Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety.

Autor:	Stamatis CA; Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America., Liu T; Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, United States of America.; Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Bethesda, MD, United States of America., Meyerhoff J; Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America., Meng Y; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States of America., Cho YM; Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, United States of America., Karr CJ; Audacious Software, Chicago, IL, United States of America., Curtis BL; Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Bethesda, MD, United States of America., Ungar LH; Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, United States of America.; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States of America., Mohr DC; Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.
Jazyk:	angličtina
Zdroj:	Internet interventions [Internet Interv] 2023 Oct 13; Vol. 34, pp. 100683. Date of Electronic Publication: 2023 Oct 13 (Print Publication: 2023).
DOI:	10.1016/j.invent.2023.100683
Abstrakt:	Background: Prior literature links passively sensed information about a person's location, movement, and communication with social anxiety. These findings hold promise for identifying novel treatment targets, informing clinical care, and personalizing digital mental health interventions. However, social anxiety symptoms are heterogeneous; to identify more precise targets and tailor treatments, there is a need for personal sensing studies aimed at understanding differential predictors of the distinct subdomains of social anxiety. Our objective was to conduct a large-scale smartphone-based sensing study of fear, avoidance, and physiological symptoms in the context of trait social anxiety over time. Methods: Participants ( n  = 1013; 74.6 % female; M age = 40.9) downloaded the LifeSense app, which collected continuous passive data (e.g., GPS, communication, app and device use) over 16 weeks. We tested a series of multilevel linear regression models to understand within- and between-person associations of 2-week windows of passively sensed smartphone data with fear, avoidance, and physiological distress on the self-reported Social Phobia Inventory (SPIN). A shifting sensor lag was applied to examine how smartphone features related to SPIN subdomains 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Results: A decrease in time visiting novel places was a strong between-person predictor of social avoidance over time (distal β  = -0.886, p  = .002; medial β  = -0.647, p  = .029; proximal β  = -0.818, p  = .007). Reductions in call- and text-based communications were associated with social avoidance at both the between- (distal β  = -0.882, p  = .002; medial β  = -0.932, p  = .001; proximal β  = -0.918, p  = .001) and within- (distal β  = -0.191, p  = .046; medial β  = -0.213, p  = .028) person levels, as well as between-person fear of social situations (distal β  = -0.860, p  < .001; medial β  = -0.892, p  < .001; proximal β  = -0.886, p  < .001) over time. There were fewer significant associations of sensed data with physiological distress. Across the three subscales, smartphone data explained 9-12 % of the variance in social anxiety. Conclusion: Findings have implications for understanding how social anxiety manifests in daily life, and for personalizing treatments. For example, a signal that someone is likely to begin avoiding social situations may suggest a need for alternative types of exposure-based interventions compared to a signal that someone is likely to begin experiencing increased physiological distress. Our results suggest that as a prophylactic means of targeting social avoidance, it may be helpful to deploy interventions involving social exposures in response to decreases in time spent visiting novel places. Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Caitlin A. Stamatis has received salary and equity support from Akili Interactive Labs and Google. Jonah Meyerhoff has accepted consulting fees from Boehringer Ingelheim. David C. Mohr has accepted honoraria and consulting fees from Otsuka Pharmaceuticals, Optum Behavioral Health, Centerstone Research Institute, and the One Mind Foundation, royalties from Oxford Press, and has an ownership interest in Adaptive Health, Inc. (© 2023 Published by Elsevier B.V.)
Databáze:	MEDLINE
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