Brief digital mental health interventions for adults: user experience, real-world uptake, AI priorities, and economic implications
DOI:
https://doi.org/10.3846/bm.2026.2562Abstract
Brief digital mental health interventions (DMHIs) represent a promising strategy for addressing the increasing global demand for accessible psychological support. Despite high usability and satisfaction reported in clinical trials, real-world adoption remains limited. This study examines the drivers of this adoption gap by synthesizing evidence from user experience research, digital health competence studies, and health technology assessment literature. The Article introduces the Pharmaco Economic Digital Twin (PEDT) concept as a framework for integrating clinical, behavioral, and economic data streams into adaptive evaluation models. Results indicate that sustained engagement with digital mental health tools depends on maintaining an equilibrium between therapeutic effectiveness and user experience quality while simultaneously strengthening workforce digital competence and establishing sustainable reimbursement pathways.
Keywords:
digital mental health, artificial intelligence, user experience, digital competence, digital twins, health economicsHow to Cite
Antonini, D., Basile, M., Di Brino, E., Falasca, G., Fiore, A., Giorgio, L., Laurita, R., Oradei, M., Rumi, F., & Cicchetti, A. (2023). HTA31 Health Technology Assessment (HTA) of clinical care pathways: Peritoneal dialysis versus hemodialysis. Value in Health, 26(12), S326. https://doi.org/10.1016/j.jval.2023.09.1716
Andrews, G., Basu, A., Cuijpers, P., Craske, M. G., McEvoy, P., English, C. L., & Newby, J. M. (2018). Computer therapy for the anxiety and depression disorders is effective, acceptable and practical health care: An updated meta-analysis. Journal of Anxiety Disorders, 55, 70–78. https://doi.org/10.1016/j.janxdis.2018.01.001
Belančić, A., & Janković, S. (2026). Pharmacoeconomic digital twins: A visionary framework for the future of precision pharmacology. Health Policy and Technology, 15(4), Article 101181. https://doi.org/10.1016/j.hlpt.2026.101181
Bennebach, M., Schmidt, L., & Kowalski, K. (2026). Digital twin monitoring of pressure vessels for predictive maintenance in extreme environments. Procedia Structural Integrity, 54, 112–119.
Cobben, D., Van der Veen, M., & Hiemstra, R. (2026). Innovation ecosystem robustness: A network-theoretic approach to systemic resilience. Journal of Business Research, 172, Article 114421.
Contreras, F., Jimenez, A., & Gupta, S. (2025). Bridging the educational divide: Healthcare training gaps in the management of obesity. Obesity Pillars, 14, Article 100158.
Dixon-Woods, M., Agarwal, S., Jones, D., Young, B., & Sutton, A. (2005). Synthesising qualitative and quantitative evidence: A review of possible methods. Journal of Health Services Research & Policy, 10(1), 45–53. https://doi.org/10.1258/1355819052801804
Geelen, S. J. G., Niemeijer, A. S., Scholten-Jaegers, S. M. H. J., van Uden, D., Verwilligen, R. A. F., Bosma, E., & Nieuwenhuis, M. K. (2026). Exploring how healthcare professionals in burn aftercare approach self-management support for burn survivors: A qualitative, grounded theory study. Patient Education and Counseling, 148, Article 109538. https://doi.org/10.1016/j.pec.2026.109538
Gehder, S., & Goeldner, M. (2025). Unpacking performance factors of innovation systems and studying Germany’s attempt to foster the role of the patient through a market access pathway for digital health applications (DiGAs): Exploratory mixed methods study. Journal of Medical Internet Research, 27, Article e66356. https://doi.org/10.2196/66356
Gupta, T., Devji, S., & Tripathi, A. K. (2025). Investigating the impact of sentiments on stock market using digital proxies: Current trends, challenges, and future directions. Expert Systems with Applications, 285, Article 127864. https://doi.org/10.1016/j.eswa.2025.127864
Gomes, M., Murray, E., & Raftery, J. (2022). Economic evaluation of digital health interventions: Methodological issues and recommendations for practice. PharmacoEconomics, 40(4), 367–378. https://doi.org/10.1007/s40273-022-01130-0
Haycock, M., Willows, L. P., & Wickstead, R. (2023). HTA34 Demonstrating similar or greater health benefits based on indirect evidence: A review of NICE evaluations that include a cost-comparison approach. Value in Health, 26(12), S326–S327. https://doi.org/10.1016/j.jval.2023.09.1719
Konttila, J., Siira, H., Kyngäs, H., Lahtinen, M., Elo, S., Kääriäinen, M., Kaakinen, P., Oikarinen, A., Yamakawa, M., Fukui, S., Utsumi, M., Higami, Y., Higuchi, A., & Mikkonen, K. (2019). Healthcare professionals’ competence in digitalisation: A systematic review. Journal of Clinical Nursing, 28(5–6), 745–761. https://doi.org/10.1111/jocn.14710
Kryzhanivska, K., Blomqvist, K., & Mention, A.-L. (2026). Digital emergent organizing and coordination of large-scale collaborative action in times of war. European Management Journal. https://doi.org/10.1016/j.emj.2026.01.003
Lee, K., Paek, H., Ofoegbu, N., Rube, S., Higashi, M. K., Dawould, D., Xu, H., Shi, L., & Wang, X. (2025). A4SLR: An agentic artificial intelligence-assisted systematic literature review framework to augment evidence synthesis for health economics and outcomes research and health technology assessment. Value in Health, 28(11), 1655–1664. https://doi.org/10.1016/j.jval.2025.08.002
Mikkonen, K., Tomietto, M., Lee, J. J., Ye, F., Mandysova, P., Pekara, J., Kommusaar, J., Kangasniemi, M., Liao, X., Cicolini, G., Simonetti, V., Unsworth, J., Vizcaya-Moreno, M. F., Domingo-Pozo, M., Liu, M. F., Yamakawa, M., Utsumi, M., Domeisen Benedetti, F., Fringer, A., .... Jarva, E. (2026). Digital health competence among healthcare professionals: A cross-sectional cluster analysis across 19 countries and regions. International Journal of Nursing Studies, 176, Article 105348. https://doi.org/10.1016/j.ijnurstu.2026.105348
Neubert, M., Sünkel, E., Planert, J., Hildebrand, A., & Klucken, T. (2026). Real-world data on uptake and use of digital mental health interventions among waitlisted patients with various mental disorders. Internet Interventions, 44, Article 100915. https://doi.org/10.1016/j.invent.2026.100915
Opie, J. E., McIntosh, J., Esler, J., D’Alfonso, S., Arnold, C., & Lancaster, T. M. J. (2026). Brief digital mental health interventions (DMHIs) Part II: Clinical integration, economic considerations, and future directions. Mental Health and Digital Technologies, 3(1), 46–73. https://doi.org/10.1108/MHDT-07-2025-0050
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491–497. https://doi.org/10.1093/jamia/ocz192
Riley, D., Fox, A., Hirst, A., Marciniak, A., & Heron, L. (2023). SA66 expert elicitation techniques: Informing application in HTA decision-making. Value in Health, 26(12), S554. https://doi.org/10.1016/j.jval.2023.09.2975
Rixon, A., Phelan, A., Ekberg, S., Senyard, J., Mashhady, A., Shaw, A., & Birdthistle, N. (2026). Entrepreneurship education for nurses and healthcare professionals: A scoping review and future research agenda. Nurse Education in Practice, 91, Article 104716. https://doi.org/10.1016/j.nepr.2026.104716
Sahoo, R. K., Sahoo, K. C., Negi, S., Baliarsingh, S. K., Panda, B., & Pati, S. (2025). Health professionals’ perspectives on the use of artificial intelligence in healthcare: A systematic review. Patient Education and Counseling, 134, Article 108680. https://doi.org/10.1016/j.pec.2025.108680
Song, P., Jiang, D., Zhou, J., Zhu, Y., Abdul Manaf, R., Adamu Bojude, D., Laurette Agbre-Yace, M., Ali, S., Allen, O., Anyasodor, A. E., Aranda, Z., Bahattab, A., Bodomo, A., Borrescio-Higa, F., Buchtova, M., Buljan, N., Deshmukh, V., Díaz-Castro, L., Cheema, S., ... Rudan, I. (2026). Research priorities for data science and artificial intelligence in global health: An international consensus exercise. The Lancet Global Health, 14(3), e455–e465. https://doi.org/10.1016/S2214-109X(25)00473-5
Seleiro, G., Menon, A., Haria, K., Whalen, J. D., & Tutein Nolthenius, J. B. (2025). HTA74 Clinical and economic factors underlying QALY weights and severity modifiers in NICE health technology evaluations: An analysis of recent appraisals. Value in Health, 28(12), S416. https://doi.org/10.1016/j.jval.2025.09.1859
Voets, M. M., Veldwijk, J., Koffijberg, H., & Frederix, G. W. J. (2026). Methodological challenges in the economic evaluation of artificial intelligence–based health technologies: A systematic review. Value in Health, 29(2), 210–220.
Zhang, J., Zhang, Q., Fang, X., Guo, G., & Wang, X. (2026). Advances in skin-on-a-chip technology: Functional innovations and translational challenges. TrAC Trends in Analytical Chemistry, 194, Article 118500. https://doi.org/10.1016/j.trac.2025.118500
Downloads
Published
Conference Event
Section
Copyright
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
