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Brief Report
July 2018

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score

Author Affiliations
  • 1Department of Computer Science, Johns Hopkins University, Baltimore, Maryland
  • 2Department of Neurology, University of Rochester Medical Center, Rochester, New York
  • 3Center for Health + Technology, University of Rochester Medical Center, Rochester, New York
  • 4Department of Mathematics, Aston University, Birmingham, England
  • 5Armstrong Institute for Patient Safety and Quality, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
  • 6Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
JAMA Neurol. 2018;75(7):876-880. doi:10.1001/jamaneurol.2018.0809
Key Points

Question  Can a smartphone be used to quantify Parkinson disease motor symptom severity?

Findings  In this study, a machine learning approach was able to generate an objective severity score for Parkinson disease from smartphone sensor data. The score captured intraday symptom fluctuations, correlated strongly with current standard rating scales, and detected response to dopaminergic therapy.

Meaning  A smartphone-derived severity score for Parkinson 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.


Importance  Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.

Objectives  To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy.

Design, Setting, and Participants  This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning–based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months.

Main Outcomes and Measures  Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication.

Results  The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease’s Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy.

Conclusions and Relevance  Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.

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