An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews
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Abstract
Background: Chatbots are an emerging technology that show potential for mental health care apps to enable effective and practical evidence-based therapies. As this technology is still relatively new, little is known about recently developed apps and their characteristics and effectiveness. Objective: In this study, we aimed to provide an overview of the commercially available popular mental health chatbots and how they are perceived by users. Methods: We conducted an exploratory observation of 10 apps that offer support and treatment for a variety of mental health concerns with a built-in chatbot feature and qualitatively analyzed 3621 consumer reviews from the Google Play Store and 2624 consumer reviews from the Apple App Store. Results: We found that although chatbots’ personalized, humanlike interactions were positively received by users, improper responses and assumptions about the personalities of users led to a loss of interest. As chatbots are always accessible and convenient, users can become overly attached to them and prefer them over interacting with friends and family. Furthermore, a chatbot may offer crisis care whenever the user needs it because of its 24/7 availability, but even recently developed chatbots lack the understanding of properly identifying a crisis. Chatbots considered in this study fostered a judgment-free environment and helped users feel more comfortable sharing sensitive information. Conclusions: Our findings suggest that chatbots have great potential to offer social and psychological support in situations where real-world human interaction, such as connecting to friends or family members or seeking professional support, is not preferred or possible to achieve. However, there are several restrictions and limitations that these chatbots must establish according to the level of service they offer. Too much reliance on technology can pose risks, such as isolation and insufficient assistance during times of crisis. Recommendations for customization and balanced persuasion to inform the design of effective chatbots for mental health support have been outlined based on the insights of our findings.
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Rule-based chatbot apps have more promising user engagement, with positive app store ratings and qualitative studies finding that users appreciate the human-like interaction and social support.
“Rule-based chatbot apps have more promising user engagement, with positive app store ratings19,20 and qualitative studies finding that users appreciate the human-like interaction and social support19–23.”
From “It happened to be the perfect thing”: experiences of generative AI chatbots for mental health (2024) · cites this paper as [22]
Meta-analyses indicate that the therapeutic effects of rule-based AI chatbots are small and not sustained over time, and users report frustration with responses that feel empty, generic, nonsensical, repetitive and constrained.
“Meta-analyses indicate that the therapeutic effects are small and not sustained over time24, and users report frustration with responses that feel empty, generic20, nonsensical, repetitive and constrained19–22.”
From “It happened to be the perfect thing”: experiences of generative AI chatbots for mental health (2024) · cites this paper as [22]
Many of the themes we developed are not new, but rather echo well-established user appreciation of rule-based chatbots' always-available, non-judgmental listening ear and abilities to create a therapeutic alliance and reframe negative thoughts.
“Many of the themes we developed are not new, but rather echo well-established user appreciation of rule-based chatbots’ always-available, non-judgmental listening ear and abilities to create a therapeutic alliance and reframe negative thoughts20,22.”
From “It happened to be the perfect thing”: experiences of generative AI chatbots for mental health (2024) · cites this paper as [22]
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- Authors
- Haque, M. D. R., & Rubya, S.
- Affiliations
- Marquette University (US)
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2023-05-22
- Volume
- 11
- Pages
- e44838
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC10242473
- OpenAlex
- W4366703942
- Cited by
- 404
- References
- 79
- Citations by year
- 2026: 120 · 2025: 182 · 2024: 84 · 2023: 18
- FWCI
- 104.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Northwestern University
- 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 › Social Robot Interaction and HRI
- Keywords
- chatbots, mental health apps, consumer reviews, user perception, crisis care, social support, digital mental health, chatbot personality, technology dependence, isolation, persuasive design, App Store
- MeSH terms
- Emotions, Humans, Mental Health, Mobile Applications
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