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Social robots as health promoting agents: An application of the health action process approach to human-robot interaction at the workplace

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ABSTRACT: Technological innovations may have the potential to improve health behavior interventions at the workplace. Using a robot as a health communicator who interacts with target individuals may be sometimes superior to human change agents. Embedded in a health behavior theory that accounts for motivational and volitional processes, an innovative study has been designed to explore operating principles and intervention effects in the domains of dietary habits, tobacco consumption, physical inactivity, and stress and anxiety. A single-arm intervention with two assessment points in time, one month apart, has been conducted with 37 employees. They were confronted with a robot that delivered a supportive interaction with the study participants addressing one of the four behavioral domains. The intervention content was pre-tested and inspired by the health action process approach (HAPA). Self-report measures of all social-cognitive constructs such as self-efficacy, outcome expectancies, risk perception, behavioral intentions, and planning were applied. Pre-post comparisons confirmed the assumption of improved scores on motivational and volitional outcome variables. Moreover, mediation analyses underscored the pivotal role of behavioral intentions that translated motivational antecedents into volitional outcomes. The intervention study highlighted the innovative potential that robots may have when it comes to design theory-based health promotion strategies at the workplace. Moreover, results also confirmed basic assumptions of the health action process approach.

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Health action process approach Human-robot interaction Health promotion intervention

Citation

Lopes, S. L., Ferreira, A. I., Prada, R., & Schwarzer, R. (2023). Social robots as health promoting agents: An application of the health action process approach to human-robot interaction at the workplace. International Journal of Human - Computer Studies, 180. https://doi.org/10.1016/j.ijhcs.2023.103124

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