Anxiety disorder is one of the  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 . Yet, such mental health assessments and surveys can be expensive, time consuming , and blocked by social stigmas. In this work, we propose an alternative way to identify individuals with anxiety and further estimate their levels of anxiety using private online activities histories from YouTube and 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 log data from volunteering participants, along with their clinically validated ground-truth anxiety assessment scores. We then engineered explainable features that capture both the temporal and semantic aspects of online behaviors. We managed to train models that not only identify individuals having anxiety disorder but also can predict the level of anxiety comparable to the gold standard Generalized Anxiety Disorder 7-item scores  with a mean square error of 1.87 based on the ubiquitous individual-level online engagements .
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 .
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world's
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. Yet, such mental health assessments and surveys
, which
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time consuming
time-consuming
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way
method
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private online activities
personal online activity
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Google search
the Google Search
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search log data
Search logs
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engineered
developed
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semantic
contextual
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We managed
Using those, we were able
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not only
(i)
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but also can predict
with an average F1 score of 0.83 and (ii) assess
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comparable to
by predicting
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(ranges from 0 to 21)
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engagements
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