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 - Kraft, Robin A1 - Schlee, Winfried A1 - Stach, Michael A1 - Reichert, Manfred A1 - Langguth, Berthold A1 - Baumeister, Harald A1 - Probst, Thomas A1 - Hannemann, Ronny A1 - Pryss, Rüdiger T1 - Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain JF - Frontiers in Neuroscience N2 - The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of mobile crowdsensing (MCS) and ecological momentary assessments (EMA) in the healthcare domain. By correlating qualitative longitudinal and ecologically valid EMA assessment data sets with sensor measurements in mobile apps, new valuable insights about patients (e.g., humans who suffer from chronic diseases) can be gained. However, there are numerous conceptual, architectural and technical, as well as legal challenges when implementing a respective software solution. Therefore, the work at hand (1) identifies these challenges, (2) derives respective recommendations, and (3) proposes a reference architecture for a MCS-EMA-platform addressing the defined recommendations. The required insights to propose the reference architecture were gained in several large-scale mHealth crowdsensing studies running for many years and different healthcare questions. To mention only two examples, we are running crowdsensing studies on questions for the tinnitus chronic disorder or psychological stress. We consider the proposed reference architecture and the identified challenges and recommendations as a contribution in two respects. First, they enable other researchers to align our practical studies with a baseline setting that can satisfy the variously revealed insights. Second, they are a proper basis to better compare data that was gathered using MCS and EMA. In addition, the combined use of MCS and EMA increasingly requires suitable architectures and associated digital solutions for the healthcare domain. KW - mobile crowdsensing (MCS) KW - crowdsourcing KW - ecological momentary assessments (EMA) KW - mobile healthcare application KW - chronic disorders KW - reference architecture Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200220 SN - 1662-453X VL - 14 IS - 164 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 - 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 -