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The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality
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As the COVID‐19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies – such as smartphone apps, virtual reality, chatbots, and social media – have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for “digital phenotyping” and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self‐management of psychological well‐being and early intervention, along with more nascent research supporting their use in clinical management of long‐term psychiatric conditions – including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders – as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real‐world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i‐PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.
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“In particular, widespread access to technology has not been accompanied by sufficient mental health advocacy, leaving the detrimental effects of technological exposure unaddressed [49].”
From SAFEvR MentalVeRse.app: Development of a Free Immersive Virtual Reality Exposure Therapy for Acrophobia and Claustrophobia (2024) · cites this paper as [49]
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“Mobile health interventions, such as smartphone applications and text messaging, are commonly used to target a wide range of health behaviors in adults and rep resent a rapidly growing area among youth 290 .”
From Substance use disorders: a comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention (2023) · cites this paper as [290]
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“Virtual reality (VR) is an emerging technology with the potential to transform mental health treatment [1-3].”
From A Virtual Reality–Based Cognitive Defusion Application for Youth Depression and Anxiety: Mixed Methods Experimental Study. (2025) · cites this paper as [3]
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“Digital mental health interventions have the potential to decrease barriers to mental health treatment.185”
From Digital Ecosystems, Children, and Adolescents: Technical Report (2026) · cites this paper as [185]
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“Research into problem gaming has advanced particularly in the areas of epidemiology, neurobiology and interventions, and has also stimulated scientific interest in problematic engagement in other online activities (e.g., social networking sites, Internet pornography use, and ecommerce) 213,214 .”
From Emerging experience with selected new categories in the ICD‐11: complex PTSD, prolonged grief disorder, gaming disorder, and compulsive sexual behaviour disorder (2022) · cites this paper as [214]
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“Moreover, analysis of text messages on social media, in combination with machine and deep-learning techniques, may provide novel ways of identifying emerging mental disorders [131].”
From Towards a youth mental health paradigm: a perspective and roadmap (2023) · cites this paper as [131]
In the citing paper
“User‐facing apps coupled with assistance from coaches, and other telepsychiatry tools, can help address the treatment gap, but barriers related to culture, policy and funding issues remain195, 238.”
From Mental health care for older adults: recent advances and new directions in clinical practice and research (2022) · cites this paper as [238]
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“An increasing number of CAIs have emerged in recent years, as the digital medical and mobile health fields have grown [26], solving and improving a larger range of mental health issues in addition to depression and anxiety.”
From Conversational Agent Interventions for Mental Health Problems: Systematic Review and Meta-analysis of Randomized Controlled Trials (2023) · cites this paper as [26]
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“Nonadherence to ADTs is associated with increased severity of depressive symptoms, risk of relapse (ie, early return of symptoms within the expected duration of a current episode) and recurrence (ie, a new major depressive episode following a full-episode remission), and healthcare resource utilization, as well as significantly higher”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [43]
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“With the widespread, everyday use of smartphones, these emerging digital tools could function as efficient, widely available mental health resources for orthopedic clinicians to offer their patients [16,20-22].”
From A Digital Mental Health Intervention in an Orthopedic Setting for Patients With Symptoms of Depression and/or Anxiety: Feasibility Prospective Cohort Study (2022) · cites this paper as [22]
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Metadata
- Authors
- Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt‐Jepsen, M., Whelan, P., Carvalho, A. F., Keshavan, M., Linardon, J., & Firth, J.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- Greater Manchester Mental Health NHS Foundation Trust (GB)
- The University of Melbourne (AU)
- Rigshospitalet (DK)
- Centre for Addiction and Mental Health (CA)
- Deakin University (AU)
- University of Manchester (GB)
- ORCID
- Venue
- World Psychiatry
- Publisher
- Wiley
- Published
- 2021-09-09
- Volume
- 20
- Issue
- 3
- Pages
- 318-335
- ISSN
- 1723-8617 (ISSN-L)
- 2051-5545
- PMC
- PMC8429349
- OpenAlex
- W3197232629
- Cited by
- 1,199
- References
- 227
- Citations by year
- 2026: 314 · 2025: 328 · 2024: 236 · 2023: 206 · 2022: 106 · 2021: 9
- FWCI
- 123.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- World Psychiatry (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Deakin Research Online (Deakin University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Research Explorer (The University of Manchester) (submittedVersion, other-oa)
- Funders
- American Psychiatric Association Foundation
- UK Research and Innovation (P123958)
- National Institute for Health and Care Research (NIHR300794)
- National Institute of Mental Health (K23MH116130)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Keywords
- digital psychiatry, mental health apps, virtual reality, chatbots, social media, digital phenotyping, artificial intelligence, telemedicine, remote intervention, i-PARIHS framework, implementation research, major depressive disorder, anxiety disorders, bipolar disorder, psychosis, eating disorders, substance use disorders, child and adolescent mental health, early intervention
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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