Mental Health Chatbot Research Articles

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.

Popularity of Mental Health Chatbots Grows

Popularity of Mental Health Chatbots Grows

"I am chatbot, your virtual mental health adviser." What drives citizens' satisfaction and continuance intention toward mental health chatbots during the COVID-19 pandemic? An empirical study in China.

IntroductionIn order to address the psychological problems during the COVID-19 pandemic, mental health chatbots have been extensively used by public sectors. According to Theory of Consumption Values, this paper proposed an analytical framework to investigate the determinants behind users’ satisfaction and continuance intention toward mental health chatbots.MethodsThe empirical study was conducted through an online survey, facilitated by the use of questionnaire posted on the WeChat platform. Seven-point Likert scale and closed-ended questions were employed. Totally 371 valid samples were collected. The research data was tested via the partial least squares structural equation modeling. Gender, age, and income were included as control variables.ResultsAnalysis of samples collected from 371 Chinese users suggested that personalization (functional value), enjoyment (emotional value), learning (epistemic value), and the condition of the COVID-19 pandemic (conditional value) have positive influences on user satisfaction and continuance intention, but such effects were weak. The findings also revealed that user satisfaction has weakly positive impact on continuance intention. However, voice interaction (functional value) was an insignificant predictor of user satisfaction and continuance intention.DiscussionThis study developed a critical perspective on the role of Theory of Consumption Values in the context of mental health chatbot usage, while Theory of Consumption Value has been increasingly employed to explain the use of AI-based public services. Thus, this research devotes to the enhancement of theoretical frameworks regarding the usage of mental health chatbots.

It Is Me, Chatbot: Working to Address the COVID-19 Outbreak-Related Mental Health Issues in China. User Experience, Satisfaction, and Influencing Factors

ABSTRACT The global spread of COVID-19 has caused a huge number of confirmed cases and deaths, which in return leads to a plethora of mental disorders across the world. In order to address citizens’ psychological problems, government agencies in many countries have employed AI-based chatbots to provide mental health services. However, there is a limited understanding of the determinants affecting citizens’ user experience and user satisfaction when mental health services supported by chatbots are provided. Thus, based on the Theory of Consumption Values (TCV), this study proposes an analytical framework to investigate the factors that are important to citizens’ user experience and user satisfaction when they interact with mental health chatbots. Analysis of data collected from 295 chatbot users in Wuhan and Chongqing reveals that personalization, enjoyment, learning, and condition are positively related to user experience and user satisfaction. However, voice interaction fails to devote to citizens’ user experience and user satisfaction. Thus, government agencies and their AI service contractors should enhance the functions and systems of mental health chatbots to ensure citizens’ user experience and user satisfaction. Also, they should more positively promote the use of mental health chatbots during the public health emergency.

Using a Chatbot to Address Psychological Distress Among First Responders

<h3>Research Objectives</h3> Frontline COVID workers (doctors, nurses, paramedics, law enforcement officers, social workers) have been especially impacted by the pandemic (1). Stigma and fear of job loss often prevent help seeking (2,3). Evidence-based interventions exist but can be hard to navigate, limiting or delaying their timely uptake (4,5). Tools are needed to better identify people in need of treatment and provide care navigation that is timely and tailored to the individuals' needs, preferences, and level of distress and helps to circumnavigate stigma. To address this need, our team is conducting formative and acceptability and feasibility research to inform the development of a chatbot-a conversational interface (e.g. Amazon Alexa) using text or speech in a conversational, human-like manner to deliver information-to screen and refer COVID frontline workers to care. <h3>Design</h3> We are conducting interviews with 20 community behavioral health providers, advocates, law enforcement officers, and people living with mental illness. We are then asking 40 naïve users to test the prototype chatbot. Feedback from naïve users will be integrated into the fully functional chatbot. Simultaneously, we are conducting a national survey with adults to test the feasibility and acceptability of using chatbot technology for behavioral health screening and referral and examine correlates of mental health chatbot use (i.e. demographics, stigma, and technology readiness). <h3>Setting</h3> The setting of this study is online with frontline COVID responders and general community members. <h3>Participants</h3> 20 stakeholder interviews, testing with 40 naive users, and feasibility and acceptability testing with 500 national participants. <h3>Interventions</h3> We will ask participants to test a chatbot that will provide screening for distress (PHQ-9) and referral. The chatbot will require about 5 minutes of time to use. <h3>Main Outcome Measures</h3> Outcome measures related to feasibility and acceptability of the chatbot include willingness to engage with the chatbot and items from the Technology Acceptance Model. <h3>Results</h3> Findings will inform modifications to the chatbot and implementation planning. <h3>Conclusions</h3> This work will inform the use of chatbots for mental health screening and referral for various populations. <h3>Author(s) Disclosures</h3> No disclosures to report.

A Suite of Mobile Conversational Agents for Daily Stress Management (Popbots): Mixed Methods Exploratory Study.

BackgroundApproximately 60%-80% of the primary care visits have a psychological stress component, but only 3% of patients receive stress management advice during these visits. Given recent advances in natural language processing, there is renewed interest in mental health chatbots. Conversational agents that can understand a user’s problems and deliver advice that mitigates the effects of daily stress could be an effective public health tool. However, such systems are complex to build and costly to develop.ObjectiveTo address these challenges, our aim is to develop and evaluate a fully automated mobile suite of shallow chatbots—we call them Popbots—that may serve as a new species of chatbots and further complement human assistance in an ecosystem of stress management support.MethodsAfter conducting an exploratory Wizard of Oz study (N=14) to evaluate the feasibility of a suite of multiple chatbots, we conducted a web-based study (N=47) to evaluate the implementation of our prototype. Each participant was randomly assigned to a different chatbot designed on the basis of a proven cognitive or behavioral intervention method. To measure the effectiveness of the chatbots, the participants’ stress levels were determined using self-reported psychometric evaluations (eg, web-based daily surveys and Patient Health Questionnaire-4). The participants in these studies were recruited through email and enrolled on the web, and some of them participated in follow-up interviews that were conducted in person or on the web (as necessary).ResultsOf the 47 participants, 31 (66%) completed the main study. The findings suggest that the users viewed the conversations with our chatbots as helpful or at least neutral and came away with increasingly positive sentiment toward the use of chatbots for proactive stress management. Moreover, those users who used the system more often (ie, they had more than or equal to the median number of conversations) noted a decrease in depression symptoms compared with those who used the system less often based on a Wilcoxon signed-rank test (W=91.50; Z=−2.54; P=.01; r=0.47). The follow-up interviews with a subset of the participants indicated that half of the common daily stressors could be discussed with chatbots, potentially reducing the burden on human coping resources.ConclusionsOur work suggests that suites of shallow chatbots may offer benefits for both users and designers. As a result, this study’s contributions include the design and evaluation of a novel suite of shallow chatbots for daily stress management, a summary of benefits and challenges associated with random delivery of multiple conversational interventions, and design guidelines and directions for future research into similar systems, including authoring chatbot systems and artificial intelligence–enabled recommendation algorithms.

A Mental Health Chatbot for Regulating Emotions (SERMO) - Concept and Usability Test

Mental disorders are widespread in countries all over the world. Nevertheless, there is a global shortage in human resources delivering mental health services. Leaving people with mental disorders untreated may increase suicide attempts and mortality. To address this matter of limited resources, conversational agents have gained momentum in the last years. In this work, we introduce SERMO, a mobile application with integrated chatbot that implements methods from cognitive behaviour therapy (CBT) to support mentally ill people in regulating emotions and dealing with thoughts and feelings. SERMO asks the user on a daily basis on events that occurred and on emotions. It determines automatically the basic emotion of a user from the natural language input using natural language processing and a lexicon-based approach. Depending on the emotion, an appropriate measurement such as activities or mindfulness exercises are suggested by SERMO. Additional functionalities are an emotion diary, a list of pleasant activities, mindfulness exercises and information on emotions and CBT in general. User experience was studied with 21 participants using the User Experience Questionnaire (UEQ). Findings show that efficiency, perspicuity and attractiveness are considered as good. The scales describing hedonic quality (stimulation and novelty), i.e., fun of use, show neutral evaluations.

Self-Harm Detection for Mental Health Chatbots.

Chatbots potentially address deficits in availability of the traditional health workforce and could help to stem concerning rates of youth mental health issues including high suicide rates. While chatbots have shown some positive results in helping people cope with mental health issues, there are yet deep concerns regarding such chatbots in terms of their ability to identify emergency situations and act accordingly. Risk of suicide/self-harm is one such concern which we have addressed in this project. A chatbot decides its response based on the text input from the user and must correctly recognize the significance of a given input. We have designed a self-harm classifier which could use the user's response to the chatbot and predict whether the response indicates intent for self-harm. With the difficulty to access confidential counselling data, we looked for alternate data sources and found Twitter and Reddit to provide data similar to what we would expect to get from a chatbot user. We trained a sentiment analysis classifier on Twitter data and a self-harm classifier on the Reddit data. We combined the results of the two models to improve the model performance. We got the best results from a LSTM-RNN classifier using BERT encoding. The best model accuracy achieved was 92.13%. We tested the model on new data from Reddit and got an impressive result with an accuracy of 97%. Such a model is promising for future embedding in mental health chatbots to improve their safety through accurate detection of self-harm talk by users.

Women’s Mental Health Chatbot Using Seq2seq With Attention

Mental healthcare is a privilege paying little heed to gender differences however the numbers show that ladies experience some emotional wellness conditions at a lot higher rates than me. This is mainly seen in the forms of anxiety and depression. Looking for help is a huge introductory stage in improving mental prosperity and getting access to mental healthcare. Diverse online organizations are instantly available for adolescents including self-composed, low force Web-based passionate health sites (eg, ReachOut), public web exhorting organizations (eg, Eheadspace), stores for information and resources concerning mental prosperity (eg, Somazone), and coordinated self-facilitated online treatment (eg, MoodGym). As the amount of psychotherapists isn't adequate, it is significant for agents to have the alternative to keep their mental prosperity in isolation. An autonomous mental clinical benefits course using VR devices has been made, and its pressing factor decline sway has been endorsed in advance. This assessment proposes another type of the course using phones and chatbots to overhaul its solace for use and to keep up customer motivation for consistently and reiterated use. The effects of pressing factor decline and motivation uphold were perceived to feel heard and give them a phase to their issues. It will allow anybody to login and profit by mental prosperity organizations in their district

Emotional Reactions and Likelihood of Response to Questions Designed for a Mental Health Chatbot Among Adolescents: Experimental Study.

BackgroundPsychological distress increases across adolescence and has been associated with several important health outcomes with consequences that can extend into adulthood. One type of technological innovation that may serve as a unique intervention for youth experiencing psychological distress is the conversational agent, otherwise known as a chatbot. Further research is needed on the factors that may make mental health chatbots destined for adolescents more appealing and increase the likelihood that adolescents will use them.ObjectiveThe aim of this study was to assess adolescents’ emotional reactions and likelihood of responding to questions that could be posed by a mental health chatbot. Understanding adolescent preferences and factors that could increase adolescents’ likelihood of responding to chatbot questions could assist in future mental health chatbot design destined for youth.MethodsWe recruited 19 adolescents aged 14 to 17 years to participate in a study with a 2×2×3 within-subjects factorial design. Each participant was sequentially presented with 96 chatbot questions for a duration of 8 seconds per question. Following each presentation, participants were asked to indicate how likely they were to respond to the question, as well as their perceived affective reaction to the question. Demographic data were collected, and an informal debriefing was conducted with each participant.ResultsParticipants were an average of 15.3 years old (SD 1.00) and mostly female (11/19, 58%). Logistic regressions showed that the presence of GIFs predicted perceived emotional valence (β=–.40, P<.001), such that questions without GIFs were associated with a negative perceived emotional valence. Question type predicted emotional valence, such that yes/no questions (β=–.23, P=.03) and open-ended questions (β=–.26, P=.01) were associated with a negative perceived emotional valence compared to multiple response choice questions. Question type also predicted the likelihood of response, such that yes/no questions were associated with a lower likelihood of response compared to multiple response choice questions (β=–.24, P=.03) and a higher likelihood of response compared to open-ended questions (β=.54, P<.001).ConclusionsThe findings of this study add to the rapidly growing field of teen-computer interaction and contribute to our understanding of adolescent user experience in their interactions with a mental health chatbot. The insights gained from this study may be of assistance to developers and designers of mental health chatbots.

Feasibility of a Software agent providing a brief Intervention for Self-help to Uplift psychological wellbeing (“SISU”). A single-group pretest-posttest trial investigating the potential of SISU to act as therapeutic agent

BackgroundSoftware agents are computer-programs that conduct conversations with a human. The present study evaluates the feasibility of the software agent “SISU” aiming to uplift psychological wellbeing. Methods: Within a one-group pretest-posttest trial, N = 30 German-speaking participants were recruited. Assessments took place before (t1), during (t2) and after (t3) the intervention. The ability of SISU to guide participants through the intervention, acceptability, and negative effects were investigated. Data analyses are based on intention-to-treat principles. Linear mixed models will be used to investigate short-term changes over time in mood, depression, anxiety. InterventionThe intervention consists of two sessions. Each session comprises writing tasks on autobiographical negative life events and an Acceptance- and Commitment Therapy-based exercise respectively. Participants interact with the software agent on two consecutive days for about 30 min each. ResultsAll participants completed all sessions within two days. User experience was positive, with all subscales of the user experience questionnaire (UEQ) M > 0.8. Participants experienced their writings as highly self-relevant and personal. However, 57% of the participants reported at least one negative effect attributed to the intervention. Results on linear mixed models indicate an increase in anxiety over time (β = 1.33, p = .001). Qualitative User Feedback revealed that the best thing about SISU was its innovativeness (13%) and anonymity (13%). As worst thing about SISU participants indicated that the conversational style of SISU often felt unnatural (73%). ConclusionSISU successfully guided participants through the two-day intervention. Moreover, SISU has the potential to enter the inner world of participants. However, intervention contents have the potential to evoke negative effects in individuals. Expectable short-term symptom deterioration due to writing about negative autobiographical life events could not be prevented by acceptance and commitment therapy-based exercises. Hence, results suggest a revision of intervention contents as well as of the conversational style of SISU. The good adherence rate indicates the useful and acceptable format of SISU as a mental health chatbot. Overall, little is known about the effectiveness of software agents in the context of psychological wellbeing. Results of the present trial underline that the innovative technology bears the potential of SISU to act as therapeutic agent but should not be used with its current intervention content. Trial-registrationThe Trial is registered at the WHO International Clinical Trials Registry Platform via the German Clinical Studies Register (DRKS): DRKS00014933 (date of registration: 20.06.2018). Link: https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00014933.

Perceptions and Opinions of Patients About Mental Health Chatbots: Scoping Review.

BackgroundChatbots have been used in the last decade to improve access to mental health care services. Perceptions and opinions of patients influence the adoption of chatbots for health care. Many studies have been conducted to assess the perceptions and opinions of patients about mental health chatbots. To the best of our knowledge, there has been no review of the evidence surrounding perceptions and opinions of patients about mental health chatbots.ObjectiveThis study aims to conduct a scoping review of the perceptions and opinions of patients about chatbots for mental health.MethodsThe scoping review was carried out in line with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) extension for scoping reviews guidelines. Studies were identified by searching 8 electronic databases (eg, MEDLINE and Embase) in addition to conducting backward and forward reference list checking of the included studies and relevant reviews. In total, 2 reviewers independently selected studies and extracted data from the included studies. Data were synthesized using thematic analysis.ResultsOf 1072 citations retrieved, 37 unique studies were included in the review. The thematic analysis generated 10 themes from the findings of the studies: usefulness, ease of use, responsiveness, understandability, acceptability, attractiveness, trustworthiness, enjoyability, content, and comparisons.ConclusionsThe results demonstrated overall positive perceptions and opinions of patients about chatbots for mental health. Important issues to be addressed in the future are the linguistic capabilities of the chatbots: they have to be able to deal adequately with unexpected user input, provide high-quality responses, and have to show high variability in responses. To be useful for clinical practice, we have to find ways to harmonize chatbot content with individual treatment recommendations, that is, a personalization of chatbot conversations is required.

"I'm like a wise little person": Notes on the Metal Performance of Woebot the Mental Health Chatbot

"I'm like a wise little person": Notes on the Metal Performance of Woebot the Mental Health Chatbot

A Survey of Mental Health Chatbots using NLP

Due to recent technological advancements, there has been a rise in digital interventions aimed at either supplementing or replacing face-to-face mental health services which include the advent of mental health chatbots, that claim to provide assistive care using a therapeutic approach. These innovations are driven by Natural Language Processing (NLP). A chatbot can be described as a computer program capable of providing smart responses to user inputs by understanding natural language using one or more NLP techniques. In this study, we discuss the application of NLP in psychotherapy and also a general analysis of existing systems was performed by comparing the responses given by the chatbot against a set of predefined user inputs pertaining to queries related to wellbeing and mental health. The general methodology involved in the creation of such chatbots includes the underlying NLP techniques like word embeddings, sentiment analysis, Models like Sequence-to-Sequence model and attention mechanism. We also propose MentalEase, a mobile application which uses NLP techniques to provide not only conversational aid but also a toolbox of helpful features to keep mental health in place. By integrating mental health assessment tools into the chatbot interface, along with regular therapy it can help patients deal with mild anxiety and depression. This can also overcome certain barriers in mental health, such as waiting lists and geographical problems that hinder attendance at face-to-face counselling appointments.

A review of mobile chatbot apps for anxiety and depression and their self-care features

BackgroundAnxiety and depression rates are at an all-time high. Smartphone-based mental health chatbots can aid psychiatrists replacing some of the costly human based interaction providing a unique opportunity to expand the availability and quality of mental health intervention whilst providing an alternative approach to fill the much-needed self-care gap. ObjectiveAssess the quality and characteristics of chatbots for anxiety and depression available on Android and iOS systems. MethodsA search was performed in the App Store and Google Play Store following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol to identify existing chatbot apps for anxiety and depression. Eligibility was assessed by two individuals based on predefined eligibility criteria. Meta-data of the included chatbots and their characteristics were extracted from their description and post-installation by two reviewers. Information on quality was assessed by following the mHONcode principles. ResultsOnly a handful (n=11) of chatbots were included from an initial search of 1000 that provide a substitute for human-human based interaction and clearly had a therapeutic human substitute goal in mind. The majority of reviewed apps had a high number of downloads indicating their popularity. The apps were also of a general high quality based on our assessment according to the mHONcode principles. ConclusionThe general popularity of apps reviewed, and results of our quality assessment indicate chatbots have a promising future within the realm of anxiety and depression. Anxiety and depression chatbot apps have the potential to increase the capacity of mental health self-care providing much needed low-cost assistance to professionals.

Preliminary Evaluation of the Engagement and Effectiveness of a Mental Health Chatbot.

Background: Mental health difficulties are highly prevalent, yet access to support is limited by barriers of stigma, cost, and availability. These issues are even more prevalent in low- and middle-income countries, and digital technology is one potential way to overcome these barriers. Digital mental health interventions are effective but often struggle with low engagement rates, particularly in the absence of human support. Chatbots could offer a scalable solution, simulating human support at a lower cost.Objective: To complete a preliminary evaluation of engagement and effectiveness of Vitalk, a mental health chatbot, at reducing anxiety, depression and stress.Methods: Real world data was analyzed from 3,629 Vitalk users who had completed the first phase of a Vitalk program (“less anxiety,” “less stress” or “better mood”). Programs were delivered through written conversation with a chatbot. Engagement was calculated from the number of responses sent to the chatbot divided by days in the program.Results: Users sent an average of 8.17 responses per day. For all three programs, target outcome scores reduced between baseline and follow up with large effect sizes for anxiety (Cohen's d = −0.85), depression (Cohen's d = −0.91) and stress (Cohen's d = −0.81). Increased engagement resulted in improved post-intervention values for anxiety and depression.Conclusion: This study highlights a chatbot's potential to reduce mental health symptoms in the general population within Brazil. While findings show promise, further research is required.

AI‐Powered mental health chatbots: Examining users’ motivations, active communicative action and engagement after mass‐shooting disasters

AbstractChatbots are gaining their popularity in society and have triggered heated discussions in academia as well. Currently, few studies explored the applications of AI‐powered mental health chatbots in a mass‐shooting disaster context. Via integrating literature from multi‐disciplines such as crisis management, mental health and digital communication, this quantitative study intends to contribute to close this gap and explore the associations between perceived gratifications and protection motivations of using mental health chatbot services, active communicative action, and online and offline engagement behaviours of solving mental health problems after disasters. This study surveyed 1,114 US participants who ever used chatbot services from top healthcare companies. Implications of the results enhance theoretical discussions on how artificial intelligence has shaped individuals’ motivations, communicative action and engagement behaviour to treat mental health problems. This study also benefits professionals who want to learn more about chatbots for mental healthcare, crisis management and customer engagement.

Hi, Can I Help? Exploring How to Design a Mental Health Chatbot for Youths

Chatbots represent new opportunities for low-threshold preventive mental health support to youths. To provide needed knowledge regarding how to design chatbots for this purpose, we present an exploratory design study where we designed and evaluated a prototype chatbot to complement the work of school nurses in the school health service. The prototype was designed with particular regard for preventive mental health support. The design process involved school nurses, digital health workers, and youths. Through user insight activities, we identified four types of support to be provided through the chatbot: informational, relational, processual, and referral. We explored these four types of support through concept development and prototyping. These results are discussed as a potential basis for a framework for understanding youths’ needs regarding chatbots for preventive mental health support. When discussing the study findings, we point out how the study contributes to theory and practice and suggest avenues for future research.

Visual presentation of mental healthcare chatbots for user experience

Today the digital healthcare market and interests in mental health chatbots are growing. People get easily accessed to chatbot interactions, and chatbots can be used to support people’s emotional stability and psychological well-being. Consequently, many mental healthcare chatbots have been designed to reduce the likelihood of the occurrence of mental health issues by improving self-metacognition through early interventions. However, only few studies have discussed specific factors, which mental healthcare chatbot design may affect user experience. Besides, although visual presentations throughout a chatbot system influence users’ positive/negative experiences, most chatbot studies so far have focused on identity design rather than graphical interfaces and non-verbal visual communication tools. While it is important to examine specific visual design elements, it is also important to examine overall visual design requirements. Therefore, this study explored the user experience of mental health chatbots in terms of identity design, chatbot interface design, and visual communication tools. In this study, participants’ data were collected by pre/post preference evaluations, the system usability scale questionnaire, and semi-structured interview related to selected chatbot systems (i.e. Replika, Youper, Sayana, Woebot). The collected data were qualitatively analysed, and consequently, considerations were suggested for designing mental healthcare chatbots.

Intelligent Conversational Agents in Mental Healthcare Services: A Thematic Analysis of User Perceptions

Abstract Background: The emerging Artificial Intelligence (AI) based Conversational Agents (CA) capable of delivering evidence-based psychotherapy presents a unique opportunity to solve longstanding issues such as social stigma and demand-supply imbalance associated with traditional mental health care services. However, the emerging literature points to several socio-ethical challenges which may act as inhibitors to the adoption in the minds of the consumers. We also observe a paucity of research focusing on determinants of adoption and use of AI-based CAs in mental healthcare. In this setting, this study aims to understand the factors influencing the adoption and use of Intelligent CAs in mental healthcare by examining the perceptions of actual users. Method: The study followed a qualitative approach based on netnography and used a rigorous iterative thematic analysis of publicly available user reviews of popular mental health chatbots to develop a comprehensive framework of factors influencing the user’s decision to adopt mental healthcare CA. Results: We developed a comprehensive thematic map comprising of four main themes, namely, perceived risk, perceived benefits, trust, and perceived anthropomorphism, along with its 12 constituent subthemes that provides a visualization of the factors that govern the user’s adoption and use of mental healthcare CA. Conclusions: Insights from our research could guide future research on mental healthcare CA use behavior. Additionally, it could also aid designers in framing better design decisions that meet consumer expectations. Our research could also guide healthcare policymakers and regulators in integrating this technology into formal healthcare delivery systems. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/1/ Recommended Citation Prakash, Ashish Viswanath and Das, Saini (2020) "Intelligent Conversational Agents in Mental Healthcare Services: A Thematic Analysis of User Perceptions," Pacific Asia Journal of the Association for Information Systems: Vol. 12: Iss. 2, Article 1. DOI: 10.17705/1pais.12201