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The Emerging Imperative for a Consensus Approach Toward the Rating and Clinical Recommendation of Mental Health Apps
Abstract
With over 10,000 mental health– and psychiatry-related smartphone apps available today and expanding, there is a need for reliable and valid evaluation of these digital tools. However, the updating and nonstatic nature of smartphone apps, expanding privacy concerns, varying degrees of usability, and evolving interoperability standards, among other factors, present serious challenges for app evaluation. In this article, we provide a narrative review of various schemes toward app evaluations, including commercial app store metrics, government initiatives, patient-centric approaches, point-based scoring, academic platforms, and expert review systems. We demonstrate that these different approaches toward app evaluation each offer unique benefits but often do not agree to each other and produce varied conclusions as to which apps are useful or not. Although there are no simple solutions, we briefly introduce a new initiative that aims to unify the current controversies in app elevation called CHART (Collaborative Health App Rating Teams), which will be further discussed in a second article in this series.
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An increasingly large number of apps claiming to support mental health needs are available for download via commercial marketplaces.
“an increasingly large number of apps claiming to support mental health needs are available for download via commercial marketplaces (Torous et al. 2018).”
From Evaluating the quality, safety, and functionality of commonly used smartphone apps for bipolar disorder mood and sleep self-management (2022) · cites this paper as Torous et al. 2018
There is no consensus approach to evaluating mHealth apps.
“Secondly, there is no consensus approach to evaluating mHeath apps (Torous et al. 2018).”
From Evaluating the quality, safety, and functionality of commonly used smartphone apps for bipolar disorder mood and sleep self-management (2022) · cites this paper as Torous et al. 2018
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Metadata
- Authors
- Torous, J., Firth, J., Huckvale, K., Larsen, M. E., Cosco, T. D., Carney, R., Chan, S., Pratap, A., Yellowlees, P., Wykes, T., Keshavan, M., & Christensen, H.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- Western Sydney University (AU)
- Black Dog Institute (AU)
- Simon Fraser University (CA)
- University of Manchester (GB)
- University of California, San Francisco (US)
- University of California, Davis (US)
- Corresponding author
- Torous, John
- Chan, Steven
- Keshavan, Matcheri
- ORCID
- Venue
- Journal of Nervous & Mental Disease
- Published
- 2018-08
- Volume
- 206
- Issue
- 8
- Pages
- 662-666
- ISSN
- 0022-3018 (ISSN-L)
- 1539-736X
- OpenAlex
- W2884982858
- Cited by
- 101
- References
- 40
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 10 · 2023: 13 · 2022: 15 · 2021: 20 · 2020: 13 · 2019: 11 · 2018: 3
- FWCI
- 14.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Nervous and Mental Disease (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- National Institute for Health and Care Research (NF-SI-0514-10028)
- National Institute of Mental Health (K23MH116130)
- 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, mobile application, app evaluation, rating systems, charts, expert review, privacy concerns, interoperability, usability, digital mental health
- MeSH terms
- Consensus Statements as Topic, Delivery of Health Care, Humans, Mental Health, Telemedicine, Consensus, 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.
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