Digital and In-Person Mindfulness-Based Interventions for University Students’ Mental Health: A Systematic Review of Randomized Controlled Trials

Background/Objectives: University students commonly experience psychological distress driven by academic demands, social transitions, and financial pressures. Mindfulness-based interventions have emerged as scalable approaches to improve mental health. However, evidence comparing their effectiveness across delivery formats remains limited. This systematic review aimed to evaluate the effectiveness of mindfulness-based interventions in reducing stress, anxiety, and depression and to compare outcomes across in-person, digital, and hybrid modalities. Methods: This review followed PRISMA 2020 guidelines and included randomized controlled trials (RCTs) published between January 2020 and December 2025 on mindfulness-based interventions among university students aged 18 years and older. Intervention duration ranged from 3 days to 12 weeks, with most lasting 4 to 8 weeks, and outcomes included validated measures of stress, anxiety, or depression. Literature research was conducted in PubMed, PsycINFO, CINAHL, Scopus, and Web of Science, and two reviewers independently screened studies, extracted data, and assessed methodological quality using the Joanna Briggs Institute checklist. Results: A total of 24 RCTs were included, with the highest representation from the United States and China (n = 4 each), followed by the United Kingdom and Canada. Mindfulness-based interventions demonstrated consistent reductions in depression and generally positive effects on anxiety, while effects on stress were more variable. Digital interventions demonstrated effectiveness comparable to in-person programs, though outcomes varied by intervention structure and level of guidance. Conclusions: Mindfulness-based interventions are effective in improving mental health among university students, particularly for depression and anxiety. Multi-week programs and guided digital delivery appear to enhance effectiveness and scalability.