DIGITAL MENTAL HEALTH: THE FUTURE IS NOW
Keywords:
Artificial Intelligence, COVID-19, Digital Health, Health Education, Precision Medicine, Technology, Telemedicine
Abstract
The COVID-19 pandemic has catalysed a significant shift towards digital health in mental health care, integrating telemedicine, telepsychiatry, and AI-driven tools as vital components of service delivery. These technologies facilitate remote support through telepsychiatry sessions, mood-tracking applications, and online cognitive behavioural therapy (CBT), demonstrating effectiveness in alleviating symptoms of anxiety and depression. Additionally, the application of artificial intelligence and machine learning enables the identification of behavioural patterns and the development of personalised treatment plans. However, the rapid adoption of these innovations raises concerns regarding privacy, ethical considerations, and digital dependency, underscoring the necessity of enhancing digital competency within mental health education. This article examines the transformative impact of digital tools on mental health care and emphasises the critical role of global organisations and educational institutions in fostering digital literacy among practitioners, thereby maximising the benefits of technology to improve care quality and accessibility for patients.
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References
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28. Mucic, J.Shore, D.M. Hilty (2023) .The World Psychiatric Association Telepsychiatry Global Guidlines. Journal of Technology in Behavioral Science. https://doi.org/10.1007/s41347-023-00339-w
29. Shore, J. H., Yellowlees, P., Caudill, R., Johnston, B., Turvey, C., Mishkind, M., & Hilty, D. M. (2018). Best practices in videocon- ferencing-based telemental health April 2018. Telemedicine and E-Health, 24(11), 827–832.
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2. Faurholt-Jepsen M, Vinberg M, Christensen EM, Frost M, Bardram JE, Kessing LV (2015).Daily electronic self-monitoring in bipolar disorder using smartphones—the MONARCA I trial: A randomized, placebo-controlled, single-blind, parallel group trial. J Med Internet Res. 2015;17(7):e193. doi: 10.2196/jmir.4205
3. Nicholas J, Fogarty AS, Boydell K, Christensen H. (2017).The Reviews Are in: A Qualitative Content Analysis of Consumer Perspectives on Apps for Bipolar Disorder. J Affect Disord. 227:716-724. doi: 10.1016/j.jad.2017.11.058
4. Ben-Zeev D, Scherer EA, Wang R, Xie H, Campbell AT. (2015). Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health. Psychiatr Rehabil J. 38(3):218-226. doi: 10.1037/prj0000130
5. Faurholt-Jepsen M, Frost M, Martiny K, Tuxen N, Rosenberg N, Busk J, et al. (2015). Reducing the rate and duration of Re-Admissions among patients with unipolar disorder and bipolar disorder using smartphone-based monitoring and treatment—The RADMIS trials. J Technol Behav Sci. 1(3):96-104. doi: 10.1007/s41347-016-0002-x
6. Torous, J., Roberts, L. W., & Joyce, M. (2017). The problem with mobile apps for bipolar disorder. JAMA Psychiatry, 74(9), 895–896. https://doi.org/10.1001/jamapsychiatry.2017.1112
7. Karyotaki E, Riper H, Twisk J, Hoogendoorn A, Kleiboer A, Mira A, Mackinnon A, Meyer B, Botella C, Littlewood E, Andersson G, Christensen H, Klein JP, Schröder J, Bretón-López J, Scheider J, Griffiths K, Farrer L, Huibers MJ, Phillips R, Gilbody S, Moritz S, Berger T, Pop V, Spek V, Cuijpers P. (2017). Efficacy of Self-guided Internet-Based Cognitive Behavioral Therapy in the Treatment of Depressive Symptoms: A Meta-analysis of Individual Participant Data. JAMA Psychiatry. 74(4):351-359. doi:10.1001/jamapsychiatry.2017.0044
8. Titov N, Dear BF, Staples LG, Terides MD, Karin E, Sheehan J, Johnston L, Gandy M, Fogliati VJ, Wootton BM, McEvoy PM. (2015). Disorder-specific versus transdiagnostic and clinician-guided versus self-guided treatment for major depressive disorder and comorbid anxiety disorders: A randomized controlled trial. J Anxiety Disord; 35:88-102. doi:10.1016/j.janxdis.2015.08.002
9. Andersson G, Carlbring P, Berger T, Almlöv J, Cuijpers P. (2009). What makes Internet therapy work? Cogn Behav Ther. 38 Suppl 1:55-60. doi:10.1080/16506070902916400
10. Sharma G, Devan K. The effectiveness of telepsychiatry: thematic review. BJPsych Bull. 2023 Apr;47(2):82-89. doi: 10.1192/bjb.2021.115.
11. Seidel, R. W., & Kilgus, M. D. (2014). Agreement between telepsychiatry assessment and face-to-face assessment for emergency department psychiatry patients. Journal of telemedicine and telecare, 20(2), 59-62.
12. Moreno, F. A., Chong, J., Dumbauld, J., Humke, M., & Byreddy, S. (2012). Use of standard Webcam and Internet equipment for telepsychiatry treatment of depression among underserved Hispanics. Psychiatric Services, 63(12), 1213-1217.
13. Ruskin, P. E., Silver-Aylaian, M., Kling, M. A., Reed, S. A., Bradham, D. D., Hebel, J. R., … & Hauser, P. (2004). Treatment outcomes in depression: comparison of remote treatment through telepsychiatry to in-person treatment. American Journal of Psychiatry, 161(8), 1471-1476.
14. Barnett, M.L., Ray, K.N., Souza, J., & Mehrotra, A. (2018). Trends in Telemedicine Use in a Large Commercially Insured Population, 2005-2017. JAMA, 320(20), 2147-2149.
15. Andersson, G., Carlbring, P., Holmstrom, A., et al. (2006). Internet-based self-help with therapist feedback and in vivo group exposure for social phobia: a randomized controlled trial. Journal of Consulting and Clinical Psychology, 74(4), 677-686.
16. Huxley CJ, Atherton H, Watkins JA, Griffiths F. (2015). Digital communication between clinician and patient and the impact on marginalised groups: a realist review in general practice. Br J Gen Pract, 65(641), e813-e821.
17. Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016). Cross-trial prediction of treatment outcome in depression: A machine learning approach. The Lancet Psychiatry, 3, 243-250. doi:10.1016/S2215-0366(15)00471-X.
18. Edgcomb JB, Zima B. Machine learning, natural language processing, and the electronic health record: innovations in mental health services research. Psychiatr Serv. 2019;70(4):346-9. doi:10.1176/appi.ps.201800401
19. Elgendi M, Menon C. Assessing anxiety disorders using wearable devices: Challenges and future directions. Brain Sci 2019;9(3):50. doi:10.3390/brainsci9030050.
20. Hilty DM, Armstrong CM, Stewart A, et al. A framework of sensor, wearable and remote patient monitoring competencies for clinical care and training: Scoping review. J Tech Behav Sci. 2021 doi:10.1007/s41347-020-00190-3.
21. Basumallick C. What is a chatbot? Meaning, working, types, and examples. Retrieved from: https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-chatbot/
22. Abd-Alrazaq AA, Alajlani M, Alalwan AA, Bewick BM, Gardner P, Househ M. An overview of the features of chatbots in mental health: A scoping review. Int J Med Inform. 2019 132:103978. doi: 10.1016/j.ijmedinf.2019.103978.
23. Vaidyam AN, Wisniewski H, Halamka, JD et al. Chatbots and conversational agents in mental health: A review of the psychiatric landscape. The Canadian Journal of Psychiatry, 2019; 64(7), 456–464. https://doi.org/10.1177/0706743719828977
24. Cheng Y, Jiang, H. How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of Broadcasting and Electronic Media, 2020b; 65(4), 592–614. doi: 10.1080/08838151.2020.1834296
25. Cheng Y, Jiang H. AI-powered mental health chatbots: Examining users’ motivations, active communicative action, and engagement after mass-shooting disasters. Journal of Contingencies and Crisis Management, 2020a; 28, 339–354. doi: 10.1111/1468-5973.12319
26. Inkster B, Sarda S. Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study. JMIR mHealth and uHealth, 2018; 6(11), e12106. https://doi.org/ 10.2196/12106
27. Hilty DM, Torous J, Parish M, et al. A literature review comparing clinicians’ approaches and skills to in-person, synchronous and asynchronous care: moving toward asynchronous competencies to ensure quality care. Telemed J E-Health. 2020;10.1089/tmj.2020.0054.
28. Mucic, J.Shore, D.M. Hilty (2023) .The World Psychiatric Association Telepsychiatry Global Guidlines. Journal of Technology in Behavioral Science. https://doi.org/10.1007/s41347-023-00339-w
29. Shore, J. H., Yellowlees, P., Caudill, R., Johnston, B., Turvey, C., Mishkind, M., & Hilty, D. M. (2018). Best practices in videocon- ferencing-based telemental health April 2018. Telemedicine and E-Health, 24(11), 827–832.
30. World Psychiatric Association. (2021). WPA telepsychiatry global guidelines. Retrieved from https://www.wpanet.org/_files/ugd/ 842ec8_ffbb5cd0d874414383cffeee34b511ec.pdf
31. DECIDE-AI Steering Group. DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence. Nat Med. 2021;27(2):186-7. doi:10.1038/s41591-021-01229-5
32. Torous J, Baker JT. Why psychiatry needs data science and data science needs psychiatry: connecting with technology. JAMA Psychiatry. 2016;73(1):3-4.
33. Chassagnon G, Dohan A. Artificial intelligence: from challenges to clinical implementation. Diagn Interv Imaging. 2020;101(12):763-4. doi:10.1016/j.diii.2020.10.007
Published
2024-09-28
How to Cite
1.
Mucic D. DIGITAL MENTAL HEALTH: THE FUTURE IS NOW. J Pak Psychiatr Soc [Internet]. 2024Sep.28 [cited 2025Jan.21];21(03):1-. Available from: https://jpps.pk/index.php/journal/article/view/967
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Editorial
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