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@ARTICLE{Goni:908810,
      author       = {Goni, Maria and Eickhoff, Simon and Sahandi Far, Mehran and
                      Patil, Kaustubh R. and Dukart, Jürgen},
      title        = {{S}martphone-{B}ased {D}igital {B}iomarkers for
                      {P}arkinson’s {D}isease in a {R}emotely-{A}dministered
                      {S}etting},
      journal      = {IEEE access},
      volume       = {10},
      issn         = {2169-3536},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-02855},
      pages        = {28361 - 28384},
      year         = {2022},
      abstract     = {Smartphone-based digital biomarker (DB) assessments provide
                      objective measures of daily-life tasks and thus hold the
                      promise to improve diagnosis and monitoring of Parkinson’s
                      disease (PD). To date, little is known about which tasks
                      perform best for these purposes and how different confounds
                      including comorbidities, age and sex affect their accuracy.
                      Here we systematically assess the ability of common
                      self-administered smartphone-based tasks to differentiate PD
                      patients and healthy controls (HC) with and without
                      accounting for the above confounds. Using a large cohort of
                      PD patients and healthy volunteers acquired in the mPower
                      study, we extracted about 700 features commonly reported in
                      previous PD studies for gait, balance, voice and tapping
                      tasks. We perform a series of experiments systematically
                      assessing the effects of age, sex and comorbidities on the
                      accuracy of the above tasks for differentiation of PD
                      patients and HC using several machine learning algorithms.
                      When accounting for age, sex and comorbidities, the highest
                      balanced accuracy on hold-out data $(73\%)$ was achieved
                      using random forest when combining all tasks followed by
                      tapping using relevance vector machine $(67\%).$ Only
                      moderate accuracies were achieved for other tasks $(60\%$
                      for balance, $56\%$ for gait and $53\%$ for voice data). Not
                      accounting for the confounders consistently yielded higher
                      accuracies of up to $77\%$ when combining all tasks. Our
                      results demonstrate the importance of controlling DB data
                      for age and comorbidities.},
      cin          = {INM-7},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000772384700001},
      doi          = {10.1109/ACCESS.2022.3156659},
      url          = {https://juser.fz-juelich.de/record/908810},
}