Motivating Health Behavior Change with Humorous Virtual Agents

Abstract

Research on the psychology of humor indicates that humor can increase the impact of persuasive messages in certain circumstances, suggesting that virtual agents could use humor to improve the effectiveness of motivational counseling with users. To explore this idea, we developed two virtual agents that attempt to motivate users to perform healthy behaviors ś either to increase exercise or fruit and vegetable consumption ś and systematically vary whether they use humor or not in their counseling conversations. Humanauthored jokes were selected from a large corpus based on the nature of the humor (empathetic affiliative humor that serves to build relational closeness), health topic relevance, and subjective ratings of funniness, as well as relevance to user stage of behavior change. We evaluated our agents in a two-treatment counterbalanced within-subjects experiment, where participants interacted with a humorous and non-humorous agent motivating either exercise or healthy diet. We found the interaction with the humorous agent led to a significantly greater change in motivation to engage in the target behavior than interacting with the non-humorous agent.

Can a Humorous Conversational Agent Enhance Learning Experience and Outcomes?

“…Similar to prior work (e.g., [44]), our results illuminate the importance of the user's personality characteristics and how they interact with the agent's-indicating that care must be taken in the design of teachable agents, with a one-size-fits-all not always being the most successful when it comes to humour, but can lead to enhanced learning experience and outcomes when matched appropriately. Self-defeating jokes appear to evoke empathy and increase effort in learners with more aggressive, self-defeating, or affiliative humour styles, improved the confidence in teaching of learners with a selfdefeating humour style themselves, but negatively impacted the experience of learners with more of a self-enhancing humour style.…”

“…In robots, four forms of humour (wit and an ice-breaker, corny jokes, subtle humour, and dry humour and self-deprecation) are suggested to enhance sociality of a robot [30], and innocent humour (riddles and punning jokes) was found to improve perception of task enjoyment, robot personality, and speaking style [41]. In interactions with virtual agents, conversational and situation-specific jokes have been found to affect how cooperation is perceived in an agent [32], humour is proposed as a means of recovering from error situations while providing a pleasant user experience [42], and affiliative humour has been shown to significantly motivate healthy behaviours [44].…”

A Mobile Application-Based Relational Agent as a Health Professional for COVID-19 Patients: Design, Approach, and Implications

“…RAs are described as virtual agents (VAs) or computational artifacts that are tasked with the responsibility of maintaining virtual socio-emotional relationships with their users over a protected period [ 18 ]. Earlier research has shown that VAs can build rapport with patients that can be useful in screening patients for various diseases, especially those that are stigmatized [ 19 , 20 ]; facilitating self-management by educating, advising, and helping patients adopt and maintain healthy habits [ 21 ]; and promoting mental health through counseling and therapy [ 22 , 23 ]. VAs can overcome many shortcomings of traditional telehealth, including the potential unavailability of HCPs in time of crisis — unlike HCPs, VAs are always available on users’ mobile devices.…”

Mia

“…Such limited-option based user response style has been also adopted in recent work with MI-based virtual agents [5,54]. Chatbot Agent Selection: Recent research has started exploring MI-based virtual agents (e.g., chatbot, embodied agent, social robots) [5,54,55] for evoking habit changes. However, in those setups, user interaction is still processed mostly as text-based input, if not through Wizard-of-Oz.…”

“…One reason is that MI is comparatively a new agent property, thus close-ended interaction allows more control over the human-machine interaction. Following this style, recent research with MI-agents successfully improved habits such as maintaining a healthy diet [54,55]. Besides agent performance, understanding task performance in terms of what strategies users apply during the actual decision-making process can also be helpful to iterate on the suggestive feedback component of the agents.…”