Artificial Intelligence Chatbot for Depression: Descriptive Study of Usage.
Open accessPublished version of a preprint
Abstract
BACKGROUND: Chatbots could be a scalable solution that provides an interactive means of engaging users in behavioral health interventions driven by artificial intelligence. Although some chatbots have shown promising early efficacy results, there is limited information about how people use these chatbots. Understanding the usage patterns of chatbots for depression represents a crucial step toward improving chatbot design and providing information about the strengths and limitations of the chatbots. OBJECTIVE: This study aims to understand how users engage and are redirected through a chatbot for depression (Tess) to provide design recommendations. METHODS: Interactions of 354 users with the Tess depression modules were analyzed to understand chatbot usage across and within modules. Descriptive statistics were used to analyze participant flow through each depression module, including characters per message, completion rate, and time spent per module. Slide plots were also used to analyze the flow across and within modules. RESULTS: Users sent a total of 6220 messages, with a total of 86,298 characters, and, on average, they engaged with Tess depression modules for 46 days. There was large heterogeneity in user engagement across different modules, which appeared to be affected by the length, complexity, content, and style of questions within the modules and the routing between modules. CONCLUSIONS: Overall, participants engaged with Tess; however, there was a heterogeneous usage pattern because of varying module designs. Major implications for future chatbot design and evaluation are discussed in the paper.
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The sample sizes included in the studies ranged from 6 to 354, but the number analysed was often lower than the recruited sample.
“The sample sizes included in the studies ranged from 6 [39] to 354 [40], but the number analysed was often lower than the recruited sample.”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [40]
The second most common use of artificial intelligence in the mental health apps was natural language conversations to deliver mental health support (6/17 studies).
“and natural language conversations to deliver mental health support (6/17 studies) [29,30,38,40,42,43].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [40]
6 of the 17 studies focused on depression.
“6 of the 17 studies focused on depression [29,31,36,40,42,44]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [40]
5 of the 17 studies did not specify what machine learning algorithm was used.
“5 of the 17 studies did not specify what machine learning algorithm was used and are thus not included in Table 5 [38–40,42,43].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [40]
Many of the studies successfully modelled data collected by the app or associated inputs to identify behaviours or responses associated with mental health conditions, provide natural language support, develop risk profiles, and deploy context-sensitive notifications or interventions.
“Many of the studies successfully modelled data collected by the app or associated inputs to identify behaviours or responses associated with mental health conditions [44], provide natural language support [29,30,38,40,42,43], develop risk profiles [31–37,41], and deploy context-sensitive notifications [28] or interventions [44].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [40]
All of the studies reviewed used data collected via mobile devices to inform AI models-for natural language analysis and output, risk stratification and prediction or for personalising the user's app experience.
“All of the studies reviewed used data collected via mobile devices to inform AI models—for natural language analysis and output [29,30,38,40,42,43], risk stratification and prediction [31,41] or for personalising the user’s app experience [28,44].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [40]
The sample sizes of the studies were relatively low, ranging from 6 to 354, with the latter being a retrospective descriptive study of real-world users, not recruited participants.
“The sample sizes of the studies were relatively low, ranging from 6 [39] to 354 [40], with the latter being a retrospective descriptive study of real-world users, not recruited participants.”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [40]
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- Authors
- Dosovitsky, G., Pineda, B. S., Jacobson, N. C., Chang, C., Escoredo, M., & Bunge, E. L.
- Affiliations
- Palo Alto University (US)
- Dartmouth College (US)
- xAI (United States) (US)
- Corresponding author
- Bunge, Eduardo L
- ORCID
- Venue
- JMIR Formative Research
- Publisher
- JMIR Publications Inc.
- Published
- 2020-11-13
- Volume
- 4
- Issue
- 11
- Pages
- e17065
- ISSN
- 2561-326X (ISSN-L)
- PMC
- PMC7695525
- OpenAlex
- W3091416888
- Cited by
- 118
- References
- 25
- Citations by year
- 2026: 19 · 2025: 21 · 2024: 25 · 2023: 22 · 2022: 20 · 2021: 11
- FWCI
- 12.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Formative Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- chatbots, depression, user engagement, behavioral health interventions, chatbot design, completion rates
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