piwik-script

Deutsch Intern
Institute for Clinical Epidemiology and Biometry

Prof. Dr. Rüdiger Pryss

Prof. Dr. Rüdiger Pryss

Professor für Medizininformatik
Am Schwarzenberg Haus A15 / A15 4.316
Phone: +49 931-201 46471
Fax: +49 931-201 647310

kurzer Lebenslauf

seit 2019

Professor für Medizininformatik am Institut für Klinische Epidemiologie und Biometrie, Universität Würzburg
2015

Promotion zum Dr. rer. nat.

2008-2015

Wissenschaftlicher Mitarbeiter am Institut für Datenbanken und Informationssysteme, Universität Ulm

2005-2008

Software-Engineer

2005

 Abschluss zum Diplom-Informatiker

1998-2005

 Studium der Informatik in Passau, Karlsruhe und Ulm

 

wissenschaftliche Schwerpunkte:

Medical Informatics, Mobile Crowdsensing, mHealth, Versorgungsforschung, Expert Systems, Medical Data Science

 

Weiteres zur Arbeitsgruppe, siehe auch:

https://medicalinformatics.ike-b.de/

 

Hauptpublikationen der letzten Jahre:

2023[ to top ]
  • 1.
    Horn A, Jírů-Hillmann S, Widmann J, Montellano FA, Salmen J, Pryss R, et al. Systematic review on the effectiveness of mobile health applications on mental health of breast cancer survivors. Journal of Cancer Survivorship [Internet]. 2023;. Available from: https://doi.org/10.1007/s11764-023-01470-6
  • 1.
    Simoes JP, Schoisswohl S, Schlee W, Basso L, Bernal-Robledano A, Boecking B, et al. The statistical analysis plan for the unification of treatments and interventions for tinnitus patients randomized clinical trial (UNITI-RCT). Trials [Internet]. 2023;24(1):472. Available from: https://doi.org/10.1186/s13063-023-07303-2
  • 1.
    Humer E, Keil T, Stupp C, Schlee W, Wildner M, Heuschmann P, et al. Associations of Country-Specific and Sociodemographic Factors With Self-Reported COVID-19-Related Symptoms: Multivariable Analysis of Data From the CoronaCheck Mobile Health Platform. JMIR Public Health Surveill. 2023;9:e40958.
  • 1.
    Beierle F, Allgaier J, Stupp C, Keil T, Schlee W, Schobel J, et al. Self-Assessment of Having COVID-19 With the Corona Check Mhealth App. IEEE J Biomed Health Inform. 2023;Pp.
  • 1.
    Unnikrishnan V, Schleicher M, Puga C, Pryss R, Vogel C, Schlee W, et al. A Similarity-Guided Framework for Error-Driven Discovery of Patient Neighbourhoods in EMA Data. In: Crémilleux B, Hess S, Nijssen S, editors. Advances in Intelligent Data Analysis XXI. Springer Nature Switzerland; 2023. pp. 459-71.
  • 1.
    Breitmayer M, Stach M, Kraft R, Allgaier J, Reichert M, Schlee W, et al. Predicting the presence of tinnitus using ecological momentary assessments. Scientific Reports [Internet]. 2023;13(1):8989. Available from: https://doi.org/10.1038/s41598-023-36172-7
  • 1.
    Winter M, Neumann H, Pryss R, Probst T, Reichert M. Defining gaze patterns for process model literacy – Exploring visual routines in process models with diverse mappings. Expert Systems with Applications [Internet]. 2023;213:119217. Available from: https://www.sciencedirect.com/science/article/pii/S0957417422022357
  • 1.
    Vogel C, Bendig E, Garatva P, Stenzel L, Idrees AR, Kraft R, et al. A Highly Configurable EMA and JITAI Mobile App Framework Utilized in a Large-Scale German Study on Breast Cancer Aftercare. In: 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). 2023. pp. 103-10.
  • 1.
    Engelke M, Simões J, Vogel C, Schoisswohl S, Schecklmann M, Wölflick S, et al. Pilot study of a smartphone-based tinnitus therapy using structured counseling and sound therapy: A multiple-baseline design with ecological momentary assessment. PLOS Digital Health [Internet]. 2023;2(1):e0000183. Available from: https://doi.org/10.1371/journal.pdig.0000183
  • 1.
    Kleinau A, Flügel S, Pryss R, Vogel C, Engelke M, Schlee W, et al. Predicting Patient-Based Time-Dependent Mobile Health Data. In: 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). 2023. pp. 79-84.
2022[ to top ]
  • 1.
    Mulansky L, Pryss R, Cohrdes C, Baumeister H, Beierle F. Social Media App Usage in Relation with PHQ-9 Depression Scores during the COVID-19 Pandemic. In: Proc. of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing. UbiComp/ISWC ’22 Adjunct; 2022.
  • 1.
    Geissler J, Buchholz H, Meerson R, Kammerer K, Göster M, Schobel J, et al. Smartphone-based behaviour analysis for challenging behaviour in intellectual and developmental disabilities and autism spectrum disorder – Study protocol for the ProVIA trial. Frontiers in Neuroscience [Internet]. 2022;16. Available from: https://www.frontiersin.org/articles/10.3389/fnins.2022.984618
  • 1.
    Karthan M, Romahn P, Pryss R, Kestler H, Schobel J. Collecting Data from Senior Citizens Using Serious Games. Stud Health Technol Inform. 2022;295:234-7.
  • 1.
    Kammerer K, Pryss R, Reichert M. Retrieving, Abstracting, and Changing Business Process Models with PQL. In: Polyvyanyy A, editor. Process Querying Methods [Internet]. Cham: Springer International Publishing; 2022. pp. 219-54. Available from: https://doi.org/10.1007/978-3-030-92875-9_8
  • 1.
    Karthan M, Kreuder A, Frick U, Pryss R, Schobel J. PeterPandemic: A Serious Game for Pandemic Management. Stud Health Technol Inform. 2022;295:161-2.
  • 1.
    Idrees AR, Kraft R, Pryss R, Reichert M, Nguyen T, Stenzel L, et al. Backend Concept of the eSano eHealth Platform for Internet- and Mobile-based Interventions. In: 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). 2022. pp. 88-93.
  • 1.
    Helmer P, Hottenrott S, Rodemers P, Leppich R, Helwich M, Pryss R, et al. Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial. J Med Internet Res. 2022;24(12):e42359.
  • 1.
    Cohrdes C, Wetzel B, Pryss R, Baumeister H, Göbel K. Adult quality of life patterns and trajectories during the COVID-19 pandemic in Germany. Current Psychology [Internet]. 2022;. Available from: https://doi.org/10.1007/s12144-022-03628-4
  • 1.
    Beyer-Wunsch P, Reichert M, Pryss R. The Adjusted Reality Vision - Emerging Technology for Digital Transformation of Disruptive Environmental Stimuli. Procedia Computer Science [Internet]. 2022;210:157-64. Available from: https://www.sciencedirect.com/science/article/pii/S1877050922015897
  • 1.
    Baumeister H, Garatva P, Pryss R, Ropinski T, Montag C. Digitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung?. Psychologische Rundschau [Internet]. 2022;74(2):89-106. Available from: https://econtent.hogrefe.com/doi/abs/10.1026/0033-3042/a000609
  • 1.
    Allgaier J, Schlee W, Probst T, Pryss R. Prediction of Tinnitus Perception Based on Daily Life MHealth Data Using Country Origin and Season. Journal of Clinical Medicine [Internet]. 2022;11(15):4270. Available from: https://www.mdpi.com/2077-0383/11/15/4270
  • 1.
    Stach M, Reichert M, Prasser F, Baumeister H, Schlee W, Heuschmann P, et al. Free Technical Solutions for Ecological Momentary Assessments - Searching GitHub plus Google. In: 2022 International Conference on Computational Science and Computational Intelligence (CSCI). 2022. pp. 1778-81.
  • 1.
    Kraft R, Hofmann F, Reichert M, Pryss R. Dealing With Inaccurate Sensor Data in the Context of Mobile Crowdsensing and mHealth. IEEE J Biomed Health Inform. 2022;26(11):5439-4.
  • 1.
    Kraft R, Reichert M, Pryss R. Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices. Sensors [Internet]. 2022;22(1):170. Available from: https://www.mdpi.com/1424-8220/22/1/170
  • 1.
    Winter M, Pryss R, Probst T, Baß J, Reichert M. Measuring the Cognitive Complexity in the Comprehension of Modular Process Models. IEEE Transactions on Cognitive and Developmental Systems. 2022;14(1):164-80.
  • 1.
    Simoes J, Bulla J, Neff P, Pryss R, Marcrum SC, Langguth B, et al. Tinnitus Subtyping with Subgrouping Within Group Iterative Multiple Model Estimation: An Ecological Momentary Assessment Study. medRxiv [Internet]. 2022;:2022.02.22.22271338. Available from: https://www.medrxiv.org/content/medrxiv/early/2022/02/23/2022.02.22.22271338.full.pdf
  • 1.
    O’Rourke T, Vogel C, John D, Pryss R, Schobel J, Haug F, et al. The Impact of Coping Styles and Gender on Situational Coping: An Ecological Momentary Assessment Study With the mHealth Application TrackYourStress. Frontiers in Psychology [Internet]. 2022;13. Available from: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.913125
  • 1.
    Schlee W, Neff P, Simões J, Langguth B, Schoisswohl S, Steinberger H, et al. Smartphone-Guided Educational Counseling and Self-Help for Chronic Tinnitus. Journal of Clinical Medicine. 2022;11(7):1825.
  • 1.
    Scheible J, Hofmann F, Reichert M, Pryss R, Schickler M. Generic Concept for Integrating Voice Assistance Into Smart Therapeutic Interventions. In: 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS). 2022. pp. 56-61.
  • 1.
    Schleicher M, Pryss R, Schlee W, Spiliopoulou M. When Can I Expect the mHealth User to Return? Prediction Meets Time Series with Gaps. In: Michalowski M, Abidi SSR, Abidi S, editors. Artificial Intelligence in Medicine. Springer International Publishing; 2022. pp. 310-2.
  • 1.
    Schleicher M, Pryss R, Schobel J, Schlee W, Spiliopoulou M. Expect the gap: A recommender approach to estimate the absenteeism of self-monitoring mHealth app users. In: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA). 2022. pp. 1-10.
  • 1.
    Schleicher M, Hamacher S, Naujoks M, Günther K, Schmidt T, Pryss R, et al. Prediction of declining engagement to self-monitoring apps on the example of tinnitus mHealth data. In: 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS). 2022. pp. 228-33.
  • 1.
    Shahania S, Unnikrishnan V, Pryss R, Kraft R, Schobel J, Hannemann R, et al. Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User’s Stream With a Contextual Multi-Armed Bandit. Frontiers in Neuroscience. 2022;16:836834.
  • 1.
    Simoes J, Bulla J, Neff P, Pryss R, Marcrum SC, Langguth B, et al. Daily Contributors of Tinnitus Loudness and Distress: An Ecological Momentary Assessment Study. Frontiers in Neuroscience [Internet]. 2022;16. Available from: https://www.frontiersin.org/articles/10.3389/fnins.2022.883665
  • 1.
    Sommer KK, Amr A, Bavendiek U, Beierle F, Brunecker P, Dathe H, et al. Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores. Life [Internet]. 2022;12(5):749. Available from: https://www.mdpi.com/2075-1729/12/5/749
  • 1.
    Stach M, Pflüger F, Reichert M, Pryss R. LAMP: a monitoring framework for mHealth application research. Procedia Computer Science [Internet]. 2022;198:203-10. Available from: https://www.sciencedirect.com/science/article/pii/S1877050921024686
  • 1.
    Weiß M, Baumeister H, Cohrdes C, Deckert J, Gründahl M, Pryss R, et al. Extraversion moderates the relationship between social media use and depression. Journal of Affective Disorders Reports [Internet]. 2022;8:100343. Available from: https://www.sciencedirect.com/science/article/pii/S2666915322000361
  • 1.
    Karthan M, Martin R, Holl F, Swoboda W, Kestler HA, Pryss R, et al. Enhancing mHealth data collection applications with sensing capabilities. Frontiers in Public Health [Internet]. 2022;10. Available from: https://www.frontiersin.org/articles/10.3389/fpubh.2022.926234
2021[ to top ]
  • 1.
    Genitsaridi E, Dode A, Qirjazi B, Mehdi M, Pryss R, Probst T, et al. An Albanian translation of a questionnaire for self-reported tinnitus assessment. International Journal of Audiology [Internet]. 2021;61(6):515-9. Available from: https://doi.org/10.1080/14992027.2021.1933221
  • 1.
    Winter M, Pryss R, Probst T, Schlee W, Tallon M, Frick U, et al. Are Non-Experts Able to Comprehend Business Process Models--Study Insights Involving Novices and Experts. arXiv preprint arXiv:2107.02030. 2021;.
  • 1.
    Schlee W, Schoisswohl S, Staudinger S, Schiller A, Lehner A, Langguth B, et al. Towards a unification of treatments and interventions for tinnitus patients: The EU research and innovation action UNITI. In: Schlee W, Langguth B, Kleinjung T, Vanneste S, De Ridder D, editors. Progress in Brain Research [Internet]. Elsevier; 2021. pp. 441-5. Available from: https://www.sciencedirect.com/science/article/pii/S0079612320302351
  • 1.
    Schlee W, Langguth B, Pryss R, Allgaier J, Mulansky L, Vogel C, et al. Using Big Data to Develop a Clinical Decision Support System for Tinnitus Treatment. In: Searchfield GD, Zhang J, editors. The Behavioral Neuroscience of Tinnitus [Internet]. Cham: Springer International Publishing; 2021. pp. 175-89. Available from: https://doi.org/10.1007/7854_2021_229
  • 1.
    Schlee W, Simoes J, Pryss R. Auricular Acupressure Combined with Self-Help Intervention for Treating Chronic Tinnitus: A Longitudinal Observational Study. Journal of Clinical Medicine [Internet]. 2021;10(18):4201. Available from: https://www.mdpi.com/2077-0383/10/18/4201
  • 1.
    Winter M, Bredemeyer C, Reichert M, Neumann H, Probst T, Pryss R. How Healthcare Professionals Comprehend Process Models - An Empirical Eye Tracking Analysis. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). 2021. pp. 313-8.
  • 1.
    Pryss R, Langguth B, Probst T, Schlee W, Spiliopoulou M, Reichert M. Editorial: Smart Mobile Data Collection in the Context of Neuroscience. Frontiers in Neuroscience [Internet]. 2021;15. Available from: https://www.frontiersin.org/articles/10.3389/fnins.2021.698597
  • 1.
    Mehdi M, Diemer F, Hennig L, Dode A, Pryss R, Schlee W, et al. TinnituSense: a Mobile Electroencephalography (EEG) Smartphone App for Tinnitus Research [Internet]. Association for Computing Machinery; 2021. pp. 252–261. Available from: https://doi.org/10.1145/3448891.3448933
  • 1.
    Winter M, Pryss R, Probst T, Reichert M. Applying Eye Movement Modeling Examples to Guide Novices’ Attention in the Comprehension of Process Models. Brain Sciences [Internet]. 2021;11(1):72. Available from: https://www.mdpi.com/2076-3425/11/1/72
  • 1.
    Allgaier J, Neff P, Schlee W, Schoisswohl S, Pryss R. Deep Learning End-to-End Approach for the Prediction of Tinnitus based on EEG Data. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. pp. 816-9.
  • 1.
    Weierstall R, Crombach A, Nandi C, Bambonyé M, Probst T, Pryss R. Effective Adoption of Tablets for Psychodiagnostic Assessments in Rural Burundi: Evidence for the Usability and Validity of Mobile Technology in the Example of Differentiating Symptom Profiles in AMISOM Soldiers 1 Year After Deployment. Front Public Health. 2021;9:490604.
  • 1.
    Vogel C, Pryss R, Schobel J, Schlee W, Beierle F. Developing apps for researching the covid-19 pandemic with the trackyourhealth platform. In: 2021 IEEE/ACM 8th International Conference on Mobile Software Engineering and Systems (MobileSoft). IEEE; 2021. pp. 65-8.
  • 1.
    Wetzel B, Pryss R, Baumeister H, Edler JS, Gonçalves ASO, Cohrdes C. “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. International Journal of Environmental Research and Public Health [Internet]. 2021;18(12):6212. Available from: https://www.mdpi.com/1660-4601/18/12/6212
  • 1.
    Winter M, Pryss R, Fink M, Reichert M. Towards Measuring and Quantifying the Comprehensibility of Process Models - The Process Model Comprehension Framework. ArXiv. 2021;abs/2106.12880.
  • 1.
    Vogel C, Schobel J, Schlee W, Engelke M, Pryss R. UNITI Mobile—EMI-Apps for a Large-Scale European Study on Tinnitus. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. pp. 2358-62.
  • 1.
    Unnikrishnan V, Shah Y, Schleicher M, Fernández-Viadero C, Strandzheva M, Velikova D, et al. Love thy Neighbours: A Framework for Error-Driven Discovery of Useful Neighbourhoods for One-Step Forecasts on EMA data. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). 2021. pp. 295-300.
  • 1.
    Winter M, Baumeister H, Frick U, Tallon M, Reichert M, Pryss R. Exploring the Usability of the German COVID-19 Contact Tracing App in a Combined Eye Tracking and Retrospective Think Aloud Study. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. pp. 2215-21.
  • 1.
    Baumeister H, Bauereiss N, Zarski AC, Braun L, Buntrock C, Hoherz C, et al. Clinical and Cost-Effectiveness of PSYCHOnlineTHERAPY: Study Protocol of a Multicenter Blended Outpatient Psychotherapy Cluster Randomized Controlled Trial for Patients With Depressive and Anxiety Disorders. Frontiers in Psychiatry [Internet]. 2021;12. Available from: https://www.frontiersin.org/articles/10.3389/fpsyt.2021.660534
  • 1.
    Beierle F, Dhakal U, Cohrdes C, Eicher S, Pryss R. Public perception of the German COVID-19 contact-tracing app corona-warn-app. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). IEEE; 2021. pp. 342-7.
  • 1.
    Paganini S, Terhorst Y, Sander LB, Catic S, Balci S, Küchler AM, et al. Quality of Physical Activity Apps: Systematic Search in App Stores and Content Analysis. JMIR Mhealth Uhealth [Internet]. 2021;9(6):e22587. Available from: https://mhealth.jmir.org/2021/6/e22587https://doi.org/10.2196/22587http://www.ncbi.nlm.nih.gov/pubmed/34106073
  • 1.
    Kammerer K, Göster M, Reichert M, Pryss R. Ambalytics: A Scalable and Distributed System Architecture Concept for Bibliometric Network Analyses. Future Internet [Internet]. 2021;13(8):203. Available from: https://www.mdpi.com/1999-5903/13/8/203
  • 1.
    Prakash S, Unnikrishnan V, Pryss R, Kraft R, Schobel J, Hannemann R, et al. Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users. Entropy [Internet]. 2021;23(12):1695. Available from: https://www.mdpi.com/1099-4300/23/12/1695
  • 1.
    Muzoora MR, El-Badawi N, Elsner C, Essenwanger A, Gocke P, Krefting D, et al. Motivating Developers to Use Interoperable Standards for Data in Pandemic Health Apps. Stud Health Technol Inform. 2021;281:1027-8.
  • 1.
    Mehdi M, Hennig L, Diemer F, Dode A, Pryss R, Schlee W, et al. Towards Mobile-Based Preprocessing Pipeline for Electroencephalography (EEG) Analyses: The Case of Tinnitus. In: Ye J, O’Grady MJ, Civitarese G, Yordanova K, editors. Wireless Mobile Communication and Healthcare. Springer International Publishing; 2021. pp. 67-86.
  • 1.
    Kraft R, Idrees AR, Stenzel L, Nguyen T, Reichert M, Pryss R, et al. eSano – An eHealth Platform for Internet- and Mobile-based Interventions. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. pp. 1997-2002.
  • 1.
    Jamaludeen N, Unnikrishnan V, Pryss R, Schobel J, Schlee W, Spiliopoulou M. Circadian Conditional Granger Causalities on Ecological Momentary Assessment Data from an mHealth App. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). 2021. pp. 354-9.
  • 1.
    Idrees AR, Kraft R, Pryss R, Reichert M, Baumeister H. Literature-based requirements analysis review of persuasive systems design for mental health applications. Procedia Computer Science [Internet]. 2021;191:143-50. Available from: https://www.sciencedirect.com/science/article/pii/S1877050921014137
  • 1.
    Holfelder M, Mulansky L, Schlee W, Baumeister H, Schobel J, Greger H, et al. Medical Device Regulation: Efforts for mHealth Apps during the COVID-19 Pandemic—An Experience Report of Corona Check and Corona Health. J [Internet]. 2021;4(2):206-22. Available from: https://www.mdpi.com/2571-8800/4/2/17
  • 1.
    Allgaier J, Schlee W, Langguth B, Probst T, Pryss R. Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform. Scientific Reports [Internet]. 2021;11(1):18375. Available from: https://doi.org/10.1038/s41598-021-96731-8
  • 1.
    Beierle F, Schobel J, Vogel C, Allgaier J, Mulansky L, Haug F, et al. Corona Health—A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic. International Journal of Environmental Research and Public Health. 2021;18(14):7395.
  • 1.
    Gallik F, Winter M, Kirikkayis Y, Pryss R, Reichert M. DyVProMo - A Lightweight Web-Based Tool for the Dynamic Visualization of Additional Information in Business Process Models. In: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW). 2021. pp. 345-8.
  • 1.
    Fleischer A, Heimeshoff L, Allgaier J, Jordan K, Gelbrich G, Pryss R, et al. Is PFS the Right Endpoint to Assess Outcome of Maintenance Studies in Multiple Myeloma? Results of a Patient Survey Highlight Quality-of-Life As an Equally Important Outcome Measure. Blood [Internet]. 2021;138:836. Available from: https://www.sciencedirect.com/science/article/pii/S000649712102824X
  • 1.
    Dode A, Mehdi M, Pryss R, Schlee W, Probst T, Reichert M, et al. Using a visual analog scale (VAS) to measure tinnitus-related distress and loudness: Investigating correlations using the Mini-TQ results of participants from the TrackYourTinnitus platform. Progress in brain research [Internet]. 2021;263:171-90. Available from: http://europepmc.org/abstract/MED/34243888https://doi.org/10.1016/bs.pbr.2020.08.008
  • 1.
    Eicher S, Pryss R, Baumeister H, Hövener C, Knoll N, Cohrdes C. Quality of life during the COVID-19 pandemic–Results of the CORONA HEALTH App study. Journal of Health Monitoring. 2021;6(Suppl 6):2-21.
  • 1.
    Budimir S, Kuska M, Spiliopoulou M, Schlee W, Pryss R, Andersson G, et al. Reasons for Discontinuing Active Participation on the Internet Forum Tinnitus Talk: Mixed Methods Citizen Science Study. JMIR Form Res [Internet]. 2021;5(4):e21444. Available from: https://formative.jmir.org/2021/4/e21444https://doi.org/10.2196/21444http://www.ncbi.nlm.nih.gov/pubmed/33830060
  • 1.
    Dode A, Genitsaridi E, Qirjazi B, Pryss R, Probst T, Reichert M, et al. Tinnitus Profiling in an Albanian Population (Preprint). 2021.
  • 1.
    Shahania S, Unnikrishnan V, Pryss R, Kraft R, Schobel J, Hannemann R, et al. User-centric vs whole-stream learning for EMA prediction. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). 2021. pp. 307-12.
2020[ to top ]
  • 1.
    Schickler M, Reichert M, Geiger P, Winkler J, Funk T, Weilbach M, et al. Flexible development of location-based mobile augmented reality applications with AREA. Journal of Ambient Intelligence and Humanized Computing [Internet]. 2020;11(12):5809-24. Available from: https://doi.org/10.1007/s12652-020-02094-9
  • 1.
    Kraft R, Birk F, Reichert M, Deshpande A, Schlee W, Langguth B, et al. Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform. Sensors [Internet]. 2020;20(12):3456. Available from: https://www.mdpi.com/1424-8220/20/12/3456
  • 1.
    Mehdi M, Dode A, Pryss R, Schlee W, Reichert M, Hauck FJ. Contemporary Review of Smartphone Apps for Tinnitus Management and Treatment. Brain Sciences [Internet]. 2020;10(11):867. Available from: https://www.mdpi.com/2076-3425/10/11/867
  • 1.
    Kraft R, Stach M, Reichert M, Schlee W, Probst T, Langguth B, et al. Comprehensive insights into the TrackYourTinnitus database. Procedia Computer Science [Internet]. 2020;175:28-35. Available from: https://www.sciencedirect.com/science/article/pii/S1877050920316872
  • 1.
    Kraft R, Schlee W, Stach M, Reichert M, Langguth B, Baumeister H, et al. Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain. Frontiers in Neuroscience [Internet]. 2020;14. Available from: https://www.frontiersin.org/articles/10.3389/fnins.2020.00164
  • 1.
    Kammerer K, Pryss R, Reichert M. Context-Aware Querying and Injection of Process Fragments in Process-Aware Information Systems. In: 2020 IEEE 24th International Enterprise Distributed Object Computing Conference (EDOC). 2020. pp. 107-14.
  • 1.
    Humer E, Stippl P, Pieh C, Pryss R, Probst T. Experiences of Psychotherapists With Remote Psychotherapy During the COVID-19 Pandemic: Cross-sectional Web-Based Survey Study. J Med Internet Res [Internet]. 2020;22(11):e20246. Available from: http://www.jmir.org/2020/11/e20246/https://doi.org/10.2196/20246http://www.ncbi.nlm.nih.gov/pubmed/33151896
  • 1.
    O’Rourke T, Pryss R, Schlee W, Probst T. Development of a multidimensional app-quality assessment tool for health-related apps (AQUA). Digital Psychology. 2020;1(2):13-2.
  • 1.
    Humer E, Stippl P, Pieh C, Pryss R, Probst T. Psychodynamic, humanistic, systemic, and behavioral psychotherapists’ experiences with remote psychotherapy during COVID-19: Better than expected but not totally comparable to face-to-face psychotherapy in personal contact (Preprint). 2020.
  • 1.
    Hoppenstedt B, Probst T, Reichert M, Schlee W, Kammerer K, Spiliopoulou M, et al. Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios. J. Vis. Exp. 2020;164:e61349.
  • 1.
    Beierle F, Probst T, Allemand M, Zimmermann J, Pryss R, Neff P, et al. Frequency and duration of daily smartphone usage in relation to personality traits. Digital Psychology. 2020;1(1):20-8.
  • 1.
    Beierle F, Tran VT, Allemand M, Neff P, Schlee W, Probst T, et al. What data are smartphone users willing to share with researchers?: Designing and evaluating a privacy model for mobile data collection apps. Journal of Ambient Intelligence and Humanized Computing. 2020;11:2277–2289.
  • 1.
    Mehdi M, Stach M, Riha C, Neff P, Dode A, Pryss R, et al. Smartphone and Mobile Health Apps for Tinnitus: Systematic Identification, Analysis, and Assessment. JMIR Mhealth Uhealth [Internet]. 2020;8(8):e21767. Available from: http://mhealth.jmir.org/2020/8/e21767/https://doi.org/10.2196/21767http://www.ncbi.nlm.nih.gov/pubmed/32808939
  • 1.
    Messner EM, Terhorst Y, Barke A, Baumeister H, Stoyanov S, Hides L, et al. The German Version of the Mobile App Rating Scale (MARS-G): Development and Validation Study. JMIR Mhealth Uhealth. 2020;8(3):e14479.
  • 1.
    Mehdi M, Riha C, Neff P, Dode A, Pryss R, Schlee W, et al. Smartphone Apps in the Context of Tinnitus: Systematic Review. Sensors [Internet]. 2020;20(6):1725. Available from: https://www.mdpi.com/1424-8220/20/6/1725
  • 1.
    Schobel J, Probst T, Reichert M, Schlee W, Schickler M, Kestler HA, et al. Measuring Mental Effort for Creating Mobile Data Collection Applications. International Journal of Environmental Research and Public Health [Internet]. 2020;17(5):1649. Available from: https://www.mdpi.com/1660-4601/17/5/1649
  • 1.
    Nandi C, Crombach A, Elbert T, Bambonye M, Pryss R, Schobel J, et al. The cycle of violence as a function of PTSD and appetitive aggression: A longitudinal study with Burundian soldiers. Aggressive Behavior [Internet]. 2020;46(5):391-9. Available from: https://doi.org/10.1002/ab.21895
  • 1.
    Winter M, Pryss R, Probst T, Reichert M. Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study. Sensors [Internet]. 2020;20(16):4561. Available from: https://www.mdpi.com/1424-8220/20/16/4561
  • 1.
    Unnikrishnan V, Shah Y, Schleicher M, Strandzheva M, Dimitrov P, Velikova D, et al. Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?. In: Appice A, Tsoumakas G, Manolopoulos Y, Matwin S, editors. Discovery Science. Springer International Publishing; 2020. pp. 659-73.
  • 1.
    Unnikrishnan V, Schleicher M, Shah Y, Jamaludeen N, Pryss R, Schobel J, et al. The Effect of Non-Personalised Tips on the Continued Use of Self-Monitoring mHealth Applications. Brain Sciences [Internet]. 2020;10(12):924. Available from: https://www.mdpi.com/2076-3425/10/12/924
  • 1.
    Unnikrishnan V, Beyer C, Matuszyk P, Niemann U, Pryss R, Schlee W, et al. Entity-level stream classification: exploiting entity similarity to label the future observations referring to an entity. International Journal of Data Science and Analytics [Internet]. 2020;9(1):1-15. Available from: https://doi.org/10.1007/s41060-019-00177-1
  • 1.
    Stach M, Kraft R, Probst T, Messner EM, Terhorst Y, Baumeister H, et al. Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). 2020. pp. 427-32.
  • 1.
    Sander LB, Schorndanner J, Terhorst Y, Spanhel K, Pryss R, Baumeister H, et al. ‘Help for trauma from the app stores?’ A systematic review and standardised rating of apps for Post-Traumatic Stress Disorder (PTSD). European Journal of Psychotraumatology [Internet]. 2020;11(1):1701788. Available from: https://doi.org/10.1080/20008198.2019.1701788
  • 1.
    Stach M, Vogel C, Gablonski TC, Andreas S, Probst T, Reichert M, et al. Technical Challenges of a Mobile Application Supporting Intersession Processes in Psychotherapy. Procedia Computer Science [Internet]. 2020;175:261-8. Available from: https://www.sciencedirect.com/science/article/pii/S187705092031718X
  • 1.
    Schleicher M, Unnikrishnan V, Neff P, Simoes J, Probst T, Pryss R, et al. Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring. Scientific Reports [Internet]. 2020;10(1):22459. Available from: https://doi.org/10.1038/s41598-020-79527-0
  • 1.
    Pryss R, Schlee W, Hoppenstedt B, Reichert M, Spiliopoulou M, Langguth B, et al. 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. J Med Internet Res [Internet]. 2020;22(6):e15547. Available from: http://www.jmir.org/2020/6/e15547/https://doi.org/10.2196/15547http://www.ncbi.nlm.nih.gov/pubmed/32602842
  • 1.
    Kammerer K, Pryss R, Hoppenstedt B, Sommer K, Reichert M. Process-Driven and Flow-Based Processing of Industrial Sensor Data. Sensors [Internet]. 2020;20(18):5245. Available from: https://www.mdpi.com/1424-8220/20/18/5245
2019[ to top ]
  • 1.
    Schickler M, Reichert M, Geiger P, Weilbach M, Pryss R. The AREA Algorithm Framework Enabling Location-based Mobile Augmented Reality Applications. Procedia Computer Science [Internet]. 2019;155:193-200. Available from: https://www.sciencedirect.com/science/article/pii/S1877050919309433
  • 1.
    Tallon M, Winter M, Pryss R, Rakoczy K, Reichert M, Greenlee MW, et al. Comprehension of business process models: Insight into cognitive strategies via eye tracking. Expert Systems with Applications [Internet]. 2019;136:145-58. Available from: https://www.sciencedirect.com/science/article/pii/S0957417419304324
  • 1.
    Pryss R, Probst T, Schlee W, Schobel J, Langguth B, Neff P, et al. Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus–stress associations based on the TrackYourTinnitus mobile platform. International Journal of Data Science and Analytics [Internet]. 2019;8(4):327-38. Available from: https://doi.org/10.1007/s41060-018-0111-4
  • 1.
    Pryss R, John D, Schlee W, Schlotz W, Schobel J, Kraft R, et al. Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessment Pilot Study. JMIR Mhealth Uhealth. 2019;7(10):e13978.
  • 1.
    Schobel J, Probst T, Reichert M, Schickler M, Pryss R. Enabling Sophisticated Lifecycle Support for Mobile Healthcare Data Collection Applications. IEEE Access. 2019;7:61204-17.
  • 1.
    Hoppenstedt B, Reichert M, Kammerer K, Probst T, Schlee W, Spiliopoulou M, et al. Dimensionality Reduction and Subspace Clustering in Mixed Reality for Condition Monitoring of High-Dimensional Production Data. Sensors [Internet]. 2019;19(18):3903. Available from: https://www.mdpi.com/1424-8220/19/18/3903
  • 1.
    Kammerer K, Hoppenstedt B, Pryss R, Stökler S, Allgaier J, Reichert M. Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings. Sensors [Internet]. 2019;19(24):5370. Available from: https://www.mdpi.com/1424-8220/19/24/5370
  • 1.
    Hendrikoff L, Kambeitz-Ilankovic L, Pryss R, Senner F, Falkai P, Pogarell O, et al. Prospective acceptance of distinct mobile mental health features in psychiatric patients and mental health professionals. Journal of Psychiatric Research [Internet]. 2019;109:126-32. Available from: https://www.sciencedirect.com/science/article/pii/S0022395618312482
  • 1.
    Hoppenstedt B, Probst T, Reichert M, Schlee W, Kammerer K, Spiliopoulou M, et al. Applicability of Immersive Analytics in Mixed Reality: Usability Study. IEEE Access. 2019;7:71921-32.
  • 1.
    Genitsaridi E, Partyka M, Gallus S, Lopez-Escamez JA, Schecklmann M, Mielczarek M, et al. Standardised profiling for tinnitus research: The European School for Interdisciplinary Tinnitus Research Screening Questionnaire (ESIT-SQ). Hearing Research [Internet]. 2019;377:353-9. Available from: https://www.sciencedirect.com/science/article/pii/S0378595518304684
  • 1.
    Cederroth CR, Gallus S, Hall DA, Kleinjung T, Langguth B, Maruotti A, et al. Editorial: Towards an Understanding of Tinnitus Heterogeneity. Frontiers in Aging Neuroscience [Internet]. 2019;11. Available from: https://www.frontiersin.org/articles/10.3389/fnagi.2019.00053
  • 1.
    Gablonski TC, Pryss R, Probst T, Vogel C, Andreas S. Intersession-Online: A Smartphone Application for Systematic Recording and Controlling of Intersession Experiences in Psychotherapy. Vols. 2. 2019. pp. 480-95.