@article{PryssSchleeHoppenstedtetal.2020,
  author    = {Pryss, R{\"u}diger and Schlee, Winfried and Hoppenstedt, Burkhard and Reichert, Manfred and Spiliopoulou, Myra and Langguth, Berthold and Breitmayer, Marius and Probst, Thomas},
  title     = {Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study},
  series = {Journal of Medical Internet Research},
  volume    = {22},
  journal   = {Journal of Medical Internet Research},
  number    = {6},
  doi       = {10.2196/15547},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229517},
  year      = {2020},
  abstract  = {Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1\% to 42.7\% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient's quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)-Android and iOS-to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider.},
  language  = {en}
}