Changes to the Psychiatric Chatbot Landscape: A Systematic Review of Conversational Agents in Serious Mental Illness: Changements du paysage psychiatrique des chatbots: une revue systématique des agents conversationnels dans la maladie mentale sérieuse

Needs-Assessment for an Artificial Intelligence-Based Chatbot for Pharmacists in HIV Care: Results from a Knowledge-Attitudes-Practices Survey

BACKGROUND

Pharmacists need up-to-date knowledge and decision-making support in HIV care. We aim to develop MARVIN-Pharma, an adapted artificial intelligence-based chatbot initially for people with HIV, to assist pharmacists in considering evidence-based needs.

METHODS

From December 2022 to December 2023, an online needs-assessment survey evaluated Québec pharmacists' knowledge, attitudes, involvement, and barriers relative to HIV care, alongside perceptions relevant to the usability of MARVIN-Pharma. Recruitment involved convenience and snowball sampling, targeting National HIV and Hepatitis Mentoring Program affiliates.

RESULTS

Forty-one pharmacists (28 community, 13 hospital-based) across 15 Québec municipalities participated. Participants perceived their HIV knowledge as moderate (M = 3.74/6). They held largely favorable attitudes towards providing HIV care (M = 4.02/6). They reported a "little" involvement in the delivery of HIV care services (M = 2.08/5), most often ART adherence counseling, refilling, and monitoring. The most common barriers reported to HIV care delivery were a lack of time, staff resources, clinical tools, and HIV information/training, with pharmacists at least somewhat agreeing that they experienced each (M ≥ 4.00/6). On average, MARVIN-Pharma's acceptability and compatibility were in the 'undecided' range (M = 4.34, M = 4.13/7, respectively), while pharmacists agreed to their self-efficacy to use online health services (M = 5.6/7).

CONCLUSION

MARVIN-Pharma might help address pharmacists' knowledge gaps and barriers to HIV treatment and care, but pharmacist engagement in the chatbot's development seems vital for its future uptake and usability.

Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review

BACKGROUND

Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field.

OBJECTIVE

This review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations.

METHODS

A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis.

RESULTS

The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts.

CONCLUSIONS

Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use.

Effects of a Chatbot-Based Intervention on Stress and Health-Related Parameters in a Stressed Sample: Randomized Controlled Trial

BACKGROUND

Stress levels and the prevalence of mental disorders in the general population have been rising in recent years. Chatbot-based interventions represent novel and promising digital approaches to improve health-related parameters. However, there is a lack of research on chatbot-based interventions in the area of mental health.

OBJECTIVE

The aim of this study was to investigate the effects of a 3-week chatbot-based intervention guided by the chatbot ELME, specifically with respect to the ability to reduce stress and improve various health-related parameters in a stressed sample.

METHODS

In this multicenter two-armed randomized controlled trial, 118 individuals with medium to high stress levels were randomized to the intervention group (n=59) or the treatment-as-usual control group (n=59). The ELME chatbot guided participants of the intervention group through 3 weeks of training based on the topics stress, mindfulness, and interoception, with practical and psychoeducative elements delivered in two daily interactive intervention sessions via a smartphone (approximately 10-20 minutes each). The primary outcome (perceived stress) and secondary outcomes (mindfulness; interoception or interoceptive sensibility; subjective well-being; and emotion regulation, including the subfacets reappraisal and suppression) were assessed preintervention (T1), post intervention (T2; after 3 weeks), and at follow-up (T3; after 6 weeks). During both conditions, participants also underwent ecological momentary assessments of stress and interoceptive sensibility.

RESULTS

There were no significant changes in perceived stress (β03=-.018, SE=.329; P=.96) and momentary stress. Mindfulness and the subfacet reappraisal significantly increased in the intervention group over time, whereas there was no change in the subfacet suppression. Well-being and momentary interoceptive sensibility increased in both groups over time.

CONCLUSIONS

To gain insight into how the intervention can be improved to achieve its full potential for stress reduction, besides a longer intervention duration, specific sample subgroups should be considered. The chatbot-based intervention seems to have the potential to improve mindfulness and emotion regulation in a stressed sample. Future chatbot-based studies and interventions in health care should be designed based on the latest findings on the efficacy of rule-based and artificial intelligence-based chatbots.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00027560; https://drks.de/search/en/trial/DRKS00027560.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-doi.org/10.3389/fdgth.2023.1046202.

A Factorial Randomized Controlled Trial to Optimize User Engagement With a Chatbot-Led Parenting Intervention: Protocol for the ParentText Optimisation Trial

BACKGROUND

Violence against children (VAC) is a serious public health concern with long-lasting adverse effects. Evidence-based parenting programs are one effective means to prevent VAC; however, these interventions are not scalable in their typical in-person group format, especially in low- and middle-income countries where the need is greatest. While digital delivery, including via chatbots, offers a scalable and cost-effective means to scale up parenting programs within these settings, it is crucial to understand the key pillars of user engagement to ensure their effective implementation.

OBJECTIVE

This study aims to investigate the most effective and cost-effective combination of external components to optimize user engagement with ParentText, an open-source chatbot-led parenting intervention to prevent VAC in Mpumalanga, South Africa.

METHODS

This study will use a mixed methods design incorporating a 2 × 2 factorial cluster-randomized controlled trial and qualitative interviews. Parents of adolescent girls (32 clusters, 120 participants [60 parents and 60 girls aged 10 to 17 years] per cluster; N=3840 total participants) will be recruited from the Ehlanzeni and Nkangala districts of Mpumalanga. Clusters will be randomly assigned to receive 1 of the 4 engagement packages that include ParentText alone or combined with in-person sessions and a facilitated WhatsApp support group. Quantitative data collected will include pretest-posttest parent- and adolescent-reported surveys, facilitator-reported implementation data, and digitally tracked engagement data. Qualitative data will be collected from parents and facilitators through in-person or over-the-phone individual semistructured interviews and used to expand the interpretation and understanding of the quantitative findings.

RESULTS

Recruitment and data collection started in August 2023 and were finalized in November 2023. The total number of participants enrolled in the study is 1009, with 744 caregivers having completed onboarding to the chatbot-led intervention. Female participants represent 92.96% (938/1009) of the sample population, whereas male participants represent 7.03% (71/1009). The average participant age is 43 (SD 9) years.

CONCLUSIONS

The ParentText Optimisation Trial is the first study to rigorously test engagement with a chatbot-led parenting intervention in a low- or middle-income country. The results of this study will inform the final selection of external delivery components to support engagement with ParentText in preparation for further evaluation in a randomized controlled trial in 2024.

TRIAL REGISTRATION

Open Science Framework (OSF); https://doi.org/10.17605/OSF.IO/WFXNE.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52145.

Dialogue agents for artificial intelligence-based conversational systems for cognitively disabled: a systematic review

PURPOSE

We present a systematic literature review of dialogue agents for Artificial Intelligence (AI) and agent-based conversational systems dealing with cognitive disability of aged and impaired people including dementia and Parkinson's disease. We analyze current applications, gaps, and challenges in the existing research body, and provide guidelines and recommendations for their future development and use.

MATERIALS AND METHODS

We perform this study by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. We performed a systematic search using relevant databases (ACM Digital Library, Google Scholar, IEEE Xplore, PubMed, and Scopus).

RESULTS

This study identified 468 articles on the use of conversational agents in healthcare. We finally selected 124 articles based on their objectives and content as directly related to our main topic.

CONCLUSION

We identified the main challenges in the field and analyzed the typical examples of the application of conversational agents in the healthcare domain, the desired characteristics of conversational agents, and chatbot support for aged people and people with cognitive disabilities. Our results contribute to a discussion on conversational health agents and emphasize current knowledge gaps and challenges for future research.IMPLICATIONS FOR REHABILITATIONA systematic literature review of dialogue agents for artificial intelligence and agent-based conversational systems dealing with cognitive disability of aged and impaired people.Main challenges and desired characteristics of the conversational agents, and chatbot support for aged people and people with cognitive disability.Current knowledge gaps and challenges for remote healthcare and rehabilitation.Guidelines and recommendations for future development and use of conversational systems.

Artificial Intelligence: A Game-Changer for Mental Health Care

Starting from the escalating global burden of mental health disorders, exacerbated by the COVID-19 pandemic, the article examines the potential of artificial intelligence (AI) to revolutionize mental health care. With nearly one in five adults facing mental health issues and suicide ranking as a leading cause of death among the young, the strained mental health system seeks innovative solutions. The text discusses the rapid evolution of AI, particularly in image analysis for early physical health diagnoses, and its promising applications in mental health, including predictive analytics for various disorders. AI's ability to analyze written language, speech characteristics, and physiological signals from wearables offers avenues for remote monitoring and early prognosis. Despite the need to address ethical considerations, particularly biases in data sets and concerns about potential patient detachment, the article advocates for AI as a complementary tool rather than a replacement for human involvement in mental health services. Overall, the article emphasizes the transformative potential of AI in enhancing diagnostics, monitoring, and treatment strategies for mental health disorders.

Current and Emerging Technologies to Address the Placebo Response Challenge in CNS Clinical Trials: Promise, Pitfalls, and Pathways Forward

Excessive placebo response rates have long been a major challenge for central nervous system (CNS) drug discovery. As CNS trials progressively shift toward digitalization, decentralization, and novel remote assessment approaches, questions are emerging about whether innovative technologies can help mitigate the placebo response. This article begins with a conceptual framework for understanding placebo response. We then critically evaluate the potential of a range of innovative technologies and associated research designs that might help mitigate the placebo response and enhance detection of treatment signals. These include technologies developed to directly address placebo response; technology-based approaches focused on recruitment, retention, and data collection with potential relevance to placebo response; and novel remote digital phenotyping technologies. Finally, we describe key scientific and regulatory considerations when evaluating and selecting innovative strategies to mitigate placebo response. While a range of technological innovations shows potential for helping to address the placebo response in CNS trials, much work remains to carefully evaluate their risks and benefits.

Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being

Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage, there is a scarcity of comprehensive evaluations of their impact on mental health and well-being. This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health and factors influencing their effectiveness and user experience. Twelve databases were searched for experimental studies of AI-based CAs' effects on mental illnesses and psychological well-being published before May 26, 2023. Out of 7834 records, 35 eligible studies were identified for systematic review, out of which 15 randomized controlled trials were included for meta-analysis. The meta-analysis revealed that AI-based CAs significantly reduce symptoms of depression (Hedge's g 0.64 [95% CI 0.17-1.12]) and distress (Hedge's g 0.7 [95% CI 0.18-1.22]). These effects were more pronounced in CAs that are multimodal, generative AI-based, integrated with mobile/instant messaging apps, and targeting clinical/subclinical and elderly populations. However, CA-based interventions showed no significant improvement in overall psychological well-being (Hedge's g 0.32 [95% CI -0.13 to 0.78]). User experience with AI-based CAs was largely shaped by the quality of human-AI therapeutic relationships, content engagement, and effective communication. These findings underscore the potential of AI-based CAs in addressing mental health issues. Future research should investigate the underlying mechanisms of their effectiveness, assess long-term effects across various mental health outcomes, and evaluate the safe integration of large language models (LLMs) in mental health care.

Qualitative analysis of mental health conversational agents messages about autism spectrum disorder: a call for action

Background

Conversational agents (CA's) have shown promise in increasing accessibility to mental health resources. This study aimed to identify common themes of messages sent to a mental health CA (Wysa) related to ASD by general users and users that identify as having ASD.

Methods

This study utilized retrospective data. Two thematic analyses were conducted, one focusing on user messages including the keywords (e.g., ASD, autism, Asperger), and the second one with messages from users who self-identified as having ASD.

Results

For the sample of general users, the most frequent themes were "others having ASD," "ASD diagnosis," and "seeking help." For the users that self-identified as having ASD (n = 277), the most frequent themes were "ASD diagnosis or symptoms," "negative reaction from others," and "positive comments." There were 3,725 emotion words mentioned by users who self-identified as having ASD. The majority had negative valence (80.3%), and few were positive (14.8%) or ambivalent (4.9%).

Conclusion

Users shared their experiences and emotions surrounding ASD with a mental health CA. Users asked about the ASD diagnosis, sought help, and reported negative reactions from others. CA's have the potential to become a source of support for those interested in ASD and/or identify as having ASD.

Artificial intelligence in psychiatry research, diagnosis, and therapy

Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.

Preliminary Evaluation of a Conversational Agent to Support Self-management of Individuals Living With Posttraumatic Stress Disorder: Interview Study With Clinical Experts

BACKGROUND

Posttraumatic stress disorder (PTSD) is a serious public health concern. However, individuals with PTSD often do not have access to adequate treatment. A conversational agent (CA) can help to bridge the treatment gap by providing interactive and timely interventions at scale. Toward this goal, we have developed PTSDialogue-a CA to support the self-management of individuals living with PTSD. PTSDialogue is designed to be highly interactive (eg, brief questions, ability to specify preferences, and quick turn-taking) and supports social presence to promote user engagement and sustain adherence. It includes a range of support features, including psychoeducation, assessment tools, and several symptom management tools.

OBJECTIVE

This paper focuses on the preliminary evaluation of PTSDialogue from clinical experts. Given that PTSDialogue focuses on a vulnerable population, it is critical to establish its usability and acceptance with clinical experts before deployment. Expert feedback is also important to ensure user safety and effective risk management in CAs aiming to support individuals living with PTSD.

METHODS

We conducted remote, one-on-one, semistructured interviews with clinical experts (N=10) to gather insight into the use of CAs. All participants have completed their doctoral degrees and have prior experience in PTSD care. The web-based PTSDialogue prototype was then shared with the participant so that they could interact with different functionalities and features. We encouraged them to "think aloud" as they interacted with the prototype. Participants also shared their screens throughout the interaction session. A semistructured interview script was also used to gather insights and feedback from the participants. The sample size is consistent with that of prior works. We analyzed interview data using a qualitative interpretivist approach resulting in a bottom-up thematic analysis.

RESULTS

Our data establish the feasibility and acceptance of PTSDialogue, a supportive tool for individuals with PTSD. Most participants agreed that PTSDialogue could be useful for supporting self-management of individuals with PTSD. We have also assessed how features, functionalities, and interactions in PTSDialogue can support different self-management needs and strategies for this population. These data were then used to identify design requirements and guidelines for a CA aiming to support individuals with PTSD. Experts specifically noted the importance of empathetic and tailored CA interactions for effective PTSD self-management. They also suggested steps to ensure safe and engaging interactions with PTSDialogue.

CONCLUSIONS

Based on interviews with experts, we have provided design recommendations for future CAs aiming to support vulnerable populations. The study suggests that well-designed CAs have the potential to reshape effective intervention delivery and help address the treatment gap in mental health.

The Chatbots Are Invading Us: A Map Point on the Evolution, Applications, Opportunities, and Emerging Problems in the Health Domain

The inclusion of chatbots is potentially disruptive in society, introducing opportunities, but also important implications that need to be addressed on different domains. The aim of this study is to examine chatbots in-depth, by mapping out their technological evolution, current usage, and potential applications, opportunities, and emerging problems within the health domain. The study examined three points of view. The first point of view traces the technological evolution of chatbots. The second point of view reports the fields of application of the chatbots, giving space to the expectations of use and the expected benefits from a cross-domain point of view, also affecting the health domain. The third and main point of view is that of the analysis of the state of use of chatbots in the health domain based on the scientific literature represented by systematic reviews. The overview identified the topics of greatest interest with the opportunities. The analysis revealed the need for initiatives that simultaneously evaluate multiple domains all together in a synergistic way. Concerted efforts to achieve this are recommended. It is also believed to monitor both the process of osmosis between other sectors and the health domain, as well as the chatbots that can create psychological and behavioural problems with an impact on the health domain.

Reinventing mental health care in youth through mobile approaches: Current status and future steps

In this perspective, we aim to bring together research on mobile assessments and interventions in the context of mental health care in youth. After the COVID-19 pandemic, one out of five young people is experiencing mental health problems worldwide. New ways to face this burden are now needed. Young people search for low-burden services in terms of costs and time, paired with high flexibility and easy accessibility. Mobile applications meet these principles by providing new ways to inform, monitor, educate, and enable self-help, thus reinventing mental health care in youth. In this perspective, we explore the existing literature reviews on mobile assessments and interventions in youth through data collected passively (e.g., digital phenotyping) and actively (e.g., using Ecological Momentary Assessments—EMAs). The richness of such approaches relies on assessing mental health dynamically by extending beyond the confines of traditional methods and diagnostic criteria, and the integration of sensor data from multiple channels, thus allowing the cross-validation of symptoms through multiple information. However, we also acknowledge the promises and pitfalls of such approaches, including the problem of interpreting small effects combined with different data sources and the real benefits in terms of outcome prediction when compared to gold-standard methods. We also explore a new promising and complementary approach, using chatbots and conversational agents, that encourages interaction while tracing health and providing interventions. Finally, we suggest that it is important to continue to move beyond the ill-being framework by giving more importance to intervention fostering well-being, e.g., using positive psychology.

A chatbot-based intervention with ELME to improve stress and health-related parameters in a stressed sample: Study protocol of a randomised controlled trial

Background

Stress levels in the general population had already been increasing in recent years, and have subsequently been exacerbated by the global pandemic. One approach for innovative online-based interventions are "chatbots" - computer programs that can simulate a text-based interaction with human users via a conversational interface. Research on the efficacy of chatbot-based interventions in the context of mental health is sparse. The present study is designed to investigate the effects of a three-week chatbot-based intervention with the chatbot ELME, aiming to reduce stress and to improve various health-related parameters in a stressed sample.

Methods

In this multicenter, two-armed randomised controlled trial with a parallel design, a three-week chatbot-based intervention group including two daily interactive intervention sessions via smartphone (á 10-20 min.) is compared to a treatment-as-usual control group. A total of 130 adult participants with a medium to high stress levels will be recruited in Germany. Assessments will take place pre-intervention, post-intervention (after three weeks), and follow-up (after six weeks). The primary outcome is perceived stress. Secondary outcomes include self-reported interoceptive accuracy, mindfulness, anxiety, depression, personality, emotion regulation, psychological well-being, stress mindset, intervention credibility and expectancies, affinity for technology, and attitudes towards artificial intelligence. During the intervention, participants undergo ecological momentary assessments. Furthermore, satisfaction with the intervention, the usability of the chatbot, potential negative effects of the intervention, adherence, potential dropout reasons, and open feedback questions regarding the chatbot are assessed post-intervention.

Discussion

To the best of our knowledge, this is the first chatbot-based intervention addressing interoception, as well as in the context with the target variables stress and mindfulness. The design of the present study and the usability of the chatbot were successfully tested in a previous feasibility study. To counteract a low adherence of the chatbot-based intervention, a high guidance by the chatbot, short sessions, individual and flexible time points of the intervention units and the ecological momentary assessments, reminder messages, and the opportunity to postpone single units were implemented.

Trial registration

The trial is registered at the WHO International Clinical Trials Registry Platform via the German Clinical Trials Register (DRKS00027560; date of registration: 06 January 2022). This is protocol version No. 1. In case of important protocol modifications, trial registration will be updated.

Evaluating Conversational Agents for Mental Health: Scoping Review of Outcomes and Outcome Measurement Instruments (Preprint)

BACKGROUND

Rapid proliferation of mental health interventions delivered through conversational agents (CAs) calls for high-quality evidence to support their implementation and adoption. Selecting appropriate outcomes, instruments for measuring outcomes, and assessment methods are crucial for ensuring that interventions are evaluated effectively and with a high level of quality.

OBJECTIVE

We aimed to identify the types of outcomes, outcome measurement instruments, and assessment methods used to assess the clinical, user experience, and technical outcomes in studies that evaluated the effectiveness of CA interventions for mental health.

METHODS

We undertook a scoping review of the relevant literature to review the types of outcomes, outcome measurement instruments, and assessment methods in studies that evaluated the effectiveness of CA interventions for mental health. We performed a comprehensive search of electronic databases, including PubMed, Cochrane Central Register of Controlled Trials, Embase (Ovid), PsychINFO, and Web of Science, as well as Google Scholar and Google. We included experimental studies evaluating CA mental health interventions. The screening and data extraction were performed independently by 2 review authors in parallel. Descriptive and thematic analyses of the findings were performed.

RESULTS

We included 32 studies that targeted the promotion of mental well-being (17/32, 53%) and the treatment and monitoring of mental health symptoms (21/32, 66%). The studies reported 203 outcome measurement instruments used to measure clinical outcomes (123/203, 60.6%), user experience outcomes (75/203, 36.9%), technical outcomes (2/203, 1.0%), and other outcomes (3/203, 1.5%). Most of the outcome measurement instruments were used in only 1 study (150/203, 73.9%) and were self-reported questionnaires (170/203, 83.7%), and most were delivered electronically via survey platforms (61/203, 30.0%). No validity evidence was cited for more than half of the outcome measurement instruments (107/203, 52.7%), which were largely created or adapted for the study in which they were used (95/107, 88.8%).

CONCLUSIONS

The diversity of outcomes and the choice of outcome measurement instruments employed in studies on CAs for mental health point to the need for an established minimum core outcome set and greater use of validated instruments. Future studies should also capitalize on the affordances made available by CAs and smartphones to streamline the evaluation and reduce participants' input burden inherent to self-reporting.

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.

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality

As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies - such as smartphone apps, vir-tual reality, chatbots, and social media - have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for "digital phenotyping" and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self-management of psychological well-being and early intervention, along with more nascent research supporting their use in clinical management of long-term psychiatric conditions - including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders - as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real-world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.

Development of a Positive Body Image Chatbot (KIT) With Young People and Parents/Carers: Qualitative Focus Group Study

BACKGROUND

Body image and eating disorders represent a significant public health concern; however, many affected individuals never access appropriate treatment. Conversational agents or chatbots reflect a unique opportunity to target those affected online by providing psychoeducation and coping skills, thus filling the gap in service provision.

OBJECTIVE

A world-first body image chatbot called "KIT" was designed. The aim of this study was to assess preliminary acceptability and feasibility via the collection of qualitative feedback from young people and parents/carers regarding the content, structure, and design of the chatbot, in accordance with an agile methodology strategy. The chatbot was developed in collaboration with Australia's national eating disorder support organization, the Butterfly Foundation.

METHODS

A conversation decision tree was designed that offered psychoeducational information on body image and eating disorders, as well as evidence-based coping strategies. A version of KIT was built as a research prototype to deliver these conversations. Six focus groups were conducted using online semistructured interviews to seek feedback on the KIT prototype. This included four groups of people seeking help for themselves (n=17; age 13-18 years) and two groups of parents/carers (n=8; age 46-57 years). Participants provided feedback on the cartoon chatbot character design, as well as the content, structure, and design of the chatbot webchat.

RESULTS

Thematic analyses identified the following three main themes from the six focus groups: (1) chatbot character and design, (2) content presentation, and (3) flow. Overall, the participants provided positive feedback regarding KIT, with both young people and parents/carers generally providing similar reflections. The participants approved of KIT's character and engagement. Specific suggestions were made regarding the brevity and tone to increase KIT's interactivity.

CONCLUSIONS

Focus groups provided overall positive qualitative feedback regarding the content, structure, and design of the body image chatbot. Incorporating the feedback of lived experience from both individuals and parents/carers allowed the refinement of KIT in the development phase as per an iterative agile methodology. Further research is required to evaluate KIT's efficacy.

The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae

Personalized medicine is among the most exciting innovations in recent clinical research, offering the opportunity for tailored screening and management at the individual level. Biomarker-enriched clinical trials have shown increased efficiency and informativeness in cancer research due to the selective exclusion of patients unlikely to benefit. In acute stress situations, clinically significant decisions are often made in time-sensitive manners and providers may be pressed to make decisions based on abbreviated clinical assessments. Up to 30% of trauma survivors admitted to the Emergency Department (ED) will develop long-lasting posttraumatic stress psychopathologies. The long-term impact of those survivors with posttraumatic stress sequelae are significant, impacting both long-term psychological and physiological recovery. An accurate prognostic model of who will develop posttraumatic stress symptoms does not exist yet. Additionally, no scalable and cost-effective method that can be easily integrated into routine care exists, even though especially the acute care setting provides a critical window of opportunity for prevention in the so-called golden hours when preventive measures are most effective. In this review, we aim to discuss emerging machine learning (ML) applications that are promising for precisely risk stratification and targeted treatments in the acute care setting. The aim of this narrative review is to present examples of digital health innovations and to discuss the potential of these new approaches for treatment selection and prevention of posttraumatic sequelae in the acute care setting. The application of artificial intelligence-based solutions have already had great success in other areas and are rapidly approaching the field of psychological care as well. New ways of algorithm-based risk predicting, and the use of digital phenotypes provide a high potential for predicting future risk of PTSD in acute care settings and to go new steps in precision psychiatry.