2023/04/03 by Emma R. Toner, Mark Rucker, Toner, Emma R. +19
Psychology · #Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes #Computers and Society (cs.CY) #Digital Mental Health Interventions #FOS: Computer and information sciences #FOS: Electrical engineering #Mental Health Research Topics #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2304.01293
openalex publication_date 2023/04/03 · openalex created_date 2023/04/07 · openalex updated_date 2026/07/28
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 disorder. In this study, we present results using passively collected data from a within-subject experiment that assessed physiological response 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 (N=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 follow-up 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, but that there is considerable variability in physiological response patterns even among these distinguishable contexts. Implications for real-world context detection and deployment of JITAIs are discussed.