Aplicações de Técnicas de Machine Learning e Gamificação no Autocuidado em Saúde: Uma Revisão Sistemática
Resumo
O autocuidado em saúde exige que o paciente esteja motivado para que ele se engaje com o seu tratamento. Gamificação e técnicas de machine learning têm sido consideradas em aplicações que apoiam o autocuidado. Este trabalho busca entender, por meio de uma revisão sistemática da literatura, as técnicas de machine learning e de gamificação que possam prover de forma efetiva recursos para que o paciente se engaje no seu autocuidado. Entre os artigos selecionados envolvendo gamificação, machine learning e autocuidado para solucionar problemas relacionados ao engajamento do paciente com o seu tratamento, 12,1% aplicam técnicas que envolvem a junção dessas áreas, e 9,1% utilizam machine learning para identificar e classificar características dos usuários visando definir seu perfil, e então prover uma gamificação personalizada ou adaptada ao contexto.
Palavras-chave:
machine-learning, autocuidado, mHealth, gamificação, engajamento
Referências
Alaghbari, S., Mitschick, A., Blichmann, G., Voigt, M., and Dachselt, R. (2020). Achiever or explorer? gamifying the creation process of training data for machine learning. ACM.
Alqahtani, F., Meier, S., and Orji, R. (2021). Personality-based approach for tailoring persuasive mental health applications. User Model User-Adapted Interac, pages 1–43.
Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., and Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of Systems and Software, 80(4):571–583. Software Performance.
Duggal, K., Gupta, L. R., and Singh, P. (2021). Gamification and machine learning inspired approach for classroom engagement and learning. Mathematical Problems in Engineering, 2021.
Khoshkangini, R., Valetto, G., Marconi, A., and Pistore, M. (2021). Automatic generation and recommendation of personalized challenges for gamification. User Modeling User-Adapted Interaction, 31(1):1–34.
Knutas, A., Van Roy, R., Hynninen, T., Granato, M., Kasurinen, J., and Ikonen, J. (2019). A process for designing algorithm-based personalized gamification. Mult Tools and App, 78(10):13593–13612.
Li, C., Rusák, Z., Horváth, I., and Ji, L. (2016). Development of engagement evaluation method and learning mechanism in an engagement enhancing rehabilitation system. Eng App of AI, 51:182–190.
Loria, E. and Marconi, A. (2021). Exploiting limited players’ behavioral data to predict churn in gamification. Electronic Commerce Research and Applications, 47:101057.
Marczewski, A. (2015). Even ninja monkeys like to play: Gamification, game thinking and motivational design. CreateSpace Independent Publishing.
Missaoui, S. and Maalel, A. (2021). Student’s profile modeling in an adaptive gamified learning environment. Education and Information Technologies, 26(5):6367–6381.
Oliveira, L. W. and Carvalho, S. T. (2020). A gamification-based framework for mhealth developers in the context of self-care. pages 138–141.
Pinto, M., Pereira, M., Raposo, D., Simões, M., and Castelo-Branco, M. (2019). Gameaal-an aal solution based on gamification and machine learning techniques. In 2019 IEEE CIBCB, pages 1–4. IEEE.
Schäfer, H., Bachner, J., Pretscher, S., Groh, G., and Demetriou, Y. (2018). Study on motivating physical activity in children with personalized gamified feedback. pages 221–226.
Sweetser Kyburz, P., Aitchison, M., et al. (2020). Do game bots dream of electric rewards?: The universality of intrinsic motivation.
Tondello, G. F., Orji, R., and Nacke, L. E. (2017). Recommender systems for personalized gamification. ACM.
Wanderley O. L., Carvalho, S. T. (2020). A gamification-based framework for mhealth developers in the context of self-care. IEEE.
Zhang, R., E. Ringland, K., Paan, M., C. Mohr, D., and Reddy, M. (2021). Designing for emotional well-being: Integrating persuasion and customization into mental health technologies. pages 1–13.
Alqahtani, F., Meier, S., and Orji, R. (2021). Personality-based approach for tailoring persuasive mental health applications. User Model User-Adapted Interac, pages 1–43.
Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., and Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of Systems and Software, 80(4):571–583. Software Performance.
Duggal, K., Gupta, L. R., and Singh, P. (2021). Gamification and machine learning inspired approach for classroom engagement and learning. Mathematical Problems in Engineering, 2021.
Khoshkangini, R., Valetto, G., Marconi, A., and Pistore, M. (2021). Automatic generation and recommendation of personalized challenges for gamification. User Modeling User-Adapted Interaction, 31(1):1–34.
Knutas, A., Van Roy, R., Hynninen, T., Granato, M., Kasurinen, J., and Ikonen, J. (2019). A process for designing algorithm-based personalized gamification. Mult Tools and App, 78(10):13593–13612.
Li, C., Rusák, Z., Horváth, I., and Ji, L. (2016). Development of engagement evaluation method and learning mechanism in an engagement enhancing rehabilitation system. Eng App of AI, 51:182–190.
Loria, E. and Marconi, A. (2021). Exploiting limited players’ behavioral data to predict churn in gamification. Electronic Commerce Research and Applications, 47:101057.
Marczewski, A. (2015). Even ninja monkeys like to play: Gamification, game thinking and motivational design. CreateSpace Independent Publishing.
Missaoui, S. and Maalel, A. (2021). Student’s profile modeling in an adaptive gamified learning environment. Education and Information Technologies, 26(5):6367–6381.
Oliveira, L. W. and Carvalho, S. T. (2020). A gamification-based framework for mhealth developers in the context of self-care. pages 138–141.
Pinto, M., Pereira, M., Raposo, D., Simões, M., and Castelo-Branco, M. (2019). Gameaal-an aal solution based on gamification and machine learning techniques. In 2019 IEEE CIBCB, pages 1–4. IEEE.
Schäfer, H., Bachner, J., Pretscher, S., Groh, G., and Demetriou, Y. (2018). Study on motivating physical activity in children with personalized gamified feedback. pages 221–226.
Sweetser Kyburz, P., Aitchison, M., et al. (2020). Do game bots dream of electric rewards?: The universality of intrinsic motivation.
Tondello, G. F., Orji, R., and Nacke, L. E. (2017). Recommender systems for personalized gamification. ACM.
Wanderley O. L., Carvalho, S. T. (2020). A gamification-based framework for mhealth developers in the context of self-care. IEEE.
Zhang, R., E. Ringland, K., Paan, M., C. Mohr, D., and Reddy, M. (2021). Designing for emotional well-being: Integrating persuasion and customization into mental health technologies. pages 1–13.
Publicado
24/10/2022
Como Citar
ANJOS, Filipe M. S. dos; OLIVEIRA, Luma W.; SOUZA, Carlos Henrique R.; CARVALHO, Sergio T..
Aplicações de Técnicas de Machine Learning e Gamificação no Autocuidado em Saúde: Uma Revisão Sistemática. In: TRILHA DE SAÚDE – ARTIGOS CURTOS - SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 21. , 2022, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 1376-1380.
DOI: https://doi.org/10.5753/sbgames_estendido.2022.226133.
