When it comes to tracking the severity and progression of Parkinson's disease, clinicians are challenged because it typically relies on the in-person evaluation during clinical visits to evaluate patients by testing their motor skills and cognitive functions. These measurements may not be reliable due to many factors. Perhaps the patient is tired after the long drive to the appointment, or is functioning differently due to a disrupted routine at home. Combine that with the fact that nearly 40% of PD patients are never treated by a neurologist or movement disorder specialist and you can see why measurements and benchmarks are more difficult to obtain accurately.
To address these problems, researchers from MIT and elsewhere demonstrated an in-home device that can monitor a patient's movement and gait speed, which can be used to evaluate Parkinson's severity, the progression of the disease, and the patient's response to medication.
The device is about the size of a Wi-Fi router, and it gathers data passively using radio signals that reflect off the patient's body as they move around the home. It doesn't require the patient to wear a gadget, and yet it can still detect Parkinson's from the person's breathing pattern even while sleeping. While radio signals pass through walls and other solid objects, they are reflected off humans due to the water in our bodies.
This creates a "human radar" that can track the movement of a person in a room. Radio waves always travel at the same speed, so the length of time it takes the signals to reflect back to the device indicates how the person is moving. The device also incorporates a machine-learning classifier that can pick out the precise radio signals reflected off the patient even when there are other people moving around the room. Sophisticated algorithms use these movement data to compute gait speed and how fast the person is walking.
Because the device operates in the background and runs all day, every day, it can collect a massive amount of data. The researchers wanted to see if they could apply machine learning to these data sets to gain insights about the disease over time. Researchers used these devices to conduct two studies that involved 50 participants. By using machine-learning algorithms to analyze the troves of data gathered, the clinicians could track Parkinson's progression more effectively.
"By being able to have a device in the home that can monitor a patient and tell the doctor remotely about the progression of the disease, and the patient's medication response so they can attend to the patient even if the patient can't come to the clinic; now they have real, reliable information that actually goes a long way toward improving equity and access," says senior author Dina Katabi, the Thuan and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS), and a principle investigator in the Computer Science and Artificial Intelligence Laboratory (CSAII) and the MIT Jameel Clinic. The co-lead authors are EECS graduate students Yingcheng Liu and Guo Zhang with research published in Science Translational Medicine.
The researchers gathered 50 participants, 34 of whom had Parkinson's, and conducted two observational studies of in-home gait measurements. One study lasted two months and the other was conducted over the course of two years. Through the studies, the researchers collected more than 300,000 individual measurements that they averaged to smooth out variability due to the condition of the device or other factors.
They used statistical methods to analyze the data and found that in-home gate speed can be used to effectively track Parkinson's progression and severity. For instance, they showed that gait speed declined almost twice as fast for individuals with Parkinson's, compared to those without.
"Monitoring the patient continuously as they moved around the room enabled us to get really good measurements of their gait speed. And with so much data, we were able to perform aggregation that allows us to see very small differences," said Guo Zhang, Co-Lead Author.
Drilling down on these variabilities offered some key insights. For instance, the researchers could see that intraday fluctuations in a patient's gait speed may improve after a dose of medication and then begin to decline after a period of time. "This gives us the possibility to objectively measure how mobility responds to medication. Previously, this was nearly impossible to do because this medication effect could only be measured by having a patient keep a journal or communicate their "off" times.
A clinician can then use this data to adjust medication dosages more effectively and accurately. This is especially important since many drugs used to treat disease symptoms can cause serious side effects if the patient receives too much. The researchers were able to demonstrate significant results regarding Parkinson's progression after studying 50 people for just one year; by contrast, "an often-cited study by the Michael J. Fox Foundation involved over 500 individuals and monitored them for more than five years," Katabi says. "For a drug or biotech company, this also helps with the development of medications to greatly reduce the burden and cost as well as speed of the development of new therapies," she added.
Katabi credits much of the study's success to the dedicated team of scientists and clinicians who worked together to tackle the many difficulties that rose along the way. For one, they began the study before the Covid-19 pandemic, so engineers initially entered people's home to set up the devices. When that was no longer possible, they developed a method to remotely deploy devices and create a user-friendly app for participants and clinicians.
Through the course of the study, they learned to automate processes and reduce effort, especially for the participants and clinical team. This knowledge proves useful as they look to deploy similar devices to study other neurological conditions. Researchers believe it could even assist in collecting a holistic set of markers to help diagnose disease earlier and then be used to track and treat it.
It is unclear when the devices will be made available publicly or specifically to residents of Alabama. The PAA will continue to monitor availability. This work is supported by the National Institute's of Health and the Michael J. Fox Foundation.
https://news.mit.edu/2022/home-wireless-parkinsons-progression-0921 Massachusetts Institute of Technology.
In-home device can monitor patient's movement and gait speed. News Medical Life Sciences. Reviewed by Emily Henderson, B.Sc., September 21, 2022. https://www.news-medical.net/news/20220921/In-home-device-can-monitor-Parkinsons-patients-movement-and-gait-speed.aspx?utm_source=Newsletter&utm_medium=Email&utm_campaign=HomeCare_Now
Journal Reference: Liu Y., et al. (2022) Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression and medication response. Science Translational Medicine. Doi.org/10.1126/scitranslmed.adc9669.