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
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- 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.…
Autor(en): | 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 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Medizinische Fakultät / Institut für Klinische Epidemiologie und Biometrie |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Journal of Medical Internet Research |
Erscheinungsjahr: | 2020 |
Band / Jahrgang: | 22 |
Heft / Ausgabe: | 6 |
Aufsatznummer: | e15547 |
Originalveröffentlichung / Quelle: | Journal of Medical Internet Research 2020;22(6):e15547 doi: 10.2196/15547 |
DOI: | https://doi.org/10.2196/15547 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Freie Schlagwort(e): | crowdsensing; ecological momentary assessment; mHealth; machine learning; mobile operating system differences; mobile phone; tinnitus |
Datum der Freischaltung: | 16.04.2021 |
Sammlungen: | Open-Access-Publikationsfonds / Förderzeitraum 2020 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |