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Anxiety disorder is one of the world's most prevalent mental healthconditions, arising from complex interactions of biological and environmentalfactors and severely interfering one's ability to lead normal life activities.Current methods for detecting anxiety heavily rely on in-person interviews,which can be expensive, time-consuming, and blocked by social stigmas. In thiswork, we propose an alternative method to identify individuals with anxiety andfurther estimate their levels of anxiety using personal online activityhistories from YouTube and the Google Search engine, platforms that are used bymillions of people daily. We ran a longitudinal study and collected multiplerounds of anonymized YouTube and Google Search logs from volunteeringparticipants, along with their clinically validated ground-truth anxietyassessment scores. We then developed explainable features that capture both thetemporal and contextual aspects of online behaviors. Using those, we were ableto train models that (i) identify individuals having anxiety disorder with anaverage F1 score of 0.83 and (ii) assess the level of anxiety by predicting thegold standard Generalized Anxiety Disorder 7-item scores (ranges from 0 to 21)with a mean square error of 1.87 based on the ubiquitous individual-levelonline engagement data. Our proposed anxiety assessment framework iscost-effective, time-saving, scalable, and opens the door for it to be deployedin real-world clinical settings, empowering care providers and therapists tolearn about anxiety disorders of patients non-invasively at any moment in time.

Individual-level Anxiety Detection and Prediction from Longitudinal YouTube and Google Search Engagement Logs