TY  - JOUR
A1  - Allgaier, Johannes
A1  - Schlee, Winfried
A1  - Langguth, Berthold
A1  - Probst, Thomas
A1  - Pryss, Rüdiger
T1  - Predicting the Gender of Individuals with Tinnitus based on Daily Life Data of the TrackYourTinnitus mHealth Platform
JF  - Scientific Reports
N2  - Tinnitus is an auditory phantom perception in the absence of an external sound stimulation. People with tinnitus often report severe constraints in their daily life. Interestingly, indications exist on gender differences between women and men both in the symptom profile as well as in the response to specific tinnitus treatments. In this paper, data of the TrackYourTinnitus platform (TYT) were analyzed to investigate whether the gender of users can be predicted. In general, the TYT mobile Health crowdsensing platform was developed to demystify the daily and momentary variations of tinnitus symptoms over time. The goal of the presented investigation is a better understanding of gender-related differences in the symptom profiles of users from TYT. Based on two questionnaires of TYT, four machine learning based classifiers were trained and analyzed. With respect to the provided daily answers, the gender of TYT users can be predicted with an accuracy of 81.7%. In this context, worries, difficulties in concentration, and irritability towards the family are the three most important characteristics for predicting the gender. Note that in contrast to existing studies on TYT, daily answers to the worst symptom question were firstly investigated in more detail. It was found that results of this question significantly contribute to the prediction of the gender of TYT users. Overall, our findings indicate gender-related differences in tinnitus and tinnitus-related symptoms. Based on evidence that gender impacts the development of tinnitus, the gathered insights can be considered relevant and justify further investigations in this direction.
KW  - computer science
KW  - machine learning
KW  - psychology
KW  - signs and symptoms
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261753
VL  - 11
IS  - 1
ER  - 
TY  - JOUR
A1  - Beierle, Felix
A1  - Schobel, Johannes
A1  - Vogel, Carsten
A1  - Allgaier, Johannes
A1  - Mulansky, Lena
A1  - Haug, Fabian
A1  - Haug, Julian
A1  - Schlee, Winfried
A1  - Holfelder, Marc
A1  - Stach, Michael
A1  - Schickler, Marc
A1  - Baumeister, Harald
A1  - Cohrdes, Caroline
A1  - Deckert, Jürgen
A1  - Deserno, Lorenz
A1  - Edler, Johanna-Sophie
A1  - Eichner, Felizitas A.
A1  - Greger, Helmut
A1  - Hein, Grit
A1  - Heuschmann, Peter
A1  - John, Dennis
A1  - Kestler, Hans A.
A1  - Krefting, Dagmar
A1  - Langguth, Berthold
A1  - Meybohm, Patrick
A1  - Probst, Thomas
A1  - Reichert, Manfred
A1  - Romanos, Marcel
A1  - Störk, Stefan
A1  - Terhorst, Yannik
A1  - Weiß, Martin
A1  - Pryss, Rüdiger
T1  - Corona Health — A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic
JF  - International Journal of Environmental Research and Public Health
N2  - Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.
KW  - mobile health
KW  - ecological momentary assessment
KW  - digital phenotyping
KW  - longitudinal studies
KW  - mobile crowdsensing
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-242658
SN  - 1660-4601
VL  - 18
IS  - 14
ER  -