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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.13725 |
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| _version_ | 1866915499050270720 |
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| author | Ahmed, Md Sabbir French, Noah Rucker, Mark Wang, Zhiyuan Myers-Brower, Taylor Petz, Kaitlyn Boukhechba, Mehdi Teachman, Bethany A. Barnes, Laura E. |
| author_facet | Ahmed, Md Sabbir French, Noah Rucker, Mark Wang, Zhiyuan Myers-Brower, Taylor Petz, Kaitlyn Boukhechba, Mehdi Teachman, Bethany A. Barnes, Laura E. |
| contents | Social anxiety is a common mental health condition linked to significant challenges in academic, social, and occupational functioning. A core feature is elevated momentary (state) anxiety in social situations, yet little prior work has measured or predicted fluctuations in this anxiety throughout the day. Capturing these intra-day dynamics is critical for designing real-time, personalized interventions such as Just-In-Time Adaptive Interventions (JITAIs). To address this gap, we conducted a study with socially anxious college students (N=91; 72 after exclusions) using our custom smartwatch-based system over an average of 9.03 days (SD = 2.95). Participants received seven ecological momentary assessments (EMAs) per day to report state anxiety. We developed a base model on over 10,000 days of external heart rate data, transferred its representations to our dataset, and fine-tuned it to generate probabilistic predictions. These were combined with trait-level measures in a meta-learner. Our pipeline achieved 60.4% balanced accuracy in state anxiety detection in our dataset. To evaluate generalizability, we applied the training approach to a separate hold-out set from the TILES-18 dataset-the same dataset used for pretraining. On 10,095 once-daily EMAs, our method achieved 59.1% balanced accuracy, outperforming prior work by at least 7%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13725 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WatchAnxiety: A Transfer Learning Approach for State Anxiety Prediction from Smartwatch Data Ahmed, Md Sabbir French, Noah Rucker, Mark Wang, Zhiyuan Myers-Brower, Taylor Petz, Kaitlyn Boukhechba, Mehdi Teachman, Bethany A. Barnes, Laura E. Machine Learning Computers and Society Social anxiety is a common mental health condition linked to significant challenges in academic, social, and occupational functioning. A core feature is elevated momentary (state) anxiety in social situations, yet little prior work has measured or predicted fluctuations in this anxiety throughout the day. Capturing these intra-day dynamics is critical for designing real-time, personalized interventions such as Just-In-Time Adaptive Interventions (JITAIs). To address this gap, we conducted a study with socially anxious college students (N=91; 72 after exclusions) using our custom smartwatch-based system over an average of 9.03 days (SD = 2.95). Participants received seven ecological momentary assessments (EMAs) per day to report state anxiety. We developed a base model on over 10,000 days of external heart rate data, transferred its representations to our dataset, and fine-tuned it to generate probabilistic predictions. These were combined with trait-level measures in a meta-learner. Our pipeline achieved 60.4% balanced accuracy in state anxiety detection in our dataset. To evaluate generalizability, we applied the training approach to a separate hold-out set from the TILES-18 dataset-the same dataset used for pretraining. On 10,095 once-daily EMAs, our method achieved 59.1% balanced accuracy, outperforming prior work by at least 7%. |
| title | WatchAnxiety: A Transfer Learning Approach for State Anxiety Prediction from Smartwatch Data |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2509.13725 |