Parkinson DiseaseKÄVELINCT03366558
Identification of Motor Symptoms Related to Parkinson's Disease Using Motion Tracking Sensors at Home
An observational study of Parkinson Disease, run by Satakunta Central Hospital at 1 site in Finland.
Trial 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. Read more about this study
- Who is it for?
- Ages 30 years and olderHealthy volunteers can take part
- Conditions the study is for
- Parkinson Disease
- What is being tested?
- UPDRS questionnaires and 20-step walking test
- How many people
- 97 participants
- Where
- 1 siteFinland
- Study dates
- 27 Mar 2018to 31 Dec 2019
- How long will I take part?
- 3 Days
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 30 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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).
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Sensor-based Motion Characterization in Patients With Parkinson's Disease
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:
Read more
- 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.
- 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.
- Conditions
- Parkinson Disease
- Being tested
- UPDRS questionnaires, 20-step walking test
- Keywords
- motion detectors, early stage Parkinson's disease, advanced Parkinson's disease, UPDRS, observational study with non-diseased controls
- Medical subject headings
- Parkinson Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
UPDRS questionnairesDiagnostic test
UPDRS (Unified Parkinson's Disease Rating Scale) questionnaires are utilized for the assessment of the disease stage.
20-step walking testDiagnostic 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)
| Study group | Role | What they receive |
|---|---|---|
| PD patients: early stage | Parkinson Disease patients with early stage of the disease: potentially hypokinesia, but no dyskinesia and motor fluctuations Receives: 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") Receives: Diagnostic Test: UPDRS questionnaires; Diagnostic Test: 20-step walking test | |
| No PD | Subjects not having diagnosed Parkinson Disease Receives: Diagnostic Test: 20-step walking test |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 3 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Dec 2017
- Study start27 Mar 2018
- Primary completion31 Dec 2019
- Study completion31 Dec 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Parkinson DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 97 participants (actual)
- Target duration
- 3 Days
Sponsor and investigators
- Lead sponsor
- Satakunta Central Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Tampere University of Technology
- Responsible party
- Principal investigator, Juha Puustinen, Satakunta Central Hospital
- Investigators
- Jari Ruokolainen, PhD, Study chair, Tampere University of TechnologyHannu Nieminen, PhD, Study director, Tampere University of TechnologyJuha Puustinen, MD, PhD, Study director, Satakunta Central Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03366558
- Sponsor's protocol ID
- 5137/31/2016
- Acronym
- KÄVELI
- Status last verified
- Nov 2020
- First submitted
- 4 Dec 2017
- Last updated
- 9 Nov 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Anonymized 3-day motion data recordings.
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.
PubMed 18344392Bot 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.
PubMed 26938265Silva 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.
PubMed 27565186Goetz 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.
PubMed 19025984Juutinen 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.
PubMed 32701955Jauhiainen 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.
PubMed 30916665Mehrang 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.
PubMed 30441010Bayle 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.
PubMed 27111531Sama 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.
PubMed 28351715Bernad-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.
PubMed 27216626Salarian 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.
PubMed 17278588Weiss 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.
PubMed 21989633
Study at a glance
From the registry record, last updated by the sponsor on 9 Nov 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Sensor-based Motion Characterization in Patients With Parkinson's Disease
- ClinicalTrials.gov ID
- NCT03366558
- Acronym / sponsor ID
- KÄVELI / 5137/31/2016
- Conditions
- Parkinson Disease
- Keywords
- motion detectors, early stage Parkinson's disease, advanced Parkinson's disease, UPDRS, observational study with non-diseased controls
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 97 participants (actual)
- Lead sponsor
- Satakunta Central HospitalUniversity, hospital or other
- Collaborators
- Tampere University of Technology
- Study start
- 27 Mar 2018 (actual)
- Primary completion
- 31 Dec 2019 (actual)
- Study completion
- 31 Dec 2019 (actual)
- First posted
- 8 Dec 2017 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- 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.
- 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.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Target follow-up
- 3 Days
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| PD patients: early stage | – | Diagnostic Test: UPDRS questionnaires, Diagnostic Test: 20-step walking testParkinson Disease patients with early stage of the disease: potentially hypokinesia, but no dyskinesia and motor fluctuations |
| PD patients: developed stage | – | Diagnostic Test: UPDRS questionnaires, Diagnostic Test: 20-step walking testPD patients having dyskinesia and motor fluctuations (described as "developed stage of the disease") |
| No PD | – | Diagnostic Test: 20-step walking testSubjects not having diagnosed Parkinson Disease |
Who may qualify
- Ages
- 30 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- 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).
Key exclusion criteria (1 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| UPDRS questionnairesUPDRS (Unified Parkinson's Disease Rating Scale) questionnaires are utilized for the assessment of the disease stage. | Diagnostic test | PD patients: developed stage, PD patients: early stage |
| 20-step walking test20-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) | Diagnostic test | No PD, PD patients: developed stage, PD patients: early stage |
Primary outcome
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.
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Nov 2020.
- Background: PubMed 18344392Jankovic J. Parkinson's disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry. 2008 Apr;79(4):368-76. doi: 10.1136/jnnp.2007.131045.
- Background: PubMed 26938265Bot 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.
- Background: PubMed 27565186Silva 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.
- Background: PubMed 19025984Goetz 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.
- Background: PubMed 32701955Juutinen 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.
- Background: PubMed 30916665Jauhiainen 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.
- Background: PubMed 30441010Mehrang 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.
- Results publication: PubMed 27111531Bayle 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.
- Results publication: PubMed 28351715Sama 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.
- Results publication: PubMed 27216626Bernad-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.
- Results publication: PubMed 17278588Salarian 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.
- Results publication: PubMed 21989633Weiss 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.
- Individual participant dataSharing planned: Undecided. Anonymized 3-day motion data recordings.
Related trials for Parkinson Disease
Not yet recruitingPhase 1/2
STAR0602 With Nivolumab in Non Small Cell Lung Cancer and in Metastatic Castrate-Resistant Prostate Cancer, and STAR0602 Alone in Thymic Carcinoma
NSCLC, Non-Small Cell Lung Cancer
Enrolling by invitationObservational study
Early Molecular Biomarkers for Differentiating Parkinsonian Syndromes
PARKINSON DISEASE (Disorder), Atypical Parkinsonism
RecruitingPhase 3
A Clinical Study of Sacituzumab Tirumotecan (MK-2870) in Combination With Pembrolizumab (MK-3475) as First-line Maintenance Treatment of Cervical Cancer (MK-2870-036/TroFuse-036/GOG-3123/ENGOT-cx22)
Cervical Cancer
Active, not recruitingPhase 1/2
Clinical Safety and Efficacy of Umbilical Cord Mesenchymal Stromal Cells-derived Exosomes in Patients With Parkinson's Disease
Parkinson Disease (PD)
Active, not recruitingObservational study
Adaptive DBS to Offset STN Dysregulation During Exercise in PD
Parkinson Disease
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jari Ruokolainen, PhD study chair
Tampere University of Technology
Hannu Nieminen, PhD study director
Tampere University of Technology
Juha Puustinen, MD, PhD study director
Satakunta Central Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Parkinson Disease, run by Satakunta Central Hospital at 1 site in Finland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 9 Nov 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Finland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of the classification of data from movement sensors in relation to the detected motor symptoms (time frame: 3 days). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03366558, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Nov 2020.