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The transition from home to formal residential care is described as stressful and emotionally difficult for people with dementia and their informal caregivers. While HCI research investigated how technology supports people with dementia at home or in formal care, there still is a need to understand how technology can support care transitions. This paper presents a practice-informed approach to gather insights collaboratively between care professionals and HCI researchers. We interviewed 42 informal caregivers of people with dementia to uncover their experiences before, during, and after care transitions. Our findings reveal how informal caregivers were: 1) navigating hurdles of information on care transitions, 2) caught up in the evolving challenges of informal caregiving, and 3) shifting from uncertainty in decision-making to acceptance of admission. Next, we formulate six design opportunities to support transitions in dementia care and encourage HCI researchers to pursue a practice-informed approach to address societal challenges in dementia.

Design ideation requires both creative and divergent thinking as well as collaboration and exchange to bring forth new insights and design possibilities. In this paper, we investigate such a collaboration with the use of emerging generative AI tools, and we explore how they may help in achieving agreement and semantic convergence between designers. We conducted workshops using Text-to-Image AI with design students and found that in addition to known advantages of brainstorming, the text prompts required by the AI system engaged students in verbal articulations that aided their design process. In collaborative contexts, the generated images shifted the centre of attention for team members to reach convergence through sharing their interpretations. We conclude that text-to-image AI can be beneficial for students’ design processes as a catalyst for brainstorming rather than a tool for generating design imagery and presentations.

This project explores the nuance of human-AI collaboration using domestic knitting machines alongside a simple, task-based, AI algorithm addressed through the research framework of more-than-human design. The textile design task is to use a broad range of knitting techniques to develop textile prototypes that mimic sea slugs, specifically the New Zealand nudibranchs Jason mirabilis and Dendrodoris krusensternii, both chosen for their colourful, organic looks. The final outcome consists of a series of textile swatches and prototypes along with a booklet documenting the digital patterns and thought processes of the development.

Health-related social control refers to intentional attempts to influence people's health behaviors, often seen in personal relationships. Social robots hold promise in influencing people's health by exerting health-related social control, but it is unclear which social control strategies used by robots are appropriate and potentially effective. This study investigates the effects of positive versus negative, and relationship-oriented versus target-oriented social control strategies from a social robot on people's psychological reactions. In an online video prototype study, participants viewed scenarios of a social robot attempting to change their sedentary behaviors by using different strategies. We found that positive (versus negative) strategies elicited stronger positive affect, enjoyment, and perceived social appropriateness, reduced perceived threats to freedom, and strengthened behavioral intention. Meanwhile, the relationship-oriented (versus target-oriented) strategies elevated people's negative affect, reduced enjoyment and perceived appropriateness, elevated perceived threats to freedom, and weakened behavioral intentions. Given these findings, we give recommendations for designing health influence strategies in social robots.

During the pre-design phase of supportive intelligent systems, interviews with future users, such as people with dementia, aim to explore the problems for which technology may present a solution. However, interviewing someone with dementia in this unstructured design phase is reported to be challenging resulting in systems that are not truly addressing the needs of the people with dementia. Existing guidelines on creating engaging interviews are often based on post-hoc reflections of researchers and participants. In this study we performed line-to-line sequential in-depth analyses on transcripts of 10 interviews conducted to co-design an interactive intelligent gaming system. Both interviewers and interviewees were subsequently asked to reflect on their experiences. Results show that information exchange with people with dementia can be supported by carefully constructed questions, well-designed probes, and an appropriate length of the interview. To create a pleasant atmosphere during the interviews, researchers should design the interview as a fulfilling experience, be prepared to respond respectfully to negative responses and be able to adapt to different signs of consent. Improving the engagement with people with dementia during interviews can enhance the design and development of intelligent systems that cater to their unique challenges and requirements.

Users with large domain knowledge can be reluctant to use prediction models. This also applies to the sports domain, where running coaches rarely rely on marathon prediction tools for race-plan advice for their runners’ next marathon. This paper studies the effect of adding interactivity to such prediction models, to incorporate and acknowledge users’ domain knowledge. In think-aloud sessions and an online study, we tested an interactive machine learning tool that allowed coaches to indicate the importance of earlier races feeding into the model. Our results show that coaches deploy rich knowledge when working with the model on runners familiar to them, and their adaptations improved model accuracy. Those coaches who could interact with the model displayed more trust and acceptance in the resulting predictions.

Advancements in artificial intelligence and (social) robotics raise pertinent questions as to how these technologies may help shape the society of the future. The main aim of the chapter is to consider the social and conceptual disruptions that might be associated with social robots, and humanoid social robots in particular. This chapter starts by comparing the concepts of robots and artificial intelligence, and briefly explores the origins of these expressions. It then explains what a social robot is, as well as what humanoid robots are. A key notion in this context is the idea of anthropomorphism: the human tendency to attribute human qualities, not only to our fellow human beings, but also to parts of nature and to technologies. This tendency to anthropomorphize technologies by responding to and interacting with them as if they have human qualities is one of the reasons why social robots – in particular social robots designed to look and behave like human beings – can be socially disruptive. As is explained in the chapter, while some ethics researchers think that anthropomorphization is a mistake that can lead to various forms of deception, others – including both ethics researchers and social roboticists – believe it can be useful or fitting to treat robots in anthropomorphizing ways. The chapter explores that disagreement by, among other things, considering recent philosophical debates about whether social robots can be moral patients; that is, whether it can make sense to treat them with moral consideration. Where one stands on this issue will depend on one’s views about whether social robots can have, imitate, or represent morally relevant properties, or on how people relate to social robots in their interactions with them. Lastly, the chapter urges that the ethics of social robots should explore intercultural perspectives, and highlights some recent research on ubuntu ethics and social robots.

The application of social robots in persuading people to change health behaviors is an increasing research topic. However, little is known in what ways, and under what conditions, effective health persuasion can be achieved in human-robot interaction (HRI). This position paper presents a conceptual model that integrates interpersonal relationship theories to postulate a mechanism through which social robots can change people’s health behaviors. In this paper, we first briefly describe the two interpersonal relationship theories we selectively focus on, namely social control and interdependence theory, and we discuss the possibility of people forming relationships with social robots. Then, we propose the conceptual model depicting the potential positive and negative influence of social robots’ health persuasion on people’s psychological and behavioral reactions and the modulating role of human-robot relationships. Finally, we discuss the implications of this model for future research.

Artificial intelligence (AI) has been applied to health behavior change research for over a decade. Current research programs include machine learning for delivering just-in-time adaptive interventions, computational modeling of behavior change processes, and the use of social AI for communication and persuasion. With new advances in AI, we propose an international workshop to bring together experts from all related disciplines to discuss and explore the potentials of AI for behavior change research. We discuss in this proposal the aims, planned activities, expected outcomes, and a promotion strategy for the workshop.

The need for touch exists below the horizon of consciousness. Interpersonal touch, or gentle physical contact between individuals, is an important aspect of human social interaction and has been shown to have numerous benefits for senior individuals. One of the main ways that interpersonal touch can benefit senior individuals is by reducing feelings of loneliness and social isolation. The outbreak of COVID-19 necessitated social distancing measures to mitigate the negative health consequences of the pandemic, which were particularly pronounced amongst vulnerable populations, especially those who live with dementia. At the same time, seniors’ psychosocial wellbeing was compromised as opportunities for interpersonal touch became severely restricted. In this paper, we share what we have learned during the pandemic both on the critical importance of social touch for senior individuals, as well as on innovative ways to ameliorate the forced absence of social touch. Through a targeted literature review and an online survey study, our findings highlights the fact that social relationships and physical contact are key to personal wellbeing and underline the importance of social touch at an advanced age.

As a consequence of the outbreak of the COVID-19 global pandemic in the spring of 2020, large-scale social distancing measures were implemented, resulting in the forced adoption of online or digital forms of psychological treatment. This sudden transition to digital care offered a unique opportunity to investigate if and how this experience impacted mental healthcare professionals’ perceptions and use of Digital Mental Health tools. The current paper presents findings of a repeated cross-sectional study consisting of three iterations of a national online survey in the Netherlands. This survey contained open and closed questions on professionals’ adoption readiness, frequency of use, perceived competency, and perceived value of Digital Mental Health collected in 2019 (before the pandemic), in 2020 (after the first wave), and in 2021 (after the second wave). The inclusion of data gathered prior to the COVID-19 pandemic offers a unique window to assess how professionals’ adoption has developed through this transition from voluntary to mandatory use of Digital Mental Health tools. Our study also re-assesses the drivers, barriers, and needs of mental healthcare professionals after having gained experience with Digital Mental Health. In total, 1,039 practitioners completed the surveys (Survey 1: n = 432, Survey 2: n = 363, and Survey 3: n = 244). Results indicate that compared to the period before the pandemic, there was a particularly large increase in use, competency, and perceived value regarding videoconferencing. Small differences were also found for some other basic tools that were crucial to ensure the continuation of care, such as e-mail, text messaging, and online screening, but not for more innovative technologies, such as virtual reality and biofeedback. Many practitioners reported to have gained skills regarding Digital Mental Health and experienced several benefits of it. They expressed the intention to continue with a blended approach, using Digital Mental H

Social touch is essential to human development and communication. Mediated social touch is suggested as a solution for circumstances where distance prevents skin-to-skin contact. However, past research aimed at demonstrating efficacy of mediated touch in reducing stress and promoting helping have produced mixed findings. These inconsistent findings could possibly be due to insufficient control of contextual factors combined with unnatural interaction scenarios. For example, touch occurs less frequently among strangers and is often accompanied with nonverbal visual cues. We investigated how visual presentation of touch, and interpersonal relationship to the sender influence perception, affective experiences, and autonomic responses the touch evoke. Fifty couples of mixed gender were recruited. A mediated touch was repeatedly applied by either the male partner or male confederate to female participants. The latter witnessed through a webcam as the sender caressed a rubber hand or touchpad to send the touch. Following our hypotheses, touch sent by one's partner was perceived softer and more comforting than stranger touch. The partner's touch also resulted in weaker skin conductance responses, particularly when sent by touching a touchpad. In sum, how a mediated touch is experienced depends both on who is touching, and on how the touch is visually represented.

As a consequence of the outbreak of the COVID-19 global pandemic in the spring of 2020, large-scale social distancing measures were implemented, resulting in the forced adoption of online or digital forms of psychological treatment. This sudden transition to digital care offered a unique opportunity to investigate if and how this experience impacted mental healthcare professionals’ perceptions and use of Digital Mental Health tools. The current paper presents findings of a repeated cross-sectional study consisting of three iterations of a national online survey in the Netherlands. This survey contained open and closed questions on professionals’ adoption readiness, frequency of use, perceived competency, and perceived value of Digital Mental Health collected in 2019 (before the pandemic), in 2020 (after the first wave), and in 2021 (after the second wave). The inclusion of data gathered prior to the COVID-19 pandemic offers a unique window to assess how professionals’ adoption has developed through this transition from voluntary to mandatory use of Digital Mental Health tools. Our study also re-assesses the drivers, barriers, and needs of mental healthcare professionals after having gained experience with Digital Mental Health. In total, 1,039 practitioners completed the surveys (Survey 1: n = 432, Survey 2: n = 363, and Survey 3: n = 244). Results indicate that compared to the period before the pandemic, there was a particularly large increase in use, competency, and perceived value regarding videoconferencing. Small differences were also found for some other basic tools that were crucial to ensure the continuation of care, such as e-mail, text messaging, and online screening, but not for more innovative technologies, such as virtual reality and biofeedback. Many practitioners reported to have gained skills regarding Digital Mental Health and experienced several benefits of it. They expressed the intention to continue with a blended approach, using Digital Menta

Biosensing technologies have advanced to a level where measurements of our physiology have become easily accessible, and new areas of application are being explored, including interpersonal communication via biosignals. To date, it is largely unknown what the communication characteristics of biosignal sharing are and how they influence experiences of biosignal-mediated interactions. By conceptualizing biosignal sharing as a new communication medium, we try to benefit from earlier theories and models that help describe and understand the communication properties that are inherently part of biosignal sharing, but have not previously been examined in this way. In this paper, we review the published literature on biosignal sharing and analyze the current state of the art using two complementary methods of analysis. First, we extend an existing framework of relevant communication dimensions and apply this to examine the characteristics of biosignal sharing systems. Second, we perform a thematic analysis on user experiences related to biosignal sharing available from literature. Then, we combine these findings and link the communication characteristics to the experiences of biosignal sharing. We discuss our findings, elucidating the way in which biosignal sharing affects the interpersonal communication process. Based on these insights, we provide several considerations for future research and design.

Autonomous mobile robots that operate in environments with people are expected to be able to deal with human proxemics and social distances. Previous research investigated how robots can approach persons or how to implement human-aware navigation algorithms. However, experimental research on how robots can avoid a person in a comfortable way is largely missing. The aim of the current work is to experimentally determine the shape and size of personal space of a human passed by a robot. In two studies, both a humanoid as well as a non-humanoid robot were used to pass a person at different sides and distances, after which they were asked to rate their perceived comfort. As expected, perceived comfort increases with distance. However, the shape was not circular: passing at the back of a person is more uncomfortable compared to passing at the front, especially in the case of the humanoid robot. These results give us more insight into the shape and size of personal space in human–robot interaction. Furthermore, they can serve as necessary input to human-aware navigation algorithms for autonomous mobile robots in which human comfort is traded off with efficiency goals.

Model-agnostic explainable AI tools explain their predictions by means of ’local’ feature contributions. We empirically investigate two potential improvements over current approaches. The first one is to always present feature contributions in terms of the contribution to the outcome that is perceived as positive by the user (“positive framing”). The second one is to add “semantic labeling”, that explains the directionality of each feature contribution (“this feature leads to +5% eligibility”), reducing additional cognitive processing steps. In a user study, participants evaluated the understandability of explanations for different framing and labeling conditions for loan applications and music recommendations. We found that positive framing improves understandability even when the prediction is negative. Additionally, adding semantic labels eliminates any framing effects on understandability, with positive labels outperforming negative labels. We implemented our suggestions in a package ArgueView[11].

Persuasion aims at changing peoples’ motivations and/or behaviors. This study explores how and when physiology reflects persuasion processes and specifically whether individual differences in motivations and behaviors affect psychophysiologic reactions to persuasive information. Participants (N = 70) with medium or high meat consumption patterns watched a persuasive video advocating limited meat consumption, while their electrodermal and cardiovascular physiology was measured. Results indicated that the video increased participants’ moral beliefs, perceived behavioral control, and reduction intentions. This study also found an increase in physiologic arousal during the persuasive video and that people with motivations less aligned to the persuasion objective had more physiologic arousal. The findings encourage further psychophysiologic persuasion research, especially as these insights can potentially be used to personalize persuasive messages of behavior change applications.

Measurement of physiological reactions to persuasive messages can improve our understanding of psychological processes of persuasion, and potentially further enhance and personalize current persuasion interventions. However, little is known about the relationship between psychophysiology and persuasive processes. This study focused on four persuasion principles: scarcity, commitment, consensus, and authority, and people's susceptibility to them. Physiological measures included the cardiovascular, respiratory, and electrodermal system, as well as facial motor systems. Psychological measures consisted of self-reported attitude towards oral care and susceptibility to persuasion (STPS). We performed a randomized within-subject experiment in which fifty-six participants viewed persuasive messages deploying the aforementioned persuasion principles to improve their oral care. Results indicated different physiological patterns during persuasion versus rest. We found no different physiological patterns in exposure to distinct persuasion principles, nor a clear correlation with susceptibility to individual persuasion principles. However, mixed model analysis illustrated that overall STPS scores help explain variance in reactivity of skin conductance level and skin conductance response, and reactivity in the zygomaticus major: lower susceptibility relates to higher reactivity. Summarizing, we have found no conclusive support for distinct psychophysiological patterns associated with different persuasion principles, although overall susceptibility seems to be reflected in physiology to some extent.

Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment. Given the reciprocal relationship between habit and behavior, changing lifestyle behaviors is largely a task of breaking old habits and creating new and healthy ones. Thus, representing users’ habit strengths can be very useful for behavior change support systems, for example, to predict behavior or to decide when an intervention reaches its intended effect. However, habit strength is not directly observable and existing self-report measures are taxing for users. In this paper, building on recent computational models of habit formation, we propose a method to enable intelligent systems to compute habit strength based on observable behavior. The hypothesized advantage of using computed habit strength for behavior prediction was tested using data from two intervention studies on dental behavior change (N= 36 and N= 75), where we instructed participants to brush their teeth twice a day for three weeks and monitored their behaviors using accelerometers. The results showed that for the task of predicting future brushing behavior, the theory-based model that computed habit strength achieved an accuracy of 68.6% (Study 1) and 76.1% (Study 2), which outperformed the model that relied on self-reported behavioral determinants but showed no advantage over models that relied on past behavior. We discuss the implications of our results for research on behavior change support systems and habit formation.

Tracking health data, for example, through wearable devices or health apps, is increasingly commonplace. Consequently, health coaches (e.g., personal trainers, dieticians) are facing growing numbers of clients who bring their data to the clinic. These data potentially add value to the coaching process, for example, by showing more objective and specific information on clients' behaviors. However, in practice, it turns out to be hard to effectively utilize health data in a coaching setting, and it is not yet fully understood how data affect the coaching process and the coach-client communication. We organized a workshop (12 coaches, 3 clients) and a field study (5 coaches, 6 clients), where we observed coach-client interactions enriched with data. By including both familiar and unfamiliar coach-client pairs, as well as alternating the timing of the data presented (i.e., at the beginning, or halfway through the session), we acquired a variety of data-driven coaching interactions and analyzed this using a mixture of qualitative and quantitative methods. Our results show that data are not “plug-and-play.” There is an extensive process of interpreting and contextualizing data, in which the client has a key role, which is essential to gain relevant and actionable insights from the data useful to the coaching process. We also observed that data affect the coach-client communication on both content and relationship levels. We will reflect on these insights in terms of design recommendations for wearable tracking devices and e-health technology to effectively support health coaches and their interactions with their clients.

Persuasive technology (PT) can assist in behavior change. PT systems often rely on user models, based on behavior and self-report data, to personalize their functionalities and thereby increase efficiency. This review paper shows how physiological measurements could be used to further improve user models for personalization of PT by means of bio-cybernetic loops and data-driven approaches. Furthermore, we outline the advantages of using physiological measures for personalization compared to self-report and behavior measurement. Additionally, we show how two types of physiological information—physiological states and physiological reactivity—can be relevant for PT adaptations. To illustrate this, we present a model with two types of physiology-based PT adaptations as part of a bio-cybernetic loop; state-based and reactivity-based. Next, we discuss the implications of physiology-aware PT for persuasive design and theory. And lastly, because of the potential impact of such systems, we also consider important ethical implications of physiology-aware PT.

Physiological responses to persuasion can help to increase our understanding of persuasive processes, and thereby the effectiveness of persuasive interventions. However, a clear relationship between psychophysiology and persuasion is not yet established. This study investigates if peripheral physiology predicts persuasion effectiveness, and whether peripheral physiology yields information that is not represented in other predictors of persuasion. We studied physiological reactions in the cardiovascular, electrodermal, facial muscle and respiratory systems of 75 participants while they read gain- or loss-framed persuasive messages advocating increased oral health care behavior. Persuasion effectiveness was measured as pre to post intervention changes in self-reported attitudes and intentions, as well as through changes in behavioral compliance over three weeks. Overall, participants showed stronger attitudes and intentions directly after the intervention (short-term persuasion), but did not show changes in behavior or attitudes two weeks later (no long-term persuasion). On an individual level, physiological reactivity parameters yielded additional information next to self-report measures to predict persuasion effectiveness on attitude, intention and behavioral compliance. To conclude, our findings suggested a positive relationship between physiological reactivity to persuasive messages and subsequent attitudes, intentions and behavior, and quantified the extra personalization that psychophysiological measures can bring to persuasive messaging

Background: A major factor hampering the adoption of technology in mental health care is a lack of knowledge and skills. Serious gaming offers a potentially effective strategy to enhance the skills needed through experiencing and learning-by-doing in a playful way. However, serious gaming solutions are not widely available for mental health care. Therefore, the development of a game-based training environment in mental health care was pursued in a design project. The first step in such a design project is to identify user requirements that should be met. Objective: This study aims to deliver user requirements that inform the design of a game-based training environment for mental health care professionals. This environment aims to support mental health care professionals’ knowledge and skill enhancement regarding the use of e–mental health (eMH); for example, video calling, mobile apps, web-based treatment modules, and techniques such as virtual or augmented reality. Methods: We used an exploratory multiple methods design consisting of a web-based questionnaire, co-design sessions, and interviews. To ensure a good representation of the target user group, professionals from various disciplines within mental health care were included in the research. The multiple methods design facilitates a broad view of user needs and in-depth knowledge of specific design requirements. We describe the protocol for this research project in a protocol paper published in the JMIR Research Protocols in February 2021. Results: The user requirements analysis revealed three types of users for the envisioned game-based training environment: mental health care professionals who want to learn about the basic possibilities of eMH, mental health care professionals who want to develop their eMH skills to the next level, and mental health care professionals who want to experiment with new technologies. This reflects the diversity of needs that were identi

Affective haptic devices (AHDs) have been developed with the aim of communicating touch acts, symbolic messages, emotions, and/or providing a sense of social awareness. Within AHDs, three categories can be distinguished: mediated social touch (MST), symbolic communication systems, and awareness systems. For each of these categories, prototypes have been developed and discussed in the literature. Each such prototype, however, describes but a small part of the design space of AHDs. What is lacking is a description of the design space itself—of all choices that can be considered during the design process. Such a description will allow for a more systematic exploration of AHD designs and provides a means of combining insights gained from individual point solutions (i.e., existing prototypes). Therefore, in this article, we provide a systematic description of the design space of AHDs and its underlying dimensions based on general (e.g., revisability or synchronicity) and AHD-specific (e.g., actuation type) communication system characteristics. This resulted in 17 design dimensions, each consisting of two or more categories (the design characteristics). Based on a systematic literature search from devices up to 2019, 89 AHD prototypes were identified, and each was classified on the design dimensions. The empirical analysis of where these AHDs are located in the design space revealed, first, that potentially interesting characteristics from mediated communication, such as revisability and reviewability, are underexplored in AHDs. Second, MST devices were found to often lack those system characteristics, such as real-time modalities, that seem crucial for providing the affordances needed to simulate social touch. In particular, when comparing symbolic and awareness devices to MST devices, we found the latter to more frequently lack some of the key characteristics of face-to-face communication (i.e., bi-directional and symmetric communication). Limitations and implication

Affective haptic devices (AHDs) are communication technologies utilizing the sense of touch, and include mediated social touch (MST), symbolic haptic messaging, and awareness systems that, for example, let one feel another person's heartbeat. The COVID-19 pandemic and consequent social distancing measures have led to a reemphasis of the importance of social touch, and many people have experienced firsthand what it is like to miss touching loved ones. This offers an excellent opportunity to study people's intention to use AHDs. For this purpose, a survey study (n = 277) was conducted combining qualitative and quantitative data analysis methods. Touch deprivation, resulting from not being able to touch a loved one, was associated with intention to use AHDs: the more deprived an individual, the higher his or her intention to use AHDs. Technology readiness and touch aversion did not affect intention to use AHDs. AHDs for symbolic messaging gained higher interest than MST and awareness devices, and long-distance relationships were seen as the most likely scenario for using AHDs. Bi-directionality, synchronicity, and symmetry were regarded as important features for providing shared meaning and a sense of connectedness. Reviewability, multimodality, and actuation type were also deemed important. Limitations of the study and implications for the design of AHDs are discussed.