Real-PD Trial: Development of Clinical Prognostic Models for Parkinson's Disease

Parkinson's Disease Clinical Trial

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT02474329
• Other study ID #: NL53034.091.41
• Status: Completed
• Start date: July 2015
• Est. completion date: November 2016

Study information

• Verified date: October 2017
• Source: Radboud University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical 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. 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 304
• Est. completion date: November 2016
• Est. primary completion date: November 2016
• Accepts healthy volunteers: No
• Gender: All
• Age group: 30 Years and older

Eligibility

Inclusion Criteria:

1. Currently own and use a smartphone device with access to the Internet

2. 30 years of age or older;

3. Diagnosed with Parkinson's disease by a physician;

4. Able to walk without any assistance.

Exclusion Criteria:

None exclusion criteria will be used.

Related Conditions & MeSH terms

• Parkinson Disease
• Parkinson's Disease

Intervention

Other:
• Clinical assessment
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.

Device:
• Fox Insight self-monitoring android app and 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.

Locations

Country	Name	City	State
Netherlands	Cohort 1	Multiple Locations	Noord-Holland

Sponsors (4)

Lead Sponsor	Collaborator
Radboud University	Intel Corporation, Michael J. Fox Foundation for Parkinson's Research, Philips Electronics Nederland B.V. acting through Philips CTO organization

Country where clinical trial is conducted

Netherlands, 

References & Publications (12)

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.

Gschwind 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. — View Citation

Hobert 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. Review. — View Citation

Lakshminarayana 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. — View Citation

Maetzler 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. Review. — View Citation

Pärkkä J, Ermes M, Korpipää P, Mäntyjärvi J, Peltola J, Korhonen I. Activity classification using realistic data from wearable sensors. IEEE Trans Inf Technol Biomed. 2006 Jan;10(1):119-28. — View Citation

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.

Patel 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. — View Citation

Patel 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. — View Citation

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.

Tsanas 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.

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. — View Citation

* Note: There are 12 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	Baseline
Primary	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.	Baseline
Primary	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.	Baseline
Primary	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.	Baseline
Secondary	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.	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.
Secondary	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.	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.
Secondary	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.	Baseline
Secondary	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.	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.
Secondary	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.	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.
Secondary	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.	Baseline
Secondary	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).	Baseline