Dr. Robin Kraft

• [ 2025 ]
• [ 2024 ]
• [ 2023 ]
• [ 2022 ]
• [ 2021 ]
• [ 2020 ]

• 1.

  Kraft R, Langguth B, Simoes J, Reichert M, Schlee W, Pryss R. Global 10 year ecological momentary assessment and mobile sensing study on tinnitus and environmental sounds. NPJ Digit Med. 2025;8(1):162.
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• 1.

  Vogel C, Kraft R, Antunes Lima R, Idrees AR, Steiger P, Pryss R. Extending a Highly Configurable EMA and JITAI Mobile App Framework with Passive Sensing, Gamification, and AI Features for a Large-Scale Physical Activity and Nutrition Study. In: Arabnia HR, Deligiannidis L, Shenavarmasouleh F, Amirian S, Ghareh Mohammadi F, editors. Computational Science and Computational Intelligence. Springer Nature Switzerland; 2025. pp. 26-41.

• 1.

  Idrees AR, Beierle F, Mutter A, Kraft R, Garatva P, Baumeister H, Reichert M, Pryss R. Engagement analysis of a persuasive-design-optimized eHealth intervention through machine learning. Sci Rep. 2024;14(1):21427.
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• 1.

  Idrees AR, Kraft R, Mutter A, Baumeister H, Reichert M, Pryss R. Persuasive technologies design for mental and behavioral health platforms: A scoping literature review. PLOS Digit Health. 2024;3(5):e0000498.
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• 1.

  Kraft R, Reichert M, Pryss R. Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis. Sensors (Basel). 2024;24(2).
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• 1.

  Breitmayer M, Stach M, Kraft R, Allgaier J, Reichert M, Schlee W, Probst T, Langguth B, Pryss R. 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
  • [ DOI ]
• 1.

  Idrees AR, Kraft R, Winter M, Küchler AM, Baumeister H, Reilly R, Reichert M, Pryss R. Exploring the usability of an internet-based intervention and its providing eHealth platform in an eye-tracking study. J Ambient Intell Humaniz Comput. 2023;14(7):9621-36.
  • [ DOI ]

• 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.

  Shahania S, Unnikrishnan V, Pryss R, Kraft R, Schobel J, Hannemann R, Schlee W, Spiliopoulou M. 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.
  • [ DOI ]
• 1.

  Idrees AR, Kraft R, Pryss R, Reichert M, Nguyen T, Stenzel L, Baumeister H. 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.
  • [ DOI ]
• 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.
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• 1.

  Shahania S, Unnikrishnan V, Pryss R, Kraft R, Schobel J, Hannemann R, Schlee W, Spiliopoulou M. 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.
  • [ DOI ]
• 1.

  Kraft R, Idrees AR, Stenzel L, Nguyen T, Reichert M, Pryss R, Baumeister H. 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.
  • [ DOI ]
• 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
  • [ DOI ]
• 1.

  Prakash S, Unnikrishnan V, Pryss R, Kraft R, Schobel J, Hannemann R, Langguth B, Schlee W, Spiliopoulou M. 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.

  Kraft R, Birk F, Reichert M, Deshpande A, Schlee W, Langguth B, Baumeister H, Probst T, Spiliopoulou M, Pryss R. 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.

  Unnikrishnan V, Schleicher M, Shah Y, Jamaludeen N, Pryss R, Schobel J, Kraft R, Schlee W, Spiliopoulou M. 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
  • [ DOI ]
• 1.

  Stach M, Kraft R, Probst T, Messner EM, Terhorst Y, Baumeister H, Schickler M, Reichert M, Sander LB, Pryss R. 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.
  • [ DOI ]
• 1.

  Kraft R, Schlee W, Stach M, Reichert M, Langguth B, Baumeister H, Probst T, Hannemann R, Pryss R. 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
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