Faculty of Psychology, University of Basel, Basel, Switzerland.
Child and Adolescent Psychiatric Research Department, Psychiatric University Hospital Basel, Basel, Switzerland.
PLoS One. 2023 Jan 17;18(1):e0280329. doi: 10.1371/journal.pone.0280329. eCollection 2023.
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.
退出型联盟破裂在边缘型人格障碍 (BPD) 青少年中较为常见。这些患者对较长的言语停顿感知负面。Safran 和 Muran 的破裂模型很有前景，但应用非常耗费精力。这项工作强度使研究成本高昂，并限制了临床应用。我们假设停顿可以用于自动检测破裂模型的一个标记物，即最小反应标记物。此外，还研究了退出型破裂与停顿之间的关联。研究共调查了 22 例青少年 BPD 和阈下 BPD 患者的 242 次心理治疗中采集的 516 次破裂。受过训练的观察者根据视频和音频记录检测破裂。相比之下，停顿是在心理治疗的音频记录中自动标记的，并且使用自动说话人定序来确定停顿发生的说话人切换模式。随机森林分类器根据停顿的数量检测具有最小反应标记物的破裂发生的时间框架。性能非常好，ROC 曲线下的面积为 0.89。停顿之前和之后都有治疗师说话的停顿是最小反应破裂的最重要预测因子。通过使用机器学习技术代替手动评分进行破裂检测，可以降低研究成本。与其他源自视频和音频的特征（如运动分析或自动面部情绪检测）相结合，未来可能能够更完整地检测破裂。这些创新的机器学习技术有助于缩小心理治疗（特别是治疗联盟）变化的机制。它们也可以用于技术增强心理治疗培训和监督。
