Embodied Conversational Agents for the Detection and Prevention of Suicidal Behaviour: Current Applications and Open Challenges

Embodied conversational agents for shared decision-making: a scoping review protocol

INTRODUCTION

Embodied conversational agents (ECAs) are computer-based dialogue systems designed to simulate face-to-face interactions by incorporating human-like physical attributes. Their capacity to establish and maintain an empathic relationship in patient interactions positions them as innovative tools that facilitate shared decision-making (SDM). To systematically synthesise the existing evidence concerning the development and application of ECAs in promoting SDM, this protocol delineates a scoping review designed to identify and present the available evidence within this domain. Specifically, the protocol outlines a review that will concentrate on the key features of ECAs in the context of SDM, including their appearance, dialogue mechanisms and emotional models, within the framework, as well as their implementation and evaluation in clinical settings.

METHODS AND ANALYSIS

The framework established by Arksey and O'Malley will be employed to guide the scoping review process. This protocol outlines the systematic retrieval of seven databases, including PubMed, EMBASE, PsycINFO, Web of Science, the Cumulative Index to Nursing and Allied Health Literature, Institute of Electrical and Electronics Engineers (IEEE) Xplore Digital Library and Association for Computing Machinery (ACM) Digital Library. The search strategy has been developed and will be conducted across each database, from its inception to September 2024. Two researchers will conduct literature screening and data extraction independently. The results will be systematically organised and presented through narrative abstracts, tables and/or figures.

ETHICS AND DISSEMINATION

Ethical approval is not necessary for this review, as it uses data that have been previously collected. Furthermore, the obtained results will be reported in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

Open Science Framework Registries (https://doi.org/10.17605/OSF.IO/BN3CM).

Embodied Conversational Agents for Chronic Diseases: Scoping Review

BACKGROUND

Embodied conversational agents (ECAs) are computer-generated animated humanlike characters that interact with users through verbal and nonverbal behavioral cues. They are increasingly used in a range of fields, including health care.

OBJECTIVE

This scoping review aims to identify the current practice in the development and evaluation of ECAs for chronic diseases.

METHODS

We applied a methodological framework in this review. A total of 6 databases (ie, PubMed, Embase, CINAHL, ACM Digital Library, IEEE Xplore Digital Library, and Web of Science) were searched using a combination of terms related to ECAs and health in October 2023. Two independent reviewers selected the studies and extracted the data. This review followed the PRISMA-ScR (Preferred Reporting Items of Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) statement.

RESULTS

The literature search found 6332 papers, of which 36 (0.57%) met the inclusion criteria. Among the 36 studies, 27 (75%) originated from the United States, and 28 (78%) were published from 2020 onward. The reported ECAs covered a wide range of chronic diseases, with a focus on cancers, atrial fibrillation, and type 2 diabetes, primarily to promote screening and self-management. Most ECAs were depicted as middle-aged women based on screenshots and communicated with users through voice and nonverbal behavior. The most frequently reported evaluation outcomes were acceptability and effectiveness.

CONCLUSIONS

This scoping review provides valuable insights for technology developers and health care professionals regarding the development and implementation of ECAs. It emphasizes the importance of technological advances in the embodiment, personalized strategy, and communication modality and requires in-depth knowledge of user preferences regarding appearance, animation, and intervention content. Future studies should incorporate measures of cost, efficiency, and productivity to provide a comprehensive evaluation of the benefits of using ECAs in health care.

Evaluation of the Current State of Chatbots for Digital Health: Scoping Review

BACKGROUND

Chatbots have become ubiquitous in our daily lives, enabling natural language conversations with users through various modes of communication. Chatbots have the potential to play a significant role in promoting health and well-being. As the number of studies and available products related to chatbots continues to rise, there is a critical need to assess product features to enhance the design of chatbots that effectively promote health and behavioral change.

OBJECTIVE

This scoping review aims to provide a comprehensive assessment of the current state of health-related chatbots, including the chatbots' characteristics and features, user backgrounds, communication models, relational building capacity, personalization, interaction, responses to suicidal thoughts, and users' in-app experiences during chatbot use. Through this analysis, we seek to identify gaps in the current research, guide future directions, and enhance the design of health-focused chatbots.

METHODS

Following the scoping review methodology by Arksey and O'Malley and guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist, this study used a two-pronged approach to identify relevant chatbots: (1) searching the iOS and Android App Stores and (2) reviewing scientific literature through a search strategy designed by a librarian. Overall, 36 chatbots were selected based on predefined criteria from both sources. These chatbots were systematically evaluated using a comprehensive framework developed for this study, including chatbot characteristics, user backgrounds, building relational capacity, personalization, interaction models, responses to critical situations, and user experiences. Ten coauthors were responsible for downloading and testing the chatbots, coding their features, and evaluating their performance in simulated conversations. The testing of all chatbot apps was limited to their free-to-use features.

RESULTS

This review provides an overview of the diversity of health-related chatbots, encompassing categories such as mental health support, physical activity promotion, and behavior change interventions. Chatbots use text, animations, speech, images, and emojis for communication. The findings highlight variations in conversational capabilities, including empathy, humor, and personalization. Notably, concerns regarding safety, particularly in addressing suicidal thoughts, were evident. Approximately 44% (16/36) of the chatbots effectively addressed suicidal thoughts. User experiences and behavioral outcomes demonstrated the potential of chatbots in health interventions, but evidence remains limited.

CONCLUSIONS

This scoping review underscores the significance of chatbots in health-related applications and offers insights into their features, functionalities, and user experiences. This study contributes to advancing the understanding of chatbots' role in digital health interventions, thus paving the way for more effective and user-centric health promotion strategies. This study informs future research directions, emphasizing the need for rigorous randomized control trials, standardized evaluation metrics, and user-centered design to unlock the full potential of chatbots in enhancing health and well-being. Future research should focus on addressing limitations, exploring real-world user experiences, and implementing robust data security and privacy measures.

A survey on agents applications in healthcare: Opportunities, challenges and trends

BACKGROUND AND OBJECTIVE

The agent abstraction is a powerful one, developed decades ago to represent crucial aspects of artificial intelligence research. The meaning has transformed over the years and now there are different nuances across research communities. At its core, an agent is an autonomous computational entity capable of sensing, acting, and capturing interactions with other agents and its environment. This review examines how agent-based techniques have been implemented and evaluated in a specific and very important domain, i.e. healthcare research.

METHODS

We survey key areas of agent-based research in healthcare, e.g. individual and collective behaviours, communicable and non-communicable diseases, and social epidemiology. We propose a systematic search and critical review of relevant recent works, introduced by an exploratory network analysis.

RESULTS

Network analysis enables to devise out 5 main research clusters, the most active authors, and 4 main research topics.

CONCLUSIONS

Our findings support discussion of some future directions for increasing the value of agent-based approaches in healthcare.

Development and Validation of Conversational Agent to Pregnancy Safe-education

Pregnant women constantly need some information to support nutritional decisions during pregnancy, and many do not receive such assistance at all. This study aims to present a conversational agent to provide reliable information to pregnant women, focusing on nutritional education and evaluating the perception of pregnant women and health professionals about the agent. As a scientific contribution, this article developed and implemented a conversational agent in a real environment capable of generating reliable responses on the basis of a set of health documents. We proposed an intervention study with 25 women and 10 healthcare providers through a survey to measure the perceptions of these groups towards conversational agents. The results show that the intended design could ensure positive support for pregnant women, clarify certain issues for the public, and remove some knowledge barriers. The results showed no significant difference between the groups (p-value = 0.713). Depending on the perception of the pregnant group, the conversational agent model can teach new knowledge during the prenatal period (Mean = 4.56). The model presented for health professionals could already be indicated as a support tool for pregnant women (Mean = 4.7).

Validity of Chatbot Use for Mental Health Assessment: Experimental Study

BACKGROUND

Mental disorders in adolescence and young adulthood are major public health concerns. Digital tools such as text-based conversational agents (ie, chatbots) are a promising technology for facilitating mental health assessment. However, the human-like interaction style of chatbots may induce potential biases, such as socially desirable responding (SDR), and may require further effort to complete assessments.

OBJECTIVE

This study aimed to investigate the convergent and discriminant validity of chatbots for mental health assessments, the effect of assessment mode on SDR, and the effort required by participants for assessments using chatbots compared with established modes.

METHODS

In a counterbalanced within-subject design, we assessed 2 different constructs-psychological distress (Kessler Psychological Distress Scale and Brief Symptom Inventory-18) and problematic alcohol use (Alcohol Use Disorders Identification Test-3)-in 3 modes (chatbot, paper-and-pencil, and web-based), and examined convergent and discriminant validity. In addition, we investigated the effect of mode on SDR, controlling for perceived sensitivity of items and individuals' tendency to respond in a socially desirable way, and we also assessed the perceived social presence of modes. Including a between-subject condition, we further investigated whether SDR is increased in chatbot assessments when applied in a self-report setting versus when human interaction may be expected. Finally, the effort (ie, complexity, difficulty, burden, and time) required to complete the assessments was investigated.

RESULTS

A total of 146 young adults (mean age 24, SD 6.42 years; n=67, 45.9% female) were recruited from a research panel for laboratory experiments. The results revealed high positive correlations (all P<.001) of measures of the same construct across different modes, indicating the convergent validity of chatbot assessments. Furthermore, there were no correlations between the distinct constructs, indicating discriminant validity. Moreover, there were no differences in SDR between modes and whether human interaction was expected, although the perceived social presence of the chatbot mode was higher than that of the established modes (P<.001). Finally, greater effort (all P<.05) and more time were needed to complete chatbot assessments than for completing the established modes (P<.001).

CONCLUSIONS

Our findings suggest that chatbots may yield valid results. Furthermore, an understanding of chatbot design trade-offs in terms of potential strengths (ie, increased social presence) and limitations (ie, increased effort) when assessing mental health were established.

Discourse Behavior of Older Adults Interacting With a Dialogue Agent Competent in Multiple Topics

We present a conversational agent designed to provide realistic conversational practice to older adults at risk of isolation or social anxiety, and show the results of a content analysis on a corpus of data collected from experiments with elderly patients interacting with our system. The conversational agent, represented by a virtual avatar, is designed to hold multiple sessions of casual conversation with older adults. Throughout each interaction, the system analyzes the prosodic and nonverbal behavior of users and provides feedback to the user in the form of periodic comments and suggestions on how to improve. Our avatar is unique in its ability to hold natural dialogues on a wide range of everyday topics – 27 topics in three groups, developed in collaboration with a team of gerontologists. The three groups vary in “degrees of intimacy”, and as such in degrees of cognitive difficulty for the user. After collecting data from 9 participants who interacted with the avatar for 7-9 sessions over a period of 3-4 weeks, we present results concerning dialogue behavior and inferred sentiment of the users. Analysis of the dialogues reveals correlations such as greater elaborateness for more difficult topics, increasing elaborateness with successive sessions, stronger sentiments in topics concerned with life goals rather than routine activities, and stronger self-disclosure for more intimate topics. In addition to their intrinsic interest, these results also reflect positively on the sophistication and practical applicability of our dialogue system.

Smart conversational agents for the detection of neuropsychiatric disorders: A systematic review

OBJECTIVE

To determine whether smart conversational agents can be used for detection of neuropsychiatric disorders. Therefore, we reviewed the technologies used, targeted mental disorders and validation procedures of relevant proposals in this field.

METHODS

We searched Scopus, PubMed, Pro-Quest, IEEE Xplore, Web of Science, CINAHL and the Cochrane Library using a predefined search strategy. Studies were included if they focused on neuropsychiatric disorders and involved conversational data for detection and diagnosis. They were assessed for eligibility by independent reviewers and ultimately included if a consensus was reached about their relevance.

RESULTS

2356 references were initially retrieved. Eventually, 17 articles - referring 9 smart conversational agents - met the inclusion criteria. Out of the selected studies, 7 are targeted at neurocognitive disorders, 7 at depression and 3 at other conditions. They apply diverse technological solutions and analysis techniques (82.4% use Artificial Intelligence), and they usually rely on gold standard tests for criterion validity assessment. Acceptability, reliability and other aspects of validity were rarely addressed.

CONCLUSION

The use of smart conversational agents for the detection of neuropsychiatric disorders is an emerging and promising field of research, with a broad coverage of mental disorders and extended use of AI. However, the few published studies did not undergo robust psychometric validation processes. Future research in this field would benefit from more rigorous validation mechanisms and standardized software and hardware platforms.

Computer-Assisted Avatar-Based Treatment for Dysfunctional Beliefs in Depressive Inpatients: A Pilot Study

Dysfunctional cognitions are a crucial part of depression. Cognitive therapy aims to modify dysfunctional beliefs. Typically, dysfunctional beliefs are questioned, and patients are trained to think of alternative functional beliefs. We developed a computer-assisted, avatar-based adjunct for cognitive therapy that aims to reduce dysfunctional beliefs and symptom severity. Besides, it aims to promote alternative functional beliefs. In a randomized controlled trial with 34 patients diagnosed with major depression currently undergoing inpatient treatment at the university psychiatric hospital in Regensburg, Germany, participants were randomly assigned to receive either treatment as usual (TAU) or computer-assisted avatar-based treatment for dysfunctional beliefs (CAT-DB) in addition to TAU. In CAT-DB participants are faced with a virtual avatar expressing their personal dysfunctional beliefs. Participants are asked to contradict these and express alternative functional beliefs. Assessments of conviction of dysfunctional beliefs, functional beliefs and symptom severity were done shortly before the intervention (pre-treatment), right after the intervention (post-treatment) and 14 days later (follow-up). The reduction in conviction of dysfunctional beliefs and symptom severity, and the increase in conviction of alternative functional beliefs at post-treatment and follow-up were significantly greater for the group receiving CAT-DB. Our study provides an indication in favor of the effectiveness of CAT-DB for depressive patients. It is a simple tool that could support classical cognitive therapy. Further studies at different centres, with larger sample sizes and varying therapeutic contexts are required to prove the effectiveness of our intervention.

Enacting 'more-than-human' care: Clients' and counsellors' views on the multiple affordances of chatbots in alcohol and other drug counselling

Forms of artificial intelligence (AI), such as chatbots that provide automated online counselling, promise to revolutionise alcohol and other drug treatment. Although the replacement of human counsellors remains a speculative prospect, chatbots for ‘narrow AI’ tasks (e.g., assessment and referral) are increasingly being used to augment clinical practice. Little research has addressed the possibilities for care that chatbots may generate in the future, particularly in the context of alcohol and other drug counselling. To explore these issues, we draw on the concept of technological ‘affordances’ and identify the range of possibilities for care that emerging chatbot interventions may afford and foreclose depending on the contexts in which they are implemented. Our analysis is based on qualitative data from interviews with clients (n=20) and focus group discussions with counsellors (n=8) conducted as part of a larger study of an Australian online alcohol and other drug counselling service. Both clients and counsellors expressed a concern that chatbot interventions lacked a ‘human’ element, which they valued in empathic care encounters. Most clients reported that they would share less information with a chatbot than a human counsellor, and they viewed this as constraining care. However, clients and counsellors suggested that the use of narrow AI might afford possibilities for performing discrete tasks, such as screening, triage or referral. In the context of what we refer to as ‘more-than-human’ care, our findings reveal complex views about the types of affordances that chatbots may produce and foreclose in online care encounters. We conclude by discussing implications for the potential ‘addiction futures’ and care trajectories that AI technologies offer, focussing on how they might inform alcohol and other drug policy, and the design of digital healthcare.

Social robots as treatment agents: Pilot randomized controlled trial to deliver a behavior change intervention

Social robots are increasingly demonstrating effectiveness as low-intensity behavior change agents. Key targets for these behavioral interventions include daily lifestyle behaviors with significant health consequences, such as the consumption of high-calorie foods and drinks ('snacks'). A pilot randomized controlled trial using a stepped-wedge design was conducted to determine the efficacy of a motivational intervention by an autonomous robot, to help reduce high-calorie snacks. Twenty-six adults were randomized to receive Immediate or 4-week Delayed treatment, with assessments at Baseline and Weeks 4 and 8. The treatment comprised motivation enhancement and self-management training using mental imagery (Functional Imagery Training). A significant condition by time effect for snack episode reduction was obtained, F(2, 32.06) = 4.30, p = .022. The Immediate condition significantly reduced snacking between Baseline and Week 4 (d = -1.06), while the Delayed condition did not (d = -0.08). Immediate participants maintained their improvement between Weeks 4 and 8 (d = -0.18), and Delayed participants then showed a significant fall (d = -1.42). Overall, 'Immediate' participants decreased their snack episodes by 54% and 'Delayed' decreased by 62% from Baseline to Week 8, and an average weight reduction of 4.4 kg was seen across over the first 2 weeks of treatment. Four weeks after starting the intervention, both conditions had significant increases in perceived confidence to control snack intake for time duration, specific scenarios and emotional states (d = 0.61 to 1.42). Working alliance was significantly correlated with reduced snack episodes. The pilot's results appear to suggest that the robot-delivered intervention may be as effective as a human clinician delivering a similar intervention. The robot-delivered pilot achieved similar snack episode reduction in the first four weeks (FIT-R, 55%) when compared with the human-delivered version by a trained clinician (FIT-H, 49%). Overall, the results provide preliminary evidence for an autonomous social robot to deliver a low-intensity treatment on dietary intake without the need for human intervention. Future trials should extend the deployment of the robot-delivered intervention protocol to other low-intensity behavioral outcomes.

Key Considerations for Incorporating Conversational AI in Psychotherapy

Conversational artificial intelligence (AI) is changing the way mental health care is delivered. By gathering diagnostic information, facilitating treatment, and reviewing clinician behavior, conversational AI is poised to impact traditional approaches to delivering psychotherapy. While this transition is not disconnected from existing professional services, specific formulations of clinician-AI collaboration and migration paths between forms remain vague. In this viewpoint, we introduce four approaches to AI-human integration in mental health service delivery. To inform future research and policy, these four approaches are addressed through four dimensions of impact: access to care, quality, clinician-patient relationship, and patient self-disclosure and sharing. Although many research questions are yet to be investigated, we view safety, trust, and oversight as crucial first steps. If conversational AI isn't safe it should not be used, and if it isn't trusted, it won't be. In order to assess safety, trust, interfaces, procedures, and system level workflows, oversight and collaboration is needed between AI systems, patients, clinicians, and administrators.

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

OBJECTIVE

Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evaluating suicide risk is highly subjective, which can limit the efficacy and accuracy of prediction efforts. Consequently, suicide detection strategies are shifting toward artificial intelligence platforms that can identify patterns within 'big data' to generate risk algorithms that can determine the effects of risk (and protective) factors on suicide outcomes, predict suicide outbreaks and identify at-risk individuals or populations. In this review, we summarize the role of artificial intelligence in optimizing suicide risk prediction and behavior management.

METHODS

This paper provides a general review of the literature. A literature search was conducted in OVID Medline, EMBASE and PsycINFO databases with coverage from January 1990 to June 2019. Results were restricted to peer-reviewed, English-language articles. Conference and dissertation proceedings, case reports, protocol papers and opinion pieces were excluded. Reference lists were also examined for additional articles of relevance.

RESULTS

At the individual level, prediction analytics help to identify individuals in crisis to intervene with emotional support, crisis and psychoeducational resources, and alerts for emergency assistance. At the population level, algorithms can identify at-risk groups or suicide hotspots, which help inform resource mobilization, policy reform and advocacy efforts. Artificial intelligence has also been used to support the clinical management of suicide across diagnostics and evaluation, medication management and behavioral therapy delivery. There could be several advantages of incorporating artificial intelligence into suicide care, which includes a time- and resource-effective alternative to clinician-based strategies, adaptability to various settings and demographics, and suitability for use in remote locations with limited access to mental healthcare supports.

CONCLUSION

Based on the observed benefits to date, artificial intelligence has a demonstrated utility within suicide prediction and clinical management efforts and will continue to advance mental healthcare forward.

Assessment of users' acceptability of a mobile-based embodied conversational agent for the prevention and detection of suicidal behaviour

The use of embodied conversational agents in mental health has increased in the last years. Several studies exist describing the benefits and advantages of this technology as a complement to psychotherapeutic interventions for the prevention and treatment of depression, anxiety, or post-traumatic stress disorder, to name a few. A small number of these works implement capabilities in the virtual agent focused on the detection and prevention of suicidality risks. The work presented in this paper describes the development of an embodied conversational agent used as the main interface in HelPath, a mobile-based application addressed to individuals detected with any of the suicidal behaviours: ideation, planning or attempt. The main objective of HelPath is to continuously collect user's information that, complemented with data from the electronic health record, supports the identification of risks associated with suicidality. Through the virtual agent, the users also receive information and suggestions based on cognitive behaviour therapy that would help them to maintain a healthy condition. The paper also presents the execution of an exploratory pilot to assess the acceptability, perception and adherence of users towards the virtual agent. The obtained results are presented and discussed, and some actions for further improvement of the embodied conversational agent are also identified.

Promoting Relational Agent for Health Behavior Change in Low and Middle - Income Countries (LMICs): Issues and Approaches

The use of contemporary technologies in healthcare systems to improve quality of care and to promote behavioral healthcare outcomes are prevalent in high-income countries. However, low and middle-income countries (LMICs) are not receiving the same advantages of technology, which may be due to inadequate technological infrastructure and financial resources, lack of interest among policy makers and healthcare service providers, lack of skills and capacity among healthcare professionals in using technology based interventions, and resistance of the public to the use of technologies for healthcare or health promotion activities. Technology-based interventions offer considerable promise to develop entirely new models of healthcare both within and outside of formal systems of care and offer the opportunity to have a large public health impact. Such technology-based interventions could be used to address targeted global health problems in LMICs, including the chronic non-communicable diseases (NCDs) - a growing health system burden in LMICs. Major preventable behavioral risk factors of chronic NCDs are increasing in LMICs, and innovative interventions are essential to address these risk factors. Computer-based or mobile-based virtual coaches or Relational Agents (RAs) are increasingly being explored for counseling patients to change their health behavior in high-income countries; however, the use of RAs in LMICs has not been studied. In this paper, we summarize the growing application of RA technology in behavior change interventions in high-income countries and describe the potential of its use in LMICs. Finally, we review the potential barriers and challenges in promoting RAs in LMICs.

A Mobile Web App to Improve Health Screening Uptake in Men (ScreenMen): Utility and Usability Evaluation Study

BACKGROUND

Globally, the uptake of health screening is suboptimal, especially in men and those of younger age. In view of the increasing internet access and mobile phone ownership, ScreenMen, a mobile Web app, was developed to improve health screening uptake in men.

OBJECTIVE

This study aimed to evaluate the utility and usability of ScreenMen.

METHODS

This study used both qualitative and quantitative methods. Healthy men working in a banking institution were recruited to participate in this study. They were purposively sampled according to job position, age, education level, and screening status. Men were asked to use ScreenMen independently while the screen activities were being recorded. Once completed, retrospective think aloud with playback was conducted with men to obtain their feedback. They were asked to answer the System Usability Scale (SUS). Intention to undergo screening pre- and postintervention was also measured. Qualitative data were analyzed using a framework approach followed by thematic analysis. For quantitative data, the mean SUS score was calculated and change in intention to screening was analyzed using McNemar test.

RESULTS

In total, 24 men participated in this study. On the basis of the qualitative data, men found ScreenMen useful as they could learn more about their health risks and screening. They found ScreenMen convenient to use, which might trigger men to undergo screening. In terms of usability, men thought that ScreenMen was user-friendly and easy to understand. The key revision done on utility was the addition of a reminder function, whereas for usability, the revisions done were in terms of attracting and gaining users' trust, improving learnability, and making ScreenMen usable to all types of users. To attract men to use it, ScreenMen was introduced to users in terms of improving health instead of going for screening. Another important revision made was emphasizing the screening tests the users do not need, instead of just informing them about the screening tests they need. A Quick Assessment Mode was also added for users with limited attention span. The quantitative data showed that 8 out of 23 men (35%) planned to attend screening earlier than intended after using the ScreenMen. Furthermore, 4 out of 12 (33%) men who were in the precontemplation stage changed to either contemplation or preparation stage after using ScreenMen with P=.13. In terms of usability, the mean SUS score of 76.4 (SD 7.72) indicated that ScreenMen had good usability.

CONCLUSIONS

This study showed that ScreenMen was acceptable to men in terms of its utility and usability. The preliminary data suggested that ScreenMen might increase men's intention to undergo screening. This paper also presented key lessons learned from the beta testing, which is useful for public health experts and researchers when developing a user-centered mobile Web app.

An Embodied Conversational Agent for Unguided Internet-Based Cognitive Behavior Therapy in Preventative Mental Health: Feasibility and Acceptability Pilot Trial

BACKGROUND

Recent years have seen an increase in the use of internet-based cognitive behavioral therapy in the area of mental health. Although lower effectiveness and higher dropout rates of unguided than those of guided internet-based cognitive behavioral therapy remain critical issues, not incurring ongoing human clinical resources makes it highly advantageous.

OBJECTIVE

Current research in psychotherapy, which acknowledges the importance of therapeutic alliance, aims to evaluate the feasibility and acceptability, in terms of mental health, of an application that is embodied with a conversational agent. This application was enabled for use as an internet-based cognitive behavioral therapy preventative mental health measure.

METHODS

Analysis of the data from the 191 participants of the experimental group with a mean age of 38.07 (SD 10.75) years and the 263 participants of the control group with a mean age of 38.05 (SD 13.45) years using a 2-way factorial analysis of variance (group × time) was performed.

RESULTS

There was a significant main effect (P=.02) and interaction for time on the variable of positive mental health (P=.02), and for the treatment group, a significant simple main effect was also found (P=.002). In addition, there was a significant main effect (P=.02) and interaction for time on the variable of negative mental health (P=.005), and for the treatment group, a significant simple main effect was also found (P=.001).

CONCLUSIONS

This research can be seen to represent a certain level of evidence for the mental health application developed herein, indicating empirically that internet-based cognitive behavioral therapy with the embodied conversational agent can be used in mental health care. In the pilot trial, given the issues related to feasibility and acceptability, it is necessary to pursue higher quality evidence while continuing to further improve the application, based on the findings of the current research.

A Systematic Literature Review of Technologies for Suicidal Behavior Prevention

Suicide is the second cause of death in young people. The use of technologies as tools facilitates the detection of individuals at risk of suicide thus allowing early intervention and efficacy. Suicide can be prevented in many cases. Technology can help people at risk of suicide and their families. It could prevent situations of risk of suicide with the technological evolution that is increasing. This work is a systematic review of research papers published in the last ten years on technology for suicide prevention. In September 2017, the consultation was carried out in the scientific databases PubMed, ScienceDirect, PsycINFO, The Cochrane Library and Google Scholar. A general search was conducted with the terms "prevention" AND "suicide" AND "technology. More specific searches included technologies such as "Web", "mobile", "social networks", and others terms related to technologies. The number of articles found following the methodology proposed was 90, but only 30 are focused on the objective of this work. Most of them were Web technologies (51.61%), mobile solutions (22.58%), social networks (12.90%), machine learning (3.23%) and other technologies (9.68%). According to the results obtained, although there are technological solutions that help the prevention of suicide, much remains to be done in this field. Collaboration among technologists, psychiatrists, patients, and family members is key to advancing the development of new technology-based solutions that can help save lives.