Embodied Conversational Agents in Clinical Psychology: A Scoping Review

A Chatbot (Juno) Prototype to Deploy a Behavioral Activation Intervention to Pregnant Women: Qualitative Evaluation Using a Multiple Case Study

BACKGROUND

Despite the increasing focus on perinatal care, preventive digital interventions are still scarce. Furthermore, the literature suggests that the design and development of these interventions are mainly conducted through a top-down approach that limitedly accounts for direct end user perspectives.

OBJECTIVE

Building from a previous co-design study, this study aimed to qualitatively evaluate pregnant women's experiences with a chatbot (Juno) prototype designed to deploy a preventive behavioral activation intervention.

METHODS

Using a multiple-case study design, the research aims to uncover similarities and differences in participants' perceptions of the chatbot while also exploring women's desires for improvement and technological advancements in chatbot-based interventions in perinatal mental health. Five pregnant women interacted weekly with the chatbot, operationalized in Telegram, following a 6-week intervention. Self-report questionnaires were administered at baseline and postintervention time points. About 10-14 days after concluding interactions with Juno, women participated in a semistructured interview focused on (1) their personal experience with Juno, (2) user experience and user engagement, and (3) their opinions on future technological advancements. Interview transcripts, comprising 15 questions, were qualitatively evaluated and compared. Finally, a text-mining analysis of transcripts was performed.

RESULTS

Similarities and differences have emerged regarding women's experiences with Juno, appreciating its esthetic but highlighting technical issues and desiring clearer guidance. They found the content useful and pertinent to pregnancy but differed on when they deemed it most helpful. Women expressed interest in receiving increasingly personalized responses and in future integration with existing health care systems for better support. Accordingly, they generally viewed Juno as an effective momentary support but emphasized the need for human interaction in mental health care, particularly if increasingly personalized. Further concerns included overreliance on chatbots when seeking psychological support and the importance of clearly educating users on the chatbot's limitations.

CONCLUSIONS

Overall, the results highlighted both the positive aspects and the shortcomings of the chatbot-based intervention, providing insight into its refinement and future developments. However, women stressed the need to balance technological support with human interactions, particularly when the intervention involves beyond preventive mental health context, to favor a greater and more reliable monitoring.

Conversational Chatbot for Cigarette Smoking Cessation: Results From the 11-Step User-Centered Design Development Process and Randomized Controlled Trial

BACKGROUND

Conversational chatbots are an emerging digital intervention for smoking cessation. No studies have reported on the entire development process of a cessation chatbot.

OBJECTIVE

We aim to report results of the user-centered design development process and randomized controlled trial for a novel and comprehensive quit smoking conversational chatbot called QuitBot.

METHODS

The 4 years of formative research for developing QuitBot followed an 11-step process: (1) specifying a conceptual model; (2) conducting content analysis of existing interventions (63 hours of intervention transcripts); (3) assessing user needs; (4) developing the chat's persona ("personality"); (5) prototyping content and persona; (6) developing full functionality; (7) programming the QuitBot; (8) conducting a diary study; (9) conducting a pilot randomized controlled trial (RCT); (10) reviewing results of the RCT; and (11) adding a free-form question and answer (QnA) function, based on user feedback from pilot RCT results. The process of adding a QnA function itself involved a three-step process: (1) generating QnA pairs, (2) fine-tuning large language models (LLMs) on QnA pairs, and (3) evaluating the LLM outputs.

RESULTS

We developed a quit smoking program spanning 42 days of 2- to 3-minute conversations covering topics ranging from motivations to quit, setting a quit date, choosing Food and Drug Administration-approved cessation medications, coping with triggers, and recovering from lapses and relapses. In a pilot RCT with 96% three-month outcome data retention, QuitBot demonstrated high user engagement and promising cessation rates compared to the National Cancer Institute's SmokefreeTXT text messaging program, particularly among those who viewed all 42 days of program content: 30-day, complete-case, point prevalence abstinence rates at 3-month follow-up were 63% (39/62) for QuitBot versus 38.5% (45/117) for SmokefreeTXT (odds ratio 2.58, 95% CI 1.34-4.99; P=.005). However, Facebook Messenger intermittently blocked participants' access to QuitBot, so we transitioned from Facebook Messenger to a stand-alone smartphone app as the communication channel. Participants' frustration with QuitBot's inability to answer their open-ended questions led to us develop a core conversational feature, enabling users to ask open-ended questions about quitting cigarette smoking and for the QuitBot to respond with accurate and professional answers. To support this functionality, we developed a library of 11,000 QnA pairs on topics associated with quitting cigarette smoking. Model testing results showed that Microsoft's Azure-based QnA maker effectively handled questions that matched our library of 11,000 QnA pairs. A fine-tuned, contextualized GPT-3.5 (OpenAI) responds to questions that are not within our library of QnA pairs.

CONCLUSIONS

The development process yielded the first LLM-based quit smoking program delivered as a conversational chatbot. Iterative testing led to significant enhancements, including improvements to the delivery channel. A pivotal addition was the inclusion of a core LLM-supported conversational feature allowing users to ask open-ended questions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03585231; https://clinicaltrials.gov/study/NCT03585231.

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.

Engagement With a Relaxation and Mindfulness Mobile App Among People With Cancer: Exploratory Analysis of Use Data and Self-Reports From a Randomized Controlled Trial

BACKGROUND

Mobile health (mHealth) apps offer unique opportunities to support self-care and behavior change, but poor user engagement limits their effectiveness. This is particularly true for fully automated mHealth apps without any human support. Human support in mHealth apps is associated with better engagement but at the cost of reduced scalability.

OBJECTIVE

This work aimed to (1) describe the theory-informed development of a fully automated relaxation and mindfulness app to reduce distress in people with cancer (CanRelax app 2.0), (2) describe engagement with the app on multiple levels within a fully automated randomized controlled trial over 10 weeks, and (3) examine whether engagement was related to user characteristics.

METHODS

The CanRelax app 2.0 was developed in iterative processes involving input from people with cancer and relevant experts. The app includes evidence-based relaxation exercises, personalized weekly coaching sessions with a rule-based conversational agent, 39 self-enactable behavior change techniques, a self-monitoring dashboard with gamification elements, highly tailored reminder notifications, an educational video clip, and personalized in-app letters. For the larger study, German-speaking adults diagnosed with cancer within the last 5 years were recruited via the web in Switzerland, Austria, and Germany. Engagement was analyzed in a sample of 100 study participants with multiple measures on a micro level (completed coaching sessions, relaxation exercises practiced with the app, and feedback on the app) and a macro level (relaxation exercises practiced without the app and self-efficacy toward self-set weekly relaxation goals).

RESULTS

In week 10, a total of 62% (62/100) of the participants were actively using the CanRelax app 2.0. No associations were identified between engagement and level of distress at baseline, sex assigned at birth, educational attainment, or age. At the micro level, 71.88% (3520/4897) of all relaxation exercises and 714 coaching sessions were completed in the app, and all participants who provided feedback (52/100, 52%) expressed positive app experiences. At the macro level, 28.12% (1377/4897) of relaxation exercises were completed without the app, and participants' self-efficacy remained stable at a high level. At the same time, participants raised their weekly relaxation goals, which indicates a potential relative increase in self-efficacy.

CONCLUSIONS

The CanRelax app 2.0 achieved promising engagement even though it provided no human support. Fully automated social components might have compensated for the lack of human involvement and should be investigated further. More than one-quarter (1377/4897, 28.12%) of all relaxation exercises were practiced without the app, highlighting the importance of assessing engagement on multiple levels.

A Digital Behavioral Activation Intervention (JuNEX) for Pregnant Women With Subclinical Depression Symptoms: Explorative Co-Design Study

BACKGROUND

Digital interventions are gaining increasing interest due to their structured nature, ready availability, and self-administered capabilities. Perinatal women have expressed a desire for such interventions. In this regard, behavioral activation interventions may be particularly suitable for digital administration.

OBJECTIVE

This study aims to exploratorily investigate and compare the feasibility of the internet-based self-help guided versus unguided version of the Brief Behavioral Activation Treatment for Depression-Revised, an empirically supported in-person behavioral activation protocol, targeting pregnant women with subclinical depression symptoms. A user-centered design is used, whereby data are collected with the intent of evaluating how to adjust the intervention in line with pregnant women's needs. Usability and user engagement were evaluated.

METHODS

A total of 11 Italian pregnant women with subclinical depressive symptoms based on the Patient Health Questionnaire-9 (scoring<15) participated in this study; of them, 6 (55%) women were randomly assigned to the guided group (age: mean 32.17, SD 4.36 years) and 5 (45%) to the unguided group (age: mean 31, SD 4.95 years). The Moodle platform was used to deliver the interventions in an e-learning format. It consisted of 6 core modules and 3 optional modules; the latter aimed at revising the content of the former. In the guided group, each woman had weekly chats with their assigned human guide to support them in the homework revisions. The intervention content included text, pictures, and videos. Semistructured interviews were conducted, and descriptive statistics were analyzed.

RESULTS

Collectively, the data suggest that the guided intervention was better accepted than the unguided one. However, the high rates of dropout (at T6: guided group: 3/6, 50%; unguided: 4/5, 80%) suggest that a digital replica of Behavioral Activation Treatment for Depression-Revised may not be feasible in an e-learning format. The reduced usability of the platform used was reported, and homework was perceived as too time-consuming and effort-intensive. Moreover, the 6 core modules were deemed sufficient for the intervention's goals, suggesting that the 3 optional modules could be eliminated. Nevertheless, participants from both groups expressed satisfaction with the content and found it relevant to their pregnancy experiences.

CONCLUSIONS

Overall, the findings have emphasized both the intervention's merits and shortcomings. Results highlight the unsuitability of replicating an in-person protocol digitally as well as of the use of nonprofessional tools for the implementation of self-help interventions, ultimately making the intervention not feasible. Pregnant women have nonetheless expressed a desire to receive psychological support and commented on the possibilities of digital psychosocial supports, particularly those that are app-based. The information collected and the issues identified here are important to guide the development and co-design of a more refined platform for the intervention deployment and to tailor the intervention's content to pregnant women's needs.

Depiction of conversational agents as health professionals: a scoping review

OBJECTIVE

The purpose of this scoping review was to examine the depiction of conversational agents as health professionals. We identified the professional characteristics that are used with these depictions and determined the prevalence of these characteristics among conversational agents that are used for health care.

INTRODUCTION

The depiction of conversational agents as health professionals has implications for both the users and the developers of these programs. For this reason, it is important to know more about these depictions and how they are implemented in practical settings.

INCLUSION CRITERIA

This review included scholarly literature on conversational agents that are used for health care. It focused on conversational agents designed for patients and health seekers, not health professionals or trainees. Conversational agents that address physical and/or mental health care were considered, as were programs that promote healthy behaviors.

METHODS

This review was conducted in accordance with JBI methodology for scoping reviews. The databases searched included MEDLINE (PubMed), Embase, CINAHL with Full Text (EBSCOhost), Scopus, Web of Science, ACM Guide to Computing Literature (Association for Computing Machinery Digital Library), and IEEE Xplore (IEEE). The main database search was conducted in June 2021, and an updated search was conducted in January 2022. Extracted data included characteristics of the report, basic characteristics of the conversational agent, and professional characteristics of the conversational agent. Extracted data were summarized using descriptive statistics. Results are presented in a narrative summary and accompanying tables.

RESULTS

A total of 38 health-related conversational agents were identified across 41 reports. Six of these conversational agents (15.8%) had professional characteristics. Four conversational agents (10.5%) had a professional appearance in which they displayed the clothing and accessories of health professionals and appeared in professional settings. One conversational agent (2.6%) had a professional title (Dr), and 4 conversational agents (10.5%) were described as having professional roles. Professional characteristics were more common among embodied vs disembodied conversational agents.

CONCLUSIONS

The results of this review show that the depiction of conversational agents as health professionals is not particularly common, although it does occur. More discussion is needed on the potential ethical and legal issues surrounding the depiction of conversational agents as health professionals. Future research should examine the impact of these depictions, as well as people's attitudes toward them, to better inform recommendations for practice.

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.

Use of automated conversational agents in improving young population mental health: a scoping review

Automated conversational agents (CAs) emerged as a promising solution in mental health interventions among young people. Therefore, the objective of this scoping review is to examine the current state of research into fully automated CAs mediated interventions for the emotional component of mental health among young people. Selected databases were searched in March 2023. Included studies were primary research, reporting on development, feasibility/usability, or evaluation of fully automated CAs as a tool to improve the emotional component of mental health among young population. Twenty-five studies were included (N = 1707). Most automated CAs applications were standalone preventions targeting anxiety and depression. Automated CAs were predominantly AI-based chatbots, using text as the main communication channel. Overall, the results of the current scoping review showed that automated CAs mediated interventions for emotional problems are acceptable, engaging and with high usability. However, the results for clinical efficacy are far less conclusive, since almost half of evaluation studies reported no significant effect on emotional mental health outcomes. Based on these findings, it can be concluded that there is a pressing need to improve the existing automated CAs applications to increase their efficacy as well as conducting more rigorous methodological research in this area.

Usability and Acceptability of a Conversational Agent Health Education App (Nthabi) for Young Women in Lesotho: Quantitative Study

BACKGROUND

Young women in Lesotho face myriad sexual and reproductive health problems. There is little time to provide health education to women in low-resource settings with critical shortages of human resources for health.

OBJECTIVE

This study aims to determine the acceptability and usability of a conversational agent system, the Nthabi health promotion app, which was culturally adapted for use in Lesotho.

METHODS

We conducted a descriptive quantitative study, using a 22-item Likert scale survey to assess the perceptions of the usability and acceptability of 172 young women aged 18-28 years in rural districts of Lesotho, who used the system on either smartphones or tablets for up to 6 weeks. Descriptive statistics were used to calculate the averages and frequencies of the variables. χ2 tests were used to determine any associations among variables.

RESULTS

A total of 138 participants were enrolled and completed the survey. The mean age was 22 years, most were unmarried, 56 (40.6%) participants had completed high school, 39 (28.3%) participants were unemployed, and 88 (63.8%) participants were students. Respondents believed the app was helpful, with 134 (97.1%) participants strongly agreeing or agreeing that the app was "effective in helping them make decisions" and "could quickly improve health education and counselling." In addition, 136 (98.5%) participants strongly agreed or agreed that the app was "simple to use," 130 (94.2 %) participants reported that Nthabi could "easily repeat words that were not well understood," and 128 (92.7%) participants reported that the app "could quickly load the information on the screen." Respondents were generally satisfied with the app, with 132 (95.6%) participants strongly agreeing or agreeing that the health education content delivered by the app was "well organised and delivered in a timely way," while 133 (96.4%) participants "enjoyed using the interface." They were satisfied with the cultural adaptation, with 133 (96.4%) participants strongly agreeing or agreeing that the app was "culturally appropriate and that it could be easily shared with a family or community members." They also reported that Nthabi was worthwhile, with 127 (92%) participants reporting that they strongly agreed or agreed that they were "satisfied with the application and intended to continue using it," while 135 (97.8%) participants would "encourage others to use it." Participants aged 18-24 years (vs those aged 25-28 years) agreed that the "Nthabi app was simple to use" (106/106, 100% vs 30/32, 98.8%; P=.01), and agreed that "the educational content was well organised and delivered in a timely way" (104/106, 98.1% vs 28/32, 87.5%; P=.01).

CONCLUSIONS

These results support further study of conversational agent systems as alternatives to traditional face-to-face provision of health education services in Lesotho, where there are critical shortages of human resources for health.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04354168; https://www.clinicaltrials.gov/study/NCT04354168.

Development and Evaluation of a Smartphone-Based Chatbot Coach to Facilitate a Balanced Lifestyle in Individuals With Headaches (BalanceUP App): Randomized Controlled Trial

BACKGROUND

Primary headaches, including migraine and tension-type headaches, are widespread and have a social, physical, mental, and economic impact. Among the key components of treatment are behavior interventions such as lifestyle modification. Scalable conversational agents (CAs) have the potential to deliver behavior interventions at a low threshold. To our knowledge, there is no evidence of behavioral interventions delivered by CAs for the treatment of headaches.

OBJECTIVE

This study has 2 aims. The first aim was to develop and test a smartphone-based coaching intervention (BalanceUP) for people experiencing frequent headaches, delivered by a CA and designed to improve mental well-being using various behavior change techniques. The second aim was to evaluate the effectiveness of BalanceUP by comparing the intervention and waitlist control groups and assess the engagement and acceptance of participants using BalanceUP.

METHODS

In an unblinded randomized controlled trial, adults with frequent headaches were recruited on the web and in collaboration with experts and allocated to either a CA intervention (BalanceUP) or a control condition. The effects of the treatment on changes in the primary outcome of the study, that is, mental well-being (as measured by the Patient Health Questionnaire Anxiety and Depression Scale), and secondary outcomes (eg, psychosomatic symptoms, stress, headache-related self-efficacy, intention to change behavior, presenteeism and absenteeism, and pain coping) were analyzed using linear mixed models and Cohen d. Primary and secondary outcomes were self-assessed before and after the intervention, and acceptance was assessed after the intervention. Engagement was measured during the intervention using self-reports and usage data.

RESULTS

A total of 198 participants (mean age 38.7, SD 12.14 y; n=172, 86.9% women) participated in the study (intervention group: n=110; waitlist control group: n=88). After the intervention, the intention-to-treat analysis revealed evidence for improved well-being (treatment: β estimate=-3.28, 95% CI -5.07 to -1.48) with moderate between-group effects (Cohen d=-0.66, 95% CI -0.99 to -0.33) in favor of the intervention group. We also found evidence of reduced somatic symptoms, perceived stress, and absenteeism and presenteeism, as well as improved headache management self-efficacy, application of behavior change techniques, and pain coping skills, with effects ranging from medium to large (Cohen d=0.43-1.05). Overall, 64.8% (118/182) of the participants used coaching as intended by engaging throughout the coaching and completing the outro.

CONCLUSIONS

BalanceUP was well accepted, and the results suggest that coaching delivered by a CA can be effective in reducing the burden of people who experience headaches by improving their well-being.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00017422; https://trialsearch.who.int/Trial2.aspx?TrialID=DRKS00017422.

Talk with Ted: an embodied conversational agent for caregivers

Communication is key to the success of any relationship. When it comes to caregivers, having a conversation with a person living with some form of cognitive impairment, such as dementia, can be a struggle. Most people living with dementia experience some form of communication impairment that reduces their ability to express their needs. In this case study, we present the design of an embodied conversation agent (ECA), Ted, designed to educate caregivers about the importance of good communication principles when engaging with people living with dementia. This training tool was trialed and compared to an online training tool, with 23 caregivers divided into two cohorts (12 in the ECA condition, and 11 in the online training tool condition), over a period of 8 weeks using a mixed evaluation approach. Our findings suggest that (a) caregivers developed an emotional connection with the ECA and retained the learning from their interactions with Ted even after 8 weeks had elapsed, (b) caregivers implemented the learnings in their practice, and (c) the changes in care practice were well received by people living with dementia.

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.

RACares: a conceptual design to guide mHealth relational agent development based on a systematic review

Background

A relational agent (RA) is a digital tool tailored to communicate with users, aiming to establish a sense of social ease and emotional bond, particularly focusing on their health and well-being concerns. A mobile health (mHealth) RA is particularly crafted to communicate with users within their mobile devices. As healthcare becomes increasingly digital, these mHealth RAs can serve as personal health assistants, e.g., guiding users through medical regimens, offering reminders for medication, providing emotional support during health crises, or even aiding in mental well-being exercises. Their accessibility, especially for those in remote areas, can bridge the gap between patients and immediate health assistance, revolutionizing the way healthcare is approached and delivered.

Methods

In this paper, our primary focus is introducing a conceptual design for mHealth RAs with the aim of enhanced user engagement, personalized health interventions, consistent support, data collection and monitoring, and enhanced multimodal accessibility. To develop this conceptual design, we employed an inductive approach. This involved conducting a qualitative analysis on data gathered from a systematic literature review of RAs. Consequently, this analysis allowed us to identify a taxonomy of key design features essential for RAs.

Results

This paper provides a conceptual design of mHealth RAs which includes five stages: user input receiving stage, input processing stage, data analysis stage, output processing stage, and output generation stage. A stage is a logical assembly of interconnected functionalities (components) that work together to accomplish a certain objective or set of goals. Each stage's outputs are used as inputs in the stages that follow after it. There is also a Data and Personalization Controller for aiding the data analysis stage. The stages are logically arranged one after another as follows: input, process, analysis, and output.

Conclusions

The conceptual design aims to create RAs for various mHealth applications, including patient education, mental health counseling, and chronic disease management. This design is crucial in digital health research as it enhances patient-RA interactions, potentially improving health outcomes and experiences in non-life-threatening scenarios where RAs can be an alternative to human healthcare professionals (HCPs).

What does the literature say about the use of personal voice assistants in older adults? A scoping review

PURPOSE

With ageing, people may experience loss of function which may be compensated for by using technology. This review aims to examine the range and extent of personal voice assistants used for older adults living in the community, their technology readiness level, associated outcomes, and the strength of evidence.

METHODS

This study complies with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. The CINAHL, EMBASE, IEEE Xplore, MEDLINE, APA PsycInfo, Scopus, and Web of Science databases were used to identify studies that explored the use of personal voice assistant technology with older adults living in the community.

RESULTS

The search yielded 499 studies, 22 of which were included for final analysis. Consumer technologies (e.g., Amazon Alexa) were evaluated in 18 studies, while four studies evaluated novel technologies. Most of the studies exploring the use of personal voice assistants with older adults evaluated technology usability and acceptance. Personal voice assistants were most commonly used by older adults for setting up reminders, searching for information, and checking the weather. None of the included studies evaluated the effectiveness of the technologies' ability to improve the management of health conditions or to facilitate the functional capacity of older adults.

CONCLUSIONS

More research is needed to determine the possible impact of using personal voice assistants for older adults living in the community. IMPLICATIONS FOR REHABILITATIONThe TRL for personal voice assistants is high.Personal voice assistants are currently being used by older adults for a range of activities including setting up reminders, searching for information, and checking the weather.Whether personal voice assistants can support older adults' ability to age in place is still unknown.The use of personal voice assistants in older adults is ripe for future enquiry and intervention-based research.

Factors influencing patient engagement in mental health chatbots: A thematic analysis of findings from a systematic review of reviews

Introduction

Mental health disorders affect millions of people worldwide. Chatbots are a new technology that can help users with mental health issues by providing innovative features. This article aimed to conduct a systematic review of reviews on chatbots in mental health services and synthesized the evidence on the factors influencing patient engagement with chatbots.

Methods

This study reviewed the literature from 2000 to 2024 using qualitative analysis. The authors conducted a systematic search of several databases, such as PubMed, Scopus, ProQuest, and Cochrane database of systematic reviews, to identify relevant studies on the topic. The quality of the selected studies was assessed using the Critical Appraisal Skills Programme appraisal checklist and the data obtained from the systematic review were subjected to a thematic analysis utilizing the Boyatzis's code development approach.

Results

The database search resulted in 1494 papers, of which 10 were included in the study after the screening process. The quality assessment of the included studies scored the papers within a moderate level. The thematic analysis revealed four main themes: chatbot design, chatbot outcomes, user perceptions, and user characteristics.

Conclusion

The research proposed some ways to use color and music in chatbot design. It also provided a systematic and multidimensional analysis of the factors, offered some insights for chatbot developers and researchers, and highlighted the potential of chatbots to improve patient-centered and person-centered care in mental health services.

Embodied Conversational Agents Providing Motivational Interviewing to Improve Health-Related Behaviors: Scoping Review

BACKGROUND

Embodied conversational agents (ECAs) are advanced human-like interfaces that engage users in natural face-to-face conversations and interactions. These traits position ECAs as innovative tools for delivering interventions for promoting health-related behavior adoption. This includes motivational interviewing (MI), a therapeutic approach that combines brief interventions with motivational techniques to encourage the adoption of healthier behaviors.

OBJECTIVE

This study aims to identify the health issues addressed by ECAs delivering MI interventions, explore the key characteristics of these ECAs (eg, appearance, dialogue mechanism, emotional model), analyze the implementation of MI principles and techniques within ECAs, and examine the evaluation methods and primary outcomes of studies that use ECAs providing MI interventions.

METHODS

We conducted a scoping review following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) methodology. Our systematic search covered the PubMed, Scopus, IEEE Xplore, ACM Digital, and PsycINFO databases for papers published between January 2008 and December 2022. We included papers describing ECAs developed for delivering MI interventions targeting health-related behaviors and excluded articles that did not describe ECAs with human appearances and without the necessary evaluation or MI explanation. In a multistage process, 3 independent reviewers performed screening and data extraction, and the collected data were synthesized using a narrative approach.

RESULTS

The initial search identified 404 articles, of which 3.5% (n=14) were included in the review. ECAs primarily focused on reducing alcohol use (n=5, 36%), took on female representations (n=9, 64%), and gave limited consideration to user ethnicity (n=9, 64%). Most of them used rules-driven dialogue mechanisms (n=13, 93%), include emotional behavior to convey empathy (n=8, 57%) but without an automatic recognition of user emotions (n=12, 86%). Regarding MI implementation, of 14 studies, 3 (21%) covered all MI principles, 4 (29%) included all processes, and none covered all techniques. Most studies (8/14, 57%) conducted acceptability, usability, and user experience assessments, whereas a smaller proportion (4/14, 29%) used randomized controlled trials to evaluate behavior changes. Overall, the studies reported positive results regarding acceptability, usability, and user experience and showed promising outcomes in changes in attitudes, beliefs, motivation, and behavior.

CONCLUSIONS

This study revealed significant advancements in the use of ECAs for delivering MI interventions aimed at promoting healthier behaviors over the past 15 years. However, this review emphasizes the need for a more in-depth exploration of ECA characteristics. In addition, there is a need for the enhanced integration of MI principles, processes, and techniques into ECAs. Although acceptability and usability have received considerable attention, there is a compelling argument for placing a stronger emphasis on assessing changes in attitudes, beliefs, motivation, and behavior. Consequently, inclusion of more randomized controlled trials is essential for comprehensive intervention evaluations.

Doctors' perceptions of using their digital twins in patient care

Recent Artificial Intelligence (AI) advancements have facilitated tools capable of generating digital twins of real human faces and voices for interactive communication. In this research, we explore utilizing Digital Twins of Doctors (DTDs) in healthcare because using a doctor's identity can provide benefits like enhancing the credibility of the health information delivered using computers. DTDs are computer-controlled AI-generated digital replicas of doctors that closely resemble their characteristics. However, there exist limitations, including the social implications of using a doctor's identity, potential negative impacts on doctor-patient communication, and liability concerns. To ensure a comprehensive understanding of DTD usage in healthcare before widespread adoption, systematic research is essential. As a step towards this direction, in this qualitative research, we report findings from 13 semi-structured interviews with doctors. Our findings indicate that doctors believe DTDs offer benefits by saving doctors' time through the efficient delivery of repetitive information and personalizing patient care. Moreover, while using a doctor's identity can enhance credibility, it also raises concerns about using a doctor's identity to spread potential misinformation. These findings contribute by informing future researchers about doctors' perspectives on utilizing DTDs in healthcare, guiding the development of effective implementation strategies for responsible DTD integration into healthcare.

Conversational Agents in Health Care: Expert Interviews to Inform the Definition, Classification, and Conceptual Framework

BACKGROUND

Conversational agents (CAs), or chatbots, are computer programs that simulate conversations with humans. The use of CAs in health care settings is recent and rapidly increasing, which often translates to poor reporting of the CA development and evaluation processes and unreliable research findings. We developed and published a conceptual framework, designing, developing, evaluating, and implementing a smartphone-delivered, rule-based conversational agent (DISCOVER), consisting of 3 iterative stages of CA design, development, and evaluation and implementation, complemented by 2 cross-cutting themes (user-centered design and data privacy and security).

OBJECTIVE

This study aims to perform in-depth, semistructured interviews with multidisciplinary experts in health care CAs to share their views on the definition and classification of health care CAs and evaluate and validate the DISCOVER conceptual framework.

METHODS

We conducted one-on-one semistructured interviews via Zoom (Zoom Video Communications) with 12 multidisciplinary CA experts using an interview guide based on our framework. The interviews were audio recorded, transcribed by the research team, and analyzed using thematic analysis.

RESULTS

Following participants' input, we defined CAs as digital interfaces that use natural language to engage in a synchronous dialogue using ≥1 communication modality, such as text, voice, images, or video. CAs were classified by 13 categories: response generation method, input and output modalities, CA purpose, deployment platform, CA development modality, appearance, length of interaction, type of CA-user interaction, dialogue initiation, communication style, CA personality, human support, and type of health care intervention. Experts considered that the conceptual framework could be adapted for artificial intelligence-based CAs. However, despite recent advances in artificial intelligence, including large language models, the technology is not able to ensure safety and reliability in health care settings. Finally, aligned with participants' feedback, we present an updated iteration of the conceptual framework for health care conversational agents (CHAT) with key considerations for CA design, development, and evaluation and implementation, complemented by 3 cross-cutting themes: ethics, user involvement, and data privacy and security.

CONCLUSIONS

We present an expanded, validated CHAT and aim at guiding researchers from a variety of backgrounds and with different levels of expertise in the design, development, and evaluation and implementation of rule-based CAs in health care settings.

Adaptation of the Gabby conversational agent system to improve the sexual and reproductive health of young women in Lesotho

Introduction

Young women from the low-middle-income country of Lesotho in southern Africa frequently report limited knowledge regarding sexual and reproductive health issues and engage in risky sexual behaviors. The purpose of this study is to describe the adaptation of an evidence-based conversational agent system for implementation in Lesotho and provide qualitative data pertaining to the success of the said adaptation.

Methods

An embodied conversational agent system used to provide preconception health advice in the United States was clinically and culturally adapted for use in the rural country of Lesotho in southern Africa. Inputs from potential end users, health leaders, and district nurses guided the adaptations. Focus group discussions with young women aged 18-28 years who had used the newly adapted system renamed "Nthabi" for 3-4 weeks and key informant interviews with Ministry of Health leadership were conducted to explore their views of the acceptability of the said adaptation. Data were analyzed using NVivo software, and a thematic content analysis approach was employed in the study.

Results

A total of 33 women aged 18-28 years used Nthabi for 3-4 weeks; eight (24.2%) of them were able to download and use the app on their mobile phones and 25 (75.8%) of them used the app on a tablet provided to them. Focus group participants (n = 33) reported that adaptations were culturally appropriate and provided relevant clinical information. The participants emphasized that the physical characteristics, personal and non-verbal behaviors, utilization of Sesotho words and idioms, and sensitively delivered clinical content were culturally appropriate for Lesotho. The key informants from the Ministry leadership (n = 10) agreed that the adaptation was successful, and that the system holds great potential to improve the delivery of health education in Lesotho. Both groups suggested modifications, such as using the local language and adapting Nthabi for use by boys and young men.

Conclusions

Clinically tailored, culturally sensitive, and trustworthy content provided by Nthabi has the potential to improve accessibility of sexual and reproductive health information to young women in the low-middle-income country of Lesotho.

StudiCare procrastination - Randomized controlled non-inferiority trial of a persuasive design-optimized internet- and mobile-based intervention with digital coach targeting procrastination in college students

BACKGROUND

Academic procrastination is widespread among college students. Procrastination is strongly negatively correlated with psychological well-being, thus early interventions are needed. Internet- and mobile-based cognitive behavioral therapy (iCBT) could provide a low-threshold treatment option. Human guidance seems to be a decisive mechanism of change in iCBT. Persuasive design optimization of iCBT and guidance by a digital coach might represent a resource-saving alternative. The study evaluated the non-inferiority of a digital coach in comparison to human guidance with regard to the primary outcome procrastination.

METHODS

The iCBT StudiCare procrastination was optimized by principles of the Persuasive System Design (PSD). A total of 233 college students were randomly assigned to either StudiCare procrastination guided by a digital coach (intervention group, IG) or by a human eCoach (control group, CG). All participants were assessed at baseline, 4-, 8- and 12-weeks post-randomization. Symptom change and between-group differences were assessed with latent growth curve models and supported by effect size levels. The non-inferiority margin was set at Cohen's d = - 0.3.

RESULTS

The primary outcome procrastination measured by the Irrational Procrastination scale (IPS) significantly decreased across groups (γ = - 0.79, p < .001, Cohen's d = -0.43 to -0.89) from baseline to 12-weeks post-randomization. There were no significant differences between groups (γ = -0.03, p = .84, Cohen's d = -0.03 to 0.08). Regarding symptoms of depression, no significant time x group effect was found (γ = 0.26, p = .09; Cohen's d = -0.15 to 0.21). There was also no significant time x group effect on the improvement of symptoms of anxiety (γ = 0.25, p = .09). However, Cohen's ds were above the non-inferiority margin 8-weeks (Cohen's d = 0.51) and 12-weeks post-randomization (Cohen's d = 0.37), preferring the CG. Of the IG, 34% and of the CG, 36% completed 80% of the modules.

CONCLUSIONS

The PSD optimized version of StudiCare procrastination is effective in reducing procrastination. The digital coach was not inferior to human guidance. Guidance by a digital coach in iCBT against procrastination for college students could be a resource-saving alternative to human guidance.

TRIAL REGISTRATION

The trial was registered at the WHO International Clinical Trials Registry Platform via the German Clinical Trial Register (ID: DRKS00025209, 30/04/2021).

A Digital Counselor-Delivered Intervention for Substance Use Among People With HIV: Development and Usability Study

BACKGROUND

Substance use disorders are prevalent and undertreated among people with HIV. Computer-delivered interventions (CDIs) show promise in expanding reach, delivering evidence-based care, and offering anonymity. Use in HIV clinic settings may overcome access barriers. Incorporating digital counselors may increase CDI engagement, and thereby improve health outcomes.

OBJECTIVE

We aim to develop and pilot a digital counselor-delivered brief intervention for people with HIV who use drugs, called "C-Raven," which is theory grounded and uses evidence-based practices for behavior change.

METHODS

Intervention mapping was used to develop the CDI including a review of the behavior change research in substance use, HIV, and digital counselors. We conducted in-depth interviews applying the situated-information, motivation, and behavior skills model and culturally adapting the content for local use with people with HIV. With a user interaction designer, we created various digital counselors and CDI interfaces. Finally, a mixed methods approach using in-depth interviews and quantitative assessments was used to assess the usability, acceptability, and cultural relevance of the intervention content and the digital counselor.

RESULTS

Participants found CDI easy to use, useful, relevant, and motivating. A consistent suggestion was to provide more information about the negative impacts of drug use and the interaction of drug use with HIV. Participants also reported that they learned new information about drug use and its health effects. The CDI was delivered by a "Raven," digital counselor, programmed to interact in a motivational interviewing style. The Raven was perceived to be nonjudgmental, understanding, and emotionally responsive. The appearance and images in the intervention were perceived as relevant and acceptable. Participants noted that they could be more truthful with a digital counselor, however, it was not unanimously endorsed as a replacement for a human counselor. The C-Raven Satisfaction Scale showed that all participants rated their satisfaction at either a 4 (n=2) or a 5 (n=8) on a 5-point Likert scale and all endorsed using the C-Raven program again.

CONCLUSIONS

CDIs show promise in extending access to care and improving health outcomes but their development necessarily requires integration from multiple disciplines including behavioral medicine and computer science. We developed a cross-platform compatible CDI led by a digital counselor that interacts in a motivational interviewing style and (1) uses evidence-based behavioral change methods, (2) is culturally adapted to people with HIV who use drugs, (3) has an engaging and interactive user interface, and (4) presents personalized content based on participants' ongoing responses to a series of menu-driven conversations. To advance the continued development of this and other CDIs, we recommend expanded testing, standardized measures to evaluate user experience, integration with clinician-delivered substance use treatment, and if effective, implementation into HIV clinical care.

Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions

OBJECTIVE

Physical inactivity is a leading modifiable cause of death and disease worldwide. Population-based interventions to increase physical activity are needed. Existing automated expert systems (e.g., computer-tailored interventions) have significant limitations that result in low long-term effectiveness. Therefore, innovative approaches are needed. This special communication aims to describe and discuss a novel mHealth intervention approach that proactively offers participants with hyper-personalised intervention content adjusted in real-time.

METHODS

Using machine learning approaches, we propose a novel physical activity intervention approach that can learn and adapt in real-time to achieve high levels of personalisation and user engagement, underpinned by a likeable digital assistant. It will consist of three major components: (1) conversations: to increase user's knowledge on a wide range of activity-related topics underpinned by Natural Language Processing; (2) nudge engine: to provide users with hyper-personalised cues to action underpinned by reinforcement learning (i.e., contextual bandit) and integrating real-time data from activity tracking, GPS, GIS, weather, and user provided data; (3) Q&A: to facilitate users asking any physical activity related questions underpinned by generative AI (e.g., ChatGPT, Bard) for content generation.

RESULTS

The detailed concept of the proposed physical activity intervention platform demonstrates the practical application of a just-in-time adaptive intervention applying various machine learning techniques to deliver a hyper-personalised physical activity intervention in an engaging way. Compared to traditional interventions, the novel platform is expected to show potential for increased user engagement and long-term effectiveness due to: (1) using new variables to personalise content (e.g., GPS, weather), (2) providing behavioural support at the right time in real-time, (3) implementing an engaging digital assistant and (4) improving the relevance of content through applying machine learning algorithms.

CONCLUSION

The use of machine learning is on the rise in every aspect of today's society, however few attempts have been undertaken to harness its potential to achieve health behaviour change. By sharing our intervention concept, we contribute to the ongoing dialogue on creating effective methods for promoting health and well-being in the informatics research community. Future research should focus on refining these techniques and evaluating their effectiveness in controlled and real-world circumstances.

A Mental Health and Well-Being Chatbot: User Event Log Analysis

BACKGROUND

Conversational user interfaces, or chatbots, are becoming more popular in the realm of digital health and well-being. While many studies focus on measuring the cause or effect of a digital intervention on people's health and well-being (outcomes), there is a need to understand how users really engage and use a digital intervention in the real world.

OBJECTIVE

In this study, we examine the user logs of a mental well-being chatbot called ChatPal, which is based on the concept of positive psychology. The aim of this research is to analyze the log data from the chatbot to provide insight into usage patterns, the different types of users using clustering, and associations between the usage of the app's features.

METHODS

Log data from ChatPal was analyzed to explore usage. A number of user characteristics including user tenure, unique days, mood logs recorded, conversations accessed, and total number of interactions were used with k-means clustering to identify user archetypes. Association rule mining was used to explore links between conversations.

RESULTS

ChatPal log data revealed 579 individuals older than 18 years used the app with most users being female (n=387, 67%). User interactions peaked around breakfast, lunchtime, and early evening. Clustering revealed 3 groups including "abandoning users" (n=473), "sporadic users" (n=93), and "frequent transient users" (n=13). Each cluster had distinct usage characteristics, and the features were significantly different (P<.001) across each group. While all conversations within the chatbot were accessed at least once by users, the "treat yourself like a friend" conversation was the most popular, which was accessed by 29% (n=168) of users. However, only 11.7% (n=68) of users repeated this exercise more than once. Analysis of transitions between conversations revealed strong links between "treat yourself like a friend," "soothing touch," and "thoughts diary" among others. Association rule mining confirmed these 3 conversations as having the strongest linkages and suggested other associations between the co-use of chatbot features.

CONCLUSIONS

This study has provided insight into the types of people using the ChatPal chatbot, patterns of use, and associations between the usage of the app's features, which can be used to further develop the app by considering the features most accessed by users.

Optimizing appreciation and persuasion of embodied conversational agents for health behavior change: A design experiment and focus group study

Embodied Conversational Agents (ECAs) can increase user engagement and involvement and can strengthen the effect of an intervention on health outcomes that is provided via an ECA. However, evidence regarding the effectiveness of ECAs on health outcomes is still limited. In this article, we report on a study that has the goal to identify the effect of a match between a health topic and the ECAs' appearance on ratings of personality characteristics, persuasiveness and intention to use. We report on an online experiment with three different ECAs and three different health topics, conducted among 732 older adults. We triangulated the quantitative results with qualitative insights from a focus group. The results reveal that older adults prefer an ECA that has an appearance matching a certain health topic, resulting in higher ratings on persuasiveness and intention to use. Personality characteristics should be measured embedded within a health topic, but are not rated higher because of a match. We furthermore provide guidelines for designing the content of the ECA.

Systematic review and meta-analysis of the effectiveness of chatbots on lifestyle behaviours

Chatbots (also known as conversational agents and virtual assistants) offer the potential to deliver healthcare in an efficient, appealing and personalised manner. The purpose of this systematic review and meta-analysis was to evaluate the efficacy of chatbot interventions designed to improve physical activity, diet and sleep. Electronic databases were searched for randomised and non-randomised controlled trials, and pre-post trials that evaluated chatbot interventions targeting physical activity, diet and/or sleep, published before 1 September 2022. Outcomes were total physical activity, steps, moderate-to-vigorous physical activity (MVPA), fruit and vegetable consumption, sleep quality and sleep duration. Standardised mean differences (SMD) were calculated to compare intervention effects. Subgroup analyses were conducted to assess chatbot type, intervention type, duration, output and use of artificial intelligence. Risk of bias was assessed using the Effective Public Health Practice Project Quality Assessment tool. Nineteen trials were included. Sample sizes ranged between 25-958, and mean participant age ranged between 9-71 years. Most interventions (n = 15, 79%) targeted physical activity, and most trials had a low-quality rating (n = 14, 74%). Meta-analysis results showed significant effects (all p < 0.05) of chatbots for increasing total physical activity (SMD = 0.28 [95% CI = 0.16, 0.40]), daily steps (SMD = 0.28 [95% CI = 0.17, 0.39]), MVPA (SMD = 0.53 [95% CI = 0.24, 0.83]), fruit and vegetable consumption (SMD = 0.59 [95% CI = 0.25, 0.93]), sleep duration (SMD = 0.44 [95% CI = 0.32, 0.55]) and sleep quality (SMD = 0.50 [95% CI = 0.09, 0.90]). Subgroup analyses showed that text-based, and artificial intelligence chatbots were more efficacious than speech/voice chatbots for fruit and vegetable consumption, and multicomponent interventions were more efficacious than chatbot-only interventions for sleep duration and sleep quality (all p < 0.05). Findings from this systematic review and meta-analysis indicate that chatbot interventions are efficacious for increasing physical activity, fruit and vegetable consumption, sleep duration and sleep quality. Chatbot interventions were efficacious across a range of populations and age groups, with both short- and longer-term interventions, and chatbot only and multicomponent interventions being efficacious.

Development of a digital biomarker and intervention for subclinical depression: study protocol for a longitudinal waitlist control study

BACKGROUND

Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.e., addressing subclinical depression).

AIM

With our study, we aim to (i) develop digital biomarkers for subclinical symptoms of depression, (ii) develop digital biomarkers for severity of subclinical depression, and (iii) investigate the efficacy of a digital intervention in reducing symptoms and severity of subclinical depression.

METHOD

Participants will interact with the digital intervention BEDDA consisting of a scripted conversational agent, the slow-paced breathing training Breeze, and actionable advice for different symptoms. The intervention comprises 30 daily interactions to be completed in less than 45 days. We will collect self-reports regarding mood, agitation, anhedonia (proximal outcomes; first objective), self-reports regarding depression severity (primary distal outcome; second and third objective), anxiety severity (secondary distal outcome; second and third objective), stress (secondary distal outcome; second and third objective), voice, and breathing. A subsample of 25% of the participants will use smartwatches to record physiological data (e.g., heart-rate, heart-rate variability), which will be used in the analyses for all three objectives.

DISCUSSION

Digital voice- and breathing-based biomarkers may improve diagnosis, prevention, and care by enabling an unobtrusive and either complementary or alternative assessment to self-reports. Furthermore, our results may advance our understanding of underlying psychophysiological changes in subclinical depression. Our study also provides further evidence regarding the efficacy of standalone digital health interventions to prevent depression. Trial registration Ethics approval was provided by the Ethics Commission of ETH Zurich (EK-2022-N-31) and the study was registered in the ISRCTN registry (Reference number: ISRCTN38841716, Submission date: 20/08/2022).

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.

Conversational Agents for Body Weight Management: Systematic Review

BACKGROUND

Obesity is a public health issue worldwide. Conversational agents (CAs), also frequently called chatbots, are computer programs that simulate dialogue between people. Owing to better accessibility, cost-effectiveness, personalization, and compassionate patient-centered treatments, CAs are expected to have the potential to provide sustainable lifestyle counseling for weight management.

OBJECTIVE

This systematic review aimed to critically summarize and evaluate clinical studies on the effectiveness and feasibility of CAs with unconstrained natural language input for weight management.

METHODS

PubMed, Embase, the Cochrane Library (CENTRAL), PsycINFO, and ACM Digital Library were searched up to December 2022. Studies were included if CAs were used for weight management and had a capability for unconstrained natural language input. No restrictions were imposed on study design, language, or publication type. The quality of the included studies was assessed using the Cochrane risk-of-bias assessment tool or the Critical Appraisal Skills Programme checklist. The extracted data from the included studies were tabulated and narratively summarized as substantial heterogeneity was expected.

RESULTS

In total, 8 studies met the eligibility criteria: 3 (38%) randomized controlled trials and 5 (62%) uncontrolled before-and-after studies. The CAs in the included studies were aimed at behavior changes through education, advice on food choices, or counseling via psychological approaches. Of the included studies, only 38% (3/8) reported a substantial weight loss outcome (1.3-2.4 kg decrease at 12-15 weeks of CA use). The overall quality of the included studies was judged as low.

CONCLUSIONS

The findings of this systematic review suggest that CAs with unconstrained natural language input can be used as a feasible interpersonal weight management intervention by promoting engagement in psychiatric intervention-based conversations simulating treatments by health care professionals, but currently there is a paucity of evidence. Well-designed rigorous randomized controlled trials with larger sample sizes, longer treatment duration, and follow-up focusing on CAs' acceptability, efficacy, and safety are warranted.

Conversational Agent Interventions for Mental Health Problems: Systematic Review and Meta-analysis of Randomized Controlled Trials

BACKGROUND

Mental health problems are a crucial global public health concern. Owing to their cost-effectiveness and accessibility, conversational agent interventions (CAIs) are promising in the field of mental health care.

OBJECTIVE

This study aims to present a thorough summary of the traits of CAIs available for a range of mental health problems, find evidence of efficacy, and analyze the statistically significant moderators of efficacy via a meta-analysis of randomized controlled trial.

METHODS

Web-based databases (Embase, MEDLINE, PsycINFO, CINAHL, Web of Science, and Cochrane) were systematically searched dated from the establishment of the database to October 30, 2021, and updated to May 1, 2022. Randomized controlled trials comparing CAIs with any other type of control condition in improving depressive symptoms, generalized anxiety symptoms, specific anxiety symptoms, quality of life or well-being, general distress, stress, mental disorder symptoms, psychosomatic disease symptoms, and positive and negative affect were considered eligible. This study followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Data were extracted by 2 independent reviewers, checked by a third reviewer, and pooled using both random effect models and fixed effects models. Hedges g was chosen as the effect size.

RESULTS

Of the 6900 identified records, a total of 32 studies were included, involving 6089 participants. CAIs showed statistically significant short-term effects compared with control conditions in improving depressive symptoms (g=0.29, 95% CI 0.20-0.38), generalized anxiety symptoms (g=0.29, 95% CI 0.21-0.36), specific anxiety symptoms (g=0.47, 95% CI 0.07-0.86), quality of life or well-being (g=0.27, 95% CI 0.16-0.39), general distress (g=0.33, 95% CI 0.20-0.45), stress (g=0.24, 95% CI 0.08-0.41), mental disorder symptoms (g=0.36, 95% CI 0.17-0.54), psychosomatic disease symptoms (g=0.62, 95% CI 0.14-1.11), and negative affect (g=0.28, 95% CI 0.05-0.51). However, the long-term effects of CAIs for the most mental health outcomes were not statistically significant (g=-0.04 to 0.39). Personalization and empathic response were 2 critical facilitators of efficacy. The longer duration of interaction with conversational agents was associated with the larger pooled effect sizes.

CONCLUSIONS

The findings show that CAIs are research-proven interventions that ought to be implemented more widely in mental health care. CAIs are effective and easily acceptable for those with mental health problems. The clinical application of this novel digital technology will conserve human health resources and optimize the allocation of mental health services.

TRIAL REGISTRATION

PROSPERO CRD42022350130; https://tinyurl.com/mvhk6w9p.

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.

Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review

BACKGROUND

Artificial intelligence (AI)-based chatbots can offer personalized, engaging, and on-demand health promotion interventions.

OBJECTIVE

The aim of this systematic review was to evaluate the feasibility, efficacy, and intervention characteristics of AI chatbots for promoting health behavior change.

METHODS

A comprehensive search was conducted in 7 bibliographic databases (PubMed, IEEE Xplore, ACM Digital Library, PsycINFO, Web of Science, Embase, and JMIR publications) for empirical articles published from 1980 to 2022 that evaluated the feasibility or efficacy of AI chatbots for behavior change. The screening, extraction, and analysis of the identified articles were performed by following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

Of the 15 included studies, several demonstrated the high efficacy of AI chatbots in promoting healthy lifestyles (n=6, 40%), smoking cessation (n=4, 27%), treatment or medication adherence (n=2, 13%), and reduction in substance misuse (n=1, 7%). However, there were mixed results regarding feasibility, acceptability, and usability. Selected behavior change theories and expert consultation were used to develop the behavior change strategies of AI chatbots, including goal setting, monitoring, real-time reinforcement or feedback, and on-demand support. Real-time user-chatbot interaction data, such as user preferences and behavioral performance, were collected on the chatbot platform to identify ways of providing personalized services. The AI chatbots demonstrated potential for scalability by deployment through accessible devices and platforms (eg, smartphones and Facebook Messenger). The participants also reported that AI chatbots offered a nonjudgmental space for communicating sensitive information. However, the reported results need to be interpreted with caution because of the moderate to high risk of internal validity, insufficient description of AI techniques, and limitation for generalizability.

CONCLUSIONS

AI chatbots have demonstrated the efficacy of health behavior change interventions among large and diverse populations; however, future studies need to adopt robust randomized control trials to establish definitive conclusions.

Comparing the Feasibility and Acceptability of a Virtual Human, Teletherapy, and an e-Manual in Delivering a Stress Management Intervention to Distressed Adult Women: Pilot Study

BACKGROUND

Virtual humans (VHs), teletherapy, and self-guided e-manuals may increase the accessibility of psychological interventions. However, there is limited research on how these technologies compare in terms of their feasibility and acceptability in delivering stress management interventions.

OBJECTIVE

We conducted a preliminary comparison of the feasibility and acceptability of a VH, teletherapy, and an e-manual at delivering 1 module of cognitive behavioral stress management (CBSM) to evaluate the feasibility of the trial methodology in preparation for a future randomized controlled trial (RCT).

METHODS

A pilot RCT was conducted with a parallel, mixed design. A community sample of distressed adult women were randomly allocated to receive 1 session of CBSM involving training in cognitive and behavioral techniques by a VH, teletherapy, or an e-manual plus homework over 2 weeks. Data were collected on the feasibility of the intervention technologies (technical support and homework access), trial methods (recruitment methods, questionnaire completion, and methodological difficulty observations), intervention acceptability (intervention completion, self-report ratings, therapist rapport, and trust), and acceptability of the trial methods (self-report ratings and observations). Qualitative data in the form of written responses to open-ended questions were collected to enrich and clarify the findings on intervention acceptability.

RESULTS

Overall, 38 participants' data were analyzed. A VH (n=12), teletherapy (n=12), and an e-manual (n=14) were found to be feasible and acceptable for delivering 1 session of CBSM to distressed adult women based on the overall quantitative and qualitative findings. Technical difficulties were minimal and did not affect intervention completion, and no significant differences were found between the conditions (P=.31). The methodology was feasible, although improvements were identified for a future trial. All conditions achieved good satisfaction and perceived engagement ratings, and no significant group differences were found (P>.40). Participants had similar willingness to recommend each technology (P=.64). There was a nonsignificant trend toward participants feeling more open to using the VH and e-manual from home than teletherapy (P=.10). Rapport (P<.001) and trust (P=.048) were greater with the human teletherapist than with the VH. The qualitative findings enriched the quantitative results by revealing the unique strengths and limitations of each technology that may have influenced acceptability.

CONCLUSIONS

A VH, teletherapy, and a self-guided e-manual were found to be feasible and acceptable methods of delivering 1 session of a stress management intervention to a community sample of adult women. The technologies were found to have unique strengths and limitations that may affect which works best for whom and in what circumstances. Future research should test additional CBSM modules for delivery by these technologies and conduct a larger RCT to compare their feasibility, acceptability, and effectiveness when delivering a longer home-based stress management program.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12620000859987; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380114&isReview=true.

Design, development, and use of conversational agents in rehabilitation for adults with brain-related neurological conditions: a scoping review

OBJECTIVE

The objective of this review was to identify how conversational agents are designed and used in rehabilitation for adults with brain-related neurological conditions.

INTRODUCTION

Adults with brain-related neurological conditions experience varied cognitive and functional challenges that can persist long term. However, rehabilitation services are time- and resource-limited, and novel rehabilitation approaches are warranted. Conversational agents provide a human-computer interface with which the user can converse. A conversational agent can be designed to meet specific user needs, such as rehabilitation and support.

INCLUSION CRITERIA

Studies focused on the design and use of conversational agents for rehabilitation for people aged 18 years or older with brain-related neurological conditions were considered for inclusion. Eligible publication types included peer-reviewed publications (quantitative, qualitative, and/or mixed methods study designs; research protocols; peer-reviewed expert opinion papers; clinical studies, including pilot trials; systematic or scoping reviews), full conference papers, and master's or PhD theses. Eligible types of research included prototype development, feasibility testing, and clinical trials.

METHODS

Online databases, including MEDLINE, Scopus, ProQuest (all databases), Web of Science, and gray literature sources were searched with no date limit. Only English publications were considered due to a lack of resourcing available for translations. Title and abstract screening and full-text review were conducted by two independent reviewers. Data extraction was shared by three independent reviewers. The data extraction instrument was iteratively refined to meet the requirements of all included papers, and covered details for technological aspects and the clinical context. Results are presented narratively and in tabular format, with emphasis on participants, concept and context, and data extraction instrument components.

RESULTS

Eleven papers were included in the review, which represented seven distinct conversational agent prototypes. Methodologies included technology description (n = 9) and initial user testing (n = 6). The intended clinical cohorts for the reported conversational agents were people with dementia (n = 5), Parkinson disease (n = 2), stroke (n = 1), traumatic brain injury (n = 1), mixed dementia and mild cognitive impairment (n = 1), and mixed dementia and Parkinson disease (n = 1). Two studies included participants who were healthy or otherwise from the general community. The design of the conversational agents considered technology aspects and clinical purposes. Two conversational agent prototypes incorporated a speaking humanoid avatar as reported in five of the papers. Topics of conversation focused on subjects enjoyable to the user (life history, hobbies, where they lived). The clinical purposes reported in the 11 papers were to increase the amount of conversation the user has each day (n = 4), reminiscence (n = 2), and one study each for anxiety management and education, Parkinson disease education, to obtain and analyze a recording of the user's voice, to monitor well-being, and to build rapport before providing daily task prompts. One study reported clinician oversight of the conversational agent use. The studies had low sample sizes (range: 1-33). No studies undertook effectiveness testing. Outcome measures focused on usability, language detection and production, and technological performance. No health-related outcomes were measured. No adverse events were reported, and only two studies reported safety considerations.

CONCLUSIONS

Current literature reporting the design and use of conversational agents for rehabilitation for adults with brain-related neurological conditions is heterogeneous and represents early stages of conversational agent development and testing. We recommend, as per our customized data extraction instrument, that studies of conversational agents for this population clearly define technical aspects, methodology for developing the conversation content, recruitment methods, safety issues, and requirements for clinician oversight.

Can virtual human clinicians help close the gap in colorectal cancer screening for rural adults in the United States? The influence of rural identity on perceptions of virtual human clinicians

Rural adults experience disparities in colorectal cancer screening, a trend even more distinct among rural Black adults. Healthcare disruptions caused by COVID-19 exacerbated inequities, heightening attention on virtual communication strategies to increase screening. Yet little is known about how rural adults perceive virtual human clinicians (VHCs). Given that identifying as rural influences perceived source credibility often through appearance judgments, the goal of this pilot was to explore how to develop VHCs that individuals highly identified with rurality find attractive. Between November 2018 and April 2019, we tested a culturally tailored, VHC-led telehealth intervention delivering evidence-based colorectal cancer prevention education with White and Black adults (N = 2079) in the United States recruited through an online panel who were non-adherent to screening guidelines and between 50 and 73 years of age. Participants were randomized on three factors (VHC race-matching, VHC gender-matching, Intervention type). Ordinal logistic regression models examined VHC appearance ratings. Participants with a high rural identity (AOR = 1.12, CI = [1.02, 1.23], p =.02) rated the VHCs more attractive. High rural belonging influenced VHC attractiveness for Black participants (AOR = 1.22, CI = [1.03, 1.44], p =.02). Also, Black participants interacting with a Black VHC and reporting high rural self-concept rated the VHC as more attractive (AOR = 2.22, CI = [1.27, 3.91], p =.01). Findings suggest adults for whom rural identity is important have more positive impressions of VHC attractiveness. For patients with strong rural identities, enhancing VHC appearance is critical to tailoring colorectal cancer prevention interventions.

Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework

BACKGROUND

Conversational agents (CAs), also known as chatbots, are computer programs that simulate human conversations by using predetermined rule-based responses or artificial intelligence algorithms. They are increasingly used in health care, particularly via smartphones. There is, at present, no conceptual framework guiding the development of smartphone-based, rule-based CAs in health care. To fill this gap, we propose structured and tailored guidance for their design, development, evaluation, and implementation.

OBJECTIVE

The aim of this study was to develop a conceptual framework for the design, evaluation, and implementation of smartphone-delivered, rule-based, goal-oriented, and text-based CAs for health care.

METHODS

We followed the approach by Jabareen, which was based on the grounded theory method, to develop this conceptual framework. We performed 2 literature reviews focusing on health care CAs and conceptual frameworks for the development of mobile health interventions. We identified, named, categorized, integrated, and synthesized the information retrieved from the literature reviews to develop the conceptual framework. We then applied this framework by developing a CA and testing it in a feasibility study.

RESULTS

The Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER) conceptual framework includes 8 iterative steps grouped into 3 stages, as follows: design, comprising defining the goal, creating an identity, assembling the team, and selecting the delivery interface; development, including developing the content and building the conversation flow; and the evaluation and implementation of the CA. They were complemented by 2 cross-cutting considerations-user-centered design and privacy and security-that were relevant at all stages. This conceptual framework was successfully applied in the development of a CA to support lifestyle changes and prevent type 2 diabetes.

CONCLUSIONS

Drawing on published evidence, the DISCOVER conceptual framework provides a step-by-step guide for developing rule-based, smartphone-delivered CAs. Further evaluation of this framework in diverse health care areas and settings and for a variety of users is needed to demonstrate its validity. Future research should aim to explore the use of CAs to deliver health care interventions, including behavior change and potential privacy and safety concerns.

Developing, Implementing, and Evaluating an Artificial Intelligence-Guided Mental Health Resource Navigation Chatbot for Health Care Workers and Their Families During and Following the COVID-19 Pandemic: Protocol for a Cross-sectional Study

Background

Approximately 1 in 3 Canadians will experience an addiction or mental health challenge at some point in their lifetime. Unfortunately, there are multiple barriers to accessing mental health care, including system fragmentation, episodic care, long wait times, and insufficient support for health system navigation. In addition, stigma may further reduce an individual’s likelihood of seeking support. Digital technologies present new and exciting opportunities to bridge significant gaps in mental health care service provision, reduce barriers pertaining to stigma, and improve health outcomes for patients and mental health system integration and efficiency. Chatbots (ie, software systems that use artificial intelligence to carry out conversations with people) may be explored to support those in need of information or access to services and present the opportunity to address gaps in traditional, fragmented, or episodic mental health system structures on demand with personalized attention. The recent COVID-19 pandemic has exacerbated even further the need for mental health support among Canadians and called attention to the inefficiencies of our system. As health care workers and their families are at an even greater risk of mental illness and psychological distress during the COVID-19 pandemic, this technology will be first piloted with the goal of supporting this vulnerable group.

Objective

This pilot study seeks to evaluate the effectiveness of the Mental Health Intelligent Information Resource Assistant in supporting health care workers and their families in the Canadian provinces of Alberta and Nova Scotia with the provision of appropriate information on mental health issues, services, and programs based on personalized needs.

Methods

The effectiveness of the technology will be assessed via voluntary follow-up surveys and an analysis of client interactions and engagement with the chatbot. Client satisfaction with the chatbot will also be assessed.

Results

This project was initiated on April 1, 2021. Ethics approval was granted on August 12, 2021, by the University of Alberta Health Research Board (PRO00109148) and on April 21, 2022, by the Nova Scotia Health Authority Research Ethics Board (1027474). Data collection is anticipated to take place from May 2, 2022, to May 2, 2023. Publication of preliminary results will be sought in spring or summer 2022, with a more comprehensive evaluation completed by spring 2023 following the collection of a larger data set.

Conclusions

Our findings can be incorporated into public policy and planning around mental health system navigation by Canadian mental health care providers—from large public health authorities to small community-based, not-for-profit organizations. This may serve to support the development of an additional touch point, or point of entry, for individuals to access the appropriate services or care when they need them, wherever they are.

International Registered Report Identifier (IRRID)

PRR1-10.2196/33717

Is Natural Necessary? Human Voice versus Synthetic Voice for Intelligent Virtual Agents

The use of intelligent virtual agents (IVA) to support humans in social contexts will depend on their social acceptability. Acceptance will be related to the human’s perception of the IVAs as well as the IVAs’ ability to respond and adapt their conversation appropriately to the human. Adaptation implies computer-generated speech (synthetic speech), such as text-to-speech (TTS). In this paper, we present the results of a study to investigate the effect of voice type (human voice vs. synthetic voice) on two aspects: (1) the IVA’s likeability and voice impression in the light of co-presence, and (2) the interaction outcome, including human–agent trust and behavior change intention. The experiment included 118 participants who interacted with either the virtual advisor with TTS or the virtual advisor with human voice to gain tips for reducing their study stress. Participants in this study found the voice of the virtual advisor with TTS to be more eerie, but they rated both agents, with recorded voice and with TTS, similarly in terms of likeability. They further showed a similar attitude towards both agents in terms of co-presence and building trust. These results challenge previous studies that favor human voice over TTS, and suggest that even if human voice is preferred, TTS can deliver equivalent benefits.

Social Humanoid Robots for Children with Autism Spectrum Disorders: A Review of Modalities, Indications, and Pitfalls

Robot-assisted therapy (RAT) is a promising area of translational neuroscience for children with autism spectrum disorders (ASDs). It has been widely demonstrated that this kind of advanced technological tool provides a reliable and efficient intervention for promoting social skills and communication in children with ASD. This type of treatment consists of a human-assisted social robot acting as an intervention mediator to increase competence and skills in children with ASD. Several social robots have been validated in the literature; however, an explicit technical comparison among devices has never been performed. For this reason, in this article, we provide an overview of the main commercial humanoid robots employed for ASD children with an emphasis on indications for use, pitfalls to be avoided, and recent advances. We conclude that, in the near future, a new generation of devices with high levels of mobility, availability, safety, and acceptability should be designed for improving the complex triadic interaction among teachers, children, and robots.

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.

Experiences of a Speech-Enabled Conversational Agent for the Self-Report of Wellbeing Among People Living with Affective Disorders: An In-The-Wild Study

The growing commercial success of smart speaker devices following recent advancements in speech recognition technology has surfaced new opportunities for collecting self-reported health and wellbeing data. Speech-enabled conversational agents (CAs) in particular, deployed in home environments using just such systems, may offer increasingly intuitive and engaging means of self-report. To date, however, few real-world studies have examined users’ experiences of engaging in the self-report of mental health using such devices, nor the challenges of deploying these systems in the home context. With these aims in mind, this paper recounts findings from a four-week ‘in-the-wild’ study during which 20 individuals with depression or bipolar disorder used a speech-enabled CA named ‘Sofia’ to maintain a daily diary log, responding also to the WHO-5 wellbeing scale every two weeks. Thematic analysis of post-study interviews highlights actions taken by participants to overcome CAs’ limitations, diverse personifications of a speech-enabled agent, and unique forms of valuing of this system among users’ personal and social circles. These findings serve as initial evidence for the potential of CAs to support the self-report of mental health and wellbeing, while highlighting the need to address outstanding technical limitations in addition to design challenges of conversational pattern matching, filling unmet interpersonal gaps, and the use of self-report CAs in the at-home social context. Based on these insights, we discuss implications for the future design of CAs to support the self-report of mental health and wellbeing.

Conversational agents in healthcare: a scoping review of their behavior change techniques and underpinning theory (Preprint)

Background

Conversational agents (CAs) are increasingly used in health care to deliver behavior change interventions. Their evaluation often includes categorizing the behavior change techniques (BCTs) using a classification system of which the BCT Taxonomy v1 (BCTTv1) is one of the most common. Previous studies have presented descriptive summaries of behavior change interventions delivered by CAs, but no in-depth study reporting the use of BCTs in these interventions has been published to date.

Objective

This review aims to describe behavior change interventions delivered by CAs and to identify the BCTs and theories guiding their design.

Methods

We searched PubMed, Embase, Cochrane’s Central Register of Controlled Trials, and the first 10 pages of Google and Google Scholar in April 2021. We included primary, experimental studies evaluating a behavior change intervention delivered by a CA. BCTs coding followed the BCTTv1. Two independent reviewers selected the studies and extracted the data. Descriptive analysis and frequent itemset mining to identify BCT clusters were performed.

Results

We included 47 studies reporting on mental health (n=19, 40%), chronic disorders (n=14, 30%), and lifestyle change (n=14, 30%) interventions. There were 20/47 embodied CAs (43%) and 27/47 CAs (57%) represented a female character. Most CAs were rule based (34/47, 72%). Experimental interventions included 63 BCTs, (mean 9 BCTs; range 2-21 BCTs), while comparisons included 32 BCTs (mean 2 BCTs; range 2-17 BCTs). Most interventions included BCTs 4.1 “Instruction on how to perform a behavior” (34/47, 72%), 3.3 “Social support” (emotional; 27/47, 57%), and 1.2 “Problem solving” (24/47, 51%). A total of 12/47 studies (26%) were informed by a behavior change theory, mainly the Transtheoretical Model and the Social Cognitive Theory. Studies using the same behavior change theory included different BCTs.

Conclusions

There is a need for the more explicit use of behavior change theories and improved reporting of BCTs in CA interventions to enhance the analysis of intervention effectiveness and improve the reproducibility of research.

Telehealth and racial disparities in colorectal cancer screening: A pilot study of how virtual clinician characteristics influence screening intentions

Introduction

Racial disparities in colorectal cancer (CRC) can be addressed through increased adherence to screening guidelines. In real-life encounters, patients may be more willing to follow screening recommendations delivered by a race concordant clinician. The growth of telehealth to deliver care provides an opportunity to explore whether these effects translate to a virtual setting. The primary purpose of this pilot study is to explore the relationships between virtual clinician (VC) characteristics and CRC screening intentions after engagement with a telehealth intervention leveraging technology to deliver tailored CRC prevention messaging.

Methods

Using a posttest-only design with three factors (VC race-matching, VC gender, intervention type), participants (N = 2267) were randomised to one of eight intervention treatments. Participants self-reported perceptions and behavioral intentions.

Results

The benefits of matching participants with a racially similar VC trended positive but did not reach statistical significance. Specifically, race-matching positively influenced screening intentions for Black participants but not for Whites (b = 0.29, p = 0.10). Importantly, perceptions of credibility, attractiveness, and message relevance significantly influenced screening intentions and the relationship with race-matching.

Conclusions

To reduce racial CRC screening disparities, investments are needed to identify patient-focused interventions to address structural barriers to screening. This study suggests that telehealth interventions that match Black patients with a Black VC can enhance perceptions of credibility and message relevance, which may then improve screening intentions. Future research is needed to examine how to increase VC credibility and attractiveness, as well as message relevance without race-matching.

What's Up With These Conversational Health Agents? From Users' Critiques to Implications for Design

Recent advancements in speech recognition technology in combination with increased access to smart speaker devices are expanding conversational interactions to ever-new areas of our lives – including our health and wellbeing. Prior human-computer interaction research suggests that Conversational Agents (CAs) have the potential to support a variety of health-related outcomes, due in part to their intuitive and engaging nature. Realizing this potential requires however developing a rich understanding of users' needs and experiences in relation to these still-emerging technologies. To inform the design of CAs for health and wellbeing, we analyze 2741 critical reviews of 485 Alexa health and fitness Skills using an automated topic modeling approach; identifying 15 subjects of criticism across four key areas of design (functionality, reliability, usability, pleasurability). Based on these findings, we discuss implications for the design of engaging CAs to support health and wellbeing.

"I Wanted to See How Bad it Was": Online Self-screening as a Critical Transition Point Among Young Adults with Common Mental Health Conditions

Young adults have high rates of mental health conditions, yet they are the age group least likely to seek traditional treatment. They do, however, seek information about their mental health online, including by filling out online mental health screeners. To better understand online self-screening, and its role in help-seeking, we conducted focus groups with 50 young adults who voluntarily completed a mental health screener hosted on an advocacy website. We explored (1) catalysts for taking the screener, (2) anticipated outcomes, (3) reactions to the results, and (4) desired next steps. For many participants, the screener results validated their lived experiences of symptoms, but they were nevertheless unsure how to use the information to improve their mental health moving forward. Our findings suggest that online screeners can serve as a transition point in young people's mental health journeys. We discuss design implications for online screeners, post-screener feedback, and digital interventions broadly.

Virtual Reality Exposure Therapy for Treating Social Anxiety: A Scoping Review of Treatment Designs and Adaptation to Stuttering

Virtual Reality Exposure Therapy (VRET) has been shown to be an effective technique for reducing social anxiety. People who stutter are at greater risk of developing heightened social anxiety. Cognitive behavior therapy protocols have shown promise in reducing social anxiety in people who stutter, but no studies have investigated VRET targeting social anxiety associated with stuttering. The aim of the current review is to provide an overview of VRET techniques used to treat social anxiety and insights into how these techniques might be adopted in the case of comorbid stuttering and social anxiety. Twelve studies were reviewed to understand key distinctions in VRET protocols used to treat social anxiety. Distinctions include exercises targeting public speaking vs. general social anxiety, computer-generated virtual environments vs. 360° video, and therapist guided vs. automated VRET. Based on the review findings, we propose how certain features could be applied in the case of stuttering. Virtual therapists, inhibitory learning techniques and integration into speech therapy may be suitable ways to tailor VRET. Regardless of these different techniques, VRET should consider the situations and cognitive-behavioral processes that underlie the experience of social anxiety amongst people who stutter.

Potential Applications of Social Robots in Robot-Assisted Interventions for Social Anxiety

Social anxiety disorder or social phobia is a condition characterized by debilitating fear and avoidance of different social situations. We provide an overview of social anxiety and evidence-based behavioural and cognitive treatment approaches for this condition. However, treatment avoidance and attrition are high in this clinical population, which calls for innovative approaches, including computer-based interventions, that could minimize barriers to treatment and enhance treatment effectiveness. After reviewing existing assistive technologies for mental health interventions, we provide an overview of how social robots have been used in many clinical interventions. We then propose to integrate social robots in conventional behavioural and cognitive therapies for both children and adults who struggle with social anxiety. We categorize the different therapeutic roles that social robots can potentially play in activities rooted in conventional therapies for social anxiety and oriented towards symptom reduction, social skills development, and improvement in overall quality of life. We discuss possible applications of robots in this context through four scenarios. These scenarios are meant as ‘food for thought’ for the research community which we hope will inspire future research. We discuss risks and concerns for using social robots in clinical practice. This article concludes by highlighting the potential advantages as well as limitations of integrating social robots in conventional interventions to improve accessibility and standard of care as well as outlining future steps in relation to this research direction. Clearly recognizing the need for future empirical work in this area, we propose that social robots may be an effective component in robot-assisted interventions for social anxiety, not replacing, but complementing the work of clinicians. We hope that this article will spark new research, and research collaborations in the highly interdisciplinary field of robot-assisted interventions for social anxiety.

Digital Therapeutics: Virtual Coaching Powered by Artificial Intelligence on Real-World Data

An ever-increasing number of people need to cope with one or more chronic conditions for a significant portion of their life. Digital Therapeutics (DTx) focused on the prevention, management, or treatment of chronic diseases are promising in alleviating the personal socio-economic burden caused. In this paper we describe a proposed DTx methodology covering three main components: observation (which data is collected), understanding (how to acquire knowledge based on the data collected), and coaching (how to communicate the acquired knowledge to the user). We focus on an emerging form of automated virtual coaching, delivered through conversational agents allowing interaction with end-users using natural language. Our methodology will be applied in the new generation of the Healthentia platform, an eClinical solution that captures clinical outcomes from mobile, medical and Internet of Things (IoT) devices, using a patient-centric mobile application and offers Artificial Intelligence (AI) driven smart services. While we are unable to provide data to prove its effectiveness, we illustrate the potential of the proposed architecture to deliver DTx by describing how the methodology can be applied to a use-case consisting of a clinical trial for treatment of a chronic condition, combining testing of a new medication and a lifestyle intervention, which will be partly implemented and evaluated in the context of the European research project RE-SAMPLE (REal-time data monitoring for Shared, Adaptive, Multi-domain and Personalised prediction, and decision making for Long-term Pulmonary care Ecosystems).

Addressing intersectional stigma as a care barrier for HIV-positive people who inject drugs: Design of an RCT in St. Petersburg, Russia

Background

HIV-positive people who inject drugs (PWID) experience stigma related to their substance use and HIV, with adverse consequences to their health care utilization and mental health. To help affected individuals cope with their intersectional stigma and reduce its negative impact on health and health care, we adapted a behavioral stigma coping intervention for this HIV key population.

Objective

To conduct a randomized controlled trial (RCT) testing the ‘Stigma Coping to Reduce HIV risks and Improve substance use Prevention and Treatment’ (SCRIPT) intervention, a community-based, adapted form of Acceptance and Commitment Therapy (ACT), for PWID living with HIV in St. Petersburg, Russia.

Methods

We recruited 100 PWID living with HIV from civil society organizations (CSO) delivering harm reduction and HIV prevention services in St. Petersburg, Russia. We randomized participants 2:1 to receive either the intervention (three adapted ACT sessions in a group format over one month and usual CSO care) or usual CSO care alone. ACT aims to help affected individuals cope with stigma by increasing their psychological flexibility to handle stigma-related negative expectations, emotions and experiences. The primary outcomes were satisfaction with the intervention, and changes in HIV and substance use stigma scores.

Conclusions

Stigma coping interventions targeting HIV-positive PWID outside of formal health care settings may help them confront negativities in their lives originating from intersectional stigma and reduce stigma's impact as a health care barrier.

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HIV-positive people who inject drugs face stigma due to both substance use and HIV.

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Interventions are scarce to help this population reduce stigma as care barrier.

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We recruited participants outside of the formal health care setting in Russia.

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We partnered with multiple organizations to recruit and retain stigmatized people.

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We evaluated the intervention's effects on stigma, health care, & health outcomes.

Participatory Development and Pilot Testing of an Adolescent Health Promotion Chatbot

Background: The use of chatbots may increase engagement with digital behavior change interventions in youth by providing human-like interaction. Following a Person-Based Approach (PBA), integrating user preferences in digital tool development is crucial for engagement, whereas information on youth preferences for health chatbots is currently limited.

Objective: The aim of this study was to gain an in-depth understanding of adolescents' expectations and preferences for health chatbots and describe the systematic development of a health promotion chatbot.

Methods: Three studies in three different stages of PBA were conducted: (1) a qualitative focus group study (n = 36), (2) log data analysis during pretesting (n = 6), and (3) a mixed-method pilot testing (n = 73).

Results: Confidentiality, connection to youth culture, and preferences when referring to other sources were important aspects for youth in chatbots. Youth also wanted a chatbot to provide small talk and broader support (e.g., technical support with the tool) rather than specifically in relation to health behaviors. Despite the meticulous approach of PBA, user engagement with the developed chatbot was modest.

Conclusion: This study highlights that conducting formative research at different stages is an added value and that adolescents have different chatbot preferences than adults. Further improvement to build an engaging chatbot for youth may stem from using living databases.

A Digital Human for Delivering a Remote Loneliness and Stress Intervention to At-Risk Younger and Older Adults During the COVID-19 Pandemic: Randomized Pilot Trial

BACKGROUND

Loneliness is a growing public health issue that has been exacerbated in vulnerable groups during the COVID-19 pandemic. Computer agents are capable of delivering psychological therapies through the internet; however, there is limited research on their acceptability to date.

OBJECTIVE

The objectives of this study were to evaluate (1) the feasibility and acceptability of a remote loneliness and stress intervention with digital human delivery to at-risk adults and (2) the feasibility of the study methods in preparation for a randomized controlled trial.

METHODS

A parallel randomized pilot trial with a mixed design was conducted. Participants were adults aged 18 to 69 years with an underlying medical condition or aged 70 years or older with a Mini-Mental State Examination score of >24 (ie, at greater risk of developing severe COVID-19). Participants took part from their place of residence (independent living retirement village, 20; community dwelling, 7; nursing home, 3). Participants were randomly allocated to the intervention or waitlist control group that received the intervention 1 week later. The intervention involved completing cognitive behavioral and positive psychology exercises with a digital human facilitator on a website for at least 15 minutes per day over 1 week. The exercises targeted loneliness, stress, and psychological well-being. Feasibility was evaluated using dropout rates and behavioral observation data. Acceptability was evaluated from behavioral engagement data, the Friendship Questionnaire (adapted), self-report items, and qualitative questions. Psychological measures were administered to evaluate the feasibility of the trial methods and included the UCLA Loneliness Scale, the 4-item Perceived Stress Scale, a 1-item COVID-19 distress measure, the Flourishing Scale, and the Scale of Positive and Negative Experiences.

RESULTS

The study recruited 30 participants (15 per group). Participants were 22 older adults and 8 younger adults with a health condition. Six participants dropped out of the study. Thus, the data of 24 participants were analyzed (intervention group, 12; waitlist group, 12). The digital human intervention and trial methods were generally found to be feasible and acceptable in younger and older adults living independently, based on intervention completion, and behavioral, qualitative, and some self-report data. The intervention and trial methods were less feasible to nursing home residents who required caregiver assistance. Acceptability could be improved with additional content, tailoring to the population, and changes to the digital human's design.

CONCLUSIONS

Digital humans are a promising and novel technological solution for providing at-risk adults with access to remote psychological support during the COVID-19 pandemic. Research should further examine design techniques to improve their acceptability in this application and investigate intervention effectiveness in a randomized controlled trial.

TRIAL REGISTRATION

Australia New Zealand Clinical Trials Registry ACTRN12620000786998; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380113.