Social robots have been used to promote health education and coaching to provide health information. Important behaviors to address and monitor include actions that can be modified, such as physical activity. These behaviours often require different personalised recommendations. Robots could be an effective way to give personalised health feedback based on scores, including in acute medical settings. This trial involved an automated social robot interaction in a health clinic to collect health data and provide personalized feedback on four key factors: exercise, diet, alcohol and cigarette use. Patients completed an 20-minute health questionnaire with a Pepper Robot in a clinic room during a health visit. The interaction was programmed to run autonomously with automatic scoring and feedback based on health scores. Instructions were delivered using co-verbal speech and detailed text on the tablet. Questions also included ratings on comfort to discuss health topics with a human or robot. Patients could choose to receive an optional follow-up in four weeks’ time. A total of 47 patients completed the session. Patients reported being as comfortable to discuss health-related topics with a robot or human for exercise, diet, alcohol, cigarette use, and mental health. Program evaluation received moderate ratings for the robot on ease of use, usefulness and motivation to change a health behavior. No significant health changes were found 30 days later due to high initial health scores, leaving little room for improvement. This initial proof-of-concept trial found that a robot-delivered service could be deployed in a live health clinic in conjunction with patient visits.

Social robots have been used to help people to make healthy changes, and one setting that could benefit from having more support services offered includes the higher education sector. This trial involved an initial test to explore how a social robot could help to deliver a low-intensity problem-solving session for students around study-related issues and challenges. A Pepper Humanoid Robot was deployed in a student centre to help students to build a problem-solving plan on a specific issue. In the trial, 72 students gave detailed responses to session questions for issues such as procrastination, life/study balance and study workload. Students reported good ratings for emotional reaction to the robot, perceived utility, intention to use the robot again, confidence to use the robot, perceived helpfulness from the robot, likelihood to use the robot for a new higher education issue, and to recommend the robot to a friend. Robot evaluation scores were correlated with scores on perceived helpfulness of the robot and confidence to try an idea in the next week. Students who reported positive robot evaluation scores were also more willing to use the session content and rate the content as helpful. One week later, most students reported that the robot session helped them to fix their chosen issue, and that they used at least one idea from the session. Overall, this study found that a session run by a social robot could provide support for a study-related issue or challenge, and that some students did receive benefit from the session content. Future studies could include enhancements and adaptations to session length, technical refinement and capacity to address new issues during the session.

This paper reports the design and qualitative evaluation of a social robot programmed to deliver a talk-based treatment program to improve health behaviour change for food intake and weight loss. A qualitative study was conducted to investigate factors that influenced human-robot interaction and its relationship to health treatment outcomes. Semi-structured interviews were undertaken on completion of a randomised controlled trial that used an autonomous robot to deliver a 4-week behavioral intervention to help coach people to decrease the consumption of high calorie foods. Questions focused on individuals’ preferences, learnings and outcomes from their participation in the trial. Twenty participants completed the treatment, and 18 conducted an interview. Content analysis found that a social robot to deliver a psychotherapeutic treatment was effective and feasible. Participants did make changes to their health behaviour change with a >50% reduction in high calorie intake and average reduction of 4.4 kilograms in weight loss. The robot received positive evaluations on its interactive nature and sociable persona. Most participants made improvements that were aligned with their chosen health goal after completing the robot-delivered sessions, and reported that the robot sessions helped them to achieve their behaviour change goals, such as consuming fewer high calorie foods. Detailed recommendations are provided for the future design of healthcare interventions by robots, including key considerations for robot behaviour, treatment content, and presentation of the program. Future recommendations are presented for the development of robot personalization to more closely resemble techniques and skills from client-centred counselling.

An individual's self-efficacy to interact with a robot has important implications around the content, utility and success of the interaction. Individuals need to achieve a high level of self-efficacy in human robot-interaction in a reasonable time-frame for positive effects to occur in shortterm human-robot scenarios. This trial explored the impact of a 2-minute automated robot-delivered tutorial designed to teach people from the general public how to use the robot as a method to increase robot self-efficacy scores. This trial assessed scores before (T1) and after (T2) an interaction with the robot to investigate changes in self-efficacy, likability and willingness to use it. The 40 participants recruited had on average very low level of robotic experience. After the tutorial, people reported significantly higher robot self-efficacy with very large effect sizes to operate a robot and apply the robot to a task (η2p= 0.727 and 0.660). Significant increases in likability and willingness to interact with the robot were also found (η2p = 0.465 and 0.480). Changes in likability and self-efficacy contributed to 64% of the variance in changes to willingness to use the robot. Initial differences were found in robot self-efficacy for older people and those with less robotics and programming experience compared with other participants, but scores across these subgroups were similar after completion of the tutorial. This demonstrated that high levels of self-efficacy, likeability and willingness to use a social robot can be reached in a very short time, and on comparable levels, regardless of age or prior robotics experience. This outcome has significant implications for future trials using social robots, since these variables can strongly influence experimental outcomes.