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An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study (Preprint)
Open accessPreprint — published as 10.2196/12106 · Paper page
Abstract
BACKGROUND: A World Health Organization 2017 report stated that major depression affects almost 5% of the human population. Major depression is associated with impaired psychosocial functioning and reduced quality of life. Challenges such as shortage of mental health personnel, long waiting times, perceived stigma, and lower government spends pose barriers to the alleviation of mental health problems. Face-to-face psychotherapy alone provides only point-in-time support and cannot scale quickly enough to address this growing global public health challenge. Artificial intelligence (AI)-enabled, empathetic, and evidence-driven conversational mobile app technologies could play an active role in filling this gap by increasing adoption and enabling reach. Although such a technology can help manage these barriers, they should never replace time with a health care professional for more severe mental health problems. However, app technologies could act as a supplementary or intermediate support system. Mobile mental well-being apps need to uphold privacy and foster both short- and long-term positive outcomes. OBJECTIVE: This study aimed to present a preliminary real-world data evaluation of the effectiveness and engagement levels of an AI-enabled, empathetic, text-based conversational mobile mental well-being app, Wysa, on users with self-reported symptoms of depression. METHODS: In the study, a group of anonymous global users were observed who voluntarily installed the Wysa app, engaged in text-based messaging, and self-reported symptoms of depression using the Patient Health Questionnaire-9. On the basis of the extent of app usage on and between 2 consecutive screening time points, 2 distinct groups of users (high users and low users) emerged. The study used mixed-methods approach to evaluate the impact and engagement levels among these users. The quantitative analysis measured the app impact by comparing the average improvement in symptoms of depression between high and low users. The qualitative analysis measured the app engagement and experience by analyzing in-app user feedback and evaluated the performance of a machine learning classifier to detect user objections during conversations. RESULTS: The average mood improvement (ie, difference in pre- and post-self-reported depression scores) between the groups (ie, high vs low users; n=108 and n=21, respectively) revealed that the high users group had significantly higher average improvement (mean 5.84 [SD 6.66]) compared with the low users group (mean 3.52 [SD 6.15]); Mann-Whitney P=.03 and with a moderate effect size of 0.63. Moreover, 67.7% of user-provided feedback responses found the app experience helpful and encouraging. CONCLUSIONS: The real-world data evaluation findings on the effectiveness and engagement levels of Wysa app on users with self-reported symptoms of depression show promise. However, further work is required to validate these initial findings in much larger samples and across longer periods.
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In the citing paper
“Empirical research on chatbots for mental health support, such as Woebot and Wysa, demonstrates that users often describe their relationship with the AI in affectively laden and anthropomorphic terms, reporting perceived understanding and companionship [35,36].”
From Delusional Experiences Emerging From AI Chatbot Interactions or “AI Psychosis” (2025) · cites this paper as [36]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“They also reported that a minority of users, around 30% or less, thought that the tool did not understand them or found their interactions unhelpful or bothersome (73, 74, 76–78, 80–82, 84, 90).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [84]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For instance, the therapeutic AI agents or chatbots identified by our systematic search (i.e., Wysa, Woebot, TEO, Tess, XiaoE, and Sexpert) were effective at delivering self-guided therapy content by communicating intelligently with their users, promoting a significant reduction in mental health symptom severity with high user satisfaction (73, 74, 76–78, 82, 84, 90).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [84]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Wysa also reported a reduction in self-reported symptoms of depression and reported that 68% of the cohort found the app helpful and encouraging [14].”
From Providing Self-Led Mental Health Support Through an Artificial Intelligence–Powered Chat Bot (Leora) to Meet the Demand of Mental Health Care (2023) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“as well as their effectiveness in reducing perceived stress [16-25]”
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 [24]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“thereby improving symptoms of anxiety [10,23-25]”
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 [24]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“When used enough, there can be positive mental health outcomes16–19.”
From Loneliness and suicide mitigation for students using GPT3-enabled chatbots (2024) · cites this paper as [19]
The cited source says what the claim attributes to it.
In the citing paper
“In terms of allegiance bias, because of the scarcity and novelty of AI-augmented interventions and other AI algorithms, most of the included articles developed and studied their own AI tools, instead of AI tools developed by other researchers (29, 33, 73, 74, 76, 79, 80, 82–87, 90).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [84]
The cited source says what the claim attributes to it.
In the citing paper
“Most studies (n=20) included >50% of female participants, with n=4 studies not reporting the percentage of males or females in their study (31, 33, 84, 90).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [84]
The cited source says what the claim attributes to it.
In the citing paper
“The meta-analysis assessing the impact of AI-augmented interventions for the management of depression symptoms considered n=7 studies (74, 76–78, 80, 84, 90) presenting pre- and post-intervention depression score outcomes.”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [84]
The cited source says what the claim attributes to it.
In the citing paper
“The meta-analysis assessing the impact of AI-augmented interventions on mental health symptoms considered n=10 studies (41, 73–78, 80, 84, 90) presenting pre- and post-intervention outcomes.”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [84]
The cited source says what the claim attributes to it.
In the citing paper
“Aside from AI-self-guided eCBT [n=6 (73, 74, 77, 78, 84, 90)] and psychoeducation [n=4 (74, 76–78)], other interventions and comparators were reported in three or fewer studies”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [84]
The cited source says what the claim attributes to it.
In the citing paper
“These intervention strategies based on positive psychology, CBT, and mindfulness practices have been recently deployed by fully automated conversational agents targeting anxiety, stress, and depression, with promising results in terms of efficacy and acceptability by users [10,16,24,25].”
The cited source says what the claim attributes to it.
In the citing paper
“depression [10,24,25]”
The cited source says what the claim attributes to it.
In the citing paper
“Overall, our quantitative and qualitative findings are aligned with recent chatbot evaluation studies [10,23-25] and will inform our design decisions for future developments.”
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The 7-item Generalized Anxiety Disorder scale (GAD-7) [34] is a 7-item self-report scale based on a 4-point Likert scale, ranging from 0 (not at all) to 3 (nearly every day).”
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 [24]
Experimental — accuracy not yet measuredIn the citing paper
“The effectiveness of health care conversational agents was assessed in 8 studies [47,52,57,61,70,75,81,84].”
From Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020) · cites this paper as [61]
In the citing paper
“Eight studies noted the effectiveness of conversational agents for mental health applications [57,61,64,67,75,80,81,84].”
From Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020) · cites this paper as [61]
In the citing paper
“In addition, 67.7% of users found the app usage to be helpful and encouraging [61].”
From Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020) · cites this paper as [61]
In the citing paper
“Inkster et al [61] employed the Patient Health Questionnaire-9 self-reported depression scale to note significant improvements in depression scores in the high user group compared with the low user group [61].”
From Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020) · cites this paper as [61]
In the citing paper
“1 study concluded that the severity of depression decreased significantly postintervention in the high user (P<.001) and low user (P=.01) groups [31].”
From Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis (2020) · cites this paper as [31]
In the citing paper
“Many chatbots have been developed for providing mental health interventions.”
From An overview of the features of chatbots in mental health: A scoping review (2019) · cites this paper as [13]
In the citing paper
“For example, conversational agents such as Wysa employ a combination of machine learning algorithms and rule‐based scripts to deliver cognitive‐behavioral therapy (CBT) interventions, demonstrating efficacy in reducing symptoms of depression and anxiety in pilot studies”
From Charting the evolution of artificial intelligence mental health chatbots from rule‐based systems to large language models: a systematic review (2025) · cites this paper as [18]
In the citing paper
“For example, two popular chatbots, Woebot and Wysa, have been shown to improve users’ depression symptoms15,16, and build therapeutic alliances that appear comparable to those formed with human therapists17,18.”
From “It happened to be the perfect thing”: experiences of generative AI chatbots for mental health (2024) · cites this paper as [16]
In the citing paper
“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 [43]
In the citing paper
“or general well-being support [30,38,43]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [43]
In the citing paper
“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 [43]
In the citing paper
“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 [43]
In the citing paper
“one found higher mean improvement of mood in the high users compared to lower users [43].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [43]
In the citing paper
“7 of the 17 studies evaluated mental health as an outcome (rather than a model input) [29,30,35,38,42–44].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [43]
In the citing paper
“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 [43]
In the citing paper
“Generally, greater engagement with the conversational agent resulted in greater reductions in psychological distress [22,26,28,29,33].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [29]
In the citing paper
“Finally, the 2 uncontrolled studies included in the review [22,29] and 2 studies that did not test for between-group effects [28,30] reported reductions in depression [28-30] and loneliness [22] postintervention.”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [29]
In the citing paper
“Generally, from studies that reported user experience outcomes (n=11), participants reported being satisfied with the conversational agent interventions offered [22,23,25,26,29,30,32].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [29]
In the citing paper
“The use of conversational agents for treating mental health problems appears to be limited but is growing quickly, with 5 of the included studies published in 2018 alone [23,24,26,29,34].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [29]
In the citing paper
“Studies that used more flexible recruitment routes such as online adverts [34] and app stores [29] recruited greater numbers of participants.”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [29]
In the citing paper
“The predominant challenges to intervention with a conversational agent included repetitive content [22,25,26,28,29,33], limitations in the agents ability to understand or respond appropriately [22,25,26,29], a shallow or superficial relationship [28,33], the sound and quality of the agents voice [32], and specific intervention tools or content [25,29].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [29]
In the citing paper
“Finally, a large proportion of agents were eclectic interventions comprising a variety of strategies and psychoeducation drawing on a range of therapeutic orientations [22,26,29,33].”
From Conversational agents in the treatment of mental health problems: mixed-method systematic review (2019) · cites this paper as [29]
In the citing paper
“As a result, we found assumptive questions (e.g., anxiety) and responses from Wysa, Woebot, and ChatGPT with no association with a clinical reference or clinical support.”
From A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement (2023) · cites this paper as Inkster et al., 2018
In the citing paper
“Wysa uses a CBT conversational agent to engage in empathetic and therapeutic conversations and activities, aiming to help users with various mental health problems (Inkster et al., 2018).”
From A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement (2023) · cites this paper as Inkster et al., 2018
In the citing paper
“The authors reported that high users (i.e., participants who responded to the two pre-post assessments and one additional in-between evaluation) showed a significantly greater improvement compared with low users, that is, participants engaging only in the two pre-post assessments [81].”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [81]
In the citing paper
“The largest proportion of them (12/31, 39%) addressed mental health issues [13,32-42]”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [36]
In the citing paper
“The most frequently raised issue with conversational agents (9 studies) was poor understanding because of limited vocabulary, voice recognition accuracy, or error management of word inputs [13,32-37,41,52].”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [36]
In the citing paper
“users in 3 studies noted disliking the repetitive conversations with the agents [13,36,37].”
From The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020) · cites this paper as [36]
In the citing paper
“Wysa, which is an established, multicomponent digital mental health intervention that uses chatbot technology and text-based access to human counselors to deliver cognitive behavioral therapy, mindfulness training, and sleep tools, among other features.”
From A Digital Mental Health Intervention in an Orthopedic Setting for Patients With Symptoms of Depression and/or Anxiety: Feasibility Prospective Cohort Study (2022) · cites this paper as [24]
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Fiske, A., Henningsen, P., & Buyx, A. (2019) · Journal of Medical Internet Research · doi:10.2196/13216 · Cited by 857
An overview of the features of chatbots in mental health: A scoping review
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Metadata
- Authors
- Inkster, B., Sarda, S., & Subramanian, V.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Inkster, Becky
- Venue
- JMIR mhealth and uhealth
- Publisher
- JMIR Publications Inc.
- Published
- 2018-09-05
- Volume
- 6
- Issue
- 11
- Pages
- e12106-e12106
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC6286427
- OpenAlex
- W2895995041
- Cited by
- 1,063
- References
- 40
- Citations by year
- 2026: 265 · 2025: 240 · 2024: 147 · 2023: 140 · 2022: 112 · 2021: 91 · 2020: 53 · 2019: 14 · 2018: 1
- FWCI
- 50.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (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
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Mental Health Treatment and Access
- Keywords
- Wysa, conversational AI agents, depression, psychological well-being, mental health apps, empathy, user engagement, app usage patterns, user feedback, mood improvement, real-world effectiveness, digital mental health, PHQ-9, user experience
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