The Nexus between Social Media Addiction, Sleep Quality, and Life Satisfaction among Medical Students: Cross-sectional Study

This study aims to address a gap in the literature by analyzing the relationships between social media addiction, sleep quality, and life satisfaction, particularly in medical students who are under significant stress. This research employed a cross-sectional design at a university medical faculty. The study population included 1210 students, with a minimum sample size calculated as 292 based on a 95% confidence level and a 5% margin of error. Data were collected voluntarily from 433 students using validated tools, including the Social Media Addiction Scale-Adult Form (SMAS-AF), the Life Satisfaction Scale (LSS), and the Republic Subjective Sleep Quality Scale. Statistical analyses included descriptive statistics, Independent Samples t-tests, Kruskal–Wallis, Pearson correlation, and multiple linear regression analysis. Of the participants, 62.4% were female, and 49.4% used social media for 2–4 h daily. A significant negative relationship was identified between social media addiction and life satisfaction (p<0.05), while a positive relationship was found between sleep quality and life satisfaction (p<0.05). Social media addiction negatively affected life satisfaction by disrupting sleep patterns, while sleep quality partially mitigated this adverse effect. Female students exhibited higher social media addiction and life satisfaction scores than male students (p<0.05). Students who used social media for more than 4 h daily reported significantly lower life satisfaction. Social media addiction significantly reduced life satisfaction among medical students, mainly by disrupting sleep quality. However, better sleep quality seemed to buffer this effect. These findings underscore the need for university programmes to reduce excessive social media use and promote healthy sleep routines. Training that fosters regular sleep habits may further support students' academic and psychological well-being. Still, the results should be interpreted with caution due to limitations such as the single-institution setting, cross-sectional design, and reliance on self-reported data, which may introduce response bias. Future research should adopt multi-centre, longitudinal designs to improve generalizability and causal understanding.