Pervasive Computing; Mental Health.
Background: Depression and anxiety disorders among the global population have worsened during the COVID-19 pandemic. Yet, current methods for screening these two issues rely on in-person interviews, which can be expensive, time-consuming, and blocked by social stigma and quarantines. Meanwhile, how individuals engage with online platforms such as Google Search and YouTube has undergone drastic shifts due to COVID-19 and subsequent lockdowns. Such ubiquitous daily behaviors on online platforms have the potential to capture and correlate with clinically alarming deteriorations in depression and anxiety profiles of users in a noninvasive manner.
Objective: The goal of this study is to examine, among college students in the United States, the relationships of deteriorating depression and anxiety conditions with the changes in user behaviors when engaging with Google Search and YouTube during COVID-19.
Methods: This study recruited a cohort of undergraduate students (N=49) from a US college campus during January 2020 (prior to the pandemic) and measured the anxiety and depression levels of each participant. The anxiety level was assessed via the General Anxiety Disorder-7 (GAD-7). The depression level was assessed via the Patient Health Questionnaire-9 (PHQ-9). This study followed up with the same cohort during May 2020 (during the pandemic), and the anxiety and depression levels were assessed again. The longitudinal Google Search and YouTube history data of all participants were anonymized and collected. From individual-level Google Search and YouTube histories, we developed 5 features that can quantify shifts in online behaviors during the pandemic: late-night activities, inter-event time, linguistic attributes (e.g., tones and affective words), and content preferences. We then assessed the correlations of deteriorating depression and anxiety profiles with each of these features. We finally demonstrated the feasibility of using the proposed features to build predictive machine learning models.
Results: Of the 49 participants, 49% (n=24) of them reported an increase in the PHQ-9 depression scores; 53% (n=26) of them reported an increase in the GAD-7 anxiety scores. The results showed that a number of online behavior features were significantly correlated with deteriorations in the PHQ-9 scores (r ranging between –0.37 and 0.75, all P values less than or equal to .03) and the GAD-7 scores (r ranging between –0.47 and 0.74, all P values less than or equal to .03). Simple machine learning models were shown to be useful in predicting the change in anxiety and depression scores (mean squared error ranging between 2.37 and 4.22, R2 ranging between 0.68 and 0.84) with the proposed features.
Conclusions: The results suggested that deteriorating depression and anxiety conditions have strong correlations with behavioral changes in Google Search and YouTube use during the COVID-19 pandemic. Though further studies are required, our results demonstrate the feasibility of using pervasive online data to establish noninvasive surveillance systems for mental health conditions that bypasses many disadvantages of existing screening methods.
My Contribution: I was the leader of this project. I designed all the experiments, extracted the features, and performed all the analyses. I solely wrote the article. I was not the leader during the initial data collection, so I decided to share the first authorship with my fantastic collaborator, Anis Zaman.
Please see our paper at JMIR Mental Health here for more details.