Parkinson's disease, a progressive brain disorder, is often tough
to treat effectively because symptoms, such as tremors and walking
difficulties, can vary dramatically over a period of days, or even hours.
To address this challenge, Johns Hopkins
University computer
scientists, working with an interdisciplinary team of experts from two other
institutions, have developed a new approach that uses sensors on a smartphone
to generate a score that reliably reflects symptom severity in patients with
Parkinson's disease.
In a study published recently online in the journal JAMA Neurology, researchers
from Johns Hopkins' Whiting School of Engineering, the University of Rochester
Medical Center, and Aston University in the United Kingdom reported that the
severity of symptoms among Parkinson's patients seen by neurologists aligned
closely with those generated by their smartphone app.
Typically, patients with Parkinson's disease are evaluated by
medical specialists during three or four clinic visits annually, with
subjective assessments capturing only a brief snapshot of a patient's
fluctuating symptoms. In their homes, patients may also be asked to fill out a
cumbersome 24-hour "motor diary" in which they keep a written record
of their mobility, involuntary twisting movements and other Parkinson's
symptoms. The doctor then uses this self-reported or imprecise data to guide
treatment.
In the new study, the researchers say patients could use a
smartphone app to objectively monitor symptoms in the home and share this data
to help doctors fine-tune their treatment.
E. Ray Dorsey, a University of Rochester Medical Center
neurologist and a co-author of the research paper, said he welcomes the
validation of Parkinson's patient severity scores produced by the smartphone
tests.
"If you think about it, it sounds crazy," he said,
"but until these types of studies, we had very limited data on how these
people function on Saturdays and Sundays because patients don't come to the
clinic on Saturdays or Sundays. We also had very limited data about how people
with Parkinson's do at two o'clock in the morning or 11 o'clock at night
because, unless they're hospitalized, they're generally not being seen in
clinics at those times."
About six years ago, while doing medical research at Johns
Hopkins, Dorsey was introduced to Suchi Saria, an assistant professor of
computer science at the university. Saria, the corresponding author of the
study and an expert in a computing technique called machine learning, had been
using it to extract useful information from health-related data that was
routinely being collected at hospitals. The two researchers, along with some of
Saria's students, teamed up to find a way to monitor the health of Parkinson's
patients as easily as people with diabetes can check their glucose levels with
a pinprick blood test.
The team members knew that neurologists evaluated their
Parkinson's patients by gathering information about how they moved, spoke and
completed certain daily tasks. "Can we do this with a cellphone?"
Saria wondered at the time. "We asked, 'What are the tricks we can use to
make that happen?' "
Using existing smartphone components such as its microphone, touch
screen and accelerometer, the team members devised five simple tasks involving
voice sensing, finger tapping, gait measurement, balance and reaction time.
They turned this into a Smartphone app called
'HopkinsPD.' Next, using a machine learning technique that the team devised,
they were able to convert the data collected with these tests and turn that
into an objective Parkinson's disease severity score—a score that better
reflected the overall severity of patients' symptoms and how well they were
responding to medication.
The researchers say this smartphone evaluation should be
particularly useful because it does not rely on the subjective observations of
a medical staff member. Moreover, it can be administered any time or day in a
clinic or within the patient's home, where the patient is less likely to be as
nervous as in a medical setting.
"The day-to-day variability of Parkinson's symptoms is so
high," Saria said. "If you happen to measure a patient at 5 p.m.
today and then three months later, again at 5 p.m., how do you know that you
didn't catch him at a good time the first time and at a bad time the second
time?"
Collecting more frequent smartphone test data in a medical setting
as well as in the home, could give doctors a clearer picture of their patients' overall heath
and how well their medications are working, Saria and her colleagues suggested.
Summarizing the importance of their finding in the JAMA Neurology report, the researchers said, "A
smartphone-derived severity score for Parkinson's disease is feasible and
provides an objective measure of motor symptoms inside and outside the clinic
that could be valuable for clinical care and therapeutic development."
Patients in the research project used Android smartphones to
download the software, available through the Parkinson's Voice Initiative website. The team has now partnered
with Apple and Sage Bionetworks to develop mPower, an iPhone version that is
available at Apple's App Store.
The study's three co-lead authors included two of Saria's students
from the Department of Computer Science at Johns Hopkins: doctoral candidate
Andong Zhan and third-year undergraduate Srihari Mohan.
Zahn, who is from Qujing , Yunnan , in China , described the project as
"a unique experience of extracting data from the physical world to a
digital world and finally seeing it become meaningful clinical
information."
Mohan, who is from Redmond ,
Washington , added, "While
not all research gets integrated tangibly into people's lives, what excites me
most is the potential for the methods we developed to be deployed seamlessly
into a patient's lifestyle and improve the quality of care."
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