On the importance of social touch for senior individuals and ways to ameliorate social isolation:Lessons learned from the COVID-19 pandemic

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

Affective and Cognitive Reactions to Robot-Initiated Social Control of Health Behaviors

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

How Might Robots Change Us? Mechanisms Underlying Health Persuasion in Human-Robot Interaction from A Relationship Perspective:A Position Paper

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.</p

How Might Robots Change Us? Mechanisms Underlying Health Persuasion in Human-Robot Interaction from A Relationship Perspective:A Position Paper

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.</p

Benefits of Human-AI Interaction for Expert Users Interacting with Prediction Models:a Study on Marathon Running

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

Design of a game-based training environment to enhance mental health care professionals' skills in using e-mental health:Multiple methods user requirements analysis

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 identified, as well as the need to develop a diversity of suitable scenarios in the environment. User requirements analysis shows that the focus of a training environment should be on increasing knowledge about the possibilities of eMH, focusing on experiencing the benefits in particular situations, and building confidence in using eMH in a therapeutic setting. This requires careful consideration of the suitable game characteristics. Conclusions: Improvement of mental health care professionals’ skills in eMH requires an environment that is user driven and flexible, and simultaneously incorporates contextual factors that are relevant for its implementation in practice. This user requirements analysis contributes to the understanding of the issues that should be considered in the development of a game-based training environment. This shows that there are multiple and diverse learning needs among mental health care professionals. Various client populations, services, and situations demand various options for training. International Registered Report Identifier (IRRID): RR2-102196/1881

Effects of Sensory Information and Prior Experience on Direct Subjective Ratings of Presence

We report three experiments using a new form of direct subjective presence evaluation that was developed from the method of continuous assessment used to assess television picture quality. Observers were required to provide a continuous rating of their sense of presence using a handheld slider. The first experiment investigated the effects of manipulating stereoscopic and motion parallax cues within video sequences presented on a 20 in. stereoscopic CRT display. The results showed that the presentation of both stereoscopic and motion parallax cues was associated with higher presence ratings. One possible interpretation of Experiment 1 is that CRT displays that contain the spatial cues of stereoscopic disparity and motion parallax are more interesting or engaging. To test this, observers in Experiment 2 rated the same stimuli first for interest and then for presence. The results showed that variations in interest did not predict the presence ratings obtained in Experiment 1. However, the subsequent ratings of presence differed significantly from those obtained in Experiment 1, suggesting that prior experience with interest ratings affected subsequent judgments of presence. To test this, Experiment 3 investigated the effects of prior experience on presence ratings. Three groups of observers rated a training sequence for interest, presence, and 3-Dness before rating the same stimuli as used for Experiments 1 and 2 for presence. The results demonstrated that prior ratings sensitize observers to different features of a display resulting in different presence ratings. The implications of these results for presence evaluation are discussed, and a combination of more-refined subjective measures and a battery of objective measures is recommended

Using AI Methods for Health Behavior Change

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.</p

Using AI Methods for Health Behavior Change

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.</p

Theory-based Habit Modeling for Enhancing Behavior Prediction

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 (e.g., toothbrushing) 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 (BCSS), 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, built 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, where we trained participants to brush their teeth twice a day for three weeks and monitored their behaviors using accelerometers. Through hierarchical cross-validation, we found that for the task of predicting future brushing behavior, computed habit strength clearly outperformed self-reported habit strength (in both studies) and was also superior to models based on past behavior frequency (in the larger second study). Our findings provide initial support for our theory-based approach of modeling user habits and encourages the use of habit computation to deliver personalized and adaptive interventions