Personalized Pychotherapy/Precision Mental Health
The research group is interested in the development and evaluation of patient-specific decision-making tools, which would support practitioners at the beginning and throughout the course of therapy. At the beginning of therapy, such tools can enable individualized predictions of the probability of therapeutic success and/or drop-out, or provide information about which interventions would be most promising for that specific individual. Machine-learning models based on large datasets are tested and information about intra-individual symptom dynamics at the individual-level (collected from patients while on the waitlist for psychotherapy) are used in order to generate such individualized case-specific predictions.
Throughout the course of treatment, these kinds of decision-making tools (based on these informative, short-form, repeated-measures patient surveys) can be used to support therapists in recognizing early warning signs for negative developments and help them to take appropriate precautions. The ultimate goal of identifying such warning signs is to reduce the number of patients who do not benefit from therapy.
Rubel, J. A., Zilcha-Mano, S., Giesemann, J., Prinz, J., & Lutz, W. (2019). Predicting personalized process-outcome associations in psychotherapy using machine learning approaches—A demonstration. Psychotherapy Research, 1-10. [LINK]
Lutz, W., Zimmermann, D., Müller, V. N., Deisenhofer, A. K., & Rubel, J. A. (2017). Randomized controlled trial to evaluate the effects of personalized prediction and adaptation tools on treatment outcome in outpatient psychotherapy: study protocol. BMC psychiatry, 17(1), 306. [PDF]
Bastiaansen, J. A., Kunkels, Y. K., Blaauw, F., Boker, S. M., Ceulemans, E., Chen, M., ... & Bringmann, L. F. (2019). Time to get personal? The impact of researchers’ choices on the selection of treatment targets using the experience sampling methodology. [PDF]
Deisenhofer, A. K., Delgadillo, J., Rubel, J. A., Böhnke, J. R., Zimmermann, D., Schwartz, B., & Lutz, W. (2018). Individual treatment selection for patients with posttraumatic stress disorder. Depression and anxiety, 35(6), 541-550. [LINK]
Fisher, A. J., Reeves, J. W., Lawyer, G., Medaglia, J. D., & Rubel, J. A. (2017). Exploring the idiographic dynamics of mood and anxiety via network analysis. Journal of abnormal psychology, 126(8), 1044.
Rubel, J. A., Fisher, A. J., Husen, K., & Lutz, W. (2018). Translating Person-Specific Network Models into Personalized Treatments: Development and Demonstration of the Dynamic Assessment Treatment Algorithm for Individual Networks (DATA-IN). Psychotherapy and psychosomatics, 87(4), 249.
Schiefele, A. K., Lutz, W., Barkham, M., Rubel, J., Böhnke, J., Delgadillo, J., ... & Lambert, M. J. (2017). Reliability of therapist effects in practice-based psychotherapy research: A guide for the planning of future studies. Administration and Policy in Mental Health and Mental Health Services Research, 44(5), 598-613. [LINK]
Lutz, W., Rubel, J., Schiefele, A. K., Zimmermann, D., Böhnke, J. R., & Wittmann, W. W. (2015). Feedback and therapist effects in the context of treatment outcome and treatment length. Psychotherapy Research, 25(6), 647-660. [LINK]
Zimmermann, D., Rubel, J., Page, A. C., & Lutz, W. (2017). Therapist effects on and predictors of non‐consensual dropout in psychotherapy. Clinical Psychology & Psychotherapy, 24(2), 312-321. [LINK]
Mechanisms of Change During Psychotherapy
Rubel, J. A., Rosenbaum, D., & Lutz, W. (2017). Patients' in-session experiences and symptom change: Session-to-session effects on a within-and between-patient level. Behaviour Research and Therapy, 90, 58-66. [LINK]
Falkenström, F., Finkel, S., Sandell, R., Rubel, J. A., & Holmqvist, R. (2017). Dynamic models of individual change in psychotherapy process research. Journal of Consulting and Clinical Psychology, 85(6), 537. [LINK]
Rubel, J. A., Bar-Kalifa, E., Atzil-Slonim, D., Schmidt, S., & Lutz, W. (2018). Congruence of therapeutic bond perceptions and its relation to treatment outcome: Within-and between-dyad effects. Journal of consulting and clinical psychology, 86(4), 341. [LINK]
Rubel, J. A., Zilcha-Mano, S., Feils-Klaus, V., & Lutz, W. (2018). Session-to-session effects of alliance ruptures in outpatient CBT: Within-and between-patient associations. Journal of consulting and clinical psychology, 86(4), 354. [LINK]