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
Please always quote using this URN: urn:nbn:de:bvb:20-opus-229517
- 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 investigateBackground: 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.…
| Author: | Rüdiger Pryss, Winfried Schlee, Burkhard Hoppenstedt, Manfred Reichert, Myra Spiliopoulou, Berthold Langguth, Marius Breitmayer, Thomas Probst |
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| URN: | urn:nbn:de:bvb:20-opus-229517 |
| Document Type: | Journal article |
| Faculties: | Medizinische Fakultät / Institut für Klinische Epidemiologie und Biometrie |
| Language: | English |
| Parent Title (English): | Journal of Medical Internet Research |
| Year of Completion: | 2020 |
| Volume: | 22 |
| Issue: | 6 |
| Article Number: | e15547 |
| Source: | Journal of Medical Internet Research 2020;22(6):e15547 doi: 10.2196/15547 |
| DOI: | https://doi.org/10.2196/15547 |
| Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
| Tag: | crowdsensing; ecological momentary assessment; mHealth; machine learning; mobile operating system differences; mobile phone; tinnitus |
| Release Date: | 2021/04/16 |
| Collections: | Open-Access-Publikationsfonds / Förderzeitraum 2020 |
| Licence (German): |  CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |