CLEVELAND, OH -- While headlines recently surged over big organizations such as Intel, Apple, and the Michael J. Fox Foundation partnering to launch pilot studies on wearables, mobile apps, and big data for Parkinson's disease... a small, Cleveland, Ohio based company continued to do what it has done best for over a decade, innovate targeted and clinically validated assessment tools for individuals with Parkinson's, and deliver them globally. Without splashy headlines, this week Great Lakes NeuroTechnologies (GLNT) launched its latest innovation, Kinesia 360 for continuous, mobile assessment of Parkinson's disease. Kinesia 360 uses wireless, patient-worn sensors and a smart phone app to monitor Parkinson's symptoms throughout the day. Data and reports are remotely transmitted to a secure web portal for access by clinicians and researchers. GLNT's Kinesia technology is a medical device with validation in over sixty publications, domestic and international medical device certifications, and intellectual property for sensor-based assessment, algorithms, and wireless transmission of Parkinson's motor symptom severity.
Why is Measuring Parkinson's Disease So Challenging?
Parkinson's impacts quality of life for millions around the world. Common symptoms include tremor, slowed movements, stiffness, freezing, and gait abnormalities. And if that's not enough, therapies used to control those symptoms can cause side effects of wild, irregular movements called "dyskinesias." "Measuring symptoms and side effects, which often fluctuate during the day, is critical both for optimizing patient care and clinical trials determining the efficacy of new therapies," says Christopher Pulliam, PhD, Product Manager. "Developing technology, such as Kinesia 360, to accurately and remotely measure Parkinson's is extremely challenging. Was an individual typing on a keyboard or did he have tremor? Was she folding the laundry or was it dyskinesia?"
During standard clinical evaluations, clinicians visually assess motor symptoms during specific tasks such as holding arms outstretched, tapping fingers, or tapping toes. "Measuring Parkinson's symptoms during activities of daily living creates an entirely new set of challenges for wearable sensors and algorithms," continued Dr. Pulliam. "Not only must the system be intelligent enough to detect symptoms and side effects, but it must distinguish those from activities which may mask or mimic those symptoms."
Big Data or Subtle Symptom Features
Motion sensors are common today in wearables, watches, and mobile devices for monitoring exercise or step counts. These gross measures of movement, however, do not provide a direct measure of Parkinson's features such as tremor, bradykinesia, or dyskinesia, as each of those symptoms have very distinct features. "As the wearables market has recently exploded with consumer actigraphy devices and motion sensors in smart phones, the ability to quickly collect big data has emerged," says Joseph P. Giuffrida, PhD, President and Principal Investigator. "However, our decade of experience on sensor-based, quantitative assessment of Parkinson's shows it's often the small things that matter. Where was the sensor positioned? What features were extracted? Having a sensor in the pocket is great if you simply want to measure movement. But to specifically measure Parkinson's symptoms, more accurate and intelligent technology is required."
According to GLNT researchers, it's not about just rapidly collecting data. The small, subtle details used to develop Kinesia 360 lie in protocol design, positioning and sensitivity of sensors, and intelligent algorithms to process data. "We've spent the last decade collecting data from individuals with Parkinson's to develop and clinically validate intelligent algorithms that actually quantify symptoms and side effects," says Dustin Heldman, PhD, Biomedical Research Manager. "We implemented strategic protocols to collect data from wide ranging symptom severities and during specific activities that could interfere with detecting the very symptoms we want to measure."
Over sixty publications validate Kinesia technology over a wide range of symptoms and applications. And it's small, subtle details, which allows their devices to detect timing and severity of specific Parkinson's symptoms. This has a big impact on understanding therapeutic effects and disease progression.
Data Mining or Validated Tools
GLNT's Kinesia technology has been integrated into a number of clinical trials as a validated tool for evaluating new Parkinson's treatments. As part of those studies, specific outcomes and expected symptom are defined upfront. Data mining refers to analyzing large amounts of data after collection to look for trends or correlations. This research technique can be incredibly valuable in many applications. However, it may not be the best strategy to implement in clinical trials, which require rigid protocols before patient enrollment. "Data mining allows researchers to dive in to a huge pile of data to see if they can make sense of anything," says Maureen Phillips, Global Clinical Trials Manager. "We work with clients and utilize our growing clinical database to determine statistics and number of subjects required to adequately power trials based on specific symptom effects. Kinesia 360 is expanding our technology offering to now assess symptoms in a truly ambulatory setting."
Highlighting this differentiation is a recent study in another movement disorder population, Multiple Sclerosis (MS). Biogen Idec in collaboration with PatientsLikeMe explored wearables to assess mobility in MS. The study collected data from 250 people with MS using the wearable Fitbit motion sensor. The outcome was that more sophisticated sensors were required to quantify movement accurately and consistently, and the technology was not validated MS assessment. So while there was high participation and compliance from study participants, the data did not provide meaningful results. This lesson can be valuable for the Parkinson's market and data mining sensors in consumer products.
Sensitive Data or Patient Compliance
Higher sensitivity of data often leads to more accurate results of assessing Parkinson's, but may come at the cost of patient compliance. "Imagine a system that used 100 motion sensors all over the body, it would likely have greater accuracy to detect symptoms than a single sensor on a smart phone in someone's pocket," says Dr. Giuffrida. "However, likelihood of a patient complying with wearing 100 sensors everyday is incredibly low. Therefore, having strategically located sensors in convenient, wearable, cosmetically acceptable devices that minimally impacts patients time or daily routine, but provides sensitive data to detect Parkinson's symptoms is what we have produced in Kinesia 360."
GLNT has conducted several studies that instrumented large numbers of sensors on individuals with Parkinson's to determine the minimum number of sensors necessary to accurately assess symptoms. As a result, Kinesia 360 utilizes two sensors, on the wrist and ankle, throughout the day. When asked why the sensor already in a phone or on a watch cannot be used, Dr. Giuffrida explained, "Those sensors are fine if you only care about general activity, but not if you truly want to assess Parkinson's symptoms. Imagine wearing only one sensor and driving down a very bumpy road. Think about the motions that might be detected by the sensor. It may look very similar to Parkinson's symptoms. By utilizing two sensors, one on the upper and lower extremity, we are able to effectively filter out those activities and improve accuracy with minimal user burden."
GLNT thanked the National Institute of Health for supporting development through the SBIR program, specifically National Institute of Neurological Disorders and Stroke and National Institute on Aging (R44AG033947, 2R44NS065554, & R44AG044293).
For more details on Kinesia 360, visit: