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Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements
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The potential of smartphone apps to improve quality and increase access to mental health care is increasingly clear. Yet even in the current global mental health crisis, real-world uptake of smartphone apps by clinics or consumers remains low. To understand this dichotomy, this paper reviews current challenges surrounding user engagement with mental health smartphone apps. While smartphone engagement metrics and reporting remains heterogeneous in the literature, focusing on themes offers a framework to identify underlying trends. These themes suggest that apps are not designed with service users in mind, do not solve problems users care most about, do not respect privacy, are not seen as trustworthy and are unhelpful in emergencies. Respecting these current issues surrounding mental health app engagement, we propose several solutions and highlight successful examples of mental health apps with high engagement. Further research is necessary to better characterise engagement with mental health apps and identify best practices for design, testing and implementation.
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“This multi-modal approach allows for personal engagement and also increases the utilization of digitally delivered interventions, which have been demonstrated to have a steep drop off in utilization when delivered alone [54].”
From Skills to Enhance Positivity in adolescents at risk for suicide: Protocol for a randomized controlled trial (2023) · cites this paper as [54]
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“Patient adherence may also be improved by providing evidence-based solutions such as 24/7 access to educational information and resources, personalized support, and targeted interventions and skills training via technology (eg, apps or websites) [21,35] in the comfort of their own homes [20,30].”
From A Digital Mental Health Support Program for Depression and Anxiety in Populations With Attention-Deficit/Hyperactivity Disorder: Feasibility and Usability Study (2023) · cites this paper as [30]
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“Digital interventions have the potential to evoke changes unavailable in traditional methods because of the ability to design personalized treatment models and measure engagement [28-30].”
From A Digital Mental Health Support Program for Depression and Anxiety in Populations With Attention-Deficit/Hyperactivity Disorder: Feasibility and Usability Study (2023) · cites this paper as [30]
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“Even when the app is downloaded, studies show that some users never use it, and those who engage with the app are unlikely to do so more than a few times, showing a decrease in use over time [49,50].”
From App-Based Mindfulness Meditation for People of Color Who Experience Race-Related Stress: Protocol for a Randomized Controlled Trial (2022) · cites this paper as [50]
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“Given the evidence of low treatment adherence in self-directed app-based mindfulness meditation [49-51], we will use a combination of app and text messaging reminders.”
From App-Based Mindfulness Meditation for People of Color Who Experience Race-Related Stress: Protocol for a Randomized Controlled Trial (2022) · cites this paper as [50]
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“Issues of treatment adherence are significant considering extensive evidence of low enrollment, poor use, and high attrition rates in self-directed app-based interventions [49-51].”
From App-Based Mindfulness Meditation for People of Color Who Experience Race-Related Stress: Protocol for a Randomized Controlled Trial (2022) · cites this paper as [50]
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“although some limitations have been found, mainly regarding low engagement by users and low completion rates [15,16].”
From Engagement and effectiveness of a healthy-coping intervention via chatbot for university students during the COVID-19 pandemic: Mixed methods proof-of-concept study (2021) · cites this paper as [15]
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“Others suggest offering a challenge may improve engagement over short periods, thereby allowing some therapeutic benefit and the potential for developing it into a habit [58].”
From Feasibility and Acceptability of the Aboriginal and Islander Mental Health Initiative for Youth App: Nonrandomized Pilot With First Nations Young People (2023) · cites this paper as [58]
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“Issues with intervention engagement are not restricted to digital interventions, and similar challenges can occur with face-to-face delivery [58].”
From Feasibility and Acceptability of the Aboriginal and Islander Mental Health Initiative for Youth App: Nonrandomized Pilot With First Nations Young People (2023) · cites this paper as [58]
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“if the tool is not used for a recommended period, it is unlikely that it will have the intended effect [17,18]”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [17]
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“A range of factors can influence engagement with DMHIs, such as the relevance of information to the user provided by a digital intervention [13], a lack of user motivation to persist with a self-guided intervention [14], and poor user experience with the technology [15].”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [15]
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“A previous review of user engagement with mental health apps theorized that one reason for low engagement is that these apps do not consider user privacy [15].”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [15]
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“However, there is little consensus on the usability and effectiveness of such systems [13].”
From A conversational artificial intelligence agent for a mental health care app: Evaluation study of its participatory design (2021) · cites this paper as [13]
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“Low engagement—defined as suboptimal levels of user access and/or adherence to an intervention (12)—is touted as one of the main reasons why the potential benefits of these interventions remain unrealized in the real world (13, 14).”
From Effect of Engagement With Digital Interventions on Mental Health Outcomes: A Systematic Review and Meta-Analysis (2021) · cites this paper as [12]
In the citing paper
“most general mental health care apps face difficulty sustaining engagement with high dropout”
From A Therapeutic Relational Agent for Reducing Problematic Substance Use (Woebot): Development and Usability Study (2021) · cites this paper as [14]
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“Several factors contribute to low DMHI engagement, including technical issues, motivation issues, and a lack of user-driven design [11,25,31,32].”
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Metadata
- Authors
- Torous, J., Nicholas, J., Larsen, M. E., Firth, J., & Christensen, H.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- Black Dog Institute (AU)
- Western Sydney University (AU)
- Corresponding author
- Torous, John
- Venue
- Evidence Based Mental Health
- Publisher
- BMJ
- Published
- 2018-06-05
- Volume
- 21
- Issue
- 3
- Pages
- 116-119
- ISSN
- 1362-0347 (ISSN-L)
- 1468-960X
- PMC
- PMC10270395
- OpenAlex
- W2806313543
- Cited by
- 790
- References
- 41
- Citations by year
- 2026: 99 · 2025: 135 · 2024: 103 · 2023: 96 · 2022: 106 · 2021: 119 · 2020: 68 · 2019: 57 · 2018: 7
- FWCI
- 8.51
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Evidence-Based Mental Health (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- Funders
- National Alliance for Research on Schizophrenia and Depression
- 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 › General Health Professions › Mental Health and Patient Involvement
- Keywords
- mental health apps, mobile application, user engagement, engagement metrics, mobile application design, privacy, trust, emergency relief, digital mental health, app uptake
- MeSH terms
- Smartphone, Mental Disorders, Humans, Mental Health Services, Patient Acceptance of Health Care, Patient Participation, Mobile Applications
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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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