DMHI for University Students

Mental health difficulties are increasingly recognised as a major global public health concern, particularly among young people (Madrid‐Cagigal, 2025). Evidence suggests that up to 60% of young adults may meet diagnostic criteria for depression or anxiety at some point during early adulthood (McGorry et al., 2024). This trend appears to have intensified in recent years. In the United States, approximately 75% of young people have reported a deterioration in their mental health since the onset of the COVID-19 pandemic (Bell et al., 2023). Rising levels of self-reported poor mental health further reflect this pattern (Wiens et al., 2020). Importantly, nearly two-thirds of mental health disorders emerge before the age of 25, highlighting adolescence and young adulthood as critical periods for early identification and intervention (Solmi et al., 2022).

Universities therefore represent a key context for mental health support. Longitudinal research indicates that around one-third of first-year undergraduate students begin university with clinically significant symptoms of depression or anxiety (Duffy et al., 2020). This early onset and high prevalence create substantial demand for campus-based counselling and psychological services, many of which are already operating under resource constraints. As a result, higher education institutions are increasingly exploring alternative and complementary approaches to expand access to care (Madrid‐Cagigal, 2025).

Digital mental health interventions (DMHIs) have emerged as one such approach. These interventions offer the potential to increase accessibility while reducing pressure on university counselling services (Schueller & Torous, 2020). Because digital platforms can be delivered at scale and often require fewer personnel resources, they are particularly well suited to student populations where demand frequently exceeds capacity (Madrid‐Cagigal, 2025).

DMHIs can be delivered in different formats. Broadly, they fall into two categories: fully automated interventions and programs that incorporate human support, such as therapist or peer guidance (Madrid‐Cagigal, 2025). Within guided models, support may be asynchronous—where communication occurs with a delay, such as via email or messaging—or synchronous, involving real-time interaction through phone or video sessions (Philippe et al., 2022). These variations allow institutions to tailor interventions according to available resources and student needs.

In terms of therapeutic orientation, cognitive behavioural therapy (CBT) remains the most widely implemented model within digital mental health interventions (Balcombe & De Leo, 2022). Internet-based CBT (iCBT) has been associated with significant reductions in psychological symptoms among university students (McCall et al., 2018). Acceptance and Commitment Therapy (ACT) has also been successfully adapted for digital delivery, with evidence supporting its effectiveness in reducing anxiety and depressive symptoms in higher education populations (Levin et al., 2022; Hemmings et al., 2021). These interventions can be delivered through a variety of technological modalities, including smartphone applications, web-based platforms, virtual or augmented reality environments, and AI-driven chatbots, thereby enhancing flexibility and reach (Madrid‐Cagigal, 2025).

Evidence from meta-analyses further supports the role of digital interventions in university settings. Ferrari et al. (2022) reported small-to-moderate improvements in psychological wellbeing among students engaging with DMHIs. Notably, CBT-based interventions demonstrated stronger effects in reducing symptoms of depression and anxiety compared to other therapeutic approaches, such as mindfulness-based therapies (Madrid‐Cagigal, 2025). These findings are consistent with an umbrella review synthesising digital health interventions for university students, which identified computer-based CBT as a key factor associated with intervention effectiveness (Harith et al., 2022).

Taken together, the evidence suggests that digital mental health interventions represent a promising and scalable response to the growing mental health needs of university students (Madrid‐Cagigal, 2025). Guided formats may provide additional benefit for individuals experiencing more severe depressive symptoms, while fully automated programs can still achieve meaningful symptom reduction, particularly for anxiety. By integrating digital interventions—especially those incorporating targeted guidance—universities may enhance accessibility, efficiency, and overall capacity within student mental health services.

References:

Balcombe, L., & De Leo, D. (2022). Evaluation of the use of digital mental health platforms and interventions: Scoping review. International Journal of Environmental Research and Public Health, 20(1), 362.

Bell, I. H., Nicholas, J., Broomhall, A., Bailey, E., Bendall, S., Boland, A., ... & Thompson, A. (2023). The impact of COVID-19 on youth mental health: A mixed methods survey. Psychiatry Research, 321, 115082.

Duffy, A., Keown-Stoneman, C., Goodday, S., Horrocks, J., Lowe, M., King, N., ... & Saunders, K. E. (2020). Predictors of mental health and academic outcomes in first-year university students: Identifying prevention and early-intervention targets. BJPsych Open, 6(3), e46.

Ferrari, M., Allan, S., Arnold, C., Eleftheriadis, D., Alvarez-Jimenez, M., Gumley, A., & Gleeson, J. F. (2022). Digital interventions for psychological well-being in university students: Systematic review and meta-analysis. Journal of Medical Internet Research, 24(9), e39686.

Harith, S., Backhaus, I., Mohbin, N., Ngo, H. T., & Khoo, S. (2022). Effectiveness of digital mental health interventions for university students: an umbrella review. PeerJ, 10, e13111.

Hemmings, N. R., Kawadler, J. M., Whatmough, R., Ponzo, S., Rossi, A., Morelli, D., ... & Plans, D. (2021). Development and feasibility of a digital acceptance and commitment therapy–based intervention for generalized anxiety disorder: Pilot acceptability study. JMIR Formative Research, 5(2), e21737.

Madrid‐Cagigal, A., Kealy, C., Potts, C., Mulvenna, M. D., Byrne, M., Barry, M. M., & Donohoe, G. (2025). Digital mental health interventions for university students with mental health difficulties: A systematic review and meta‐analysis. Early Intervention in Psychiatry, 19(3), e70017.

McCall, H. C., Richardson, C. G., Helgadottir, F. D., & Chen, F. S. (2018). Evaluating a web-based social anxiety intervention among university students: Randomized controlled trial. Journal of Medical Internet Research, 20(3), e91.

McGorry, P. D., Mei, C., Dalal, N., Alvarez-Jimenez, M., Blakemore, S. J., Browne, V., ... & Killackey, E. (2024). The Lancet Psychiatry Commission on youth mental health. The Lancet Psychiatry, 11(9), 731–774.

Schueller, S. M., & Torous, J. (2020). Scaling evidence-based treatments through digital mental health. American Psychologist, 75(8), 1093.

Solmi, M., Radua, J., Olivola, M., Croce, E., Soardo, L., Salazar de Pablo, G., ... & Fusar-Poli, P. (2022). Age at onset of mental disorders worldwide: Large-scale meta-analysis of 192 epidemiological studies. Molecular Psychiatry, 27(1), 281–295.

Wiens, K., Bhattarai, A., Pedram, P., Dores, A., Williams, J., Bulloch, A., & Patten, S. (2020). A growing need for youth mental health services in Canada: Examining trends in youth mental health from 2011 to 2018. Epidemiology and Psychiatric Sciences, 29, e115.