Mental Health and the Perceived Usability of Digital Mental Health Tools Among Essential Workers and People Unemployed Due to COVID-19: Cross-sectional Survey Study

Background: COVID-19 has created serious mental health consequences for essential workers or people who have become unemployed as a result of the pandemic. Digital mental health tools have the potential to address this problem in a timely and efficient manner. Objective: The purpose of this study was to document the extent of digital mental health tool (DMHT) use by essential workers and those unemployed due to COVID-19, including asking participants to rate the usability and user burden of the DMHT they used most to cope. We also explored which aspects and features of DMHTs were seen as necessary for managing stress during a pandemic by having participants design their own ideal DMHT. Methods: A total of 2000 people were recruited from an online research community (Prolific) to complete a one-time survey about mental health symptoms, DMHT use, and preferred digital mental health features. Results: The final sample included 1987 US residents that identified as either an essential worker or someone who was unemployed due to COVID-19. Almost three-quarters of the sample (1479/1987, 74.8%) reported clinically significant emotional distress. Only 14.2% (277/1957) of the sample used a DMHT to cope with stress associated with COVID-19. Of those who used DMHTs to cope with COVID-19, meditation apps were the most common (119/261, 45.6%). Usability was broadly in the acceptable range, although participants unemployed due to COVID-19 were less likely to report user burden with DMHTs than essential workers (t198.1=–3.89, P<.001). Individuals with emotional distress reported higher financial burden for their DMHT than nondistressed individuals (t69.0=–3.21, P=.01). When the sample was provided the option to build their own DMHT, the most desired features were a combination of mindfulness/meditation (1271/1987, 64.0%), information or education (1254/1987, 63.1%), distraction tools (1170/1987, 58.9%), symptom tracking for mood and sleep (1160/1987, 58.4%), link to mental health resources (1140/1987, 57.4%), and positive psychology (1131/1986, 56.9%). Subgroups by employment, distress, and previous DMHT use status had varied preferences. Of those who did not use a DMHT to cope with COVID-19, most indicated that they did not consider looking for such a tool to help with coping (1179/1710, 68.9%). Conclusions: Despite the potential need for DMHTs, this study found that the use of such tools remains similar to prepandemic levels. This study also found that regardless of the level of distress or even past experience using an app to cope with COVID-19, it is possible to develop a COVID-19 coping app that would appeal to a majority of essential workers and unemployed persons. JMIR Ment Health 2021 | vol. 8 | iss. 8 | e28360 | p. 1 https://mental.jmir.org/2021/8/e28360 (page number not for citation purposes) Mata-Greve et al JMIR MENTAL HEALTH


Background
The COVID-19 pandemic has led to necessary public health mandates, such as physical distancing and stay-at-home orders.While these orders are important to contain the outbreak, they have led to concerns about increased isolation and loneliness among the general population, and prolonged exposure to stress among essential workers (eg, those working in food distribution, construction, mail delivery, etc) and those who are unemployed or furloughed owing to the pandemic [1][2][3][4].Rates of negative mental health outcomes, especially fear, anxiety, and stress, in the general population during this pandemic are higher compared to prepandemic times [1,5].
Individuals struggling financially are reporting challenges with job security (ie, being laid off), housing costs, and making enough money to make ends meet [6].Essential workers and those unemployed due to COVID-19 have many unique stressors, including but not limited to, concern about COVID-19 exposure, caring for family while working or searching for work, uncertainty about their job security, financial stress, guilt about not contributing to frontline COVID-19 efforts, underor uninsured status, and access to no or nonmedical grade personal protective equipment [1][2][3][4].While both groups have shared concerns, recent studies have shown that half of all essential workers are likely experiencing at least one adverse mental health symptom and increased anxiety or fatigue due to work demands in high stress or changing settings [3,7].For the unemployed, there is concern about higher rates of suicidality and suicide attempts.Previous pandemics, such as the Spanish flu of 1918 and the 2003 SARS (severe acute respiratory syndrome) epidemic, led to an increase in suicide, and loss of employment and financial stress are risk factors for suicide [4,8].Although the recent availability of vaccines and the eventual reopening of services mean that these concerns will eventually resolve, the need to understand how to best support essential workers and unemployed people emotionally during this time is still important, as future pandemics are predicted to be likely [9], and the long-term emotional impact of the current pandemic is still unknown [10].
In response to these mental health concerns, public health systems and digital mental health companies responded by increasing access to existing technology-based care (ie, telemedicine) or modifying digital mental health tools (DMHTs), such as online resources or mobile phone apps to address perceived concerns specific to COVID-19.For example, in the United States, Medicare restrictions on telemedicine were lifted to allow for better access to health care [11].DMHTs are also available as potential solutions to decrease stress and mental health symptoms and address the mental health care shortage during COVID-19 [8,12].In anticipation of the need for lowor no-cost care, organizations such as the Veterans Affairs Health Care System created a free mobile app to help veterans cope with COVID-19.A report from March 2020, as physical distancing began in the United States, found that there was an increased volume of people using these tools [13].In addition, many organizations and tech companies are turning to DMHTs to support the emotional well-being of frontline health care workers [14].
These recent events lend an important opportunity to learn about the utility of digital mental health to support populations impacted by prolonged pandemic conditions.No research has evaluated the use of DMHTs by two of the most affected populations outside of frontline health care workers and older adults or adults with disability: essential workers and those unemployed due to COVID-19.As identified in several studies, the use of DMHTs tends to be poor, with most people downloading then discontinuing use of these tools in quick succession [15,16].As Mohr and colleagues [17] have noted, digital mental health service use could be improved if intervention developers better understood what features people felt were important to have, the usability of these tools, and what role these services should have in the context of mental wellness [18][19][20].

This Study
Considering the need to better understand the mental health challenges faced by essential workers and those unemployed due to COVID-19, the potential long-term effects of the societal challenges imposed by the pandemic, the potential for future pandemics, and the limited information we have on the usability and user burden of DMHTs to cope with the stress of COVID-19, we conducted a study with the following aims: • Aim 1: Document psychological distress through clinically validated measures by the total sample, employment status (ie, unemployed due to COVID-19 and essential workers), and DMHT use (ie, reported using DMHTs to cope with COVID-19, reported not using DMHTs to cope with COVID-19); • Aim 2: Explore DMHT use in response to COVID-19-related stress and differences by employment status and psychological distress (ie, distressed, not distressed); • Aim 3: Assess usability and user burden ratings of DMHTs by total sample, employment status, and psychological distress; • Aim 4: Understand the needs of these at-risk populations by identifying what DMHT features were ranked as most important by employment status, psychological distress, and DMHT use during this time.

Recruitment
A total of 2000 adults (≥18 years old) were recruited from Prolific Research Platform [21].Using online research platforms

Inclusion Screening
Participants must have been ≥18 years old, speak English, and self-reported as either an essential worker during COVID-19 or unemployed or furloughed due to COVID-19.They also had the opportunity to indicate their current job (if an essential worker) or past job (if an unemployed worker).Participants were excluded if they were under 18 years of age, did not speak English, had no access to a mobile device (eg, smartphone or tablet), did not report being an essential worker or unemployed due to COVID-19, or lived outside of the United States.

Bad-Actor Screening
Even with the best safeguards in place, online recruitment can sometimes result in the accidental inclusion of individuals who participate in bad faith to accumulate monetary incentives ("bad actors") [24].We instituted the procedures explained below to identify potential bad actors.
The first was to use research platforms (described above) that conduct their own extensive participant vetting.These procedures include but are not limited to: (1) every account needing a unique non-VOIP (voice over IP) phone number to verify, (2) restricting signups based on IP address and internet service provider, (3) limiting the number of accounts that can use the same IP address and machine to prevent duplicate accounts, (4) limiting the number of unique IP addresses per study, and (5) unique payment accounts (eg, PayPal) for each participant account.For example, in order to have 2 participant accounts that receive payment from Prolific, a participant would need to have 2 PayPal accounts.Payment accounts, such as PayPal, have steps to prevent duplicate accounts, such as analyzing internal data to monitor for patterns of unusual use [25].
The second method involved the use of an attention check built into our survey [26].This method consisted of one question where participants were given this instruction: "To confirm you are paying attention, please select 'strongly disagree'" and then choices between strongly agree to strongly disagree were provided.
The third method involved the review of open-ended responses to screen out bot-like communication, repetitious, and nonsensical responses.Each of these methods confirmed that the final sample in this study could be qualified as comprising "good actors."

Demographics
Participants completed a questionnaire about demographics, which collected information about age, race, ethnicity, gender identity, sexual orientation, marital status, education, employment status, income, and living situation.

Mental Health and Possible Substance Use Disorder
Participants completed the 2-item Patient Health Questionnaire (PHQ-2) [27], the 2-item Generalized Anxiety Disorder (GAD-2) [28], and the Cut-Annoyed-Guilty-Eye Adapted to Include Drugs (CAGE-AID) [29].The PHQ-2 and GAD-2 have good sensitivity and specificity with sensitivity to change over time in comparison to the PHQ-9 and GAD-7 [28][29][30].The CAGE-AID demonstrates good sensitivity and poor specificity for substance use disorders.As a result, individuals who scored beyond the cut-off on the CAGE-AID (≥1) were categorized as a possible case of substance use disorder, in accordance with the National HIV Curriculum [29,31].

Suicidal Behaviors
Suicidal behaviors were measured using the Suicide Behaviors Questionnaire-Revised (SBQ-R) [32], a 4-item self-report measure that assesses suicide attempts, ideation, communication, and intent in one's lifetime.If the total score is greater than or equal to 7, the score is deemed to have good sensitivity and specificity for identifying individuals at risk for suicidal behaviors in a nonpsychiatric general adult population.Given some limitations of the SBQ-R, a single validated item (ie, "Have you attempted to kill yourself?")was added.The addition of this item provides further accuracy and classification of individuals at risk of suicide [33].

DMHT Questionnaire
This questionnaire was developed by the research team with expertise in digital mental health (author PA).The measure was tested for face validity, understandability, and respondent burden among the internal group.The questionnaire consisted of three distinct tasks: use of DMHTs during COVID-19, usability and burden of DMHTs during COVID-19, and design of an ideal DMHT for COVID-19, which are described below.

Use of DMHTs
All participants were asked whether they have used an app to cope with stress associated with COVID-19.If the participant responded yes, they were asked to list which apps they used, and if they used more than one, to list the app they used the most to cope with COVID-19.Participants were then asked to rate the app that they used most frequently in terms of features they liked, features they did not like, and then on the app's usability and user burden.If participants did not report using an app to cope with COVID-19 stress, they were asked to provide reasons for why they did not use an app (Figure 1).

Usability
Usability was measured with the System Usability Scale (SUS) [34], a 10-item measure that examines the usability of a particular intervention.The scale assesses a system's likability, learnability, complexity, need for technical support, system integration, and efficiency.The SUS is the industry standard for measuring the usability of a variety of digital tools and systems and has normative data to allow for cross system and app comparisons, even between those that are outwardly very dissimilar to one another [35].

User Burden
User burden was measured using the 20-item Use Burden Scale (UBS) [36].This scale creates five subscales to assess different types of user burden: difficulty of use ("this app demands too much mental effort"), physical demands ("use of this app is too physically demanding"), time and social burden ("I spend too much time using this app"; "using this app has a negative impact on my social life"), mental and emotional burden ("this app presents too much information at once"), and privacy and financial burden ("the value of the app is not worth the cost for me").This measure was developed in order to assess the adoption, retention, and experience of various technologies with the ability to compare and calibrate burden across different tools.User burden is linked to app retention and has been used in the context of mobile app research [37].

Design of a COVID-19 App
All participants, regardless of whether they reported app use for stress associated with COVID-19, were asked which features they thought would be helpful to include in an app for coping with COVID-19 (ie, information or education, meditation/mindfulness, symptom tracking, brain games, distraction tools, gratitude exercises, links to resources, chatbot, or tips to cope with COVID-19) on a scale from 0="not at all important" to 9="very important."This method of asking opinions of those who do and do not use digital technology, particularly when the needs of a given population are unknown, is commonly used in app development.The opinions of people familiar and unfamiliar with apps are needed to design a digital tool with the broadest reach [38].
After indicating which features participants preferred in an app to cope with COVID-19, they were then asked to build their own app, by selecting from a preset list of features and then adding their own desired features that were not previously listed.The app feature list was created using premade categories from One Mind Psyberguide [39], a nonprofit tool that reviews digital mental health tools for consumers, and M-Health Index and Navigation Database (MIND) [40] (see Multimedia Appendix 1 for the full survey).

Statistical Analysis
To describe the sample, we ran crosstabulations (with chi-square tests or Fisher exact tests) and independent samples t tests to examine possible differences in the demographic and descriptive variables by employment status (ie, unemployed vs essential worker groups) and DMHT use (ie, DMHT user vs non-DMHT user).For variables with multiple discrete categories (eg, education), if these analyses indicated a significant omnibus chi-square test, we examined standardized residuals to identify which categories were responsible for the omnibus significant difference, and reported on all categories with absolute value standardized residuals greater than 2.
For the first aim, descriptive statistics were used to document the frequencies and means of the psychological distress composite among the entire sample and stratified by employment status.We also compared those who reported using an app to cope with COVID-19 to those who reported not using an app to cope with COVID-19.Specific reports on depression, anxiety, possible substance use disorder, suicidal behavior, and history of suicide attempt may be found in Multimedia Appendix 2.
For the second aim, we calculated frequencies and differences in DMHT use for the whole sample, between essential workers and those unemployed and between those reporting distress and no distress.
For the third aim, we computed means and SDs to examine DMHT ratings from the SUS and the UBS only for those who reported using a DMHT to cope with COVID-19.Differences across the top 3 apps were assessed using an ANOVA (analysis of variance).For the sample that did not report using a DMHT to cope during COVID-19, we provided the reasons for not using a DMHT and the frequency by which those reasons were endorsed in the sample.
For the fourth aim, we computed frequencies and central tendencies of the data to assess preferred DMHT components for the whole sample and compared these findings first between essential workers and those unemployed, then between distressed and nondistressed subsamples, and finally between those who reported having used a DMHT and those who did not.
The aims described above that examined significant differences by employment, distress, or DMHT use status were assessed using chi-square tests, Fisher exact tests, or independent samples t tests.All statistical analyses were performed with SAS version 9.4 (SAS Institute Inc).To adjust for increased type 1 error rates due to multiple tests, we applied the Benjamini-Hochberg procedure, which applies the acceptable fraction of tests that may be erroneously statistically significant, deemed the "false discovery rate" [41,42].We applied a false discovery rate (Q) of 10% to 119 statistical tests.
Open-ended responses from the DMHT survey for categories (ie, "What app did you try?If you tried more than one app, please pick the one you liked the most") and app features listed during the create-your-own-app survey were qualitatively coded.Like Rubanovich et al [43], the first author (FM-G) referenced the Apple App Store and Google Play to verify spelling and DMHT titles.As an example, Calm, CALM, Calm App, Calm, and Camh were all coded as "Calm."If a DMHT was unable to be identified via Google Play, Apple App Store, or an internet search, or the participant response was undecipherable (eg, "IDK," "NA"), it was categorized as missing (n=18).
Categorization of DMHTs was completed by authors FM-G and MJ.Informed by a modified grounded theory approach [44], each response was reviewed in order to identify meaningful units of information.Responses were compared with one another and grouped based on common responses until categories were identified.If the authors were unfamiliar with a DMHT, they read descriptions and reviews of the DMHT to determine its main feature.Some participants described DMHTs instead of names.In these cases, the response was coded for a DMHT category, but not for a specific DMHT title.As an example, the following responses, "I used a few meditation apps and one about CBT," "mindfulness app," and "meditation app" were coded into the mindfulness/meditation category.Categories and definitions were informed by Psyberguide, MIND, and experience working with digital mental health researchers.An identical process was conducted to code desired app features.

Data Exclusion and Cleaning
Duplicate cases were identified and removed.Missingness accounted for less than 5% of the data evaluated item by item.Measures were scored unless all items were missing.As an exception, PHQ-2, GAD-2, and CAGE-AID required all items to be answered to attain a final score.

Sample Description
A total of 2485 participants completed the initial screener.Of this, 598 (23.7%) observations were deleted due to missing IDs, duplicate responses, "bad actors," or not meeting inclusion criteria.The final analytic sample (Table 1) consisted of 1987 adults with 1013 (50.9%) participants reporting unemployment due to COVID-19 and 974 (49.0%) identifying as an essential worker during COVID-19.The most common open-ended responses for jobs among essential workers included education, customer service or retail, management, information technology (IT), health care, pharmacy, delivery or postal work, and food service (eg, cashiers, servers, restaurant workers, grocery store workers).Although we sampled throughout the United States, compared to the US census, the majority of the overall sample was European American (1538/1987, 77.4%, compared to the US census figure of 60%), with a somewhat higher representation of Asian Americans (238/1987, 12.0% vs 5% US census) and a lower representation of African Americans (172/1987, 8.7% vs 13% US census) and Latinx Americans (212/1956, 10.8% vs 18% US census) [45].The sample was almost split evenly between male and female (female: 1027/1987, 52.2%).
Compared to the essential workers, the unemployed group had significantly more people who identified as being: Hispanic or Latinx, or an unlisted race; younger; any gender other than male; any sexuality other than straight; and never married.The group comprised significantly less White individuals.Of note, there were almost twice as many in the "single or never married" category than what would be expected compared to the US census data [46]; however, our sample was relatively young (ie, early 30s) compared to the US population [47].Additionally, there were socioeconomic differences across groups.Compared to the essential workers, the unemployed group had significantly more individuals with lower education, less income, and lived somewhere other than a house or apartment.
Compared to participants that did not use a DMHT to cope with COVID-19 stress, DMHT users had a significantly higher proportion of individuals who identified as transgender and a lower proportion of individuals who identified as women or men.DMHT users were more likely to be married compared to non-DMHT users.In terms of socioeconomic differences, DMHT users had a significantly smaller proportion of individuals with lower levels of education and a higher percentage of individuals with higher education compared to non-DMHT users.Finally, compared to non-DMHT users, DMHT users were less likely to live in a house and more likely to live in an apartment.

Aim 1: Document Psychological Distress Among the Sample
Table 2 reports psychological distress (see the Measures section for calculation of the composite score) for the whole sample with stratification by employment status and DMHT-use status.We found that almost three-quarters of the sample fell into the "distressed" category (1479/1976, 74.8%), meaning they had scores at or above the clinical cut-off for at least one of the following: depression (PHQ-2), anxiety (GAD-2), risk for substance use disorder (CAGE-AID), risk for suicidal behaviors (SBQ-R), and history of suicide attempt.The unemployed group was more likely to be distressed than the essential worker group (815/1013, 81.2% vs 664/974, 68.3%; χ 2 1 =43.40,P<.001; Table 2).DMHT users were significantly more likely to be distressed compared to non-DMHT users (236/277, 85.2% vs 1234/1680, 73.5%; χ 2 1 =17.55,P<.001; Table 2).Table S1 in Multimedia Appendix 3 provides a further breakdown of depression, anxiety, risk for substance use disorder, risk for suicidal behaviors, and history of suicide attempt by total sample, employment status, and DMHT-use status.b P values <.05 and less than the Benjamini-Hochberg critical value were considered to be statistically significant.

Most Used DMHTs
Total Sample Among the total sample, which included 261 responses, the most used DMHTs were 2 meditation apps, Calm (41/261, 15.7%) and Headspace (38/261, 14.6%), followed by BetterHelp (11/261, 4.2%).A total of 119 participants (45.6%) reported using meditation apps, 25 (9.6%) reported using virtual therapy or DMHTs that facilitated contact with a virtual provider, and 21 (8.1%) used DMHTs with a chat feature (Table 3).Information about international or national occurrences (eg, WHO Info) News 1 (0.4) Using a DMHT to manage crisis or safety (eg, suicide) Crisis 1 (0.4) Using a DMHT in order to practice or learn a new language Language learning a A total of 18 responses were coded as "missing" due to being indecipherable or unidentifiable; percentages do not reflect missingness.

Employment Status
The leading entries by the unemployed sample were 3 meditation apps: Calm (

Reasons for Lack of Use
Most of the sample (1710/1957, 85.9%) reported that they did not use a DMHT to cope with COVID-19.The primary reasons for not using a DMHT to cope with COVID-19 were (1) not thinking to look for an app (1179/1710, 68.9%), (2) not thinking apps would help them (605/1710, 35.4%), and (3) having other ways of coping (421/1710, 24.6%).Table S3 in Multimedia Appendix 3 lists all reasons for lack of use.These top 3 responses were endorsed by all subgroups.There were differences that emerged by employment status and distress status.Compared to essential workers, those who were

Aim 3: Assess DMHT Usability and User Burden
Data for the following analyses were taken from the 277 participants who reported using a DMHT to cope with COVID-19.Individuals who did not report using a DMHT to cope with COVID-19 did not complete the SUS or UBS (Figure 1).

Employment Status
As shown in  c P values <.05 and less than the Benjamini-Hochberg critical value were considered to be statistically significant.
Finally, we explored the user burden and usability ratings of the three most used apps (ie, Calm, Headspace, and BetterHelp; shown in Table S4 in Multimedia Appendix 3).There were no statistically significant differences among the apps in terms of the total SUS, total UBS, and UBS subscales, except for privacy burden (Calm: mean 1.54, SD 2.82 vs Headspace: mean 0.50, SD 1.03 vs BetterHelp: mean 2.00, SD 2.14; F 2.87 =3.25, P=.04).
Participants also had the option to write in what DMHT features they felt were important to include but were not provided in the list of options.The top suggested features among the 764 responses were the ability to chat with a mental health XSL • FO RenderX professional, support personnel, or peer (n=57); entertainment and distraction (n=39); and positive psychology (n=29).The feature "entertainment and distraction" was defined as "different forms of entertainment such as music, movies, movie clips, GIFs, memes, games, or other forms of distraction."Additionally, participants reported wanting regularly occurring (ie, daily) gratitude exercises or activities to promote positivity, such as verses, quotes, and uplifting or hopeful stories, which we categorized as "positive psychology" features.Example responses included: "give positive messages in the morning or something like that," "daily gratitude," and "a good news section… I don't want to be told COVID-19 isn't a problem.I want to know what hope there is." When provided the option to build their own app, the sample most frequently endorsed the following features: mindfulness/meditation (1271/1987, 64.0%), information or education (1254/1987, 63.1%), and distraction tools (1170/1987, 58.9%) (Table 5).b P values <.05 and less than the Benjamini-Hochberg critical value were considered to be statistically significant.

DMHT Use Status
Participants who used DMHTs to cope during COVID-19 reported the following features as having the highest importance for a DMHT: (1) mindfulness/meditation (mean 7.10, SD 2.05); (2) tools to focus on the positive events and influences in life (mean 6.23, SD 2.24); (3) link to resources, counseling, or crisis support (mean 5.94, SD 2.64); and (4) symptom tracking (mean 5.90, SD 2.40).On the other hand, non-DMHT users indicated their most important features were (1) information or education (mean 6.  b P values <.05 and less than the Benjamini-Hochberg critical value were considered to be statistically significant.

Principal Findings
This study documented DMHT use among essential workers and unemployed individuals during the COVID-19 pandemic and determined which features such users would prefer to have in a DMHT offering.DMHT use has been deemed by many in the field to be subpar, and some have suggested that poor uptake and adherence to such tools is the result of user burden and inadequate match to user needs [17].Indeed, our findings indicate that despite reports of increased downloads [48] and user registration by digital mental health companies [13], use of DMHTs by essential workers and those unemployed due to COVID-19 is very similar to prepandemic reports (14%).Compared to our study (14%), previous studies found that 10% of outpatient psychiatric clinic patients used a DMHT [49] and only 17% of a sample with no self-reported mental health distress report downloading an app "to help relax" [50].
Of those who reported using a DMHT, by far the most common DMHTs were those that focused on mindfulness/meditation strategies (46%), with access to virtual therapy (10%) in second place.This finding did not vary by level of distress or employment status except among the nondistressed group using COVID-19 contact tracing (8% of this subsample).This finding is nearly identical to another recent study that found that Calm and Headspace were the top 2 downloaded apps among iPhone users during COVID-19 [48].
Additionally, when asked to rate the usability and user burden of the DMHT tool participants used the most, system usability fell in the "acceptable" range [34], and time, mental and emotional, physical, financial, and privacy burdens were seen as acceptable, with essential workers finding these tools to be more burdensome than the unemployed group.Increased perceived burdensome may be partially explained by previous findings suggesting that essential workers have increased fatigue from elevated anxiety and work demands during the ongoing pandemic [3].
Individuals with increased mental health needs (ie, the distressed group) reported more financial burden of DMHTs than the nondistressed.It is understandable that during a pandemic, where people are struggling financially, there would be concerns about the costs of DMHTs, given that many popular and widely publicized tools require a paid subscription.In the United States, those who lost their jobs during COVID-19 are faced with insufficient insurance to cover the costs of mental health care [51], and those who are struggling financially likely have additional financial concerns aside from a DMHT subscription fee, such as the cost of data plans and the technology needed to use these services.In fact, an earlier study noted that most individuals with depression and/or anxiety symptoms preferred using health apps that were free or had low cost for download (eg, <$5) [43].As such, reimbursement is one part of the solution for increasing access to care for everyone, and until technology is more affordably available to all, the use of these services will be compromised [52].
When asked to design their own DMHT for coping with COVID-19, again mindfulness/meditation was listed as an important feature for all subgroups in this study.Interestingly, information and education about COVID-19 was also consistently listed as an important feature in all subgroups except for people who had used DMHTs during the pandemic.In addition to mindfulness/meditation, people who used DMHTs to cope with COVID-19 preferred positive psychology tools and mood and sleep tracking.Figure 2 illustrates the preferences between the unemployed and essential worker groups.This finding has important implications for DMHT development focused on pandemic response and other prolonged environmental disasters.Developers would be able to create a single tool that includes mindfulness/meditation, information and education about COVID-19 coping, and distraction tools, which would appeal to a wide group of people with different needs during COVID-19, with only a few added features for specific populations.
A final finding in this study was reasons for not turning to DMHTs to cope with COVID-19.Most of the sample indicated that they did not use a DMHT because they did not think to look for such a tool.Past reports suggest that this result may be due to a lack of information about how DMHTs might be effective [53].This assumption is further supported by the fact that one-third of the sample did not think a DMHT would be helpful to them, and one-quarter of the sample indicated that they had other means of coping.The potential lack of confidence in DMHTs might be addressed through education to health providers on the effectiveness of DMHTs [54], the creation of reimbursement codes in the United States that would allow providers to prescribe these services [55], or the further use of a human-centered design from DMHT companies to create tools that are appropriately targeting user needs and concerns.

Comparison With Prior Work
A strength of this study is that we explicitly asked a large sample of users about their app preferences and perceived importance of various features.This survey was different from previous studies that have primarily focused on downloads and user metrics [48], insight from providers and private digital health companies [56], and self-report from individuals exclusively with mild depression or mild anxiety symptoms with exclusion of severer mental health conditions (eg, suicidality) [43].It is also novel in its consideration of user-centered design principles (eg, ease of use and learnability) when developing and identifying DMHT features that would be most acceptable to a very large sample of potential target consumers.Consistent with emerging models that integrate community-based research, implementation science, and user-centered design principles [57,58], this is an important first step in a well-planned process of DMHT design to identify the needs and preferred features that users, both experienced and unexperienced, and preferences for what tools they would like to see in a DMHT.Previous studies that used self-report of physical health and mental health apps found that users typically only use an app for one feature [43].It might be that future apps need to have multiple features incorporated to meet the overarching needs of similar populations.As Mohr et al [17] have noted previously, health app developers tend to create a tool based on what the developer feels is essential and historically only designs around these developer-driven features, rather than asking the end-user what role they see digital health playing in their lives, what needs they have that are unmet, and what functions they want these tools to have.By starting with understanding end-user needs and preferences, DMHT developers may see not only an increase in DMHT uptake but long-term use as well.
The findings of this study differ from findings in recent studies on the use of technology to cope with the consequences of COVID-19.According to recent research in the general population, there has been increased desire for apps or online resources that allow for fitness at home, owing to physical distancing and stay-at-home orders that have led to a shift from gyms and group fitness classes to exercise at home [59].During prepandemic times, Rubanovich et al [43] found that people with depression and anxiety symptoms reported more frequently using health apps featuring fitness, pedometers, or heart rate monitoring apps than DMHTs.Conversely, in our study, fitness apps and tools were listed very low in the list of tools participants used for coping with COVID-19.Although studies on the use of fitness apps among essential workers and employee groups are sparse, existing research suggests that the use of such tools in practice is low [60], which may explain why these tools were not in the top group of DMHTs listed by these participants.According to past research, those who are unemployed may likewise not have resources to engage in fitness apps, and generally are less likely to engage in fitness tracking [61].

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Finally, another COVID-19 study found that more contact tracing and COVID-19 informational apps were being downloaded than DMHTs in North America [62].We note here that downloads are often not equivalent to tool use as recent research has found that many people do download such tools but rarely use them long term [20,63].Our study specifically asked about which DMHTs people used to cope with COVID-19 stress.
Our study adds to the existing body of work by understanding how DMHTs could be made to be more accessible to those at risk for the emotional consequences of COVID-19.Many experts in digital mental health have argued for the need to better personalize such tools [54] and to include the perspectives of the intended consumer in the design of such tools [8].

Limitations
Although this study has important implications regarding the use of DMHTs from a human-centered design approach, it does have limitations.First, this is a cross-sectional study surveying the US participants' experiences and opinions at one point in time.Second, the participants of this sample are likely to be more accepting of digital tools, as they were recruited from an online research platform.As such, the information from this study is limited to those who are currently using and familiar with technology.Third, this study did not consider cross-cultural acceptance of DMHTs, which is an important caveat since a DMHT may be different in countries that already support such tools as part of their health care system.Fourth, we are unable to explicitly comment on the sample's overall experience with apps or DMHTs during prepandemic times.The focus of this paper was to explore whether users were using available, low-cost DMHTs to address COVID-19-related stress.Future studies should conduct a more thorough assessment of both current and previous DMHT use.

Conclusions
Despite the limitations, this study provides important information to the mental health care system and to those who develop and provide DMHTs during prolonged stressful events.Policy makers and providers may not be able to rely on existing DMHTs to address the emotional health of essential workers and people who are unemployed.This study points to the need to ensure DMHTs address the needs that the intended consumer feels is most important, that these tools are not burdensome under high-stress conditions, and that they are affordable to people who have limited means.©Felicia Mata-Greve, Morgan Johnson, Michael D Pullmann, Emily C Friedman, Isabell Griffith Fillipo, Katherine A Comtois, Patricia Arean.Originally published in JMIR Mental Health (https://mental.jmir.org),05.08.2021.This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited.The complete bibliographic information, a link to the original publication on https://mental.jmir.org/,as well as this copyright and license information must be included.

Finance 4 (
1.5)A DMHT with primarily writing or journaling features (eg, Day One,
a Chi-square test.bFisherexact test.cUnequal variance two-sample t test.

Table 2 .
Psychological distress stratified by employment status and digital mental health tool (DMHT) use.
a Chi-square test.

Table 3 .
Categories of digital mental health tools (DMHTs).

Table 4 .
User burden and system usability stratified by workers and psychological distress.
a Equal variance two-sample t test.bUnequal variance two-sample t test.

Table 5 .
Digital mental health tool (DMHT) features stratified by worker status and psychological distress.
a Chi-square test.

Table 6 .
Digital mental health tool (DMHT) features stratified by user status.