@article{PrakashUnnikrishnanPryssetal.2021,
  author    = {Prakash, Subash and Unnikrishnan, Vishnu and Pryss, R{\"u}diger and Kraft, Robin and Schobel, Johannes and Hannemann, Ronny and Langguth, Berthold and Schlee, Winfried and Spiliopoulou, Myra},
  title     = {Interactive system for similarity-based inspection and assessment of the well-being of mHealth users},
  series = {Entropy},
  volume    = {23},
  journal   = {Entropy},
  number    = {12},
  issn      = {1099-4300},
  doi       = {10.3390/e23121695},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252333},
  year      = {2021},
  abstract  = {Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users' condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported.},
  language  = {en}
}
@article{KraftBirkReichertetal.2020,
  author    = {Kraft, Robin and Birk, Ferdinand and Reichert, Manfred and Deshpande, Aniruddha and Schlee, Winfried and Langguth, Berthold and Baumeister, Harald and Probst, Thomas and Spiliopoulou, Myra and Pryss, R{\"u}diger},
  title     = {Efficient processing of geospatial mHealth data using a scalable crowdsensing platform},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {12},
  issn      = {1424-8220},
  doi       = {10.3390/s20123456},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207826},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{HolfelderMulanskySchleeetal.2021,
  author    = {Holfelder, Marc and Mulansky, Lena and Schlee, Winfried and Baumeister, Harald and Schobel, Johannes and Greger, Helmut and Hoff, Andreas and Pryss, R{\"u}diger},
  title     = {Medical device regulation efforts for mHealth apps during the COVID-19 pandemic — an experience report of Corona Check and Corona Health},
  series = {J — Multidisciplinary Scientific Journal},
  volume    = {4},
  journal   = {J — Multidisciplinary Scientific Journal},
  number    = {2},
  issn      = {2571-8800},
  doi       = {10.3390/j4020017},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-285434},
  pages     = {206 -- 222},
  year      = {2021},
  abstract  = {Within the healthcare environment, mobile health (mHealth) applications (apps) are becoming more and more important. The number of new mHealth apps has risen steadily in the last years. Especially the COVID-19 pandemic has led to an enormous amount of app releases. In most countries, mHealth applications have to be compliant with several regulatory aspects to be declared a "medical app". However, the latest applicable medical device regulation (MDR) does not provide more details on the requirements for mHealth applications. When developing a medical app, it is essential that all contributors in an interdisciplinary team — especially software engineers — are aware of the specific regulatory requirements beforehand. The development process, however, should not be stalled due to integration of the MDR. Therefore, a developing framework that includes these aspects is required to facilitate a reliable and quick development process. The paper at hand introduces the creation of such a framework on the basis of the Corona Health and Corona Check apps. The relevant regulatory guidelines are listed and summarized as a guidance for medical app developments during the pandemic and beyond. In particular, the important stages and challenges faced that emerged during the entire development process are highlighted.},
  language  = {en}
}