Table 1 shows the weighted means and standard errors for continuous variables, as well as the weighted proportions of individuals within each categorical variable group. Overall, the average age was 36.99, and most of the sample was male, non-Hispanic White, and married or living in a marital-like relationship. Most of the sample did not experience a change in employment status due to the COVID-19 pandemic, had an income level greater than US $50,000, and had greater than a high school education. Close to half of the sample experienced symptoms indicative of clinical levels of depression (45.45%) or anxiety (40.04%).

Table 2 shows the results of the two adjusted models regressing the prevalence of clinically meaningful symptoms of depression and anxiety, respectively, on the county COVID-19 case rate per 10 people, time point, and the covariates. As shown, COVID-19 case rate was significantly associated with an increase in the likelihood of experiencing clinically meaningful symptoms of depression (odds ratio [OR] 2.06, 95% CI 1.27-3.35), but not anxiety (OR 1.21, 95% CI 0.77-1.88). Additionally, the likelihood of experiencing symptoms of depression or symptoms of anxiety increased over time (OR 1.19, 95% CI 1.12-1.27 and OR 1.12, 95% CI 1.05-1.19, respectively). Results associated with each covariate are shown in Multimedia Appendix 1.

The adjusted association between depression, anxiety, county case rate per 10 people, time point, and use of each of the seven categories of digital mental health tools and other technologies is presented in Table 3. Results pertaining to each covariate are shown in Multimedia Appendix 1. Both symptoms of depression and anxiety were significantly associated with the likelihood of using each type of tool and technology. Those with depressive symptoms were three to six times more likely to use digital mental health tools than those without depressive symptoms. Those with symptoms of anxiety were two to three times more likely to use digital mental health tools than those without symptoms of anxiety. Specifically, those with depressive symptoms or symptoms of anxiety were more likely than those without symptoms to use mental health forums, websites, or apps (OR 6.01, 95% CI 4.70-7.70 and OR 2.95, 95% CI 2.37-3.66, respectively), phone-based or text-based crisis lines (OR 4.98, 95% CI 3.66-6.77 and OR 2.85, 95% CI 2.22-3.66, respectively), and other health forums, websites, or apps (OR 3.44, 95% CI 2.81-4.20 and OR 2.55, 95% CI 2.11-3.10, respectively).

The findings pertaining to digital mental health tools mirrored the results from models assessing use of other technologies not necessarily related to health. Specifically, those with symptoms indicative of depression and anxiety were more likely, as compared to those without symptoms, to engage in use of social media and blogs (OR 1.56, 95% CI 1.31-1.86 and OR 1.80, 95% CI 1.51-2.14, respectively); online, computer, or console gaming/video gaming (OR 1.63, 95% CI 1.36-1.95 and OR 1.67, 95% CI 1.40-1.99, respectively); online calendar, checklist, planner, Word, Notepad, Google Docs, spreadsheet, or Google Sheets (OR 1.91, 95% CI 1.60-2.28 and OR 2.09, 95% CI 1.75-2.50, respectively); and email, texting or messaging software, or video conferencing software (OR 1.66, 95% CI 1.39-1.98 and OR 1.82, 95% CI 1.52-2.17, respectively). However, in general, even though significant, the odds ratios for these other technologies tended to be much smaller, with the largest being 2.09 and all others below 2.00, while all the odds ratios for the mental health technologies were well above 2.00, ranging from 2.55 to 6.01.

With regard to the association between county-level COVID-19 case rate and digital mental health tool and other technology use, a higher county case rate per 10 people was associated with increased likelihood of using digital mental health tools, specifically mental health forums, websites, or apps (OR 2.70, 95% CI 1.49-4.88) and other health forums, websites, or apps (OR 2.60, 95% CI 1.55-4.34). Additionally, over time, there was an increase in the likelihood of using mental health forums, websites, or apps (OR 1.20, 95% CI 1.11-1.30); phone-based or text-based crisis lines (OR 1.20, 95% CI 1.10-1.31); other health forums, websites, or apps (OR 1.12, 95% CI 1.05-1.20); and online, computer, or console gaming/video gaming (OR 1.12, 95% CI 1.05-1.19). The predicted probabilities of using each type of technology at each time point while holding the covariates at their mean values are graphed in Multimedia Appendix 2. Multimedia Appendix 3 shows the total number of respondents using each type of technology at each time point.

Lastly, models assessing the interaction of county-level COVID-19 case rate and symptoms indicative of anxiety or depression did not yield significant findings (analyses available on request). For this reason, interaction terms were not subject to further analyses.

To our knowledge, this is the first study to examine rates of use of digital mental health tools and other technologies to manage one’s mental health among a sample of people during the early stages of the COVID-19 pandemic. Similar to other studies [29], rates of COVID-19 cases were associated with increased rates of depressive symptoms. Both depressive and anxiety symptoms and COVID-19 county case rates were associated with the largest increased likelihood of using digital mental health tools, specifically mental health forums, websites, and apps; phone-based/text-based crisis lines; and other health forums, websites, and apps, reflecting the greatest increase in likelihood of use for products designed specifically for health and mental health. Interestingly, we also found a general increase in the likelihood of using other technologies to support one’s mental health, including gaming and work products, among those experiencing symptoms of mental illness. This increase in the use of other technologies may reflect the transition to working from home that occurred for many people.

We also sought to examine if the likelihood of using these tools and technologies was the highest among people who were both living in counties with high COVID-19 case rates and exhibiting symptoms indicative of clinically meaningful levels of depression or anxiety. The findings suggested, in fact, that there was no additional significant likelihood of increased use among respondents with depressive or anxious symptoms living in counties with high COVID-19 case rates compared to individuals with similar symptom levels in counties with low COVID-19 rates. Of course, it is likely that other factors, such as the impacts of policy measures aimed at mitigating risks (eg, physical distancing, working from home, or school closures) may have had a greater impact on mental health symptoms, resulting in people turning to digital tools for mental health support. In the absence of in-person connections or services, digital tools appear to play an important role in combatting the mental health impact of the pandemic.

We also found that the odds of having used these technologies generally increased over time. This increase in use of digital mental health tools and other technologies may be due, in part, to increased access to some of these technologies. For example, cities like New York City [30] have made technologies freely available to their residents and/or shared recommendations for particular products that have been vetted. Additionally, health insurance companies, such as Kaiser Permanente and others, have also made mental health products freely available for their enrollees [31]. It might also be due to the fact that people are spending more time interacting with technologies and these may be convenient ways to receive mental health support.

Our findings that people are increasingly using various technologies—both those specifically designed for mental health support as well as those that are not—are consistent with studies that explore the various ways people use technology to self-manage their mental health [32-34]. These findings also support recent calls for understanding how the technology ecosystem might impact mental health and lead to opportunities for prevention and intervention tools [35]. Some efforts, such as Google’s integration of mental health screening into their search engine, have been launched [36]. In light of our findings, it appears as though there is consumer interest for such resources. Furthermore, an important consideration for future work is to ensure that consumers find effective and safe resources, and have the proper support for using such resources appropriately.

Indeed, despite this increased use and interest, evidence-based and safe resources are rarely available for consumers. One study has suggested that only 2.08% of publicly available psychosocial wellness and stress management mobile apps have published, peer-reviewed evidence of feasibility and/or efficacy [37]. Furthermore, few products provide sufficient information to gauge their safety and privacy [38], and even among those that do, many share information with third parties in ways that might not be disclosed in those policies [39]. Although various efforts have been launched that either evaluate or offer evaluative frameworks for mental health apps (including One Mind PsyberGuide [40], ORCHA [41], and the American Psychiatric Association app evaluation framework [42]), no widely accepted or coordinated effort at regulation and evaluation exists in the United States, despite multiple calls for such models (see National Institutes of Health, National Advisory Work Group [43]). Indeed, better regulation and better access to information at point-of-access could be a significant improvement in helping guide consumers [44], who are demonstrating a clear interest in such resources, and might serve an important need in light of the COVID-19 pandemic.

Several study limitations should be noted. First, this survey was completed by MTurk workers who regularly use the computer, and therefore have the necessary technology, mobility, and digital literacy to participate [45]. This might contribute to the rates of technology use reported and the ability to use technologies—both those designed for mental health and otherwise—to support their mental health. Additionally, it is likely that people with the lowest socioeconomic status, those that are the most isolated, and those with limited access to technology were omitted from the study. These individuals may represent those most affected by COVID-19 and thus may have the greatest mental health needs [46]. Second, the survey was only available in English, and thus the findings may not hold among groups with limited English proficiency who also might be less likely to use these technologies, especially because many are not designed for diverse populations [47]. Third, this study only included people living in the United States, and thus, findings cannot be generalized to other countries [1]. Fourth, there exist a number of scales pertaining to examining the self-reported impact of COVID-19 on mental health [48-51]. These works were primarily published during the development and implementation of this survey, and as such, they were not included in this study. Fifth, there exists the possibility that at least some of the survey responses were bot-generated. To test for the potential influence of bots, we executed post hoc diagnostics previously suggested by Chmielewski and Kucker [52]. Removing those responses suspected to be bot-generated did not substantially change the findings. Thus, we reported on those findings generated from the full analytic sample. However, we also have included the results garnered after removing tagged responses (Multimedia Appendix 4).

Finally, this study focused on examining the extent to which people used mobile health technologies throughout the early period of the pandemic. The findings do not shed light on the effectiveness of these particular strategies for managing mental health. For many, engagement in digital mental health tool and other technology use has been critical for enhancing social connectedness, managing stress and anxiety, and providing greatly needed entertainment [53]. Although the vast majority of the increase in digital technology use is adaptive, it is important to note that there are likely negative impacts of this growth, including the spread of false information [54-56]. Furthermore, there exist subgroups of vulnerable individuals that are at risk of developing problematic usage patterns [53]. Excessive engagement in specific online activities such as video gaming, social media use, and shopping has been linked with severe problems and can elevate the risk of disordered or addictive use [57,58].

In conclusion, this study provides an important description of the prevalence of symptoms of anxiety and depression and digital mental health tool and other technology use during the early stages of the COVID-19 pandemic. Specifically, we found evidence of an increased likelihood of experiencing depressive symptoms both when considering differences between county COVID-19 case rates and changes over time. Furthermore, COVID-19 case rate and time were generally associated with an increased likelihood of using digital mental health tools and other technologies. Lastly, those experiencing symptoms of depression or anxiety were more likely than those without such symptoms to use tools and technologies to manage their mental health.

As the pandemic is expected to continue well into the spring and summer of 2021, and as depression [59,60], anxiety [7,61], and suicidal ideation rates continue to climb [1,8,62], the importance of providing easy access to tools and technologies to manage one’s mental health will become even more important. The current shortage of mental health professionals demands the need to explore more scalable solutions that might be able to be adapted and deployed to meet the needs of various populations. The findings from this study—as well as future research that may address more specific issues designed to understand how digital mental health tools and other technologies can be more accessible and effective for the populations who need them—could inform public health efforts.