Predicting Depression Among College Students Through Academic, Lifestyle, and Smartphone Factors

Abstract

Purpose: This study examines how academic, lifestyle, and smartphone usage factors jointly predict depression severity among college students in Nepal, with the aim of identifying key risk and protective determinants of mental health.

Design/methodology/approach: A quantitative, cross-sectional survey was administered to 394 college students. The questionnaire captured academic stress, lifestyle behaviors, and smartphone habits, alongside the Patient Health Questionnaire (PHQ-9) to measure depressive symptoms. Data were analyzed using Python through descriptive, correlational, and multiple regression techniques to identify significant predictors.

Findings and conclusion: Results showed that 62% of students experienced mild to moderate depression. Multiple regression analysis (R² = 0.56) identified sleep quality (β = -0.37), academic stress (β = 0.29), and social support (β = -0.23) as the strongest predictors. Nighttime smartphone use (β = 0.21) and low physical activity (β = -0.18) also had significant effects. The study concludes that poor sleep, academic overload, and weak social ties elevate depressive symptoms, while social support and healthy lifestyle behaviors act as protective factors.

 Implications: The findings underscore the importance of holistic student mental health strategies combining stress management, digital hygiene, and lifestyle promotion.

Originality/value: This research provides one of the first integrative, data-driven analyses in Nepal linking academic, behavioral, and technological domains to student depression, offering actionable insights for higher education institutions to strengthen well-being initiatives.