion screening in a free-living setting in which the subjects are wearing the abovementioned sensors for as long duration as they comfortably can and are willing. This 3-day study starts immediately after completion of the 20-step walking test in the hospital. During the 3-day study, subjects are free to live their lives without any additional tests. Subjects mark down the time when they take their Parkinson medication.

Detailed Description

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Conditions

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Parkinson Disease

Keywords

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motion detectors early stage Parkinson's disease advanced Parkinson's disease UPDRS observational study with non-diseased controls

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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PD patients: early stage

Parkinson Disease patients with early stage of the disease: potentially hypokinesia, but no dyskinesia and motor fluctuations

UPDRS questionnaires

Intervention Type DIAGNOSTIC_TEST

UPDRS (Unified Parkinson's Disease Rating Scale) questionnaires are utilized for the assessment of the disease stage.

20-step walking test

Intervention Type DIAGNOSTIC_TEST

20-step walking test is utilized either for assessing the disease stage (subjects having Parkinson disease) or for assessing the normal walking (subjects not having Parkinson disease)

PD patients: developed stage

PD patients having dyskinesia and motor fluctuations (described as "developed stage of the disease")

UPDRS questionnaires

Intervention Type DIAGNOSTIC_TEST

UPDRS (Unified Parkinson's Disease Rating Scale) questionnaires are utilized for the assessment of the disease stage.

20-step walking test

Intervention Type DIAGNOSTIC_TEST

20-step walking test is utilized either for assessing the disease stage (subjects having Parkinson disease) or for assessing the normal walking (subjects not having Parkinson disease)

No PD

Subjects not having diagnosed Parkinson Disease

20-step walking test

Intervention Type DIAGNOSTIC_TEST

20-step walking test is utilized either for assessing the disease stage (subjects having Parkinson disease) or for assessing the normal walking (subjects not having Parkinson disease)

Interventions

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UPDRS questionnaires

UPDRS (Unified Parkinson's Disease Rating Scale) questionnaires are utilized for the assessment of the disease stage.

Intervention Type DIAGNOSTIC_TEST

20-step walking test

20-step walking test is utilized either for assessing the disease stage (subjects having Parkinson disease) or for assessing the normal walking (subjects not having Parkinson disease)

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

(A) participants must be 30 years of age or older. (B) (for the Parkinson groups) diagnosed with PD (ICD-10 code G20) by a physician (neurologist or physician specializing in neurology). (C) They should be able to walk at least 20 steps unassisted (subjects are allowed to get help from assistive devices but not from other persons).

Exclusion Criteria

(A) The subjects must not be receiving any deep brain stimulation (DBS) treatment while they are participating, but intraduodenal administration of levodopa (Duodopa®) or intradermal administration of apomorphine (Apogo® or Dacepton®) is accepted. (B) .Other extrapyramidal syndromes such as MSA (multiple system atrophy), PSP (progressive supranuclear palsy), CBD (corticobasal degeneration), LBD (Lewy body dementia) or dopamine antagonist drug (such as antipsychotic drug, metoclopramide) induced Parkinsonism will be excluded.
Minimum Eligible Age

30 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Tampere University of Technology

OTHER

Sponsor Role collaborator

Satakunta Central Hospital

OTHER

Sponsor Role lead

Responsible Party

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Juha Puustinen

MD, PhD, Adjunct Professor (Docent)

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Jari Ruokolainen, PhD

Role: STUDY_CHAIR

Tampere University of Technology

Hannu Nieminen, PhD

Role: STUDY_DIRECTOR

Tampere University of Technology

Juha Puustinen, MD, PhD

Role: STUDY_DIRECTOR

Satakunta Central Hospital

Locations

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Satakunta Central Hospital, Unit of Neurology

Pori, , Finland

Site Status

Countries

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Finland

References

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Reference Type BACKGROUND
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Bot BM, Suver C, Neto EC, Kellen M, Klein A, Bare C, Doerr M, Pratap A, Wilbanks J, Dorsey ER, Friend SH, Trister AD. The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci Data. 2016 Mar 3;3:160011. doi: 10.1038/sdata.2016.11.

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Silva de Lima AL, Hahn T, de Vries NM, Cohen E, Bataille L, Little MA, Baldus H, Bloem BR, Faber MJ. Large-Scale Wearable Sensor Deployment in Parkinson's Patients: The Parkinson@Home Study Protocol. JMIR Res Protoc. 2016 Aug 26;5(3):e172. doi: 10.2196/resprot.5990.

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Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stern MB, Dodel R, Dubois B, Holloway R, Jankovic J, Kulisevsky J, Lang AE, Lees A, Leurgans S, LeWitt PA, Nyenhuis D, Olanow CW, Rascol O, Schrag A, Teresi JA, van Hilten JJ, LaPelle N; Movement Disorder Society UPDRS Revision Task Force. Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord. 2008 Nov 15;23(15):2129-70. doi: 10.1002/mds.22340.

Reference Type BACKGROUND
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Juutinen M, Wang C, Zhu J, Haladjian J, Ruokolainen J, Puustinen J, Vehkaoja A. Parkinson's disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study. PLoS One. 2020 Jul 23;15(7):e0236258. doi: 10.1371/journal.pone.0236258. eCollection 2020.

Reference Type BACKGROUND
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Jauhiainen M, Puustinen J, Mehrang S, Ruokolainen J, Holm A, Vehkaoja A, Nieminen H. Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KAVELI): Protocol for an Observational Case-Control Study. JMIR Res Protoc. 2019 Mar 27;8(3):e12808. doi: 10.2196/12808.

Reference Type BACKGROUND
PMID: 30916665 (View on PubMed)

Mehrang S, Jauhiainen M, Pietil J, Puustinen J, Ruokolainen J, Nieminen H. Identification of Parkinson's Disease Utilizing a Single Self-recorded 20-step Walking Test Acquired by Smartphone's Inertial Measurement Unit. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2913-2916. doi: 10.1109/EMBC.2018.8512921.

Reference Type BACKGROUND
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Reference Type RESULT
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Sama A, Perez-Lopez C, Rodriguez-Martin D, Catala A, Moreno-Arostegui JM, Cabestany J, de Mingo E, Rodriguez-Molinero A. Estimating bradykinesia severity in Parkinson's disease by analysing gait through a waist-worn sensor. Comput Biol Med. 2017 May 1;84:114-123. doi: 10.1016/j.compbiomed.2017.03.020. Epub 2017 Mar 23.

Reference Type RESULT
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Reference Type RESULT
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Salarian A, Russmann H, Wider C, Burkhard PR, Vingerhoets FJ, Aminian K. Quantification of tremor and bradykinesia in Parkinson's disease using a novel ambulatory monitoring system. IEEE Trans Biomed Eng. 2007 Feb;54(2):313-22. doi: 10.1109/TBME.2006.886670.

Reference Type RESULT
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Weiss A, Sharifi S, Plotnik M, van Vugt JP, Giladi N, Hausdorff JM. Toward automated, at-home assessment of mobility among patients with Parkinson disease, using a body-worn accelerometer. Neurorehabil Neural Repair. 2011 Nov-Dec;25(9):810-8. doi: 10.1177/1545968311424869.

Reference Type RESULT
PMID: 21989633 (View on PubMed)

Other Identifiers

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5137/31/2016

Identifier Type: -

Identifier Source: org_study_id