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 - TY - JOUR A1 - Kraft, Robin A1 - Birk, Ferdinand A1 - Reichert, Manfred A1 - Deshpande, Aniruddha A1 - Schlee, Winfried A1 - Langguth, Berthold A1 - Baumeister, Harald A1 - Probst, Thomas A1 - Spiliopoulou, Myra A1 - Pryss, Rüdiger T1 - Efficient processing of geospatial mHealth data using a scalable crowdsensing platform JF - Sensors N2 - Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case. KW - mHealth KW - crowdsensing KW - tinnitus KW - geospatial data KW - cloud-native KW - stream processing KW - scalability KW - architectural design Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207826 SN - 1424-8220 VL - 20 IS - 12 ER - TY - JOUR A1 - Allgaier, Johannes A1 - Schlee, Winfried A1 - Probst, Thomas A1 - Pryss, Rüdiger T1 - Prediction of tinnitus perception based on daily life mHealth data using country origin and season JF - Journal of Clinical Medicine N2 - Tinnitus is an auditory phantom perception without external sound stimuli. This chronic perception can severely affect quality of life. Because tinnitus symptoms are highly heterogeneous, multimodal data analyses are increasingly used to gain new insights. MHealth data sources, with their particular focus on country- and season-specific differences, can provide a promising avenue for new insights. Therefore, we examined data from the TrackYourTinnitus (TYT) mHealth platform to create symptom profiles of TYT users. We used gradient boosting engines to classify momentary tinnitus and regress tinnitus loudness, using country of origin and season as features. At the daily assessment level, tinnitus loudness can be regressed with a mean absolute error rate of 7.9% points. In turn, momentary tinnitus can be classified with an F1 score of 93.79%. Both results indicate differences in the tinnitus of TYT users with respect to season and country of origin. The significance of the features was evaluated using statistical and explainable machine learning methods. It was further shown that tinnitus varies with temperature in certain countries. The results presented show that season and country of origin appear to be valuable features when combined with longitudinal mHealth data at the level of daily assessment. KW - tinnitus KW - gradient boosting machine KW - mobile health KW - machine learning KW - multimodal data KW - explainable machine learning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281812 SN - 2077-0383 VL - 11 IS - 15 ER -