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

• Published in: IEEE transactions on affective computing pp. 1 - 12
• Main Authors: Toner, Emma R., Rucker, Mark, Wang, Zhiyuan, Larrazabal, Maria A., Cai, Lihua, Datta, Debajyoti, Lone, Haroon, Boukhechba, Mehdi, Teachman, Bethany A., Barnes, Laura E.
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Affective computing; Anxiety disorders; context awareness; Feature extraction; Medical diagnostic imaging; Mental health; Oral communication; physiological responses; Physiology; Psychology; Skin; Wearable sensors
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2025.3562787

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 vs. with others), social phases (i.e., pre- and post-interaction vs. during an interaction), social interaction sizes (i.e., dyadic vs. group interactions), and levels of social threat (i.e., implicit vs. explicit social evaluation). Participants in the study (<inline-formula><tex-math notation="LaTeX">N=46</tex-math></inline-formula>) reported moderate to severe social anxiety symptoms as assessed by the Social Interaction Anxiety Scale (<inline-formula><tex-math notation="LaTeX">\geq</tex-math></inline-formula>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.