Assessing mental health apps marketplaces with objective metrics from 29,190 data points from 278 apps
Abstract
OBJECTIVE: Utilizing a standard framework that may help clinicians and patients to identify relevant mental health apps, we sought to gain a comprehensive picture of the space by searching for, downloading, and reviewing 278 mental health apps from both the iOS and Android stores. METHODS: 278 mental health apps from the Apple iOS store and Google Play store were downloaded and reviewed in a standardized manner by trained app raters using a validated framework. Apps were evaluated with this framework comprising 105 questions and covering app origin and accessibility, privacy and security, inputs and outputs, clinical foundation, features and engagement style, and interoperability. RESULTS: Our results confirm that app stars and downloads-even for the most popular apps by these metrics-did not correlate with more clinically relevant metrics related to privacy/security, effectiveness, and engagement. Most mental health apps offer similar functionality, with 16.5% offering both mood tracking and journaling and 7% offering psychoeducation, deep breathing, mindfulness, journaling, and mood tracking. Only 36.4% of apps were updated with a 100-day window, and 7.5% of apps had not been updated in four years. CONCLUSION: Current app marketplace metrics commonly used to evaluate apps do not offer an accurate representation of individual apps or a comprehensive overview of the entire space. The majority of apps overlap in terms of features offered, with many domains and other features not well represented. Selecting an appropriate app continues to require personal matching given no clear trends or guidance offered by quantitative metrics alone.
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Mental health apps oftentimes include techniques for which there is no clear evidence.
“a recent meta-analysis has shown that mental health apps oftentimes include techniques for which there is no clear evidence (Lagan et al., 2021)”
From Virtual Reality exposure therapy in the treatment of public speaking anxiety and social anxiety disorder (2024) · cites this paper as Lagan et al., 2021
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- Authors
- Lagan, S., D'Mello, R., Vaidyam, A., Bilden, R., & Torous, J.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- Department of Psychiatry Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA
- Corresponding author
- Torous, John
- Venue
- Acta Psychiatrica Scandinavica
- Publisher
- Wiley
- Published
- 2021-04-29
- Volume
- 144
- Issue
- 2
- Pages
- 201-210
- ISSN
- 0001-690X (ISSN-L)
- 1600-0447
- OpenAlex
- W3141253249
- Cited by
- 65
- References
- 33
- Citations by year
- 2026: 11 · 2025: 8 · 2024: 9 · 2023: 20 · 2022: 13 · 2021: 4
- FWCI
- 10.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Acta Psychiatrica Scandinavica (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Argosy Foundation
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- mental health apps, privacy and security, app updates, app features, user engagement, interoperability, Android and iOS apps, app selection
- MeSH terms
- Humans, Mental Health, Benchmarking, Mobile Applications
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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