TY - JOUR A1 - Prakash, Subash A1 - Unnikrishnan, Vishnu A1 - Pryss, Rüdiger A1 - Kraft, Robin A1 - Schobel, Johannes A1 - Hannemann, Ronny A1 - Langguth, Berthold A1 - Schlee, Winfried A1 - Spiliopoulou, Myra T1 - Interactive system for similarity-based inspection and assessment of the well-being of mHealth users JF - Entropy N2 - 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. KW - medical analytics KW - condition prediction KW - ecological momentary assessment KW - visual analytics KW - time series Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-252333 SN - 1099-4300 VL - 23 IS - 12 ER - TY - JOUR A1 - Wetzel, Britta A1 - Pryss, Rüdiger A1 - Baumeister, Harald A1 - Edler, Johanna-Sophie A1 - Gonçalves, Ana Sofia Oliveira A1 - Cohrdes, Caroline T1 - “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 JF - International Journal of Environmental Research and Public Health N2 - 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. KW - loneliness KW - social well-being KW - passive data KW - app KW - smartphone communication KW - COVID-19 KW - social media use KW - age differences KW - public mental health KW - mental health monitoring Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-241033 SN - 1660-4601 VL - 18 IS - 12 ER - TY - JOUR A1 - Kammerer, Klaus A1 - Göster, Manuel A1 - Reichert, Manfred A1 - Pryss, Rüdiger T1 - Ambalytics: a scalable and distributed system architecture concept for bibliometric network analyses JF - Future Internet N2 - 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. KW - system architecture design KW - bibliometric analysis KW - community detection Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-244916 SN - 1999-5903 VL - 13 IS - 8 ER - TY - JOUR A1 - Kraft, Robin A1 - Reichert, Manfred A1 - Pryss, Rüdiger T1 - Towards the interpretation of sound measurements from smartphones collected with mobile crowdsensing in the healthcare domain: an experiment with Android devices JF - Sensors N2 - 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. KW - mHealth KW - crowdsensing KW - tinnitus KW - noise measurement KW - environmental sound Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-252246 SN - 1424-8220 VL - 22 IS - 1 ER - TY - JOUR A1 - Winter, Michael A1 - Pryss, Rüdiger A1 - Probst, Thomas A1 - Reichert, Manfred T1 - Applying Eye Movement Modeling Examples to guide novices' attention in the comprehension of process models JF - Brain Sciences N2 - 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. KW - Business Process Models KW - Process Model Comprehension KW - Eye Movement Modeling Examples KW - eye tracking KW - human-centered design KW - cognition Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-222966 SN - 2076-3425 VL - 11 IS - 1 ER - TY - JOUR A1 - Schlee, Winfried A1 - Simoes, Jorge A1 - Pryss, Rüdiger T1 - Auricular acupressure combined with self-help intervention for treating chronic tinnitus: a longitudinal observational study JF - Journal of Clinical Medicine N2 - 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. KW - tinnitus KW - acupressure KW - self-help KW - ecological momentary assessment KW - stress Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-246209 SN - 2077-0383 VL - 10 IS - 18 ER - 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 - Holfelder, Marc A1 - Mulansky, Lena A1 - Schlee, Winfried A1 - Baumeister, Harald A1 - Schobel, Johannes A1 - Greger, Helmut A1 - Hoff, Andreas A1 - Pryss, Rüdiger T1 - Medical device regulation efforts for mHealth apps during the COVID-19 pandemic — an experience report of Corona Check and Corona Health JF - J — Multidisciplinary Scientific Journal N2 - 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. KW - mHealth KW - mobile application KW - MDR KW - medical device regulation KW - medical device software Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-285434 SN - 2571-8800 VL - 4 IS - 2 SP - 206 EP - 222 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 -