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This paper is in the following e-collection/theme issue:

New Methods (1208) mHealth for Data Collection and Research (1303) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1866) Machine Learning (2337) Patient-Reported Outcome Measures (PROMs) (398) Ecological Momentary Assessment (EMA) (451) Hearing Impairment and Hearing Loss (95)

Published on in Vol 22, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15547, first published .
iPhone screen showing the "Track Your Tinnitus" app with sliders for stress, mood, and arousal.

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

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

Authors of this article:

Rüdiger Pryss1 Author Orcid Image ;   Winfried Schlee2 Author Orcid Image ;   Burkhard Hoppenstedt3 Author Orcid Image ;   Manfred Reichert3 Author Orcid Image ;   Myra Spiliopoulou4 Author Orcid Image ;   Berthold Langguth2 Author Orcid Image ;   Marius Breitmayer3 Author Orcid Image ;   Thomas Probst5 Author Orcid Image
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