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

NCT03366558 | Completed

• 1 other identifier: 5137/31/2016
• Study Type: observational
• Target: 97
• Locations: 1 country
• Sites: 1

Brief Summary

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. 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. 2.Can the time when the Parkinson medicine is taken be detected from the movement signals?

Conditions

Parkinson Disease

Keywords

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

Outcome Measures

Primary Outcomes (2)

• Accuracy of the classification of data from movement sensors in relation to the detected motor symptoms

  Accuracy and consistency of the classification of the subjects in the 3 categories (early stage disease, developed stage of disease, no disease) based on movement signals recorded with accelerometers and gyroscopes. Sensitivity and specificity of the classification are analyzed. Several features and methods of classification are tested including time-domain features, time-frequency domain features and machine learning both from raw data and calculated feature sets.

  3 days

• Accuracy of the detection of the time when the Parkinson medicine was taken

  Accuracy and consistency of detecting the time when the medicine is taken based on movement signals recorded with accelerometers and gyroscopes. Sensitivity and specificity of the detection are analyzed. Several features and methods of analysis are tested including time-domain features, time-frequency domain features and machine learning both from raw data and calculated feature sets.

  3 days

Study Arms (3)

PD patients: early stage

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

Diagnostic Test: UPDRS questionnaires; Diagnostic Test: 20-step walking test

PD patients: developed stage

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

Diagnostic Test: UPDRS questionnaires; Diagnostic Test: 20-step walking test

No PD

Subjects not having diagnosed Parkinson Disease

Diagnostic Test: 20-step walking test

Interventions

UPDRS questionnaires DIAGNOSTIC_TEST

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

PD patients: developed stage; PD patients: early stage

20-step walking test 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; PD patients: developed stage; PD patients: early stage

Eligibility Criteria

• Age: 30 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients having Parkinson's disease will be recruited with two methods: A) Unit of Neurology at Satakunta Central Hospital searches from the patient records potential participants, who have visited the hospital during the past 5 years with ICD-10 code G20. B) In case not enough participants are found from the Unit of Neurology, advertisements in local newspapers, in local Parkinson's disease patient association newsletters and in the web site of the hospital will be utilized. For recruiting volunteers without Parkinson's disease into the reference group, an advertisement is placed in local newspapers and in the web pages of the hospital.

You may qualify if:

• (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).

You may not qualify if:

• (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.

Sponsors & Collaborators

• Satakunta Central Hospital: lead
• Tampere University of Technology: collaborator

Study Sites (1)

Satakunta Central Hospital, Unit of Neurology

Pori, Finland

Location

Related Publications (12)

• Jankovic J. Parkinson's disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry. 2008 Apr;79(4):368-76. doi: 10.1136/jnnp.2007.131045.

  PMID: 18344392 BACKGROUND

• 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.

  PMID: 26938265 BACKGROUND

• 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.

  PMID: 27565186 BACKGROUND

• 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.

  PMID: 19025984 BACKGROUND

• 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.

  PMID: 32701955 BACKGROUND

• 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.

  PMID: 30916665 BACKGROUND

• 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.

  PMID: 30441010 BACKGROUND

• Bayle N, Patel AS, Crisan D, Guo LJ, Hutin E, Weisz DJ, Moore ST, Gracies JM. Contribution of Step Length to Increase Walking and Turning Speed as a Marker of Parkinson's Disease Progression. PLoS One. 2016 Apr 25;11(4):e0152469. doi: 10.1371/journal.pone.0152469. eCollection 2016.

  PMID: 27111531 RESULT

• 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.

  PMID: 28351715 RESULT

• Bernad-Elazari H, Herman T, Mirelman A, Gazit E, Giladi N, Hausdorff JM. Objective characterization of daily living transitions in patients with Parkinson's disease using a single body-fixed sensor. J Neurol. 2016 Aug;263(8):1544-51. doi: 10.1007/s00415-016-8164-6. Epub 2016 May 23.

  PMID: 27216626 RESULT

• 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.

  PMID: 17278588 RESULT

• 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.

  PMID: 21989633 RESULT

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

• Jari Ruokolainen, PhD

  Tampere University of Technology

  STUDY CHAIR

• Hannu Nieminen, PhD

  Tampere University of Technology

  STUDY DIRECTOR

• Juha Puustinen, MD, PhD

  Satakunta Central Hospital

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Target Duration: 3 Days
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD, PhD, Adjunct Professor (Docent)

Study Record Dates

• First Submitted: December 4, 2017
• First Posted: December 8, 2017
• Study Start: March 27, 2018
• Primary Completion: December 31, 2019
• Study Completion: December 31, 2019
• Last Updated: November 9, 2020

Record last verified: 2020-11

Locations

FI (1)