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

By Becky Inkster, Shubhankar Sarda, V. Subramanian. JMIR mHealth and uHealth, 2018

868 citations recorded by the source corpus.

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.

Subfields: Applied Psychology, General Health Professions, Social Psychology

View Paper: https://www.semanticscholar.org/paper/eb5583763621788b6a1c8dfe25091dc20a21e4b1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Psychology

1. A. Parks
2. Jiangen He
3. Yuting Liao
4. D. Chan

In General Health Professions

1. S. Kwon
2. A. Parks
3. Tobias Kowatsch
4. Jungrak Hong
5. Shafiq R. Joty
6. Sung-Woo Choi
7. Jiangen He
8. H. Lee

In Social Psychology

1. Enrico W. Coiera
2. A. Parks
3. Jiangen He
4. Yuting Liao
5. Abeba Birhane

Citations in the corpus

• Using Health Chatbots for Behavior Change: A Mapping Study (2019, background): “Finally, [28] highlights the value of chatbots as an efficient tool to address a global public health challenge, the lack of enough human psychotherapists to cope with the increasing mental health problems among the population.”
• Physician Confidence in Artificial Intelligence: An Online Mobile Survey (2018, background): “First, AI cannot engage in high-level conversation with patients to gain their trust, reassure them, or express empathy [47].”
• An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews (2022, neutral): “They were designed to identify MH concerns [34], track moods [49], deliver cognitive behavioral therapy (CBT) [47], and promote positive psychology [50].”
• Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey (2018, background): “Early research has demonstrated the benefits of using health care chatbots, such as helping with diagnostic decision support [15,16], promoting and increasing physical activity [17], and cognitive behavioral therapy for psychiatric and somatic disorders [18-24], which provide effective, acceptable, ”
• Racial mirroring effects on human-agent interaction in psychotherapeutic conversations (2020, methodology): “Empathy [11,28] “I understand things could be difficult.”
• The Personalization of Conversational Agents in Health Care: Systematic Review (2019, methodology): “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].”
• Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020, methodology): “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].”
• Conversational AI: Social and Ethical Considerations (2019, background): “Evaluation of the use of CAs often mentions the importance of ethical considerations but fails to explicitly discuss such concerns or provide mechanisms to address them such as in [19] [35] and [54].”
• Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy (2018, neutral): “Increasingly, artificially intelligent virtual and robotic agents are not only available for relatively low-level elements of mental health support, such as comfort or social interaction, but also perform high-level therapeutic interventions that used to be offered exclusively by highly trained, ski”
• The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (2020, methodology): “9 (82) 1 1 0 1 0 1 1 1 1 1 1 Inkster [36]”
• Artificially intelligent chatbots in digital mental health interventions: a review (2021, background): “In one study, 68% of users found Wysa encouraging and helpful [90].”