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

• ClinicalTrials.gov Identifier: NCT03366558
• Recruitment Status: Completed
• First Posted: December 8, 2017
• Last Update Posted: November 9, 2020
• Sponsor: Satakunta Central Hospital
• Collaborator: Tampere University of Technology
• Information provided by (Responsible Party): Juha Puustinen, Satakunta Central Hospital

Study Description

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. 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.

Condition or disease	Intervention/treatment
Parkinson Disease	Diagnostic Test: UPDRS questionnaires; Diagnostic Test: 20-step walking test

Study Design

• Study Type: Observational [Patient Registry]
• Actual Enrollment: 97 participants
• Observational Model: Case-Control
• Time Perspective: Prospective
• Target Follow-Up Duration: 3 Days
• Official Title: Sensor-based Motion Characterization in Patients With Parkinson's Disease
• Actual Study Start Date: March 27, 2018
• Actual Primary Completion Date: December 31, 2019
• Actual Study Completion Date: December 31, 2019

Groups and Cohorts

Group/Cohort	Intervention/treatment
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; UPDRS (Unified Parkinson's Disease Rating Scale) questionnaires are utilized for the assessment of the disease stage.; 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)
PD patients: developed stage; PD patients having dyskinesia and motor fluctuations (described as "developed stage of the disease")	Diagnostic Test: UPDRS questionnaires; UPDRS (Unified Parkinson's Disease Rating Scale) questionnaires are utilized for the assessment of the disease stage.; 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)
No PD; Subjects not having diagnosed Parkinson Disease	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)

Outcome Measures

Primary Outcome Measures :

1. Accuracy of the classification of data from movement sensors in relation to the detected motor symptoms [ Time Frame: 3 days ]
   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.
2. Accuracy of the detection of the time when the Parkinson medicine was taken [ Time Frame: 3 days ]
   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.

Eligibility Criteria

• Ages Eligible for Study: 30 Years and older (Adult, Older Adult)
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: Yes
• 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.

Criteria

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.

Contacts and Locations

Locations

Finland

Satakunta Central Hospital, Unit of Neurology

Pori, Finland

Sponsors and Collaborators

Satakunta Central Hospital

Tampere University of Technology

Investigators

• Study Chair: Jari Ruokolainen, PhD; Tampere University of Technology
• Study Director: Hannu Nieminen, PhD; Tampere University of Technology
• Study Director: Juha Puustinen, MD, PhD; Satakunta Central Hospital

More Information

Additional Information:

Publications of Results:

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.

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.

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.

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.

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.

Other Publications:

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.

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.

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.

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.

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.

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.

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.

• Responsible Party: Juha Puustinen, MD, PhD, Adjunct Professor (Docent), Satakunta Central Hospital
• ClinicalTrials.gov Identifier: NCT03366558
• Other Study ID Numbers: 5137/31/2016
• First Posted: December 8, 2017
• Last Update Posted: November 9, 2020
• Last Verified: November 2020

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: Undecided
• Plan Description: Anonymized 3-day motion data recordings.

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Keywords provided by Juha Puustinen, Satakunta Central Hospital:

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

Additional relevant MeSH terms:

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