title: Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study.
abstract: BACKGROUND
  Remote, non-invasive and objective tests that can be used to support expert diagnosis for Parkinson's disease (PD) are lacking.
  
  
  METHODS
  Participants underwent baseline in-clinic assessments, including the Unified Parkinson's Disease Rating Scale (UPDRS), and were provided smartphones with an Android operating system that contained a smartphone application that assessed voice, posture, gait, finger tapping, and response time. Participants then took the smart phones home to perform the five tasks four times a day for a month. Once a week participants had a remote (telemedicine) visit with a Parkinson disease specialist in which a modified (excluding assessments of rigidity and balance) UPDRS performed. Using statistical analyses of the five tasks recorded using the smartphone from 10 individuals with PD and 10 controls, we sought to: (1) discriminate whether the participant had PD and (2) predict the modified motor portion of the UPDRS.
  
  
  RESULTS
  Twenty participants performed an average of 2.7 tests per day (68.9% adherence) for the study duration (average of 34.4 days) in a home and community setting. The analyses of the five tasks differed between those with Parkinson disease and those without. In discriminating participants with PD from controls, the mean sensitivity was 96.2% (SD 2%) and mean specificity was 96.9% (SD 1.9%). The mean error in predicting the modified motor component of the UPDRS (range 11-34) was 1.26 UPDRS points (SD 0.16).
  
  
  CONCLUSIONS
  Measuring PD symptoms via a smartphone is feasible and has potential value as a diagnostic support tool.
authors.affiliation: Nonlinearity and Complexity Research Group, Aston University, Birmingham, United Kingdom; Somerville College, University of Oxford, Oxford, United Kingdom.
authors.forename: S
authors.initials: S
authors.lastname: Arora
authors.affiliation: Duke University School of Medicine, Durham, NC, United States.
authors.forename: V
authors.initials: V
authors.lastname: Venkataraman
authors.affiliation: Johns Hopkins University, Baltimore, MD, United States.
authors.forename: A
authors.initials: A
authors.lastname: Zhan
authors.affiliation: Johns Hopkins University, Baltimore, MD, United States.
authors.forename: S
authors.initials: S
authors.lastname: Donohue
authors.affiliation: Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States.
authors.forename: K M
authors.initials: KM
authors.lastname: Biglan
authors.affiliation: Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States; CHET, University of Rochester Medical Center, Rochester, NY, United States. Electronic address: ray.dorsey@chet.rochester.edu.
authors.forename: E R
authors.initials: ER
authors.lastname: Dorsey
authors.affiliation: Nonlinearity and Complexity Research Group, Aston University, Birmingham, United Kingdom; Media Lab, Massachusetts Institute of Technology, Boston, MA, United States.
authors.forename: M A
authors.initials: MA
authors.lastname: Little
mesh_terms: D000368:Aged; D005260:Female; D005385:Fingers; D005684:Gait; D006801:Humans; D008297:Male; D008875:Middle Aged; D009048:Motor Skills; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology; D010865:Pilot Projects; D011187:Posture; D011930:Reaction Time / Q000502:physiology; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D000068997:Smartphone / Q000295:instrumentation*; D017216:Telemedicine; D014831:Voice
keywords: Diagnostic test assessment; Motor assessment; Parkinson's disease; Smartphone; Speech