CoChat
Sign Up
Cited by 282Digital Mental Health InterventionsMobile Health and mHealth ApplicationsMental Health Treatment and Access

The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.

Finding a Depression App: A Review and Content Analysis of the Depression App Marketplace

Open access
Shen, Nelson, Levitan, Michael-Jane, Johnson, Andrew, Bender, Jacqueline Lorene, Hamilton-Page, Michelle, Jadad, Alejandro (Alex) R, Wiljer, David·2015·JMIR mHealth and uHealth
DOI 10.2196/mhealth.3713PMC PMC4376135
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/mhealth.3713

Cites

85 of 85 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Flow diagram illustrating the exclusion of apps at various stages of the study.
Figure 1 Flow diagram illustrating the exclusion of apps at various stages of the study.CC BY · PubMed Central
Figure 2 Distribution of depression apps by function.
Figure 2 Distribution of depression apps by function.CC BY · PubMed Central
Figure 3 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.
Figure 3 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.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 85 references

Research works printed but not linked: 4

  1. App store sales top $10 billion in 2013 press release20142014-03-30Cupertino, CAApple Inchttps://www.apple.com/pr/library/2014/01/07App-Store-Sales-Top-10-Billion-in-2013.html6OT5C6Ym0
  2. Lippman, H, 2013, J Fam Pract, 62, 362
  3. 10.3899/jrheum.081101 · DOI
  4. Huang, TL, 2008, Altern Ther Health Med, 14, 38

Cited by

Sorted by citations

Items 201–282 of 282

Related

See all

Metadata

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
ORCID
Venue
JMIR mHealth and uHealth
Publisher
JMIR Publications Inc.
Published
2015-02-16
Volume
3
Issue
1
Pages
e16
ISSN
2291-5222 (ISSN-L)
DOI
10.2196/mhealth.3713
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
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
  • Health Sciences › Medicine › Psychiatry and Mental health › 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 ›

Are you an author, or do you see a problem with this page? Request a review