@article{AllgaierSchleeLangguthetal.2021,
  author    = {Allgaier, Johannes and Schlee, Winfried and Langguth, Berthold and Probst, Thomas and Pryss, R{\"u}diger},
  title     = {Predicting the Gender of Individuals with Tinnitus based on Daily Life Data of the TrackYourTinnitus mHealth Platform},
  series = {Scientific Reports},
  volume    = {11},
  journal   = {Scientific Reports},
  number    = {1},
  doi       = {10.1038/s41598-021-96731-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-261753},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{AllgaierSchleeProbstetal.2022,
  author    = {Allgaier, Johannes and Schlee, Winfried and Probst, Thomas and Pryss, R{\"u}diger},
  title     = {Prediction of tinnitus perception based on daily life mHealth data using country origin and season},
  series = {Journal of Clinical Medicine},
  volume    = {11},
  journal   = {Journal of Clinical Medicine},
  number    = {15},
  issn      = {2077-0383},
  doi       = {10.3390/jcm11154270},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-281812},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{BeierlePryssAizawa2023,
  author    = {Beierle, Felix and Pryss, R{\"u}diger and Aizawa, Akiko},
  title     = {Sentiments about mental health on Twitter — before and during the COVID-19 pandemic},
  series = {Healthcare},
  volume    = {11},
  journal   = {Healthcare},
  number    = {21},
  issn      = {2227-9032},
  doi       = {10.3390/healthcare11212893},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-355192},
  year      = {2023},
  abstract  = {During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make comparisons with the pre-pandemic situation. We collected tweets that included the hashtags \#MentalHealth and \#Depression from before and during the pandemic (8.5 months each). We used LDA (Latent Dirichlet Allocation) for topic modeling and LIWC, VADER, and NRC for sentiment analysis. We used three machine-learning classifiers to seek evidence regarding an automatically detectable change in tweets before vs. during the pandemic: (1) based on TF-IDF values, (2) based on the values from the sentiment libraries, (3) based on tweet content (deep-learning BERT classifier). Topic modeling revealed that Twitter users who explicitly used the hashtags \#Depression and especially \#MentalHealth did so to raise awareness. We observed an overall positive sentiment, and in tough times such as during the COVID-19 pandemic, tweets with \#MentalHealth were often associated with gratitude. Among the three classification approaches, the BERT classifier showed the best performance, with an accuracy of 81\% for \#MentalHealth and 79\% for \#Depression. Although the data may have come from users familiar with mental health, these findings can help gauge public sentiment on the topic. The combination of (1) sentiment analysis, (2) topic modeling, and (3) tweet classification with machine learning proved useful in gaining comprehensive insight into public sentiment and could be applied to other data sources and topics.},
  language  = {en}
}
@article{BeierleSchobelVogeletal.2021,
  author    = {Beierle, Felix and Schobel, Johannes and Vogel, Carsten and Allgaier, Johannes and Mulansky, Lena and Haug, Fabian and Haug, Julian and Schlee, Winfried and Holfelder, Marc and Stach, Michael and Schickler, Marc and Baumeister, Harald and Cohrdes, Caroline and Deckert, J{\"u}rgen and Deserno, Lorenz and Edler, Johanna-Sophie and Eichner, Felizitas A. and Greger, Helmut and Hein, Grit and Heuschmann, Peter and John, Dennis and Kestler, Hans A. and Krefting, Dagmar and Langguth, Berthold and Meybohm, Patrick and Probst, Thomas and Reichert, Manfred and Romanos, Marcel and St{\"o}rk, Stefan and Terhorst, Yannik and Weiß, Martin and Pryss, R{\"u}diger},
  title     = {Corona Health — A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic},
  series = {International Journal of Environmental Research and Public Health},
  volume    = {18},
  journal   = {International Journal of Environmental Research and Public Health},
  number    = {14},
  issn      = {1660-4601},
  doi       = {10.3390/ijerph18147395},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-242658},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{GablonskiPryssProbstetal.2019,
  author    = {Gablonski, Thorsten-Christian and Pryss, R{\"u}diger and Probst, Thomas and Vogel, Carsten and Andreas, Sylke},
  title     = {Intersession-Online: A smartphone application for systematic recording and controlling of intersession experiences in psychotherapy},
  series = {J — Multidisciplinary Scientific Journal},
  volume    = {2},
  journal   = {J — Multidisciplinary Scientific Journal},
  number    = {4},
  issn      = {2571-8800},
  doi       = {10.3390/j2040031},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-285597},
  pages     = {480 -- 495},
  year      = {2019},
  abstract  = {Mobile health technologies have become more and more important in psychotherapy research and practice. The market is being flooded by several psychotherapeutic online services for different purposes. However, mobile health technologies are particularly suitable for data collection and monitoring, as data can be recorded economically in real time. Currently, there is no appropriate method to assess intersession experiences systematically in psychotherapeutic practice. The aim of our project was the development of a smartphone application framework for systematic recording and controlling of intersession experiences. Intersession-Online, an iOS- and Android-App, offers the possibility to collect data on intersession experiences easily, to provide the results to therapists in an evaluated form and, if necessary, to induce or interrupt intersession experiences with the primary aim to improve outcome of psychotherapy. In general, the smartphone application could be a helpful, evidence-based tool for research and practice. Overall speaking, further research to investigate the efficacy of Intersession-Online is necessary.},
  language  = {en}
}
@article{HelmerHottenrottRodemersetal.2022,
  author    = {Helmer, Philipp and Hottenrott, Sebastian and Rodemers, Philipp and Leppich, Robert and Helwich, Maja and Pryss, R{\"u}diger and Kranke, Peter and Meybohm, Patrick and Winkler, Bernd E. and Sammeth, Michael},
  title     = {Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial},
  series = {Journal of Medical Internet Research},
  volume    = {24},
  journal   = {Journal of Medical Internet Research},
  number    = {12},
  doi       = {10.2196/42359},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299679},
  year      = {2022},
  abstract  = {Background: Over the recent years, technological advances of wrist-worn fitness trackers heralded a new era in the continuous monitoring of vital signs. So far, these devices have primarily been used for sports. Objective: However, for using these technologies in health care, further validations of the measurement accuracy in hospitalized patients are essential but lacking to date. Methods: We conducted a prospective validation study with 201 patients after moderate to major surgery in a controlled setting to benchmark the accuracy of heart rate measurements in 4 consumer-grade fitness trackers (Apple Watch 7, Garmin Fenix 6 Pro, Withings ScanWatch, and Fitbit Sense) against the clinical gold standard (electrocardiography). Results: All devices exhibited high correlation (r≥0.95; P<.001) and concordance (rc≥0.94) coefficients, with a relative error as low as mean absolute percentage error <5\% based on 1630 valid measurements. We identified confounders significantly biasing the measurement accuracy, although not at clinically relevant levels (mean absolute error<5 beats per minute). Conclusions: Consumer-grade fitness trackers appear promising in hospitalized patients for monitoring heart rate.},
  language  = {en}
}
@article{HelmerRodemersHottenrottetal.2023,
  author    = {Helmer, Philipp and Rodemers, Philipp and Hottenrott, Sebastian and Leppich, Robert and Helwich, Maja and Pryss, R{\"u}diger and Kranke, Peter and Meybohm, Patrick and Winkler, Bernd E. and Sammeth, Michael},
  title     = {Evaluating blood oxygen saturation measurements by popular fitness trackers in postoperative patients: a prospective clinical trial},
  series = {iScience},
  volume    = {26},
  journal   = {iScience},
  number    = {11},
  issn      = {2589-0042},
  doi       = {10.1016/j.isci.2023.108155},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349913},
  year      = {2023},
  abstract  = {Summary Blood oxygen saturation is an important clinical parameter, especially in postoperative hospitalized patients, monitored in clinical practice by arterial blood gas (ABG) and/or pulse oximetry that both are not suitable for a long-term continuous monitoring of patients during the entire hospital stay, or beyond. Technological advances developed recently for consumer-grade fitness trackers could—at least in theory—help to fill in this gap, but benchmarks on the applicability and accuracy of these technologies in hospitalized patients are currently lacking. We therefore conducted at the postanaesthesia care unit under controlled settings a prospective clinical trial with 201 patients, comparing in total >1,000 oxygen blood saturation measurements by fitness trackers of three brands with the ABG gold standard and with pulse oximetry. Our results suggest that, despite of an overall still tolerable measuring accuracy, comparatively high dropout rates severely limit the possibilities of employing fitness trackers, particularly during the immediate postoperative period of hospitalized patients. Highlights •The accuracy of O2 measurements by fitness trackers is tolerable (RMSE ≲4\%) •Correlation with arterial blood gas measurements is fair to moderate (PCC = [0.46; 0.64]) •Dropout rates of fitness trackers during O2 monitoring are high (∼1/3 values missing) •Fitness trackers cannot be recommended for O2 measuring during critical monitoring},
  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}
}
@article{KammererGoesterReichertetal.2021,
  author    = {Kammerer, Klaus and G{\"o}ster, Manuel and Reichert, Manfred and Pryss, R{\"u}diger},
  title     = {Ambalytics: a scalable and distributed system architecture concept for bibliometric network analyses},
  series = {Future Internet},
  volume    = {13},
  journal   = {Future Internet},
  number    = {8},
  issn      = {1999-5903},
  doi       = {10.3390/fi13080203},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-244916},
  year      = {2021},
  abstract  = {A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability.},
  language  = {en}
}
@article{KammererHoppenstedtPryssetal.2019,
  author    = {Kammerer, Klaus and Hoppenstedt, Burkhard and Pryss, R{\"u}diger and St{\"o}kler, Steffen and Allgaier, Johannes and Reichert, Manfred},
  title     = {Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings},
  series = {Sensors},
  volume    = {19},
  journal   = {Sensors},
  number    = {24},
  issn      = {1424-8220},
  doi       = {10.3390/s19245370},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193885},
  pages     = {5370},
  year      = {2019},
  abstract  = {o build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly.},
  language  = {en}
}
@article{KammererPryssHoppenstedtetal.2020,
  author    = {Kammerer, Klaus and Pryss, R{\"u}diger and Hoppenstedt, Burkhard and Sommer, Kevin and Reichert, Manfred},
  title     = {Process-driven and flow-based processing of industrial sensor data},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {18},
  issn      = {1424-8220},
  doi       = {10.3390/s20185245},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213089},
  year      = {2020},
  abstract  = {For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.},
  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{KraftReichertPryss2021,
  author    = {Kraft, Robin and Reichert, Manfred and Pryss, R{\"u}diger},
  title     = {Towards the interpretation of sound measurements from smartphones collected with mobile crowdsensing in the healthcare domain: an experiment with Android devices},
  series = {Sensors},
  volume    = {22},
  journal   = {Sensors},
  number    = {1},
  issn      = {1424-8220},
  doi       = {10.3390/s22010170},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252246},
  year      = {2021},
  abstract  = {The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users' individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.},
  language  = {en}
}
@article{KraftSchleeStachetal.2020,
  author    = {Kraft, Robin and Schlee, Winfried and Stach, Michael and Reichert, Manfred and Langguth, Berthold and Baumeister, Harald and Probst, Thomas and Hannemann, Ronny and Pryss, R{\"u}diger},
  title     = {Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain},
  series = {Frontiers in Neuroscience},
  volume    = {14},
  journal   = {Frontiers in Neuroscience},
  number    = {164},
  issn      = {1662-453X},
  doi       = {10.3389/fnins.2020.00164},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200220},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{MehdiDodePryssetal.2020,
  author    = {Mehdi, Muntazir and Dode, Albi and Pryss, R{\"u}diger and Schlee, Winfried and Reichert, Manfred and Hauck, Franz J.},
  title     = {Contemporary review of smartphone apps for tinnitus management and treatment},
  series = {Brain Sciences},
  volume    = {10},
  journal   = {Brain Sciences},
  number    = {11},
  issn      = {2076-3425},
  doi       = {10.3390/brainsci10110867},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-219367},
  year      = {2020},
  abstract  = {Tinnitus is a complex and heterogeneous psycho-physiological disorder responsible for causing a phantom ringing or buzzing sound albeit the absence of an external sound source. It has a direct influence on affecting the quality of life of its sufferers. Despite being around for a while, there has not been a cure for tinnitus, and the usual course of action for its treatment involves use of tinnitus retaining and sound therapy, or Cognitive Behavioral Therapy (CBT). One positive aspect about these therapies is that they can be administered face-to-face as well as delivered via internet or smartphone. Smartphones are especially helpful as they are highly personalized devices, and offer a well-established ecosystem of apps, accessible via respective marketplaces of differing mobile platforms. Note that current therapeutic treatments such as CBT have shown to be effective in suppressing the tinnitus symptoms when administered face-to-face, their effectiveness when being delivered using smartphones is not known so far. A quick search on the prominent market places of popular mobile platforms (Android and iOS) yielded roughly 250 smartphone apps offering tinnitus-related therapies and tinnitus management. As this number is expected to steadily increase due to high interest in smartphone app development, a contemporary review of such apps is crucial. In this paper, we aim to review scientific studies validating the smartphone apps, particularly to test their effectiveness in tinnitus management and treatment. We use the PRISMA guidelines for identification of studies on major scientific literature sources and delineate the outcomes of identified studies.},
  language  = {en}
}
@article{NandiCrombachElbertetal.2020,
  author    = {Nandi, Corina and Crombach, Anselm and Elbert, Thomas and Bambonye, Manass{\´e} and Pryss, R{\"u}diger and Schobel, Johannes and Weierstall-Pust, Roland},
  title     = {The cycle of violence as a function of PTSD and appetitive aggression: A longitudinal study with Burundian soldiers},
  series = {Aggressive Behavior},
  volume    = {46},
  journal   = {Aggressive Behavior},
  number    = {5},
  doi       = {10.1002/ab.21895},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-218235},
  pages     = {391 -- 399},
  year      = {2020},
  abstract  = {During deployment, soldiers face situations in which they are not only exposed to violence but also have to perpetrate it themselves. This study investigates the role of soldiers' levels of posttraumatic stress disorder (PTSD) symptoms and appetitive aggression, that is, a lust for violence, for their engaging in violence during deployment. Furthermore, factors during deployment influencing the level of PTSD symptoms and appetitive aggression after deployment were examined for a better comprehension of the maintenance of violence. Semi-structured interviews were conducted with 468 Burundian soldiers before and after a 1-year deployment to Somalia. To predict violent acts during deployment (perideployment) as well as appetitive aggression and PTSD symptom severity after deployment (postdeployment), structural equation modeling was utilized. Results showed that the number of violent acts perideployment was predicted by the level of appetitive aggression and by the severity of PTSD hyperarousal symptoms predeployment. In addition to its association with the predeployment level, appetitive aggression postdeployment was predicted by violent acts and trauma exposure perideployment as well as positively associated with unit support. PTSD symptom severity postdeployment was predicted by the severity of PTSD avoidance symptoms predeployment and trauma exposure perideployment, and negatively associated with unit support. This prospective study reveals the importance of appetitive aggression and PTSD hyperarousal symptoms for the engagement in violent acts during deployment, while simultaneously demonstrating how these phenomena may develop in mutually reinforcing cycles in a war setting.},
  language  = {en}
}
@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{PryssSchleeHoppenstedtetal.2020,
  author    = {Pryss, R{\"u}diger and Schlee, Winfried and Hoppenstedt, Burkhard and Reichert, Manfred and Spiliopoulou, Myra and Langguth, Berthold and Breitmayer, Marius and Probst, Thomas},
  title     = {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},
  series = {Journal of Medical Internet Research},
  volume    = {22},
  journal   = {Journal of Medical Internet Research},
  number    = {6},
  doi       = {10.2196/15547},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229517},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{SchicklerReichertGeigeretal.2020,
  author    = {Schickler, Marc and Reichert, Manfred and Geiger, Philip and Winkler, Jens and Funk, Thomas and Weilbach, Micha and Pryss, R{\"u}diger},
  title     = {Flexible development of location-based mobile augmented reality applications with AREA},
  series = {Journal of Ambient Intelligence and Humanized Computing},
  volume    = {11},
  journal   = {Journal of Ambient Intelligence and Humanized Computing},
  issn      = {1868-5137},
  doi       = {10.1007/s12652-020-02094-9},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-232773},
  pages     = {5809-5824},
  year      = {2020},
  abstract  = {Mobile applications have garnered a lot of attention in the last years. The computational capabilities of mobile devices are the mainstay to develop completely new application types. The provision of augmented reality experiences on mobile devices paves one alley in this field. For example, in the automotive domain, augmented reality applications are used to experience, inter alia, the interior of a car by moving a mobile device around. The device's camera then detects interior parts and shows additional information to the customer within the camera view. Another application type that is increasingly utilized is related to the combination of serious games with mobile augmented reality functions. Although the latter combination is promising for many scenarios, technically, it is a complex endeavor. In the AREA (Augmented Reality Engine Application) project, a kernel was implemented that enables location-based mobile augmented reality applications. Importantly, this kernel provides a flexible architecture that fosters the development of individual location-based mobile augmented reality applications. The work at hand shows the flexibility of AREA based on a developed serious game. Furthermore, the algorithm framework and major features of it are presented. As the conclusion of this paper, it is shown that mobile augmented reality applications require high development efforts. Therefore, flexible frameworks like AREA are crucial to develop respective applications in a reasonable time.},
  language  = {en}
}
@article{SchleeNeffSimoesetal.2022,
  author    = {Schlee, Winfried and Neff, Patrick and Simoes, Jorge and Langguth, Berthold and Schoisswohl, Stefan and Steinberger, Heidi and Norman, Marie and Spiliopoulou, Myra and Schobel, Johannes and Hannemann, Ronny and Pryss, R{\"u}diger},
  title     = {Smartphone-guided educational counseling and self-help for chronic tinnitus},
  series = {Journal of Clinical Medicine},
  volume    = {11},
  journal   = {Journal of Clinical Medicine},
  number    = {7},
  issn      = {2077-0383},
  doi       = {10.3390/jcm11071825},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-267295},
  year      = {2022},
  abstract  = {Tinnitus is an auditory phantom perception in the ears or head in the absence of a corresponding external stimulus. There is currently no effective treatment available that reliably reduces tinnitus. Educational counseling is a treatment approach that aims to educate patients and inform them about possible coping strategies. For this feasibility study, we implemented educational material and self-help advice in a smartphone app. Participants used the educational smartphone app unsupervised during their daily routine over a period of four months. Comparing the tinnitus outcome measures before and after smartphone-guided treatment, we measured changes in tinnitus-related distress, but not in tinnitus loudness. Improvements on the Tinnitus Severity numeric rating scale reached an effect size of 0.408, while the improvements on the Tinnitus Handicap Inventory (THI) were much smaller with an effect size of 0.168. An analysis of user behavior showed that frequent and intensive use of the app is a crucial factor for treatment success: participants that used the app more often and interacted with the app intensively reported a stronger improvement in the tinnitus. Between study allocation and final assessment, 26 of 52 participants dropped out of the study. Reasons for the dropouts and lessons for future studies are discussed in this paper.},
  language  = {en}
}
@article{SchleeSimoesPryss2021,
  author    = {Schlee, Winfried and Simoes, Jorge and Pryss, R{\"u}diger},
  title     = {Auricular acupressure combined with self-help intervention for treating chronic tinnitus: a longitudinal observational study},
  series = {Journal of Clinical Medicine},
  volume    = {10},
  journal   = {Journal of Clinical Medicine},
  number    = {18},
  issn      = {2077-0383},
  doi       = {10.3390/jcm10184201},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246209},
  year      = {2021},
  abstract  = {Tinnitus is a phantom sound perception in the ears or head and can arise from many different medical disorders. Currently, there is no standard treatment for tinnitus that reliably reduces tinnitus. Individual patients reported that acupressure at various points around the ear can help to reduce tinnitus, which was investigated here. With this longitudinal observational study, we report a systematic evaluation of auricular acupressure on 39 tinnitus sufferers, combined with a self-help smartphone app. The participants were asked to report on tinnitus, stress, mood, neck, and jaw muscle tensions twice a day using an ecological momentary assessment study design for six weeks. On average, 123.6 questionnaires per person were provided and used for statistical analysis. The treatment responses of the participants were heterogeneous. On average, we observed significant negative trends for tinnitus loudness (Cohen's d effect size: -0.861), tinnitus distress (d = -0.478), stress (d = -0.675), and tensions in the neck muscles (d = -0.356). Comparison with a matched control group revealed significant improvements for tinnitus loudness (p = 0.027) and self-reported stress level (p = 0.003). The positive results of the observational study motivate further research including a randomized clinical trial and long-term assessment of the clinical improvement.},
  language  = {en}
}
@article{SchobelProbstReichertetal.2020,
  author    = {Schobel, Johannes and Probst, Thomas and Reichert, Manfred and Schlee, Winfried and Schickler, Marc and Kestler, Hans A. and Pryss, R{\"u}diger},
  title     = {Measuring mental effort for creating mobile data collection applications},
  series = {International Journal of Environmental Research and Public Health},
  volume    = {17},
  journal   = {International Journal of Environmental Research and Public Health},
  number    = {5},
  issn      = {1660-4601},
  doi       = {10.3390/ijerph17051649},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203176},
  year      = {2020},
  abstract  = {To deal with drawbacks of paper-based data collection procedures, the QuestionSys approach empowers researchers with none or little programming knowledge to flexibly configure mobile data collection applications on demand. The mobile application approach of QuestionSys mainly pursues the goal to mitigate existing drawbacks of paper-based collection procedures in mHealth scenarios. Importantly, researchers shall be enabled to gather data in an efficient way. To evaluate the applicability of QuestionSys, several studies have been carried out to measure the efforts when using the framework in practice. In this work, the results of a study that investigated psychological insights on the required mental effort to configure the mobile applications are presented. Specifically, the mental effort for creating data collection instruments is validated in a study with N=80 participants across two sessions. Thereby, participants were categorized into novices and experts based on prior knowledge on process modeling, which is a fundamental pillar of the developed approach. Each participant modeled 10 instruments during the course of the study, while concurrently several performance measures are assessed (e.g., time needed or errors). The results of these measures are then compared to the self-reported mental effort with respect to the tasks that had to be modeled. On one hand, the obtained results reveal a strong correlation between mental effort and performance measures. On the other, the self-reported mental effort decreased significantly over the course of the study, and therefore had a positive impact on measured performance metrics. Altogether, this study indicates that novices with no prior knowledge gain enough experience over the short amount of time to successfully model data collection instruments on their own. Therefore, QuestionSys is a helpful instrument to properly deal with large-scale data collection scenarios like clinical trials.},
  language  = {en}
}
@article{SommerAmrBavendieketal.2022,
  author    = {Sommer, Kim K. and Amr, Ali and Bavendiek, Udo and Beierle, Felix and Brunecker, Peter and Dathe, Henning and Eils, J{\"u}rgen and Ertl, Maximilian and Fette, Georg and Gietzelt, Matthias and Heidecker, Bettina and Hellenkamp, Kristian and Heuschmann, Peter and Hoos, Jennifer D. E. and Keszty{\"u}s, Tibor and Kerwagen, Fabian and Kindermann, Aljoscha and Krefting, Dagmar and Landmesser, Ulf and Marschollek, Michael and Meder, Benjamin and Merzweiler, Angela and Prasser, Fabian and Pryss, R{\"u}diger and Richter, Jendrik and Schneider, Philipp and St{\"o}rk, Stefan and Dieterich, Christoph},
  title     = {Structured, harmonized, and interoperable integration of clinical routine data to compute heart failure risk scores},
  series = {Life},
  volume    = {12},
  journal   = {Life},
  number    = {5},
  issn      = {2075-1729},
  doi       = {10.3390/life12050749},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-275239},
  year      = {2022},
  abstract  = {Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.},
  language  = {en}
}
@article{WetzelPryssBaumeisteretal.2021,
  author    = {Wetzel, Britta and Pryss, R{\"u}diger and Baumeister, Harald and Edler, Johanna-Sophie and Gon{\c{c}}alves, Ana Sofia Oliveira and Cohrdes, Caroline},
  title     = {"How come you don't call me?" Smartphone communication app usage as an indicator of loneliness and social well-being across the adult lifespan during the COVID-19 pandemic},
  series = {International Journal of Environmental Research and Public Health},
  volume    = {18},
  journal   = {International Journal of Environmental Research and Public Health},
  number    = {12},
  issn      = {1660-4601},
  doi       = {10.3390/ijerph18126212},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-241033},
  year      = {2021},
  abstract  = {Loneliness and lack of social well-being are associated with adverse health outcomes and have increased during the COVID-19 pandemic. Smartphone communication data have been suggested to help monitor loneliness, but this requires further evidence. We investigated the informative value of smartphone communication app data for predicting subjective loneliness and social well-being in a sample of 364 participants ranging from 18 to 78 years of age (52.2\% female; mean age = 42.54, SD = 13.22) derived from the CORONA HEALTH APP study from July to December 2020 in Germany. The participants experienced relatively high levels of loneliness and low social well-being during the time period characterized by the COVID-19 pandemic. Apart from positive associations with phone call use times, smartphone communication app use was associated with social well-being and loneliness only when considering the age of participants. Younger participants with higher use times tended to report less social well-being and higher loneliness, while the opposite association was found for older adults. Thus, the informative value of smartphone communication use time was rather small and became evident only in consideration of age. The results highlight the need for further investigations and the need to address several limitations in order to draw conclusions at the population level.},
  language  = {en}
}
@article{WinterPryssProbstetal.2020,
  author    = {Winter, Michael and Pryss, R{\"u}diger and Probst, Thomas and Reichert, Manfred},
  title     = {Towards the applicability of measuring the electrodermal activity in the context of process model comprehension: feasibility study},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {16},
  issn      = {1424-8220},
  doi       = {10.3390/s20164561},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-211276},
  year      = {2020},
  abstract  = {Process model comprehension is essential in order to understand the five Ws (i.e., who, what, where, when, and why) pertaining to the processes of organizations. However, research in this context showed that a proper comprehension of process models often poses a challenge in practice. For this reason, a vast body of research exists studying the factors having an influence on process model comprehension. In order to point research towards a neuro-centric perspective in this context, the paper at hand evaluates the appropriateness of measuring the electrodermal activity (EDA) during the comprehension of process models. Therefore, a preliminary test run and a feasibility study were conducted relying on an EDA and physical activity sensor to record the EDA during process model comprehension. The insights obtained from the feasibility study demonstrated that process model comprehension leads to an increased activity in the EDA. Furthermore, EDA-related results indicated significantly that participants were confronted with a higher cognitive load during the comprehension of complex process models. In addition, the experiences and limitations we learned in measuring the EDA during the comprehension of process models are discussed in this paper. In conclusion, the feasibility study demonstrated that the measurement of the EDA could be an appropriate method to obtain new insights into process model comprehension.},
  language  = {en}
}
@article{WinterPryssProbstetal.2021,
  author    = {Winter, Michael and Pryss, R{\"u}diger and Probst, Thomas and Reichert, Manfred},
  title     = {Applying Eye Movement Modeling Examples to guide novices' attention in the comprehension of process models},
  series = {Brain Sciences},
  volume    = {11},
  journal   = {Brain Sciences},
  number    = {1},
  issn      = {2076-3425},
  doi       = {10.3390/brainsci11010072},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-222966},
  year      = {2021},
  abstract  = {Process models are crucial artifacts in many domains, and hence, their proper comprehension is of importance. Process models mediate a plethora of aspects that are needed to be comprehended correctly. Novices especially face difficulties in the comprehension of process models, since the correct comprehension of such models requires process modeling expertise and visual observation capabilities to interpret these models correctly. Research from other domains demonstrated that the visual observation capabilities of experts can be conveyed to novices. In order to evaluate the latter in the context of process model comprehension, this paper presents the results from ongoing research, in which gaze data from experts are used as Eye Movement Modeling Examples (EMMEs) to convey visual observation capabilities to novices. Compared to prior results, the application of EMMEs improves process model comprehension significantly for novices. Novices achieved in some cases similar performances in process model comprehension to experts. The study's insights highlight the positive effect of EMMEs on fostering the comprehension of process models.},
  language  = {en}
}