As the second most common neurodegenerative disease, Parkinson’s Disease (PD) is an important subject of ongoing research and innovation.

However, it also presents unique challenges as the pathophysiology behind the disease remains complex and often medication is not effective for all patients. Additionally, clinical rating scales are limited in their ability to provide quantitative data for researchers. When conducting clinical trials, patients suffering from PD are often limited in their ability to travel to sites, and as a result, this burden can be a barrier to participation. In regard to these concerns, recent advances in medical devices and digital health technologies, such as wearables and apps, may offer new solutions for physicians and researchers, and determine the direction of treatment in the future.

Leveraging medical devices for therapy

When PD patients do not respond well to medication or experience a decline in medication effectiveness over time, technology offers a different method of treatment. Deep brain stimulation (DBS) is a well-established therapy for motor symptoms and involves the implantation of a small device that sends electrical signals to targeted areas of the brain. This therapy can improve patient quality of life by treating motor symptoms of the disease, including tremor, stiffness and slowed movement.

Advances in DBS technology have focused on making these electrical signals more adaptive to fluctuations in symptoms (1) or even brain signals (2) that the device can detect, rather than sending a constant signal predetermined by the patient’s doctor. As the study of DBS grows, more information will be discovered about this technology’s ability to offer patients better treatment options that can adjust to their changing needs.

Further, the use of telehealth systems can help physicians to remotely manage patients undergoing DBS at home (3), both making access to healthcare easier for patients and illustrating a method for clinical researchers to gather data in real time and generate endpoint data.

Gathering quantitative data through wearables

One challenge in treating patients and managing clinical trials has been obtaining quantitative data in characterising PD symptoms, as clinical rating scales are limited by patient memory and physician interpretation. The use of accelerometers and wearable sensors is a promising approach as wearables can assess tremor, rigor, micrography and gait abnormalities as key symptoms of PD. Researchers are developing new ways to use wearables to monitor patients: for example, sensors designed to monitor motion (and thus motor symptoms) with high precision while at the same time also monitoring neurophysiological signals in the heart, brain and muscles (4).

Another avenue of research focuses on determining the applicability of accelerometers in tracking motor symptoms (5), as it can reduce patient burden. Using numerous sensors on multiple limbs can be difficult for patients in long-term studies; being able to use the accelerometers in devices such as smartwatches and smartphones instead would be less cumbersome and easier for patients participating in trials. The ability to gather this information over the long-term offers clinical studies the possibility of collecting more quantitative data with more ease for both patients and researchers. When that data is transmitted wirelessly and automatically uploaded into cloud-based databases for remote data gathering, it can provide better options for generating objective endpoints.

Applying AI to data collection

In addition to incorporating wearables into clinical research, researchers are also exploring how AI and machine learning might contribute to better understanding PD. AI algorithms are in development to analyse data from patient-worn accelerometers or sensors as patients perform sets of movements, as well as data captured at home. The AI then identifies movements that are affected by PD, and analyses the symptom severity (6). This technology is specifically intended to improve clinical trials by helping to analyse the data collected. Potentially, this will also help to put such remotely collected data in a plausibility context, e.g. disregard periods when the smartphone was creating odd signals because it was placed in the car. Currently, these plausibility checks need to happen through direct feed-back from the patient, e.g. by answering a question what he/she is doing through the device software the very moment an unusual signal is detected.

Using apps for long-term studies and real-world data

Mobile apps have the potential to transform how data is collected in PD research and in dialy patient care, and may provide beneficial real-world data. Because symptoms can vary from day-to-day, apps can be valuable in tracking trends in symptoms. Through a combination of self-assessment questions and the use of smartphone touch and motion sensors to measure symptoms, one app was able to assess patients with results similar to those of physicians at nearly 80 percent accuracy (7).

In another example, an app allowed patients to track medication dosing and timing as well as symptoms, providing physicians a clearer picture of the effects of a treatment and how best to make adjustments (8). Such apps can be augmented with notifications and reminders to help ensure consistent patient use. Using mobile apps, such as these, can help physicians obtain more complete information on what their patients are experiencing and make telehealth tools like virtual clinics more effective (9) .  Apps also hold the possibility of integration into studies for gathering  real-world data or alternative study endpoints.

The future of digital health in PD

The design of clinical trials evaluating treatments for neurodegenerative diseases can be complex, however digital health technologies have the ability to significantly improve PD clinical research. From reducing patient burdens for trial participation to improving data collection,  medical devices, wearables, apps, and even AI, all have significant roles to play in the future of PD treatment and research. The regulatory authorities actively support any such initiative. The FDA’s center for devices and radiological health (CDRH) created a dedicated “Digital Health Inbox” at for digital health policy questions. In Europe, EMA regulates such tools under the Device Regulations EU 2017/745 and 2017/746.

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(1) Henry Ford Health System. (2021, February 18). Henry Ford Health System First in the U.S. to Offer Next Generation Deep Brain Stimulation System For Parkinson’s Disease [Press Release].

(2) Medtronic. (2021, January 14). Medtronic Launches First-of-Its-Kind Adaptive Deep Brain Stimulation (aDBS) Trial in Parkinson's Disease Patients [Press Release].

(3) Wicklund, E. (2021, March 30). Chicago Hospital Uses Telehealth to Manage DBS Treatment at Home. mHealth Intelligence.

(4) Zhang, H., Li, C., Liu, W., Wang, J. et al (2020). A Multi-Sensor Wearable System for the Quantitative Assessment of Parkinson’s Disease. Sensors.

(5) Daneault, JF., Vergara-Diaz, G., Parisi, F. et al. (2021). Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson’s disease. Sci Data 8, 48.

(6) Wicklund, E. (2020, August 17). Researchers Use mHealth to Track, Assess Parkinson’s Movements. mHealth Intelligence.

(7) University College London. (2020, December 18). New app to monitor Parkinson’s progression at home.

(8) American Parkinson Disease Association. (2020, November 28). American Parkinson Disease Association Releases Symptom Tracker Mobile App with Expanded Features [Press Release].

(9) Evans, L., Mohamed, B., Thomas, EC. (2020) Using telemedicine and wearable technology to establish a virtual clinic for people with Parkinson’s disease. BMJ Open Quality.