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Aldenaini, N., Oyebode, O., Orji, R., Sampalli, S.: Mobile phone-based persuasive know-how for bodily exercise and sedentary habits: a scientific evaluation. Front. Comput. Sci. 2, 1–17 (2020)
Baumer, E.P., et al.: Prescriptive persuasion and open-ended social consciousness: increasing the design house of cellular well being. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, CSCW ’12, pp. 475–484. Association for Computing Machinery, New York (2012)
Belloc, N.B., Breslow, L.: Relationship of bodily well being standing and well being practices. Prev. Med. 1(3), 409–421 (1972)
Calle, P., et al.: Towards ai-driven healthcare: systematic optimization, linguistic evaluation, and clinicians’ analysis of enormous language fashions for smoking cessation interventions. In: Proceedings of the CHI Conference on Human Factors in Computing Systems. CHI ’24. Association for Computing Machinery, New York (2024)
Chen, M., et al.: The way forward for cognitive strategy-enhanced persuasive dialogue brokers: new views and developments (2024)
Chen, W., et al.: From hole to synergy: Enhancing contextual understanding by human-machine collaboration in personalised methods. In: Proceedings of the thirty sixth Annual ACM Symposium on User Interface Software and Technology. UIST ’23. Association for Computing Machinery, New York (2023)
De Carolis, B.N., Palestra, G., Oranger, E.: Social robots vs. chatbots: evaluating the impact as a persuasive know-how for kids within the wholesome consuming area. In: Proceedings of the 2024 International Conference on Advanced Visual Interfaces. AVI ’24. Association for Computing Machinery, New York (2024)
Fogg, B.J.: Persuasive know-how: utilizing computer systems to vary what we expect and do. Ubiquity 2002(December) (2002)
Getson, C., Nejat, G.: Care suppliers’ views on the design of assistive persuasive behaviors for socially assistive robots. J. Am. Med. Dir. Assoc. 25(8), 105084 (2024)
Ghazali, A.S., Ham, J., Barakova, E., Markopoulos, P.: Persuasive robots acceptance mannequin (pram): roles of social responses throughout the acceptance mannequin of persuasive robots. Int. J. Soc. Robot. 12(5), 1075–1092 (2020)
Heerink, M., Krose, B., Evers, V., Wielinga, B.: Measuring acceptance of an assistive social robotic: a recommended toolkit. In: RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 528–533. IEEE (2009)
Van der Heijden, H.: User acceptance of hedonic info methods. MIS Q. 695–704 (2004)
Ikeda, F., Sasao, T.: Hugbot: designing a persuasive robotic for smartphone dependancy management. In: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’24, pp. 1233–1236. Association for Computing Machinery, New York (2024)
Kaplan, R., Stone, A.: Bringing the laboratory and clinic to the neighborhood: cellular applied sciences for well being promotion and illness prevention. Ann. Rev. Psychol. 64 (2012)
Khoo, W., et al.: Spill the tea: when robotic dialog brokers help well-being for older adults. In: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’23, pp. 178–182. Association for Computing Machinery, New York (2023)
King, A., et al.: Harnessing completely different motivational frames through cell phones to advertise each day bodily exercise and cut back sedentary habits in growing older adults. PloS One 8, e62613 (2013)
Lyzwinski, L.N., Caffery, L.J., Bambling, M., Edirippulige, S.: A scientific evaluation of digital mindfulness-based therapeutic interventions for weight, weight-related behaviors, and psychological stress. Telemed. J. e-health Off. J. Am. Telemed. Assoc. 24(3), 173–184 (2017)
Mahadevan, Ok., et al.: Generative expressive robotic behaviors utilizing massive language fashions. In: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’24, pp. 482–491. Association for Computing Machinery, New York (2024)
Michie, S., van Stralen, M., West, R.: The behaviour change wheel: a brand new technique for characterising and designing behaviour change interventions. Implement. Sci. IS 6, 42 (2011)
Nurbakova, D., Serna, A., Omiri, A., Boutet, A.: Adaptive and privacy-aware persuasive methods to advertise wholesome consuming habits: place paper. In: Adjunct Proceedings of the thirty first ACM Conference on User Modeling, Adaptation and Personalization, UMAP ’23 Adjunct, pp. 129–131. Association for Computing Machinery, New York (2023)
O’Keefe, D.J.: Elaboration Likelihood Model. John Wiley & Sons, Ltd., Hoboken (2008)
de Oliveira, R.S.C., Oinas-Kukkonen, H.: Persuasive methods options in digital well being interventions for diabetes administration: a scoping evaluation. In: Persuasive Technology: nineteenth International Conference. PERSUASIVE 2024, Wollongong, NSW, Australia, 10–12 April 2024, Proceedings, pp. 89–99. Springer, Heidelberg (2024). https://doi.org/10.1007/978-3-031-58226-4_8
Orji, R., Mandryk, R., Vassileva, J.: Gender, age, and responsiveness to Cialdini’s persuasion methods (2015)
Paay, J., et al.: Quitty: utilizing know-how to influence people who smoke to give up. In: Proceedings of the eighth Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational, NordiCHI ’14, pp. 551–560. Association for Computing Machinery, New York (2014)
Petty, R., Cacioppo, J.: The elaboration chance mannequin of persuasion. Adv. Hydrosci. 19, 124–205 (1986)
Saunderson, S., Nejat, G.: Investigating methods for robotic persuasion in social human-robot interplay. IEEE Trans. Cybern. 52(1), 641–653 (2020)
Sayis, B., Gunes, H.: Technology-assisted journal writing for bettering pupil psychological wellbeing: humanoid robotic vs. voice assistant. In: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’24, pp. 945–949. Association for Computing Machinery, New York (2024)
Stevens, V.J., et al.: Design and implementation of an interactive web site to help long-term upkeep of weight reduction. J. Med. Internet Res. 10(1), e1 (2008)
Sun, X., Liu, Y., De Wit, J., Bosch, J.A., Li, Z.: Trust by interface: how completely different consumer interfaces form human belief in well being info from massive language fashions. In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, pp. 1–7 (2024)
Venkatesh, V., Bala, H.: Technology acceptance mannequin 3 and a analysis agenda on interventions. Decis. Sci. 39(2), 273–315 (2008)
Wang, Z., Reisert, P., Nichols, E., Gomez, R.: Ain’t misbehavin’ – utilizing LLMs to generate expressive robotic habits in conversations with the tabletop robotic haru. In: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’24. pp. 1105–1109. Association for Computing Machinery, New York (2024)
Wei, J., Kim, S., Jung, H., Kim, Y.H.: Leveraging massive language fashions to energy chatbots for gathering consumer self-reported knowledge. Proc. ACM Hum.-Comput. Interact. 8(CSCW1) (2024)
Widmer, R.J., Collins, N.M., Collins, C.S., West, C.P., Lerman, L.O., Lerman, A.: Digital well being interventions for the prevention of heart problems: a scientific evaluation and meta-analysis. Mayo Clin. Proc. 90(4), 469–480 (2015)
Wu, R., et al.: Mindshift: leveraging massive language fashions for mentalstates- based mostly problematic smartphone use intervention. In: Proceedings of the CHI Conference on Human Factors in Computing Systems. CHI ’24. Association for Computing Machinery, New York (2024)
Xu, J., Zhang, C., Cuijpers, R., Ijsselsteijn, W.: Affective and cognitive reactions to robot-initiated social management of well being behaviors, pp. 810–819 (2024)
Zhang, R.E., Ringland, Ok., Paan, M., C. Mohr, D., Reddy, M.: Designing for emotional well-being: integrating persuasion and customization into psychological well being applied sciences. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. CHI ’21. Association for Computing Machinery, New York (2021)
Zhang, Y.B., et al.: Combined way of life elements, all-cause mortality and heart problems: a scientific evaluation and meta-analysis of potential cohort research. J. Epidemiol. Community Health 75, 92–99 (2021)
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