Should Machines Express Sympathy and Empathy? Experiments with a Health Advice Chatbot

Assessing the impact of chatbots on health decision-making: A multifactorial experimental approach

BackgroundChatbots are increasingly integrated into healthcare, offering personalized and accessible health advice. However, the impact of factors such as chatbot authority, health information type, and interaction style on users' decision-making remains unclear.ObjectiveThis study aims to investigate how these elements influence users' willingness to adopt health advice provided by chatbots.MethodsA 2 × 2 × 2 factorial experiment was conducted with 480 university students to examine the effects of chatbot authority (authoritative vs. non-authoritative), health information type (preventive vs. treatment-related), and interaction style (formal vs. informal). Participants' willingness to adopt the health advice was measured before and after interacting with the chatbot.ResultsThe study found that a authoritative chatbot delivering treatment-related advice in a formal style significantly increased willingness to adopt the advice. Conversely, preventive information was more effective when presented informally by a non-authoritative chatbot. These results support the media evocation paradigm, which suggests that chatbots framed as authoritative figures evoke greater user engagement and trust in health contexts.ConclusionThe findings extend the media evocation paradigm by demonstrating that chatbot authority, information type, and interaction style should be aligned with the nature of health advice to maximize effectiveness. This study provides insights for designing chatbots that improve health decision-making by tailoring their communication strategies.

Patient perceptions of empathy in physician and artificial intelligence chatbot responses to patient questions about cancer

Artificial intelligence chatbots can draft empathetic responses to cancer questions, but how patients perceive chatbot empathy remains unclear. Here, we found that people with cancer rated chatbot responses as more empathetic than physician responses. However, differences between patient and physician perceptions of empathy highlight the need for further research to tailor clinical messaging to better meet patient needs. Chatbots may be effective in generating empathetic template responses to patient questions under clinician oversight.

Navigating the design of simulated exercising peers: insights from a participatory design study

Background

To fight sedentary lifestyles, researchers have introduced various technological interventions aimed at promoting physical activity through social support. These interventions encourage people to exercise together, maintaining high levels of motivation. However, the unpredictable nature of human peers makes it challenging to control behavior and balance these interventions effectively. Artificial intelligence agents, on the other hand, can provide consistent social support and are more controllable. Hence, we propose Simulated Exercising Peers (SEPs) as a promising solution for providing agent-based social support for physical activity.

Method

Participatory design sessions were conducted, involving young adults in the creation of SEP-based interventions. Sixteen participants generated four prototypes that varied in aesthetics, behavior, and communication style, with outcomes analyzed through the lens of Self-Determination Theory to better understand the motivational implications of each design.

Results

Findings highlight key components crucial for designing SEPs that enhance acceptance and efficiently integrate into physical activity interventions. Additionally, the study revealed how the aesthetics and behavior of SEPs could potentially deceive users, which can lead to user disengagement from interventions involving SEPs. Participants also defined two distinct social roles for the SEPs, i.e., coach, and companion, each associated with unique communication styles.

Conclusion

This study offers five design guidelines for the development of SEPs, AI agents aimed at promoting physical activity through social support, and highlights opportunities for their integration into broader physical activity interventions.

Counselor type (Human/AI) and consultation intention: a moderated mediation model of trust and psychological counseling scenarios

OBJECTIVES

In some services involving social and emotional needs (such as psychological counseling), people seem to prefer human services over AI. Exploring the psychological mechanisms behind this preference can help increase people's acceptance of AI-based psychological counseling. This study aims to explore the differences in people's consultation intention for human versus AI across different counseling scenarios, as well as the role of trust (including cognitive and emotional trust) between humans and AI.

METHODS

A total of 477 participants (297 in Study 1 and 180 in Study 2) were randomly assigned to different groups for counseling imagination tasks and then completed self-report questionnaires.

RESULTS

The results of Study 1 demonstrated a significantly higher consultation intention towards human counselors in social emotional scenarios, while no significant preference was observed in cognitive analytical scenarios. Study 2 replicated the findings of Study 1, and further revealed that: (1) cognitive and affective trust played a multiple mediating role between counselor type and consultation intention in social emotional scenarios; (2) there existed a suppressing effect in the relationship model between cognitive trust, counselor type, and counseling intentions in cognitive analytical scenarios; (3) psychological counseling scenarios moderated the relationship between cognitive trust/counselor type and consultation intention.

CONCLUSION

These findings offer practical guidance for the development of AI-driven psychological consultation products and carry theoretical implications for research pertaining to human-AI interaction.

[Digital innovations in vaccination communication]

Despite the significant success of vaccinations, increasing vaccine hesitancy poses a threat to public health, making effective vaccination communication essential. Both personalized, needs-based conversations between healthcare providers and patients and large-scale, standardized vaccination campaigns through traditional media are important tools for addressing patients' concerns, correcting misinformation, and conveying positive arguments for the benefits of vaccination. This article describes innovative digital solutions for vaccination communication that combine the strengths of these two approaches, potentially becoming vital components of vaccination communication in an increasingly digital society. We focus on four digital solutions: social media, smartphone apps, chatbots, and immersive technologies (particularly virtual reality). We describe the features of these media and technologies, potential target groups for their application, selected scientific evidence regarding their effectiveness in vaccination communication, and potential research gaps and opportunities. Finally, we offer suggestions for possible application areas of these digital solutions in vaccination communication. While research on the use of innovative digital solutions in vaccination communication is still in its early stages, we see great potential for integrating these technologies as complementary measures into comprehensive vaccination communication strategies to enhance the effectiveness of future vaccination campaigns and promote global health.

Exploring Client Perceptions on Gaining Infant Feeding Information Through the Texas Women, Infants, and Children (WIC) Chatbot

The modernization of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) program is a priority. The Texas WIC chatbot, Maya, streamlines client interactions through dialog-based responses. This qualitative study explored client capabilities, motivations, and opportunities for seeking nutrition information about breastfeeding, formula feeding, infant feeding safety, adequacy of infant feeding, and complementary feeding via a chatbot. A team conducted in-depth semi-structured interviews with Texas WIC clients (n = 19 women). All interviews were transcribed and subjected to a two-coder, four-phase process utilizing a theory-based codebook. Codes were compiled and thematically categorized. Identified themes included (1) motivations through necessity or resource availability, (2) client capabilities and Maya usability, and (3) opportunities for connection, support, and encouragement. Texas WIC clients that participated in this study expressed motivations, capabilities, and opportunities to engage with nutrition information through Maya. They described Maya as a favorable resource for behavior changes, and a trusted source of nutrition information, citing the credibility of WIC and reliability of the chatbot. The findings may inform future research and development of public health chatbots. Additional research is required to explore how different factors such as language and technology usage may impact client capabilities, motivations, and opportunities to seek nutrition information with regard to infant feeding.

Effectiveness and User Experience of a Smoking Cessation Chatbot: Mixed Methods Study Comparing Motivational Interviewing and Confrontational Counseling

BACKGROUND

Cigarette smoking poses a major public health risk. Chatbots may serve as an accessible and useful tool to promote cessation due to their high accessibility and potential in facilitating long-term personalized interactions. To increase effectiveness and acceptability, there remains a need to identify and evaluate counseling strategies for these chatbots, an aspect that has not been comprehensively addressed in previous research.

OBJECTIVE

This study aims to identify effective counseling strategies for such chatbots to support smoking cessation. In addition, we sought to gain insights into smokers' expectations of and experiences with the chatbot.

METHODS

This mixed methods study incorporated a web-based experiment and semistructured interviews. Smokers (N=229) interacted with either a motivational interviewing (MI)-style (n=112, 48.9%) or a confrontational counseling-style (n=117, 51.1%) chatbot. Both cessation-related (ie, intention to quit and self-efficacy) and user experience-related outcomes (ie, engagement, therapeutic alliance, perceived empathy, and interaction satisfaction) were assessed. Semistructured interviews were conducted with 16 participants, 8 (50%) from each condition, and data were analyzed using thematic analysis.

RESULTS

Results from a multivariate ANOVA showed that participants had a significantly higher overall rating for the MI (vs confrontational counseling) chatbot. Follow-up discriminant analysis revealed that the better perception of the MI chatbot was mostly explained by the user experience-related outcomes, with cessation-related outcomes playing a lesser role. Exploratory analyses indicated that smokers in both conditions reported increased intention to quit and self-efficacy after the chatbot interaction. Interview findings illustrated several constructs (eg, affective attitude and engagement) explaining people's previous expectations and timely and retrospective experience with the chatbot.

CONCLUSIONS

The results confirmed that chatbots are a promising tool in motivating smoking cessation and the use of MI can improve user experience. We did not find extra support for MI to motivate cessation and have discussed possible reasons. Smokers expressed both relational and instrumental needs in the quitting process. Implications for future research and practice are discussed.

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.

Using ChatGPT for Kidney Transplantation: Perceived Information Quality by Race and Education Levels

BACKGROUND

Kidney transplantation is a complex process requiring extensive preparation and ongoing monitoring. Artificial intelligence (AI)-powered chatbots hold potential for providing accessible health information, but our understanding of their role in offering health advice for kidney transplantation and how individuals assess such advice remains limited. This study investigates how individuals evaluate ChatGPT's responses to kidney transplantation questions in terms of information quality and empathy, focusing on potential differences across race/ethnicity and educational backgrounds.

METHODS

We collected Reddit posts (N = 4624) regarding kidney transplantation and selected 86 questions to represent typical clinician inquiries. These questions were used as input prompts for ChatGPT. A total of 565 participants assessed ChatGPT's responses through online surveys, rating information quality and empathy using Likert scales.

RESULTS

Multilevel analyses (N = 2825) show that there is a significant interaction between race/ethnicity and education levels in various measures related to perceived information quality, but not perceived empathy of ChatGPT's responses: accuracy (p < 0.05); authenticity (p < 0.01); believability (p < 0.05); informativeness (p = 0.053); usefulness (p < 0.05); recognizing users' feelings (p = 0.70) and understanding feelings and situations (p = 0.65). Among non-White individuals, higher education levels predicted higher perceived quality of ChatGPT's responses across all information quality measures. Notably, this trend was reversed for White individuals, where higher education levels led to lower perceived information quality.

CONCLUSIONS

Our results highlight the importance of developing AI tools sensitive to diverse communication styles and information needs.

ChatGPT: perspectives from human-computer interaction and psychology

The release of GPT-4 has garnered widespread attention across various fields, signaling the impending widespread adoption and application of Large Language Models (LLMs). However, previous research has predominantly focused on the technical principles of ChatGPT and its social impact, overlooking its effects on human-computer interaction and user psychology. This paper explores the multifaceted impacts of ChatGPT on human-computer interaction, psychology, and society through a literature review. The author investigates ChatGPT's technical foundation, including its Transformer architecture and RLHF (Reinforcement Learning from Human Feedback) process, enabling it to generate human-like responses. In terms of human-computer interaction, the author studies the significant improvements GPT models bring to conversational interfaces. The analysis extends to psychological impacts, weighing the potential of ChatGPT to mimic human empathy and support learning against the risks of reduced interpersonal connections. In the commercial and social domains, the paper discusses the applications of ChatGPT in customer service and social services, highlighting the improvements in efficiency and challenges such as privacy issues. Finally, the author offers predictions and recommendations for ChatGPT's future development directions and its impact on social relationships.

On the relationship between mind perception and social support of chatbots

The social support provided by chatbots is typically designed to mimic the way humans support others. However, individuals have more conflicting attitudes toward chatbots providing emotional support (e.g., empathy and encouragement) compared to informational support (e.g., useful information and advice). This difference may be related to whether individuals associate a certain type of support with the realm of the human mind and whether they attribute human-like minds to chatbots. In the present study, we investigated whether perceiving human-like minds in chatbots affects users' acceptance of various support provided by the chatbot. In the experiment, the chatbot posed questions about participants' interpersonal stress events, prompting them to write down their stressful experiences. Depending on the experimental condition, the chatbot provided two kinds of social support: informational support or emotional support. Our results showed that when participants explicitly perceived a human-like mind in the chatbot, they considered the support to be more helpful in resolving stressful events. The relationship between implicit mind perception and perceived message effectiveness differed depending on the type of support. More specifically, if participants did not implicitly attribute a human-like mind to the chatbot, emotional support undermined the effectiveness of the message, whereas informational support did not. The present findings suggest that users' mind perception is essential for understanding the user experience of chatbot social support. Our findings imply that informational support can be trusted when building social support chatbots. In contrast, the effectiveness of emotional support depends on the users implicitly giving the chatbot a human-like mind.

Longitudinal Examination of the Relationship Between Virtual Companionship and Social Anxiety: Emotional Expression as a Mediator and Mindfulness as a Moderator

Purpose

As the interweaving of human interaction and Artificial Intelligence (AI) intensifies, understanding the psychological impact, especially regarding social anxiety, of engaging with AI-driven virtual companionship becomes crucial. While a substantial body of research on social anxiety has concentrated on interactions between individuals, both online and offline, there is a noticeable deficit in explorations concerning how human-computer interactions influence social anxiety. This study offers a comprehensive, longitudinal examination of this underinvestigated relationship, intricately dissecting the roles of emotional expression and mindfulness within the context of AI-based interactions.

Methods

We use social support theory and emotion regulation theory as our theoretical foundation. Data were collected from 618 undergraduate students in Eastern China over two intervals (May 15, 2023 and September 15, 2023). We utilized SPSS 26.0 to conduct descriptive statistics, while AMOS 25.0 facilitated multi-group confirmatory factor analysis (CFA) and the cross-lagged panel modeling.

Results

Our findings indicate that as the frequency of virtual companionship use increases, there's a decline in online social anxiety but a rise in offline social anxiety. Emotional expression emerges as a significant mediator, with heightened emotional expression leading to reduced social anxiety in both contexts. Mindfulness serves as a potent moderator, suggesting its protective role against the potential pitfalls of frequent virtual interactions.

Conclusion

This research not only deepens our theoretical understanding of the dynamics between virtual interactions and social anxiety but also serves as a cornerstone for future endeavors aimed at optimizing AI and devising therapeutic interventions tailored for the digital generation.

Improving counseling effectiveness with virtual counselors through nonverbal compassion involving eye contact, facial mimicry, and head-nodding

An effective way to reduce emotional distress is by sharing negative emotions with others. This is why counseling with a virtual counselor is an emerging methodology, where the sharer can consult freely anytime and anywhere without having to fear being judged. To improve counseling effectiveness, most studies so far have focused on designing verbal compassion for virtual counselors. However, recent studies showed that virtual counselors' nonverbal compassion through eye contact, facial mimicry, and head-nodding also have significant impact on the overall counseling experience. To verify this, we designed the virtual counselor's nonverbal compassion and examined its effects on counseling effectiveness (i.e., reduce the intensity of anger and improve general affect). A total of 40 participants were recruited from the university community. Participants were then randomly assigned to one of two virtual counselor conditions: a neutral virtual counselor condition without nonverbal compassion and a compassionate virtual counselor condition with nonverbal compassion (i.e., eye contact, facial mimicry, and head-nodding). Participants shared their anger-inducing episodes with the virtual counselor for an average of 16.30 min. Note that the virtual counselor was operated by the Wizard-of-Oz method without actually being technically implemented. Results showed that counseling with a compassionate virtual counselor reduced the intensity of anger significantly more than counseling with a neutral virtual counselor (F(1, 37) = 30.822, p < 0.001, ηp2 = 0.454). In addition, participants who counseled with a compassionate virtual counselor responded that they experienced higher empathy than those who counseled with a neutral virtual counselor (p < 0.001). These findings suggest that nonverbal compassion through eye contact, facial mimicry, and head-nodding of the virtual counselor makes the participants feel more empathy, which contributes to improving the counseling effectiveness by reducing the intensity of anger.

Exploring the potential of digital therapeutics: An assessment of progress and promise

Digital therapeutics (DTx), a burgeoning subset of digital health solutions, has garnered considerable attention in recent times. These cutting-edge therapeutic interventions employ diverse technologies, powered by software algorithms, to treat, manage, and prevent a wide array of diseases and disorders. Although DTx shows significant promise as an integral component of medical care, its widespread integration is still in the preliminary stages. This limited adoption can be largely attributed to the scarcity of comprehensive research that delves into DTx's scope, including its technological underpinnings, potential application areas, and challenges-namely, regulatory hurdles and modest physician uptake. This review aims to bridge this knowledge gap by offering an in-depth overview of DTx products' value to both patients and clinicians. It evaluates the current state of maturity of DTx applications driven by digital technologies and investigates the obstacles that developers and regulators encounter in the market introduction phase.

'I Hope You Can Rise Again': Linguistic Variation in Online Condolences

In the wake of tragic events such as the 'Paris Attacks of 2015', the expression of condolences through e-messages has become a common way for individuals to offer support and sympathy to those affected. However, limited research has been conducted on the linguistic aspects of condolence e-messages and how they reflect the speech act of condolence. This study aims to fill this gap by examining the syntactic and pragmatic elements present in these messages. The aim is to understand how the syntactic and pragmatic elements of these messages contribute to the expression of the communicative speech act of condolence. Condolence e-messages were identified and analyzed using an adapted version of Elwood's (2004) coding scheme. The analysis focused on common themes in the condolence sentences, revealing that some linguistic functions were overtly used to express grief. Additionally, specific words such as 'pray', 'love', and 'condolence' were frequently used in conjunction with the expressions of condolence. The findings highlight the influence of sociocultural factors in shaping the norms and variations in the production of speech acts across different cultures. Understanding these linguistic variations can contribute to effective communication and cultural sensitivity in expressing condolences.

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

BACKGROUND

In March 2020, New Zealand was plunged into its first nationwide lockdown to halt the spread of COVID-19. Our team rapidly adapted our existing chatbot platform to create Aroha, a well-being chatbot intended to address the stress experienced by young people aged 13 to 24 years in the early phase of the pandemic. Aroha was made available nationally within 2 weeks of the lockdown and continued to be available throughout 2020.

OBJECTIVE

In this study, we aimed to evaluate the acceptability and relevance of the chatbot format and Aroha's content in young adults and to identify areas for improvement.

METHODS

We conducted qualitative in-depth and semistructured interviews with young adults as well as in situ demonstrations of Aroha to elicit immediate feedback. Interviews were recorded, transcribed, and analyzed using thematic analysis assisted by NVivo (version 12; QSR International).

RESULTS

A total of 15 young adults (age in years: median 20; mean 20.07, SD 3.17; female students: n=13, 87%; male students: n=2, 13%; all tertiary students) were interviewed in person. Participants spoke of the challenges of living during the lockdown, including social isolation, loss of motivation, and the demands of remote work or study, although some were able to find silver linings. Aroha was well liked for sounding like a "real person" and peer with its friendly local "Kiwi" communication style, rather than an authoritative adult or counselor. The chatbot was praised for including content that went beyond traditional mental health advice. Participants particularly enjoyed the modules on gratitude, being active, anger management, job seeking, and how to deal with alcohol and drugs. Aroha was described as being more accessible than traditional mental health counseling and resources. It was an appealing option for those who did not want to talk to someone in person for fear of the stigma associated with mental health. However, participants disliked the software bugs. They also wanted a more sophisticated conversational interface where they could express themselves and "vent" in free text. There were several suggestions for making Aroha more relevant to a diverse range of users, including developing content on navigating relationships and diverse chatbot avatars.

CONCLUSIONS

Chatbots are an acceptable format for scaling up the delivery of public mental health and well-being-enhancing strategies. We make the following recommendations for others interested in designing and rolling out mental health chatbots to better support young people: make the chatbot relatable to its target audience by working with them to develop an authentic and relevant communication style; consider including holistic health and lifestyle content beyond traditional "mental health" support; and focus on developing features that make users feel heard, understood, and empowered.

Modeling users' satisfaction and visit intention using AI-based chatbots

AI-based chatbots are an emerging technology disrupting the tourism industry. Although chatbots have received increasing attention, there is little evidence of their impact on tourists' decisions to visit a destination. This study evaluates the key attributes of chatbots and their effects on user satisfaction and visit intention. We use structural equation modeling with covariance procedures to test the proposed model and its hypotheses. The results showed that informativeness, empathy, and interactivity are critical attributes for satisfaction, which drive tourists' intention to visit a destination.

How people interact with a chatbot against disinformation and fake news in COVID-19 in Brazil: The CoronaAI case

BACKGROUND

The search for valid information was one of the main challenges encountered during the COVID-19 pandemic, which resulted in the development of several online alternatives.

OBJECTIVES

To describe the development of a computational solution to interact with users of different levels of digital literacy on topics related to COVID-19 and to map the correlations between user behavior and events and news that occurred throughout the pandemic.

METHOD

CoronaAI, a chatbot based on Google's Dialogflow technology, was developed at a public university in Brazil and made available on WhatsApp. The dataset with users' interactions with the chatbot comprises approximately 7,000 hits recorded throughout eleven months of CoronaAI usage.

RESULTS

CoronaAI was widely accessed by users in search of valuable and updated information on COVID-19, including checking the veracity of possible fake news about the spread of cases, deaths, symptoms, tests and protocols, among others. The mapping of users' behavior revealed that as the number of cases and deaths increased and as COVID-19 became closer, users showed a greater need for information applicable to self-care compared to following the statistical data. In addition, they showed that the constant updating of this technology may contribute to public health by enhancing general information on the pandemic and at the individual level by clarifying specific doubts about COVID-19.

CONCLUSION

Our findings reinforce the potential usefulness of chatbot technology to resolve a wide spectrum of citizens' doubts about COVID-19, acting as a cost-effective tool against the parallel pandemic of misinformation and fake news.

Developing an AI-Assisted Educational Chatbot for Radiotherapy Using the IBM Watson Assistant Platform

Objectives: This study aims to make radiotherapy knowledge regarding healthcare accessible to the general public by developing an AI-powered chatbot. The interactive nature of the chatbot is expected to facilitate better understanding of information on radiotherapy through communication with users. Methods: Using the IBM Watson Assistant platform on IBM Cloud, the chatbot was constructed following a pre-designed flowchart that outlines the conversation flow. This approach ensured the development of the chatbot with a clear mindset and allowed for effective tracking of the conversation. The chatbot is equipped to furnish users with information and quizzes on radiotherapy to assess their understanding of the subject. Results: By adopting a question-and-answer approach, the chatbot can engage in human-like communication with users seeking information about radiotherapy. As some users may feel anxious and struggle to articulate their queries, the chatbot is designed to be user-friendly and reassuring, providing a list of questions for the user to choose from. Feedback on the chatbot's content was mostly positive, despite a few limitations. The chatbot performed well and successfully conveyed knowledge as intended. Conclusions: There is a need to enhance the chatbot's conversation approach to improve user interaction. Including translation capabilities to cater to individuals with different first languages would also be advantageous. Lastly, the newly launched ChatGPT could potentially be developed into a medical chatbot to facilitate knowledge transfer.

Support provision in a digitalized world: The consequences of social sharing across different communication channels

People tend to share their emotional experiences with others, with sharing increasingly done online. This raises questions about the quality of computer-mediated vs. face-to-face sharing. The present review (1) outlines conditions for sharing to improve emotional and relational well-being, (2) discusses when computer-mediated sharing with other people may (not) be conducive to these conditions, and (3) reviews recent research on the effectiveness of computer-mediated sharing with humans and virtual agents. It is concluded that the emotional and relational consequences of sharing are dependent on the responsiveness of the listener, regardless of the communication channel. Differences exist, however, in the extent to which channels are conducive to various forms of responding, with implications for speakers' emotional and relational well-being.

A Transgender Chatbot (Amanda Selfie) to Create Pre-exposure Prophylaxis Demand Among Adolescents in Brazil: Assessment of Acceptability, Functionality, Usability, and Results

BACKGROUND

HIV incidence rates have increased in adolescent men who have sex with men (AMSM) and adolescent transgender women (ATGW). Thus, it is essential to promote access to HIV prevention, including pre-exposure prophylaxis (PrEP), among these groups. Moreover, using artificial intelligence and online social platforms to create demand and access to health care services are essential tools for adolescents and youth.

OBJECTIVE

This study aims to describe the participative process of developing a chatbot using artificial intelligence to create demand for PrEP use among AMSM and ATGW in Brazil. Furthermore, it analyzes the chatbot's acceptability, functionality, and usability and its results on the demand creation for PrEP.

METHODS

The chatbot Amanda Selfie integrates the demand creation strategies based on social networks (DCSSNs) of the PrEP1519 study. She was conceived as a Black transgender woman and to function as a virtual peer educator. The development process occurred in 3 phases (conception, trial, and final version) and lasted 21 months. A mixed methodology was used for the evaluations. Qualitative approaches, such as in-depth adolescent interviews, were used to analyze acceptability and usability, while quantitative methods were used to analyze the functionality and result of the demand creation for PrEP based on interactions with Amanda and information from health care services about using PrEP. To evaluate Amanda's result on the demand creation for PrEP, we analyzed sociodemographic profiles of adolescents who interacted at least once with her and developed a cascade model containing the number of people at various stages between the first interaction and initiation of PrEP (PrEP uptake). These indicators were compared with other DCSs developed in the PrEP1519 study using chi-square tests and residual analysis (P=.05).

RESULTS

Amanda Selfie was well accepted as a peer educator, clearly and objectively communicating on topics such as gender identity, sexual experiences, HIV, and PrEP. The chatbot proved appropriate for answering questions in an agile and confidential manner, using the language used by AMSM and ATGW and with a greater sense of security and less judgment. The interactions with Amanda Selfie combined with a health professional were well evaluated and improved the appointment scheduling. The chatbot interacted with most people (757/1239, 61.1%) reached by the DCSSNs. However, when compared with the other DCSSNs, Amanda was not efficient in identifying AMSM/ATGW (359/482, 74.5% vs 130/757, 17.2% of total interactions, respectively) and in PrEP uptake (90/359, 25.1% vs 19/130, 14.6%). The following profiles were associated (P<.001) with Amanda Selfie's demand creation, when compared with other DCS: ATGW and adolescents with higher levels of schooling and White skin color.

CONCLUSIONS

Using a chatbot to create PrEP demand among AMSM and ATGW was well accepted, especially for ATGW with higher levels of schooling. A complimentary dialog with a health professional increased PrEP uptake, although it remained lower than the results of the other DCSSNs.

You Are Not Alone: A Serial Mediation of Social Attraction, Privacy Concerns, and Satisfaction in Voice AI Use

The popularity of voice-activated artificial intelligence (voice AI) has grown rapidly as people continue to use smart speakers such as Amazon Alexa and Google Home to support everyday tasks. However, little is known about how loneliness relates to voice AI use, or the potential mediators in this association. This study investigates the mediating roles of users' perceptions (i.e., social attraction, privacy concerns, and satisfaction) in the relationship between users' social loneliness and intentions to continue using voice AI. A serial mediation model based on survey data from current voice AI users showed that users' perceptions were positively associated with behavioral intentions. Several full serial mediations were observed: people who felt lonely perceived (1) voice AI as a more socially attractive agent and (2) had fewer privacy concerns. These aspects were each tied to satisfaction and subsequent usage intention. Theoretical and practical implications are discussed.

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Background

Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.

Objectives

The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?

Materials and methods

A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.

Results

Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.

Conclusion

There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.

Implications

In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.

Designing Emotions for Health Care Chatbots: Text-Based or Icon-Based Approach

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Effect of AI chatbot emotional disclosure on user satisfaction and reuse intention for mental health counseling: a serial mediation model

This study explored the effect of chatbot emotional disclosure on user satisfaction and reuse intention for a chatbot counseling service. It also examined the independent and sequential mediation roles of user emotional disclosure intention and perceived intimacy with a chatbot on the relationship between chatbot emotional disclosure, user satisfaction, and reuse intention for chatbot counseling. In total, 348 American adults were recruited to participate in a mental health counseling session with either of the two types of artificial intelligence-powered mental health counseling chatbots. These included a chatbot disclosing factual information only or a chatbot disclosing humanlike emotions. The results revealed that chatbot emotional disclosure significantly increased user satisfaction and reuse intention for a chatbot counseling service. The results further revealed that user emotional disclosure intention and perceived intimacy with a chatbot independently and serially mediates the effect of chatbot emotional disclosure on user satisfaction and chatbot counseling service reuse intention. The results indicate positive effects of artificial emotions and their disclosure in the context of chatbot moderated mental health counseling. Practical implications and psychological mechanisms are discussed.

Effects of personalization and source expertise on users' health beliefs and usage intention toward health chatbots: Evidence from an online experiment

Objective

Based on the heuristic–systematic model (HSM) and health belief model (HBM), this study aims to investigate how personalization and source expertise in responses from a health chatbot influence users’ health belief-related factors (i.e. perceived benefits, self-efficacy and privacy concerns) as well as usage intention.

Methods

A 2 (personalization vs. non-personalization) × 2 (source expertise vs. non-source expertise) online between-subject experiment was designed. Participants were recruited in China between April and May 2021. Data from 260 valid observations were used for the data analysis.

Results

Source expertise moderated the effects of personalization on health belief factors. Perceived benefits and self-efficacy mediated the relationship between personalization and usage intention when the source expertise cue was presented. However, the privacy concerns were not influenced by personalization and source expertise and did not significantly affect usage intention toward the health chatbot.

Discussion

This study verified that in the health chatbot context, source expertise as a heuristic cue may be a necessary condition for effects of the systematic cue (i.e. personalization), which supports the HSM's arguments. By introducing the HBM in the chatbot experiment, this study is expected to provide new insights into the acceptance of healthcare AI consulting services.

When a Chatbot Smiles at You: The Psychological Mechanism Underlying the Effects of Friendly Language Use by Product Recommendation Chatbots

Based on the computers are social actors theory and social presence theory, the current study investigates the psychological mechanism by which the use of friendly language by a personalized product recommendation chatbot influences product attitudes. Results indicated that the effect of the friendly chatbot on more positive product attitudes was sequentially mediated by social presence and user satisfaction. Previous experience with product recommendation chatbots was found to moderate the serial mediation effects. Furthermore, the current study found that a friendly chatbot led to higher rates of contact information disclosure by consumers. Theoretical and practical implications are discussed.

Chatbot as an emergency exist: Mediated empathy for resilience via human-AI interaction during the COVID-19 pandemic

As a global health crisis, the COVID-19 pandemic has also made heavy mental and emotional tolls become shared experiences of global communities, especially among females who were affected more by the pandemic than males for anxiety and depression. By connecting multiple facets of empathy as key mechanisms of information processing with the communication theory of resilience, the present study examines human-AI interactions during the COVID-19 pandemic in order to understand digitally mediated empathy and how the intertwining of empathic and communicative processes of resilience works as coping strategies for COVID-19 disruption. Mixed methods were adopted to explore the using experiences and effects of Replika, a chatbot companion powered by AI, with ethnographic research, in-depth interviews, and grounded theory-based analysis. Findings of this research extend empathy theories from interpersonal communication to human-AI interactions and show five types of digitally mediated empathy among Chinese female Replika users with varying degrees of cognitive empathy, affective empathy, and empathic response involved in the information processing processes, i.e., companion buddy, responsive diary, emotion-handling program, electronic pet, and tool for venting. When processing information obtained from AI and collaborative interactions with the AI chatbot, multiple facets of mediated empathy become unexpected pathways to resilience and enhance users’ well-being. This study fills the research gap by exploring empathy and resilience processes in human-AI interactions. Practical implications, especially for increasing individuals’ psychological resilience as an important component of global recovery from the pandemic, suggestions for future chatbot design, and future research directions are also discussed.

The Effects of Chatbot Service Recovery With Emotion Words on Customer Satisfaction, Repurchase Intention, and Positive Word-Of-Mouth

This study sought to examine the effect of the quality of chatbot services on customer satisfaction, repurchase intention, and positive word-of-mouth by comparing two groups, namely chatbots with and without emotion words. An online survey was conducted for 2 weeks in May 2021. A total of 380 responses were collected and analyzed using structural equation modeling to test the hypothesis. The theoretical basis of the study was the SERVQUAL theory, which is widely used in measuring and managing service quality in various industries. The results showed that the assurance and reliability of chatbots positively impact customer satisfaction for both groups. However, empathy and interactivity positively affect customer satisfaction only for chatbots with emotion words. Responsiveness did not have an impact on customer satisfaction for both groups. Customer satisfaction positively impacts repurchase intention and positive word-of-mouth for both groups. The findings of this study can serve as a priori research to empirically prove the effectiveness of chatbots with emotion words.

“I don't Know what you Mean by ‘I am Anxious’”: A New Method for Evaluating Conversational Agent Responses to Standardized Mental Health Inputs for Anxiety and Depression

Conversational agents (CAs) are increasingly ubiquitous and are now commonly used to access medical information. However, we lack systematic data about the quality of advice such agents provide. This paper evaluates CA advice for mental health (MH) questions, a pressing issue given that we are undergoing a mental health crisis. Building on prior work, we define a new method to systematically evaluate mental health responses from CAs. We develop multi-utterance conversational probes derived from two widely used mental health diagnostic surveys, the PHQ-9 (Depression) and the GAD-7 (Anxiety). We evaluate the responses of two text-based chatbots and four voice assistants to determine whether CAs provide relevant responses and treatments. Evaluations were conducted both by clinicians and immersively by trained raters, yielding consistent results across all raters. Although advice and recommendations were generally low quality, they were better for Crisis probes and for probes concerning symptoms of Anxiety rather than Depression. Responses were slightly improved for text versus speech-based agents, and when CAs had access to extended dialogue context. Design implications include suggestions for improved responses through clarification sub-dialogues. Responses may also be improved by the incorporation of empathy although this needs to be combined with effective treatments or advice.

The Effects of Health Care Chatbot Personas With Different Social Roles on the Client-Chatbot Bond and Usage Intentions: Development of a Design Codebook and Web-Based Study

BACKGROUND

The working alliance refers to an important relationship quality between health professionals and clients that robustly links to treatment success. Recent research shows that clients can develop an affective bond with chatbots. However, few research studies have investigated whether this perceived relationship is affected by the social roles of differing closeness a chatbot can impersonate and by allowing users to choose the social role of a chatbot.

OBJECTIVE

This study aimed at understanding how the social role of a chatbot can be expressed using a set of interpersonal closeness cues and examining how these social roles affect clients' experiences and the development of an affective bond with the chatbot, depending on clients' characteristics (ie, age and gender) and whether they can freely choose a chatbot's social role.

METHODS

Informed by the social role theory and the social response theory, we developed a design codebook for chatbots with different social roles along an interpersonal closeness continuum. Based on this codebook, we manipulated a fictitious health care chatbot to impersonate one of four distinct social roles common in health care settings-institution, expert, peer, and dialogical self-and examined effects on perceived affective bond and usage intentions in a web-based lab study. The study included a total of 251 participants, whose mean age was 41.15 (SD 13.87) years; 57.0% (143/251) of the participants were female. Participants were either randomly assigned to one of the chatbot conditions (no choice: n=202, 80.5%) or could freely choose to interact with one of these chatbot personas (free choice: n=49, 19.5%). Separate multivariate analyses of variance were performed to analyze differences (1) between the chatbot personas within the no-choice group and (2) between the no-choice and the free-choice groups.

RESULTS

While the main effect of the chatbot persona on affective bond and usage intentions was insignificant (P=.87), we found differences based on participants' demographic profiles: main effects for gender (P=.04, ηp2=0.115) and age (P<.001, ηp2=0.192) and a significant interaction effect of persona and age (P=.01, ηp2=0.102). Participants younger than 40 years reported higher scores for affective bond and usage intentions for the interpersonally more distant expert and institution chatbots; participants 40 years or older reported higher outcomes for the closer peer and dialogical-self chatbots. The option to freely choose a persona significantly benefited perceptions of the peer chatbot further (eg, free-choice group affective bond: mean 5.28, SD 0.89; no-choice group affective bond: mean 4.54, SD 1.10; P=.003, ηp2=0.117).

CONCLUSIONS

Manipulating a chatbot's social role is a possible avenue for health care chatbot designers to tailor clients' chatbot experiences using user-specific demographic factors and to improve clients' perceptions and behavioral intentions toward the chatbot. Our results also emphasize the benefits of letting clients freely choose between chatbots.

Power-Posing Robots: The Influence of a Humanoid Robot’s Posture and Size on its Perceived Dominance, Competence, Eeriness, and Threat

When interacting with sophisticated digital technologies, people often fall back on the same interaction scripts they apply to the communication with other humans—especially if the technology in question provides strong anthropomorphic cues (e.g., a human-like embodiment). Accordingly, research indicates that observers tend to interpret the body language of social robots in the same way as they would with another human being. Backed by initial evidence, we assumed that a humanoid robot will be considered as more dominant and competent, but also as more eerie and threatening once it strikes a so-called power pose. Moreover, we pursued the research question whether these effects might be accentuated by the robot’s body size. To this end, the current study presented 204 participants with pictures of the robot NAO in different poses (expansive vs. constrictive), while also manipulating its height (child-sized vs. adult-sized). Our results show that NAO’s posture indeed exerted strong effects on perceptions of dominance and competence. Conversely, participants’ threat and eeriness ratings remained statistically independent of the robot’s depicted body language. Further, we found that the machine’s size did not affect any of the measured interpersonal perceptions in a notable way. The study findings are discussed considering limitations and future research directions.

MICA: Motivational Interviewing Conversational Agent for Parents as Proxies for their Children in Healthy Eating (Preprint)

Background

Increased adoption of off-the-shelf conversational agents (CAs) brings opportunities to integrate therapeutic interventions. Motivational Interviewing (MI) can then be integrated with CAs for cost-effective access to it. MI can be especially beneficial for parents who often have low motivation because of limited time and resources to eat healthy together with their children.

Objective

We developed a Motivational Interviewing Conversational Agent (MICA) to improve healthy eating in parents who serve as a proxy for health behavior change in their children. Proxy relationships involve a person serving as a catalyst for behavior change in another person. Parents, serving as proxies, can bring about behavior change in their children.

Methods

We conducted user test sessions of the MICA prototype to understand the perceived acceptability and usefulness of the MICA prototype by parents. A total of 24 parents of young children participated in 2 user test sessions with MICA, approximately 2 weeks apart. After parents’ interaction with the MICA prototype in each user test session, we used qualitative interviews to understand parents’ perceptions and suggestions for improvements in MICA.

Results

Findings showed participants’ perceived usefulness of MICAs for helping them self-reflect and motivating them to adopt healthier eating habits together with their children. Participants further suggested various ways in which MICA can help them safely manage their children’s eating behaviors and provide customized support for their proxy needs and goals.

Conclusions

We have discussed how the user experience of CAs can be improved to uniquely offer support to parents who serve as proxies in changing the behavior of their children. We have concluded with implications for a larger context of designing MI-based CAs for supporting proxy relationships for health behavior change.

Is Artificial Intelligence Customer Service Satisfactory? Insights Based on Microblog Data and User Interviews

A growing number of sectors are delivering customer services powered by artificial intelligence (AI) instead of humans, with evidence indicating labor cost reduction and efficiency improvement. However, it would be worthwhile to examine the extent to which consumers are satisfied with AI service agents. In two studies based on an analysis of 17,673 Weibo data (Study 1) and 33 interviews (Study 2), we constructed a pair of theoretical models of consumer attitudes toward AI services: a sentiment model and an evaluation model. The results from Weibo data analysis showed that consumers display a stronger negative attitude toward AI customer service than toward their human counterparts. Complaints regarding AI customer service is mainly about its poor problem-solving ability, while untimely response and lack of human touch also dissatisfy customers. Whether consumers offer positive feedback mainly depends on voice traits and service attitudes. The results from the interviews confirm an overall negative attitude of consumers toward AI customer service. Consumers also recognize AI customer service agents as human like and social interaction stress relieving. Taken together, these findings reveal Chinese customers' attitudes toward AI service solutions and provide concrete suggestions for the development and upgrade of AI customer services.

An Artificial Intelligence Chatbot for Young People's Sexual and Reproductive Health in India (SnehAI): Instrumental Case Study

Background

Leveraging artificial intelligence (AI)–driven apps for health education and promotion can help in the accomplishment of several United Nations sustainable development goals. SnehAI, developed by the Population Foundation of India, is the first Hinglish (Hindi + English) AI chatbot, deliberately designed for social and behavioral changes in India. It provides a private, nonjudgmental, and safe space to spur conversations about taboo topics (such as safe sex and family planning) and offers accurate, relatable, and trustworthy information and resources.

Objective

This study aims to use the Gibson theory of affordances to examine SnehAI and offer scholarly guidance on how AI chatbots can be used to educate adolescents and young adults, promote sexual and reproductive health, and advocate for the health entitlements of women and girls in India.

Methods

We adopted an instrumental case study approach that allowed us to explore SnehAI from the perspectives of technology design, program implementation, and user engagement. We also used a mix of qualitative insights and quantitative analytics data to triangulate our findings.

Results

SnehAI demonstrated strong evidence across fifteen functional affordances: accessibility, multimodality, nonlinearity, compellability, queriosity, editability, visibility, interactivity, customizability, trackability, scalability, glocalizability, inclusivity, connectivity, and actionability. SnehAI also effectively engaged its users, especially young men, with 8.2 million messages exchanged across a 5-month period. Almost half of the incoming user messages were texts of deeply personal questions and concerns about sexual and reproductive health, as well as allied topics. Overall, SnehAI successfully presented itself as a trusted friend and mentor; the curated content was both entertaining and educational, and the natural language processing system worked effectively to personalize the chatbot response and optimize user experience.

Conclusions

SnehAI represents an innovative, engaging, and educational intervention that enables vulnerable and hard-to-reach population groups to talk and learn about sensitive and important issues. SnehAI is a powerful testimonial of the vital potential that lies in AI technologies for social good.

Improving User Experience of Virtual Health Assistants: Scoping Review

BACKGROUND

Virtual assistants can be used to deliver innovative health programs that provide appealing, personalized, and convenient health advice and support at scale and low cost. Design characteristics that influence the look and feel of the virtual assistant, such as visual appearance or language features, may significantly influence users' experience and engagement with the assistant.

OBJECTIVE

This scoping review aims to provide an overview of the experimental research examining how design characteristics of virtual health assistants affect user experience, summarize research findings of experimental research examining how design characteristics of virtual health assistants affect user experience, and provide recommendations for the design of virtual health assistants if sufficient evidence exists.

METHODS

We searched 5 electronic databases (Web of Science, MEDLINE, Embase, PsycINFO, and ACM Digital Library) to identify the studies that used an experimental design to compare the effects of design characteristics between 2 or more versions of an interactive virtual health assistant on user experience among adults. Data were synthesized descriptively. Health domains, design characteristics, and outcomes were categorized, and descriptive statistics were used to summarize the body of research. Results for each study were categorized as positive, negative, or no effect, and a matrix of the design characteristics and outcome categories was constructed to summarize the findings.

RESULTS

The database searches identified 6879 articles after the removal of duplicates. We included 48 articles representing 45 unique studies in the review. The most common health domains were mental health and physical activity. Studies most commonly examined design characteristics in the categories of visual design or conversational style and relational behavior and assessed outcomes in the categories of personality, satisfaction, relationship, or use intention. Over half of the design characteristics were examined by only 1 study. Results suggest that empathy and relational behavior and self-disclosure are related to more positive user experience. Results also suggest that if a human-like avatar is used, realistic rendering and medical attire may potentially be related to more positive user experience; however, more research is needed to confirm this.

CONCLUSIONS

There is a growing body of scientific evidence examining the impact of virtual health assistants' design characteristics on user experience. Taken together, data suggest that the look and feel of a virtual health assistant does affect user experience. Virtual health assistants that show empathy, display nonverbal relational behaviors, and disclose personal information about themselves achieve better user experience. At present, the evidence base is broad, and the studies are typically small in scale and highly heterogeneous. Further research, particularly using longitudinal research designs with repeated user interactions, is needed to inform the optimal design of virtual health assistants.

A growth mindset about human minds promotes positive responses to intelligent technology

Perceiving minds in technology agents, for example, robots designed with artificial intelligence (AI), is common and crucial in modern life. However, past studies have revealed that robots with a high level of minds elicit polarized responses. From a human-robot interaction perspective, we proposed that people' responses to robots, in part, originate from differences between fixed and growth mindsets about human minds-the beliefs regarding whether humans' mental capacities are fixed or incremental. We conducted five studies to test this assumption. A growth mindset about human minds was associated with or led to lower levels of negative feelings about robots (Study 1), more perceptions of robots as allies versus enemies (Study 2), more support for robotic research (Studies 3 and 4), and greater willingness to interact with robots (Study 5). Furthermore, the effect of a growth mindset about human minds on favorable responses to robots was more pronounced when robots were perceived as having high (versus low) levels of minds (Studies 3-5) and mediated by decreased concerns about robots (Study 5). By emphasizing the nuanced role of mindset beliefs about human minds in responses to intelligent technology, this research provides not only a new perspective on research into minds but also important implications for human-technology relationships.

Connect With Me. Exploring Influencing Factors in a Human-Technology Relationship Based on Regular Chatbot Use

Companion technologies, such as social robots and conversational chatbots, take increasing responsibility for daily tasks and support our physical and mental health. Especially in the domain of healthcare, where technologies are often applied for long-term use, our experience with and relationship to such technologies become ever more relevant. Based on a 2-week interaction period with a conversational chatbot, our study (N = 58) explores the relationship between humans and technology. In particular, our study focuses on felt social connectedness of participants to the technology, possibly related characteristics of technology and users (e.g., individual tendency to anthropomorphize, individual need to belong), as well as possibly affected outcome variables (e.g., desire to socialize with other humans). The participants filled in short daily and 3 weekly questionnaires. Results showed that interaction duration and intensity positively predicted social connectedness to the chatbot. Thereby, perceiving the chatbot as anthropomorphic mediated the interrelation of interaction intensity and social connectedness to the chatbot. Also, the perceived social presence of the chatbot mediated the relationship between interaction duration as well as interaction intensity and social connectedness to the chatbot. Characteristics of the user did not affect the interrelations of chatbot interaction duration or intensity and perceived anthropomorphism or social presence. Furthermore, we did not find a negative correlation between felt social connectedness of users to the technology and their desire to socialize with other humans. In sum, our findings provide both theoretical and practical contributions. Our study suggests that regular interaction with a technology can foster feelings of social connectedness, implying transferability of dynamics known from interpersonal interaction. Moreover, social connectedness could be supported by technology design that facilitates perceptions of anthropomorphism and social presence. While such means could help to establish an intense relationship between users and technology and long-term engagement, the contexts in which anthropomorphic design is, actually, the means of choice should be carefully reflected. Future research should examine individual and societal consequences to foster responsible technology development in healthcare and beyond.

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.

Human versus chatbot: Understanding the role of emotion in health marketing communication for vaccines

Based on the theoretical framework of agency effect, this study examined the role of affect in influencing the effects of chatbot versus human brand representatives in the context of health marketing communication about HPV vaccines. We conducted a 2 (perceived agency: chatbot vs. human) × 3 (affect elicitation: embarrassment, anger, neutral) between-subject lab experiment with 142 participants, who were randomly assigned to interact with either a perceived chatbot or a human representative. Key findings from self-reported and behavioral data highlight the complexity of consumer-chatbot communication. Specifically, participants reported lower interaction satisfaction with the chatbot than with the human representative when anger was evoked. However, participants were more likely to disclose concerns of HPV risks and provide more elaborate answers to the perceived human representative when embarrassment was elicited. Overall, the chatbot performed comparably to the human representative in terms of perceived usefulness and influence over participants' compliance intention in all emotional contexts. The findings complement the Computers as Social Actors paradigm and offer strategic guidelines to capitalize on the relative advantages of chatbot versus human representatives.