Worldwide mental health problems such as depression, anxiety, and suicidal ideation have severely worsened during the COVID-19 pandemic [1-3], specifically for college students [4-7]. Yet, current methods for screening mental health issues and identifying vulnerable individuals rely on in-person interviews. Such assessments can be expensive, time-consuming, and blocked by social stigma, not to mention the reluctancy induced by travel restrictions and exposure risks. It has been reported that few patients in need were correctly identified and received proper mental health treatments on time under the current health care system [8,9]. Even with emerging telehealth technologies and online surveys, the screening requires patients to actively reach out to care providers.

At the same time, because of the lockdown caused by the global pandemic outbreak, people’s engagements with online platforms underwent notable changes, particularly in search engine trends [10-12], exposures to media reports [13,14], and through quotidian smartphone use for COVID-19 information [5]. Reliance on the internet has significantly increased due to the overnight change in lifestyles, for example, remote working and learning, imposed by the pandemic on society. The sort of content consumed, the time and duration spent online, and the purpose of online engagements may be influenced by COVID-19. Furthermore, the digital footprints left by online interactions may reveal information about these changes in user behaviors.

Most importantly, such ubiquitous online footprints may provide useful signals of deteriorating mental health profiles (eg, depression and anxiety) of users during COVID-19. They may capture insights into what was going on in the mind of the user through a noninvasive manner, especially since Google and YouTube searches are short and succinct, and can be quite rich in providing the real-time cognitive state of a person. On one hand, online engagements can cause fluctuations in mental health. On the other hand, having certain mental health conditions can cause certain types of online behaviors. This opens up possibilities for potential health care frameworks that leverage pervasive computing approaches to monitor mental health conditions and deliver interventions on time. However, the findings of this study do not imply any causal relationship between specific types of online activities and one’s level of anxiety or depression at a given point in time.

Extensive research has been conducted on a population level, correlating mental health problems with user behaviors on social platforms [15,16], especially among young adolescents. Researchers monitored Twitter to understand mental health profiles of the general population, such as suicidal ideation [17] and depression [18]. Similar research has been done with Reddit, where anxiety [19], suicidal ideation [17], and other general disorders were studied [20,21]. Another popular public platform is Facebook, and experiments have been done studying anxiety, depression, body-shaming, and stress online [22,23]. In addition, it has been shown that college student communities rely heavily on YouTube for both academic and entertainment purposes [24,25]. Yet, abundant use may lead to compulsive YouTube engagements [26], and researchers have found that social anxiety is associated with YouTube consumption in a complex way [27].

During COVID-19, multiple studies have reported deteriorating mental health conditions in various communities [1-3,28] such as nationwise [29,30], across the health care industry [31,32], and among existing mental health patients [33]. Recently, it has been shown that greater use of social media during COVID-19 may induce increasing levels of anxiety and depression at both population and individual levels [14,34]. In addition, online behaviors during COVID-19 have been explored, especially for web searches related to the pandemic [10-12] and abnormal TV consumption during the lockdown [13]. Many of the behavioral studies also discussed the effects of online interactions on the spread, misinformation, knowledge, and protective measures of COVID-19, including the roles of YouTube [35-37] and other platforms [38]. Lyu et al [39] investigated hate speech targeting the Chinese and Asian communities on Twitter during COVID-19. A study in 2009 showed the opposite effect in mental health risk factors: a communitywide crisis may reduce self-harm ideation behaviors [40].

Ubiquitous data has been proved to be useful in detecting mental health conditions. Mobile sensor data such as GPS logs [41,42]; electrodermal activity; and sleep behavior, motion, and phone use patterns [43,44] have been applied in investigating depressive symptoms. Zaman et al [45] found that individual private Google Search histories can be used to detect low self-esteem conditions among college students. Huckins et al [5] examined the longitudinal changes in mental health and smartphone use through ecological momentary assessments during COVID-19 among college populations. Although studies exploring anxiety and depression have been conducted in the past, none of them have leveraged individual-level Google Search and YouTube activity logs to examine the effect of COVID-19 on college students.

It has been shown that online platforms preserve useful information about the mental health conditions of users, and COVID-19 is jeopardizing the mental well-being of the global community. Thus, we demonstrate the richness of online engagement logs and how they can be leveraged to uncover alarming mental health conditions during COVID-19. In this study, we aim to examine whether the changes in user behaviors during COVID-19 have a relationship with deteriorating depression and anxiety profiles. We focused on Google Search and YouTube use, and we investigated if the behavior shifts when engaging with these two platforms signify worsened mental health conditions.

The scope of the study covers undergraduate students in the United States. We envision this project as a pilot study; it may lay a foundation for mental health surveillance and help delivery frameworks based on pervasive computing and ubiquitous online data. Compared to traditional interviews and surveys, such a noninvasive system may be cheaper and efficient, and avoid being blocked by social stigma while notifying caregivers on time about individuals at risk.

We recruited a cohort of undergraduate students, all of whom were at least 18 years of age and have an active Google account for at least 2 years, from the University of Rochester River Campus, Rochester, NY. Participation was voluntary, and individuals had the option to opt out of the study at any time, although we did not encounter any such cases. We collected individual-level longitudinal online data (Google Search and YouTube) in the form of private history logs from the participants. For every participant, we measured the depression and anxiety levels via the clinically validated Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7), respectively. Basic demographic information was also recorded. There were in total two rounds of data collection: the first round during January 2020 (prior to the pandemic) and the second round during May 2020 (during the pandemic). During each round and for each participant, the anxiety and depression scores were assessed, and the change in mental health conditions was calculated in the end. The entire individual online history data up until the date of participation was also collected in both rounds from the participants. Figure 1 gives an illustration of the recruitment timeline and two rounds of data collections. All individuals participated in both rounds and were compensated with US $10 Amazon gift cards during each round of participation.

Given the sensitivity and proprietary nature of private Google Search and YouTube histories, we leveraged the Google Takeout web interface [46] to share the data with the research team. Prior to any data cleaning and analysis, all sensitive information such as the name, email, phone number, social security number, and credit card information was automatically removed via the Data Loss Prevention application programming interface (API) [47] of Google Cloud. For online data and survey response storage, we used a Health Insurance Portability and Accountability Act–compliant cloud-based secure storing pipeline. The whole study design, pipelines, and survey measurements involved were similar to our previous setup [45] and have been approved by the Institutional Review Board of the University of Rochester.

To address participation bias, the study was advertised among the college population via campus wide digital announcements. The text in the study advertisements and consent materials were generic with text such as “help uncover mental health understanding via your online activities.” There was no explicit mention of anxiety or depression in the advertisement. Participation was voluntary with the option to opt out of the study anytime, and their data would not be part of the research study. The intent of the study was clearly explained at the beginning of the recruitment process via one-on-one interviews with the recruiter. We did not have anyone declining to participate or withdrawing in the middle of the study.

The Google Takeout platform enables users to share the entire private history logs associated with their Google accounts, and as long as the account of the user was logged-in, all histories would be recorded regardless of which device the individual was using. Each activity in Google Search and YouTube engagement logs were time stamped, signifying when the activity happened to the precision of seconds. Furthermore, for each Google Search, the history log contained the query text input by the user. It also recorded the URL if the user directly inputted a website address to the search engine. For each YouTube video watched by the user, the history log contained the URL to the video. If the individual directly searched with one or more keywords on the YouTube platform, the history log also recorded the URL to the search results.

To capture the change in online behaviors for the participants, we first introduced a set of features that quantified certain aspects of how individuals interact with Google Search and YouTube. The set of features was calculated for each participant separately. Individual-level behavior changes were then obtained by examining the variations of the feature between January to March 1, 2020 (a week before the state of emergency in New York State), and March 28 to May 2020 (after the outbreak, following the lockdown and mandated social distancing).

We excluded the online data generated between March 1, 2020, and March 28, 2020, to account for any acute or temporal behavior changes concentrated around the initial lockdown or due to adapting to remote work. We focused on the persistent and stabilized online behaviors throughout the time after the lockdown. Furthermore, the spring break at our institution started on March 7, and the state of emergency in New York State was issued on the same day. All students were asked to leave campus at the start of spring break and complete the rest of the semester remotely.

Concretely, we defined 5 features and cut the longitudinal data of each participant into two segments: (1) from January 1 to February 29, 2020, and (2) from March 29 to May 31. Each segment spanned 2 months. We excluded online data from March 1 to March 28 to account for the fact that not all individuals transitioned into work from home on a specific date or practiced a strict social distancing lifestyle, although all of our participants are residents of New York State. The same feature was extracted from both segments of data, and the change was calculated. Such change was referred to as the behavior shifts during the pandemic and lockdown. Figure 1 gives an illustration of data segmentations and feature development pipelines.

Before any analysis of mental health conditions, to eliminate the possibility of annual confounding factors interfering with the shifts in online behaviors, two-tailed paired independent t tests were performed. We inspected, in terms of the five quantitative features, whether the online behavior changes happened every year, such as due to seasonal factors, or only during COVID-19 for the whole study population. As previously mentioned, we collected the entire Google history log back to the registration date of the Google accounts of all participants.

We now use the example of SEIs to illustrate the idea. For each participant, we obtained 4 SEIs counts from 4 periods of time: January 1, 2019, to February 28, 2019; March 29, 2019, to May 31, 2019; January 1, 2020, to February 29, 2020; and March 29, 2020, to May 31, 2020. These counts are represented as 4 points on a Cartesian coordinate plane, where the y-axis represents the counts and the x-axis represents the time. We then calculated the slope (S) of the line connecting the two points from the same year. With the aforementioned process, we achieved two measurements for each participant, namely, S₂₀₁₉ and S₂₀₂₀. Viewing all the participants as a cohort, we computed the S₂₀₁₉ and S₂₀₂₀ for all features and performed multiple paired t tests. This enabled us to estimate the seasonal confounding factors. We could not perform the paired t tests on the changes of behavioral features directly because it may only validate a change in the intercept (baseline) amount of activity while ignoring the slope for each feature.

In the main experiments, with each of the aforementioned 5 features and various thresholds, we investigated the correlation of online behavioral changes with deteriorations in the GAD-7 and PHQ-9 scores, which did not require arbitrary discretization decisions. The dependent variables were the 5 behavior changes extracted from the longitudinal individual online data. Experiments were carried out in a one-on-one fashion: anxiety or depression condition was the single independent variable, and one of the five online behavior changes was the single dependent variable each time. Both of them were continuous variables.

We recruited 49 participants in total, and all of them participated in both rounds of the study (100% response rate). On average, each participant made 2357 (95% CI 2106.28 to 2433.45) Google Searches and 2901 (95% CI 2556.92 to 3248.67) YouTube interactions from January to February 29, 2020, and 2497 (95% CI 2069.45 to 2901.34) Google Searches and 3105 (95% CI 2702.48 to 3487.56) YouTube interactions from March 29 to the end of May. Of the 49 participants, 49% (n=24) of them reported an increase in the PHQ-9 score, and 53% (n=26) of them reported an increase in the GAD-7 score. An increase in the PHQ-9 score≥5 was reported by 41% (n=20) of participants, and 45% (n=22) of them reported an increase in the GAD-7 score≥5.

Figure 4 shows the baseline (collected on January 1, 2020) and follow-up postlockdown (collected on May 31, 2020) distributions of depression and anxiety scores in our sample student population. The PHQ-9 scores are shown on the left, ranging from 0 to 27. The GAD-7 scores are shown on the right, ranging from 0 to 21. Each dot represents a participant. The x-axis represents the baseline score in January, and the y-axis represents the follow-up score during the lockdown in May. Figure 5 shows the distributions of the change in PHQ-9 depression and GAD-7 anxiety scores before and after the lockdown. Again, the PHQ-9 scores are shown on the left, and the GAD-7 scores are shown on the right. The changes were calculated as the follow-up scores in May subtracted by the baseline scores in January. Putting the pandemic into context, the deterioration in anxiety or depression levels may have been triggered by the fear of getting infected, loss of jobs, the death of family members or friends, and many other negative impacts from COVID-19. Particularly for college students, other major reasons may be the pressure of online learning, loss of financial aids, and living alone. In contrast, students that underwent quarantines with their families safely may not have shown signals of deteriorating anxiety or depression, compared to the high stress levels during normal school days.

Of the 49 participants, 61% (n=30) of the them were female, 35% (n=17) were male, and 4% (n=2) reported nonbinary genders. First-, second-, third-, and forth-year students occupied 22% (n=11), 41% (n=20), 31% (n=15), and 6% (n=3) of the whole cohort, respectively. A total of 80% (n=39) of the participants were US citizens, and the rest (n=10) were international students. A complete breakdown of demographics with respect to the deteriorating anxiety and depressive disorders are given in Table 1.

The two-tailed paired independent t tests mentioned at the beginning of the Statistical Analysis section were designed to rule out seasonal factors in online behavior changes and focus on COVID-19 before any of the main experiments. All features had P values less than .003. Hence, the presence of annual or seasonal factors accountable for online behavior changes was neglectable, and it was safe to carry out the following main experiment. This is consistent with one of the main conclusions of Huckins et al [5] that, when comparing the longitudinal data between different years, behaviors during COVID-19 shifted drastically.

We calculated the Pearson product-moment correlations, r, of online behavior shifts with deteriorations in anxiety and depression levels. We reported the correlation coefficients with P values and 95% CIs obtained for each of the aforementioned features.

In this section, we probe the feasibility of using common machine learning models to predict the change (eg, deterioration) in PHQ-9 and GAD-7 scores. We framed the task as a supervised regression problem and treated the aforementioned behavioral changes as feature inputs to the model. The goal was to predict the change in the PHQ-9 or GAD-7 scores. Given our small sample size, we evaluated the model performance by multiple (N=49) leave-one-out train (n=48) and test (n=1) splits. For each of the splits, we tuned model hyperparameters with another complete leave-one-out cross-validation on the 48 training samples.

We developed the feature vector by concatenating some of the significant behavioral shifts (scalars) previously mentioned. Nonetheless, given the small sample size, it was reasonable to avoid high-dimensional feature vectors. Thus, we first separated the aforementioned 5 behavioral changes into 2 groups, namely, the temporal and semantic features. Specifically, we picked the online activity distributions at 2 AM, 3 AM, and 4 AM; the last seen activities before 4 AM and 5 AM; and the short YouTube intervals for the temporal feature vector, and hence, it had a dimensionality of 6. We then concatenated the 4 significant LIWC categories for Google Search, the 4 significant LIWC categories for YouTube, and the adult Google Search content as the semantic feature vector. The dimensionality was 9.

We experimented with two of the most common linear models: ordinary least square regression and lasso regression. In Figure 8, we present the performances of the regression models. We reported the mean squared error (MSE) and the average coefficient of determination (R²). The mean and SDs were calculated from the 49 leave-one-out splits. Overall, temporal features performed better than semantic ones, regardless of predicting the change in PHQ-9 or GAD-7. The best average performance in predicting the change of PHQ-9 was achieved by the temporal features (MSE=2.37, R²=0.84). The best average performance in predicting the change of GAD-7 was also achieved by the temporal features (MSE=2.48, R²=0.81).

We examined the coefficient weights from the linear models for PHQ-9 and GAD-7 predictions. Since lasso regression performed the best in all cases, we looked at the weights from the fitted lasso models. For the 6 temporal features, as shown in Table 6, online activity distributions at 4 AM had the most significant importance in both prediction tasks. For the 9 semantic features, as shown in Table 7, keywords related to the anxiety dimension in the LIWC from YouTube histories had the most significant importance. Moreover, as the aforementioned statistical tests have shown that neither the friends dimension in LIWC nor the adult Google Search category were significant for deteriorating GAD-7, the lasso regression indeed assigned zero weights to these two features. We used an alpha value of .0001 for the regularization term in the lasso model.

In this study, we collected longitudinal individual-level Google Search and YouTube data from college students, and we measured their anxiety (GAD-7) and depression (PHQ-9) levels before and after the outbreak of COVID-19. We then developed explainable features from Google Search and YouTube logs and quantified various online behavior shifts of the participants during the pandemic. We also calculated the change in mental health conditions for all participants. Our experiment examined the correlations of online behavior features with deteriorating anxiety and depression levels. We finally demonstrated the feasibility of building simple predictive machine learning models with the proposed behavioral signals. To the best of our knowledge, we are the first to conduct observational studies on how anxiety and depression problems and Google Search and YouTube use of college students are related during COVID-19.

Our results showed that online behavior changes have significant correlations with worsened depression and anxiety profiles during the pandemic. The features we developed based on online activities were all explainable and preserved certain levels of interpretability. For example, the SEIs and online activity distributions measured the consecutive use and hourly activity volumes of Google Search and YouTube, which were inspired by previous studies on excessive YouTube use [26], internet addictions [59], and positive associations with social anxiety among college students [27]. Our results indicated that individuals with increasing anxiety or depressive disorders during the pandemic tended to have long use sessions (multiple consecutive activities with short time intervals) when engaging with Google Search and YouTube.

Moreover, the increasing activities during late night hours in online activity distributions and the positive shifts of medians of last seen events corresponded with previous studies in sleep deprivation and subsequent positive correlations with mental health deteriorations [60,61]. Our results demonstrated that individuals with worsened anxiety or depressive symptoms during the pandemic were indeed likely to stay up late and engage more online. The aforementioned three features captured the temporal aspects of user online behaviors, and they generated the best performance in the regression tasks.

Additionally, our analysis found that the amount of porn consumption had significant correlations with deteriorating depression, which adheres to previous findings that people with depression and loneliness are likely to consume excessive pornography [62,63]. For the LIWC features, participants with significant increases in anxiety watched more videos with anxiety, sadness, and negative tone, and previous research has shown that negative YouTube videos tended to receive more attention from vulnerable individuals [64]. They also consumed more videos related to social activities and friends keywords. The friends keywords did not show any significance in the depression analysis. This is consistent with studies on patterns of social withdrawal and depression [42,65,66], and social interactions and isolations have been recognized by Leigh-Hunt et al [67] as one of the priorities in mental illness prevention, especially during COVID-19 [30]. For Google Search, both the participants with significant increases in anxiety and depression searched more content related to work, money, and death, focusing on real-life practices. None of the emotional dimensions were significant in Google Search logs. Instead, LIWC considered the search queries from depressed and anxious individuals more honest and vulnerable (eg, asking for help) after the lockdown, given the authentic score. Although prior research has shown that individuals living with depression tend to use more first-person languages [68], we did not observe any similar pattern. This is probably due to the fact that search queries are more succinct, imperative, and functional, which leaves less necessity for personal references. These attributes captured the semantic aspect of user online behaviors. The prevalence of personal affair, social activity, and negative keywords as well as porn consumption has shown statistically significant correlations.

Many researchers have reported that there has been a significant boost in health- and news-related topics at the population level in various online platforms during COVID-19. This is partly due to additional measures taken by individuals, various stakeholders, and agencies with regard to preventive measures [11,36,37], daily statistics [10,12,13], and health care information and misinformation [35,37,38]. However, unlike many, our investigation was carried out considering individual-level Google Search and YouTube engagement logs, and our analysis did not reveal any significant spikes in the news and health and illness categories among individuals with deteriorating anxiety and depression during the pandemic. One possible explanation for such observation could be due to the target population (college students) of our study, who may prefer to follow news from other popular platforms such as social media.

Finally, COVID-19 has forced us to alter daily lifestyles. The world was not ready for such a viral outbreak. Since there is no cure for COVID-19, it, or an even more deadly viral disease, may resurface at different capacities in the near future. Society may be forced to rely on technologies even more and employ remote learning, working, and socializing for a longer period of time. It is important that we learn from our experience of living through the initial COVID-19 outbreak and take necessary measures to uncover the changes in online behaviors, investigating how that can be leveraged to understand and monitor various mental health conditions of individuals in the least invasive manner. Furthermore, we hope our work paves the path for technology stakeholders to consider incorporating various mental health assessment monitoring systems using user engagements, following users’ consent in a privacy-preserving manner. They can periodically share the mental health monitoring assessment report with users based on their online activities and education, and inform users about their current mental health. This can eventually encourage individuals to acknowledge the importance of mental health and take better care of themselves.

First, although most of the online behavioral features we developed showed significant correlations, our study cohort only represented a small portion of the whole population with mental health difficulties. Therefore, further studies are required to investigate if the significant behavioral changes still hold among more general communities not limited to college students. It is possible that the relationship of worsened anxiety and depression with online activities on Google Search and YouTube of our college population is different from that of other populations whose education and social backgrounds may vary. There may also be differences in mental health for substudent populations, such as those living in harmful environments and those depending on financial aid, all of whom may experience more physical and economic crises during COVID-19. Nonetheless, we argue that the explainable features we constructed, such as late-night activities, continuous use, inactivity, pornography, and certain keywords, can remain behaviorally representative and be applied universally across experiments exploring the relationship of anxiety and depression with online activities during the pandemic.

Second, in this study, we explored the relationship between user online behaviors and the fluctuations in anxiety and depression conditions during COVID-19. Any causal relationship between online behavior and mental disorders is beyond the scope of this study. As one can readily imagine, online behavioral changes could both contribute to or be caused by deteriorating anxiety or depressive disorders.

Third, we acknowledge that it may be impossible to obtain data without noise because one may seldom, or even never, search on Google or watch YouTube videos. Such concealed information makes it impossible for the proposed model to flag alarming symptoms that are reflected in the PHQ-9 and GAD-7 questionnaires.

Fourth, though we included preliminary demographic information as covariates, there remains the possibility of other confounding factors. In fact, both the shifts in online behaviors and deteriorating mental health profiles may be due to common factors such as living conditions, financial difficulties, and other health problems during the pandemic. There was also no causal direction implied between COVID-19 and online behavior changes, which was introduced in the first paragraph of the Statistical Analysis section as a precaution before the main experiments.

Despite being a pilot study, our results indicate that it is possible to build an anxiety and depression surveillance system based on passively collected private Google data histories during COVID-19. Such noninvasive systems shall be subject to rigorous data security and anonymity checks. Necessary measures need to be in place to ensure personal safety and privacy concerns when collecting sensitive and proprietary data such as Google Search logs and YouTube histories. Even in pilot studies, participants should have full rights over their data; they may choose to opt out of the study at any stage and remove any data shared in the system.

Moreover, anonymity and systematic bias elimination should be enforced. As an automatic medical screening system based on pervasive data, it has been shown that such frameworks are prone to implicit machine learning bias during data collection or training phases [69-71]. Black-box methods should be avoided, as they are known to be vulnerable to adversarial attacks and produce unexplainable distributional representations [72,73]. Anonymizing data and obscuring identity information should be the first step in data debiasing.

In the end, to what extent should caregivers trust a clinical decision made by machines remains an open question. We believe that possible pervasive computing frameworks shall play the role of a smart assistant, at most, to the care providers. Any final intervention or help delivery decision should be made by health care professionals who understand both the mental health problems and the limitations of automatic detection systems in clinical settings.