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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

Zitieren Sie bitte immer diese 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.zeige mehrzeige weniger

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Autor(en): Rüdiger Pryss, Winfried Schlee, Burkhard Hoppenstedt, Manfred Reichert, Myra Spiliopoulou, Berthold Langguth, Marius Breitmayer, Thomas Probst
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):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International