Project Summary
This study aims to empirically test the theoretical mechanisms of relational perceptions in the context of building and testing a relational artificial intelligence (AI) chatbot for improving physical activity (PA) behaviors among a sedentary adult population in the U.S.
The aim of the study is to build and experimentally test relational capacities of AI chatbot in inducing positive human-AI relationship and leading to higher PA behavior change intention.
During the 7-day intervention, the relational chatbot will educate participants on physical activity using 5 types of relational messages during a PA intervention including 1) social dialogue, 2) empathy, 3) self-disclosure, 4) meta-relational communication, and 5) humor.
Publications
- Oh, Y. J., Hoffmann, T. J., & Fukuoka, Y. (2023). A Novel Approach to Assess Weekly Self-efficacy for Meeting Personalized Physical Activity Goals Via a Cellphone: 12-Week Longitudinal Study. JMIR Formative Research, 7(1), e38877. https://formative.jmir.org/2023/1/e38877
- Oh, Y. J., Zhang, J., Ji, X., Liao, W., & Feng, B. (2022, June). Efficacy of a Chatbot-Based Sleep Intervention on Sleep Quality Improvement among Young Adults. Sleep (Vol. 45, pp. A42-A42). 10.1093/sleep/zsac079.091
- Oh, Y. J., Zhang, J., Fang, M. L., & Fukuoka, Y. (2021). A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. International Journal of Behavioral Nutrition and Physical Activity, 18(1), 1-25. https://doi.org/10.1186/s12966-021-01224-6
- Liang, K. H., Lange, P., Oh, Y. J., Zhang, J., Fukuoka, Y., & Yu, Z. (2021). Evaluation of In-Person Counseling Strategies To Develop Physical Activity Chatbot for Women. SIGDIAL. https://arxiv.org/pdf/2107.10410.pdf
- Zhang, J., Oh, Y. J., Lange, P., Yu, Z., & Fukuoka, Y. (2020). Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet. Journal of Medical Internet Research, 22(9), e22845. https://doi.org/10.2196/22845
- Shi, W., Wang, X., Oh, Y. J., Zhang, J., Sahay, S., & Yu, Z. (2020). Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-13). https://dl.acm.org/doi/10.1145/3313831.3376843
- Wang, X., Shi, W., Kim, R., Oh, Y. J., Yang, S., Zhang, J., & Yu, Z. (2019). Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 5635-5649). Best Paper Nomination Award. https://arxiv.org/pdf/1906.06725.pdf
Presentations
- Liao, W., Oh, Y. J., Feng, B., & Zhang, J. (2021, May). Are Bots Agentic Enough? Understanding Agentic Perceptions and Social Influence From Taking Sleep Advice with Human versus Artificial Agent. Paper accepted to the 71st Annual Convention of International Communication Association, Denver, CO. *Virtual conference due to COVID-19.
- Zhang. J., Oh, Y. J., & Feng, S. (2020, May). Predicting successful persuasion based on conversational dynamics: Markov Chain models and sequence analyses. Paper accepted for presentation at the annual convention of the International Communication Association, Gold Coast, Australia.
- Zhang, J., Oh, Y. J., Wang, X., Kim, R., Yang, S., & Yu, Z. (2019, May). First step towards an automated personalized persuasive conversational system: Investigating moderating effects of psychological factors. Paper accepted for presentation at the annual convention of the International Communication Association, Washington, D.C