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  - Pryss, Rüdiger
A1  - Schlee, Winfried
A1  - Hoppenstedt, Burkhard
A1  - Reichert, Manfred
A1  - Spiliopoulou, Myra
A1  - Langguth, Berthold
A1  - Breitmayer, Marius
A1  - Probst, Thomas
T1  - 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
JF  - Journal of Medical Internet Research
N2  - 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 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.
KW  - crowdsensing
KW  - ecological momentary assessment
KW  - mHealth
KW  - machine learning
KW  - mobile operating system differences
KW  - tinnitus
KW  - mobile phone
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-229517
VL  - 22
IS  - 6
ER  -