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

Status Completed

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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Parkinson Disease
• Parkinson's Disease

• NCT number: NCT02474329
• Study type: Observational [Patient Registry]
• Source: Radboud University
• Status: Completed
• Start date: July 2015
• Completion date: November 2016