Detecting Temporal Phases of Anxiety in The Wild: Toward Continuously Adaptive Self-Regulation Technologies

Senaratne, Hashini

doi:10.1145/3340555.3356091

Cited by 1 publication

(2 citation statements)

References 26 publications

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“…In accordance with the findings of the above studies, a recent interview study conducted with anxiety specialist clinicians has suggested that four temporal phases likely occur within diverse types of anxiety experiences [56]. Suggested phases are non-anxious, pre-anxious, peak-anxious, and post-anxious phases.…”

Section: Related Worksupporting

confidence: 72%

“…Further, our findings propose the ability to objectively detect distinct state anxiety experiences, contributing towards the long-term aims of minimizing the burden of anxiety disorders. For example, real-time detection capabilities can support implementing intelligent mobile technologies to guide appropriate and timely interventions to patients outside therapy [8], ultimately supporting to break the vicious cycles of anxiety, therefore; improving the recovery speed or preventing anxiety disorders [56]. Longitudinal analytics on temporal pattern changes across anxiety experiences could estimate risk levels and guide the general public to clinicians on time to receive early support.…”

Section: Hypotheses Novelty and Contributionsmentioning

confidence: 99%

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A Multimodal Dataset and Evaluation for Feature Estimators of Temporal Phases of Anxiety

Senaratne

Kuhlmann

Ellis

et al. 2021

Proceedings of the 2021 International Conference on Multimodal Interaction

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Vicious cycles of anxiety responses underlie the onset of increasingly prevalent and highly impairing anxiety disorders and also contribute to their maintenance. Our goal is to evaluate whether different anxiety responses are evident in temporal patterns of physiological and behavioral features. Consequently, we established a rich multimodal-multisensor dataset of cardiac, electrodermal, movement, posture, and speech measures from 95 young adults during two anxiety experiments that induce social anxiety and bug-phobic anxiety. A subset of this dataset is publicly available at "Anxiety Phases Dataset" Figshare repository. We adopted a generalized mixed model approach and found that 10 out of 14 feature trajectories modeled for high-and low-anxiety groups differ significantly at 0.001 level in magnitude, creating at least two temporal phases in both groups. Further differences in magnitude, duration and the number of phases were observed for responses of confrontation, safety behaviors, escape, and avoidance in the high-anxiety group. Our findings contribute to the long-term aim of designing multimodal systems that have great potential to reduce the impacts of anxiety disorders and improve therapy.

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Section: Related Worksupporting

confidence: 72%

Section: Hypotheses Novelty and Contributionsmentioning

confidence: 99%