Large-Scale Wearable Sensor Deployment in Parkinson’s Patients: The Parkinson@Home Study Protocol

Abstract

BackgroundLong-term management of Parkinson’s disease does not reach its full potential because we lack knowledge about individual variations in clinical presentation and disease progression. Continuous and longitudinal assessments in real-life (ie, within the patients’ own home environment) might fill this knowledge gap.ObjectiveThe primary aim of the Parkinson@Home study is to evaluate the feasibility and compliance of using multiple wearable sensors to collect clinically relevant data. Our second aim is to address the usability of these data for answering clinical research questions. Finally, we aim to build a database for future validation of novel algorithms applied to sensor-derived data from Parkinson’s patients during daily functioning.MethodsThe Parkinson@Home study is a two-phase observational study involving 1000 Parkinson’s patients and 250 physiotherapists. Disease status is assessed using a short version of the Parkinson's Progression Markers Initiative protocol, performed by certified physiotherapists. Additionally, participants will wear a set of sensors (smartwatch, smartphone, and fall detector), and use these together with a customized smartphone app (Fox Insight), 24/7 for 3 months. The sensors embedded within the smartwatch and fall detector may be used to estimate physical activity, tremor, sleep quality, and falls. Medication intake and fall incidents will be measured via patients’ self-reports in the smartphone app. Phase one will address the feasibility of the study protocol. In phase two, mathematicians will distill relevant summary statistics from the raw sensor signals, which will be compared against the clinical outcomes.ResultsRecruitment of 300 participants for phase one was concluded in March, 2016, and the follow-up period will end in June, 2016. Phase two will include the remaining participants, and will commence in September, 2016.ConclusionsThe Parkinson@Home study is expected to generate new insights into the feasibility of integrating self-collected information from wearable sensors into both daily routines and clinical practices for Parkinson’s patients. This study represents an important step towards building a reliable system that translates and integrates real-life information into clinical decisions, with the long-term aim of delivering personalized disease management support.ClinicalTrialClinicalTrials.gov NCT02474329; https://clinicaltrials.gov/ct2/show/NCT02474329 (Archived at http://www.webcitation.org/6joEc5P1v)

Physical therapy and occupational therapy in Parkinson's disease

“…However, much work remains needed in this field, for example to demonstrate the feasibility of wearable sensor technology (e.g. compliance and usability), to develop reliable algorithms, and to study the impact on the clinical decision making process [123,124]. Finally, technology can be used to improve long-term adherence to various treatment strategies.…”

Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial

“…Data analysis of these results is pending. The Parkinson@home study deployed the Fox Wearable Companion app to 953 participants in the Netherlands (13‐week study) and North America (6‐week study) to passively monitor behavior and rate the severity of selected symptoms. Adherence was 68% in the Netherlands and 62% in North America, and not affected by demographics, clinical characteristics, or attitude toward technology .…”

“…Adherence was 68% in the Netherlands and 62% in North America, and not affected by demographics, clinical characteristics, or attitude toward technology. 26,30 Further data analyses are pending. These preliminary findings represent major milestones in the development of digital biomarkers, and at the same time highlight the challenges yet to be overcome for the successful use of digital biomarker approaches in clinical research or patient monitoring, namely long-term adherence, consistency of sensor-derived feature data, and clinical validity of long-term, remote patient testing.…”

Is It Possible to Conduct a Multi-Arm Multi-Stage Platform Trial in Parkinson’s Disease: Lessons Learned from Other Neurodegenerative Disorders and Cancer

“…DHTs can be broadly categorised into passive (such as wearables) and active (apps that require data input or task completion) data capture. Where active measures are used, compliance may create problems with accuracy and reliability of data sets over long study periods [69][70][71][72]. Uploading of data collected by apps and sensors represents an additional challenge, making studies liable to data gaps due to patients living in poor network areas or outdated hospital IT systems.…”