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Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications
Open accessAbstract
Mental disorders are highly prevalent and often remain untreated. Many limitations of conventional face-to-face psychological interventions could potentially be overcome through Internet-based and mobile-based interventions (IMIs). This chapter introduces core features of IMIs, describes areas of application, presents evidence on the efficacy of IMIs as well as potential effect mechanisms, and delineates how Artificial Intelligence combined with IMIs may improve current practices in the prevention and treatment of mental disorders in adults. Meta-analyses of randomized controlled trials clearly show that therapist-guided IMIs can be highly effective for a broad range of mental health problems. Whether the effects of unguided IMIs are also clinically relevant, particularly under routine care conditions, is less clear. First studies on IMIs for the prevention of mental disorders have shown promising results. Despite limitations and challenges, IMIs are increasingly implemented into routine care worldwide. IMIs are also well suited for applications of Artificial Intelligence and Machine Learning, which provides ample opportunities to improve the identification and treatment of mental disorders. Together with methodological innovations, these approaches may also deepen our understanding of how psychological interventions work, and why. Ethical and professional restraints as well as potential contraindications of IMIs, however, should also be considered. In sum, IMIs have a high potential for improving the prevention and treatment of mental health disorders across various indications, settings, and populations. Therefore, implementing IMIs into routine care as both adjunct and alternative to face-to-face treatment is highly desirable. Technological advancements may further enhance the variability and flexibility of IMIs, and thus even further increase their impact in people's lives in the future.
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“In 2019, Ebert et al [45] reported that only 4% of commercial apps for depression and anxiety symptoms (not only CAI apps) had been subjected to rigorous clinical studies.”
InfluentialEngagement in digital interventions.
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“It provides an overview of the current state of digital mental health interventions and an introduction to their potential to improve the efficacy of treatment and intervention strategies through AI.”
BackgroundChatGPT is not ready yet for use in providing mental health assessment and interventions
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“AI chatbots, as these tools are commonly referred to, may be useful in the delivery of evidence-based mental health interventions (10).”
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“DMHI span a wide range of interventions (Ebert et al., 2019), including virtual/augmented reality programs (e.g., Aboujaoude et al., 2015; Carl et al., 2019;)”
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Self-citationFactors affecting mental health of seafarers on board merchant ships: a systematic review
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“Through ITCs, the tools and solutions with which to face, improve and manage the mental health of this category of workers, all too neglected to date, shall be identified [52 – 54].”
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Dederichs, M., Weber, J., Pischke, C. R., Angerer, P., & Apolinário-Hagen, J. (2021) · Internet Interventions · doi:10.1016/j.invent.2021.100398 · Cited by 50
Self-citationPersonalised depression forecasting using mobile sensor data and ecological momentary assessment
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Self-citationZantvoort, K., Isacsson, N. H., Funk, B., & Kaldo, V. (2024) · Digital Health · doi:10.1177/20552076241248920 · Cited by 18
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“, asynchronous feedback), and technical implementation (e.g., virtual reality; Ebert et al., 2019).”
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“Solche eMHSs umfassen ein breites Spektrum an digitalen Interventionen, von Stressmanagement-Apps bis hin zur Psychotherapie via Videokonferenz, die den Zugang zu Gesundheitsangeboten erleichtern und bestimmte Zielgruppen besser erreichen könnten als Face-toface-Angebotealleine[13].”
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“…by their application field (eg, prevention vs therapy), technical modality (eg, app and virtual reality), guidance (varying in source, timing, and intensity; eg, via videoconference) or application area (eg, stand-alone vs blended care), and theory base (eg, cognitive behavioral therapy) [5,6].”
BackgroundSelf-citationFaurholt‐Jepsen, M., Kyster, N. B., Dyreholt, M. S., Christensen, E. M., Bondo-Kozuch, P., Lerche, A. S., Smidt, B., Knorr, U., Brøndmark, K., Cardoso, A.-M. B., et al. (2023) · Trials · doi:10.1186/s13063-023-07625-1 · Cited by 10
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Fabritiis, M. D., Trisolini, F., Bertuletti, G., Fagadau, I. D., Ginelli, D., Lalopa, K. P., Peverelli, L., Pirola, A., Sala, G., Maisto, M., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19052711 · Cited by 7
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“…et al., 2023) and promising results from studies on psychological detachment and recovery training interventions (e.g., Hahn et al., 2011; Karabinski et al., 2021), a majority of individuals suffering from stress and other mental health-related disorders, remain untreated (Ebert et al., 2019).”
ResultTherapy as an NLP Task: Comparing LLMs and Human Peers Behaviors in CBT Sessions
Iftikhar, Z., Ransom, S., Xiao, A., Nugent, N., & Huang, J. (2024) · arXiv · doi:10.48550/arxiv.2409.02244 · Cited by 6
“Asignif-icantareaoffocusistheuseofconversationalagentsorchatbotsforstandalonesupport[14,22,29,54], eitherasagentsprovidingpsychoeducationalcontentorasapsychotherapist[38,70,86].”
Therapy as an NLP Task: Comparing LLMs and Human Peers Behaviors in CBT Sessions
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“Asignif-icantareaoffocusistheuseofconversationalagentsorchatbotsforstandalonesupport[14,22,29,54], eitherasagentsprovidingpsychoeducationalcontentorasapsychotherapist[38,70,86].”
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“In ML, many neural network models can account for time-dependent input, but they require large datasets to perform well (Bzdok and Meyer-Lindenberg, 2018; Ebert et al., 2019).”
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“…(Cognition Companion, Betthauser et al., 2020; Autonomous Connectivity Independent System for Remote align with current theory, which suggests that digital interventions may serve as an independent intervention or may be used as a complement to other intervention approaches (Ebert et al., 2019).”
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Metadata
- Authors
- Ebert, D. D., Harrer, M., Apolinário-Hagen, J., & Baumeister, H.
- Affiliations
- Vrije Universiteit Amsterdam (NL)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- FernUniversität in Hagen (DE)
- Universität Ulm (DE)
- Corresponding author
- Ebert, David Daniel
- Venue
- Advances in Experimental Medicine and Biology
- Publisher
- Springer Singapore
- Published
- 2019
- Volume
- 1192
- Pages
- 583-627
- ISSN
- 0065-2598 (ISSN-L)
- 2214-8019
- OpenAlex
- W2985217336
- Semantic Scholar
- 5a72a586e0eb3ea08d584944e4fb08ff2fb960f0
- Cited by
- 76
- References
- 333
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 21 · 2023: 10 · 2022: 9 · 2021: 12 · 2020: 2
- FWCI
- 35.41
- Type
- book chapter
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Advances in experimental medicine and biology (publishedVersion)
- PubMed (publishedVersion)
- VU Research Portal (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Psychiatry and Mental health › Mental Health Treatment and Access
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- online intervention, mobile-based interventions, digital mental health, mental illness, artificial intelligence, machine learning, therapist-guided intervention, mental disorder prevention, treatment efficacy, effect mechanism, adults
- MeSH terms
- Adult, Artificial Intelligence, Mental Disorders, Humans, Psychotherapy, Telemedicine, Internet, Mobile Applications
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