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Depression is highly prevalent and causes considerable suffering and disease burden despite the existence of wide-ranging treatment options. Mobile phone apps offer the potential to help close this treatment gap by confronting key barriers to accessing support for depression.
Our goal was to identify and characterize the different types of mobile phone depression apps available in the marketplace.
A search for depression apps was conducted on the app stores of the five major mobile phone platforms: Android, iPhone, BlackBerry, Nokia, and Windows. Apps were included if they focused on depression and were available to people who self-identify as having depression. Data were extracted from the app descriptions found in the app stores.
Of the 1054 apps identified by the search strategy, nearly one-quarter (23.0%, 243/1054) unique depression apps met the inclusion criteria. Over one-quarter (27.7%, 210/758) of the excluded apps failed to mention depression in the title or description. Two-thirds of the apps had as their main purpose providing therapeutic treatment (33.7%, 82/243) or psychoeducation (32.1%, 78/243). The other main purpose categories were medical assessment (16.9%, 41/243), symptom management (8.2%, 20/243), and supportive resources (1.6%, 4/243). A majority of the apps failed to sufficiently describe their organizational affiliation (65.0%, 158/243) and content source (61.7%, 150/243). There was a significant relationship (
Without guidance, finding an appropriate depression app may be challenging, as the search results yielded non-depression–specific apps to depression apps at a 3:1 ratio. Inadequate reporting of organization affiliation and content source increases the difficulty of assessing the credibility and reliability of the app. While certification and vetting initiatives are underway, this study demonstrates the need for standardized reporting in app stores to help consumers select appropriate tools, particularly among those classified as medical devices.
Depression is a serious, common, and recurring disorder linked to diminished functioning, quality of life, medical morbidity, and mortality [
Information and communication technologies (ICTs) hold tremendous promise to expand the reach of quality mental health care [
Many consider apps as an opportunity to increase patient access to evidence-based mental health (and addictions) treatments [
The discrepancy between availability and evaluation is problematic because many of these products will continue to be marketed with unfounded claims of health improvement to attract health consumers [
We used a systematic review and content analysis approach based on a study by Bender et al [
Apps were organized as either “potentially relevant” or “not relevant” based on the app title, store description, and available screenshots. Apps were categorized as “potentially relevant” and included in the final analysis if they met three criteria: (1) the term “depression” was in the title or store description, (2) the app targeted health consumers (ie, those who self-identify as needing support for depression, including family or caregivers), rather than health care professionals, and (3) the app had an English-language interface or English translation (if in another language).
Apps were excluded from the study if they did not provide sufficient information, did not have a clear focus on depression, used the term depression in an unrelated context (eg, the Great Depression), used the term depression as a keyword in a list of unrelated items or as background information, and were duplicates appearing in multiple markets or for other devices (ie, optimized for tablets). The duplicate that provided the most information for data extraction was retained based on the following hierarchy (most to least information): Google Play, iTunes, AppWorld, Ovi, and Marketplace.
After independent screening for relevance, the 2 reviewers exchanged a random selection of 5% (104 apps) of their search yields to verify eligibility. Interrater reliability (IRR) of the random samples, as determined by Cohen’s kappa (kappa=.77,
Information was extracted from the store descriptions of the apps for the following variables: commercial information (ie, year of release/update, cost, developer name, audience, downloads), organizational affiliation, content source, main purpose, user interface, media type, and popularity (ie, rating, number of raters, number of comments). The 2 reviewers (MJL and NS) collectively and iteratively developed a preliminary coding scheme by analyzing the content of 20.5% (108/528) of randomly selected “potentially relevant” apps. The coding for the main purpose variable used the Luxton et al [
The remaining sample was divided for data extraction based on odd and even numbering to ensure that the reviewers had equal proportions of apps from each marketplace. After independent review, 20% (combined 41 apps) of each reviewer’s sample was randomly selected, exchanged, and coded to assess IRR. The results were all significant (
Final codebook for content analysis.
Variable | Code | Description |
Organizational affiliation | UNI | UNIVERSITY: Produced in affiliation with a university or other academic institution |
MEDC | MEDICAL CENTER: Produced in affiliation with a medical institution | |
GOVT | GOVERNMENT: Produced in affiliation with a government institution | |
INST | INSTITUTION: An explicit association (ie, foundation, center, NGO, church) | |
OTHER | OTHER: There is a clear but unclassifiable affiliation (eg, LLC, LLP, Inc.), not .com | |
INSUFF | INSUFFICIENT: The affiliation cannot be confirmed by available info | |
Content source | EXP | EXPERT: Developed by/with an accredited medical professional (eg, Dr., LCSW) |
EXT | EXTERNAL SOURCE: From specific external source (eg, BDI, DSM, Bible) but not “based on” or inspired by a theory/practice (eg, cognitive behavioral therapy) | |
LAY | LAYPERSON: Source identified but no credential mentioned. Non-medical expertise clearly indicated by detailed bio or qualifier (eg, years of experience) | |
PLE | PERSON LIVED EXPERIENCE: Indication that app is developed by people with lived experience | |
INSUFF | INSUFFICIENT: No direct information provided about origin of intervention | |
Audience | ADULT | ADULT: Adult or high maturity, age 18+ |
YADULT | YOUNG ADULT: Medium maturity, age 12+ | |
YOUTH | YOUTH: Low maturity, age 9+ | |
ALL | ALL: “Everyone,” age 4+, “general,” no rating | |
Main purpose | PE | PSYCHOEDUCATION: Educational material that includes books or guides, news or journal articles, commentaries/opinions, tips, and lessons |
MA | MEDICAL ASSESSMENT: Allows users to screen, diagnose, assess risk, determine treatment | |
SM | SYMPTOM MANAGEMENT: Allows users to track symptoms – only for mood diaries | |
SR | SUPPORTIVE RESOURCES: Provides referrals for help or connects users with support. May include the use of forums | |
TT | THERAPEUTIC TREATMENT: Provides therapy and includes functions that support relaxation (eg, hypnosis, binaural beats); meditation, spiritual faith-based solutions; holistic therapy (eg, diet, exercise, nutrition, lifestyle, cannabis); and positive affirmation | |
MULTI | MULTIPLE PURPOSES: Use only if indistinguishable overlap of categories | |
User interface | INFO | INFORMATION ONLY: Static user interface that provides minimal interaction (eg, e-book). The only interactions available are for settings or navigation |
TOOL | TOOL: Dynamic user interface that provides an interactive component to app (ie, games, social media consultation) or allows users to input data | |
Media type | AUD | AUDIO: Audio only (with supporting background images/text) |
TXT | TEXT ONLY: Text only (with supporting background images) – eg, e-book | |
PIC | PICTORIAL: Pictures only (eg, wallpaper) | |
VID | VIDEO: Video only | |
VIS | VISUAL: Animations or graphics or charts (ie, no audio or video) | |
MULTI | MULTIMEDIA: Used more than one of the categories above | |
INSUFF | INSUFFICIENT: Not enough information to determine types of media used |
Cohen’s kappa and descriptive statistics were computed using SPSS version 20. Chi-square tests of independence examined the relationship between the variables data source, user interface and multimedia, and the main purpose of the app. Statistical significance was set at
The initial search yielded 1054 apps, of which 53 were excluded as duplicates (31 were available in two stores, eight in three stores, two in four stores, and one in all stores). Of the remaining apps, 243 met the inclusion criteria.
Windows (4.5%, 11/243), Nokia (2.5%, 6/243), and BlackBerry (2.5%, 6/243) accounted for less than 10% of the included sample, as the majority of apps were from the Google (53.5%, 130/243) and Apple (37.0%, 90/243) marketplaces. The apps spanned 32 different store categories, with 79.9% (194/243) of the apps found under four categories: health and fitness (41.2%, 100/243), medical (17.3%, 42/243), lifestyle (14.4%, 35/243), and books (7.0%, 17/243). Six (2.5%, 6/243) apps had no categorization. The average price for paid apps (152/243; 62.6%) was CAN $3.15 and ranged from $0.99 to $15.99. The majority of paid apps (73.7%, 112/152) were sold for less than $4.99, with the mode price of $0.99 (18.9%, 46/243).
Only the release date was provided by the iTunes store, whereas Google Play, BlackBerry, and Windows provided dates of the last app update. Nokia did not provide this information. The earliest date reported by the app stores was 2009 (3.7%, 9/243). Two-thirds (66.0%, 156/237) of the apps were released or updated in 2012 (36.2%, 88/243) and the first quarter of 2013 (28.0%; 68/243). Google Play was the only market that reported the number of installs (ie, downloaded and installed on an Android mobile device) and was reported in ranges; 40 apps (30.8%, 40/130) were installed less than 50 times. The most frequent ranges of installation were 100-500 and 1000-5000, each registering 16.9% (22/130) of the sample. One app (0.4%, 1/243) was installed in the 1 million to 5 million range, and four apps fell into the 100,000 to 500,000 range.
Flow diagram illustrating the exclusion of apps at various stages of the study.
There were 190 developers in the sample, with 35 accounting for multiple apps. Of this group, 27 developers created two apps, three developed three apps, and four developed four apps. The top developer, MOZ, created nine apps. Only 5.3% (10/190) of the developers were either medical centers (1.0%, 2/190), universities (1.0%, 2/190), and institutions (3.2%, 6/190). A total of 56 developers indicated that they were a commercial developer (eg, LLC, LLP, Inc.), while 124 developers did not provide sufficient information about their affiliation.
Of the 113 rated apps (46.5%, 113/243), there was an average of 37.2 raters (95% CI 21.6-52.81) per app. One app had 583 raters. The average rating (out of five stars) was 3.5 stars (95% CI 3.3-3.7). There was an average of 5.9 comments per rated app (95% CI 4.2-7.7), with a range from zero to 56 comments.
Over 80% of the apps had the main purpose of providing therapeutic treatment (33.7%, 82/243), psychoeducation (32.1%, 78/243), or medical assessment (16.9%, 21/243). Apps with multiple purposes accounted for 7.4% (18/243) of the sample. Only 38.3% (93/243) of the apps reported the content source in sufficient detail and mainly cited an external (17.7%, 42/243) or expert (14.0%, 30/243) source. The majority (72.4%, 176/243) featured a dynamic user interface. Over half of the apps were text-only (51.9%, 126/243), while 14.4% (35/243) used multiple forms of media.
The chi-square tests of independence yielded significant results (
Distribution of depression apps by variable and main purpose.
Variable and Value | Main purpose, n (%)a | ||||||||
|
TT | PE | MA | SM | SR | MP | Total | ||
Overallb | 82 (33.7) | 78 (32.1) | 41 (16.9) | 20 (8.2) | 4 (1.6) | 18 (7.4) | 243 | ||
|
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|
|
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|
|
Institution | 1 (1.2) | 2 (2.6) |
|
1 (5.0) | 3 (75.0) |
|
7 (2.9) |
Academic |
|
|
1 (2.4) |
|
1 (25.0) |
|
2 (0.8) | ||
Medical center | 1 (1.2) |
|
1 (2.4) |
|
|
|
2 (0.8) | ||
Other | 27 (32.9) | 21 (26.9) | 14 (34.1) | 6 (30.0) |
|
6 (33.3) | 74 (30.5) | ||
Insufficient information | 53 (64.6) | 55 (70.5) | 25 (61.0) | 13 (65.0) |
|
12 (66.6) | 158 (65.0) | ||
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|
External | 8 (9.8) | 6 (7.7) | 21 (26.9) | 1 (5.0) | 1 (25.0) | 5 (27.8) | 42 (17.3) |
Expert | 3 (3.7) | 10 (12.8) | 11 (26.8) |
|
|
6 (33.3) | 30 (12.3) | ||
Patient lived experience |
|
7 (9.0) | 1 (2.4) | 2 (10.0) |
|
1 (5.6) | 11 (4.5) | ||
Layperson | 9 (11.0) | 1 (1.3) |
|
|
|
|
10 (4.1) | ||
Insufficient information | 62 (75.6) | 54 (69.2) | 8 (19.5) | 17 (85.0) | 3 (75.0) | 6 (33.3) | 150 (61.7) | ||
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|
Tool (dynamic) | 75 (91.5) | 18 (23.1) | 41 (100.0) | 20 (100.0) | 4 (100.0) | 18 (100.0) | 176 (72.4) | |
Information only (static) | 7 (8.5) | 60 (76.9) |
|
|
|
|
67 (27.6) | ||
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|
Text only | 17 (20.7) | 61 (78.2) | 35 (85.4) | 4 (20.0) | 2 (50.0) | 7 38.9) | 126 (51.9) | |
Audio only | 36 (43.9) | 3 (3.8) |
|
|
|
|
39 (16.0) | ||
Multimedia | 16 (19.5) | 9 (11.5) | 1 (2.4) | 3 (15.0) | 2 (50.0) | 4 (22.2) | 35 (14.4) | ||
Visual | 8 (9.8) | 4 (5.1) | 4 (9.8) | 11 (55.0) |
|
7 (38.9) | 34 (14.0) | ||
Pictorial | 5 (6.1) |
|
|
1 (5.0) |
|
|
6 (2.5) | ||
Insufficient information |
|
1 (1.3) | 1 (2.4) | 1 (5.0) |
|
|
3 (1.2) |
aCalculated as percentage within main purpose category; TT=therapeutic treatment, PE=psychoeducation, MA=medical assessment, SM=symptom management, SR=supportive resources, MP=multiple purposes.
bTotal was calculated as percentage within the whole sample (N=243).
cThe denoted variables were collapsed into binary categories for chi-square analysis.
dNone of the apps were video based.
Audio (44%, 36/82) was the most frequently used media for therapeutic treatment apps, which accounted for 92% (36/39) of audio apps found in the entire sample. Similarly, therapeutic treatment apps most frequently used multimedia, which represented 46% (16/35) of multimedia apps in the entire sample. Half (41/82) of the therapeutic treatment apps supported audio therapy in the form of hypnosis (n=14), brainwave entrainment (n=23), music therapy (n=3), or nature sounds (n=1). Five of the audio therapy apps included other types of media. One hypnosis app used visual media only. Nine of the 11 relaxation therapy apps reported layperson as the source, which accounts for 90% (9/10) of the layperson-sourced apps in the sample. Other types of therapy included spiritual/faith-based (n=10), entertainment (n=10), positive affirmation (n=7), behavior training (n=7), and light/visual (n=3). Two apps provided exercise-based therapy consisting of breathing techniques and yoga. One app focused on diet and one provided activity suggestions. There were ten apps that provided cognitive behavioral therapy and were classified under the multipurpose category.
The psychoeducation category of apps predominantly used a static (ie, read-only) interface (n=60) and represented 90.0% (60/67) of the static interface apps in the sample. The most frequently used media was the text-only category (n=61) and represented roughly half of all text-only apps (48.4%; 61/126) in the entire sample. Fifty psychoeducation apps were general e-books about depression, of which two were fiction and seven were reference manuals (ie, medication library), 12 apps provided tips or advice on how to overcome depression, and 11 apps provided education through learning modules or lessons. Five apps provided a collection of resources such as news and journal articles. The psychoeducation category had the greatest number of apps based on patient lived experience (n=7). Five of these were general e-books, one provided tips, and one provided lessons.
Of the medical assessment apps, 33 (81%; 33/41) reported the content source, which is the highest proportion and number of sourced apps within a main purpose category. External sources were reported 21 times and used 11 different questionnaires. The most frequently used questionnaire was the Patient Health Questionnaire (PHQ-9) [
Only 15% (3/20) of symptom management apps reported the content source, the lowest proportion of all the main purpose categories. Over half of the symptom management apps used visual media (55%; 11/20). Nine apps allowed users to track their moods and eight tracked lifestyle factors (eg, mood, sleep, diet, medication, exercise). Two apps allowed users to keep a journal, and one app used a checklist system.
Half of the apps (50%; 2/4) were text-only, while the other half were multimedia. One app reported the content source and cited an external source. Two apps provided resources (online and offline) and references for help. The other two apps connected users to a community via online forums.
Two-thirds (67%; 12/18) of the multipurpose apps reported the source, with almost all citing an expert (n=6) or external (n=5) source. All the apps used text (n=7) or visual (n=7) as the primary media. Four apps were multimedia, and 17 apps (94%; 17/18) used a combination of medical assessment and symptom management. Ten of these apps specifically focused on cognitive behavioral therapy (CBT), while seven used a questionnaire and allowed users to track depression over time. The questionnaires consisted of PHQ-9 (n=2) [
Distribution of depression apps by function.
This review found that depression apps provided support on five different dimensions: therapeutic treatment, psychoeducation, medical assessment, and supportive resources. Through the iterative development of this typology and understanding of the available commercial information, the results provided some insights into the user experience of those seeking depression support through apps. Similar to a recent study by Martinez-Perez et al [
Of the apps included in the study, there were three times more text-only apps than any other media category; furthermore, almost all the text-only apps with static interfaces were found in the psychoeducation app category. The reviewers found that these apps, based on screenshots and descriptions, were rudimentary in function and minimal in design. The proliferation of these apps may be a result of the low barrier to entry into the marketplace in the form of prerequisite resources and skills, thereby allowing those with minimal programming skills and resources to develop and publish their own apps [
The lack of apps that incorporate authoritative sources remains problematic. It has been estimated that one in five of paid apps claim to treat or cure medical ailments [
Based on the app store categories used in this study, 42 apps were defined as medical; however, this category included apps that are considered innocuous, such as those that help patients organize their health information or look up information about treatments [
An example of white labeling where the apps have the same description but are labeled as different apps. The word depression (circled in red) is only one in a list of unrelated terms and is an example of how such lists allow non-depression apps to enter the search.
The most common function of depression apps provides users with information about depression through an e-book modality. Despite the potential to translate books or bibliotherapeutic guides, only 13 of the 50 e-books cited a content source. The majority of these books were self-help guides, often with titles that claimed they would help users overcome depression. Examples include “Beat Depression”, “Defeat Depression”, and “Stomping Out Depression”. While these non-sourced books do pose the potential to distribute erroneous or biased information to people seeking help, the Google dataset shows that two-thirds of these apps are installed less than 100 times and indicates that users do exercise some discretion before purchasing or installing apps. Nettleton et al [
Medical assessment was the only app category with a high rate of reporting content source. All of these apps were screening tools that allowed users to self-diagnose for depression. There is an absence of published data investigating the impact of patient self-diagnosis using apps or the Internet; however, some studies have identified false positive assessments as a potential source of harm [
Audio therapy apps may have a similar potential to that of medical assessment apps [
The fourth most prevalent function of depression apps was offering behavior training or therapy, with most apps focusing on CBT. Internet-based CBT (ICBT) has shown to be an effective treatment for depression [
While the development of regulations and certification standards for assessing the quality of apps is underway, this study used the information available in the app store description (ie, developer affiliation and content source) to understand how depression apps are advertised to health consumers seeking depression apps. The information provided about affiliation and content source was accepted prima facie based on the developed inclusion criteria. The high percentage of insufficient reporting of affiliation may be an overestimation, since the developer websites were not examined to corroborate their status. Similarly, the reported content sources were not further examined. It is acknowledged that the apps themselves may contain more information and that not downloading and testing the apps is a limitation of this study. The lack of physical testing mirrors the actual user experience when making the decision to download apps [
A second limitation lies in the possibility that many of the apps excluded from this study because they were not depression specific could potentially be useful for people with depression. ICBT apps are prime examples of potentially useful non-depression-specific apps. ICBT is regarded as a well-established treatment for depression, panic disorder, and social phobia, but it is also an option for 25 other clinical disorders. While ICBT apps could be the prototypical depression app [
This study represents a snapshot of depression apps found in Canadian app stores in March of 2013. This may be a limitation in three ways. First, the landscape of the depression market will have changed at the time of submission of this publication. Second, the findings from this study may not be representative of all the depression apps available on the global market because certain apps may be localized or licensed only to specific countries. The study by Martinez-Perez et al in Spain found over 1537 depression apps available on the five major platforms. In comparison, the current review yielded 1001 unique apps, with a large part of the discrepancy attributed to Google Play app count. Moreover, a sample of Android apps may be missing because this study was conducted just prior to the Amazon announcement [
This study found that finding an appropriate depression app may be challenging due to the large quantity available. The search results yielded non–depression-specific apps to depression apps at a ratio of 3:1. Over one-quarter of the apps excluded from the study failed to even mention depression in their description or title and exemplify the role of metadata in populating the search results. The lack of reporting of organizational affiliation and content source brings the credibility into question. Whether the content is evidence-based is a whole other issue. This lack of information was most common among symptom management apps, followed by therapeutic treatment and psychoeducation apps. Only medical assessment apps, many of which were based on well-established depression questionnaires, adequately described their sources. As the app phenomenon and health consumerism continue to grow, the user’s ability to find a reliable and credible app may become increasingly difficult. While efforts are underway to populate the marketplace with certifications and professional vetting, this study delineates the need for standards in reporting and for a framework to enable people with depression or other conditions to use proxy measures to assess the legitimacy of apps.
List of included apps (N=243).
cognitive behavioral therapy
Edinburgh Postnatal Depression Scale
Internet-based cognitive behavioral therapy
information and communication technologies
interrater reliability
Patient Health Questionnaire
Zung Self-Rating Depression Scale
The authors of this study would like to thank Hema Zbogar for her editorial assistance in preparing the manuscript for submission. Dr Jadad was supported by the Canada Research Chair in eHealth Innovation, which he holds at the University of Toronto and the University Health Network.
None declared.