May 30, 2023

Accelerating proactive treatment of Parkinson’s Disease through translating everyday, wearable sensor data into digital biomarkers

Accelerating proactive treatment of Parkinson’s Disease through translating everyday, wearable sensor data into digital biomarkers

It’s an exciting moment for biomedical researchers who are tackling the complex challenge of Parkinson’s Disease (PD).

We know that tools that support earlier diagnosis, monitoring, and intervention can improve quality of life for people with PD and can help clinicians improve PD treatment outcomes. For example, during April’s annual focus on Parkinson’s awareness, the Michael J. Fox Foundation (MJFF) announced a game-changing biological test that will enable better pre-symptomatic interventions for people at risk of PD.

Researchers have also been working hard to develop better noninvasive methods for detecting, measuring, and monitoring Parkinson’s symptoms. Sage Bionetworks’ Biomarker and Endpoint Assessment to Track Parkinson’s Disease (BEAT-PD) Challenge, funded by MJFF, is bringing researchers together to identify new digital biomarkers to monitor PD symptoms and enable better treatment.

Wearable sensors like those in smartwatches, fitness trackers, and smartphones, as well as highly specialized research-grade wearable sensors, can help us capture and understand the variability in symptoms that people with PD experience day-by-day, hour-by-hour, and minute-by-minute. These “digital biomarkers” have the potential to reshape how clinicians and patients make care decisions and shift treatment to be more proactive rather than reactive.

Sage Bionetworks and our research partners have shown that we can use wearable devices or smartphones to measure the severity of PD symptoms when patients perform short, fixed tasks (like walking for a short segment or touching their finger to their nose). What we still need to investigate is if passively collected data – in other words, the digital data people with PD generate naturally as they go about their regular lives – could be used for the same purpose. Being able to use these “free-living” data would lessen the burden of measuring PD symptoms and may even teach us more about PD, its treatment, and progression.

Converting sensor-based data into digital biomarkers is complex. There are no standard methods to guide this conversion process. And this was the focus of our BEAT-PD Challenge, a PD-focused DREAM Challenge. DREAM Challenges are collaborative competitions that allow quick exploration of a large space of solutions by engaging communities of researchers from around the world. Sage Bionetworks develops and maintains tools to support DREAM Challenges, including Synapse, a set of web services and tools that make it easier for researchers to aggregate, organize, analyze, and share scientific data, code, and insights. Even though DREAM Challenges take an open science approach, thanks to tools like Synapse, the privacy of patient data is secure and protected. At the same time, all of the methods used in DREAM Challenge participants are shared back with the research community, accelerating scientific discovery.

As we recently shared in PLOS, BEAT-PD has been an accelerator for PD digital biomarker research. We designed BEAT-PD to be a first-of-its-kind attempt to benchmark methods for processing free-living sensor data from smartwatches of people with PD in order to identify digital biomarkers that are predictive of PD severity.

Forty-two BEAT-PD Challenge teams around the world developed algorithms to interpret patient accelerometer and gyroscope sensor data. The team of organizers scored these submissions to determine those that performed the best. Through this effort, we confirmed that PD symptom severity can be measured passively through smartwatch data collected as people with PD about their daily activities, and that the models validate against doctors’ assessments, thus bringing us closer to the goal of noninvasive, passive, yet highly detailed monitoring of PD symptoms.

DREAM Challenges frameworks, like the one that BEAT-PD used, can produce results that lead directly to actionable progress in disease care. For instance, two of the top three performing teams from 2017’s Digital Mammography DREAM Challenge have secured FDA approval for their artificial intelligence algorithms to detect the presence of breast cancer at an earlier stage.

Since our founding in 2009, Sage Bionetworks has been committed to collaborative, reproducible, open-source research methods to accelerate research from the lab bench to the bedside. To this end, we believe scientific data and approaches that serve society, including methods like artificial intelligence and machine learning models, like the ones used in BEAT-PD, should be made publicly auditable, and, when possible, should be made fully open source.