The Relationships of Deteriorating Depression and Anxiety With Longitudinal Behavioral Changes in Google and YouTube Use During COVID-19: Observational Study

Abstract

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. 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.

Anxiety during the COVID-19 pandemic in hospital staff: systematic review plus meta-analysis

“…The literature has approved that the COVID-19 pandemic has caused serious mental, physical and emotional problems among hospital staff who are at a higher risk of contracting the infection or even passing on the disease to others. [27][28][29] The other cause of anxiety among HCWs might be working in high-infectious work environments with inadequate access to PPE. 30 A number of systematic reviews and meta-analyses have reported the prevalence of anxiety among HCWs.…”

What can internet users' behaviours reveal about the mental health impacts of the COVID-19 pandemic? A systematic review

“…At the end of the screening process, 19 articles were included in the systematic review. 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52

Fig. 1 Flow diagram of the study-selection process.

…”

Social media monitoring of mental health during disasters: A scoping review of methods and applications

“…However, it remains unclear whether spikes in posts with negative affect detected during and following disasters are clinically meaningful changes warranting intervention or the natural course of psychopathology following a distressing event (Galea & Norris, 2006). More participatory research could address this issue by combining passive SNS monitoring with validated psychological measures capable of capturing both distress and dysfunction in the affected population (e.g., Zhang et al, 2020). Further, little research has examined the use of SNS to assess the impact of disasters on the mental health of vulnerable populations, including those with pre-existing mental health issues.…”

“…Next, two studies used mental health forum membership to indicate mental health problems (Low et al, 2020;Saha & De Choudhury, 2017), with Saha and De Choudhury (2017) using the novel method of transfer learning from a classifier trained on a mental health subreddit (r/stress) and a random sample of Reddit posts to identify posts with high stress on college-specific subreddits following gun-related violence on campus. Finally, mental health questionnaires were used in two studies; specifically, the PHQ-9 and GAD-7 in Zhang et al (2020) and PWB Scale in Wang et al (2020).…”

“…Finally, a few studies used other techniques including web crawlers (n=8), third-party companies (n=2), and other novel methods (n=2, n=6 not reported) such as having participants download and share their usage patterns via Google Takeout (Zhang et al, 2020). In most studies (n=39), individual posts were the unit of analysis, rather than the user contributing those posts (n=8).…”