TY  - JOUR
A1  - Pryss, Rüdiger
A1  - Schlee, Winfried
A1  - Hoppenstedt, Burkhard
A1  - Reichert, Manfred
A1  - Spiliopoulou, Myra
A1  - Langguth, Berthold
A1  - Breitmayer, Marius
A1  - Probst, Thomas
T1  - 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
JF  - Journal of Medical Internet Research
N2  - 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.
KW  - crowdsensing
KW  - ecological momentary assessment
KW  - mHealth
KW  - machine learning
KW  - mobile operating system differences
KW  - tinnitus
KW  - mobile phone
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-229517
VL  - 22
IS  - 6
ER  -