Finding a Depression App: A Review and Content Analysis of the Depression App Marketplace
Open accessAbstract
BACKGROUND: 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. OBJECTIVES: Our goal was to identify and characterize the different types of mobile phone depression apps available in the marketplace. METHODS: 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. RESULTS: 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 (χ(2) 5=50.5, P<.001) between the main purpose of the app and the reporting of content source, with most medical assessment apps reporting their content source (80.5%, 33/41). A fifth of the apps featured an e-book (20.6%, 50/243), audio therapy (16.9%, 41/243), or screening (16.9%, 41/243) function. Most apps had a dynamic user interface (72.4%, 176/243) and used text as the main type of media (51.9%, 126/243), and over a third (14.4%, 35/243) incorporated more than one form of media. CONCLUSION: 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.
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Over 90% of available apps for depression can be found in either the Apple App Store or Google Play.
“over 90% of available apps for depression can be found in either of these stores [46].”
From Apps with maps-anxiety and depression mobile apps with evidence-based frameworks: Systematic search of major app stores (2020) · cites this paper as [46]
Research on consumer smartphone applications for mental health suggests that few future consumer VR applications released on ordinary digital marketplaces can be expected to be evidence-based and effective.
“Research on consumer smartphone applications for mental health (Larsen et al. 2019;Shen et al. 2015) suggests that few future consumer VR applications that will be released on ordinary digital marketplaces can be expected to be evidence-based and effective.”
From Better, Virtually: the Past, Present, and Future of Virtual Reality Cognitive Behavior Therapy (2021) · cites this paper as Shen et al. 2015
Predictive models that combine information from physiological and behavioural signals can automatically estimate the patient's future mood, stress level, and self-reported health one or a few days in advance.
“Alternatively, predictive models that combine information from physiological and behavioural signals to estimate the patient’s future mood, stress level, and self-reported health (one or a few days in advance) can be automatically inferred [89].”
From Current state and future directions of technology-based ecological momentary assessment and intervention for major depressive disorder: a systematic review (2019) · cites this paper as [89]
Recent studies obtained promising results for stress recognition via model personalisation and for mood prediction via deep learning, using a combination of behavioural and physiological markers in non-clinical populations.
“Building on recent advances of machine learning, recent studies obtained promising results by means of model personalisation for stress recognition [88] and deep learning for mood prediction [89] using a combination of behavioural and physiological markers in non-clinical populations.”
From Current state and future directions of technology-based ecological momentary assessment and intervention for major depressive disorder: a systematic review (2019) · cites this paper as [89]
Although consumer app stores offer over 200 depression-specific apps, judging the credibility and efficacy of those apps is difficult without clinical validation.
“Unfortunately, although the consumer app stores offer over 200 depression-specific apps, judging the credibility and efficacy of those apps is difficult without clinical validation [69].”
From MoodHacker Mobile Web App With Email for Adults to Self-Manage Mild-to-Moderate Depression: Randomized Controlled Trial (2016) · cites this paper as [69]
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- Authors
- Shen, N., Levitan, M.-J., Johnson, A., Bender, J. L., Hamilton-Page, M., Jadad, A. (. R., & Wiljer, D.
- Affiliations
- Centre for Addiction and Mental Health (CA)
- Toronto General Hospital (CA)
- Corresponding author
- Wiljer, David
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2015-02-16
- Volume
- 3
- Issue
- 1
- Pages
- e16
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC4376135
- OpenAlex
- W1973546009
- Cited by
- 282
- References
- 85
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 14 · 2023: 10 · 2022: 22 · 2021: 40 · 2020: 41 · 2019: 33 · 2018: 45 · 2017: 26 · 2016: 29 · 2015: 7
- FWCI
- 4.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University Health Network
- University of Toronto
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Keywords
- mobile health apps, depression, app descriptions, psychoeducation, e-books, sound therapy, depression screening, user interface design, mental health treatment gap, app store security
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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