Wearable Sensor-Based Multimodal Physiological Responses of Socially Anxious Individuals in Social Contexts on Zoom

IF 9.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Affective Computing Pub Date : 2025-04-21 DOI:10.1109/TAFFC.2025.3562787
Emma R. Toner;Mark Rucker;Zhiyuan Wang;Maria A. Larrazabal;Lihua Cai;Debajyoti Datta;Haroon Lone;Mehdi Boukhechba;Bethany A. Teachman;Laura E. Barnes
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Abstract

Correctly identifying an individual’s social context from passively worn sensors holds promise for delivering just-in-time adaptive interventions (JITAIs) to treat social anxiety. In this study, we present results using passively collected data from a within-subjects experiment that assessed physiological responses across different social contexts (i.e., alone versus with others), social phases (i.e., pre- and post-interaction versus during an interaction), social interaction sizes (i.e., dyadic versus group interactions), and levels of social threat (i.e., implicit versus explicit social evaluation). Participants in the study ($N=46$) reported moderate to severe social anxiety symptoms as assessed by the Social Interaction Anxiety Scale ($\geq$34 out of 80). Univariate paired difference tests, multivariate random forest models, and cluster analyses were used to explore physiological response patterns across different social and non-social contexts. Our results suggest that social context is more reliably distinguishable than social phase, group size, or level of social threat, and that there is considerable variability in physiological response patterns even among distinguishable contexts. Implications for real-world context detection and future deployment of JITAIs are discussed.
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基于可穿戴传感器的Zoom社交情境下社交焦虑个体的多模态生理反应
从被动佩戴的传感器中正确识别个人的社会背景,有望提供及时的适应性干预(JITAIs)来治疗社交焦虑。在这项研究中,我们使用被动式的实验数据来评估不同社会背景(即单独与他人)、社会阶段(即互动前后与互动期间)、社会互动规模(即二元互动与群体互动)和社会威胁水平(即内隐与外显社会评价)的生理反应。研究参与者($N=46$)报告了中度至重度社交焦虑症状,并通过社交互动焦虑量表($\geq$ 34 / 80)进行了评估。采用单变量配对差异检验、多变量随机森林模型和聚类分析探讨了不同社会和非社会环境下的生理反应模式。我们的研究结果表明,社会环境比社会阶段、群体规模或社会威胁水平更可靠地可区分,并且即使在可区分的环境中,生理反应模式也存在相当大的差异。讨论了jitai对现实环境检测和未来部署的影响。
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来源期刊
IEEE Transactions on Affective Computing
IEEE Transactions on Affective Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
15.00
自引率
6.20%
发文量
174
期刊介绍: The IEEE Transactions on Affective Computing is an international and interdisciplinary journal. Its primary goal is to share research findings on the development of systems capable of recognizing, interpreting, and simulating human emotions and related affective phenomena. The journal publishes original research on the underlying principles and theories that explain how and why affective factors shape human-technology interactions. It also focuses on how techniques for sensing and simulating affect can enhance our understanding of human emotions and processes. Additionally, the journal explores the design, implementation, and evaluation of systems that prioritize the consideration of affect in their usability. We also welcome surveys of existing work that provide new perspectives on the historical and future directions of this field.
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