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Functionality of Top-Rated Mobile Apps for Depression

Systematic Search and Evaluation

ABSTRACT

Background: In the last decade, there has been a proliferation of mobile apps claiming to support the needs of people living with depression. However, it is unclear what functionality is actually provided by apps for depression, or for whom they are intended.

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Objective: This paper aimed to explore the key features of top-rated apps for depression, including descriptive characteristics, functionality, and ethical concerns, to better inform the design of apps for depression.

Methods: We reviewed top-rated iPhone OS (iOS) and Android mobile apps for depression retrieved from app marketplaces in spring 2019. We applied a systematic analysis to review the selected apps, for which data were gathered from the 2 marketplaces and through direct use of the apps. We report an in-depth analysis of app functionality, namely, screening, tracking, and provision of interventions. Of the initially identified 482 apps, 29 apps met the criteria for inclusion in this review. Apps were included if they remained accessible at the moment of evaluation, were offered in mental health–relevant categories, received a review score greater than 4.0 out of 5.0 by more than 100 reviewers, and had depression as a primary target.

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Results: The analysis revealed that a majority of apps specify the evidence base for their intervention (18/29, 62%), whereas a smaller proportion describes receiving clinical input into their design (12/29, 41%). All the selected apps are rated as suitable for children and adolescents on the marketplace, but 83% (24/29) do not provide a privacy policy consistent with their rating. The findings also show that most apps provide multiple functions. The most commonly implemented functions include provision of interventions (24/29, 83%) either as a digitalized therapeutic intervention or as support for mood expression; tracking (19/29, 66%) of moods, thoughts, or behaviors for supporting the intervention; and screening (9/29, 31%) to inform the decision to use the app and its intervention. Some apps include overtly negative content.

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Conclusions: Currently available top-ranked apps for depression on the major marketplaces provide diverse functionality to benefit users across a range of age groups; however, guidelines and frameworks are still needed to ensure users’ privacy and safety while using them. Suggestions include clearly defining the age of the target population and explicit disclosure of the sharing of users’ sensitive data with third parties. In addition, we found an opportunity for apps to better leverage digital affordances for mitigating harm, for personalizing interventions, and for tracking multimodal content. The study further demonstrated the need to consider potential risks while using depression apps, including the use of nonvalidated screening tools, tracking negative moods or thinking patterns, and exposing users to negative emotional expression content.

KEYWORDS

mobile apps; depression; review; ethics; mHealth 

INTRODUCTION

​Background

Depression is a major affective disorder with significant socioeconomic cost [1], affecting over 300 million people worldwide [2] across the life span [3]. However, access to treatment is problematic [4] given the acknowledged barriers such as high treatment cost, time constraints [4], geographical location [5], and stigma [4-7]. With over 90% worldwide penetration [8], mobile phones have significant potential to scale up the provision of interventions targeting depression [9]. They are especially useful to reach users who do not normally seek professional support, such as adolescents [10]. Prior work has already indicated a high user acceptance and effectiveness of mobile-delivered interventions for depression [11,12]. The number of mobile apps available on marketplaces offering treatment for depression has also been growing rapidly [9,13].

The apps available on mobile phone marketplaces provide access to a range of interventions targeting depression [14-16], which people can select and download to fit their needs [17]. Yet, users acting independently can only select apps based on information that is available at the point of download, ie, popularity, user ratings, or app descriptions provided on the marketplaces. Evidence for supporting assessment of the quality of an app, ie, structured description of its main features, evidence-based functionality, and potential risks, is not reflected in user ratings of apps [18,19]. Additionally, marketplaces do not require app developers to provide such information [20,21]. As a result, concerns have been raised regarding the lack of an evidence base for mental health apps [15,19,22] and poor regulation of the major mobile marketplaces [23-25] hosting them. Prior work [26] has also suggested the importance of having controlled clinical trials to determine the efficacy of new therapeutic treatments. In this newly established field of mobile health (mHealth) apps, most apps claim to be informed by evidence-based treatments rather than presenting rigorous evaluations of the app itself.

Besides efficacy, understanding patients (eg, their characteristics, needs, and behaviors) is also key for improving the uptake of apps [26,27]. Most human-computer interaction (HCI) studies on understanding [28-30] or supporting depression have focused on designing and evaluating mobile technologies in research contexts rather than marketplaces [31-33]. Scholarly work has also called for the evaluation of commercial apps for depression to support the effective development of the rapidly growing market of commercial apps [10,13,15]. However, such evaluations tend to focus in isolation on specific aspects such as ethics [34] and safety [35] or on specific interventions such as cognitive behavior therapy (CBT) or acceptance and commitment therapy (ACT) [10,19]. Moreover, previous evaluations tend to analyze app information from marketplaces without the actual experience of using the apps [15].

Objectives

This paper addresses these limitations by focusing on a broader range of interventions and functionality of the top-rated apps for depression. Thus, we focused on the following research questions:

  1. Which are the key functionalities of the top-rated apps for depression available on iPhone OS (iOS) and Android marketplaces?

  2. Is this functionality described and delivered in a way that supports user privacy and safety?

METHODS

Overview

This paper focuses on apps selected in spring 2019 from 2 major marketplaces, iOS and Android, whose analysis triangulates (1) reviewing app ratings on marketplaces to identify the top-rated apps for depression, (2) reviewing app descriptions on marketplaces, and (3) experimental evaluation through author interaction with the apps as expert HCI researchers [36,37].

App Selection

We now describe the selection process (Figure 1). The apps were initially identified through the 2 keywords “depression” and “depressed” entered into App Crawler and Google Play search engines. A script was used [38] to extract all the apps shown in the search results. The script automatically downloaded information for each app from its marketplace, including name, category, marketplace description, price, review score, and number of reviewers. This resulted in 482 apps, and after removing duplicates, 444 apps were included in the later selection.

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The strategy for app selection outlined in Figure 1 aimed to include top-rated publicly available apps targeting primarily depression. From the initially identified 444 apps, we excluded those that (1) had less than 100 reviews; (2) were inaccessible at the time of selection; (3) belonged to irrelevant marketplace categories such as social, casual, business, news, or book; and (4) had average user review scores lower than 4.0 (out of 5.0). The application of these criteria on the initial set of 444 apps resulted in 94 apps for consideration.

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From these apps, we further excluded those that did not focus primarily on depression by employing the following criteria: (1) the words “depression” or “depressed” do not appear in the app’s title or marketplace description of the app, (2) the primary target is not depression (eg, yoga tracker), and (3) their marketplace description mentions that people with depression should not use the app. These criteria led to 31 apps, from which we further excluded 2 more apps as their functionality was limited to the provision of therapy sessions to be purchased in-app. The remaining 29 apps were analyzed in this review (see Multimedia Appendix 1).

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Data Extraction

Descriptive characteristics of the apps were extracted from the information provided on the marketplace. These included category, costs, target audience, whether they claimed to be evidence-based (including explicit scientific underpinning and clinical input), and data supporting analysis of ethical aspects such as the privacy policy.

To extract data on app functionality, between June and October 2019, 2 rounds of experimental evaluation [36,37] were used in which the authors as HCI experts interacted with the apps using both Android and iPhone mobile devices (ie, Samsung tablet and Xiaomi phone for Android apps and iPhone for iOS apps). The entire set of apps was evaluated by 2 authors (CQ and CD), and 21% (6/29) of the apps were evaluated by all authors. The coding scheme was iteratively revised until agreement was reached among all the coders. The coding process was hybrid, integrating both deductive and inductive coding. Informed by prior work on the classification of mHealth apps [14], the deductive codes consisted of 3 main types of functionality of depression apps: screening, tracking, and provision of interventions (Table 1). The inductive coding [39] allowed the identification of specific subcodes under each of the main functionality described above. For instance, the screening function was broken down into subcodes such as symptom monitoring, self-diagnosis, and basis for personalization.

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Table 1. Main codes and subcodes from functionality’s evaluation.

View this table

RESULTS

Overview

The description of findings is organized into 3 parts. The first outlines a broader picture focusing on descriptive app characteristics (eg, categorization). The second part covers ethical considerations. The third part looks in more depth into specific functionality such as screening, tracking, and provision of interventions.

Descriptive Characteristics

This section describes the characteristics of the selected apps, for example, the main categories under which depression apps are classified on marketplaces, their target audience, costs, evidence base, medical disclaimer, and whether involving of clinicians’ guidance while using the apps.

Categorization

The 29 apps reviewed in this study belong to 3 categories used to describe apps on the marketplaces. The most popular category is health and fitness (18/29, 62% apps), followed by lifestyle (4/29, 14% apps) and medical (7/29, 24% apps).

Targeted Audience (Age Group)

An important finding is that app marketplaces rate all apps as suitable for nonadult users (Multimedia Appendix 2). Most of the selected apps were classified as being suitable for children from preschool age: 76% (22/29) of apps were rated for those older than 3 years, 3% (1/29) for those older than 4 years, 7% (2/29) for those older than 12 years, 3% (1/29) for those older than 16 years, and 10% (3/29) with parental guidance.

However, only 41% (12/29) of the apps provide a privacy policy intended to protect children’s data. Half of these privacy policies (7/12, 58%) claim to restrict users to a specific age group, albeit this approach is inconsistent with the app’s age rating on the marketplace. For instance, one app (A8, see app_ID in Multimedia Appendix 2) states in its privacy policy that the app does not provide services to users who are younger than 18 years; in contrast, it is rated on the marketplace as Pan European Game Information (PEGI) 3. This may be because of a mismatch between age rating definitions oriented around the inclusion of material such as violent content, and health care apps that should have age restrictions because of the personal and sensitive nature of the content, with associated risk for harm.

In addition, all the apps apply the same design across all ages, and we did not find any customization for users who are children, such as involving in-app interactions to allow parents to collaborate or monitor their children while using the app [40].

Targeted Audience (Clinical Nosology)

All included apps claim to target users with depression. Most of the apps (20/29, 69%) represent depression as a lack of well-being (eg, feeling stressed or having low mood). Less than one-fifth of the apps (5/29, 17%) actually represent depression as a mental disorder, whereas only 1 app (A18) employs Patient Health Questionnaire-9 (PHQ-9) [41] to assess the severity of symptoms. Another 14% (4/29) of apps do not claim to target depression as a disorder, yet employ validated tools for assessing users’ depressive symptoms. Furthermore, none of the apps claims to target users with a specific level of severity (ie, mild, moderate, or severe depression).

Costs

An important finding is that although most of the apps (28/29, 97%) are free to download, at least some of their costs are covered either directly or indirectly by users (Multimedia Appendix 2). The direct costs consist of explicit charges for more advanced features, whereas indirect costs relate to users’ forced consumption of in-app advertisements. In-app purchase was offered by 66% (19/29) of the apps, mostly as a subscription priced between US $3.99 to US $29.99 per month, or as paid online therapy sessions (US $35/hourly session over call, video, or chat, A11). Advertisements were provided by 34% (10/29) of apps, which raises privacy concerns. Of the apps with advertisements, 80% (8/10) stated specifically in their privacy policies that users’ information, captured for instance through cookies, would be collected and shared with third parties, including advertisers or analytics providers. Only 1 app that offered advertisements claimed that users’ data would not be collected or shared (A29), whereas another app (A7) did not provide a privacy policy in English. Only 17% (5/29) of apps that are free to download neither request in-app purchase nor provide advertisement. Only 1 app requires purchase (for US $4.99) before downloading.

Evidence Base

Developers of 62% (18/29) of the apps have specified a scientific underpinning for their app design, whereas another 38% (11/29) do not make such a claim (Multimedia Appendix 3). Almost half of the apps (14/29, 48%) claim to be designed based on validated psychological treatments (eg, CBT, ACT, dialectical behavior therapy, and mindfulness). The remaining 14% (4/29) are designed based on theories pertaining to gamification, hypnosis, and affirmations. However, only 7% (2/29) of the apps provide direct evidence in the form of peer-reviewed scholarly work on the efficacy of the app for reducing depression symptoms [42,43], whereas another 34% (10/29) of apps provide indirect evidence of efficacy of their underpinning theories without referencing any academic work. For instance, 8 apps (A3, A4, A5, A15, A16, A17, A18, and A28) are promoted as evidence-based therapeutic tools by claims that their design is grounded on evidence-based treatments (ie, CBT). In addition, 41% (12/29) are described as being designed with input from clinicians (eg, psychologists, psychiatrists, and therapists), whereas 59% (17/29) do not mention the involvement of mental health professionals in their design.

Medical Disclaimer

A medical disclaimer is presented in 66% (19/29) of the apps, outlining that the app is not a replacement for clinical treatment (Multimedia Appendix 3). However, 11 out of these 19 apps (11/19, 58%) only present this disclaimer in their terms of use policy, which is difficult to find and unlikely to be read by users. Another 35% (10/29) of apps do not provide any disclaimer on either marketplace or app’s website. No app presented itself as an alternative to clinical treatment (ie, drug treatment or face-to-face psychotherapy).

Clinical Involvement

All apps are designed to be used independently and do not require professional guidance while using them (Multimedia Appendix 3). In addition, 5 apps (5/29, 17%) provide opportunities to involve health experts while using the app. Of these, 2 apps support access to coaching and counseling sessions as an additional intervention for a price ranging from US $29.99 per month (A27) to US $35 per hour (A11). The other 3 apps allow users to share their in-app data (eg, health tracking report) with their health care providers (A16, A22, and A24).

Ethical Considerations

This section describes the ethical considerations raised while reviewing selected apps.

Negative Content

Aligned with the concerns raised by prior work that apps with poor design present an increased risk of potential harm [15,44], the results show that 2 out of 29 apps are categorized as so-called wallpaper apps. Such apps support people, “reflecting the true nature of the pain and loneliness in [your] heart […] give permission to feel the way you do” (A12). We found that these 2 apps include images or quotes capturing negative thinking (eg, “Do you ever get in those moods where you just don’t feel like existing,” A12). Surprisingly, these 2 apps with potentially disturbing content are rated as PEGI 3 (A12) or PEGI 12 (A6) on the marketplace, which indicates that the apps’ content merely includes bad language. As prior studies [45,46] have indicated, adolescents’ exposure to negative content may trigger negative behavior such as self-harm. Therefore, there is a clear need to explore safeguarding strategies for protecting vulnerable users such as those at risk of self-harm or suicide, especially given that these 2 apps are highly rated on the marketplace, ie, between 4.4 and 4.6 out of 5, and are subsequently more likely to be selected for use, adoption, or appropriation [47].

Safety

Strikingly, despite the increased vulnerability of people living with depression, 72% (21/29) of apps do not provide any information for handling or preventing the risk of suicide (Multimedia Appendix 4). Only 28% (8/29) of apps provide such information; in particular, most of these apps (5/8, 63%) provide information on accessing suicide prevention helplines, counseling websites, or support services, whereas 25% (2/8) provide information advising users to contact local emergency services if in critical risk of harm. In addition, 1 app (A18) assists users in creating a personalized safety plan for handling crises.

Functionality Review

We now discuss the functionality of reviewed apps such as screening, tracking, and providing interventions.

Screening

A total of 9 apps offer functionality to screen for depression; their features are summarized in Multimedia Appendix 5. Almost half of the apps that provide screening functionality (4/9, 44%) aim to assess changes in users’ depression symptoms during engagement with the app-provided intervention. Interestingly, despite the acknowledged benefit of personalization to support adherence [48], most of these apps (3/4, 75%) provide predefined psychoeducation articles upon informing users of their screening result, rather than tailored information for addressing particular issues identified through screening. All 4 of these apps employ the PHQ-9, a validated screening tool. An interesting outcome in this context relates to the frequency of the screening. Although 2 apps supported periodic repeated measures of users’ depression (ie, apps suggest or limit access to the screening tool only once in a fortnight), another 2 apps instead allowed on-demand momentary screening of users’ depression (ie, users can access screening tools as frequently as they want with no instructions regarding an appropriate frequency).

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In addition, 33% (3/9) of the apps provide stand-alone screening functionality for self-diagnosis purposes. Furthermore, 2 out of 3 apps classified into this category provide only screening functionality (A29 and A24), whereas another app (A16) also provides mood regulation strategies in addition to screening as its primary function. The first 2 apps (A29 and A24) do not use validated screening tools and do not provide direct in-app links to professional help upon informing users of the severity of their screening results. We found that the other app (A16) enables the potential benefits of screening while avoiding harm, as it provides support for both psychoeducation and for discussing the diagnosis and its implications with mHealth professionals [15,19]. In addition, the app (A16) provides screening as the main functionality through the use of International Classification of Diseases-10 [49], a validated screening tool, and in-app links to professional support. A16 also allows users to generate a report of the screening result to show to their own health care professionals.

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The other apps (2/9, 22%) provide a screening function to inform the delivery of personalized app content. One app asks users to self-report their disorder and symptoms (A19), whereas another app uses a questionnaire as a screening tool (A11), although it provides neither the source of this questionnaire and information on its validity nor evidence for the personalization of intervention. This app offers in-app purchase of online therapy sessions; however, this is not integrated with users’ progress through the intervention or their screening results.

Functionality Review

We now discuss the functionality of reviewed apps such as screening, tracking, and providing interventions.

Screening

A total of 9 apps offer functionality to screen for depression; their features are summarized in Multimedia Appendix 5. Almost half of the apps that provide screening functionality (4/9, 44%) aim to assess changes in users’ depression symptoms during engagement with the app-provided intervention. Interestingly, despite the acknowledged benefit of personalization to support adherence [48], most of these apps (3/4, 75%) provide predefined psychoeducation articles upon informing users of their screening result, rather than tailored information for addressing particular issues identified through screening. All 4 of these apps employ the PHQ-9, a validated screening tool. An interesting outcome in this context relates to the frequency of the screening. Although 2 apps supported periodic repeated measures of users’ depression (ie, apps suggest or limit access to the screening tool only once in a fortnight), another 2 apps instead allowed on-demand momentary screening of users’ depression (ie, users can access screening tools as frequently as they want with no instructions regarding an appropriate frequency).

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In addition, 33% (3/9) of the apps provide stand-alone screening functionality for self-diagnosis purposes. Furthermore, 2 out of 3 apps classified into this category provide only screening functionality (A29 and A24), whereas another app (A16) also provides mood regulation strategies in addition to screening as its primary function. The first 2 apps (A29 and A24) do not use validated screening tools and do not provide direct in-app links to professional help upon informing users of the severity of their screening results. We found that the other app (A16) enables the potential benefits of screening while avoiding harm, as it provides support for both psychoeducation and for discussing the diagnosis and its implications with mHealth professionals [15,19]. In addition, the app (A16) provides screening as the main functionality through the use of International Classification of Diseases-10 [49], a validated screening tool, and in-app links to professional support. A16 also allows users to generate a report of the screening result to show to their own health care professionals.

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The other apps (2/9, 22%) provide a screening function to inform the delivery of personalized app content. One app asks users to self-report their disorder and symptoms (A19), whereas another app uses a questionnaire as a screening tool (A11), although it provides neither the source of this questionnaire and information on its validity nor evidence for the personalization of intervention. This app offers in-app purchase of online therapy sessions; however, this is not integrated with users’ progress through the intervention or their screening results.

Functionality Review

We now discuss the functionality of reviewed apps such as screening, tracking, and providing interventions.

Screening

A total of 9 apps offer functionality to screen for depression; their features are summarized in Multimedia Appendix 5. Almost half of the apps that provide screening functionality (4/9, 44%) aim to assess changes in users’ depression symptoms during engagement with the app-provided intervention. Interestingly, despite the acknowledged benefit of personalization to support adherence [48], most of these apps (3/4, 75%) provide predefined psychoeducation articles upon informing users of their screening result, rather than tailored information for addressing particular issues identified through screening. All 4 of these apps employ the PHQ-9, a validated screening tool. An interesting outcome in this context relates to the frequency of the screening. Although 2 apps supported periodic repeated measures of users’ depression (ie, apps suggest or limit access to the screening tool only once in a fortnight), another 2 apps instead allowed on-demand momentary screening of users’ depression (ie, users can access screening tools as frequently as they want with no instructions regarding an appropriate frequency).

​

In addition, 33% (3/9) of the apps provide stand-alone screening functionality for self-diagnosis purposes. Furthermore, 2 out of 3 apps classified into this category provide only screening functionality (A29 and A24), whereas another app (A16) also provides mood regulation strategies in addition to screening as its primary function. The first 2 apps (A29 and A24) do not use validated screening tools and do not provide direct in-app links to professional help upon informing users of the severity of their screening results. We found that the other app (A16) enables the potential benefits of screening while avoiding harm, as it provides support for both psychoeducation and for discussing the diagnosis and its implications with mHealth professionals [15,19]. In addition, the app (A16) provides screening as the main functionality through the use of International Classification of Diseases-10 [49], a validated screening tool, and in-app links to professional support. A16 also allows users to generate a report of the screening result to show to their own health care professionals.

​

The other apps (2/9, 22%) provide a screening function to inform the delivery of personalized app content. One app asks users to self-report their disorder and symptoms (A19), whereas another app uses a questionnaire as a screening tool (A11), although it provides neither the source of this questionnaire and information on its validity nor evidence for the personalization of intervention. This app offers in-app purchase of online therapy sessions; however, this is not integrated with users’ progress through the intervention or their screening results.

Conclusions and Future Work

The rapid increase of mobile apps for reducing depression can benefit from a closer look and evaluation of the functionality such apps actually deliver and the potential ethical issues that they raise. From a systematic analysis of 29 top-rated depression apps on the major marketplaces, we suggest that developers of marketplaces should regulate depression apps to mitigate ethical risks, including missing, inadequate, or inconsistent privacy policies, ie, sharing data with third parties, child data protection, and safeguarding of vulnerable user groups. In addition, the analysis of app functionality provided new insights into opportunities for mitigating harm regarding the consumption of the negative content, unrestricted access by children (with related privacy concerns), and the provision of screening tools with less scientific validation.

Acknowledgments

This work has been supported by AffecTech: Personal Technologies for Affective Health, Innovative Training Network funded by the H2020 People Programme under Marie Sklodowska-Curie grant agreement number 722022. The research of GD is funded in part by SFI grant number 13/RC/2106 to the Adapt Centre.

Conflicts of Interest

GD is a cofounder of SilverCloud Health, a provider of supported online treatment, and has a financial interest in the company.​

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