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

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 (<i>N</i> = 46) reported moderate to severe social anxiety symptoms as assessed by the Social Interaction Anxiety Scale (≥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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Publication Details

Journal
IEEE Transactions on Affective Computing
Published
2025-04-21
DOI
https://doi.org/10.1109/taffc.2025.3562787
Citations
3
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
6.35
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article

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

Laura E. Barnes, Maria A. Larrazabal, Mark Rucker, Emma R. Toner et al.
3 citations
IEEE Transactions on Affective Computing
Emotion and Mood Recognition
6.35
article

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

Laura E. Barnes, Maria A. Larrazabal, Mark Rucker, Emma R. Toner, Mehdi Boukhechba, Lihua Cai, Bethany Ann Teachman, Zhiyuan Wang, Haroon R. Lone, Debajyoti Datta
article en
3 citations

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 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 (<i>N</i> = 46) reported moderate to severe social anxiety symptoms as assessed by the Social Interaction Anxiety Scale (≥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.

IEEE Transactions on Affective ComputingVol. 16(3)
Johnson & Johnson (United States) (US), South China Normal University (CN), Indian Institute of Science Education and Research, Bhopal (IN), Janssen (United States) (US), University of Virginia (US)
Reduced inequalities
Openalex Percentile: Top 4%
Emotion and Mood Recognition
6.35
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