Physical activity as a predictor of fast-food consumption in university students of education in Puno, Peru
DOI:
https://doi.org/10.47197/retos.v72.114529Keywords:
physical activity, non-recommended foods, processed foods, social networks, obesityAbstract
Introduction and Objective. Low physical activity among college students may lead to increased fast-food consumption, thereby raising the risk of health problems. The objective of this study was to evaluate whether eating habits, physical activity, the influence of social networks, gender, year of study, and peer influence serve as predictors of fast-food consumption among university students in the Faculty of Educational Sciences at the National University of the Altiplano of Puno, Peru.
Methodology. This study employed a quantitative, non-experimental design with an explanatory research scope. A total of 507 students participated, with a mean age of 21.34 ± 3.42 years, of whom 298 were female (58.9%) and 209 male (41.2%). Data analysis included multiple linear regression and path analysis to determine the predictors of fast-food consumption.
Results. The analysis revealed that fast-food consumption is significantly influenced by multiple factors. Eating habits (β = 0.163, p < 0.01), physical activity (β = -0.012, p < 0.01), and the influence of social networks (β = 0.412, p < 0.01) emerged as key determinants in predicting eating behavior.
Discussion. These findings emphasize the role of eating habits, physical activity, and social networks in shaping food choices among university students. The results are consistent with previous studies highlighting the social and lifestyle dimensions of fast-food consumption in young populations.
Conclusions.The study confirms that eating habits, physical activity, and social network influence are strong predictors of fast-food consumption. However, the exclusion of variables such as socioeconomic status, access to healthy foods, and psychological factors may limit the model’s accuracy. Future research is recommended to incorporate these additional variables to improve predictive validity.
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