Univ.-Prof. Dr. Thomas Probst

Beierle, F.; Tran, V.T.; Allemand, M.; Neff, P.; Schlee, W.; Probst, T.; Zimmermann, J.; Pryss, R. (2019). What data are smartphone users willing to share with researchers? Journal of Ambient Intelligence and Humanized Computing, accepted: in press

Cederroth, C.; Gallus, S.; Hall, D.; Kleinjung, T.; Langguth, B.; Maruotti, A.; Meyer, M.; Norena A.; Probst, T.; Pryss, R.; Searchfield, G.; Shekhawat, G.; Spiliopoulou, M.; Vanneste, S.; Schlee, W. (2019). Editorial: Towards an understanding of tinnitus heterogeneity. Frontiers in Aging Neuroscience, 11: Article 53

Fiegl, S.; Lahmann, C.; O'Rourke, T.; Probst, T.; Pieh, C. (2019). Depression according to ICD-10 clinical interview vs. depression according to the Epidemiologic Studies Depression Scale to predict pain therapy outcomes. Frontiers in Psychology, 10: Article 1862: doi: 10.3389/fpsyg.2019.01862

Gmeinwieser, S.; Hagmayer, Y.; Pieh, C.; Probst, T. (2019). General change mechanisms in the early treatment phase and their associations with the outcome of Cognitive-Behavioral Therapy in patients with different levels of motivational incongruence. Clinical Psychology and Psychotherapy, accepted: in press

Gmeinwieser, S.; Kuhlencord, M.; Ruhl, U.; Hagmayer, Y.; Probst, T. (2019). Early developments in general change mechanisms predictreliable improvement in addition to early symptom trajectories in cognitive behavioral therapy. Psychotherapy Research, accepted: in press

Hoppenstedt, B.; Probst, T.; Reichert, M.; Schlee, W.; Kammerer, K.; Spiliopoulou, M.; Schobel, J.; Zimoch, M.; Felnhofer, A.; Kothgassner, O.; Pryss, R. (2019). Applicability of immersive analytics in mixed reality: Usability study. IEEE Access, 7: 71921-71932

Hoppenstedt, B.; Reichert, M.; Kammerer, K.; Probst, T.; Schlee, W.; Spiliopoulou, M.; Pryss, R. (2019). Dimensionality reduction and subspace clustering in mixed reality for condition monitoring of high-dimensional production data. Sensors, 19: Article 3903

Kleinstäuber, M.; Gottschalk, J.-M.; Ruckmann, J.; Probst, T.; Rief, W. (2019). Acceptance and cognitive reappraisal as regulation strategies for symptom annoyance in individuals with medically unexplained physical symptoms. Cognitive Therapy and Research, 43: 570–584

Odyniec, P.; Probst, T.; Göllner, R.; Margraf, J.; Willutzki, U. (2019). An exploratory study of patients’ sudden losses during outpatient CBT and therapists’ experience of difficulties. Journal of Clinical Psychology, Volume 75, Issue 10: 1790-1809

Odyniec, P.; Probst, T.; Margraf, J.; Willutzki, U. (2019). Psychotherapist trainees’ professional self-doubt and negative personal reaction: Changes during cognitive behavioral therapy and association with patient progress. Psychotherapy Research, 29: 123-138

Petrowski, K.; Berth, H.; Schurig, S.; Probst, T. (2019). Suppressor effects in associations between patient attachment to therapist and psychotherapy outcome. Clinical Psychology and Psychotherapy, 26: 105-109

Probst, T.; Baumeister, H.; McCracken, L. M.; Lin, J. (2019). Baseline psychological inflexibility moderates the outcome pain interference in a randomized controlled trial on Internet-based acceptance and commitment therapy for chronic pain. Journal of Clinical Medicine, 8: Article 24

Probst, T.; Jank, R.; Dreyer, N.; Seel, S.; Wagner, R.; Hanshans, K.; Reyersbach, R.; Mühlberger, A.; Lahmann, C.; Pieh, C. (2019). Early changes in pain acceptance predict pain outcomes in interdisciplinary treatment for chronic pain. Journal of Clinical Psychology, 8: Article 1373

Probst, T.; O’Rourke, T.; Decker, V.; Kießling, E.; Meyer, S.; Bofinger, C.; Niklewsk, G.; Mühlberger, A.; Pieh, C. (2019). Effectiveness of a 5-week inpatient dialectical behavior therapy for borderline personality disorder. Journal of Psychiatric Practice, 25: 192-198

Probst, T.; Weise, C.; Andersson, G.; Kleinstäuber, M. (2019). Differences in baseline and process variables between non-responders and responders in Internet-based cognitive behaviour therapy for chronic tinnitus. Cognitive Behaviour Therapy, 48: 52-64

Schobel, J.; Probst, T.; Reichert, M.; Schickler, M.; Pryss, R. (2019). Enabling sophisticated lifecycle support for mobile healthcare data collection applications. IEEE Access, 7: 61204-61217

Kraft, R.; Birk, F.; Reichert, M.; Deshpande, A.; Schlee, W.; Langguth, B.; Baumeister, H.; Probst, T.; Spiliopoulou, M.; Pryss, R. (2019). Design and implementation of a scalable crowdsensing platform for geospatial data of tinnitus patients. In: 32th IEEE International Symposium on Computer-Based Medical Systems; CBMS 2019: accepted, xx, xx

Pryss, R.; John, D.; Reichert, M.; Hoppenstedt, B.; Schmid, L.; Schlee, W.; Spiliopoulou, M.; Schobel, J.; Kraft, R.; Schickler, M.; Langguth, B.; Probst, T. (2019). Machine learning findings on geospatial data of users from the TrackYourStress mHealth crowdsensing platform. In: IEEE, 20th IEEE International Conference on Information Reuse and Integration for Data Science; IEE IRI 2019: 350-355, Curran Associates, Los Angeles, CA, USA

Pryss, R.; Schlee, W.; Reichert, M.; Kurthen, I.; Giroud, N.; Jagoda, L.; Neuschwander, P.; Meyer, M.; Neff, P.; Schobel, J.; Hoppenstedt, B.; Spiliopoulou, M.; Langguth, B.; Probst, T. (2019). Ecological momentary assessment based differences between Android and iOS users of the TrackYourHearing mHealth crowdsensing platform. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society; EMBC 2019: accepted, IEEE, Berlin

Zimoch, M.; Pryss, R.; Probst, T.; Schlee, W.; Reichert M. (2019). The repercussions of business process modeling notations on mental load and mental effort. In: Daniel F.; Sheng Q.; Motahari H., Business Process Management Workshops; BPM 2018. Lecture Notes in Business Information Processing, vol 342: 133-145, Springer, Cham