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. 2021 Jan 25;2020:906–914.

Evaluating Commercially Available Mobile Apps for Depression Self-Management

Annie Myers 1, Lewis Chesebrough 2, Ruixuan Hu 1, Meghan Reading Turchioe 1, Jyotishman Pathak 1, Ruth Masterson Creber 1
PMCID: PMC8075488  PMID: 33936466

Abstract

Clinical depression affects 17.3 million adults in the U.S. However, 37% of these adults receive no treatment, and many symptoms remain unmanaged. Mobile health apps may complement in-person treatment and address barriers to treatment, yet their quality has not been systematically appraised. We conducted a systematic review of apps for depression by searching in three major app stores. Apps were selected using specific inclusion and exclusion criteria. The final apps were downloaded and independently evaluated using the Mobile Application Rating Scale (MARS), IMS Institute for Healthcare Informatics functionality score, and six features specific to depression self-management. Mobile health apps for depression self-management exhibit a wide range of quality, but more than half (74%) of the apps had acceptable quality, with 32% having MARS scores ≥ 4.0 out of 5.0. These high scoring apps indicate that mobile apps have the potential to improve patient self-management, treatment engagement, and mental health outcomes.

Introduction

Clinical depression— defined as a prolonged period of sadness, loss of interest, and impairment in daily functioning— is highly prevalent in the U.S.; it affects more than 17.3 million adults including 13.1% of young adults between the ages of 18 and 25 and 13.3% of adolescents between ages 12 and 17.1 Approximately 35% of adults with depression and over half of adolescents do not receive any form of treatment.1 Patients face numerous barriers to accessing professional treatment for depression, including social stigma, lack of financial resources and health insurance, lack of appropriate transportation and time, not even knowing where to receive treatment, and lack of faith in the efficacy of treatment.2 Lack of treatment for depression leads to several consequences such as worsening symptom severity, societal burdens such as higher healthcare costs, and suicide.3,4

Mobile health (mHealth) technology presents exciting opportunities to address barriers to access and delivery of depression treatment. Currently, 96% of American adults own a cellphone and 81% own smartphones.5 In 2018, 95% of adolescents reported that they use smartphones daily.6 Further, younger adults spend an average of three hours a day on their smartphones.7 As such, mHealth applications (apps) on smartphones can be leveraged to offer a convenient, affordable, and accessible option for the self-management of depression, especially as many adults and adolescents with depression do not formally engage in mental health treatment.8 Specifically, mHealth apps offer low-cost, in-home treatment by reducing travel to in-person psychiatric care, concerns about cost, stigma, and time associated with seeking depression treatment may be mitigated. Apps may also offer a broader set of self-management tools for depression including promoting mindfulness, social support, healthy sleep and eating habits, appointment and therapy adherence, and diet, nutrition and/or exercise. While some in-person care is clinically necessary for many patients, apps may be a complementary solution to in-person care that reduces the burden of depression treatment for patients.

Several studies have shown that the use of mHealth apps can effectively reduce symptoms of depression.9-11 In one study of 96 individuals with severe symptoms of depression and anxiety, participants reported a reduction in depressive symptoms by 50% after the mHealth intervention.9 Another study measured the effectiveness of a mobile app for reducing depressive symptoms in the workplace and found that after five weeks of use, depressive symptoms were significantly lower, self-reported job performance was higher, and more than 90% claimed that it helped improve their mental fitness.10 A 2019 study that reviewed 19 RCTs focused on mHealth apps for mental health disorders including depression found that individuals using apps targeting overall depression had significant pooled effects on improvements in self-reported symptoms using validated surveys compared to control groups.12

While these literature reviews have appraised the effectiveness of a small number of mental health apps, many of which are used only in academic research settings, they do not address the abundance of commercially available depression apps that consumers are more likely to find and use without adequate clinical guidance or recommendation. In particular, one of the existing challenges consumers face when considering mHealth apps for self-management of depressive symptoms is uncertainty around app quality, which can be defined as the caliber of an app’s technical functioning (navigation, aesthetics, usability) and content (evidence-based information, useful features).13 In previous systematic reviews of mHealth apps for mental health, the lack of clinical guidelines and efficacy have limited widespread adoption and use.14-16 Mobile interventions that are not rigorously evaluated may provide unhelpful or even harmful mental health information for consumers. Further, apps that are perceived as ineffective may reinforce the belief that there are no efficacious treatments or self-management tools for mental health. This study aimed to systematically review commercially available apps for depression self-management, including a thorough assessment of quality, functionality, and features using a validated review methodology.

Methods

We followed a previously established methodology for systematically searching and reviewing commercially available apps.17 From February to March 2019, a systematic review of mobile apps for depression was conducted by entering six relevant search terms across three major mobile app marketplaces: Android Google Play Store, Apple App Store, and the Amazon Appstore. Search terms were: "depression," "mood improvement," "mental health self-management," "depression monitoring," "depression screening," and "depression self-management." Four rounds of reviews were completed to account for the pre- specified exclusion criteria (Figure 1). During the first round, most of the excluded apps were unrelated to depression or were games or books. During the second round, apps were excluded if they were educational (for students) or diagnostic only, or only tracked mood using a diary feature. In the third round, apps that were targeted for anxiety only, no longer available, or required a health system login were also excluded. The final apps were downloaded and screened to ensure that they were functioning and not duplicative, ending with a final list of 31 evaluable apps. Each app was reviewed by at least two reviewers using either iOS on an iPhone, or Android OS on a Samsung phone, depending on where the app was available; some apps were only available in one app store.

Figure 1.

Figure 1.

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PRISMA flowchart of screening process

The final apps were independently reviewed and rated according to the Mobile Application Rating Scale (MARS), IMS Institute for Healthcare Informatics functionality score, and six specific depression self-management features. The MARS evaluates apps on four objective quality scales: engagement, functionality, aesthetics, and information quality.18 MARS also includes behavior change (anticipated effect of the app on the user’s behaviors) and subjective quality (overall satisfaction with the app) scales. All MARS scales are scored using a 5-point Likert scale, with 1 being inadequate and 5 being excellent quality.

The IMS functionality score consists of seven functionality criteria, including the ability to communicate, display and record data, and guide, inform, instruct, and remind users.19 The record criteria includes four sub-criteria: collect, share, evaluate, and intervene upon data. These functionality criteria delineate specific app functions, compared to the MARS functionality scale which evaluates how well the app functions overall.

Finally, each app was evaluated on six specific self-management behaviors for depression: treatment adherence, social support, exercise/nutrition, sleep, and mindfulness. These behaviors were selected based on reviews of existing mental health app features, in the absence of an official guideline of mHealth app features for depression.20-22

Data were also extracted from the app stores including average star rating, number of downloads, version number, and cost. Each reviewer first rated the same four apps to evaluate inter-rater reliability (IRR) using the kappa statistic, with κ ≥ 0.7 indicating good reliability. All subsequent apps were reviewed by at least two independent reviewers.

Results

Out of the 1,198 potentially relevant apps retrieved from the initial searches using the six search terms, 31 apps met the final inclusion criteria. The selection process and exclusion criteria are shown in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram (Figure 1). Most apps were excluded because they were unrelated to depression (n= 394), were games, books, videos, etc. (n=154), were designed for educational purposes (for students) only or diagnostic purposes only (n= 117).

Table 1 provides the full list of the included apps and their characteristics. Among the 31 included apps, 77% (n=24) were free to download, but 23% (n=7) ranged in cost from $4.99 to $99 per month. All of them offered at least a three-day free trial for users to experience before purchasing. The vast majority of apps (84%, n=26) were updated within the past year, and most of the apps were updated within the past three months (73%, n=19). The average consumer star rating across the apps was 4.36 out of 5. Overall, among the 18 apps with accessible rating information, 67% (n=12) had fewer than 5,000 ratings, and 33% (n=6) had 5,000 to 500,000 ratings. Over 90% (n=28) of the apps had in-app privacy policies, though interpretability of these policies and the use of lay language was not evaluated.

Table 1.

Descriptive Characteristics of Included Apps

App Name Platform(s) Star Rating Installs Version Cost Privacy Policy Total IMS
Funct Score▪
Pacifica (now Sanvello) Apple, Google 4.8 N/A 7.1.3 Free Yes 10
Snapclarity: Online Therapy Apple, Google 4.9 1,000+ 1.5.2 $39.99/w Yes 10
Joyable Apple, Google 4.3 N/A 3.6.4 $99/m Yes 9
BetterHelp: Online Counseling Apple, Google 4.4 100,000+ 7 $35/w Yes 9
Wysa: Anxiety and Depression Bot Apple, Google 4.4 N/A 4.4.4 Free Yes 8
Youper: AIMindfulness Apple, Google 5 500,000+ 6.06.008 Free Yes 8
HelloJoy Apple 5 N/A 1.4.5 Free Yes 8
InnerHour: Live Happier Apple, Google 4.4 N/A 3.04 Free Yes 8
DBT Daily Apple, Google 4.6 5,000+ 2.0.5 Free Yes 8
Depression Manager Apple N/R N/A 10.3.1 Free Yes 8
Talkspace: Online Therapy Apple. Google 4.1 N/A 8.48.06 Free Yes 7
Woebot: Your Self-Care Expert Apple, Google 4.6 N/A 3.2.5 Free Yes 7
Elevatr Apple 4.8 N/A 1.7.6 $4.99/m Yes 7
Moodpath: Depression and Anxiety Apple, Google 4.7 N/A 3.0.0 Free Yes 7
CBT Mental Health Application Apple, Google N/R N/A 1.01 Free No 7
GG Depression Apple, Google 5 100+ 1.3 Free Yes 6
iPrevail Apple, Google 3.5 N/A 3.6.5 Free Yes 6
Lift: Depression and Anxiety Apple, Google 2.5 100+ 1.0.2 Free Yes 6
Mental Health Intervention Apple, Google 4.2 1,000+ 1.6.1 $9.99/m Yes 6
AkiliHealth Google 5 10+ 1.3 Free No 5
BoosterBuddy Apple, Google 4.25 100,000+ 1.6 Free Yes 5
feel better: Moods & Goals Apple, Google 4.4 5,000+ 1.0.2 Free Yes 5
Depression Support Apple, Google 4.3 1,000+ 11.7.0 Free Yes 4
dew.: Mental Health Navigator Google 3.8 100+ 1.0.2 Free Yes 4
Psychologist: Anxiety, Depression, Stress, Couples Google 4.2 50,000+ 1.59 $24.99/w Yes 4
Unthink: CBT and Mental Health Apple, Google N/R N/A 1.0.3 Free Yes 4
PsyHeal Google 5 100+ 1.0.7 $8.40/m Yes 3
What’s Up?: A Mental Health App Apple, Google 4.6 100,000+ 2.3.1 Free Yes 2
LightExistence: Depression Aid Google 4.5 500+ 1.2 Free No 2
Life Reboot: Fight Depression Google 3.9 10,000+ 1.6 Free Yes 1
Depression Group Therapy Google 3 100+ 02.2019 Free Yes 0
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Abbreviations: m= monthly, N/A: not applicable; N/R: not reported, w= weekly, Star rating: 0-5

▪

This total functionality score refers to the IMS Functionality ranging from 0-11 (including the four sub-criteria for the “record” criteria).

Functionality

Figure 2 displays the functionalities of the included apps based on their IMS Institute for Health Informatics functionality scores. Across the 31 apps, the average number of functionalities was six out of a possible 11 (including the four sub-criteria for the “record” criteria). Most (71%, n=22) had seven or fewer functions, and 32% of apps (n=10) had four or fewer functions. Almost all of the apps had a record function (90%, n=28), and inform function (87%), n=27). Twenty-two had functions to remind or alert (71%), 16 to display (52%), 14 to instruct (45%), 13 to communicate (42%), and 11 to guide (35%). Pacifica and Snapclarity had the highest scores for functionality, both offering 10 of the 11 functionalities (Table 1).

Figure 2.

Figure 2.

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Functionality of apps based on IMS scores

Among the 28 apps that had a record function, 96% (n=27) could collect data but only 25% (n=7) had the option to share the data with healthcare providers. Eight apps (29%) could evaluate the entered data, but only 7% (n=2) had a function to intervene by sending text alerts based on the self-reported data collected. Most of the apps focused on recording and collecting data for daily mood, sleep, and physical activity. They also provided educational information about understanding and managing depressive symptoms. However, personal data analysis and feedback was not always provided within the apps.

There were interactive features within some apps, including either communicating with an automated chatbot, or contacting licensed therapists or counselors. For example, Woebot, Wysa Anxiety and Depression Bot, and Youper offer real-time interactions with an artificial intelligence (AI) enabled chatbot to collect and record daily mood. Snapclarity, Talkspace, and Betterhelp offer users the option to talk one-on-one with licensed therapists and counselors. Further, Snapclarity provides users with shareable personalized mental health strategies, and connects them with qualified professionals who specialize in their areas of concern based on an initial in-app assessment. Users can connect to a therapist at any time through text or video chat. Pacifica has a feature that allows users to share their mood, goals, stress, and personal experiences to a peer support community designed to share daily accomplishments, anecdotes, and inspiration. Users also have the option to create or join specific chat groups such as an “anxiety” group or “afraid to fly” group to connect with other users going through the same experiences.

MARS App Quality Scores

Table 2 presents the overall MARS quality scores for the included 31 apps, and the four subscale scores (engagement, functionality, aesthetics, and information quality). The IRR between reviewers was considered high across the four MARS quality factors (κ=0.75 – 0.83). The overall MARS scores ranged from 1.6 to 4.6, and the average was 3.5 out of 5. More than half of the apps (74%, n=23) had acceptable MARS scores (≥3.0) and among those, 32% (n=10) had MARS scores ≥ 4.0. The functionality subscale had the highest average (4.1 out of 5), and the satisfaction subscale had the lowest (2.8 out of 5). Pacifica had the highest average total MARS score (4.6 out of 5), followed by Wysa Anxiety & Depression Bot (4.2), Youper – AIMindfulness (4.1), InnerHour- Live Happier (4.1), Elevatr (4.1), and GG Depression (4.1).

Table 2.

Top Rated Apps based on the Mobile Application Rating Scale (MARS)*

App Name Engage Function Aesthetic Info Satisfaction Behavior Change Overall Score
Pacifica (now Sanvello) 4.8 4.6 4.5 4.2 4.6 4.8 4.6
Wysa: Anxiety and Depression Bot 4.5 4.6 4.6 3.9 3.6 4.3 4.2
Youper: AIMindfulness 4.4 4.4 4.3 3.9 3.8 4.2 4.1
InnerHour: Live Happier 4.2 4.8 4.1 3.6 4.0 4.1 4.1
Elevatr 4.3 4.5 4.5 3.4 3.6 4.4 4.1
GG Depression 4.3 4.4 3.6 3.6 3.6 4.3 4.1
Woebot: Your Self-Care Expert 3.8 4.4 4.2 4.2 3.8 3.8 4.0
Talkspace: Online Therapy 3.8 4.8 4.2 3.7 3.4 4.1 4.0
Joyable 3.7 4.3 4.4 4.0 3.4 3.9 4.0
Moodpath: Depression and Anxiety 4.2 4.4 4.7 3.8 3.8 3.8 4.0
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*

MARS scales are scored using a 5-point scale, with 1 being inadequate and 5 being excellent quality.

Behavior Change

Using the MARS behavior change score, the average score for behavior change was 3.5 out of 5. More than half of the apps (81%, n=25) had acceptable behavior change scores (≥3.0). Pacifica had the highest score for behavior change (4.8), followed by Elevatr (4.4), Wysa Anxiety & Depression Bot (4.3), and GG Depression (4.3).

Depression Self-Management Features

Table 3 includes the six depression self-management features. The most common feature addressed within the apps was promoting mindfulness and a positive attitude (81%, n=25), followed by social support (52%, n=16), healthy sleep habits (45%, n=14), treatment adherence (42%, n=13), nutrition and/or exercise (32%, n=10) and medication adherence (13%, n=4). The highest performing apps were AkiliHealth, Booster Buddy, InnerHour-Live Happier, Depression Manager, and Lift, which addressed five of the six depression self-management features.

Table 3.

Top Rated Apps based on Depression Self-Management Features Included

App Name Medication Adherence Social Support Treatment Adherence Exercise/Nutrition Sleep Habits Mindfulness Overall Score
AkiliHealth ✓ ✓ ✓ ✓ ✓ 5
BoosterBuddy ✓ ✓ ✓ ✓ ✓ 5
Depression Manager ✓ ✓ ✓ ✓ ✓ 5
InnerHour: Live Happier ✓ ✓ ✓ ✓ ✓ 5
Lift: Depression and Anxiety ✓ ✓ ✓ ✓ ✓ 5
Pacifica (now Sanvello) ✓ ✓ ✓ ✓ 4
Woebot: Your Self-Care Expert ✓ ✓ ✓ ✓ 4
Youper: AIMindfulness ✓ ✓ ✓ ✓ 4
DBT Daily ✓ ✓ ✓ 3
feel better: Moods & Goals ✓ ✓ ✓ 3
iPrevail ✓ ✓ ✓ 3
Joyable ✓ ✓ ✓ 3
Wysa: Anxiety and Depression Bot ✓ ✓ ✓ 3
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Overall App Quality

After factoring in the MARS score, IMS Institute for Healthcare Informatics functionality score, and depression self- management features, the highest performing apps included Pacifica, Youper, Innerhour-Live Happier, Wysa, Joyable, and Woebot.

Discussion

The purpose of this study was to systematically evaluate the quality and functionality of mHealth apps for depression self-management. Based on our review, there was a wide variation in quality when evaluating apps using the MARS and IMS Functionality Score. While evidence suggests mHealth apps hold significant promise in delivering treatment for depression more broadly, our review highlights key areas of improvement for commercially available apps.

In the past five years, research on the effects of mobile apps included in this review has grown and are showing positive outcomes. Results from a RCT using Woebot found that participants who used the app for two weeks showed a greater reduction in depressive symptoms compared to participants who received an informational e-Book on depression.23 Another recent study encouraged adults with depressive symptoms to use Pacifica as a self-management tool and found that participants who used Pacifica consistently had improved Patient Health Questionnaire-9 scores compared to infrequent users.24 Regarding apps that provide mobile therapy and counseling, a pilot trial found that adults who engaged in Talkspace found the app to be effective, convenient, and affordable.25 Another recent feasibility study found that the use of Betterhelp reduced depressive symptom severity in individuals who have not previously engaged in psychotherapy.26 Further, there has been growing research focusing on the effectiveness of chatbots, which were available in three apps in this review. Chatbots, driven by AI, are programmed to mimic human interactions, interpret the text and emoticons that the user shares and offer responsive conversations, guidance, and helpful advice.27 While talking with the Chatbot, users may also have access to educational information, training courses, and diagnostic test results.

However, our review also highlighted issues with existing commercially available apps that require further attention. First, consumers may have difficulty locating salient applications for depression treatment. In a study looking at the process of identifying an appropriate app for depression, the authors found that significant barriers exist for users to find an appropriate app in the three marketplaces.28 Overall, irrelevant apps outnumbered relevant apps by 3:1. Furthermore, 27.7% of the apps did not include depression in their names, 65% failed to describe their organizational affiliations, and 61.7% did not cite their content sources, making it very difficult for users to assess credibility and reliability. Our search process encountered these challenges, and from our initial list of 1,198 apps, a majority were irrelevant to depression. Many of the apps that were evaluated had questionable quality of information without any clear or reliable source. Since prior reviews of depression mHealth apps were completed, the number of available apps has greatly increased and is apparent by comparing the 243 apps identified in a previous systematic review28 to our initial list of 1,198 apps. This rapidly changing landscape reinforces the need for more streamlined and efficient methods to support patients in locating relevant apps for depression.

Once downloaded, we noted tradeoffs in the apps between cost, quality, and functionality. Specifically, of the seven out of 31 apps we reviewed that cost money, four scored above a 3.0 on the MARS, and two scored a 4.0 or higher on the MARS. Notably, there were several high-quality apps that were free, with seven free apps scoring 4.0 or higher on the MARS. However, there was also discordance between quality and functionality within apps; many of the apps that scored high on overall quality had limited features, and those that had many features had poorer quality. These findings are concordant with other literature reviews of apps for depression. Another study of apps that offered evidence-based methods for cognitive behavioral therapy conducted in 2016 found a high variance in functionality and quality, and almost all of the apps lacked a privacy or safety policy statement.14 We found a similarly high level of variance in quality and usability, but in contrast, we found that only three apps lacked a privacy policy. While we did not study the policies in-depth, this general trend would suggest the app developers are more aware of app user and data privacy issues.

mHealth technologies could play a significant role in addressing the current knowledge gaps that exist within depression self-management. As stated, many adults and adolescents do not receive any treatment for their depression. mHealth technologies could help bridge the gap by increasing access to care and accurate depression screening and help determine if this untreated population can benefit from mobile technology self-management approaches. mHealth also has the ability to generate vast amounts of data to help us understand how depression affects our population, and what self-management approaches produce beneficial outcomes. Further, the data generated could be rebuilt into apps as informatics-driven health interventions. For example, a certain event recorded in the app could automatically notify a provider when a patient is having a serious depressive episode or is at immediate risk for suicide. mHealth apps can enable research into the population who suffer without options for treatment and self-management, lead to more cost- effective methods of managing depression, and reduce the overall burden on our health system. This vision for use of depression apps in practice is supported by a recent systematic review examining mental health apps in RCTs, which found significant positive effects for the apps tailored to depression.12 Nonetheless, these apps are not recommended to act as standalone mental health treatment at this time.

In order for the promise of mHealth apps for depression to be realized, significant future research and app development efforts are needed. Specifically, there is a need to support consumers in appraising the quality of commercially available apps. Moreover, building on the early positive results of RCTs of depression apps, implementation science studies on the integration of these apps into clinical workflows as a complement to in-person clinical care are needed. Moreover, more multilingual apps and inclusion of telehealth features would expand access and use by a greater segment of the population, particularly disadvantaged individuals. Furthermore, future research on depression mobile app quality could classify the apps into categories based on their intended functionality. The various therapeutic approaches should not be evaluated as equal, given that their features and goals differ significantly ranging from chatbots to social support networks. A challenge to such a sub-analysis is that many of the apps reviewed incorporated a variety of approaches to self-management. For instance, an app might include a combination of video-based therapy, mood tracking, and education on therapeutic concepts. Meta-analyses to evaluate improvements in depressive symptoms and other health outcomes based on different app features, or combinations of features, could be valuable in understanding which features are most beneficial.

Limitations

A challenge to this study is the subjective nature and risk of bias when reviewing mHealth apps designed for mental health. The standardized methodology of the MARS and the usage of IRR to ensure agreement helps mitigate subjectivity. The authors were limited in the scope of apps that were available to evaluate, since a few promising apps were only accessible for institutional users, and non-English apps were excluded. Another limitation of this study is the lack of validated guidelines for mHealth interventions specifically for depression self-management. The depression-management app features were based on a previous literature review16 and the authors’ judgment of which features would be incorporated into a comprehensive set of approaches for depressive symptom management. Ideally, an authoritative body of mental health and mHealth experts would establish standard, evidence-based criteria for beneficial self-management features for depression.

Conclusion

Apps designed to help manage depressive symptoms hold great potential for individuals living with depression. However existing tradeoffs in app quality and functionality, and lack of guidelines for incorporating evidence-based behavior change for depression management into mHealth, limit the full potential of these apps from being realized. As such, there is an urgent need for peer-reviewed research on mHealth interventions for depression to strengthen our understanding of what features and approaches are efficacious in reducing symptoms and improving quality of life and long-term patient outcomes.

Acknowledgements

The authors would like to thank Lingchen Lou who assisted with app reviews and evaluations. This work was funded in part by funding from NIH R01MH105384, R01MH119177, R01MH121922, P50MH113838, and R00NR016275.

Figures & Table

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Articles from AMIA Annual Symposium Proceedings are provided here courtesy of American Medical Informatics Association

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