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Cited by 78Digital Mental Health InterventionsMental Health Treatment and AccessMental Health Research Topics

The DOI matches its Crossref record; 333 of the 334 references it cites were found in a registry or an index, and 1 are not checked yet.

Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications

Open access
Ebert, David Daniel, Harrer, Mathias, Apolinário-Hagen, Jennifer, Baumeister, Harald·2019·Advances in Experimental Medicine and Biology
DOI 10.1007/978-981-32-9721-0_29
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Abstract

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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Crossref · 10.1007/978-981-32-9721-0_29

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Research works printed but not linked: 10

  1. World Health Organization. The Global Burden of Disease: 2004 update. 2008 PMID:15572474.
  2. Buntrock C, Ebert DD, Lehr D, Smit F, Riper H, Berking M, Cuijpers P. Effect of a web-based guided self-help intervention for prevention of major depression in adults with subthreshold depression. JAMA. 2016;315(17):1854. https://doi.org/10.1001/jama.2016.4326 American Medical Association. · DOI · Paper page
  3. Riper H, Blankers M, Hadiwijaya H, Cunningham J, Clarke S, Wiers R, Ebert DD, Cuijpers P. Effectiveness of guided and unguided low-intensity internet interventions for adult alcohol misuse: a meta-analysis. PLoS One. 2014;9(6):e99912 PMID:24937483, Stewart R, editor, Public Library of Science. · DOI · Paper page
  4. Ebert DD, Berking M, Cuijpers P, Lehr D, Pörtner M, Baumeister H. Increasing the acceptance of internet-based mental health interventions in primary care patients with depressive symptoms. A randomized controlled trial. J Affect Disord. 2015; https://doi.org/10.1016/j.jad.2015.01.056 . · DOI · Paper page
  5. Ebert DD, Berking M, Thiart H, Riper H, Laferton JAC, Cuijpers P, Sieland B, Lehr D. Restoring depleted resources: efficacy and mechanisms of change of an internet-based unguided recovery training for better sleep and psychological detachment from work. Heal Psychol. 2015;34(Suppl):1240–51 PMID:26651465. · DOI · Paper page
  6. Ebert DD, Lehr D, Heber E, Riper H, Cuijpers P, Berking M. Internet-and mobile-based stress management for employees with adherence-focused guidance: efficacy and mechanism of change. Scand J Environ Heal. 2016;42(5):382–94. · DOI · Paper page
  7. Terhorst Y, Rathner EM, Baumeister H, Sander L. Help from the App-Store? a systematic review of apps for depression (in press). Verhaltenstherapie 2017.
  8. Wampold BE, Imel ZE. The great psychotherapy debate: the evidence for what makes psychotherapy work. Routledge; 2015. ISBN:1136672605. · DOI · Paper page
  9. Cadwalladr C, Graham-Harrison E. Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. Guard 2018;17.
  10. Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2017;52(6):446–62 Oxford University Press US. · DOI · Paper page

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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
ORCID
Venue
Advances in Experimental Medicine and Biology
Publisher
Springer Singapore
Published
2019
Volume
1192
Pages
583-627
ISSN
  • 0065-2598 (ISSN-L)
  • 2214-8019
DOI
10.1007/978-981-32-9721-0_29
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
Licence
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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