  Anxiety disorder is one of the world's most prevalent mental health
conditions, arising from complex interactions of biological and environmental
factors 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 this
work, we propose an alternative method to identify individuals with anxiety and
further estimate their levels of anxiety using personal online activity
histories from YouTube and the Google Search engine, platforms that are used by
millions of people daily. We ran a longitudinal study and collected multiple
rounds of anonymized YouTube and Google Search logs from volunteering
participants, along with their clinically validated ground-truth anxiety
assessment scores. We then developed explainable features that capture both the
temporal and contextual aspects of online behaviors. Using those, we were able
to train models that (i) identify individuals having anxiety disorder with an
average F1 score of 0.83 and (ii) assess the level of anxiety by predicting the
gold 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-level
online engagement data. Our proposed anxiety assessment framework is
cost-effective, time-saving, scalable, and opens the door for it to be deployed
in real-world clinical settings, empowering care providers and therapists to
learn about anxiety disorders of patients non-invasively at any moment in time.
