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Smartphone App Helps Quantify Parkinson’s Disease

By HospiMedica International staff writers
Posted on 25 Apr 2018
A mobile health application uses machine learning and sensors to enable patients with Parkinson’s disease (PD) perform at-home assessments of their condition.

Researchers at Johns Hopkins University (JHU; Baltimore, MD, USA), the University of Rochester Medical Center (URMC; NY, USA), and other institutions conducted an observational study to assess the validity of a smartphone app that uses a machine-learning approach to objectively measure PD features that can be derived from five smartphone activities--voice, finger tapping, gait, balance, and reaction time--in order to generate a mobile Parkinson disease score (mPDS) on a scale of 0 to 100 (where higher scores indicate greater severity).

Image: A smartphone app can help score Parkinson’s Disease severity (Photo courtesy of URMC).
Image: A smartphone app can help score Parkinson’s Disease severity (Photo courtesy of URMC).

The study examined 6,148 patient assessments from 129 people (mean age 58.7 years; 43.4% women), both with and without PD. In addition, 23 individuals with PD and 17 without PD completed standard in-person assessments during a period of six months. The main outcomes and measures were the ability of the mPDS score to identify intraday symptom fluctuations, examine how the grade score correlated with current standard PD outcome measures, and whether the mPDS grading could account for dopaminergic medication.

The results revealed that gait features contributed most to the total mPDS, at 33.4%; mPDS detected symptom fluctuations with a mean intraday change of 13.9 points on a scale of 0 to 100. The measure correlated well with The International Parkinson and Movement Disorder Society Unified Parkinson Disease’s Rating Scale, the Time Up and Go (TUG) assessment, and the Hoehn and Yahr stage tool. The mPDS improved by a mean 16.3 points in response to dopaminergic therapy. The study was published on March 26, 2018, in JAMA Neurology.

“The ability to remotely monitor patients on a much more frequent basis, more accurately track the symptoms and progression of the disease, and monitor the impact of exercise, sleep, and medications and their side effects holds the potential to transform how we treat Parkinson’s disease,” concluded study co-author Christopher Tarolli, MD, of URMC, and colleagues. “This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.”

PD is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Dementia becomes common in the advanced stages of the disease. Depression and anxiety are also common occurring in more than a third of people with PD. Other symptoms include sensory, sleep, and emotional problems.

Related Links:
Johns Hopkins University
University of Rochester Medical Center

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