Parkinson's DiseaseNCT02474329
Real-PD Trial: Development of Clinical Prognostic Models for Parkinson's Disease
An observational study of Parkinson's Disease, run by Radboud University Medical Center at 1 site in Netherlands.
Trial summary
Background: Long-term management of Parkinson's disease (PD) does not reach its full potential due to lack of knowledge about disease progression. The Real-PD study aim to evaluate the feasibility and compliance of usage of wearable sensors in PD patients in real life. Read more about this study
- Who is it for?
- Ages 30 years and older
- Conditions the study is for
- Parkinson's Disease
- What is being tested?
- Clinical assessment and Fox Insight self-monitoring android app and falls detector
- Type of treatment
- Medical device
- How many people
- 304 participants
- Where
- 1 siteNetherlands
- Study dates
- Jul 2015to Nov 2016
- How long will I take part?
- 13 Weeks
- Phase
- Observational
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
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
- No healthy volunteers
- Adult, Older adult
Study population: Dutch Parkinson patients.
Who may be able to join
- Currently own and use a smartphone device with access to the Internet
- 30 years of age or older;
- Diagnosed with Parkinson's disease by a physician;
- Able to walk without any assistance.
Who cannot join
- None exclusion criteria will be used.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Real-PD: Development of Clinical Prognostic Models for Parkinson's Disease From Large-scale Wearable Sensor Deployment and Clinical Data - a Population Based Trial
Background: Long-term management of Parkinson's disease (PD) does not reach its full potential due to lack of knowledge about disease progression. The Real-PD study aim to evaluate the feasibility and compliance of usage of wearable sensors in PD patients in real life. Moreover, an explorative analysis concerning activity level, medication intake and mood will be done.
Methods: Overall, 1000 PD patients and 250 physiotherapist will be enrolled in this observational study. Dutch PD patients will be recruited across the country and an assessment will be performed using a short version of the Parkinson's Progression Markers Initiative (PPMI) protocol. Moreover, participants will wear a set of medical devices (Pebble Smartwatch, fall detector) and they will use a smartphone with The Fox Insight App (Android app), 24/7, during 13 weeks. Primary measures of interest are: 1) physical activity, falls and tremor, measured by the axial accelerometers embedded in the Pebble watch and fall detector; and 2) medication intake and mood reports measured by patients' self-report in the Android app. To measure motor impact, an assessment will be performed by physiotherapists who are all certified to perform the Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS).
Discussion: Management of PD patients is complex and appears to be a challenging task for health care professionals. The main reason is the lack of knowledge in the disease pattern. This issue could be solved by a long term follow-up of patients' during their everyday life, and wearable medical devices can act as a way to collect data about every day life activities. Therefore, the Real-PD study will be a first contribution in increasing the lack of knowledge in disease progression, developing a new medical decision system and improving PD patients' care.
Read the detailed description
Rationale: Today's management of patients with a chronic disorder like Parkinson's disease (PD) is imperfect. The understanding of clinical profiles is based on observations in small, selective populations with brief follow-up. Moreover, treatment decisions are based on averaged population results that may not apply to a specific individual context. These drawbacks will be addressed with a "big data" approach. Ambulatory sensors will be used as an objective measure of patients' performance under everyday circumstances, for longer periods of time. The researchers aim to explore the potential of using longitudinal ambulatory data to enrich a standardized clinical dataset, which reflects current clinical practice for the assessment of disease status.
Objective: The study will include a total of 250 physiotherapists and 1000 patients. The aims of this study are: (1) to perform "big data" analyses on the raw sensor data, in relation to concurrently acquired clinical data in these patients (limited version of the PPMI (Parkinson's Progression Markers Initiative) protocol) to develop patient profiles; and (2) to correlate the ambulatory sensor data to simple self-assessments made during follow-up.
Study design: Observational descriptive study.
Study population: Dutch Parkinson patients, male or female, age 30 years or older, with PD diagnosis given by a physician, and own a suitable smartphone.
lntervention: 250 ParkinsonNet physiotherapists and 1000 eligible patients will be included in this study. Patients and physiotherapists will be recruited in 5 consecutive cohorts based on geographic region. Patients will be asked to wear a smartwatch and a pendant movement sensor, both with triaxial accelerometers, during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. During the 13 week follow-up, trained physiotherapists will perform a standardized clinical assessment, based on the PPMI protocol (www.ppmi-info.org) for every included patient. This assessment will last for 60 minutes. The smartphone is used to transmit data from the watch to a cloud-based data platform. lntel developed this dedicated data analysis platform for ambulatory data. lntel will receive coded data only.
Main study parameters/endpoints: Study endpoints include parameters registered with the smartwatch, the pendant movement sensor, the self-monitoring app and collected with the PPMI assessment. The smartwatch data provides, after data processing, a measure for the level of physical activity during the day. Falls will be registered with the pendant movement sensor. Medication intake and mood are registered using the smartphone. Finally, PPMI assessment includes assessment of motor symptoms, cognition, depression, sleep and daily activity. Correlations will be determined between the above mentioned parameters.
Nature and extent of the burden and risks associated with participation, benefit and group relatedness: First, participants are asked to wear the devices 24/7 and data will be recorded continuously, for a total duration of 13 weeks. Second, data will be transmitted to a data platform developed and managed by lntel, on behalf of the Michael J. Fox Foundation for Parkinson's Research. To access these data, researchers can grant permission for research purposes, provided by Michael J. Fox Foundation. Patients will be asked for permission to share the raw coded data for dissemination to the research community, analysis and use in future publications. Participation in the study warrants that patients provide written permission for this.
- Conditions
- Parkinson's Disease
- Being tested
- Clinical assessment, Fox Insight self-monitoring android app and falls detector
- Keywords
- Parkinson's disease, Ambulatory monitoring, Disease progression
- Medical subject headings
- Parkinson Disease, Disease Progression
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Clinical assessmentOther
Also called PPMI (Parkinson's Progression Markers Initiative) protocol
During the 13 week follow-up, trained physiotherapists will perform a standardized clinical assessment, based on the PPMI protocol (www.ppmi-info.org) for every included patient. This assessment will last for 60 minutes, and it will be done once.
Fox Insight self-monitoring android app and falls detectorDevice
Also called Pebble smartphone, Philips falls detector
Patients will be asked to wear a smartwatch and a pendant movement sensor, both with triaxial accelerometers, during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling.
| Study group | Role | What they receive |
|---|---|---|
| Cohort 1 | Dutch Parkinson's patients resident in the region of Noord-Holland whose fulfill the eligibility criteria.
Interventions/Exposures to be administered:
1. PPMI (Parkinson's Progression Markers Initiative) protocol Trained physiotherapists will perform once a standardized clinical assessment to every included patient. This assessment will last for 60 minutes, and it will be done once.
2. Fox Insight self-monitoring android app and falls detector Patients will wear a smartwatch and a pendant movement sensor during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. Receives: Other: Clinical assessment; Device: Fox Insight self-monitoring android app and falls detector | |
| Cohort 2 | Dutch Parkinson's patients resident in the region of Zuid-Holland whose fulfill the eligibility criteria.
Interventions/Exposures to be administered:
1. PPMI (Parkinson's Progression Markers Initiative) protocol Trained physiotherapists will perform once a standardized clinical assessment to every included patient. This assessment will last for 60 minutes, and it will be done once.
2. Fox Insight self-monitoring android app and falls detector Patients will wear a smartwatch and a pendant movement sensor during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. Receives: Other: Clinical assessment; Device: Fox Insight self-monitoring android app and falls detector | |
| Cohort 3 | Dutch Parkinson's patients resident in the region of Gelderland and Utrecht whose fulfill the eligibility criteria.
Interventions/Exposures to be administered:
1. PPMI (Parkinson's Progression Markers Initiative) protocol Trained physiotherapists will perform once a standardized clinical assessment to every included patient. This assessment will last for 60 minutes, and it will be done once.
2. Fox Insight self-monitoring android app and falls detector Patients will wear a smartwatch and a pendant movement sensor during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. Receives: Other: Clinical assessment; Device: Fox Insight self-monitoring android app and falls detector | |
| Cohort 4 | Dutch Parkinson's patients resident in the region of Groningen, Friesland, Drenthe and Overijssel whose fulfill the eligibility criteria.
Interventions/Exposures to be administered:
1. PPMI (Parkinson's Progression Markers Initiative) protocol Trained physiotherapists will perform once a standardized clinical assessment to every included patient. This assessment will last for 60 minutes, and it will be done once.
2. Fox Insight self-monitoring android app and falls detector Patients will wear a smartwatch and a pendant movement sensor during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. Receives: Other: Clinical assessment; Device: Fox Insight self-monitoring android app and falls detector | |
| Cohort 5 | Dutch Parkinson's patients resident in the region of Zeeland, Noord-Brabant and Limburg whose fulfill the eligibility criteria.
Interventions/Exposures to be administered:
1. PPMI (Parkinson's Progression Markers Initiative) protocol Trained physiotherapists will perform once a standardized clinical assessment to every included patient. This assessment will last for 60 minutes, and it will be done once.
2. Fox Insight self-monitoring android app and falls detector Patients will wear a smartwatch and a pendant movement sensor during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. Receives: Other: Clinical assessment; Device: Fox Insight self-monitoring android app and falls detector |
What is being measured
The primary outcome is the main question the study is designed to answer.
Parkinson's Disease Symptoms
The MDS-UPDRS is a revision of the Unified Parkinson's Disease Rating Scale (UPDRS). It was developed to evaluate various aspects of Parkinson's disease including non-motor and motor experiences of daily living, as well as motor complications. The MDS-UPDRS characterizes the extent and burden of disease across various populations. Here, we used data from one-point assessment (baseline). The total score used here was calculated by a sum of all scores from the 4 sub-scales (i.e. part I, up to part IV) composing the MDS-UPDRS. Total score ranges from 0 to 272. A higher score indicates higher disease severity and burden, being thus a worse outcome.
Measured: Baseline
Depression Scores as a Measure of Depression Rates
The scores obtained with the Geriatric Depression Scale were analysed in order to create a percentage of probably depressed participants. The total score goes from 0 to 15. A score higher than 6 indicates higher probability of suffer from a depression.
Measured: Baseline
Cognitive Impairment.
Total sum score obtained with the Montreal Cognitive Assessment. We analyzed the full score to investigate percentage of participants with a possible cognitive impairment. The Montreal Cognitive sum scores ranges from 0 to 30, in which a score lower or equal to 26 is considered as possible cognitive decline.
Measured: Baseline
Independency Level
The total sum score obtained with the Schwab and England activities of daily living scale were analyzed to describe the functional level of the sample. The total sum scores varies from 0 to 100, in which lower scores are associated with more dependency of others to perform daily life activities.
Measured: Baseline
Secondary outcomes (7)
Number of Falls Per Patient Registered by the Falls Detector.
The fall event is recognized by the falls detector. Every time that the patient falls, the algorithm embedded at the falls detector recognize as a fall and record the fall event. At the end of the follow-up time, a sum of the falls event for each patient will be done.
Measured: Patients will be automatically assessed during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week.
Number of Mood Reports for Each Patient Measured With a Four Point Scale
The number of mood reports will be collected through the smartphone application. A four point scale (very good, good, poor and fair) will be available, and by pressing the button which correspond to how the patient feels at that moment the report can be performed. At the end of the follow-up time a sum of all the reports will be done in order to measure the number of mood reports over the follow-up time.
Measured: Patients will be assessed during the follow-up time (up to 13 weeks after the enrollment date). It is expected that the assessment (self-report) will be performed as many times as the patient wants to report how they feel or at least once a day.
Number of Medication Intake Annotations Made by Each Participant Via the Self-report App.
The number of medication intake annotations made by the patients will be collected through the smartphone application. Every time that the patient take medication they must press the button reporting that they took the medication. At the end of the follow-up time a sum of all the reports will be done in order to measure the number of medication intake over the follow-up time.
Measured: Baseline
Time That Each Patient Was Active During the Day
The time that the patient was active during the day is calculated automatically through the app at the smartphone. The calculation is performed by using an algorithm, which analyze the patterns of walk. This algorithm is able to predict when the patient was active in a zone above his/her usual threshold (e.g. when the patient was performing one activity that makes him/her more active than during a quiet time). At the end of the follow-up time a sum of all active hours will be done in order to measure the amount of time that the patient was active over the follow-up time.
Measured: Patients will be automatically assessed continuously during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week. The analyses was limited to walking activities.
Level of Activity for Each Patient During the Day
The level of activity for each patient is calculated automatically through the app at the smartphone. The calculation is performed by using the data collect with the accelerometers embedded in the smartwatch. An algorithm installed in the phone, which analyze the data collected with the smartwatch, can calculate the level of activity for each patient throughout the day.
Measured: Patients will be automatically assessed during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week.
Scores in Autonomic Dysfunctions Measure With the Autonomic Dysfunctions Scale
The scores for autonomic dysfunctions will be obtained with the Assessment of autonomic dysfunction in Parkinson's disease (SCOPA-AUT). The total sum scores ranges from 0 to 100, in which high scores are correlated with more burden of autonomic dysfunctions in Parkinson's patients.
Measured: Baseline
Sleepiness Rates in the Epworth Sleepiness Scale as a Measure of Sleep Quantity.
The Epworth sleepiness scale was used to rate the level of sleepiness during the day. The scale's scores are related to the usual duration of sleep at night and increase with relative sleep deprivation. Here, we used data from one-point assessment (baseline). Then, we can suggest that if the sample has high scores they will have a low sleep quantity. Total sum score ranges from 0 (no sleepiness at all) to 24 (excessive sleepiness).
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Jun 2015 (estimated)
- Study startJul 2015
- Primary completionNov 2016
- Study completionNov 2016
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (13)Papers the sponsor has linked to this study.
- Other trials for Parkinson's DiseaseStudies of the same condition, open and under way.
- More trials run by Radboud University Medical CenterThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 304 participants (actual)
- Target duration
- 13 Weeks
Sponsor and investigators
- Lead sponsor
- Radboud University Medical Center
- Sponsor type
- University, hospital or other
- Collaborators
- Michael J. Fox Foundation for Parkinson's Research, Philips Clinical & Medical Affairs Global, Intel Corporation
- Responsible party
- Sponsor
- Investigators
- Bastiaan R Bloem, Prof. Dr., Principal investigator, Radboud University Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02474329
- Sponsor's protocol ID
- NL53034.091.41
- Status last verified
- Oct 2017
- First submitted
- 2 Jun 2015
- Last updated
- 6 Dec 2021
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- Yes
Publications
Parkka J, Ermes M, Korpipaa P, Mantyjarvi J, Peltola J, Korhonen I. Activity classification using realistic data from wearable sensors. IEEE Trans Inf Technol Biomed. 2006 Jan;10(1):119-28. doi: 10.1109/titb.2005.856863.
PubMed 16445257Hobert MA, Maetzler W, Aminian K, Chiari L. Technical and clinical view on ambulatory assessment in Parkinson's disease. Acta Neurol Scand. 2014 Sep;130(3):139-47. doi: 10.1111/ane.12248. Epub 2014 Apr 1.
PubMed 24689772Maetzler W, Domingos J, Srulijes K, Ferreira JJ, Bloem BR. Quantitative wearable sensors for objective assessment of Parkinson's disease. Mov Disord. 2013 Oct;28(12):1628-37. doi: 10.1002/mds.25628. Epub 2013 Sep 12.
PubMed 24030855Patel S, Lorincz K, Hughes R, Huggins N, Growdon J, Standaert D, Akay M, Dy J, Welsh M, Bonato P. Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors. IEEE Trans Inf Technol Biomed. 2009 Nov;13(6):864-73. doi: 10.1109/TITB.2009.2033471. Epub 2009 Oct 20.
PubMed 19846382Patel S, Chen BR, Mancinelli C, Paganoni S, Shih L, Welsh M, Dy J, Bonato P. Longitudinal monitoring of patients with Parkinson's disease via wearable sensor technology in the home setting. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1552-5. doi: 10.1109/IEMBS.2011.6090452.
PubMed 22254617Tsanas A, Little MA, McSharry PE, Ramig L. Using the cellular mobile telephone network to remotely monitor parkinsons disease symptom severity. IEEE Transactions on Biomedical Engineering. 2012.
Arora S, Venkataraman V, Donohue S, Biglan KM, Dorsey ER, Little MA, editors. High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones. Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on; 2014: IEEE.
Pastorino M, Arredondo M, Cancela J, Guillen S, editors. Wearable sensor network for health monitoring: the case of Parkinson disease. Journal of Physics: Conference Series; 2013: IOP Publishing.
Sharma V, Mankodiya K, De La Torre F, Zhang A, Ryan N, Ton TG, et al. SPARK: Personalized Parkinson Disease Interventions through Synergy between a Smartphone and a Smartwatch. Design, User Experience, and Usability User Experience Design for Everyday Life Applications and Services: Springer; 2014. p. 103-14.
Tzallas AT, Tsipouras MG, Rigas G, Tsalikakis DG, Karvounis EC, Chondrogiorgi M, Psomadellis F, Cancela J, Pastorino M, Waldmeyer MT, Konitsiotis S, Fotiadis DI. PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease. Sensors (Basel). 2014 Nov 11;14(11):21329-57. doi: 10.3390/s141121329.
PubMed 25393786Lakshminarayana R, Wang D, Burn D, Chaudhuri KR, Cummins G, Galtrey C, Hellman B, Pal S, Stamford J, Steiger M, Williams A; SMART-PD Investigators. Smartphone- and internet-assisted self-management and adherence tools to manage Parkinson's disease (SMART-PD): study protocol for a randomised controlled trial (v7; 15 August 2014). Trials. 2014 Sep 25;15:374. doi: 10.1186/1745-6215-15-374.
PubMed 25257518Gschwind YJ, Eichberg S, Marston HR, Ejupi A, Rosario Hd, Kroll M, Drobics M, Annegarn J, Wieching R, Lord SR, Aal K, Delbaere K. ICT-based system to predict and prevent falls (iStoppFalls): study protocol for an international multicenter randomized controlled trial. BMC Geriatr. 2014 Aug 20;14:91. doi: 10.1186/1471-2318-14-91.
PubMed 25141850Silva 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 27565186
Study at a glance
From the registry record, last updated by the sponsor on 6 Dec 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Real-PD: Development of Clinical Prognostic Models for Parkinson's Disease From Large-scale Wearable Sensor Deployment and Clinical Data - a Population Based Trial
- ClinicalTrials.gov ID
- NCT02474329
- Acronym / sponsor ID
- NL53034.091.41
- Conditions
- Parkinson's Disease
- Keywords
- Parkinson's disease, Ambulatory monitoring, Disease progression
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 304 participants (actual)
- Lead sponsor
- Radboud University Medical CenterUniversity, hospital or other
- Collaborators
- Michael J. Fox Foundation for Parkinson's Research, Philips Clinical & Medical Affairs Global, Intel Corporation
- Study start
- Jul 2015
- Primary completion
- Nov 2016 (actual)
- Study completion
- Nov 2016 (actual)
- First posted
- 17 Jun 2015 (estimated)
- Results
- Posted 6 Dec 2021
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Background: Long-term management of Parkinson's disease (PD) does not reach its full potential due to lack of knowledge about disease progression. The Real-PD study aim to evaluate the feasibility and compliance of usage of wearable sensors in PD patients in real life. Moreover, an explorative analysis concerning activity level, medication intake and mood will be done.
Methods: Overall, 1000 PD patients and 250 physiotherapist will be enrolled in this observational study. Dutch PD patients will be recruited across the country and an assessment will be performed using a short version of the Parkinson's Progression Markers Initiative (PPMI) protocol. Moreover, participants will wear a set of medical devices (Pebble Smartwatch, fall detector) and they will use a smartphone with The Fox Insight App (Android app), 24/7, during 13 weeks. Primary measures of interest are: 1) physical activity, falls and tremor, measured by the axial accelerometers embedded in the Pebble watch and fall detector; and 2) medication intake and mood reports measured by patients' self-report in the Android app. To measure motor impact, an assessment will be performed by physiotherapists who are all certified to perform the Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS).
Discussion: Management of PD patients is complex and appears to be a challenging task for health care professionals. The main reason is the lack of knowledge in the disease pattern. This issue could be solved by a long term follow-up of patients' during their everyday life, and wearable medical devices can act as a way to collect data about every day life activities. Therefore, the Real-PD study will be a first contribution in increasing the lack of knowledge in disease progression, developing a new medical decision system and improving PD patients' care.
Detailed description
Rationale: Today's management of patients with a chronic disorder like Parkinson's disease (PD) is imperfect. The understanding of clinical profiles is based on observations in small, selective populations with brief follow-up. Moreover, treatment decisions are based on averaged population results that may not apply to a specific individual context. These drawbacks will be addressed with a "big data" approach. Ambulatory sensors will be used as an objective measure of patients' performance under everyday circumstances, for longer periods of time. The researchers aim to explore the potential of using longitudinal ambulatory data to enrich a standardized clinical dataset, which reflects current clinical practice for the assessment of disease status.
Objective: The study will include a total of 250 physiotherapists and 1000 patients. The aims of this study are: (1) to perform "big data" analyses on the raw sensor data, in relation to concurrently acquired clinical data in these patients (limited version of the PPMI (Parkinson's Progression Markers Initiative) protocol) to develop patient profiles; and (2) to correlate the ambulatory sensor data to simple self-assessments made during follow-up.
Study design: Observational descriptive study.
Study population: Dutch Parkinson patients, male or female, age 30 years or older, with PD diagnosis given by a physician, and own a suitable smartphone.
lntervention: 250 ParkinsonNet physiotherapists and 1000 eligible patients will be included in this study. Patients and physiotherapists will be recruited in 5 consecutive cohorts based on geographic region. Patients will be asked to wear a smartwatch and a pendant movement sensor, both with triaxial accelerometers, during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. During the 13 week follow-up, trained physiotherapists will perform a standardized clinical assessment, based on the PPMI protocol (www.ppmi-info.org) for every included patient. This assessment will last for 60 minutes. The smartphone is used to transmit data from the watch to a cloud-based data platform. lntel developed this dedicated data analysis platform for ambulatory data. lntel will receive coded data only.
Main study parameters/endpoints: Study endpoints include parameters registered with the smartwatch, the pendant movement sensor, the self-monitoring app and collected with the PPMI assessment. The smartwatch data provides, after data processing, a measure for the level of physical activity during the day. Falls will be registered with the pendant movement sensor. Medication intake and mood are registered using the smartphone. Finally, PPMI assessment includes assessment of motor symptoms, cognition, depression, sleep and daily activity. Correlations will be determined between the above mentioned parameters.
Nature and extent of the burden and risks associated with participation, benefit and group relatedness: First, participants are asked to wear the devices 24/7 and data will be recorded continuously, for a total duration of 13 weeks. Second, data will be transmitted to a data platform developed and managed by lntel, on behalf of the Michael J. Fox Foundation for Parkinson's Research. To access these data, researchers can grant permission for research purposes, provided by Michael J. Fox Foundation. Patients will be asked for permission to share the raw coded data for dissemination to the research community, analysis and use in future publications. Participation in the study warrants that patients provide written permission for this.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 13 Weeks
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| Cohort 1 | – | Other: Clinical assessment, Device: Fox Insight self-monitoring android app and falls detectorDutch Parkinson's patients resident in the region of Noord-Holland whose fulfill the eligibility criteria. Interventions/Exposures to be administered: 1. PPMI (Parkinson's Progression Markers Initiative) protocol Trained physiotherapists will perform once a standardized clinical assessment to every included patient. This assessment will last for 60 minutes, and it will be done once. 2. Fox Insight self-monitoring android app and falls detector Patients will wear a smartwatch and a pendant movement sensor during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. |
| Cohort 2 | – | Other: Clinical assessment, Device: Fox Insight self-monitoring android app and falls detectorDutch Parkinson's patients resident in the region of Zuid-Holland whose fulfill the eligibility criteria. Interventions/Exposures to be administered: 1. PPMI (Parkinson's Progression Markers Initiative) protocol Trained physiotherapists will perform once a standardized clinical assessment to every included patient. This assessment will last for 60 minutes, and it will be done once. 2. Fox Insight self-monitoring android app and falls detector Patients will wear a smartwatch and a pendant movement sensor during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. |
| Cohort 3 | – | Other: Clinical assessment, Device: Fox Insight self-monitoring android app and falls detectorDutch Parkinson's patients resident in the region of Gelderland and Utrecht whose fulfill the eligibility criteria. Interventions/Exposures to be administered: 1. PPMI (Parkinson's Progression Markers Initiative) protocol Trained physiotherapists will perform once a standardized clinical assessment to every included patient. This assessment will last for 60 minutes, and it will be done once. 2. Fox Insight self-monitoring android app and falls detector Patients will wear a smartwatch and a pendant movement sensor during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. |
| Cohort 4 | – | Other: Clinical assessment, Device: Fox Insight self-monitoring android app and falls detectorDutch Parkinson's patients resident in the region of Groningen, Friesland, Drenthe and Overijssel whose fulfill the eligibility criteria. Interventions/Exposures to be administered: 1. PPMI (Parkinson's Progression Markers Initiative) protocol Trained physiotherapists will perform once a standardized clinical assessment to every included patient. This assessment will last for 60 minutes, and it will be done once. 2. Fox Insight self-monitoring android app and falls detector Patients will wear a smartwatch and a pendant movement sensor during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. |
| Cohort 5 | – | Other: Clinical assessment, Device: Fox Insight self-monitoring android app and falls detectorDutch Parkinson's patients resident in the region of Zeeland, Noord-Brabant and Limburg whose fulfill the eligibility criteria. Interventions/Exposures to be administered: 1. PPMI (Parkinson's Progression Markers Initiative) protocol Trained physiotherapists will perform once a standardized clinical assessment to every included patient. This assessment will last for 60 minutes, and it will be done once. 2. Fox Insight self-monitoring android app and falls detector Patients will wear a smartwatch and a pendant movement sensor during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. |
Who may qualify
- Ages
- 30 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Dutch Parkinson patients.
Key inclusion criteria (4 listed)
- Currently own and use a smartphone device with access to the Internet
- 30 years of age or older;
- Diagnosed with Parkinson's disease by a physician;
- Able to walk without any assistance.
Key exclusion criteria (1 listed)
- None exclusion criteria will be used.
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 |
|---|---|---|
| Clinical assessmentAlso: PPMI (Parkinson's Progression Markers Initiative) protocolDuring the 13 week follow-up, trained physiotherapists will perform a standardized clinical assessment, based on the PPMI protocol (www.ppmi-info.org) for every included patient. This assessment will last for 60 minutes, and it will be done once. | Other | Cohort 1, Cohort 2, Cohort 3, Cohort 4, Cohort 5 |
| Fox Insight self-monitoring android app and falls detectorAlso: Pebble smartphone, Philips falls detectorPatients will be asked to wear a smartwatch and a pendant movement sensor, both with triaxial accelerometers, during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a Smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. | Device | Cohort 1, Cohort 2, Cohort 3, Cohort 4, Cohort 5 |
Primary outcome
Parkinson's Disease Symptoms
Time frame: Baseline
The MDS-UPDRS is a revision of the Unified Parkinson's Disease Rating Scale (UPDRS). It was developed to evaluate various aspects of Parkinson's disease including non-motor and motor experiences of daily living, as well as motor complications. The MDS-UPDRS characterizes the extent and burden of disease across various populations. Here, we used data from one-point assessment (baseline). The total score used here was calculated by a sum of all scores from the 4 sub-scales (i.e. part I, up to part IV) composing the MDS-UPDRS. Total score ranges from 0 to 272. A higher score indicates higher disease severity and burden, being thus a worse outcome.
Depression Scores as a Measure of Depression Rates
Time frame: Baseline
The scores obtained with the Geriatric Depression Scale were analysed in order to create a percentage of probably depressed participants. The total score goes from 0 to 15. A score higher than 6 indicates higher probability of suffer from a depression.
Cognitive Impairment.
Time frame: Baseline
Total sum score obtained with the Montreal Cognitive Assessment. We analyzed the full score to investigate percentage of participants with a possible cognitive impairment. The Montreal Cognitive sum scores ranges from 0 to 30, in which a score lower or equal to 26 is considered as possible cognitive decline.
Independency Level
Time frame: Baseline
The total sum score obtained with the Schwab and England activities of daily living scale were analyzed to describe the functional level of the sample. The total sum scores varies from 0 to 100, in which lower scores are associated with more dependency of others to perform daily life activities.
7 secondary outcomes
Number of Falls Per Patient Registered by the Falls Detector.
Time frame: Patients will be automatically assessed during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week.
Number of Mood Reports for Each Patient Measured With a Four Point Scale
Time frame: Patients will be assessed during the follow-up time (up to 13 weeks after the enrollment date). It is expected that the assessment (self-report) will be performed as many times as the patient wants to report how they feel or at least once a day.
Number of Medication Intake Annotations Made by Each Participant Via the Self-report App.
Time frame: Baseline
Time That Each Patient Was Active During the Day
Time frame: Patients will be automatically assessed continuously during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week. The analyses was limited to walking activities.
Level of Activity for Each Patient During the Day
Time frame: Patients will be automatically assessed during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week.
Scores in Autonomic Dysfunctions Measure With the Autonomic Dysfunctions Scale
Time frame: Baseline
Sleepiness Rates in the Epworth Sleepiness Scale as a Measure of Sleep Quantity.
Time frame: Baseline
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Dec 2021.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Background: PubMed 16445257Parkka J, Ermes M, Korpipaa P, Mantyjarvi J, Peltola J, Korhonen I. Activity classification using realistic data from wearable sensors. IEEE Trans Inf Technol Biomed. 2006 Jan;10(1):119-28. doi: 10.1109/titb.2005.856863.
- Background: PubMed 24689772Hobert MA, Maetzler W, Aminian K, Chiari L. Technical and clinical view on ambulatory assessment in Parkinson's disease. Acta Neurol Scand. 2014 Sep;130(3):139-47. doi: 10.1111/ane.12248. Epub 2014 Apr 1.
- Background: PubMed 24030855Maetzler W, Domingos J, Srulijes K, Ferreira JJ, Bloem BR. Quantitative wearable sensors for objective assessment of Parkinson's disease. Mov Disord. 2013 Oct;28(12):1628-37. doi: 10.1002/mds.25628. Epub 2013 Sep 12.
- Background: PubMed 19846382Patel S, Lorincz K, Hughes R, Huggins N, Growdon J, Standaert D, Akay M, Dy J, Welsh M, Bonato P. Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors. IEEE Trans Inf Technol Biomed. 2009 Nov;13(6):864-73. doi: 10.1109/TITB.2009.2033471. Epub 2009 Oct 20.
- Background: PubMed 22254617Patel S, Chen BR, Mancinelli C, Paganoni S, Shih L, Welsh M, Dy J, Bonato P. Longitudinal monitoring of patients with Parkinson's disease via wearable sensor technology in the home setting. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1552-5. doi: 10.1109/IEMBS.2011.6090452.
- BackgroundTsanas A, Little MA, McSharry PE, Ramig L. Using the cellular mobile telephone network to remotely monitor parkinsons disease symptom severity. IEEE Transactions on Biomedical Engineering. 2012.
- BackgroundArora S, Venkataraman V, Donohue S, Biglan KM, Dorsey ER, Little MA, editors. High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones. Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on; 2014: IEEE.
- BackgroundPastorino M, Arredondo M, Cancela J, Guillen S, editors. Wearable sensor network for health monitoring: the case of Parkinson disease. Journal of Physics: Conference Series; 2013: IOP Publishing.
- BackgroundSharma V, Mankodiya K, De La Torre F, Zhang A, Ryan N, Ton TG, et al. SPARK: Personalized Parkinson Disease Interventions through Synergy between a Smartphone and a Smartwatch. Design, User Experience, and Usability User Experience Design for Everyday Life Applications and Services: Springer; 2014. p. 103-14.
- Background: PubMed 25393786Tzallas AT, Tsipouras MG, Rigas G, Tsalikakis DG, Karvounis EC, Chondrogiorgi M, Psomadellis F, Cancela J, Pastorino M, Waldmeyer MT, Konitsiotis S, Fotiadis DI. PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease. Sensors (Basel). 2014 Nov 11;14(11):21329-57. doi: 10.3390/s141121329.
- Background: PubMed 25257518Lakshminarayana R, Wang D, Burn D, Chaudhuri KR, Cummins G, Galtrey C, Hellman B, Pal S, Stamford J, Steiger M, Williams A; SMART-PD Investigators. Smartphone- and internet-assisted self-management and adherence tools to manage Parkinson's disease (SMART-PD): study protocol for a randomised controlled trial (v7; 15 August 2014). Trials. 2014 Sep 25;15:374. doi: 10.1186/1745-6215-15-374.
- Background: PubMed 25141850Gschwind YJ, Eichberg S, Marston HR, Ejupi A, Rosario Hd, Kroll M, Drobics M, Annegarn J, Wieching R, Lord SR, Aal K, Delbaere K. ICT-based system to predict and prevent falls (iStoppFalls): study protocol for an international multicenter randomized controlled trial. BMC Geriatr. 2014 Aug 20;14:91. doi: 10.1186/1471-2318-14-91.
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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.
Bastiaan R Bloem, Prof. Dr. principal investigator
Radboud University Medical Center
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's Disease, run by Radboud University Medical Center at 1 site in Netherlands.
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 4 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 6 Dec 2021. 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 Netherlands. 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?
Yes. Results are posted in the registry (first posted 6 Dec 2021). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
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?
Parkinson's Disease Symptoms (time frame: Baseline). The study lists 4 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 NCT02474329, which also lists 13 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 6 Dec 2021.