Robot Companion for Better Wellbeing

Globally, depression affects more than 264 million people of all ages, and is a leading cause of disability worldwide. Several interactive technologies for mental health have been developed to make various therapeutic services (e.g. psycho-education, health monitoring, clinical assessment, etc.) more accessible and scalable. However, most of them are designed to engage users only within the therapy/intervention tasks. In this project, we present a social robot that delivers interactive positive psychology interventions designed to improve people's psychological wellbeing, and provides other useful skills to build rapport with people over time in their homes. Our previous work showed that college students showed a significant improvement in psychological wellbeing, mood, and motivation to change after completing seven positive psychology interactions with the robot. However, we also found that students' personality traits were also shown to be associated with the intervention outcomes as well as their working alliance with the robot and their satisfaction with the interventions. Also, students' working alliance with the robot was shown to be associated with their pre-to-post change in motivation for better wellbeing. 

Based on these results, we are investigating how a social robot can support highly neurotic people better by delivering the mental health interventions in a non-threatening and socially supportive manner. We present a new way for social agents to deliver mental health interventions in a companion-like style, in which the agent uses gentler language to prompt users to engage in the intervention. In this new style, the robot does not instruct the human user but demonstrates the intervention activity first and invites him/her to join in, instead of using explicit directives for instructions. A eight-week home deployment study is currently conducted with people living in the U.S. in order to compare the efficacy of this new companion-like style with the traditional coach-like intervention style. Results from our study will give insight into design guidelines for personalizing interactive technologies' intervention and behaviors based on users' traits and behavioral cues for better mental health outcomes.