Hacia una Educación Digital Sostenible: Modelo Predictivo  para la Prevención de la Adicción a Plataformas Sociales  en Población Universitaria.

Francisco Jacobo Murillo-López

doi:10.55965/setp.5.10.a4

Authors

Francisco Jacobo Murillo-López Universidad Autónoma de Aguascalientes, Aguascalientes, México https://orcid.org/0009-0005-2104-2634

DOI:

https://doi.org/10.55965/setp.5.10.a4

Keywords:

social media addiction, educational innovation, predictive model, sustainable development, digital wellbeing, gender, university students, SD3, SDG4

Abstract

Context. The maladaptive use of social media represents a growing public health concern worldwide, particularly affecting young individuals and university settings. In Mexico, prevalence rates among higher education students range from 18% to 42%, highlighting the need to identify predictive factors and develop tailored intervention strategies for this population.

Problem. There is a lack of consensus regarding the moderating role of demographic variables such as age and gender in PSMU - Problematic Social Media Use, challenging the efficacy of interventions based solely on usage time reduction.

Purpose. This study aims to identify the main predictors of social media addiction among Mexican university students using a logistic regression model, focusing on variables such as age, daily usage time, and gender, in alignment with Sustainable Development Goals 3 and 4.

Methodology. A cross-sectional study conducted between January and March 2025 with 705 students from the Universidad Autónoma de Aguascalientes (UAA), Mexico. The validated Bergen Social Media Addiction Scale (BSMAS) was administered (α=0.89; r=0.76 with IAT), and binary logistic regression was performed controlling for gender and academic year.

Theoretical and practical findings. Age demonstrated a protective effect (OR=0.37, p=0.006), reducing the probability of addiction by 63% per additional year. Male gender was associated with higher risk (69.6% vs. 60.1%, p=0.012). Hours of use were not statistically significant. These findings support the I-PACE (Interaction of Person-Affect-Cognition-Execution) model and suggest the need for gender- and academic year-specific interventions.

Originality. Integration of developmental and gender variables into a predictive model applicable to the Mexican university context, employing a process innovation framework (Oslo Manual).

Conclusions and limitations. Age and gender are more robust predictors than usage time. The cross-sectional design limitation underscores the need for longitudinal studies to establish causality.

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Author Biography

Francisco Jacobo Murillo-López, Universidad Autónoma de Aguascalientes, Aguascalientes, México

Research Professor at the Profesor investigador Universidad Autónoma de Aguascalientes, Aguascalientes, Mexico.

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