Mental Health Chatbot Research Articles

AI Chatbots And Psychotherapy: A Match Made In Heaven?

Dear Editor, Artificial Intelligence (AI) is revolutionizing psychotherapy by combating its inaccessibility.1 AI chatbots and conversational agents are among the most promising integrations in psychiatry and psychology. By harnessing the power of machine learning and artificial intelligence (Figure I), AI chatbots and conversational agents can provide a level of interaction and support previously unattainable in the landscape of mental health. In this letter, I will explore some of their numerous benefits. Chatbots have evolved from early examples such as ELIZA, a programmable natural language processing program, and PARRY, a program that mimicked the behaviour of a human with schizophrenia, to modern ones such as Woebot, Tess, and Replika, which offer cognitive behavioural therapy, sessions, and self-understanding, respectively. Some chatbots such as Kaspar and Nao are also embodied in physical robots, which help children with autism spectrum disorder (ASD) learn social skills and facial recognition. AI chatbots can address the challenges of mental health care, such as the shortage of professionals2, especially in underdeveloped countries; the stigma of seeking help, which is prevalent in many cultures; the cost of therapy, which can be prohibitive for many people; and the limitations of human therapists, who can have variable attention spans, attention-to-detail, and memory capacities. AI chatbots can also offer benefits such as accessibility, anonymity, personalization, and effectiveness. Studies have shown that AI chatbots can improve the outcomes and experiences of mental health patients in various ways, such as enhancing the performance of those with ASD with robotic "therapists"1, increasing their disclosure of sensitive information to chatbots2, and reducing their distress after talking about their problems with conversational agents3. The benefits of AI chatbots for mental health are clear. However, they also face many challenges that limit their usability and reliability. They may lack the emotional skills of human therapists and may not be accountable for their diagnoses and recommendations. They may also not be based on sound mental health knowledge and evidence, as technology companies may not have expertise and experience in this field. Therefore, technology companies and mental health professionals must collaborate and adopt a rational approach to develop and evaluate chatbots. Only then can chatbots realize their full potential and complement existing mental health services safely and ethically.

Dr. Mine: Mental Health Chatbot Web App with Personalized Recommendation System

Dr. Mine: Mental Health Chatbot Web App with Personalized Recommendation System

Exploring Young Adults' Views About Aroha, a Chatbot for Stress Associated With the COVID-19 Pandemic: Interview Study Among Students.

In March 2020, New Zealand was plunged into its first nationwide lockdown to halt the spread of COVID-19. Our team rapidly adapted our existing chatbot platform to create Aroha, a well-being chatbot intended to address the stress experienced by young people aged 13 to 24 years in the early phase of the pandemic. Aroha was made available nationally within 2 weeks of the lockdown and continued to be available throughout 2020. In this study, we aimed to evaluate the acceptability and relevance of the chatbot format and Aroha's content in young adults and to identify areas for improvement. We conducted qualitative in-depth and semistructured interviews with young adults as well as in situ demonstrations of Aroha to elicit immediate feedback. Interviews were recorded, transcribed, and analyzed using thematic analysis assisted by NVivo (version 12; QSR International). A total of 15 young adults (age in years: median 20; mean 20.07, SD 3.17; female students: n=13, 87%; male students: n=2, 13%; all tertiary students) were interviewed in person. Participants spoke of the challenges of living during the lockdown, including social isolation, loss of motivation, and the demands of remote work or study, although some were able to find silver linings. Aroha was well liked for sounding like a "real person" and peer with its friendly local "Kiwi" communication style, rather than an authoritative adult or counselor. The chatbot was praised for including content that went beyond traditional mental health advice. Participants particularly enjoyed the modules on gratitude, being active, anger management, job seeking, and how to deal with alcohol and drugs. Aroha was described as being more accessible than traditional mental health counseling and resources. It was an appealing option for those who did not want to talk to someone in person for fear of the stigma associated with mental health. However, participants disliked the software bugs. They also wanted a more sophisticated conversational interface where they could express themselves and "vent" in free text. There were several suggestions for making Aroha more relevant to a diverse range of users, including developing content on navigating relationships and diverse chatbot avatars. Chatbots are an acceptable format for scaling up the delivery of public mental health and well-being-enhancing strategies. We make the following recommendations for others interested in designing and rolling out mental health chatbots to better support young people: make the chatbot relatable to its target audience by working with them to develop an authentic and relevant communication style; consider including holistic health and lifestyle content beyond traditional "mental health" support; and focus on developing features that make users feel heard, understood, and empowered.

Factors Predicting Intentions of Adoption and Continued Use of Artificial Intelligence Chatbots for Mental Health: Examining the Role of UTAUT Model, Stigma, Privacy Concerns, and Artificial Intelligence Hesitancy.

Background: Artificial intelligence-based chatbots (AI chatbots) can potentially improve mental health care, yet factors predicting their adoption and continued use are unclear. Methods: We conducted an online survey with a sample of U.S. adults with symptoms of depression and anxiety (N = 393) in 2021 before the release of ChatGPT. We explored factors predicting the adoption and continued use of AI chatbots, including factors of the unified theory of acceptance and use of technology model, stigma, privacy concerns, and AI hesitancy. Results: Results from the regression indicated that for nonusers, performance expectancy, price value, descriptive norm, and psychological distress are positively related to the intention of adopting AI chatbots, while AI hesitancy and effort expectancy are negatively associated with adopting AI chatbots. For those with experience in using AI chatbots for mental health, performance expectancy, price value, descriptive norm, and injunctive norm are positively related to the intention of continuing to use AI chatbots. Conclusions: Understanding the adoption and continued use of AI chatbots among adults with symptoms of depression and anxiety is essential given that there is a widening gap in the supply and demand of care. AI chatbots provide new opportunities for quality care by supporting accessible, affordable, efficient, and personalized care. This study provides insights for developing and deploying AI chatbots such as ChatGPT in the context of mental health care. Findings could be used to design innovative interventions that encourage the adoption and continued use of AI chatbots among people with symptoms of depression and anxiety and who have difficulty accessing care.

Assisted Technology and Mental Health

This manuscript discusses the potential use of assistive technology in promoting the mental health and well-being of the growing elderly population in the Arab world. While the use of assistive technology to address physical needs has been around for some time, its use in enhancing mental health and well-being is still in its early days. The manuscript highlights that assistive technology interventions have been shown to benefit the mental health of older adults. However, there are limited publications on the use of assistive technology in the Arab world, and certain assumptions held by healthcare professionals may hinder the access of older adults to these programs. The manuscript suggests introducing the topic of assistive technology in the medical and nursing undergraduate curriculum to address any misconceptions and make the technology more user-friendly. The manuscript also describes two specific types of assistive technology, mental health chatbots, and GPS trackers, and their potential benefits for older adults.

User-Friendly Chatbot to Mitigate the Psychological Stress of Older Adults During the COVID-19 Pandemic: Development and Usability Study.

To safeguard the most vulnerable individuals during the COVID-19 pandemic, numerous governments enforced measures such as stay-at-home orders, social distancing, and self-isolation. These social restrictions had a particularly negative effect on older adults, as they are more vulnerable and experience increased loneliness, which has various adverse effects, including increasing the risk of mental health problems and mortality. Chatbots can potentially reduce loneliness and provide companionship during a pandemic. However, existing chatbots do not cater to the specific needs of older adult populations. We aimed to develop a user-friendly chatbot tailored to the specific needs of older adults with anxiety or depressive disorders during the COVID-19 pandemic and to examine their perspectives on mental health chatbot use. The primary research objective was to investigate whether chatbots can mitigate the psychological stress of older adults during COVID-19. Participants were older adults belonging to two age groups (≥65 years and <65 years) from a psychiatric outpatient department who had been diagnosed with depressive or anxiety disorders by certified psychiatrists according to the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria. The participants were required to use mobile phones, have internet access, and possess literacy skills. The chatbot's content includes monitoring and tracking health data and providing health information. Participants had access to the chatbot for at least 4 weeks. Self-report questionnaires for loneliness, depression, and anxiety were administered before and after chatbot use. The participants also rated their attitudes toward the chatbot. A total of 35 participants (mean age 65.21, SD 7.51 years) were enrolled in the trial, comprising 74% (n=26) female and 26% (n=9) male participants. The participants demonstrated a high utilization rate during the intervention, with over 82% engaging with the chatbot daily. Loneliness significantly improved in the older group ≥65 years. This group also responded positively to the chatbot, as evidenced by changes in University of California Los Angeles Loneliness Scale scores, suggesting that this demographic can derive benefits from chatbot interaction. Conversely, the younger group, <65 years, exhibited no significant changes in loneliness after the intervention. Both the older and younger age groups provided good scores in relation to chatbot design with respect to usability (mean scores of 6.33 and 6.05, respectively) and satisfaction (mean scores of 5.33 and 5.15, respectively), rated on a 7-point Likert scale. The chatbot interface was found to be user-friendly and demonstrated promising results among participants 65 years and older who were receiving care at psychiatric outpatient clinics and experiencing relatively stable symptoms of depression and anxiety. The chatbot not only provided caring companionship but also showed the potential to alleviate loneliness during the challenging circumstances of a pandemic.

An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews.

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. In this study, we aimed to provide an overview of the commercially available popular mental health chatbots and how they are perceived by users. 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. 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. 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.

"A Great Way to Start the Conversation": Evidence for the Use of an Adolescent Mental Health Chatbot Navigator for Youth at Risk of HIV and Other STIs.

Chatbot use is increasing for mobile health interventions on sensitive and stigmatized topics like mental health because of their anonymity and privacy. This anonymity provides acceptability to sexual and gendered minority youth (ages 16-24) at increased risk of HIV and other STIs with poor mental health due to higher levels of stigma, discrimination, and social isolation. This study evaluates the usability of Tabatha-YYC, a pilot chatbot navigator created to link these youth to mental health resources.Tabatha-YYC was developed using a Youth Advisory Board (n = 7). The final design underwent user testing (n = 20) through a think-aloud protocol, semi-structured interview, and a brief survey post-exposure which included the Health Information Technology Usability Evaluation Scale.The chatbot was found to be an acceptable mental health navigator by participants.This study provides important design methodology considerations and key insights into chatbot design preferences of youth at risk of STIs seeking mental health resources.

Feasibility and impact of a mental health chatbot on postpartum mental health: a randomized controlled trial

Perinatal mood disorders are common yet underdiagnosed and un- or undertreated. Barriers exist to accessing perinatal mental health services, including limited availability, time, and cost. Automated conversational agents (chatbots) can deliver evidence-based cognitive behavioral therapy content through text message-based conversations and reduce depression and anxiety symptoms in select populations. Such digital mental health technologies are poised to overcome barriers to mental health care access but need to be evaluated for efficacy, as well as for preliminary feasibility and acceptability among perinatal populations. To evaluate the acceptability and preliminary efficacy of a mental health chatbot for mood management in a general postpartum population. An unblinded randomized controlled trial was conducted at a tertiary academic center. English-speaking postpartum women aged 18 years or above with a live birth and access to a smartphone were eligible for enrollment prior to discharge from delivery hospitalization. Baseline surveys were administered to all participants prior to randomization to a mental health chatbot intervention or to usual care only. The intervention group downloaded the mental health chatbot smartphone application with perinatal-specific content, in addition to continuing usual care. Usual care consisted of routine postpartum follow up and mental health care as dictated by the patient's obstetric provider. Surveys were administered during delivery hospitalization (baseline) and at 2-, 4-, and 6-weeks postpartum to assess depression and anxiety symptoms. The primary outcome was a change in depression symptoms at 6-weeks as measured using two depression screening tools: Patient Health Questionnaire-9 and Edinburgh Postnatal Depression Scale. Secondary outcomes included anxiety symptoms measured using Generalized Anxiety Disorder-7, and satisfaction and acceptability using validated scales. Based on a prior study, we estimated a sample size of 130 would have sufficient (80%) power to detect a moderate effect size (d=.4) in between group difference on the Patient Health Questionnaire-9. A total of 192 women were randomized equally 1:1 to the chatbot or usual care; of these, 152 women completed the 6-week survey (n=68 chatbot, n=84 usual care) and were included in the final analysis. Mean baseline mental health assessment scores were below positive screening thresholds. At 6-weeks, there was a greater decrease in Patient Health Questionnaire-9 scores among the chatbot group compared to the usual care group (mean decrease=1.32, standard deviation=3.4 vs mean decrease=0.13, standard deviation=3.01, respectively). 6-week mean Edinburgh Postnatal Depression Scale and Generalized Anxiety Disorder-7 scores did not differ between groups and were similar to baseline. 91% (n=62) of the chatbot users were satisfied or highly satisfied with the chatbot, and 74% (n=50) of the intervention group reported use of the chatbot at least once in 2 weeks prior to the 6-week survey. 80% of study participants reported being comfortable with the use of a mobile smartphone application for mood management. Use of a chatbot was acceptable to women in the early postpartum period. The sample did not screen positive for depression at baseline and thus the potential of the chatbot to reduce depressive symptoms in this population was limited. This study was conducted in a general obstetric population. Future studies of longer duration in high-risk postpartum populations who screen positive for depression are needed to further understand the utility and efficacy of such digital therapeutics for that population.

The Role of Chatbots in Mental Health Interventions: User Experiences

This study aims to explore the user experiences of individuals interacting with mental health chatbots, identifying key themes related to engagement, efficacy, satisfaction, barriers, and potential improvements. The objective is to inform the design and implementation of chatbot interventions to better meet the needs of users seeking mental health support. A qualitative research design was employed, utilizing semi-structured interviews with 22 participants who have used mental health chatbots. Thematic analysis was conducted to extract major and minor themes from the transcribed interviews, focusing on the aspects of user interaction, perceived benefits, and areas for enhancement in chatbot functionality. Five major themes were identified: User Engagement and Interaction, Perceived Efficacy, User Satisfaction and Trust, Barriers and Limitations, and Future Directions and Improvements. These encompassed various categories such as ease of use, personalization, emotional support, privacy concerns, technological issues, and suggestions for advanced AI and better integration with professional care. Participants valued chatbots for their accessibility and personalized support but highlighted the need for improvements in emotional understanding and technical reliability. Mental health chatbots are a promising tool for supporting individuals with their mental health needs, offering benefits in terms of engagement, personalized support, and perceived efficacy. However, addressing identified barriers such as technological limitations and emotional disconnect is crucial for enhancing user satisfaction and trust. Future developments should focus on incorporating advanced AI technologies, ensuring user privacy, and integrating chatbots more closely with traditional mental health services.

Chatbot features for anxiety and depression: A scoping review.

Chatbots can provide valuable support to patients in assessing and guiding management of various health problems particularly when human resources are scarce. Chatbots can be affordable and efficient on-demand virtual assistants for mental health conditions, including anxiety and depression. We review features of chatbots available for anxiety or depression. Six bibliographic databases were searched including backward and forwards reference list checking. The initial search returned 1302 citations. Post-filtering, 42 studies remained forming the final dataset for this scoping review. Most of the studies were from conference proceedings (62%, 26/42), followed by journal articles (26%, 11/42), reports (7%, 3/42), or book chapters (5%, 2/42). About half of the reviewed chatbots had functionality targeting both anxiety and depression (60%, 25/42), whereas 38% (16/42) targeted only depression, 38% (16/42) anxiety and the remaining addressed other mental health issues along with anxiety and depression. Avatars or fictional characters were rarely used in these studies only 26% (11/42) despite their increasing popularity. Mental health chatbots could benefit in helping patients with anxiety and depression and provide valuable support to mental healthcare workers, particularly when resources are scarce. Real-time personal virtual assistance fills in this gap. Their role in mental health care is expected to increase.

To chat or bot to chat: Ethical issues with using chatbots in mental health.

This paper presents a critical review of key ethical issues raised by the emergence of mental health chatbots. Chatbots use varying degrees of artificial intelligence and are increasingly deployed in many different domains including mental health. The technology may sometimes be beneficial, such as when it promotes access to mental health information and services. Yet, chatbots raise a variety of ethical concerns that are often magnified in people experiencing mental ill-health. These ethical challenges need to be appreciated and addressed throughout the technology pipeline. After identifying and examining four important ethical issues by means of a recognised ethical framework comprised of five key principles, the paper offers recommendations to guide chatbot designers, purveyers, researchers and mental health practitioners in the ethical creation and deployment of chatbots for mental health.

Development of a chatbot for depression: adolescent perceptions and recommendations.

Chatbots are a relatively new technology that has shown promising outcomes for mental health symptoms in adults; however, few studies have been done with adolescents or reported adolescent user experiences and recommendations for chatbot development. Twenty three participants ages 13-18 (Mage =14.96) engaged in user testing of a chatbot developed to psychoeducate adolescents on depression, teach behavioral activation, and change negative thoughts. Thematic analysis was conducted of participants' responses to user experience questions, impressions, and recommendations. Over half (56.5%) of the sample completed the full intervention and provided user experience feedback online. The average NPS score was 6.04 (SD=2.18), and 64.3% (n=9) said they would use the chatbot in the future. Of all user experience responses, 54.5% were positive. The most common impressions were related to symptom improvement (61.1%) and availability (52.8%) The most frequent recommendations were related to solving technical problems (66%). Chatbots for mental health are acceptable to some adolescents, a population that tends to be reluctant to engage with traditional mental health services. Most participants reported positive experiences with the chatbot, believing that it could help with symptom improvement and is highly available. Adolescents highlighted some technical and stylistic problems that developers should consider. More pilot and user testing is needed to develop mental health chatbots that are appealing and relevant to adolescents.

"For an App Supposed to Make Its Users Feel Better, It Sure is a Joke" - An Analysis of User Reviews of Mobile Mental Health Applications

Mobile mental health applications are seen as a promising way to fulfill the growing need for mental health care. Although there are more than ten thousand mental health apps available on app marketplaces, such as Google Play and Apple App Store, many of them are not evidence-based, or have been minimally evaluated or regulated. The real-life experience and concerns of the app users are largely unknown. To address this knowledge gap, we analyzed 2159 user reviews from 117 Android apps and 2764 user reviews from 76 iOS apps. Our findings include the critiques around inconsistent moderation standards and lack of transparency. App-embedded social features and chatbots were criticized for providing little support during crises. We provide research and design implications for future mental health app developers, discuss the necessity of developing a comprehensive and centralized app development guideline, and the opportunities of incorporating existing AI technology in mental health chatbots.

Mental Health Chatbot and Mental Illness Identification System

Abstract: In this advanced world today, the Internet has changed the way we communicate with one another which has led to an increase in mental disorders. Mental illness has been considered taboo since ages. The main causes of Mental disorders are : abuse, deadlines, medications, loneliness, relationships, conflicts, genetics, major diseases, etc. People are afraid of discussing their issues with others because of the fear of getting judged. So they decide to go to a psychiatrist. But the fees of psychiatrists are very high and common people cannot afford a full course. So this is the perfect situation for artificial Intelligence to come into action. People can happily share their symptoms and problems with Chat-bot and get satisfactory answers. In this paper, we proposed an intelligent social system which predicts the mental disorder of a user. Users will go through a few questionnaires. As per the responses provided by the user, a score will be generated.

Chatbot System for Mental Health in Bahasa Malaysia

Chatbot has been the driving force of modern communication for business, customer service and even mental healthcare. At the same time, there are not many research and project regarding mental health chatbots in Bahasa Malaysia. This project focuses on developing a chatbot application for mental healthcare in Bahasa Malaysia. This chatbot system is integrated with artificial intelligence and natural language processing. This chatbot utilize the feedforward neural network model to train the datasets. Apart from the backend of the application, Kivy and KivyMD are used to create the app's graphical user interface.

Chatbots to Support Young Adults’ Mental Health: An Exploratory Study of Acceptability

Despite the prevalence of mental health conditions, stigma, lack of awareness, and limited resources impede access to care, creating a need to improve mental health support. The recent surge in scientific and commercial interest in conversational agents and their potential to improve diagnosis and treatment seems a potentially fruitful area in this respect, particularly for young adults who widely use such systems in other contexts. Yet, there is little research that considers the acceptability of conversational agents in mental health. This study, therefore, presents three research activities that explore whether conversational agents and, in particular, chatbots can be an acceptable solution in mental healthcare for young adults. First, a survey of young adults (in a university setting) provides an understanding of the landscape of mental health in this age group and of their views around mental health technology, including chatbots. Second, a literature review synthesises current evidence relating to the acceptability of mental health conversational agents and points to future research priorities. Third, interviews with counsellors who work with young adults, supported by a chatbot prototype and user-centred design techniques, reveal the perceived benefits and potential roles of mental health chatbots from the perspective of mental health professionals, while suggesting preconditions for the acceptability of the technology. Taken together, these research activities: provide evidence that chatbots are an acceptable solution to offering mental health support for young adults; identify specific challenges relating to both the technology and environment; and argue for the application of user-centred approaches during development of mental health chatbots and more systematic and rigorous evaluations of the resulting solutions.

Development process of artificial intelligence based chatbot to support and promote mental wellbeing in sparsely populated areas of five European countries

IntroductionIn many countries, people face problems regarding access to care, 24/7 support and evidence-based support. Digital interventions and services, such as chatbots, can be one option to tackle these challenges. There is a lack knowledge regarding how mental health chatbots are developed and how to ensure that there is collaboration between mental health and digital technology experts and users.ObjectivesThis presentation describes the phases of the development for the ChatPal mental health and wellbeing chatbot.MethodsDevelopment process was conducted in five and with four different languages. First, using an electronic survey for mental health professionals (n =190) we screened how familiar they are with chatbots and how they evaluated their potential. Second, university students and staff, mental health professionals and service users (n=78) participated in workshops to design the chatbot content. Finally, the content and scripts of chatbot were written in multi-professional and multi-national collaboration.ResultsChatPal is based on the PERMAH model of positive psychology and on the idea that we all have mental health which needs boosting and support from time to time. ChatPal includes relevant mental health information, exercises, mood diaries and simple monitoring and self-care tools. Based on preliminary evaluations, the ChatPal chatbot offers an option to offer support in areas where other mental health services are lacking or are insufficient.ConclusionsChatPal is already freely available in application stores and first scientific trials are have started. Preliminary results of 4-week and subsequent 12-week in-the-wild trials will be in place at the time of EPA 2022 conference.DisclosureNo significant relationships.

Coproducing multilingual conversational scripts for a mental wellbeing chatbot - where healthcare domain experts become chatbot designers

IntroductionDigital mental health interventions, such as chatbots that promote mental health and wellbeing are a promising way to deliver low-threshold support 24/7 for those in need. According to current knowledge about the topic, health care professionals should participate in the design and development processes for digital interventions.ObjectivesThe aim of this presentation is to describe the interdisciplinary content development process of the ChatPal chatbot.MethodsThe content development process started in co-operation with mental health professionals and potential users to identify requirements. Content was created, evaluated and tested in international, multi-disciplinary group workshops, and online tools were used to allow the collaboration. Initial conversational scripts were drafted in English, and translated into Finnish, Swedish and Scottish Gaelic.ResultsA multilingual chatbot was developed and the conversation scripts were structured and stored using a spreadsheet. The conversation scripts will be made freely available online in due course using this structured approach to formatting chatbot dialogue content. It will allow repurposing the content as well as facilitating studies that wish to assess the design of conversation scripts for mental health chatbots. Conversation design process also highlighted some challenges in turning empathetic and supportive conversations to short utterances suitable for a chatbot.ConclusionsThe ChatPal chatbot is now available in four languages. As literature about the topic is still scarce, it is important to describe and document the content development processes of mental health chatbots. Future work will develop a conversational UX toolkit that would allow health professionals to design chatbot scripts using design guidelines.DisclosureNo significant relationships.

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring.

Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.