Rubin Mikael, Hickson Robert, Suen Caitlyn, Vaishnav Shreya
Department of Psychology, Palo Alto University, Palo Alto, CA 94304, USA;
Department of Counseling, Palo Alto University, Palo Alto, CA 94304, USA;
J Eye Mov Res. 2025 Aug 12;18(4):36. doi: 10.3390/jemr18040036. eCollection 2025 Aug.
This empirical pilot study explored the use of wearable eye-tracking technology to gain objective insights into interpersonal interactions, particularly in healthcare provider training. Traditional methods of understanding these interactions rely on subjective observations, but wearable tech offers a more precise, multimodal approach. This multidisciplinary study integrated counseling perspectives on therapeutic alliance with an empirically motivated wearable framework informed by prior research in clinical psychology. The aims of the study were to describe the complex data that can be achieved with wearable technology and to test our primary hypothesis that the therapeutic alliance in clinical training interactions is associated with certain behaviors consistent with stronger interpersonal engagement. One key finding was that a single multimodal feature predicted discrepancies in client versus therapist working alliance ratings (b = -4.29, 95% CI [-8.12, -0.38]), suggesting clients may have perceived highly structured interactions as less personal than therapists did. Multimodal features were more strongly associated with therapist rated working alliance, whereas linguistic analysis better captured client rated working alliance. The preliminary findings support the utility of multimodal approaches to capture clinical interactions. This technology provides valuable context for developing actionable insights without burdening instructors or learners. Findings from this study will motivate data-driven methods for providing actionable feedback to clinical trainees.
这项实证性试点研究探索了可穿戴式眼动追踪技术在深入了解人际互动方面的应用，特别是在医疗保健提供者培训中。理解这些互动的传统方法依赖于主观观察，但可穿戴技术提供了一种更精确的多模态方法。这项多学科研究将治疗联盟的咨询观点与基于临床心理学先前研究的具有实证依据的可穿戴框架相结合。该研究的目的是描述通过可穿戴技术能够获得的复杂数据，并检验我们的主要假设，即临床培训互动中的治疗联盟与某些与更强人际参与度一致的行为相关。一个关键发现是，单一的多模态特征预测了客户与治疗师工作联盟评分的差异（b = -4.29，95%置信区间[-8.12, -0.38]），这表明客户可能比治疗师更认为高度结构化的互动缺乏人情味。多模态特征与治疗师评定的工作联盟关联更强，而语言分析能更好地捕捉客户评定的工作联盟。初步研究结果支持了多模态方法在捕捉临床互动方面的效用。这项技术为形成可采取行动的见解提供了有价值的背景信息，而不会给教师或学习者带来负担。本研究的结果将推动采用数据驱动的方法为临床实习生提供可采取行动的反馈。
