An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study (Preprint)

Inkster, Becky; Sarda, Shubhankar; Subramanian, Vinod

doi:10.2196/preprints.12106

PreprintCited by 1,063Digital Mental Health InterventionsMobile Health and mHealth ApplicationsMental Health Treatment and Access

An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study (Preprint)

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Inkster, Becky, Sarda, Shubhankar, Subramanian, Vinod·2018·JMIR mhealth and uhealth

DOI 10.2196/preprints.12106PMC PMC6286427

Preprint — published as 10.2196/12106 · Paper page

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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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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.
“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]
The Results (Qualitative Analysis; no page given) report that Wysa users often found the conversations helpful and encouraging. The Discussion (Comparison With Prior Work; no page given) also reports favorable experiences with Wysa and another chatbot study. Neither passage shows that users commonly described the AI as a companion or in anthropomorphic terms; some instead wanted it to understand them better. (LLM, conf=0.84)
Experimental — accuracy not yet measured
A minority of users, around 30% or less, thought that the tool did not understand them or found their interactions unhelpful or bothersome.
“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]
The Results (Qualitative Analysis) report feedback that conversations or tools were unhelpful, that the app did not understand users, and that conversations bothered some users. But the approximately 30% figure describes responses (91/282, or 32%), not users: those responses came from 53 of the 95 users who gave feedback. (LLM, conf=0.86)
Experimental — accuracy not yet measured
The therapeutic AI agents or chatbots identified by our systematic search (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.
“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]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Wysa reported a reduction in self-reported symptoms of depression and 68% of the cohort found the app helpful and encouraging.
“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]
The Abstract Results and Discussion report reduced self-reported depression symptoms, particularly among high Wysa users. The Abstract Results also say that 67.7% of user-provided feedback responses described the app as helpful and encouraging; this is not necessarily 68% of the cohort. (LLM, conf=0.88)
Experimental — accuracy not yet measured
A growing number of studies report effectiveness of chatbots in reducing perceived stress.
“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]
The Discussion’s Comparison With Prior Work reports chatbot studies with effects on depression or psychological well-being; one used the Perceived Stress Scale. It does not state that Wysa reduced perceived stress or specify a stress-reduction result from the comparison study. No page number is available in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured

A growing number of studies report effectiveness of chatbots in improving symptoms of anxiety.
“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]
The Discussion’s Comparison With Prior Work describes chatbot studies reporting improvements in depressive symptoms or psychological well-being, and the Principal Findings report improvement in self-reported depressive symptoms among high Wysa users (pages not provided). The paper says Wysa offers tools to manage anxiety, but the passages found do not report an improvement in anxiety symptoms. (LLM, conf=0.83)
Experimental — accuracy not yet measured
When used enough, chatbots can have positive mental health outcomes.
“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]
In terms of allegiance bias, most included articles developed and studied their own AI tools instead of AI tools developed by other researchers.
“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]
Most studies (n=20) included >50% female participants, with n=4 studies not reporting percentage of males or females.
“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 meta-analysis for depression considered n=7 studies presenting pre- and post-intervention depression score outcomes.
“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 meta-analysis assessing impact of AI-augmented interventions on mental health symptoms considered n=10 studies presenting pre- and post-intervention outcomes.
“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]
Aside from AI-self-guided eCBT (n=6) and psychoeducation (n=4), other interventions and comparators were reported in three or fewer studies.
“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]
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.
“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].”
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]
A growing number of studies report effectiveness of chatbots in improving symptoms of depression.
“depression [10,24,25]”
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]
Overall, our quantitative and qualitative findings are aligned with recent chatbot evaluation studies and will inform our design decisions for future developments.
“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.”
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]
The GAD-7 is a 7-item self-report scale assessing anxiety symptoms over the past 2 weeks, with total scores categorized as none (0-4), mild (5-9), moderate (10-14), and severe (≥15).
“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]
The Methods section, 'Quantitative Measurement and Screening' (page unavailable), describes PHQ-9 and PHQ-2 depression screening, not the GAD-7. Searches for GAD-7 and the claimed response options and score categories found no passage in our copy addressing the anxiety scale. (LLM, conf=0.76)
Experimental — accuracy not yet measured
The effectiveness of health care conversational agents was assessed in 8 studies.
“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]
Eight studies noted the effectiveness of conversational agents for mental health applications.
“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 Inkster et al, 67.7% of users found the app usage to be helpful and encouraging.
“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]
Inkster et al 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.
“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]
One quasiexperiment concluded that the severity of depression decreased significantly postintervention in the high user (P<.001) and low user (P=.01) groups.
“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]
Many chatbots have been developed for providing mental health interventions.
“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]
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.
“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]
Two popular chatbots, Woebot and Wysa, have been shown to improve users' depression symptoms, and build therapeutic alliances that appear comparable to those formed with human therapists.
“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]
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 [43]
3 of the 17 studies focused on general well-being support.
“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]
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 [43]
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 [43]
One study found higher mean improvement of mood in the high users compared to lower users.
“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]
7 of the 17 studies evaluated mental health as an outcome (rather than a model input).
“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]
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 [43]
Greater engagement with the conversational agent resulted in greater reductions in psychological distress.
“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]
The 2 uncontrolled studies and 2 studies that did not test for between-group effects reported reductions in depression and loneliness postintervention.
“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]
Participants reported being satisfied with the conversational agent interventions offered.
“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]
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.
“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]
Studies that used more flexible recruitment routes such as online adverts and app stores recruited greater numbers of participants.
“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]
The predominant challenges to intervention with a conversational agent included repetitive content, limitations in the agents ability to understand or respond appropriately, a shallow or superficial relationship, the sound and quality of the agents voice, and specific intervention tools or content.
“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]
A large proportion of agents were eclectic interventions comprising a variety of strategies and psychoeducation drawing on a range of therapeutic orientations.
“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]
Existing VMHAs Woebot, Wysa, and ChatGPT initiated responsive conversation without focusing on context, producing assumptive questions and responses with no association with a clinical reference or clinical support.
“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
Wysa uses a CBT conversational agent to engage in empathetic and therapeutic conversations and activities, aiming to help users with various mental health problems.
“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 one pre-post investigation, high users showed significantly greater improvement compared with low users.
“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]
The largest proportion of included conversational agents (12/31, 39%) addressed mental health issues.
“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]
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.
“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]
Users in 3 studies noted disliking repetitive conversations with the agents.
“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]
Wysa 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.
“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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Smartphone-Supported versus Full Behavioural Activation for Depression: A Randomised Controlled Trial
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Assessing the Efficacy of Mobile Health Apps Using the Basic Principles of Cognitive Behavioral Therapy: Systematic Review
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Does Internet-based guided-self-help for depression cause harm? An individual participant data meta-analysis on deterioration rates and its moderators in randomized controlled trials
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A randomised active-controlled trial to examine the effects of an online mindfulness intervention on executive control, critical thinking and key thinking dispositions in a university student sample
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A protocol for a randomised active-controlled trial to evaluate the effects of an online mindfulness intervention on executive control, critical thinking and key thinking dispositions in a university student sample
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[12]British Medical Association 2018 02 26 New BMA research unveils blindspot in mental healthcare https://www.bma.org.uk/news/media-centre/press-releases/2018/february/new-bma-research-unveils-blindspot-in-mental-healthcare

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Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy
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An overview of the features of chatbots in mental health: A scoping review
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Conversational Agents in Health Care: Scoping Review and Conceptual Analysis
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“Moreover, 19 studies allowed free text user inputs [45,48,51,56,57,60,61,66,69,70,72,74,77,78,80,81,85,86,89], and 4 studies used both fixed and free text user inputs [53,64,65,73].”
BackgroundMethodInfluential
The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review
Milne-Ives, M., de Cock, C., Lim, E., Shehadeh, M. H., de Pennington, N., Mole, G., Normando, E., & Meinert, E. (2020) · Journal of Medical Internet Research · doi:10.2196/20346 · Cited by 626 · see citing works
Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being
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Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis
Abd-Alrazaq, A. A., Rababeh, A., Alajlani, M., Bewick, B. M., & Househ, M. (2020) · Journal of Medical Internet Research · doi:10.2196/16021 · Cited by 503 · see citing works
“Of these 6 studies, 4 studies were RCTs [27-30], and the remaining 2 studies were pretest-posttest quasiexperiments [31,32].”
An overview of and recommendations for more accessible digital mental health services
Lattie, E. G., Stiles-Shields, C., & Graham, A. K. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-021-00003-1 · Cited by 446 · see citing works
Artificially intelligent chatbots in digital mental health interventions: a review
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Physicians’ perceptions of chatbots in health Care: cross-sectional web-based survey
Palanica, A., Flaschner, P., Thommandram, A., Li, M., & Fossat, Y. (2019) · Journal of Medical Internet Research · doi:10.2196/12887 · Cited by 384 · see citing works
AI in mental health
D’Alfonso, S. (2020) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2020.04.005 · Cited by 408 · see citing works
An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews
Haque, M. D. R., & Rubya, S. (2023) · JMIR mHealth and uHealth · doi:10.2196/44838 · Cited by 404 · see citing works
Physician Confidence in Artificial Intelligence: An Online Mobile Survey
Oh, S., Kim, J. H., Choi, S.-W., Lee, H. J., Hong, J., & Kwon, S. H. (2019) · Journal of Medical Internet Research · doi:10.2196/12422 · Cited by 387 · see citing works
Artificial intelligence in medical education: a cross-sectional needs assessment
Civaner, M. M., Uncu, Y., Bulut, F., Chalil, E. G., & Tatli, A. (2022) · BMC Medical Education · doi:10.1186/s12909-022-03852-3 · Cited by 363 · see citing works
The Personalization of Conversational Agents in Health Care: Systematic Review
Kocaballi, A. B., Berkovsky, S., Quiroz, J. C., Laranjo, L., Tong, H. L., Rezazadegan, D., Briatore, A., & Coiera, E. (2019) · Journal of Medical Internet Research · doi:10.2196/15360 · Cited by 332 · see citing works
“Across all the studies, data explicitly entered by the users included personal goals [35,37,46,47], symptoms and medications [37,44,45], measurement of vital signs [39,40], knowledge level on a specific topic [38], and daily practices [38].”
BackgroundMethodInfluential
Perceptions and opinions of patients about mental health chatbots: scoping review
Abd-Alrazaq, A. A., Alajlani, M., Ali, N., Denecke, K., Bewick, B. M., & Househ, M. (2021) · Journal of Medical Internet Research · doi:10.2196/17828 · Cited by 301 · see citing works
Conversational agents in the treatment of mental health problems: mixed-method systematic review
Gaffney, H., Mansell, W., & Tai, S. (2019) · JMIR Mental Health · doi:10.2196/14166 · Cited by 275 · see citing works
Using Chatbots as AI Conversational Partners in Language Learning
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Can Your Phone Be Your Therapist? Young People’s Ethical Perspectives on the Use of Fully Automated Conversational Agents (Chatbots) in Mental Health Support
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A systematic review on artificial intelligence dialogue systems for enhancing English as foreign language students’ interactional competence in the university
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Artificial intelligence technologies and compassion in healthcare: A systematic scoping review
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Evaluating the Therapeutic Alliance With a Free-Text CBT Conversational Agent (Wysa): A Mixed-Methods Study
Beatty, C., Malik, T., Meheli, S., & Sinha, C. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.847991 · Cited by 224 · see citing works
A smartphone-based health care chatbot to promote self-management of chronic pain (SELMA): pilot randomized controlled trial
Hauser-Ulrich, S., Künzli, H., Meier-Peterhans, D., & Kowatsch, T. (2020) · JMIR mHealth and uHealth · doi:10.2196/15806 · Cited by 210 · see citing works
A Mental Health Chatbot for Regulating Emotions (SERMO) - Concept and Usability Test
Denecke, K., Vaaheesan, S., & Arulnathan, A. (2020) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2020.2974478 · Cited by 240 · see citing works
“It happened to be the perfect thing”: experiences of generative AI chatbots for mental health
Siddals, S., Torous, J., & Coxon, A. (2024) · npj Mental Health Research · doi:10.1038/s44184-024-00097-4 · Cited by 241 · see citing works
The Opportunities and Risks of Large Language Models in Mental Health
Lawrence, H. R., Schneider, R. A., Rubin, S. B., Matarić, M. J., McDuff, D. J., & Jones Bell, M. (2024) · JMIR Mental Health · doi:10.2196/59479 · Cited by 235 · see citing works
Loneliness and suicide mitigation for students using GPT3-enabled chatbots
Maples, B., Cerit, M., Vishwanath, A., & Pea, R. (2024) · npj Mental Health Research · doi:10.1038/s44184-023-00047-6 · Cited by 250 · see citing works
Your robot therapist is not your therapist: understanding the role of AI-powered mental health chatbots
Khawaja, Z., & Bélisle-Pipon, J.-C. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1278186 · Cited by 214 · see citing works
Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review
Rahsepar Meadi, M., Sillekens, T., Metselaar, S., van Balkom, A., Bernstein, J., & Batelaan, N. (2025) · JMIR Mental Health · doi:10.2196/60432 · Cited by 211 · see citing works
Artificial Intelligence–Based Chatbot for Anxiety and Depression in University Students: Pilot Randomized Controlled Trial
Klos, M. C., Escoredo, M., Joerin, A., Lemos, V. N., Rauws, M., & Bunge, E. L. (2021) · JMIR Formative Research · doi:10.2196/20678 · Cited by 200 · see citing works
Using Health Chatbots for Behavior Change: A Mapping Study
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Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers.
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Digital transformation of mental health services
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Engagement and retention in digital mental health interventions: a narrative review
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Attitudes and perceptions of dental students towards artificial intelligence
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A Scoping Review of Machine Learning in Psychotherapy Research
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Mental Health Chatbot for Young Adults With Depressive Symptoms During the COVID-19 Pandemic: Single-Blind, Three-Arm Randomized Controlled Trial
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The therapeutic effectiveness of artificial intelligence-based chatbots in alleviation of depressive and anxiety symptoms in short-course treatments: A systematic review and meta-analysis
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Artificial Intelligence for Chatbots in Mental Health: Opportunities and Challenges
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Designing Human-centered AI for Mental Health: Developing Clinically Relevant Applications for Online CBT Treatment
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The Potential of Chatbots for Emotional Support and Promoting Mental Well-Being in Different Cultures: Mixed Methods Study
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AI Chatbots in Digital Mental Health
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Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement
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Technical metrics used to evaluate health care chatbots: scoping review
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A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring
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Artificial intelligence and mobile apps for mental healthcare: a social informatics perspective
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Combating Depression in Students using an Intelligent ChatBot: A Cognitive Behavioral Therapy
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Mobile app-based chatbot to deliver cognitive behavioral therapy and psychoeducation for adults with attention deficit: A development and feasibility/usability study
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Acceptability and Effectiveness of Artificial Intelligence Therapy for Anxiety and Depression (Youper): Longitudinal Observational Study
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AI-based conversational agents: A scoping review from technologies to future directions
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Designing a Chatbot for a Brief Motivational Interview on Stress Management: Qualitative Case Study
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The Use of Artificial Intelligence–Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations
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Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review
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The Development and Use of Chatbots in Public Health: Scoping Review
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Engagement and effectiveness of a healthy-coping intervention via chatbot for university students during the COVID-19 pandemic: Mixed methods proof-of-concept study
Gabrielli, S., Rizzi, S., Bassi, G., Carbone, S., Maimone, R., Marchesoni, M., & Forti, S. (2021) · JMIR mHealth and uHealth · doi:10.2196/27965 · Cited by 125 · see citing works
“The total scores are separated into the following four categories to determine the anxiety symptom levels: none (0-4), mild (5-9), moderate (10-14), and severe (≥15) [23,24].”
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Efficacy of mobile app-based interactive cognitive behavioral therapy using a chatbot for panic disorder
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Health-focused conversational agents in person-centered care: a review of apps
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Human-AI Interaction in Human Resource Management: Understanding Why Employees Resist Algorithmic Evaluation at Workplaces and How to Mitigate Burdens
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Providing Self-Led Mental Health Support Through an Artificial Intelligence–Powered Chat Bot (Leora) to Meet the Demand of Mental Health Care
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“Another way AI is being used is in chatbots, where it has the potential to make an impact in the area of mental health [14,15].”
Considering the role of human empathy in AI-driven therapy
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Charting the evolution of artificial intelligence mental health chatbots from rule‐based systems to large language models: a systematic review
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Authors: Inkster, B., Sarda, S., & Subramanian, V.
Affiliations: University of Cambridge (GB)
Corresponding author: Inkster, Becky
ORCID: Inkster, Becky
Sarda, Shubhankar
Subramanian, Vinod
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)
DOI: 10.2196/preprints.12106
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
Social Sciences › Psychology › Social Psychology › 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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An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study (Preprint)

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