In the scope of project 178/12 - How collaboration in psychotherapy becomes therapeutic: a study of interactive and psychophysiological processes in good and poor outcome cases, supported by the BIAL Foundation, Eugénia Pereira and colleagues published the paper Data mining techniques in psychotherapy: applications for studying therapeutic alliance in the journal Scientific Reports. Applying data mining and Machine Learning (ML) techniques, the study aimed to identify the key factors or variables that significantly impact the strength of the therapeutic alliance (TA) between clients and psychotherapists. The results showed that the heart rate (HR) of the therapist was negatively associated with the therapist’s TA and the electrodermal activity (EDA) emerged as the most influential biological feature in the prediction of the TA, in the client but not in the therapist. Thus, the results from the ML algorithm document the differential importance of the physiological variables in the therapist and client (HR and EDA, respectively), for predicting TA, suggesting different experiences during therapy sessions for the dyad and with different underlying neurophysiological mechanisms.
ABSTRACT
Therapeutic Alliance (TA) has been consistently reported as a robust predictor of therapy outcomes and is one of the most investigated therapy relational factors. Research on therapists' and clients’ contributions to the alliance development and the alliance-outcome relationship had shown mixed results. The relation of the therapist’s and client’s biological markers with the alliance is an important and under-investigated topic. Taking advantage of data mining techniques, this exploratory study aimed to investigate the role of different therapist and client factors, including heart rate (HR) and electrodermal activity (EDA), in relation to TA. Twenty-two dyads with 6 therapists and 22 clients participated in the study. The Working Alliance Inventory (WAI) was used to evaluate the client’s and therapist's perception of the alliance at the end of each session and through the therapy processes. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to explore patterns that may contribute to TA. Machine Learning (ML) models have been employed to provide insights into the predictors and correlates of TA. Our results showed that Linear Regression (LR) was the best technique for predicting the therapist’s TA, with client “Diagnostic” and therapy “Termination” being identified as significant predictors of the therapist’s TA. In addition, for clients’ TA, the Random Forest (RF) was shown to have the best performance. The therapist’s TA and therapy “Outcome” were observed as the most influential predictors for the client’s TA. In addition, while the Heart Rate (therapist) was negatively associated with the therapist’s TA, EDA in the client was a physiological indicator related to the client’s TA. Overall, these findings can assist in identifying key factors that therapists should focus on to enhance the quality of therapeutic alliance. Results are discussed in terms of their consistency with empirical literature, innovative and interdisciplinary research on the therapeutic alliance field, and, in particular, the use of the Data Mining approach in a psychotherapy context.
