@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{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{UnnikrishnanSchleicherShahetal.2020, author = {Unnikrishnan, Vishnu and Schleicher, Miro and Shah, Yash and Jamaludeen, Noor and Pryss, Ruediger and Schobel, Johannes and Kraft, Robin and Schlee, Winfried and Spiliopoulou, Myra}, title = {The effect of non-personalised tips on the continued use of self-monitoring mHealth applications}, series = {Brain Sciences}, volume = {10}, journal = {Brain Sciences}, number = {12}, issn = {2076-3425}, doi = {10.3390/brainsci10120924}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-219435}, year = {2020}, abstract = {Chronic tinnitus, the perception of a phantom sound in the absence of corresponding stimulus, is a condition known to affect patients' quality of life. Recent advances in mHealth have enabled patients to maintain a 'disease journal' of ecologically-valid momentary assessments, improving patients' own awareness of their disease while also providing clinicians valuable data for research. In this study, we investigate the effect of non-personalised tips on patients' perception of tinnitus, and on their continued use of the application. The data collected from the study involved three groups of patients that used the app for 16 weeks. Groups A \& Y were exposed to feedback from the start of the study, while group B only received tips for the second half of the study. Groups A and Y were run by different supervisors and also differed in the number of hospital visits during the study. Users of Group A and B underwent assessment at baseline, mid-study, post-study and follow-up, while users of group Y were only assessed at baseline and post-study. It is seen that the users in group B use the app for longer, and also more often during the day. The answers of the users to the Ecological Momentary Assessments are seen to form clusters where the degree to which the tinnitus distress depends on tinnitus loudness varies. Additionally, cluster-level models were able to predict new unseen data with better accuracy than a single global model. This strengthens the argument that the discovered clusters really do reflect underlying patterns in disease expression.}, 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{HuflageKunzHendeletal.2023, author = {Huflage, Henner and Kunz, Andreas Steven and Hendel, Robin and Kraft, Johannes and Weick, Stefan and Razinskas, Gary and Sauer, Stephanie Tina and Pennig, Lenhard and Bley, Thorsten Alexander and Grunz, Jan-Peter}, title = {Obesity-related pitfalls of virtual versus true non-contrast imaging — an intraindividual comparison in 253 oncologic patients}, series = {Diagnostics}, volume = {13}, journal = {Diagnostics}, number = {9}, issn = {2075-4418}, doi = {10.3390/diagnostics13091558}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313519}, year = {2023}, abstract = {Objectives: Dual-source dual-energy CT (DECT) facilitates reconstruction of virtual non-contrast images from contrast-enhanced scans within a limited field of view. This study evaluates the replacement of true non-contrast acquisition with virtual non-contrast reconstructions and investigates the limitations of dual-source DECT in obese patients. Materials and Methods: A total of 253 oncologic patients (153 women; age 64.5 ± 16.2 years; BMI 26.6 ± 5.1 kg/m\(^2\)) received both multi-phase single-energy CT (SECT) and DECT in sequential staging examinations with a third-generation dual-source scanner. Patients were allocated to one of three BMI clusters: non-obese: <25 kg/m\(^2\) (n = 110), pre-obese: 25-29.9 kg/m\(^2\) (n = 73), and obese: >30 kg/m\(^2\) (n = 70). Radiation dose and image quality were compared for each scan. DECT examinations were evaluated regarding liver coverage within the dual-energy field of view. Results: While arterial contrast phases in DECT were associated with a higher CTDI\(_{vol}\) than in SECT (11.1 vs. 8.1 mGy; p < 0.001), replacement of true with virtual non-contrast imaging resulted in a considerably lower overall dose-length product (312.6 vs. 475.3 mGy·cm; p < 0.001). The proportion of DLP variance predictable from patient BMI was substantial in DECT (R\(^2\) = 0.738) and SECT (R\(^2\) = 0.620); however, DLP of SECT showed a stronger increase in obese patients (p < 0.001). Incomplete coverage of the liver within the dual-energy field of view was most common in the obese subgroup (17.1\%) compared with non-obese (0\%) and pre-obese patients (4.1\%). Conclusion: DECT facilitates a 30.8\% dose reduction over SECT in abdominal oncologic staging examinations. Employing dual-source scanner architecture, the risk for incomplete liver coverage increases in obese patients.}, language = {en} }