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

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 resear...

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Bibliographic Details
Published in:	PloS one Vol. 18; no. 1; p. e0280329
Main Authors:	Künsch, Christophe, Fürer, Lukas, Steppan, Martin, Schenk, Nathalie, Blum, Kathrin, Kaess, Michael, Koenig, Julian, Schmeck, Klaus, Zimmermann, Ronan
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 17-01-2023 Public Library of Science (PLoS)
Subjects:	Adolescent Adolescents Audio equipment Automation Behavior Borderline personality disorder Borderline Personality Disorder - psychology Borderline Personality Disorder - therapy Care and treatment Case studies Clinical medicine Complications and side effects Computer and Information Sciences Emotion recognition Emotions Engineering and Technology Humans Learning algorithms Machine learning Medicine and Health Sciences Mental disorders Mentally ill teenagers Methods Patient outcomes Patients Personality Personality disorders Psychological aspects Psychotherapy Psychotherapy - methods Rupture Social Sciences Speaking Speech Speech production Teenagers Therapeutic Alliance Therapists Therapy Voice recognition Withdrawal Switzerland
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0280329