Towards precision psychotherapy: Leveraging session-level data to predict session-by-session treatment outcomes
Year: 2026
Type: preprint
Abstract: Theoretical Background: Clinical support tools can potentially enhance cognitive behavioral therapy (CBT) by providing session-by-session treatment recommendations. To enable such tools, prediction models are needed that can accurately track therapy outcomes based on assessable session data. This study developed models that predict session-by-session mental health levels and identified variables that influence these predictions.Methods: We analyzed data from a monocentric, naturalistic therapy-process study of outpatients with affective, anxiety, and psychotic disorders (n=325) participating in up to 24 weekly sessions of CBT in routine care in Germany. Within a nested cross-validation approach, we used mixed-effect random forest models to predict session-by-session scores on a brief self-report measure of mental health (i.e., symptoms, mood, distress, functioning) assessed in the next session. Included predictors were baseline psychopathology, type of CBT interventions conducted in the prior session, and patient- and therapist-reported mechanisms of change (e.g., cognitive change, self-understanding, coping) assessed in the prior session. Variable importance was examined using SHAP values.Results: The mixed-effects random forest predicting next-session outcome achieved an R²=0.25 across the test folds. The most influential predictors were baseline psychopathology and prior session self-reported behavioral mechanisms of change, i.e., engagement in meaningful/positive activities and coping.Conclusion: Session-by-session prediction of mental health outcomes using routine outcome monitoring data yielded promising predictive accuracy. Self-reported mechanisms of change are particularly valuable for predicting session-by-session mental health levels. These findings may suggest that activating behavioral mechanisms of change in therapy may have a stronger impact on short-term mental health than activating cognitive mechanisms of change.
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7223012498