Usabilidade de Interfaces Conversacionais com Inteligência Artificial Generativa em Aplicações mHealth: Uma revisão sistemática
Resumo
Esta revisão sistemática tem como objetivo principal identificar e analisar os métodos e estratégias empregados para avaliar a usabilidade e a experiência do usuário em interfaces conversacionais de Inteligência Artificial Generativa no contexto de aplicações mHealth. O estudo identificou que as estratégias de avaliação, tanto quantitativas quanto qualitativas, focam em critérios como facilidade de uso, utilidade, satisfação e qualidade da informação. Também foram considerados aspectos como interatividade, personalização, engajamento e privacidade dos dados. Espera-se que esta revisão sistemática contribua para o desenvolvimento de interfaces conversacionais mais acessíveis, culturalmente sensíveis e inclusivas, bem como para o apoio a estudos que foquem em aplicações em mHealth.
Palavras-chave:
Usabilidade, Experiência do Usuário, IA Generativa, Interfaces Conversacionais, mHealth
Referências
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Ashton, L. M., Adam, M. T., Whatnall, M., Rollo, M. E., Burrows, T. L., Hansen, V., and Collins, C. E. (2023). Exploring the design and utility of an integrated web-based chatbot for young adults to support healthy eating: a qualitative study. International Journal of Behavioral Nutrition and Physical Activity, 20(1):119.
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Colakoglu, S., Durmus, M., Polat, Z. P., Yildiz, A., and Sezgin, E. (2025). User engagement with a multimodal conversational agent for self-care and chronic disease management: A retrospective analysis. Journal of Medical Systems, 49(1):76.
D, U., S, K., Chakradhar, V., Teja, M. S., and Teja, M. S. (2024). Enabling mental wellness through interactive platforms. In 2024 9th International Conference on Communication and Electronics Systems (ICCES), pages 1970–1975.
De la Puente, G., Silva, A., and Felix, R. (2024). Development of a chatbot powered by artificial intelligence to diagnose and improve stress and anxiety levels in university students. In 2024 IEEE XXXI International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pages 1–8.
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Raj, S., Shekhar, S., and P, B. (2025). Medihealth: An ai-driven mobile solution for enhanced health care decision-making and accessibility. In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), pages 874–880.
van Heerden, A., Bosman, S., Swendeman, D., and Comulada, W. S. (2023). Chatbots for hiv prevention and care: a narrative review. Current HIV/AIDS Reports, 20(6):481–486.
Wang, Y., Zhu, T., Zhou, T., Wu, B., Tan, W., Ma, K., Yao, Z., Wang, J., Li, S., Qin, F., Xu, Y., Tan, L., Liu, J., and Wang, J. (2025). Hyper-dream, a multimodal digital transformation hypertension management platform integrating large language model and digital phenotyping: Multicenter development and initial validation study. Journal of Medical Systems, 49(1):42.
Zhang, S. and Song, J. (2024). A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model. Scientific Reports, 14(1):17118.
Arigo, D., Jake-Schoffman, D. E., and Pagoto, S. L. (2025). The recent history and near future of digital health in the field of behavioral medicine: an update on progress from 2019 to 2024. Journal of Behavioral Medicine, 48(1):120–136.
Ashton, L. M., Adam, M. T., Whatnall, M., Rollo, M. E., Burrows, T. L., Hansen, V., and Collins, C. E. (2023). Exploring the design and utility of an integrated web-based chatbot for young adults to support healthy eating: a qualitative study. International Journal of Behavioral Nutrition and Physical Activity, 20(1):119.
Azam, A., Naz, Z., and Khan, M. U. G. (2024). Pharmallm: A medicine prescriber chatbot exploiting open-source large language models. Human-Centric Intelligent Systems, 4(4):527–544.
Cevasco, K. E., Morrison Brown, R. E., Woldeselassie, R., and Kaplan, S. (2024). Patient engagement with conversational agents in health applications 2016–2022: A systematic review and meta-analysis. Journal of Medical Systems, 48(1):40.
Choi, S., Seo, J., Hernandez, M., and Kitsiou, S. (2024). Conversational agents in mhealth: use patterns, challenges, and design opportunities for individuals with visual impairments. Journal of Technology in Behavioral Science, 9(4):912–923.
Colakoglu, S., Durmus, M., Polat, Z. P., Yildiz, A., and Sezgin, E. (2025). User engagement with a multimodal conversational agent for self-care and chronic disease management: A retrospective analysis. Journal of Medical Systems, 49(1):76.
D, U., S, K., Chakradhar, V., Teja, M. S., and Teja, M. S. (2024). Enabling mental wellness through interactive platforms. In 2024 9th International Conference on Communication and Electronics Systems (ICCES), pages 1970–1975.
De la Puente, G., Silva, A., and Felix, R. (2024). Development of a chatbot powered by artificial intelligence to diagnose and improve stress and anxiety levels in university students. In 2024 IEEE XXXI International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pages 1–8.
Esmaeilzadeh, P. (2025). Decoding the cry for help: Ai’s emerging role in suicide risk assessment. AI and Ethics.
Gabarron, E., Larbi, D., Rivera-Romero, O., and Denecke, K. (2024). Human factors in AI-Driven digital solutions for increasing physical activity: Scoping review. JMIR Hum Factors, 11:e55964.
Galvão, M. C. B. and Ricarte, I. L. M. (2019). RevisÃo sistemÁtica da literatura: ConceituaÇÃo, produÇÃo e publicaÇÃo. Logeion: Filosofia da Informação, 6(1):57–73.
Golden, A. and Aboujaoude, E. (2024). Describing the framework for AI tool assessment in mental health and applying it to a generative AI Obsessive-Compulsive disorder platform: Tutorial. JMIR Form Res, 8:e62963.
Görtz, M., Baumgärtner, K., Schmid, T., Muschko, M., Woessner, P., Gerlach, A., Byczkowski, M., Sültmann, H., Duensing, S., and Hohenfellner, M. (2023). An artificial intelligence-based chatbot for prostate cancer education: Design and patient evaluation study. Digit Health, 9:20552076231173304.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. 2.
Lewis, D.-M., DeRenzi, B., Misomali, A., Nyirenda, T., Phiri, E., Chifisi, L., Makwenda, C., and Lesh, N. (2024). Human review for post-training improvement of low-resource language performance in large language models. In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), pages 592–597.
Ma, Y., Achiche, S., Pomey, M.-P., Paquette, J., Adjtoutah, N., Vicente, S., Engler, K., MARVIN chatbots Patient Expert Committee, Laymouna, M., Lessard, D., Lemire, B., Asselah, J., Therrien, R., Osmanlliu, E., Zawati, M. H., Joly, Y., and Lebouché, B. (2024a). Adapting and evaluating an AI-Based chatbot through patient and stakeholder engagement to provide information for different health conditions: Master protocol for an adaptive platform trial (the MARVIN chatbots study). JMIR Res Protoc, 13:e54668.
Ma, Y., Achiche, S., Tu, G., Vicente, S., Lessard, D., Engler, K., Lemire, B., MARVIN chatbots Patient Expert Committee, Laymouna, M., de Pokomandy, A., Cox, J., and Lebouché, B. (2024b). The first AI-based chatbot to promote HIV self-management: A mixed methods usability study. HIV Med, 26(2):184–206.
Nguyen, M. H., Sedoc, J., and Taylor, C. O. (2024). Usability, engagement, and report usefulness of Chatbot-Based family health history data collection: Mixed methods analysis. J Med Internet Res, 26:e55164.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2022). [the PRISMA 2020 statement: an updated guideline for reporting systematic reviewsdeclaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas]. Rev Panam Salud Publica, 46:e112.
Raj, S., Shekhar, S., and P, B. (2025). Medihealth: An ai-driven mobile solution for enhanced health care decision-making and accessibility. In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), pages 874–880.
van Heerden, A., Bosman, S., Swendeman, D., and Comulada, W. S. (2023). Chatbots for hiv prevention and care: a narrative review. Current HIV/AIDS Reports, 20(6):481–486.
Wang, Y., Zhu, T., Zhou, T., Wu, B., Tan, W., Ma, K., Yao, Z., Wang, J., Li, S., Qin, F., Xu, Y., Tan, L., Liu, J., and Wang, J. (2025). Hyper-dream, a multimodal digital transformation hypertension management platform integrating large language model and digital phenotyping: Multicenter development and initial validation study. Journal of Medical Systems, 49(1):42.
Zhang, S. and Song, J. (2024). A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model. Scientific Reports, 14(1):17118.
Publicado
04/12/2025
Como Citar
LIMA, Diego E. da Silva; VAZ, Noeli A. Pimentel; CARVALHO, Sérgio T.; BERRETTA, Luciana de O..
Usabilidade de Interfaces Conversacionais com Inteligência Artificial Generativa em Aplicações mHealth: Uma revisão sistemática. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 13. , 2025, Luziânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 139-148.
DOI: https://doi.org/10.5753/erigo.2025.17065.
