Social robots are increasingly applied as health behavior change interventions, yet actionable knowledge to guide their design and evaluation remains limited. This systematic review synthesizes (1) the behavior change strategies used in existing HRI studies employing social robots to promote health behavior change, and (2) the evaluation methods applied to assess behavior change outcomes. Relevant literature was identified through systematic database searches and hand searches. Analysis of 39 studies revealed four overarching categories of behavior change strategies: coaching strategies, counseling strategies, social influence strategies, and persuasion-enhancing strategies. These strategies highlight the unique affordances of social robots as behavior change interventions and offer valuable design heuristics. The review also identified key characteristics of current evaluation practices, including study designs, settings, durations, and outcome measures, on the basis of which we propose several directions for future HRI research.

This study investigates how interaction scenarios of human caregiving for robots affect humans’ perceived bond with robots. In a between-subjects lab experiment (n = 88), participants played a game with a social robot during which they provided either 1) emotional care (comforting the robot); 2) instrumental care (helping with battery charging); or 3) no care for the robot. Results indicated that caregiving did not significantly affect human-robot bonding according to explicit relationship measures including closeness, social attraction, or desire for future interaction. However, caregiving mattered when bonding was measured implicitly. Those in the emotional caregiving scenario were more hesitant to replace the robot and invested more effort in a voluntary task requested by the robot than those who provided no care. These findings provide empirical evidence that emotional caregiving interactions can effectively foster initial human-robot bonding, highlighting a promising design scenario for human-robot interaction.

To design social robots to effectively promote health behavior change, it is essential to understand how people respond to various health communication strategies employed by these robots. This study examines the effectiveness of two types of social control strategies from a social robot-relationship-focused strategies (emphasizing relational consequences) and target-focused strategies (emphasizing health consequences)-in encouraging people to reduce sedentary behavior. A two-session lab experiment was conducted (n = 135), where participants first played a game with a robot, followed by the robot persuading them to stand up and move using one of the strategies. Half of the participants joined a second session to have a repeated interaction with the robot. Results showed that relationship-focused strategies motivated participants to stay active longer. Repeated sessions did not strengthen participants' relationship with the robot, but those who felt more attached to the robot responded more actively to the target-focused strategies. These findings offer valuable insights for designing persuasive strategies for social robots in health communication contexts.

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.

It has been shown that providing explanations about AI-based systems’ decisions can be an effective way to increase users’ trust and acceptance. The effect of explanation design in smart home systems on users’ acceptance and perceptions is however less known. We therefore explored the effect of different explanation designs on acceptance in the context of the Philips Hue smart home lighting system. We conducted interviews (N = 10) and an online experiment (N = 452) using three everyday smart home lighting scenarios with different explanation types. The results showed that although participants indicated a positive attitude towards explanations, receiving an explanation can potentially reduce the perceived control of the lighting system. Furthermore, participants preferred system-based explanations rather than user-based explanations. Our study also provides recommendations for the design of explanations in smart home systems.

Anxiety in children is gradually becoming a problem that needs to be addressed urgently. Socially Assistive Robots (SARs) have shown great potential in anxiety treatment among adults and elders. However, the application on childhood anxiety has scarcely been tested. Autonomy is also an influential factor of psychological therapy sessions in children. This study, using state anxiety as a kick-off point, aims to investigate the effectiveness of SARs and the role of autonomy in reducing children's anxiety by using Progressive Muscle Relaxation (PMR). Participants were 69 Chinese children aged from 10 to 12. We found that SARs significantly reduced state anxiety levels in all conditions. But no difference between each level of intervention and autonomy was found. Further research on various perspectives is suggested.

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 article 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 might bring to persuasive messaging.

Abstract 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 ( $$N = 36$$ N=36 and $$N = 75$$ 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.