Understanding State Social Anxiety in Virtual Social Interactions using Multimodal Wearable Sensing Indicators
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908275949174784 |
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| author | Larrazabal, Maria A. Wang, Zhiyuan Rucker, Mark Toner, Emma R. Boukhechba, Mehdi Teachman, Bethany A. Barnes, Laura E. |
| author_facet | Larrazabal, Maria A. Wang, Zhiyuan Rucker, Mark Toner, Emma R. Boukhechba, Mehdi Teachman, Bethany A. Barnes, Laura E. |
| contents | Mobile sensing is ubiquitous and offers opportunities to gain insight into state mental health functioning. Detecting state elevations in social anxiety would be especially useful given this phenomenon is highly prevalent and impairing, but often not disclosed. Although anxiety is highly dynamic, fluctuating rapidly over the course of minutes, most work to date has examined anxiety at a scale of hours, days, or longer. In the present work, we explore the feasibility of detecting fluctuations in state social anxiety among N = 46 undergraduate students with elevated symptoms of trait social anxiety. Participants engaged in two dyadic and two group social interactions via Zoom. We evaluated participants' state anxiety levels as they anticipated, immediately after experiencing, and upon reflecting on each social interaction, spanning a time frame of 2-6 minutes. We collected biobehavioral features (i.e., PPG, EDA, skin temperature, and accelerometer) via Empatica E4 devices as they participated in the varied social contexts (e.g., dyadic vs. group; anticipating vs. experiencing the interaction; experiencing varying levels of social evaluation). We additionally measured their trait mental health functioning. Mixed-effect logistic regression and leave-one-subject-out machine learning modeling indicated biobehavioral features significantly predict state fluctuations in anxiety, though balanced accuracy tended to be modest (59%). However, our capacity to identify instances of heightened versus low state anxiety significantly increased (with balanced accuracy ranging from 69% to 84% across different operationalizations of state anxiety) when we integrated contextual data alongside trait mental health functioning into our predictive models.. We discuss these and other findings in the context of the broader anxiety detection literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15637 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding State Social Anxiety in Virtual Social Interactions using Multimodal Wearable Sensing Indicators Larrazabal, Maria A. Wang, Zhiyuan Rucker, Mark Toner, Emma R. Boukhechba, Mehdi Teachman, Bethany A. Barnes, Laura E. Human-Computer Interaction Mobile sensing is ubiquitous and offers opportunities to gain insight into state mental health functioning. Detecting state elevations in social anxiety would be especially useful given this phenomenon is highly prevalent and impairing, but often not disclosed. Although anxiety is highly dynamic, fluctuating rapidly over the course of minutes, most work to date has examined anxiety at a scale of hours, days, or longer. In the present work, we explore the feasibility of detecting fluctuations in state social anxiety among N = 46 undergraduate students with elevated symptoms of trait social anxiety. Participants engaged in two dyadic and two group social interactions via Zoom. We evaluated participants' state anxiety levels as they anticipated, immediately after experiencing, and upon reflecting on each social interaction, spanning a time frame of 2-6 minutes. We collected biobehavioral features (i.e., PPG, EDA, skin temperature, and accelerometer) via Empatica E4 devices as they participated in the varied social contexts (e.g., dyadic vs. group; anticipating vs. experiencing the interaction; experiencing varying levels of social evaluation). We additionally measured their trait mental health functioning. Mixed-effect logistic regression and leave-one-subject-out machine learning modeling indicated biobehavioral features significantly predict state fluctuations in anxiety, though balanced accuracy tended to be modest (59%). However, our capacity to identify instances of heightened versus low state anxiety significantly increased (with balanced accuracy ranging from 69% to 84% across different operationalizations of state anxiety) when we integrated contextual data alongside trait mental health functioning into our predictive models.. We discuss these and other findings in the context of the broader anxiety detection literature. |
| title | Understanding State Social Anxiety in Virtual Social Interactions using Multimodal Wearable Sensing Indicators |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.15637 |