Association between lifestyle and television exposure time: a machine learning and regression models approach

Authors

DOI:

https://doi.org/10.14571/brajets.v18.n4.1338-1348

Keywords:

sedentary behavior, screen time, dietary habits, statistical modeling

Abstract

Excessive television viewing has been associated with sedentary behavior and metabolic risks, being influenced by factors such as physical activity level, dietary habits, and socioeconomic characteristics. This study employed machine learning techniques and statistical modeling to identify the main predictors of weekly television exposure time among high school students. Categorical variables, including sex, physical activity level, dietary consumption, and socioeconomic factors, were analyzed using R software. Variable selection was performed using Random Forest and Recursive Feature Elimination, followed by statistical analyses with Poisson and Quasi-Poisson regression models. The results indicated that time spent using computers, video games, or other devices on weekends and male sex showed greater predictive relevance for weekly television exposure time. Soft drink consumption showed a statistically significant association in the Quasi-Poisson model, although with low predictive importance in the Random Forest model. These findings suggest that behavioral and sociodemographic factors are associated with television exposure time among students, and that the role of soft drink consumption should be interpreted cautiously, reinforcing the need for further investigations.

Author Biographies

  • Rodrigo Mercês Reis Fonseca , Federal University of Bahia

    PhD candidate in Knowledge Diffusion at the Federal University of Bahia (UFBA), recipient of a CNPq doctoral scholarship (Process No. 140507/2025-5, GD modality), and holds a Master’s degree in Physical Education from the State University of Southwest Bahia (UESB). Works as a researcher and lecturer, with an emphasis on public health, risk behaviors, youth, and social inequalities. Has published in academic journals and books, with experience in academic advising and additional training in quantitative analysis and machine learning applied to research.

  • Bruna Maria Palatino Ferreira, Southwest Bahia State University

    Currently a resident in Public Health at the Municipal Health Foundation, Ponta Grossa. Academic Master’s degree in Physical Education, with a focus on epidemiology and health (health programs for the elderly) at the State University of Southwest Bahia (UESB, 2022–2024). Bachelor’s degree in Physical Education from the State University of Ponta Grossa (UEPG, 2011–2015). Participated in an academic exchange program at the University of Coimbra, Portugal, as a Santander Ibero-America scholarship recipient (UC, 2014–2015). Specialist in spinal pathology, geriatrics, and gerontology (Unibf, 2021).

  • Cristiane dos Santos Silva, Southwest Bahia State University

    Master’s degree holder from the Graduate Program in Physical Education (PPGEF) at the State University of Southwest Bahia (UESB) and the State University of Santa Cruz (UESC). Lato Sensu postgraduate degree in Teacher Education and Pedagogical Practices from the Federal Institute of Education, Science and Technology of Bahia (IFBA), 2021.1. Bachelor’s degree in Physical Education from Leonardo Da Vinci University Center (UNIASSELVI), 2020.1. Licensed in Physical Education from the University of Northern Paraná (UNOPAR), 2019.1. Contributes to improving physical fitness conditions for youth, adults, and the elderly. Researcher affiliated with the Interdisciplinary Center for Studies and Research on Human Aging (NIEPEH/UESB). Works in administrative and financial areas. Holds a degree in Economics from the Integrated College Euclides Fernandes (FIEF), 2016.2.

  • Rafaelle Dayanne Barros, Instituto Federal do Amapá

    Tenured professor at the Federal Institute of Amapá (IFAP), Santana Campus. Specialist in School Physical Education (Faculdade de Macapá – FAMA); Specialist in Exercise Physiology (Faculdade de Macapá – FAMA); Specialist in Clinical Exercise Physiology (Federal University of São Carlos – UFSCAR); Master’s degree in Educational Sciences (Absolute Christian University – ACU); Master’s degree in Physical Education (State University of Southwest Bahia – UESB). Member of the Center for Studies in Aging Epidemiology (NEPE – UESB). Teaching experience includes instruction at the lower secondary level (Ensino Fundamental II), PROEJA, high school, technical education, and higher education. Interested in topics related to physical education at the high school and university levels, as well as health-related issues, particularly concerning the elderly population.

  • Hector Luiz Rodrigues Munaro, Southwest Bahia State University

    Holds a degree in Physical Education from the Federal University of Espírito Santo (1997), a Master’s degree in Physical Education from the Federal University of Santa Catarina (2007), and a PhD in Physical Education from the same institution (2016). Currently serves as Associate Professor B at the State University of Southwest Bahia (UESB), teaching Kinesiology and Biomechanics in the Physical Education and Physiotherapy programs. Research leader of the Center for Studies in Physical Activity and Health (NEAFIS) and the Center for Population Health Studies (NESP). Founding member of the Brazilian Society of Physical Activity and Health. Permanent researcher in the Graduate Program in Teaching at UESB, as well as in the Joint Graduate Program in Physical Education (UESB/UESC). Has experience in the field of Physical Education, with an emphasis on studies related to physical activity and its relationship to health.

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Published

28-12-2025

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Vol. 18 No. 4 (2025)

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Copyright (c) 2025 Rodrigo Mercês Reis Fonseca, Bruna Maria Palatino Ferreira, Cristiane dos Santos Silva, Rafaelle Dayanne Barros, Hector Luiz Rodrigues Munaro

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How to Cite

Mercês Reis Fonseca , R., Palatino Ferreira, B. M., dos Santos Silva, C., Barros, R. D., & Rodrigues Munaro, H. L. (2025). Association between lifestyle and television exposure time: a machine learning and regression models approach. Cadernos De Educação Tecnologia E Sociedade, 18(4), 1338-1348. https://doi.org/10.14571/brajets.v18.n4.1338-1348