Withdrawal ruptures in adolescents with borderline personality disorder psychotherapy are marked by increased speech pauses-can minimal responses be automatically detected?

Christophe Künsch; Lukas Fürer; Martin Steppan; Nathalie Schenk; Kathrin Blum; Michael Kaess; Julian Koenig; Klaus Schmeck; Ronan Zimmermann

doi:10.1371/journal.pone.0280329

Withdrawal ruptures in adolescents with borderline personality disorder psychotherapy are marked by increased speech pauses-can minimal responses be automatically detected?

PLoS One. 2023 Jan 17;18(1):e0280329. doi: 10.1371/journal.pone.0280329. eCollection 2023.

Authors

Christophe Künsch¹, Lukas Fürer², Martin Steppan², Nathalie Schenk², Kathrin Blum¹, Michael Kaess^{3

4}, Julian Koenig^{4

5}, Klaus Schmeck², Ronan Zimmermann^{1

2}

Affiliations

¹ Faculty of Psychology, University of Basel, Basel, Switzerland.
² Child and Adolescent Psychiatric Research Department, Psychiatric University Hospital Basel, Basel, Switzerland.
³ Department of Child and Adolescent Psychiatry, Centre of Psychosocial Medicine, University of Heidelberg, Heidelberg, Germany.
⁴ University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
⁵ Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.

Abstract

Alliance ruptures of the withdrawal type are prevalent in adolescents with borderline personality disorder (BPD). Longer speech pauses are negatively perceived by these patients. Safran and Muran's rupture model is promising but its application is very work intensive. This workload makes research costly and limits clinical usage. We hypothesised that pauses can be used to automatically detect one of the markers of the rupture model i.e. the minimal response marker. Additionally, the association of withdrawal ruptures with pauses was investigated. A total of 516 ruptures occurring in 242 psychotherapy sessions collected in 22 psychotherapies of adolescent patients with BPD and subthreshold BPD were investigated. Trained observers detected ruptures based on video and audio recordings. In contrast, pauses were automatically marked in the audio-recordings of the psychotherapy sessions and automatic speaker diarisation was used to determine the speaker-switching patterns in which the pauses occur. A random forest classifier detected time frames in which ruptures with the minimal response marker occurred based on the quantity of pauses. Performance was very good with an area under the ROC curve of 0.89. Pauses which were both preceded and followed by therapist speech were the most important predictors for minimal response ruptures. Research costs can be reduced by using machine learning techniques instead of manual rating for rupture detection. In combination with other video and audio derived features like movement analysis or automatic facial emotion detection, more complete rupture detection might be possible in the future. These innovative machine learning techniques help to narrow down the mechanisms of change of psychotherapy, here specifically of the therapeutic alliance. They might also be used to technologically augment psychotherapy training and supervision.

Copyright: © 2023 Künsch et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Borderline Personality Disorder* / psychology
Borderline Personality Disorder* / therapy
Emotions
Humans
Psychotherapy / methods
Speech
Therapeutic Alliance*

Grants and funding

The study was funded by the University of Basel (https://www.unibas.ch/en.html) and the Psychiatric University Clinics of the University of Basel (https://www.upk.ch/en/home.html). Additionally, we would like to thank the 'Thalmann-Stiftung'' (https://www.fundraiso.ch/sponsor/thalmann-stiftung) and the 'Stiftung zur Förderung von Psychiatrie und Psychotherapie' (https://stiftung-fpp.ch) for the financial support. Finally, we want to thank the Open access funding provided by the University of Basel (https://www.oa-fund.unibas.ch/de/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.