Affiliation:
1. University of Dubrovnik , Department of Economics and Business Economics , Dubrovnik , Croatia
Abstract
Abstract
Background
Digital technologies have significantly changed the way adolescents perceive the world around them. The perception of the social environment is crucial for their well-being and health.
Objectives
This paper aims to evaluate the relationship between the perceived life circumstances of adolescents, such as dietary habits, physical activity, obesity, subjective health, the use of digital technology devices, and the level of occupancy with school obligations.
Methods/Approach
The survey research was conducted on a sample of adolescents between the ages of 11 and 15. Data was analysed using regression analysis and association rules.
Results
The results present a moderate positive correlation between the level of school obligations and life satisfaction or subjective health, while for the independent variable, time spent in front of screens, the strength of the relationship is moderate and negative.
Conclusions
The model represents a useful starting point for the recommendations for creating patterns to influence life satisfaction and well-being in adolescence. It provides insight into the potential optimisation of school obligations of adolescents according to the level of life satisfaction, subjective perception of health, and time spent in front of the screen.
Reference67 articles.
1. Abu Hanifah, R., Mohamed, M. N. A., Jaafar, Z., Mohsein, N. A.-S. A., Jalaludin, M. Y., Majid, H. A., Murray, L., Cantwell, M., & Su, T. T. (2013). The correlates of body composition with heart rate recovery after step test: An exploratory study of Malaysian adolescents. PloS One, 8(12), e82893. https://doi.org/10.1371/journal.pone.0082893
2. Adelantado-Renau, M., Moliner-Urdiales, D., Cavero-Redondo, I., Beltran-Valls, M. R., Martínez-Vizcaíno, V., & Álvarez-Bueno, C. (2019). Association Between Screen Media Use and Academic Performance Among Children and Adolescents: A Systematic Review and Meta-analysis. JAMA Pediatrics, 173(11), 1058–1067. https://doi.org/10.1001/jamapediatrics.2019.3176
3. Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, 487–499.
4. Barr, R., Lauricella, A., Zack, E., & Calvert, S. (2010). Infant and Early Childhood Exposure to Adult-Directed and Child-Directed Television Programming Relations with Cognitive Skills at Age Four. Merrill-Palmer Quarterly, 56, 21–48. https://doi.org/10.1353/mpq.0.0038
5. Botelho, G., Aguiar, M., & Abrantes, C. (2013). How critical is the effect of body mass index on physical fitness and physical activity performance in adolescents? Journal of Physical Education and Sport, 13, 19–26. https://doi.org/10.7752/jpes.2013.01004