Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 4, 2019
Date Accepted: Feb 27, 2020
Date Submitted to PubMed: Apr 29, 2020
Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping using Passive Smartphone Sensors
ABSTRACT
Background:
Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform healthcare decisions, offers a possible method of addressing this assessment barrier
Objective:
To determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity.
Methods:
In this study, participants (N = 59) completed an assessment of their social anxiety symptom severity and installed an application which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) across two weeks. Next this passive sensor data was used to form digital biomarkers which were paired with machine learning models to predict participants’ social anxiety symptom severity.
Results:
The results suggested that this passive sensor data could be utilized to accurately predict participants’ social anxiety symptom severity (r = 0.702 between predicted and observed symptom severity), and demonstrated discriminant validity between depression, negative affect, and positive affect.
Conclusions:
These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity.
Citation
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Copyright
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