Um Modelo Inteligente para Auxílio a Pessoas com Uso Problemático de Smartphones
Resumo
O Uso Problemático de Smartphones (UPS) é caracterizado por padrões de uso compulsivos que podem gerar impactos negativos na rotina e no bem-estar. Este artigo apresenta o modelo Kratos, que integra consciência de contextos, análise de Históricos de Contextos, aprendizado de máquina e inferência ontológica para detectar UPS e gerar intervenções personalizadas. O modelo foi avaliado por meio de dados simulados de 49 usuários ao longo de 30 dias, demonstrando eficácia na identificação e na redução do uso problemático. O Kratos permite a detecção de padrões de risco e a recomendação de intervenções personalizadas, complementando métodos tradicionais baseados em questionários e intervenções manuais.Referências
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Bavaresco, R., Ren, Y., Barbosa, J., and Li, G. (2024). An ontology-based framework for worker’s health reasoning enabled by machine learning. Computers Industrial Engineering, 193:110310.
Bouazza, S., Abbouyi, S., El Kinany, S., El Rhazi, K., and Zarrouq, B. (2023). Association between problematic use of smartphones and mental health in the middle east and north africa (mena) region: A systematic review. International Journal of Environmental Research and Public Health, 20(4):2891.
Busch, P. A. and McCarthy, S. (2021). Antecedents and consequences of problematic smartphone use: A systematic literature review of an emerging research area. Computers in Human Behavior, 114:106414.
Choi, J., Rho, M. J., Kim, Y., Yook, I. H., Yu, H., Kim, D.-J., and Choi, I. Y. (2017). Smartphone dependence classification using tensor factorization. PLOS ONE, 12(6):1–12.
Elhai, J. D., Dvorak, R. D., Levine, J. C., and Hall, B. J. (2017). Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. Journal of Affective Disorders, 207:251–259.
Elhai, J. D., Rozgonjuk, D., Alghraibeh, A. M., and Yang, H. (2021). Disrupted Daily Activities From Interruptive Smartphone Notifications: Relations With Depression and Anxiety Severity and the Mediating Role of Boredom Proneness. Social Science Computer Review, 39(1):20–37.
Filippetto, A. S., Lima, R., and Barbosa, J. L. V. (2021). A risk prediction model for software project management based on similarity analysis of context histories. Information and Software Technology, 131:106497.
Gratch, I., Choo, T.-H., Galfalvy, H., Keilp, J. G., Itzhaky, L., Mann, J. J., Oquendo, M. A., and Stanley, B. (2021). Detecting suicidal thoughts: The power of ecological momentary assessment. Depression and anxiety, 38(1):8–16.
Grüning, D. J., Riedel, F., and Lorenz-Spreen, P. (2023). Directing smartphone use through the self-nudge app one sec. Proceedings of the National Academy of Sciences, 120(8).
Horwood, S. and Anglim, J. (2018). Personality and problematic smartphone use: A facet-level analysis using the five factor model and HEXACO frameworks. Computers in Human Behavior, 85:349–359.
Kent, S., Masterson, C., Ali, R., Parsons, C. E., and Bewick, B. M. (2021). Digital intervention for problematic smartphone use. International Journal of Environmental Research and Public Health, 18(24):13165.
Kwon, M., Kim, D.-J., Cho, H., and Yang, S. (2013). The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS ONE, 8(12):e83558.
Lee, H., Ahn, H., Choi, S., and Choi, W. (2014). The sams: Smartphone addiction management system and verification. Journal of medical systems, 38(1):1–10.
Lee, J. and Kim, W. (2021). Prediction of problematic smartphone use: A machine learning approach. International Journal of Environmental Research and Public Health, 18(12).
Lee, M., Han, M., and Pak, J. (2018). Analysis of behavioral characteristics of smartphone addiction using data mining. Applied Sciences, 8(7).
Lovibond, P. F. and Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 33(3):335–343.
Lu, X., An, X., and Chen, S. (2024). Trends and influencing factors in problematic smartphone use prevalence (2012–2022): A systematic review and meta-analysis. Cyberpsychology, Behavior, and Social Networking, 27(9):616–634.
Marciano, L. and Camerini, A.-L. (2022). Duration, frequency, and time distortion: Which is the best predictor of problematic smartphone use in adolescents? a trace data study. PLOS ONE, 17(2):1–19.
Norris, G. A. (2001). The requirement for congruence in normalization. The International Journal of Life Cycle Assessment, 6(2).
Olson, J. A., Sandra, D. A., Chmoulevitch, D., Raz, A., and Veissière, S. P. (2022). A nudge-based intervention to reduce problematic smartphone use: Randomised controlled trial. International Journal of Mental Health and Addiction, pages 1–23.
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Patel, V. R. and Mehta, R. G. (2011). Impact of outlier removal and normalization approach in modified k-means clustering algorithm. International Journal of Computer Science Issues (IJCSI), 8(5):331.
Rahman, M. A., Duradoni, M., and Guazzini, A. (2022). Identification and prediction of phubbing behavior: a data-driven approach. Neural Computing and Applications, 34(5):3885–3894.
Rentz, D. M., Heckler, W. F., and Barbosa, J. L. V. (2023). A computational model for assisting individuals with suicidal ideation based on context histories. Universal Access in the Information Society, 23(3):1447–1466.
Schroeder, G. L., Heckler, W., Francisco, R., and Barbosa, J. L. V. (2022). Problematic smartphone use on mental health: a systematic mapping study and taxonomy. Behaviour & Information Technology, 42(16):2808–2831.
Turner, A. (2021). How many smartphones are in the world? [link]. Accessed: 2025-01-11.
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., and Campbell, A. T. (2014). StudentLife. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM.
Yasudomi, K., Hamamura, T., Honjo, M., Yoneyama, A., and Uchida, M. (2021). Usage prediction and effectiveness verification of app restriction function for smartphone addiction. 2020 IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020.
Yildirim, C. and Correia, A.-P. (2015). Exploring the dimensions of nomophobia: Development and validation of a self-reported questionnaire. Computers in Human Behavior, 49:130–137.
Yu, J., Amores, J., Sebe, N., Radeva, P., and Tian, Q. (2008). Distance learning for similarity estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3):451–462.
Bavaresco, R., Ren, Y., Barbosa, J., and Li, G. (2024). An ontology-based framework for worker’s health reasoning enabled by machine learning. Computers Industrial Engineering, 193:110310.
Bouazza, S., Abbouyi, S., El Kinany, S., El Rhazi, K., and Zarrouq, B. (2023). Association between problematic use of smartphones and mental health in the middle east and north africa (mena) region: A systematic review. International Journal of Environmental Research and Public Health, 20(4):2891.
Busch, P. A. and McCarthy, S. (2021). Antecedents and consequences of problematic smartphone use: A systematic literature review of an emerging research area. Computers in Human Behavior, 114:106414.
Choi, J., Rho, M. J., Kim, Y., Yook, I. H., Yu, H., Kim, D.-J., and Choi, I. Y. (2017). Smartphone dependence classification using tensor factorization. PLOS ONE, 12(6):1–12.
Elhai, J. D., Dvorak, R. D., Levine, J. C., and Hall, B. J. (2017). Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. Journal of Affective Disorders, 207:251–259.
Elhai, J. D., Rozgonjuk, D., Alghraibeh, A. M., and Yang, H. (2021). Disrupted Daily Activities From Interruptive Smartphone Notifications: Relations With Depression and Anxiety Severity and the Mediating Role of Boredom Proneness. Social Science Computer Review, 39(1):20–37.
Filippetto, A. S., Lima, R., and Barbosa, J. L. V. (2021). A risk prediction model for software project management based on similarity analysis of context histories. Information and Software Technology, 131:106497.
Gratch, I., Choo, T.-H., Galfalvy, H., Keilp, J. G., Itzhaky, L., Mann, J. J., Oquendo, M. A., and Stanley, B. (2021). Detecting suicidal thoughts: The power of ecological momentary assessment. Depression and anxiety, 38(1):8–16.
Grüning, D. J., Riedel, F., and Lorenz-Spreen, P. (2023). Directing smartphone use through the self-nudge app one sec. Proceedings of the National Academy of Sciences, 120(8).
Horwood, S. and Anglim, J. (2018). Personality and problematic smartphone use: A facet-level analysis using the five factor model and HEXACO frameworks. Computers in Human Behavior, 85:349–359.
Kent, S., Masterson, C., Ali, R., Parsons, C. E., and Bewick, B. M. (2021). Digital intervention for problematic smartphone use. International Journal of Environmental Research and Public Health, 18(24):13165.
Kwon, M., Kim, D.-J., Cho, H., and Yang, S. (2013). The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS ONE, 8(12):e83558.
Lee, H., Ahn, H., Choi, S., and Choi, W. (2014). The sams: Smartphone addiction management system and verification. Journal of medical systems, 38(1):1–10.
Lee, J. and Kim, W. (2021). Prediction of problematic smartphone use: A machine learning approach. International Journal of Environmental Research and Public Health, 18(12).
Lee, M., Han, M., and Pak, J. (2018). Analysis of behavioral characteristics of smartphone addiction using data mining. Applied Sciences, 8(7).
Lovibond, P. F. and Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 33(3):335–343.
Lu, X., An, X., and Chen, S. (2024). Trends and influencing factors in problematic smartphone use prevalence (2012–2022): A systematic review and meta-analysis. Cyberpsychology, Behavior, and Social Networking, 27(9):616–634.
Marciano, L. and Camerini, A.-L. (2022). Duration, frequency, and time distortion: Which is the best predictor of problematic smartphone use in adolescents? a trace data study. PLOS ONE, 17(2):1–19.
Norris, G. A. (2001). The requirement for congruence in normalization. The International Journal of Life Cycle Assessment, 6(2).
Olson, J. A., Sandra, D. A., Chmoulevitch, D., Raz, A., and Veissière, S. P. (2022). A nudge-based intervention to reduce problematic smartphone use: Randomised controlled trial. International Journal of Mental Health and Addiction, pages 1–23.
Padgham, L. and Winikoff, M. (2003). Prometheus: A methodology for developing intelligent agents. In Giunchiglia, F., Odell, J., and Weiß, G., editors, Agent-Oriented Software Engineering III, pages 174–185, Berlin, Heidelberg. Springer Berlin Heidelberg.
Patel, V. R. and Mehta, R. G. (2011). Impact of outlier removal and normalization approach in modified k-means clustering algorithm. International Journal of Computer Science Issues (IJCSI), 8(5):331.
Rahman, M. A., Duradoni, M., and Guazzini, A. (2022). Identification and prediction of phubbing behavior: a data-driven approach. Neural Computing and Applications, 34(5):3885–3894.
Rentz, D. M., Heckler, W. F., and Barbosa, J. L. V. (2023). A computational model for assisting individuals with suicidal ideation based on context histories. Universal Access in the Information Society, 23(3):1447–1466.
Schroeder, G. L., Heckler, W., Francisco, R., and Barbosa, J. L. V. (2022). Problematic smartphone use on mental health: a systematic mapping study and taxonomy. Behaviour & Information Technology, 42(16):2808–2831.
Turner, A. (2021). How many smartphones are in the world? [link]. Accessed: 2025-01-11.
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., and Campbell, A. T. (2014). StudentLife. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM.
Yasudomi, K., Hamamura, T., Honjo, M., Yoneyama, A., and Uchida, M. (2021). Usage prediction and effectiveness verification of app restriction function for smartphone addiction. 2020 IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020.
Yildirim, C. and Correia, A.-P. (2015). Exploring the dimensions of nomophobia: Development and validation of a self-reported questionnaire. Computers in Human Behavior, 49:130–137.
Yu, J., Amores, J., Sebe, N., Radeva, P., and Tian, Q. (2008). Distance learning for similarity estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3):451–462.
Publicado
09/06/2025
Como Citar
SCHROEDER, Gustavo Lazarotto; FRANCISCO, Rosemary; BARBOSA, Jorge Luis Victória.
Um Modelo Inteligente para Auxílio a Pessoas com Uso Problemático de Smartphones. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 56-67.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.6929.
