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

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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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References

  1. Kessler RC, Aguilar-Gaxiola S, Alonso J, Chatterji S, Lee S, Ormel J, Ustün TB, Wang PS. The global burden of mental disorders: an update from the WHO World Mental Health (WMH) surveys. Epidemiol Psichiatr Soc NIH Public Access; 2009;18(1):23–33 PMID:19378696.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Steel Z, Marnane C, Iranpour C, Chey T, Jackson JW, Patel V, Silove D. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980–2013. Int J Epidemiol. 2014;43(2):476–93 PMID:24648481.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, Charlson FJ, Norman RE, Flaxman AD, Johns N, Burstein R, Murray CJ, Vos T. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet. 2013;382(9904):1575–86. https://doi.org/10.1016/S0140-6736(13)61611-6.

    Article  PubMed  Google Scholar 

  4. Saarni SI, Suvisaari J, Sintonen H, Pirkola S, Koskinen S, Aromaa A, Lönnqvist J. Impact of psychiatric disorders on health-related quality of life: general population survey. Br J Psychiatry. 2007;190:326–32.

    Article  PubMed  Google Scholar 

  5. Hysenbegasi A, Hass SL, Rowland CR. The impact of depression on the academic productivity of university students. J Ment Health Policy Econ. 2005;8(3):145.

    Google Scholar 

  6. Eisenberg D, Golberstein E, Hunt JB. Mental health and academic success in college. B E J Econom Anal Policy. 2009;9(1). https://doi.org/10.2202/1935-1682.2191.

  7. Ishii T, Tachikawa H, Shiratori Y, Hori T, Aiba M, Kuga K, Arai T. What kinds of factors affect the academic outcomes of university students with mental disorders? A retrospective study based on medical records. Asian J Psychiatr. 2018;32:67–72. https://doi.org/10.1016/j.ajp.2017.11.017.

    Article  PubMed  Google Scholar 

  8. Kessler RC, Foster CL, Saunders WB, Stang PE. Social consequences of psychiatric disorders, I: educational attainment. Am J Psychiatry Am Psychiatr Assoc. 1995;152(7):1026.

    Google Scholar 

  9. Fergusson DM, Woodward LJ. Mental health, educational, and social role outcomes of adolescents with depression. Arch Gen Psychiatry Am Med Assoc. 2002;59(3):225–31.

    Article  PubMed  Google Scholar 

  10. Cuijpers P, Smit F. Excess mortality in depression: a meta-analysis of community studies. J Affect Disord. 2002;72(3):227–36 PMID:12450639.

    Article  PubMed  Google Scholar 

  11. Nock MK, Hwang I, Sampson N, Kessler RC, Angermeyer M, Beautrais A, Borges G, Bromet E, Bruffaerts R, De Girolamo G. Cross-national analysis of the associations among mental disorders and suicidal behavior: findings from the WHO World Mental Health Surveys. PLoS Med Public Libr Sci. 2009;6(8):e1000123.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Nock MK, Hwang I, Sampson NA, Kessler RC. Mental disorders, comorbidity and suicidal behavior: results from the national comorbidity survey replication. Mol Psychiatry. 2009;15:868. http://dx.doi.org/10.1038/mp.2009.29 Macmillan Publishers Limited.

  13. World Health Organization. The Global Burden of Disease: 2004 update. 2008 PMID:15572474.

    Google Scholar 

  14. Barth J, Munder T, Gerger H, Nüesch E, Trelle S, Znoj H, Jüni P, Cuijpers P. Comparative efficacy of seven psychotherapeutic interventions for patients with depression: a network meta-analysis. Focus (Madison) Am Psychiatric Assoc. 2016;14(2):229–43.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cuijpers P, van Straten A, Andersson G, van Oppen P. Psychotherapy for depression in adults: a meta-analysis of comparative outcome studies. J Consult Clin Psychol. 2008;76(6):909–22 PMID:19045960.

    Article  PubMed  Google Scholar 

  16. Cuijpers P, Smit F, Bohlmeijer E, Hollon SD, Andersson G. Efficacy of cognitive–behavioural therapy and other psychological treatments for adult depression: meta-analytic study of publication bias. Br J Psychiatry RCP. 2010;196(3):173–78.

    Google Scholar 

  17. Hofmann SG, Smits JAJ. Cognitive-behavioral therapy for adult anxiety disorders: a meta-analysis of randomized placebo-controlled trials. J Clin Psychiatry. 2008;69(4):621–32 PMID:18363421.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Cuijpers P, Sijbrandij M, Koole S, Huibers M, Berking M, Andersson G. Psychological treatment of generalized anxiety disorder: a meta-analysis. Clin Psychol Rev. 2014;34(2):130–40 Elsevier.

    Article  PubMed  Google Scholar 

  19. Mayo-Wilson E, Dias S, Mavranezouli I, Kew K, Clark DM, Ades AE, Pilling S. Psychological and pharmacological interventions for social anxiety disorder in adults: a systematic review and network meta-analysis. Lancet Psychiatry. 2014;1(5):368–76 PMID:26361000.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bisson JI, Ehlers A, Matthews R, Pilling S, Richards D, Turner S. Psychological treatments for chronic post-traumatic stress disorder: systematic review and meta-analysis. Br J Psychiatry Camb Univ Press. 2007;190(2):97–104.

    Google Scholar 

  21. Powers MB, Halpern JM, Ferenschak MP, Gillihan SJ, Foa EB. A meta-analytic review of prolonged exposure for posttraumatic stress disorder. Clin Psychol Rev. 2010;30(6):635–41 Elsevier.

    Article  PubMed  Google Scholar 

  22. Leichsenring F, Leibing E. The effectiveness of psychodynamic therapy and cognitive behavior therapy in the treatment of personality disorders: a meta-analysis. Focus (Madison) Am Psychiatric Assoc. 2005;160(3):1223–1428.

    Article  Google Scholar 

  23. Cristea IA, Gentili C, Cotet CD, Palomba D, Barbui C, Cuijpers P. Efficacy of psychotherapies for borderline personality disorder: a systematic review and meta-analysis. Jama Psychiatry Am Med Assoc. 2017;74(4):319–28.

    Article  PubMed  Google Scholar 

  24. Kleinstäuber M, Witthöft M, Hiller W. Efficacy of short-term psychotherapy for multiple medically unexplained physical symptoms: a meta-analysis. Clin Psychol Rev. 2011;31(1):146–60 Elsevier.

    Article  PubMed  Google Scholar 

  25. Olatunji BO, Kauffman BY, Meltzer S, Davis ML, Smits JAJ, Powers MB. Cognitive-behavioral therapy for hypochondriasis/health anxiety: a meta-analysis of treatment outcome and moderators. Behav Res Ther. 2014;58:65–74 Elsevier.

    Article  PubMed  Google Scholar 

  26. Glombiewski JA, Sawyer AT, Gutermann J, Koenig K, Rief W, Hofmann SG. Psychological treatments for fibromyalgia: a meta-analysis. PAIN®. 2010;151(2):280–95 Elsevier.

    Article  PubMed  Google Scholar 

  27. Frühauf S, Gerger H, Schmidt HM, Munder T, Barth J. Efficacy of psychological interventions for sexual dysfunction: a systematic review and meta-analysis. Arch Sex Behav Springer; 2013;42(6):915–33.

    Article  PubMed  Google Scholar 

  28. Auerbach RP, Alonso J, Axinn WG, Cuijpers P, Ebert DD, Green JG, Hwang I, Kessler RC, Liu H, Mortier P, Nock MK, Pinder-Amaker S, Sampson NA, Aguilar-Gaxiola S, Al-Hamzawi A, Andrade LH, Benjet C, Caldas-de-Almeida JM, Demyttenaere K, Florescu S, de Girolamo G, Gureje O, Haro JM, Karam EG, Kiejna A, Kovess-Masfety V, Lee S, McGrath JJ, O’Neill S, Pennell B-E, Scott K, Ten Have M, Torres Y, Zaslavsky AM, Zarkov Z, Bruffaerts R. Mental disorders among college students in the World Health Organization world mental health surveys. Psychol Med. 2016 Oct 3 [cited 2017 Mar 20];46(14):1–16 PMID:27484622.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kessler RC, Berglund PA, Bruce ML, Koch RJ, Laska EM, Leaf PJ, Manderscheid RW, Rosenheck RA, Walters EE, Wang PS. The prevalence and correlates of untreated serious mental illness. Health Serv Res. 2001;36(6):987–1007.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Bijl RV, de Graaf R, Hiripi E, Kessler RC, Kohn R, Offord DR, Ustun TB, Vicente B, Vollebergh WAM, Walters EE. The prevalence of treated and untreated mental disorders in five countries. Health Aff Project HOPE-The People-to-People Health Foundation, Inc.; 2003;22(3):122–33.

    Google Scholar 

  31. Andrade LH, Alonso J, Mneimneh Z, Wells JE, Al-Hamzawi A, Borges G, Bromet E, Bruffaerts R, de Girolamo G, de Graaf R, Florescu S, Gureje O, Hinkov HR, Hu C, Huang Y, Hwang I, Jin R, Karam EG, Kovess-Masfety V, Levinson D, Matschinger H, O’Neill S, Posada-Villa J, Sagar R, Sampson NA, Sasu C, Stein DJ, Takeshima T, Viana MC, Xavier M, Kessler RC. Barriers to mental health treatment: results from the WHO World Mental Health surveys. Psychol Med. 2014;44(6):1303–17 PMID:23931656, Cambridge University Press.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Clement S, Schauman O, Graham T, Maggioni F, Evans-Lacko S, Bezborodovs N, Morgan C, Rüsch N, Brown JSL, Thornicroft G. What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies. Psychol Med. 2015 [cited 2017 May 10];45(1):11–27 PMID:24569086.

    Article  PubMed  Google Scholar 

  33. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication. Arch Gen Psychiatry. 2005;62(6):593–602 PMID:18188442.

    Article  PubMed  Google Scholar 

  34. Kupfer DJ, Frank E, Phillips ML. Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet. 2012;379(9820):1045–55 Elsevier.

    Article  Google Scholar 

  35. Ebert DD, Van Daele T, Nordgreen T, Karekla M, Compare AT, Zarbo C, Brugnera A, Oeverland S, Trebbi G, Jensen K, Kaehlke F, Baumeister H. Internet- and mobile-based psychological interventions: applications, efficacy, and potential for improving mental health. Eur Psychol 2018. https://doi.org/10.1027/1016-9040/a000318.

    Article  Google Scholar 

  36. Craske MG, Stein MB. No psychotherapy monoculture for anxiety disorders; Authors’ reply. Lancet. 2017;389(10082):1883. https://doi.org/10.1016/s0140-6736(17)31208-4 Elsevier.

    Article  Google Scholar 

  37. Cuijpers P, Cristea IA, Karyotaki E, Reijnders M, Huibers MJH. How effective are cognitive behavior therapies for major depression and anxiety disorders? A meta‐analytic update of the evidence. World Psychiatry. 2016;15(3):245–58 Wiley Online Library.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Berking M, Rief W, editors. Klinische Psychologie und Psychotherapie. Epidemiology. Berlin Heidelberg: Springer;2012. ISBN:9783642130175 PMID:15003161.

    Google Scholar 

  39. Andersson G, Carlbring P, Lindefors N. History and current status of ICBT. In: Lindefors N, Andersson G, editors. Guid internet-based treat psychiatry. Cham: Springer International Publishing; 2016. p. 1–16. https://doi.org/10.1007/978-3-319-06083-5_1.

    Google Scholar 

  40. Andersson G, Paxling B, Roch-Norlund P, Ostman G, Norgren A, Almlöv J, Georén L, Breitholtz E, Dahlin M, Cuijpers P, Carlbring P, Silverberg F. Internet-based psychodynamic versus cognitive behavioral guided self-help for generalized anxiety disorder: a randomized controlled trial. Psychother Psychosom. 2012;81(6):344–55 PMID:22964540.

    Article  PubMed  Google Scholar 

  41. Johansson R, Hesser H, Ljótsson B, Frederick RJ, Andersson G. Transdiagnostic, affect-focused, psychodynamic, guided self-help for depression and anxiety through the internet: study protocol for a randomised controlled trial. BMJ Open. 2012;2(6):1–6 PMID:23257775.

    Article  Google Scholar 

  42. Donker T, Batterham PJ, Warmerdam L, Bennett K, Bennett A, Cuijpers P, Griffiths KM, Christensen H. Predictors and moderators of response to internet-delivered Interpersonal Psychotherapy and Cognitive Behavior Therapy for depression. J Affect Disord. 2013;151(1):343–51.

    Article  CAS  PubMed  Google Scholar 

  43. Mak WWS, Chan ATY, Cheung EYL, Lin CLY, Ngai KCS. Enhancing Web-based mindfulness training for mental health promotion with the health action process approach: randomized controlled trial. J Med Internet Res. 2015;17(1):e8 PMID:25599904.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Lin J, Lüking M, Ebert DD, Buhrman M, Andersson G, Baumeister H. Effectiveness and cost-effectiveness of a guided and unguided internet-based acceptance and commitment therapy for chronic pain: study protocol for a three-armed randomised controlled trial. Internet Interv 2015;2(1). https://doi.org/10.1016/j.invent.2014.11.005.

    Article  Google Scholar 

  45. Johansson R, Björklund M, Hornborg C, Karlsson S, Hesser H, Ljótsson B, Rousseau A, Frederick RJ, Andersson G. Affect-focused psychodynamic psychotherapy for depression and anxiety through the Internet: a randomized controlled trial. PeerJ. 2013;1(Suppl 2):e102- PMID:23862104.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Garcia-Palacios A, Hoffman H, Carlin A, Furness TA, Botella C. Virtual reality in the treatment of spider phobia: a controlled study. Behav Res Ther. 2002;40(9):983–93 PMID:12296495.

    Article  CAS  PubMed  Google Scholar 

  47. Freeman D, Haselton P, Freeman J, Spanlang B, Kishore S, Albery E, Denne M, Brown P, Slater M, Nickless A. Automated psychological therapy using immersive virtual reality for treatment of fear of heights: a single-blind, parallel-group, randomised controlled trial. Lancet Psychiatry. 2018;5(8):625–32 Elsevier.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Pot-Kolder RMCA, Geraets CNW, Veling W, van Beilen M, Staring ABP, Gijsman HJ, Delespaul PAEG, van der Gaag M. Virtual-reality-based cognitive behavioural therapy versus waiting list control for paranoid ideation and social avoidance in patients with psychotic disorders: a single-blind randomised controlled trial. Lancet Psychiatry. 2018;5(3):217–26 Elsevier.

    Article  PubMed  Google Scholar 

  49. Fleming T, Dixon R, Frampton C, Merry S. A pragmatic randomized controlled trial of computerized CBT (SPARX) for symptoms of depression among adolescents excluded from mainstream education. Behav Cogn Psychother. 2012;40(5):529–41. https://doi.org/10.1017/s1352465811000695 12/06. Fleming, Theresa, University of Auckland, Department of Psychological Medicine, Private Bag 92019, Auckland, New Zealand, 1142.

    Article  PubMed  Google Scholar 

  50. An LC, Demers MRS, Kirch MA, Considine-Dunn S, Nair V, Dasgupta K, Narisetty N, Resnicow K, Ahluwalia J. A randomized trial of an avatar-hosted multiple behavior change intervention for young adult smokers. J Natl Cancer Inst Monogr. 2013;2013(47):209–15 PMID:24395994, Oxford University Press.

    Article  Google Scholar 

  51. Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment Heal. 2017;4(2) JMIR Publications Inc.

    Google Scholar 

  52. Schlicker S, Ebert DD, Middendorf T, Titzler I, Berking M. Evaluation of a text-message-based maintenance intervention for Major Depressive Disorder after inpatient cognitive behavioral therapy. J Affect Disord. 2017;227:305–12 Elsevier.

    Article  PubMed  Google Scholar 

  53. Lin J, Ebert DD, Lehr D, Berking M, Baumeister H. Internetbasierte Gesundheitsinterventionen: State of the Art und Einsatzmöglichkeiten in der Rehabilitation. Rehabilitation. 2013;52(03):155–63. https://doi.org/10.1055/s-0033-1343491.

    Article  CAS  Google Scholar 

  54. Backhaus A, Agha Z, Maglione ML, Repp A, Ross B, Zuest D, Rice-Thorp NM, Lohr J, Thorp SR. Videoconferencing psychotherapy: a systematic review. Psychol Serv. 2012;9(2):111–31 PMID:22662727.

    Article  PubMed  Google Scholar 

  55. Ebert DD, Lehr D, Smit F, Zarski A-C, Riper H, Heber E, Cuijpers P, Berking M. Efficacy and cost-effectiveness of minimal guided and unguided internet-based mobile supported stress-management in employees with occupational stress: a three-armed randomised controlled trial. BMC Public Health. 2014;14(1):807. https://doi.org/10.1186/1471-2458-14-807.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Schueller SM, Tomasino KN, Mohr DC. Integrating human support into behavioral intervention technologies: the efficiency model of support. Clin Psychol Sci Pract. 2016;24(1):27–45. https://doi.org/10.1111/cpsp.12173.

    Article  Google Scholar 

  57. Zarski A-C, Lehr D, Berking M, Riper H, Cuijpers P, Ebert DD. Adherence to internet-based mobile-supported stress management: a pooled analysis of individual participant data from three randomized controlled trials. J Med Internet Res. 2016;18(6):e146. https://doi.org/10.2196/jmir.4493.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Buntrock C, Ebert DD, Lehr D, Cuijpers P, Riper H, Smit F, Berking M. Evaluating the efficacy and cost-effectiveness of web-based indicated prevention of major depression: design of a randomised controlled trial. BMC Psychiatry BioMed Cent. 2014;14:25. https://doi.org/10.1186/1471-244x-14-25.

  59. Lal S, Adair CE. E-mental health: a rapid review of the literature. Psychiatr Serv Am Psychiatric Assoc. 2014;65(1):24–32.

    Article  PubMed  Google Scholar 

  60. Henderson C, Evans-Lacko S, Thornicroft G. Mental illness stigma, help seeking, and public health programs. Am J Public Health 2013 [cited 2017 May 10];103(5):777–80 PMID:23488489.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Muñoz RF, Bunge EL, Chen K, Schueller SM, Bravin JI, Shaughnessy EA, Pérez-Stable EJ. Massive open online interventions: a novel model for delivering behavioral-health services worldwide. Clin Psychol Sci. 2016;4(2):194–205 Los Angeles, CA: Sage Publications Sage CA.

    Google Scholar 

  62. Fairburn CG, Patel V. The impact of digital technology on psychological treatments and their dissemination. Behav Res Ther. 2017;88:19–25 Elsevier.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Erbe D, Eichert H-C, Riper H, Ebert DD. Blending face-to-face and internet-based interventions for the treatment of mental disorders in adults: systematic review. J Med Internet Res. 2017;19(9):e306 PMID:28916506.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Ebert DD, Nobis S, Lehr D, Baumeister H, Riper HM, Auerbach RP, Snoek F, Cuijpers P, Berking M. The 6-month effectiveness of Internet-based guided self-help for depression in adults with Type 1 and 2 diabetes mellitus. Diabet Med. 2016; https://doi.org/10.1111/dme.13173.

    Article  PubMed  Google Scholar 

  65. Nobis S, Lehr D, Ebert DD, Baumeister H, Snoek F, Riper H, Berking M. Efficacy of a web-based intervention with mobile phone support in treating depressive symptoms in adults with type 1 and type 2 diabetes: a randomized controlled trial. Diabetes Care. 2015 PMID:25710923.

    Google Scholar 

  66. Sander L, Paganini S, Lin J, Schlicker S, Ebert DD, Buntrock C, Baumeister H. Effectiveness and cost-effectiveness of a guided Internet- and mobile-based intervention for the indicated prevention of major depression in patients with chronic back pain-study protocol of the PROD-BP multicenter pragmatic RCT. BMC Psychiatry. 2017;17(1). https://doi.org/10.1186/s12888-017-1193-6.

  67. van Bastelaar KMP, Pouwer F, Cuijpers P, Riper H, Snoek FJ. Web-based depression treatment for type 1 and type 2 diabetic patients: a randomized, controlled trial. Diabetes Care. 2011;34(2):320–5 PMID:21216855.

    Article  PubMed  PubMed Central  Google Scholar 

  68. van Bastelaar KMP, Pouwer F, Cuijpers P, Riper H, Twisk JWR, Snoek FJ. Is a severe clinical profile an effect modifier in a Web-based depression treatment for adults with type 1 or type 2 diabetes? Secondary analyses from a randomized controlled trial. J Med Internet Res. 2012;14(1):e2 PMID:22262728.

    Article  PubMed  PubMed Central  Google Scholar 

  69. 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 a randomized clinical trial. JAMA J Am Med Assoc. 2016;315(17). https://doi.org/10.1001/jama.2016.4326.

    Article  CAS  PubMed  Google Scholar 

  70. Ebert DD, Buntrock C, Cuijpers P, K van Z, P C, C B, C B, H B. Online intervention for prevention of major depression—reply. JAMA. 2016;316(8):881. https://doi.org/10.1001/jama.2016.9586 American Medical Association.

    Article  PubMed  Google Scholar 

  71. Bockting CLH, Kok GD, van der Kamp L, Smit F, van Valen E, Schoevers R, van Marwijk H, Cuijpers P, Riper H, Dekker J, Beck AT. Disrupting the rhythm of depression using Mobile Cognitive Therapy for recurrent depression: randomized controlled trial design and protocol. BMC Psychiatry. 2011;11:12 PMID:21235774.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Ebert DD, Gollwitzer M, Riper H, Cuijpers P, Baumeister H, Berking M. For whom does it work? moderators of outcome on the effect of a transdiagnostic internet-based maintenance treatment after inpatient psychotherapy: randomized controlled trial. J Med Internet Res. 2013;15(10):e191 PMID:24113764.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Ebert DD, Tarnowski T, Gollwitzer M, Sieland B, Berking M. A transdiagnostic internet-based maintenance treatment enhances the stability of outcome after inpatient cognitive behavioral therapy: a randomized controlled trial. Psychother Psychosom. 2013;82(4):246–56 PMID:23736751.

    Article  PubMed  Google Scholar 

  74. Golkaramnay V, Bauer S, Haug S, Wolf M, Kordy H. The exploration of the effectiveness of group therapy through an Internet chat as aftercare: a controlled naturalistic study. Psychother Psychosom. 2007;76(4):219–25 PMID:17570960.

    Article  PubMed  Google Scholar 

  75. Kok G, Bockting C, Burger H, Smit F, Riper H. Mobile cognitive therapy: adherence and acceptability of an online intervention in remitted recurrently depressed patients. Internet Interv. 2014;1(2):65–73. https://doi.org/10.1016/j.invent.2014.05.002.

    Article  Google Scholar 

  76. Holländare F, Johnsson S, Randestad M, Tillfors M, Carlbring P, Andersson G, Engström I. Randomized trial of Internet‐based relapse prevention for partially remitted depression. Acta Psychiatr Scand. 2011;124(4):285–94 Wiley Online Library.

    Article  PubMed  Google Scholar 

  77. Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N. Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: a meta-analysis. PLoS One. 2010;5(10):e13196 PMID:20967242.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Ebert DD, Zarski A-C, Christensen H, Stikkelbroek Y, Cuijpers P, Berking M, Riper H. Internet and computer-based cognitive behavioral therapy for anxiety and depression in youth: a meta-analysis of randomized controlled outcome trials. PLoS One 2015;10(3). https://doi.org/10.1371/journal.pone.0119895.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Mayo-Wilson E, Montgomery P. Media-delivered cognitive behavioural therapy and behavioural therapy (self-help) for anxiety disorders in adults. Cochrane database Syst Rev. 2013;9:CD005330 PMID:24018460.

    Google Scholar 

  80. Richards D, Richardson T. Computer-based psychological treatments for depression: a systematic review and meta-analysis. Clin Psychol Rev. 2012;32(4):329–42 PMID:22466510 Elsevier Ltd.

    Article  PubMed  Google Scholar 

  81. Richards D, Richardson T, Timulak L, McElvaney J. The efficacy of internet-delivered treatment for generalized anxiety disorder: a systematic review and meta-analysis. Internet Interv. 2015;2(3):272–82. https://doi.org/10.1016/j.invent.2015.07.003 Elsevier B.V.

    Article  Google Scholar 

  82. Olthuis JV, Watt MC, Bailey K, Hayden JA, Stewart SH. Therapist-supported Internet cognitive behavioural therapy for anxiety disorders in adults. Cochrane database Syst Rev. 2015;3:CD011565 PMID:25742186.

    Google Scholar 

  83. Josephine K, Josefine L, Philipp D, David E, Harald B. Internet- and mobile-based depression interventions for people with diagnosed depression: a systematic review and meta-analysis. J Affect Disord. 2017;223:28–40 PMID:28715726 Elsevier.

    Article  PubMed  Google Scholar 

  84. Kuester A, Niemeyer H, Knaevelsrud C. Internet-based interventions for posttraumatic stress: a meta-analysis of randomized controlled trials. Clin Psychol Rev. 2016; 1–16 PMID:26655959 Elsevier Ltd.

    Article  PubMed  Google Scholar 

  85. Zachariae R, Lyby MS, Ritterband L, O’Toole MS. Efficacy of Internet-delivered cognitive-behavioral therapy for insomnia–a systematic review and meta-analysis of randomized controlled trials. Sleep Med Rev. 2015;30:1–10 PMID:26615572.

    Article  PubMed  Google Scholar 

  86. Melioli T, Bauer S, Franko DL, Moessner M, Ozer F, Chabrol H, Rodgers RF. Reducing eating disorder symptoms and risk factors using the internet: a meta-analytic review. Int J Eat Disord. 2016;49(1):19–31. https://doi.org/10.1002/eat.22477.

    Article  PubMed  Google Scholar 

  87. Buhrman M, Gordh T, Andersson G. Internet interventions for chronic pain including headache: a systematic review. Internet Interv. 2016;4:17–34. https://doi.org/10.1016/j.invent.2015.12.001.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Riper H, Blankers M, Hadiwijaya H, Cunningham J, Clarke S, Wiers R, Ebert D, Cuijpers P. Effectiveness of guided and unguided low-intensity internet interventions for adult alcohol misuse: a meta-analysis. PLoS One. 2014;9(6). https://doi.org/10.1371/journal.pone.0099912.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Andersson E, Enander J, Andrén P, Hedman E, Ljótsson B, Hursti T, Bergström J, Kaldo V, Lindefors N, Andersson G, Rück C. Internet-based cognitive behaviour therapy for obsessive–compulsive disorder: a randomized controlled trial. Psychol Med. 2012;42(10):2193–203 PMID:22348650.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Herbst N, Voderholzer U, Thiel N, Schaub R, Knaevelsrud C, Stracke S, Hertenstein E, Nissen C, Külz AK. No talking, just writing! Efficacy of an Internet-based cognitive behavioral therapy with exposure and response prevention in obsessive compulsive disorder. Psychother Psychosom. 2014;83(3):165–75 PMID:24732962.

    Article  PubMed  Google Scholar 

  91. Lenhard F, Andersson E, Mataix-Cols D, Rück C, Vigerland S, Högström J, Hillborg M, Brander G, Ljungström M, Ljótsson B, Serlachius E. Therapist-guided, internet-delivered cognitive-behavioral therapy for adolescents with obsessive-compulsive disorder: a randomized controlled trial. J Am Acad Child Adolesc Psychiatry. 2017;56(1):10–19.e2. https://doi.org/10.1016/j.jaac.2016.09.515.

    Article  PubMed  Google Scholar 

  92. Storch EA, Caporino NE, Morgan JR, Lewin AB, Rojas A, Brauer L, Larson MJ, Murphy TK. Preliminary investigation of web-camera delivered cognitive-behavioral therapy for youth with obsessive-compulsive disorder. Psychiatry Res. 2011;189(3):407–12 PMID:21684018.

    Article  PubMed  Google Scholar 

  93. Gottlieb JD et al. Web-based cognitive-behavioral therapy for auditory hallucinations in persons with psychosis: a pilot study. Schizophr Res. 2013;145(1–3):82–7 PMID:23410709 Elsevier.

    Article  PubMed  Google Scholar 

  94. Harper KM. An Investigation of an Internet-based cognitive behavioral Therapy Program fpr auditory hallucinations. University of North Carolina at Chapel Hill; 2013.

    Google Scholar 

  95. Enander J, Andersson E, Mataix-Cols D, Lichtenstein L, Alstroem K, Andersson G, Ljotsson B, Rueck C. Therapist guided internet based cognitive behavioural therapy for body dysmorphic disorder: single blind randomised controlled trial. BMJ-BRITISH Med J. 2016;352. https://doi.org/10.1136/bmj.i241.

  96. Hidalgo-Mazzei D, Mateu A, Reinares M, Matic A, Vieta E, Colom F. Internet-based psychological interventions for bipolar disorder: review of the present and insights into the future. J Affect Disord. 2015;188:1–13 PMID:26342885.

    Article  PubMed  Google Scholar 

  97. Andersson E, Walén C, Hallberg J, Paxling B, Dahlin M, Almlöv J, Källström R, Wijma K, Carlbring P, Andersson G. A randomized controlled trial of guided Internet-delivered cognitive behavioral therapy for erectile dysfunction. J Sex Med. 2011;8(10):2800–9 PMID:21797983.

    Article  PubMed  Google Scholar 

  98. Jones LM, Mccabe MP. The effectiveness of an internet-based psychological treatment program for female sexual dysfunction. J Sex Med. 2011;8(10):2781–92 PMID:21771279.

    Article  PubMed  Google Scholar 

  99. Schover LR, Canada AL, Yuan Y, Sui D, Neese L, Jenkins R, Rhodes MM. A randomized trial of internet-based versus traditional sexual counseling for couples after localized prostate cancer treatment. Cancer. 2012;118(2):500–9 PMID:21953578.

    Article  PubMed  Google Scholar 

  100. van Lankveld JJ, Leusink P, van Diest S, Gijs L, Slob AK. Internet-based brief sextherapy for heterosexual men with sexual dysfunctions: a randomized controlled pilot trial. J Sex Med. 2009;6(8):2224–36.

    Article  PubMed  Google Scholar 

  101. Zarski A-C, Berking M, Fackiner C, Rosenau C, Ebert DD. Internet-based guided self-help for vaginal penetration difficulties: results of a randomized controlled pilot trial. J Sex Med. 2017;14(2). https://doi.org/10.1016/j.jsxm.2016.12.232.

    Article  PubMed  Google Scholar 

  102. Abbott J-AM, Kaldo V, Klein B, Austin D, Hamilton C, Piterman L, Williams B, Andersson G. A Cluster Randomised Trial of an Internet-Based Intervention Program for Tinnitus Distress in an Industrial Setting. Cogn Behav Ther 2009;38(3):162–73 PMID:19675959.

    Article  PubMed  Google Scholar 

  103. Nyenhuis N, Zastrutzki S, Weise C, Jäger B, Kröner-Herwig B. The efficacy of minimal contact interventions for acute tinnitus: a randomised controlled study. Cogn Behav Ther. 2013;42(2):127–38 PMID:22413736.

    Article  PubMed  Google Scholar 

  104. Andersson G, Strömgren T, Ström L, Lyttkens L. Randomized controlled trial of internet-based cognitive behavior therapy for distress associated with tinnitus. Psychosom Med. 64(5):810–6 PMID:12271112.

    PubMed  Google Scholar 

  105. Weise C, Kleinstäuber M, Andersson G. Internet-delivered cognitive-behavior therapy for tinnitus. Psychosom Med. 2016;78(4):501–10 PMID:26867083.

    Article  PubMed  Google Scholar 

  106. Kaldo V, Levin S, Widarsson J, Buhrman M, Larsen H-C, Andersson G. Internet versus group cognitive-behavioral treatment of distress associated with tinnitus: a randomized controlled trial. Behav Ther. 2008;39(4):348–59 PMID:19027431.

    Article  PubMed  Google Scholar 

  107. Nyenhuis N, Zastrutzki S, Jäger B, Kröner-Herwig B. An internet-based cognitive-behavioural training for acute tinnitus: secondary analysis of acceptance in terms of satisfaction, trial attrition and non-usage attrition. Cogn Behav Ther. 2013;42(2):139–45 PMID:23205617.

    Article  PubMed  Google Scholar 

  108. Hesser H, Westin VZ, Andersson G. Acceptance as a mediator in internet-delivered acceptance and commitment therapy and cognitive behavior therapy for tinnitus. J Behav Med. 2014;37(4):756–67 PMID:23881309.

    Article  PubMed  Google Scholar 

  109. Hesser H, Gustafsson T, Lundén C, Henrikson O, Fattahi K, Johnsson E, Westin VZ, Carlbring P, Mäki-Torkko E, Kaldo V, Andersson G. A randomized controlled trial of internet-delivered cognitive behavior therapy and acceptance and commitment therapy in the treatment of tinnitus. J Consult Clin Psychol. 2012;80(4):649–61 PMID:22250855.

    Article  PubMed  Google Scholar 

  110. Jasper K, Weise C, Conrad I, Andersson G, Hiller W, Kleinstäuber M. Internet-based guided self-help versus group cognitive behavioral therapy for chronic tinnitus: a randomized controlled trial. Psychother Psychosom. 2014;83(4):234–46 PMID:24970708.

    Article  PubMed  Google Scholar 

  111. Andersson G, Cuijpers P, Carlbring P, Riper H, Hedman E. Guided Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: a systematic review and meta-analysis. World Psychiatry. 2014;13(3):288–95 PMID:25273302.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Wagner B, Knaevelsrud C, Maercker A. Internet-based cognitive-behavioral therapy for complicated grief: a randomized controlled trial. Death Stud. 2006;30(5):429–53 PMID:16610157.

    Article  PubMed  Google Scholar 

  113. Wagner B, Maercker A. A 1.5-year follow-up of an internet-based intervention for complicated grief. J Trauma Stress. 2007;20(4):625–29. https://doi.org/10.1002/jts.20230 Wiley Subscription Services, Inc., A Wiley Company.

    Article  PubMed  Google Scholar 

  114. Eisma MC, Boelen PA, van den Bout J, Stroebe W, Schut HAW, Lancee J, Stroebe MS. Internet-based exposure and behavioral activation for complicated grief and rumination: a randomized controlled trial. Behav Ther. 2015;46(6):729–48 Elsevier.

    Article  PubMed  Google Scholar 

  115. Luquiens A, Tanguy M-L, Lagadec M, Benyamina A, Aubin H-J, Reynaud M. The efficacy of three modalities of internet-based psychotherapy for non-treatment-seeking online problem gamblers: a randomized controlled trial. J Med Internet Res. 2016;18(2):e36 PMID:26878894.

    Article  PubMed  PubMed Central  Google Scholar 

  116. Carlbring P, Smit F. Randomized trial of internet-delivered self-help with telephone support for pathological gamblers. J Consult Clin Psychol. 2008;76(6):1090–4 PMID:19045977.

    Article  PubMed  Google Scholar 

  117. Nijhof SL, Priesterbach LP, Uiterwaal C, Bleijenberg G, Kimpen JLL, Putte EM. Internet-based therapy for adolescents with chronic fatigue syndrome: long-term follow-up. Pediatrics; 2013. p. e1788–95. https://doi.org/10.1542/peds.2012-2007.

    Article  PubMed  Google Scholar 

  118. Nijhof SL, et al. Effectiveness of internet-based cognitive behavioural treatment for adolescents with chronic fatigue syndrome (FITNET): a randomised controlled trial. Lancet (London, England). 2012;379(9824):1412–8 PMID:22385683 Elsevier.

    Article  Google Scholar 

  119. Franklin JC, Fox KR, Franklin CR, Kleiman EM, Ribeiro JD, Jaroszewski AC, Hooley JM, Nock MK. A brief mobile app reduces nonsuicidal and suicidal self-injury: evidence from three randomized controlled trials. J Consult Clin Psychol. 2016;84(6):544–57 PMID:27018530.

    Article  PubMed  Google Scholar 

  120. Mohr DC, Ho J, Hart TL, Baron KG, Berendsen M, Beckner V, Cai X, Cuijpers P, Spring B, Kinsinger SW, Schroder KE, Duffecy J. Control condition design and implementation features in controlled trials: a meta-analysis of trials evaluating psychotherapy for depression. Transl Behav Med. 2014 [cited 2017 May 10];4(4):407–23 PMID:25584090 Springer.

    Article  PubMed  PubMed Central  Google Scholar 

  121. Andersson G, Topooco N, Havik O, Nordgreen T. Internet-supported versus face-to-face cognitive behavior therapy for depression. Expert Rev Neurother. 2016;16(1):55–60 PMID:26610160.

    Article  CAS  PubMed  Google Scholar 

  122. Carlbring P, Andersson G, Cuijpers P, Riper H, Hedman-Lagerlöf E. Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: an updated systematic review and meta-analysis. Cogn Behav Ther. 2018;47(1):1–18 PMID:29215315.

    Article  PubMed  Google Scholar 

  123. Ebert DD, Berking M, Cuijpers P, Lehr D, P?rtner 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;176. https://doi.org/10.1016/j.jad.2015.01.056.

    Article  CAS  PubMed  Google Scholar 

  124. Baumeister H, Nowoczin L, Lin J, Seifferth H, Seufert J, Laubner K, Ebert DD. Impact of an acceptance facilitating intervention on diabetes patients’ acceptance of Internet-based interventions for depression: a randomized controlled trial. Diabetes Res Clin Pract. 2014;105(1):30–9. https://doi.org/10.1016/j.diabres.2014.04.031.

    Article  CAS  PubMed  Google Scholar 

  125. Apolinário-Hagen J, Vehreschild V, Alkoudmani RM. Current views and perspectives on e-mental health: an exploratory survey study for understanding public attitudes toward internet-based psychotherapy in Germany. JMIR Ment Heal. 2017;4(1):e8 PMID:28232298.

    Article  Google Scholar 

  126. Apolinário-Hagen J, Harrer M, Kählke F, Fritsche L, Salewski C, Ebert DD. Public attitudes toward guided internet-based therapies: web-based survey study. JMIR Ment Health. 2018;5(2):e10735 PMID:29764797, JMIR Publications Inc.

    Article  PubMed  PubMed Central  Google Scholar 

  127. Dorow M, Löbner M, Pabst A, Stein J, Riedel-Heller SG. Preferences for depression treatment including internet-based interventions: results from a large sample of primary care patients. Front Psychiatry. 2018;9 Frontiers Media SA.

    Google Scholar 

  128. Sethi S, Campbell AJ, Ellis LA. The use of computerized self-help packages to treat adolescent depression and anxiety. J Technol Hum Serv. 2010, 31;28(3):144–60. https://doi.org/10.1080/15228835.2010.508317 Routledge.

    Article  Google Scholar 

  129. Lindhiem O, Bennett CB, Rosen D, Silk J. Mobile technology boosts the effectiveness of psychotherapy and behavioral interventions: a meta-analysis. Behav Modif. 2015;39(6):785–804 PMID:26187164.

    Article  PubMed  PubMed Central  Google Scholar 

  130. Kenwright M, Liness S, Marks I. Reducing demands on clinicians by offering computer-aided self-help for phobia/panic. Feasibility study. Br J Psychiatry. 2001;179:456–9 PMID:11689405.

    Article  CAS  PubMed  Google Scholar 

  131. Marks IM, Kenwright M, McDonough M, Whittaker M, Mataix-Cols D. Saving clinicians’ time by delegating routine aspects of therapy to a computer: a randomized controlled trial in phobia/panic disorder. Psychol Med. 2004 PMID:14971623, Cambridge University Press.

    Google Scholar 

  132. Kenter RMF, van de Ven PM, Cuijpers P, Koole G, Niamat S, Gerrits RS, Willems M, van Straten A. Costs and effects of Internet cognitive behavioral treatment blended with face-to-face treatment: results from a naturalistic study. Internet Interv. 2015;2(1):77–83. https://doi.org/10.1016/j.invent.2015.01.001.

    Article  Google Scholar 

  133. Kleiboer A, Smit J, Bosmans J, Ruwaard J, Andersson G, Topooco N, Berger T, Krieger T, Botella C, Ba?os R, Chevreul K, Araya R, Cerga-Pashoja A, Cie?lak R, Rogala A, Vis C, Draisma S, Schaik A, Kemmeren L, Ebert D, Berking M, Funk B, Cuijpers P, Riper H. European COMPARative Effectiveness research on blended Depression treatment versus treatment-as-usual (E-COMPARED): study protocol for a randomized controlled, non-inferiority trial in eight European countries. Trials 2016;17(1). https://doi.org/10.1186/s13063-016-1511-1.

  134. Kooistra LC, Wiersma JE, Ruwaard J, van Oppen P, Smit F, Lokkerbol J, Cuijpers P, Riper H. Blended vs. face-to-face cognitive behavioural treatment for major depression in specialized mental health care: study protocol of a randomized controlled cost-effectiveness trial. BMC Psychiatry 2014;14(1):290 PMID:25326035.

    Google Scholar 

  135. Romijn G, Riper H, Kok R, Donker T, Goorden M, Roijen LH Van, Kooistra L, Balkom A Van, Koning J. Cost-effectiveness of blended vs. face-to- face cognitive behavioural therapy for severe anxiety disorders : study protocol of a randomized controlled trial. BMC Psychiatry BMC Psychiatry; 2015;1–10. https://doi.org/10.1186/s12888-015-0697-1.

  136. Vara D, Herrero R, Etchemendy E, Espinoza M, Baños R, García-Palacios A, Lera G, Folch B, Palop-Larrea V, Vázquez P. Efficacy and cost-effectiveness of a blended cognitive behavioral therapy for depression in Spanish primary health care: study protocol for a randomised non-inferiority trial. BMC Psychiatry BioMed Central. 2018;18(1):74.

    Google Scholar 

  137. Ebert DD, Cuijpers P, Muñoz RF, Baumeister H. Prevention of mental health disorders using internet and mobile-based interventions: a narrative review and recommendations for future research. Front Psychiatry Front. 2017;8:116. https://doi.org/10.3389/fpsyt.2017.00116.

  138. 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.

    Article  CAS  PubMed  Google Scholar 

  139. Buntrock C, Ebert D, Lehr D, Riper H, Smit F, Cuijpers P, Berking M. Effectiveness of a web-based cognitive behavioural intervention for subthreshold depression: pragmatic randomised controlled trial. Psychother Psychosom. 2015;84(6). https://doi.org/10.1159/000438673.

    Article  PubMed  Google Scholar 

  140. Buntrock C, Berking M, Smit F, Lehr D, Nobis S, Riper H, Cuijpers P, Ebert D. Preventing depression in adults with subthreshold depression: health-economic evaluation alongside a pragmatic randomized controlled trial of a web-based intervention. J Med Internet Res. 2017;19(1):e5. https://doi.org/10.2196/jmir.6587.

    Article  PubMed  PubMed Central  Google Scholar 

  141. Holländare F, Anthony SA, Randestad M, Tillfors M, Carlbring P, Andersson G, Engström I. Two-year outcome of internet-based relapse prevention for partially remitted depression. Behav Res Ther. 2013. https://doi.org/10.1016/j.brat.2013.08.002.

    Article  PubMed  Google Scholar 

  142. Klein NS, Kok GD, Burger H, van Valen E, Riper H, Cuijpers P, Dekker J, Smit F, van der Heiden C, Bockting CLH. No sustainable effects of an internet-based relapse prevention program over 24 months in recurrent depression: primary outcomes of a randomized controlled trial. Psychother Psychosom. 2018;87(1):55–57 Karger Publishers.

    Article  PubMed  Google Scholar 

  143. 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.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Boß L, Lehr D, Schaub MP, Paz Castro R, Riper H, Berking M, Ebert DD. Efficacy of a web‐based intervention with and without guidance for employees with risky drinking: results of a three‐arm randomized controlled trial. Addiction Wiley Online Library; 2017.

    Google Scholar 

  145. Ebert DD, Berking M, Thiart H, Riper H, Laferton J, 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;Suppl(1). https://doi.org/10.1037/hea0000277.

    Article  Google Scholar 

  146. Thiart H, Lehr D, Ebert DD, Berking M, Riper H. Log in and breathe out: internet-based recovery training for sleepless employees with work-related strain–results of a randomized controlled trial. Scand J Work Environ Health. 2015; https://doi.org/10.5271/sjweh.3478 Online Fir.

    Article  Google Scholar 

  147. Lehr D, Heber E, Sieland B, Hillert A, Funk B, Ebert DD. Occupational eMental Health and teachers’ health: a meta-analytic review on the efficacy of internet-based intervention for promoting mental health in teachers | “Occupational eMental Health” in der Lehrergesundheit: Ein metaanalytisches Review zur Wirksa. Pravention und Gesundheitsforderung 2016; https://doi.org/10.1007/s11553-016-0541-6.

    Article  Google Scholar 

  148. Heber E, Lehr D, Ebert DD, Berking M, Riper H. Web-based and mobile stress management intervention for employees: results of a randomised controlled trial. J Med Internet Res. 2016;18(1):e21. https://doi.org/10.2196/jmir.5112.

    Article  PubMed  PubMed Central  Google Scholar 

  149. Ebert DD, Heber E, Berking M, Riper H, Cuijpers P, Funk B, Lehr D. Self-guided internet-based and mobile-based stress management for employees: results of a randomised controlled trial. Occup Environ Med. 2016;73(5). https://doi.org/10.1136/oemed-2015-103269.

    Article  PubMed  Google Scholar 

  150. 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 Work Environ Health. 2016 Sept 1 [cited 2017 May 2];42(5):382–94 PMID:27249161.

    Article  Google Scholar 

  151. Harrer M, Adam SH, Fleischmann RJ, Baumeister H, Auerbach RP, Bruffaerts R, Cuijpers P, Kessler RC, Berking M, Lehr D, Ebert DD. Effectiveness of an internet- and app-based intervention for college students with elevated stress: results of a randomized controlled trial (in press). J Med Internet Res. 2018.

    Google Scholar 

  152. Heber E, Ebert DD, Lehr D, Cuijpers P, Berking M, Nobis S, Riper H. The benefit of web- and computer-based interventions for stress: a systematic review and meta-analysis. J Med Internet Res. 2017;19(2):e32 PMID:28213341.

    Article  PubMed  PubMed Central  Google Scholar 

  153. van Zoonen K, Buntrock C, Ebert DD, Smit F, Reynolds III CF, Beekman ATF, Cuijpers P. Preventing the onset of major depressive disorder: a meta-analytic review of psychological interventions. Int J Epidemiol. 2014;43(2). https://doi.org/10.1093/ije/dyt175.

    Article  PubMed  PubMed Central  Google Scholar 

  154. Moreno-Peral P, Conejo-Cerón S, Rubio-Valera M, Fernández A, Navas-Campaña D, Rodríguez-Morejón A, Motrico E, Rigabert A, Luna J de D, Martín-Pérez C, Rodríguez-Bayón A, Ballesta-Rodríguez MI, Luciano JV, Bellón JÁ. Effectiveness of psychological and/or educational interventions in the prevention of anxiety. JAMA Psychiatry. 2017;56(9):1026–33. https://doi.org/10.1001/jamapsychiatry.2017.2509 Wiley, New York.

    Article  PubMed  PubMed Central  Google Scholar 

  155. Üstün TB, Ayuso-Mateos JL, Chatterji S, Mathers C, Murray CJL. Global burden of depressive disorders in the year 2000. Br J psychiatry RCP. 2004;184(5):386–92.

    Article  Google Scholar 

  156. Olesen J, Gustavsson A, Svensson M, Wittchen H, Jönsson B. The economic cost of brain disorders in Europe. Eur J Neurol. 2012;19(1):155–62 Wiley Online Library.

    Article  Google Scholar 

  157. Sobocki P, Jönsson B, Angst J, Rehnberg C. Cost of depression in Europe. J Ment Health Policy Econ. 2006;9(2):87–98 PMID:17007486.

    PubMed  Google Scholar 

  158. Drummond MF, Sculpher MJ, Torrance GW, O’Brien BJ, Stoddart GL. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press; 2005.

    Google Scholar 

  159. Donker T, Blankers M, Hedman E, Ljotsson B, Petrie K, Christensen H. Economic evaluations of Internet interventions for mental health: a systematic review. Psychol Med. 2015;45(16):3357–76 Cambridge University Press.

    Article  CAS  PubMed  Google Scholar 

  160. Paganini S, Teigelkötter W, Buntrock C, Baumeister H. Economic evaluations of internet- and mobile-based interventions for the treatment and prevention of depression: a systematic review. J Affect Disord. 2017;225:733–55. https://doi.org/10.1016/j.jad.2017.07.018 Elsevier.

    Article  PubMed  Google Scholar 

  161. Andrews G, Williams AD. Internet psychotherapy and the future of personalized treatment. Depress Anxiety. 2014;31(11):912–5 PMID:25407580.

    Article  PubMed  Google Scholar 

  162. Williams AD, O’Moore K, Mason E, Andrews G. The effectiveness of internet cognitive behaviour therapy (iCBT) for social anxiety disorder across two routine practice pathways. Internet Interv. 2014;1(4):225–9. https://doi.org/10.1016/j.invent.2014.11.001.

    Article  Google Scholar 

  163. Hedman E, Ljótsson B, Kaldo V, Hesser H, El Alaoui S, Kraepelien M, Andersson E, Rück C, Svanborg C, Andersson G, Lindefors N. Effectiveness of Internet-based cognitive behaviour therapy for depression in routine psychiatric care. J Affect Disord. 2014;155(1):49–58 PMID:24238951.

    Article  PubMed  Google Scholar 

  164. El Alaoui S, Hedman E, Ljótsson B, Lindefors N. Long-term effectiveness and outcome predictors of therapist-guided internet-based cognitive-behavioural therapy for social anxiety disorder in routine psychiatric care. BMJ Open. 2015;5(6):e007902 PMID:26105031.

    Article  PubMed  PubMed Central  Google Scholar 

  165. El Alaoui S, Hedman E, Kaldo V, Hesser H, Kraepelien M, Andersson E, Rück C, Andersson G, Ljótsson B, Lindefors N. Effectiveness of Internet-based cognitive-behavior therapy for social anxiety disorder in clinical psychiatry. J Consult Clin Psychol. 2015;83(5):902–14 PMID:26009780.

    Article  PubMed  Google Scholar 

  166. Nordgreen T, Gjestad R, Andersson G, Carlbring P, Havik OE. The implementation of guided Internet-based cognitive behaviour therapy for panic disorder in a routine-care setting: effectiveness and implementation efforts. Cogn Behav Ther. 2017;1–14 PMID:28714775.

    Google Scholar 

  167. Titov N, Dear BF, Staples LG, Bennett-Levy J, Klein B, Rapee RM, Andersson G, Purtell C, Bezuidenhout G, Nielssen OB. The first 30 months of the MindSpot Clinic: evaluation of a national e-mental health service against project objectives. Aust New Zeal J Psychiatry. 2016;000486741667159 PMID:27733709.

    Google Scholar 

  168. Titov N, Dear BF, Staples LG, Bennett-Levy J, Klein B, Rapee RM, Shann C, Richards D, Andersson G, Ritterband L, Purtell C, Bezuidenhout G, Johnston L, Nielssen OB. MindSpot Clinic: an accessible, efficient, and effective online treatment service for anxiety and depression. Psychiatr Serv. 2015;66(10):1043–50 PMID:26130001.

    Article  PubMed  Google Scholar 

  169. Titov N, Dear B, Nielssen O, Staples L, Hadjistavropoulos H, Nugent M, Adlam K, Nordgreen T, Bruvik KH, Hovland A. ICBT in routine care: a descriptive analysis of successful clinics in five countries. Internet Interv. 2018 Elsevier.

    Google Scholar 

  170. 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 MJH, Phillips R, Gilbody S, Moritz S, Berger T, Pop V, Spek V, Cuijpers P. Efficacy of Self-guided Internet-Based Cognitive Behavioral Therapy in the Treatment of Depressive Symptoms. JAMA Psychiatry. 2017. https://doi.org/10.1001/jamapsychiatry.2017.0044.

    Article  PubMed  Google Scholar 

  171. Ebert DD, Baumeister H. Internet-based self-help interventions for depression in routine care. JAMA Psychiatry. 2017;1(4):205–15. https://doi.org/10.1001/jamapsychiatry.2017.1394.

    Article  Google Scholar 

  172. Lin J, Faust B, Ebert DD, Krämer L, Baumeister H. A web-based acceptance-facilitating intervention for identifying patients’ acceptance, uptake, and adherence of internet-and mobile-based pain interventions: randomized controlled trial. J Med Internet Res. 2018;20(8):e244 Toronto, Canada: JMIR Publications Inc.

    Google Scholar 

  173. Littlewood E, Duarte A, Hewitt C, Knowles S, Palmer S, Walker S, Andersen P, Araya R, Barkham M, Bower P, Brabyn S, Brierley G, Cooper C, Gask L, Kessler D, Lester H, Lovell K, Muhammad U, Parry G, Richards DA, Richardson R, Tallon D, Tharmanathan P, White D, Gilbody S. A randomised controlled trial of computerised cognitive behaviour therapy for the treatment of depression in primary care: the Randomised Evaluation of the Effectiveness and Acceptability of Computerised Therapy (REEACT) trial. Health Technol Assess. 2015;19(101):1–174 PMID:26685904.

    Article  PubMed Central  Google Scholar 

  174. Brabyn S, Araya R, Barkham M, Bower P, Cooper C, Duarte A, Kessler D, Knowles S, Lovell K, Littlewood E, Mattock R, Palmer S, Pervin J, Richards D, Tallon D, White D, Walker S, Worthy G, Gilbody S. The second Randomised Evaluation of the Effectiveness, cost-effectiveness and Acceptability of Computerised Therapy (REEACT-2) trial: does the provision of telephone support enhance the effectiveness of computer-delivered cognitive behaviour therapy? A ra. Health Technol Assess (Rockv). 2016;20(89):1–64 PMID:27922448.

    Article  Google Scholar 

  175. Muñoz RF. The efficiency model of support and the creation of digital apothecaries. Clin Psychol Sci Pract. 2017;24(1):46–9. https://doi.org/10.1111/cpsp.12174.

    Article  Google Scholar 

  176. Mohr DC, Weingardt KR, Reddy M, Schueller SM. Three problems with current digital mental health research and three things we can do about them. Psychiatr Serv. 2017;68(5):427–29. https://doi.org/10.1176/appi.ps.201600541 American Psychiatric Association Arlington, VA.

    Article  PubMed  Google Scholar 

  177. Kaltenthaler E, Parry G, Beverley C, Ferriter M. Computerised cognitive-behavioural therapy for depression: systematic review. Br J Psychiatry. 2008;193(3):181–84 Cambridge University Press.

    Article  Google Scholar 

  178. Lillevoll K, Vangberg H, Griffiths K, Waterloo K, Eisemann M. Uptake and adherence of a self-directed internet-based mental health intervention with tailored e-mail reminders in senior high schools in Norway. BMC Psychiatry. 2014. https://doi.org/10.1186/1471-244x-14-14.

  179. Whiteside U, Lungu A, Richards J, Simon GE, Clingan S, Siler J, Snyder L, Ludman E. Designing messaging to engage patients in an online suicide prevention intervention: survey results from patients with current suicidal ideation. J Med Internet Res. 2014;16(2) JMIR Publications Inc.

    Google Scholar 

  180. Woodford J, Farrand P, Bessant M, Williams C. Recruitment into a guided internet based CBT (iCBT) intervention for depression: lesson learnt from the failure of a prevalence recruitment strategy. Contemp Clin Trials. 2011;32(5):641–8. https://doi.org/10.1016/j.cct.2011.04.013.

    Article  PubMed  Google Scholar 

  181. Paul CL, Piterman L, Shaw JE, Kirby C, Forshaw KL, Robinson J, Thepwongsa I, Sanson-Fisher RW. Poor uptake of an online intervention in a cluster randomised controlled trial of online diabetes education for rural general practitioners. Trials Engl. 2017;18(1):137 PMID:28335809.

    Google Scholar 

  182. Meisel SF, Drury H, Perera-Delcourt RP. Therapists’ attitudes to offering eCBT in an inner-city IAPT service: a survey study. Cogn Behav Ther. 2018;11 Cambridge University Press.

    Google Scholar 

  183. Hennemann S, Beutel ME, Zwerenz R. Ready for eHealth? Health professionals’ acceptance and adoption of ehealth interventions in inpatient routine care. J Health Commun. 2017;22(3):274–84 Taylor & Francis.

    Google Scholar 

  184. Mitchell N, Gordon PK. Attitudes towards computerised CBT for depression amongst a student population. Behav Cogn Psychother. 2007 [cited 2017 Mar 22];35(04):421–30. https://doi.org/10.1017/s1352465807003700.

    Article  Google Scholar 

  185. Cranen K, Veld RH in’t, Ijzerman M, Vollenbroek-Hutten M. Change of patients’ perceptions of telemedicine after brief use. Telemed J e-Health. 2011;17(7):530–35. https://doi.org/10.1089/tmj.2010.0208.

    Article  PubMed  Google Scholar 

  186. Baumeister H, Seifferth H, Lin J, Nowoczin L, L?king M, Ebert D. Impact of an acceptance facilitating intervention on patients’ acceptance of internet-based pain interventions: a randomized controlled trial. Clin J Pain. 2015;31(6). https://doi.org/10.1097/ajp.0000000000000118.

    Article  PubMed  Google Scholar 

  187. 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.

    Article  CAS  PubMed  Google Scholar 

  188. Ebert DD, Donkin L, Andersson G, Andrews G, Berger T, Carlbring P, Rozenthal A, Choi I, Laferton JAC, Johansson R, Kleiboer A, Lange A, Lehr D, Reins JA, Funk B, Newby J, Perini S, Riper H, Ruwaard J, Sheeber L, Snoek FJ, Titov N, Ünlü Ince B, Van Bastelaar K, Vernmark K, Van Straten A, Warmerdam L, Salsman N, Cuijpers P. Does Internet-based guided-self-help for depression cause harm? An individual participant data meta-analysis on deterioration rates and its moderators in randomized controlled trials. Psychol Med. 2016;46(13). https://doi.org/10.1017/s0033291716001562.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Christensen H, Farrer L, Batterham PJ, Mackinnon A, Griffiths KM, Donker T. The effect of a web-based depression intervention on suicide ideation: secondary outcome from a randomised controlled trial in a helpline. BMJ Open. 2013;3(6):1–10 PMID:23811172.

    Article  Google Scholar 

  190. Mewton L, Andrews G. Cognitive behaviour therapy via the internet for depression: a useful strategy to reduce suicidal ideation. J Affect Disord. 2014;170C:78–84 PMID:25233243.

    Google Scholar 

  191. Rozental A, Boettcher J, Andersson G, Schmidt B, Carlbring P. Negative effects of internet interventions: a qualitative content analysis of patients’ experiences with treatments delivered online. Cogn Behav Ther. 2015;44(3):223–36 PMID:25705924.

    Article  PubMed  Google Scholar 

  192. Boettcher J, Rozental A, Andersson G, Carlbring P. Side effects in Internet-based interventions for Social Anxiety Disorder. Internet Interv. 2014;1(1):3–11. https://doi.org/10.1016/j.invent.2014.02.002.

    Article  Google Scholar 

  193. Rozental A, Magnusson K, Boettcher J, Andersson G, Carlbring P. For better or worse: an individual patient data meta-analysis of deterioration among participants receiving Internet-based cognitive behavior therapy. J Consult Clin Psychol. 2017;85(2):160–77 PMID:27775414.

    Article  PubMed  Google Scholar 

  194. Wampold BE, Imel ZE. The Great psychotherapy debate. the evidence for what makes psychotherapy work. 2nd ed. New York: Routledge; 2015.

    Book  Google Scholar 

  195. Domhardt M, Geßlein H, von Rezori RE, Baumeister H. Internet‐and mobile‐based interventions for anxiety disorders: a meta‐analytic review of intervention components. Depress Anxiety. 2018 Wiley Online Library.

    Google Scholar 

  196. 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.

    Article  Google Scholar 

  197. Warmerdam L, van Straten A, Jongsma J, Twisk J, Cuijpers P. Online cognitive behavioral therapy and problem-solving therapy for depressive symptoms: exploring mechanisms of change. J Behav Ther Exp Psychiatry. 2010;41(1):64–70 PMID:19913781.

    Article  PubMed  Google Scholar 

  198. 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.

    Article  Google Scholar 

  199. Boettcher J, Renneberg B, Berger T. Patient expectations in internet-based self-help for social anxiety. Cogn Behav Ther. 2013;42(3):203–14 PMID:23697570.

    Article  PubMed  Google Scholar 

  200. Lévesque A, Campbell ANC, Pavlicova M, Hu M-C, Walker R, McClure EA, Ghitza UE, Bailey G, Stitzer M, Nunes EV. Coping strategies as a mediator of internet-delivered psychosocial treatment: secondary analysis from a NIDA CTN multisite effectiveness trial. Addict Behav. 2017;65:74–80 PMID:27776269.

    Article  PubMed  Google Scholar 

  201. Mogoașe C, Cobeanu O, David O, Giosan C, Szentagotai A. Internet-based psychotherapy for adult depression: what about the mechanisms of change? J Clin Psychol. 2017;73(1):5–64 PMID:27684405.

    Article  PubMed  Google Scholar 

  202. Hedman E, Andersson E, Andersson G, Lindefors N, Lekander M, Rück C, Ljótsson B. Mediators in internet-based cognitive behavior therapy for severe health anxiety. PLoS One. 2013;8(10):e77752. https://doi.org/10.1371/journal.pone.0077752 Thombs B, editor. Guilford Press.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  203. Graham AL, Papandonatos GD, Cobb CO, Cobb NK, Niaura RS, Abrams DB, Tinkelman DG. Internet and telephone treatment for smoking cessation: mediators and moderators of short-term abstinence. Nicotine Tob Res. 2015;17(3):299–308 PMID:25156528, Oxford University Press.

    Article  Google Scholar 

  204. Ljótsson B, Hesser H, Andersson E, Lindfors P, Hursti T, Rück C, Lindefors N, Andersson G, Hedman E. Mechanisms of change in an exposure-based treatment for irritable bowel syndrome. J Consult Clin Psychol. 2013;81(6):1113–26 PMID:23750460.

    Article  PubMed  Google Scholar 

  205. Morgan AJ, Mackinnon AJ, Jorm AF. Behavior change through automated e-mails: mediation analysis of self-help strategy use for depressive symptoms. Behav Res Ther. 2013;51(2):57–62 PMID:23262114.

    Article  PubMed  Google Scholar 

  206. Alpay L, van der Boog P, Dumaij A. An empowerment-based approach to developing innovative e-health tools for self-management. Health Inf J. 2011;17(4):247–55 Sage Publications Sage UK: London, England.

    Google Scholar 

  207. Crisp D, Griffiths K, Mackinnon A, Bennett K, Christensen H. An online intervention for reducing depressive symptoms: secondary benefits for self-esteem, empowerment and quality of life. Psychiatry Res. 2014;216(1):60–66 Elsevier.

    Article  PubMed  Google Scholar 

  208. Samoocha D, Bruinvels DJ, Elbers NA, Anema JR, van der Beek AJ. Effectiveness of web-based interventions on patient empowerment: a systematic review and meta-analysis. J Med Internet Res. 2010;12. https://doi.org/10.2196/jmir.1286.

    Article  PubMed  PubMed Central  Google Scholar 

  209. Kuijpers W, Groen WG, Aaronson NK, van Harten WH. A systematic review of web-based interventions for patient empowerment and physical activity in chronic diseases: relevance for cancer survivors. J Med Internet Res. 2013;15(2) JMIR Publications Inc.

    Google Scholar 

  210. Cowpertwait L, Clarke D. Effectiveness of web-based psychological interventions for depression: a meta-analysis. Int J Ment Health Addict. 2013;11(2):247–68. https://doi.org/10.1007/s11469-012-9416-z Springer.

    Article  Google Scholar 

  211. Baumeister H, Reichler L, Munzinger M, Lin J. The impact of guidance on Internet-based mental health interventions-a systematic review. Internet Interv. 2014:205–15. https://doi.org/10.1016/j.invent.2014.08.003.

    Article  Google Scholar 

  212. Johansson R, Andersson G. Internet-based psychological treatments for depression. Expert Rev Neurother. 2012;12(7):861–9; quiz 870 PMID:22853793.

    Article  CAS  PubMed  Google Scholar 

  213. Andersson G, Carlbring P, Berger T, Almlöv J, Cuijpers P. What makes internet therapy work? Cogn Behav Ther. 2009;38(sup1):55–60 PMID:19675956.

    Article  PubMed  Google Scholar 

  214. Andersson G, Paxling B, Wiwe M, Vernmark K, Felix CB, Lundborg L, Furmark T, Cuijpers P, Carlbring P. Therapeutic alliance in guided internet-delivered cognitive behavioural treatment of depression, generalized anxiety disorder and social anxiety disorder. Behav Res Ther. 2012;50(9):544–50 PMID:22728647.

    Article  PubMed  Google Scholar 

  215. Ebert DD, Hannig W, Tarnowski T, Sieland B, Götzky B, Berking M. Web-based rehabilitation aftercare following inpatient psychosomatic treatment. Rehabilitation (Stuttg). 2013;52(3):164–72 PMID:23761205.

    Article  CAS  Google Scholar 

  216. Preschl B, Maercker A, Wagner B. The working alliance in a randomized controlled trial comparing online with face-to-face cognitive-behavioral therapy for depression. BMC Psychiatry. 2011;11(1):189 PMID:22145768.

    Article  PubMed  PubMed Central  Google Scholar 

  217. Cook JE, Doyle C. Working alliance in online therapy as compared to face-to-face therapy: preliminary results. Cyberpsychol Behav. 2002;5(2):95–105 PMID:12025884.

    Article  PubMed  Google Scholar 

  218. Bengtsson J, Nordin S, Carlbring P. Therapists’ Experiences of conducting cognitive behavioural therapy online vis-à-vis face-to-face. Cogn Behav Ther. 2015;1–10 PMID:26090947, Routledge.

    Google Scholar 

  219. Knaevelsrud C, Maercker A. Does the quality of the working alliance predict treatment outcome in online psychotherapy for traumatized patients? J Med Internet Res. 2006;8(4):e31 PMID:17213049.

    Article  PubMed  PubMed Central  Google Scholar 

  220. Mohr DC, Cuijpers P, Lehman K. Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions. J Med Internet Res. 2011 Mar 10 [cited 2017 May 15];13(1):e30 PMID:21393123.

    Article  PubMed  PubMed Central  Google Scholar 

  221. Terhorst Y, Rathner EM, Baumeister H, Sander L. Help from the App-Store? a systematic review of apps for depression (in press). Verhaltenstherapie 2017.

    Google Scholar 

  222. Sucala M, Cuijpers P, Muench F, Cardoș R, Soflau R, Dobrean A, Achimas‐Cadariu P, David D. Anxiety: there is an app for that. A systematic review of anxiety apps. Depress Anxiety. 2017;34(6):518–25 Wiley Online Library.

    Article  PubMed  Google Scholar 

  223. Neary M, Schueller SM. State of the field of mental health apps. Cogn Behav Pract. 2018 Elsevier.

    Google Scholar 

  224. Klein JP, Knaevelsrud C, Bohus M, Ebert DD, Gerlinger G, Günther K, Jacobi C, Löbner M, Riedel-Heller SG, Sander J. Internetbasierte Selbstmanagementinterventionen. Nervenarzt Springer; 2018;1–9.

    Google Scholar 

  225. Alpaydin E. Introduction to machine learning. MIT press; 2009. ISBN:0262303264.

    Google Scholar 

  226. Yarkoni T, Westfall J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect Psychol Sci. 2017;12(6):1100–22 SAGE Publications Sage CA: Los Angeles, CA.

    Google Scholar 

  227. Breiman L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat Sci. 2001;16(3):199–231 Institute of Mathematical Statistics.

    Article  Google Scholar 

  228. Ward JS, Barker A. Undefined by data: a survey of big data definitions; 2013. arXiv:13095821.

  229. Becker D, van Breda W, Funk B, Hoogendoorn M, Ruwaard J, Riper H. Predictive modeling in e-mental health: a common language framework. Internet Interv Neth. 2018;12:57–67 PMID:30135769.

    Article  Google Scholar 

  230. Wongkoblap A, Vadillo MA, Curcin V. Researching mental health disorders in the era of social media: systematic review. J Med Internet Res. 2017;19(6):e228 PMID:28663166 Canada.

    Article  PubMed  PubMed Central  Google Scholar 

  231. Kang K, Yoon C, Kim EY. Identifying depressive users in Twitter using multimodal analysis. In: 2016 International conference on big data smart comput (BigComp), IEEE;2016. p. 231–38.

    Google Scholar 

  232. Park S, Kim I, Lee SW, Yoo J, Jeong B, Cha M. Manifestation of depression and loneliness on social networks: a case study of young adults on Facebook. In: Proceedings of the 18th ACM conference on comput support cooperative work social computing, ACM; 2015. p. 557–70.

    Google Scholar 

  233. Hu Q, Li A, Heng F, Li J, Zhu T. Predicting depression of social media user on different observation windows. In: 2015 IEEE/WIC/ACM International conference on web intelligence agent technology (WI-IAT), IEEE; 2015. p. 361–64.

    Google Scholar 

  234. Tsugawa S, Kikuchi Y, Kishino F, Nakajima K, Itoh Y, Ohsaki H. Recognizing depression from twitter activity. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, ACM; 2015. p. 3187–96.

    Google Scholar 

  235. Park S, Lee SW, Kwak J, Cha M, Jeong B. Activities on Facebook reveal the depressive state of users. J Med Internet Res. 2013;15(10) JMIR Publications Inc.

    Google Scholar 

  236. Wang X, Zhang C, Sun L. An improved model for depression detection in micro-blog social network. In: 2013 IEEE 13th international conference on data mining workshops, IEEE; 2013. p. 80–87.

    Google Scholar 

  237. De Choudhury M, Counts S, Horvitz E. Social media as a measurement tool of depression in populations. In: Proceedings of the 5th annual ACM web science conference, ACM; 2013. p. 47–56.

    Google Scholar 

  238. Mowery D, Smith H, Cheney T, Stoddard G, Coppersmith G, Bryan C, Conway M. Understanding depressive symptoms and psychosocial stressors on Twitter: a corpus-based study. J Med Internet Res. 2017;19(2):e48 PMID:28246066 Canada.

    Article  PubMed  PubMed Central  Google Scholar 

  239. Reece A. Chronology and patterns of psychiatric morbidity in substance-dependent and medical patients. Australas Psychiatry. 2009;17(2):170–71. https://doi.org/10.1177/103985620901700203 US: Informa Healthcare.

    Article  Google Scholar 

  240. Reece AG, Danforth CM. Instagram photos reveal predictive markers of depression. EPJ Data Sci. 2017;6(1):15 SpringerOpen.

    Google Scholar 

  241. Lin H, Jia J, Guo Q, Xue Y, Li Q, Huang J, Cai L, Feng L. User-level psychological stress detection from social media using deep neural network. In: Proceedings of the 22nd ACM international conference on multimedia, ACM; 2014. p. 507–16.

    Google Scholar 

  242. Schwartz HA, Sap M, Kern ML, Eichstaedt JC, Kapelner A, Agrawal M, Blanco E, Dziurzynski L, Park G, Stillwell D. Predicting individual well-being through the language of social media. In: Biocomput 2016 proceedings of the pacific symposium world scientific; 2016. p. 516–27.

    Google Scholar 

  243. Liu P, Tov W, Kosinski M, Stillwell DJ, Qiu L. Do Facebook status updates reflect subjective well-being? Cyberpsychology, Behav Soc Netw. 2015;18(7):373–79 Mary Ann Liebert, Inc., 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA.

    Article  Google Scholar 

  244. Settanni M, Azucar D, Marengo D. Predicting individual characteristics from digital traces on social media: a meta-analysis. Cyberpsychology Behav Soc Netw. 2018;21(4):217–28 Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA.

    Article  Google Scholar 

  245. Coppersmith G, Dredze M, Harman C. Quantifying mental health signals in Twitter. In: Proceedings of the workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality; 2014. p. 51–60.

    Google Scholar 

  246. Harman G, Dredze MH. Measuring post traumatic stress disorder in Twitter. In: ICWSM 2014.

    Google Scholar 

  247. Chancellor S, Lin Z, Goodman EL, Zerwas S, De Choudhury M. Quantifying and predicting mental illness severity in online pro-eating disorder communities. In: Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing, ACM; 2016. p. 1171–84.

    Google Scholar 

  248. Moessner M, Feldhege J, Wolf M, Bauer S. Analyzing big data in social media: text and network analyses of an eating disorder forum. Int J Eat Disord. 2018 PMID:29746710 United States.

    Google Scholar 

  249. Song J, Song TM, Seo D-C, Jin JH. Data mining of web-based documents on social networking sites that included suicide-related words among korean adolescents. J Adolesc Health. 2016;59(6):668–73 PMID:27693129 United States.

    Article  Google Scholar 

  250. Braithwaite SR, Giraud-Carrier C, West J, Barnes MD, Hanson CL. Validating machine learning algorithms for Twitter data against established measures of suicidality. JMIR Ment Heal. 2016;3(2) JMIR Publications Inc.

    Google Scholar 

  251. O’Dea B, Wan S, Batterham PJ, Calear AL, Paris C, Christensen H. Detecting suicidality on Twitter. Internet Interv. 2015;2(2):183–88 Elsevier.

    Article  Google Scholar 

  252. Zhang L, Huang X, Liu T, Li A, Chen Z, Zhu T. Using linguistic features to estimate suicide probability of Chinese microblog users. In: International conference on human centered computing, Springer; 2014. p. 549–59.

    Google Scholar 

  253. Burnap P, Colombo G, Amery R, Hodorog A, Scourfield J. Multi-class machine classification of suicide-related communication on Twitter. Online Soc networks media. 2017;2:32–44 Elsevier.

    Article  Google Scholar 

  254. Torous J, Larsen ME, Depp C, Cosco TD, Barnett I, Nock MK, Firth J. Smartphones, sensors, and machine learning to advance real-time prediction and interventions for suicide prevention: a review of current progress and next steps. Curr Psychiatry Rep. 2018;20(7):51 PMID:29956120 United States.

    Google Scholar 

  255. Kornfield R, Sarma PK, Shah DV, McTavish F, Landucci G, Pe-Romashko K, Gustafson DH. Detecting recovery problems just in time: application of automated linguistic analysis and supervised machine learning to an online substance abuse forum. J Med Internet Res. 2018;20(6):e10136 PMID:29895517 Canada.

    Article  PubMed  PubMed Central  Google Scholar 

  256. Cepeda MS, Reps J, Fife D, Blacketer C, Stang P, Ryan P. Finding treatment‐resistant depression in real‐world data: how a data‐driven approach compares with expert‐based heuristics. Depress Anxiety. 2017. https://doi.org/10.1002/da.22705 US: Wiley.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  257. Morales S, Barros J, Echavarri O, Garcia F, Osses A, Moya C, Maino MP, Fischman R, Nunez C, Szmulewicz T, Tomicic A. Acute mental discomfort associated with suicide behavior in a clinical sample of patients with affective disorders: ascertaining critical variables using artificial intelligence tools. Front Psychiatry. 2017;8:7 PMID:28210230 Switzerland.

    Google Scholar 

  258. Whelan R, Watts R, Orr CA, Althoff RR, Artiges E, Banaschewski T, Barker GJ, Bokde ALW, Buchel C, Carvalho FM, Conrod PJ, Flor H, Fauth-Buhler M, Frouin V, Gallinat J, Gan G, Gowland P, Heinz A, Ittermann B, Lawrence C, Mann K, Martinot J-L, Nees F, Ortiz N, Paillere-Martinot M-L, Paus T, Pausova Z, Rietschel M, Robbins TW, Smolka MN, Strohle A, Schumann G, Garavan H. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 2014;512(7513):185–89 PMID:25043041 England.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  259. Chekroud AM, Foster D, Zheutlin AB, Gerhard DM, Roy B, Koutsouleris N, Chandra A, Esposti MD, Subramanyan G, Gueorguieva R, Paulus M, Krystal JH. Predicting barriers to treatment for depression in a U.S. National sample: a cross-sectional, proof-of-concept study. Psychiatr Serv. 2018;69(8):927–34 PMID:29962307 United States.

    Article  PubMed  Google Scholar 

  260. Jordan P, Shedden-Mora MC, Lowe B. Predicting suicidal ideation in primary care: an approach to identify easily assessable key variables. Gen Hosp Psychiatry. 2018;51:106–11 PMID:29428582 United States.

    Article  PubMed  Google Scholar 

  261. Tran T, Luo W, Phung D, Harvey R, Berk M, Kennedy RL, Venkatesh S. Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments. BMC Psychiatry. 2014;14(1):76 BioMed Central.

    Google Scholar 

  262. Hoexter MQ, Dougherty DD, Shavitt RG, D’Alcante CC, Duran FLS, Lopes AC, Diniz JB, Batistuzzo MC, Evans KC, Bressan RA, Busatto GF, Miguel EC. Differential prefrontal gray matter correlates of treatment response to fluoxetine or cognitive-behavioral therapy in obsessive–compulsive disorder. Eur Neuropsychopharmacol. 2013;23(7):569–80. https://doi.org/10.1016/j.euroneuro.2012.06.014 Hoexter, Marcelo Q., Department of Psychiatry, University of Sao Paulo Medical School, Rua Ovidio Pires de Campos 785–31 andar Ala Norte-sala 9 (PROTOC), CEP 05403-010, Sao Paulo, Brazil: Elsevier Science.

    Article  CAS  PubMed  Google Scholar 

  263. Lueken U, Straube B, Yang Y, Hahn T, Beesdo-Baum K, Wittchen H-U, Konrad C, Strohle A, Wittmann A, Gerlach AL, Pfleiderer B, Arolt V, Kircher T. Separating depressive comorbidity from panic disorder: a combined functional magnetic resonance imaging and machine learning approach. J Affect Disord Neth. 2015;184:182–92 PMID:26093832.

    Article  Google Scholar 

  264. Jimenez-Serrano S, Tortajada S, Garcia-Gomez JM. A mobile health application to predict postpartum depression based on machine learning. Telemed J E Health. 2015;21(7):567–74 PMID:25734829 United States.

    Article  PubMed  Google Scholar 

  265. Marzano L, Bardill A, Fields B, Herd K, Veale D, Grey N, Moran P. The application of mHealth to mental health: opportunities and challenges. Lancet Psychiatry. 2015;2(10):942–48 Elsevier.

    Article  PubMed  Google Scholar 

  266. Pantelopoulos A, Bourbakis NG. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man, Cybern Part C (Appl Rev IEEE). 2010;40(1):1–12.

    Article  Google Scholar 

  267. Schueller SM, Begale M, Penedo FJ, Mohr DC. Purple: a modular system for developing and deploying behavioral intervention technologies. J Med Internet Res. 2014;16(7) JMIR Publications Inc.

    Google Scholar 

  268. Torous J, Kiang M V, Lorme J, Onnela J-P. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment Health. 2016;3(2) JMIR Publications Inc.

    Google Scholar 

  269. Mohr DC, Zhang M, Schueller SM. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annu Rev Clin Psychol. 2017;13:23–47 PMID:28375728 United States.

    Article  PubMed  PubMed Central  Google Scholar 

  270. Bourla A, Mouchabac S, El Hage W, Ferreri F. e-PTSD: an overview on how new technologies can improve prediction and assessment of Posttraumatic Stress Disorder (PTSD). Eur J Psychotraumatol. 2018;9(sup1):1424448 PMID:29441154 United States.

    Article  PubMed  PubMed Central  Google Scholar 

  271. Trull TJ, Solhan MB, Tragesser SL, Jahng S, Wood PK, Piasecki TM, Watson D. Affective instability: measuring a core feature of borderline personality disorder with ecological momentary assessment. J Abnorm Psychol. 2008;117(3):647 American Psychological Association.

    Google Scholar 

  272. Dennis ML, Scott CK, Funk RR. Ecological momentary assessment to predict the risk of relapse. Drug Alcohol Depend. 2015;146:e262. https://doi.org/10.1016/j.drugalcdep.2014.09.179.

    Article  Google Scholar 

  273. Chih M-Y, Patton T, McTavish FM, Isham AJ, Judkins-Fisher CL, Atwood AK, Gustafson DH. Predictive modeling of addiction lapses in a mobile health application. J Subst Abuse Treat. 2014;46(1):29–35 PMID:24035143 United States.

    Article  PubMed  Google Scholar 

  274. Saeb S, Kording K, Mohr DC. Making activity recognition robust against deceptive behavior. PLoS One. 2015;10(12):e0144795 PMID:26659118 United States.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  275. Pratap A, Atkins DC, Renn BN, Tanana MJ, Mooney SD, Anguera JA, Arean PA. The accuracy of passive phone sensors in predicting daily mood. Depress Anxiety. 2018 PMID:30129691 United States.

    Google Scholar 

  276. Hung GC-L, Yang P-C, Chang C-C, Chiang J-H, Chen Y-Y. Predicting negative emotions based on mobile phone usage patterns: an exploratory study. JMIR Res Protoc. 2016;5(3):e160 PMID:27511748 Canada.

    Article  PubMed  PubMed Central  Google Scholar 

  277. Jaques N, Taylor S, Azaria A, Ghandeharioun A, Sano A, Picard R. Predicting students’ happiness from physiology, phone, mobility, and behavioral data. In: International conference on affective computing and intelligent interaction and workshops [proceedings]. ACII United States; 2015:222–28 PMID:28515966.

    Google Scholar 

  278. Sano A. Measuring college students’ sleep, stress, mental health and wellbeing with wearable sensors and mobile phones. Massachusetts Institute of Technology; 2016 [cited 2017 May 10]. https://dspace.mit.edu/handle/1721.1/106066.

  279. Sano A, Taylor S, McHill AW, Phillips AJ, Barger LK, Klerman E, Picard R. Identifying objective physiological markers and modifiable behaviors for self-reported stress and mental health status using wearable sensors and mobile phones: observational study. J Med Internet Res. 2018;20(6):e210 PMID:29884610 Canada.

    Article  PubMed  PubMed Central  Google Scholar 

  280. Lu H, Frauendorfer D, Rabbi M, Mast MS, Chittaranjan GT, Campbell AT, Gatica-Perez D, Choudhury T. Stresssense: detecting stress in unconstrained acoustic environments using smartphones. In: Proceedings of the 2012 ACM conference on ubiquitous computing, ACM; 2012. p. 351–60.

    Google Scholar 

  281. Abbas H, Garberson F, Glover E, Wall DP. Machine learning approach for early detection of autism by combining questionnaire and home video screening. J Am Med Inform Assoc. 2018;25(8):1000–07 PMID:29741630, England.

    Article  PubMed  PubMed Central  Google Scholar 

  282. Sathyanarayana A, Joty S, Fernandez-Luque L, Ofli F, Srivastava J, Elmagarmid A, Arora T, Taheri S. Sleep quality prediction from wearable data using deep learning. JMIR mHealth uHealth. 2016;4(4):e125 PMID:27815231 Canada.

    Article  PubMed  PubMed Central  Google Scholar 

  283. Barnett I, Torous J, Staples P, Sandoval L, Keshavan M, Onnela J-P. Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology. 2018;1 Nature Publishing Group.

    Google Scholar 

  284. Depp CA, Thompson WK, Frank E, Swartz HA. Prediction of near-term increases in suicidal ideation in recently depressed patients with bipolar II disorder using intensive longitudinal data. J Affect Disord. 2017;208:363–8 Elsevier.

    Article  PubMed  Google Scholar 

  285. Hofmann SG, Curtiss J, McNally RJ. A complex network perspective on clinical science. Perspect Psychol Sci. 2016;11(5):597–605 PMID:27694457.

    Article  PubMed  PubMed Central  Google Scholar 

  286. van de Leemput IA, Wichers M, Cramer AOJ, Borsboom D, Tuerlinckx F, Kuppens P, van Nes EH, Viechtbauer W, Giltay EJ, Aggen SH. Critical slowing down as early warning for the onset and termination of depression. Proc Natl Acad Sci. 2014;111(1):87–92.

    Google Scholar 

  287. Fried EI, van Borkulo CD, Cramer AOJ, Boschloo L, Schoevers RA, Borsboom D. Mental disorders as networks of problems: a review of recent insights. Soc Psychiatry Psychiatr Epidemiol. 2017;52(1):1–10. https://doi.org/10.1007/s00127-016-1319-z.

    Article  PubMed  Google Scholar 

  288. Wampold BE, Imel ZE. The great psychotherapy debate: the evidence for what makes psychotherapy work. Routledge; 2015. ISBN:1136672605.

    Google Scholar 

  289. Loerinc AG, Meuret AE, Twohig MP, Rosenfield D, Bluett EJ, Craske MG. Response rates for CBT for anxiety disorders: need for standardized criteria. Clin Psychol Rev. 2015;42:72–82 Elsevier.

    Article  PubMed  Google Scholar 

  290. Hofmann SG, Asnaani A, Vonk IJJ, Sawyer AT, Fang A. The efficacy of cognitive behavioral therapy: a review of meta-analyses. Cognit Ther Res. 2012;36(5):427–40. https://doi.org/10.1007/s10608-012-9476-1.

    Article  PubMed  PubMed Central  Google Scholar 

  291. Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D, Niederehe G, Thase ME, Lavori PW, Lebowitz BD. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR* D report. Am J Psychiatry Am Psychiatric Assoc. 2006;163(11):1905–17.

    Article  Google Scholar 

  292. Kessler RC. The potential of predictive analytics to provide clinical decision support in depression treatment planning. Curr Opin Psychiatry. 2018;31(1):32–39 Wolters Kluwer.

    Article  PubMed  Google Scholar 

  293. Riley RD, Ensor J, Snell KIE, Debray TPA, Altman DG, Moons KGM, Collins GS. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ. 2016;353:i3140 PMID:27334381 England.

    Google Scholar 

  294. Gori A, Lauro-Grotto R, Giannini M, Schuldberg D. Predicting treatment outcome by combining different assessment tools: toward an integrative model of decision support in psychotherapy. J Psychother Integr. 2010;20(2):251–69. https://doi.org/10.1037/a0019768 Gori, Alessio, Department of Psychology, University of Florence, via di San Salvi, 12, 50135, Florence, Italy: Educational Publishing Foundation.

    Article  Google Scholar 

  295. Hoogendoorn M, Berger T, Schulz A, Stolz T, Szolovits P. Predicting social anxiety treatment outcome based on therapeutic email conversations. IEEE J Biomed Health Inf. 2017;21(5):1449–59 IEEE.

    Article  PubMed  Google Scholar 

  296. Lenhard F, Sauer S, Andersson E, Mansson KN, Mataix-Cols D, Ruck C, Serlachius E. Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: a machine learning approach. Int J Methods Psychiatr Res. 2018;27(1) PMID:28752937 United States.

    Article  PubMed Central  Google Scholar 

  297. Mansson KNT, Frick A, Boraxbekk C-J, Marquand AF, Williams SCR, Carlbring P, Andersson G, Furmark T. Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning. Transl Psychiatry 2015;5:e530 PMID:25781229 United States.

    Article  PubMed  PubMed Central  Google Scholar 

  298. Askland KD, Garnaat S, Sibrava NJ, Boisseau CL, Strong D, Mancebo M, Greenberg B, Rasmussen S, Eisen J. Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy. Int J Methods Psychiatr Res. 2015;24(2):156–69 Wiley Online Library.

    Article  PubMed  PubMed Central  Google Scholar 

  299. Sundermann B, Bode J, Lueken U, Westphal D, Gerlach AL, Straube B, Wittchen H-U, Strohle A, Wittmann A, Konrad C, Kircher T, Arolt V, Pfleiderer B. Support vector machine analysis of functional magnetic resonance imaging of interoception does not reliably predict individual outcomes of cognitive behavioral therapy in panic disorder with agoraphobia. Front Psychiatry. 2017;8:99 PMID:28649205, Switzerland.

    Google Scholar 

  300. Kessler RC, van Loo HM, Wardenaar KJ, Bossarte RM, Brenner LA, Cai T, Ebert DD, Hwang I, Li J, de Jonge P, Nierenberg AA, Petukhova MV, Rosellini AJ, Sampson NA, Schoevers RA, Wilcox MA, Zaslavsky AM. Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Mol Psychiatry. 2016;21(10):1366–71. https://doi.org/10.1038/mp.2015.198.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  301. Kessler RC, Hwang I, Hoffmire CA, McCarthy JF, Petukhova M V, Rosellini AJ, Sampson NA, Schneider AL, Bradley PA, Katz IR. Developing a practical suicide risk prediction model for targeting high‐risk patients in the Veterans health Administration. Int J Methods Psychiatr Res. 2017;26(3):e1575 Wiley Online Library.

    Google Scholar 

  302. Perlis RH. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol Psychiatry. 2013;74(1):7–14 Elsevier.

    Article  PubMed  PubMed Central  Google Scholar 

  303. Mikus A, Hoogendoorn M, Rocha A, Gama J, Ruwaard J, Riper H. Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data. Internet Interv Neth. 2018;12:105–10 PMID:30135774.

    Article  Google Scholar 

  304. Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, Cannon TD, Krystal JH, Corlett PR. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry. 2016;3(3):243–250 Elsevier.

    Article  PubMed  Google Scholar 

  305. Chekroud AM, Gueorguieva R, Krumholz HM, Trivedi MH, Krystal JH, McCarthy G. Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiatry. 2017;74(4):370–378 American Medical Association.

    Article  PubMed  PubMed Central  Google Scholar 

  306. Spring Health. https://www.springhealth.com/about/.

  307. Bremer V, Becker D, Kolovos S, Funk B, van Breda W, Hoogendoorn M, Riper H. Predicting therapy success and costs for personalized treatment recommendations using baseline characteristics: data-driven analysis. J Med Internet Res. 2018;20(8):e10275 PMID:30131318 Canada.

    Article  PubMed  PubMed Central  Google Scholar 

  308. Ebert DD, Cuijpers P. It time to invest in the prevention of depression. JAMA Netw. 2018;1(2):e180335. https://doi.org/10.1001/jamanetworkopen.2018.0335 Open American Medical Association.

    Article  PubMed  Google Scholar 

  309. Walton A, Nahum‐Shani I, Crosby L, Klasnja P, Murphy S. Optimizing digital integrated care via micro‐randomized trials. Clin Pharmacol Ther. 2018 Wiley Online Library.

    Google Scholar 

  310. Collins LM. Optimization of behavioral, biobehavioral, and biomedical interventions: the multiphase optimization strategy (MOST). Springer; 2018. ISBN:3319722069.

    Google Scholar 

  311. Nahum-Shani I, Hekler EB, Spruijt-Metz D. Building health behavior models to guide the development of just-in-time adaptive interventions: a pragmatic framework. Heal Psychol. 2015;34(S):1209 American Psychological Association.

    Google Scholar 

  312. 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. 1–17. https://doi.org/10.1007/s12160-016-9830-8 Springer US.

    Article  PubMed Central  Google Scholar 

  313. Goldstein SP, Evans BC, Flack D, Juarascio A, Manasse S, Zhang F, Forman EM. Return of the JITAI: applying a just-in-time adaptive intervention framework to the development of m-health solutions for addictive behaviors. Int J Behav Med. 2017;24(5):673–82 PMID:28083725 England.

    Article  PubMed  PubMed Central  Google Scholar 

  314. Jaimes LG, Llofriu M, Raij A. A stress-free life: just-in-time interventions for stress via real-time forecasting and intervention adaptation. In: BODYNETS 2014-9th international conference on body area networks 2014;1:197–203. https://doi.org/10.4108/icst.bodynets.2014.258237.

  315. Thomas JG, Bond DS. Behavioral response to a just-in-time adaptive intervention (JITAI) to reduce sedentary behavior in obese adults: implications for JITAI optimization. Health Psychol. 2015;34(Suppl):1261–7 PMID:26651467.

    Article  PubMed Central  Google Scholar 

  316. Spruijt-Metz D, Nilsen W. Dynamic models of behavior for just-in-time adaptive interventions. IEEE Pervasive Comput. 2014;13(3):13–7. https://doi.org/10.1109/MPRV.2014.46.

    Article  Google Scholar 

  317. Juarascio AS, Parker MN, Lagacey MA, Godfrey KM. Just-in-time adaptive interventions: a novel approach for enhancing skill utilization and acquisition in cognitive behavioral therapy for eating disorders. Int J Eat Disord. 2018 PMID:30051495 United States.

    Google Scholar 

  318. Wahle F, Kowatsch T, Fleisch E, Rufer M, Weidt S. Mobile sensing and support for people with depression: a pilot trial in the wild. JMIR mHealth uHealth. 2016;4(3):e111 PMID:27655245 Canada.

    Article  PubMed  PubMed Central  Google Scholar 

  319. Burns MN, Begale M, Duffecy J, Gergle D, Karr CJ, Giangrande E, Mohr DC. Harnessing context sensing to develop a mobile intervention for depression. J Med Internet Res. 2011;13(3):e55 PMID:21840837 Canada.

    Article  PubMed  PubMed Central  Google Scholar 

  320. Ben-Zeev D, Kaiser SM, Brenner CJ, Begale M, Duffecy J, Mohr DC. Development and usability testing of FOCUS: a smartphone system for self-management of schizophrenia. Psychiatr Rehbil J. 2013;36(4):289–96 PMID:24015913.

    Article  Google Scholar 

  321. Gustafson DH, McTavish FM, Chih M-Y, Atwood AK, Johnson RA, Boyle MG, Levy MS, Driscoll H, Chisholm SM, Dillenburg L, Isham A, Shah D. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiatry. 2014;71(5):566–72 PMID:24671165.

    Article  PubMed  PubMed Central  Google Scholar 

  322. Pina L, Rowan K, Roseway a, Johns P, Hayes GR, Czerwinski M. In situ cues for ADHD parenting strategies using mobile technology. In: Proceedings of the-PERVASIVEHEALTH 2014 8th international conference on pervasive computing technologies for healthcare 2014. p. 17–24. https://doi.org/10.4108/icst.pervasivehealth.2014.254958.

  323. Forman EM, Goldstein SP, Zhang F, Evans BC, Manasse SM, Butryn ML, Juarascio AS, Abichandani P, Martin GJ, Foster GD. OnTrack: development and feasibility of a smartphone app designed to predict and prevent dietary lapses. Transl Behav Med. 2018 PMID:29617911 England.

    Google Scholar 

  324. Luxton DD. Artificial intelligence in behavioral and mental health care. Academic Press; 2015. ISBN:0128007923.

    Google Scholar 

  325. DeVault D, Artstein R, Benn G, Dey T, Fast E, Gainer A, Georgila K, Gratch J, Hartholt A, Lhommet M. SimSensei Kiosk: a virtual human interviewer for healthcare decision support. In: Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems. International Foundation for Autonomous Agents and Multiagent Systems; 2014. p. 1061–68.

    Google Scholar 

  326. Bird T, Mansell W, Wright J, Gaffney H, Tai S. Manage your life online: a web-based randomized controlled trial evaluating the effectiveness of a problem-solving intervention in a student sample. Behav Cogn Psychother. 2018;1–13 PMID:29366432 United States.

    Google Scholar 

  327. Burton C, Tatar AS, McKinstry B, Matheson C, Matu S, Moldovan R, Macnab M, Farrow E, David D, Pagliari C, Blanco AS, Wolters M. Pilot randomised controlled trial of Help4Mood, an embodied virtual agent-based system to support treatment of depression. J Telemed Telecare. 2016;22(6):348–355. https://doi.org/10.1177/1357633x15609793 Burton, Christopher, University of Aberdeen Aberdeen, Aberdeen, United Kingdom: Sage Publications.

    Article  PubMed  Google Scholar 

  328. Crutzen R, Peters G-JY, Portugal SD, Fisser EM, Grolleman JJ. An artificially intelligent chat agent that answers adolescents’ questions related to sex, drugs, and alcohol: an exploratory study. J Adolesc Health. 2011;48(5):514–19 PMID:21501812 United States.

    Article  Google Scholar 

  329. Provoost S, Lau HM, Ruwaard J, Riper H. Embodied conversational agents in clinical psychology: a scoping review. J Med Internet Res. 2017;19(5) JMIR Publications Inc.

    Google Scholar 

  330. Khoury MJ, Ioannidis JPA. Big data meets public health. Science. 2014;346(6213):1054–55 (80-) American Association for the Advancement of Science.

    Google Scholar 

  331. Pearl J. Myth, confusion, and science in causal analysis. 2009.

    Google Scholar 

  332. Goldstein BA, Navar AM, Pencina MJ, Ioannidis J. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inf Assoc. 2017;24(1):198–208 Oxford University Press.

    Google Scholar 

  333. Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1–W73 American College of Physicians.

    Article  PubMed  Google Scholar 

  334. DeMasi O, Kording K, Recht B. Meaningless comparisons lead to false optimism in medical machine learning. PLoS One. 2017;12(9):e0184604 PMID:28949964 United States.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  335. Pearl J, MacKenzie D. The book of why: the new science of cause and effect. 1st ed. New York: Basic Books; 2018.

    Google Scholar 

  336. Chen JH, Asch SM. Machine learning and prediction in medicine—beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507 NIH Public Access.

    Article  Google Scholar 

  337. Cadwalladr C, Graham-Harrison E. Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. Guard 2018;17.

    Google Scholar 

  338. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018.

    Google Scholar 

  339. 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.

    Google Scholar 

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Ebert, D.D., Harrer, M., Apolinário-Hagen, J., Baumeister, H. (2019). Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications. In: Kim, YK. (eds) Frontiers in Psychiatry. Advances in Experimental Medicine and Biology, vol 1192. Springer, Singapore. https://doi.org/10.1007/978-981-32-9721-0_29

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