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Adapting Evidence-Based Treatments for Digital Technologies: a Critical Review of Functions, Tools, and the Use of Branded Solutions

  • Psychiatry in the Digital Age (J Shore, Section Editor)
  • Published:
Current Psychiatry Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

We provide a critical review of digital technologies in evidence-based treatments (EBTs) for mental health with a focus on the functions technologies are intended to serve. The review highlights issues related to clarity of purpose, usability, and assumptions related to EBT technology integration, branding, and packaging.

Recent Findings

Developers continue to use technology in creative ways, often combining multiple functions to convey existing EBTs or to create new technology-enabled EBTs. Developers have a strong preference for creating and investigating whole-source, branded solutions related to specific EBTs, in comparison to developing or investigating technology tools related to specific components of behavior change, or developing specific clinical protocols that can be delivered via existing technologies.

Summary

Default assumptions that new applications are required for each individual EBT, that EBTs are best served by the use of only one technology solution rather than multiple tools, and that an EBT-specific technology product should include or convey all portions of an EBT slow scientific progress and increase risk of usability issues that negatively impact uptake. We contend that a purposeful, functions-based approach should guide the selection, development, and application of technology in support of EBT delivery.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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

    Google Scholar 

  2. Hollis C, Morriss R, Martin J, Amani S, Cotton R, Denis M, et al. Technological innovations in mental healthcare: harnessing the digital revolution. Br J Psychiatry. 2015;206(4):263–5.

    PubMed  Google Scholar 

  3. • Neary M, Schueller SM. State of the field of mental health apps. Cogn Behav Pract. 2018;25(4):531–7. This review organizes a wide variety of intervention-oriented applications by considering level of engagement with providers, along a continuum of no support, some at-distance support, and full in-person support.

    Google Scholar 

  4. Valmaggia LR, Latif L, Kempton MJ, Rus-Calafell M. Virtual reality in the psychological treatment for mental health problems: a systematic review of recent evidence. Psychiatry Res. 2016;236:189–95.

    Google Scholar 

  5. Sliwinski J, Katsikitis M, Jones CM. A review of interactive technologies as support tools for the cultivation of mindfulness. Mindfulness. 2017;8(5):1150–9.

    Google Scholar 

  6. Montagni I, Tzourio C, Cousin T, Sagara JA, Bada-Alonzi J, Horgan A. Mental health-related digital use by university students: a systematic review. Telemedicine e-Health 2019.

  7. Grist R, Croker A, Denne M, Stallard P. Technology delivered interventions for depression and anxiety in children and adolescents: a systematic review and meta-analysis. Clin Child Fam Psychol Rev. 2019;22(2):147–71.

    PubMed  Google Scholar 

  8. Archangeli C, Marti FA, Wobga-Pasiah EA, Zima B. Mobile health interventions for psychiatric conditions in children: a scoping review. Child Adolesc Psychiatr Clin N Am. 2017;26(1):13–31.

    PubMed  Google Scholar 

  9. Huguet A, Rao S, McGrath PJ, Wozney L, Wheaton M, Conrod J, et al. A systematic review of cognitive behavioral therapy and behavioral activation apps for depression. PLoS One. 2016;11(5):e0154248.

    PubMed  PubMed Central  Google Scholar 

  10. Fleming TM, Bavin L, Stasiak K, Hermansson-Webb E, Merry SN, Cheek C, et al. Serious games and gamification for mental health: current status and promising directions. Frontiers in Psychiatr. 2017;7:215.

    Google Scholar 

  11. Kuester A, Niemeyer H, Knaevelsrud C. Internet-based interventions for posttraumatic stress: a meta-analysis of randomized controlled trials. Clin Psychol Rev. 2016;43:1–6.

    PubMed  Google Scholar 

  12. Whiteside SP. Mobile device-based applications for childhood anxiety disorders. J Child Adolesc Psychopharmacol. 2016;26(3):246–51.

    PubMed  Google Scholar 

  13. Tuerk PW, Keller SM, Acierno R. Treatment for anxiety and depression via clinical videoconferencing: evidence base and barriers to expanded access in practice. Focus. 2018;16(4):363–9.

    Google Scholar 

  14. Geven A, Sefelin R, Tscheligi M. Depth and breadth away from the desktop: the optimal information hierarchy for mobile use. In Proceedings of the 8th conference on human-computer interaction with mobile devices and services 2006 (pp. 157-164). ACM.

  15. Vaananen-Vainio-Mattila K, Ruuska S. Designing mobile phones and communicators for consumers’ needs at Nokia. In: Bergman E, editor. Information appliances and beyond: interaction design for consumer products. San Francisco: Morgan Kaufman; 2000. p. 169–204.

    Google Scholar 

  16. Svanæs D, Alsos OA, Dahl Y. Usability testing of mobile ICT for clinical settings: methodological and practical challenges. Int J Med Inform. 2010;79(4):e24–34.

    PubMed  Google Scholar 

  17. Kukulska-Hulme, A. Mobile usability and user experience. In Mobile learning. Routledge; 2007. p. 61–72.

  18. Nagata, S. F. Multitasking and interruptions during mobile web tasks. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Sage: SAGE Publications; 2003. 47(11):1341–1345.

    Google Scholar 

  19. Doherty G, Coyle D, Matthews M. Design and evaluation guidelines for mental health technologies. Interact Comput. 2010;22(4):243–52.

    Google Scholar 

  20. Marzano L, Bardill A, Fields B, Herd K, Veale D, Grey N, et al. The application of mHealth to mental health: opportunities and challenges. Lancet Psychiatr. 2015;2(10):942–8.

    Google Scholar 

  21. Murnane EL, Cosley D, Chang P, Guha S, Frank E, Gay G, et al. Self-monitoring practices, attitudes, and needs of individuals with bipolar disorder: implications for the design of technologies to manage mental health. J Am Med Inform Assoc. 2016;23(3):477–84.

    PubMed  Google Scholar 

  22. Lauritsen L, Andersen L, Olsson E, Søndergaard SR, Nørregaard LB, Løventoft PK, et al. Usability, acceptability, and adherence to an electronic self-monitoring system in patients with major depression discharged from inpatient wards. J Med Internet Res. 2017;19(4):e123.

    PubMed  PubMed Central  Google Scholar 

  23. Chan S, Li L, Torous J, Gratzer D, Yellowlees PM. Review of use of asynchronous technologies incorporated in mental health care. Current Psychiatr Reports. 2018;20(10):85.

    Google Scholar 

  24. Gee BL, Griffiths KM, Gulliver A. Effectiveness of mobile technologies delivering ecological momentary interventions for stress and anxiety: a systematic review. J Am Med Inform Assoc. 2016;23:221–9. https://doi.org/10.1093/jamia/ocv043.

    Article  Google Scholar 

  25. Versluis A, Verkuil B, Spinhoven P, van der Ploeg MM, Brosschot JF. Changing mental health and positive psychological well-being using ecological momentary interventions: a systematic review and meta-analysis. J Med Internet Res. 2016;18(6):e152.

    PubMed  PubMed Central  Google Scholar 

  26. • Schueller SM, Aguilera A, Mohr DC. Ecological momentary interventions for depression and anxiety. Depress Anxiety. 2017;34(6):540–5. This work provides a good introduction and description of ecological momentary interventions with tangible examples.

    PubMed  Google Scholar 

  27. Hoffman JE, Kuhn E, Owen JE, Ruzek JI. Mobile apps to improve outreach, engagement, self-management, and treatment for posttraumatic stress disorder. Complement Altern Med for PTSD 2016;331.

  28. Rickard N, Arjmand HA, Bakker D, Seabrook E. Development of a mobile phone app to support self-monitoring of emotional well-being: a mental health digital innovation. JMIR mental health. 2016;3(4):e49.

    PubMed  PubMed Central  Google Scholar 

  29. Jain SH, Powers BW, Hawkins JB, Brownstein JS. The digital phenotype. Nat Biotechnol. 2015;33(5):462.

    CAS  PubMed  Google Scholar 

  30. Onnela JP, Rauch SL. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacol. 2016;41(7):1691–6.

    Google Scholar 

  31. Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, et al. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J Med Internet Res. 2015;17(7):e175.

    PubMed  PubMed Central  Google Scholar 

  32. Coppersmith G, Dredze M, Harman C, Hollingshead K, Mitchell M. Clpsych 2015 shared task: depression and PTSD on Twitter. In: Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Denver, Colorado. 2015. p. 31–39.

  33. Cavazos-Rehg PA, Krauss MJ, Sowles S, Connolly S, Rosas C, Bharadwaj M, et al. A content analysis of depression-related tweets. Comput Hum Behav. 2016;54:351–7.

    Google Scholar 

  34. Maxhuni A, Muñoz-Meléndez A, Osmani V, Perez H, Mayora O, Morales EF. Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients. Pervasive Mob Comput. 2016;31:50–66.

    Google Scholar 

  35. Ben-Zeev D, Scherer EA, Wang R, Xie H, Campbell AT. Next-generation psychiatric assessment: using smartphone sensors to monitor behavior and mental health. Psychiatri Rehabilitation J. 2015;38(3):218–26.

    Google Scholar 

  36. Ferrás C, García Y, Aguilera A, Rocha Á. How can geography and mobile phones contribute to psychotherapy? J Med Syst. 2017;41(6):92.

    PubMed  Google Scholar 

  37. Nicholas J, Larsen ME, Proudfoot J, Christensen H. Mobile apps for bipolar disorder: a systematic review of features and content quality. J Med Internet Res. 2015;17(8):e198.

    PubMed  PubMed Central  Google Scholar 

  38. Brás S, Soares SC, Moreira R, Fernandes JM. BeMonitored: monitoring psychophysiology and behavior using Android in phobias. Behav Res Methods. 2016;48(3):1100–8.

    PubMed  Google Scholar 

  39. Hinrichs R, Michopoulos V, Winters S, Rothbaum AO, Rothbaum BO, Ressler KJ, et al. Mobile assessment of heightened skin conductance in posttraumatic stress disorder. Depress Anxiety. 2017;34(6):502–7.

    PubMed  PubMed Central  Google Scholar 

  40. Raugh IM, Chapman HC, Bartolomeo LA, Gonzalez C, Strauss GP. A comprehensive review of psychophysiological applications for ecological momentary assessment in psychiatric populations. Psychol Assess. 2019;31(3):304–17.

    PubMed  Google Scholar 

  41. Sano A, Taylor S, McHill AW, Phillips AJ, Barger LK, Klerman E, et al. 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.

    PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  43. Nahum-Shani I, Smith SN, Spring B, Collins LM, Witkiewitz K, Tewari A, et al. Just in time adaptive interventions (JITAIS) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2016;52:1–17. https://doi.org/10.1007/s12160-016-9830-8.

    Article  Google Scholar 

  44. Abdullah S, Matthews M, Frank E, Doherty G, Gay G, Choudhury T. Automatic detection of social rhythms in bipolar disorder. J Am Med Inform Assoc. 2016;23:583e543.

    Google Scholar 

  45. Grünerbl A, Muaremi A, Osmani V, Bahle G, Oehler S, Tröster G, et al. Smartphone-based recognition of states and state changes in bipolar disorder patients. IEEE J Biomed Health Inf. 2015;19(1):140–8.

    Google Scholar 

  46. Ben-Zeev D, Brenner CJ, Begale M, Duffecy J, Mohr DC, Mueser KT. Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia. Schizophr Bull. 2014 Mar 8;40(6):1244–53.

    PubMed  PubMed Central  Google Scholar 

  47. • Schaeffer CM, Dimeff LA. A mobile phone app to support caregivers in the management of youth conduct problems. Stratford: Presented at the CARES Institute's 11th Annual Best Practice Symposium; 2017. This work employs geofencing to automatically provide support to teens with conduct disorder and to notify caregivers if their teens are in the wrong places at the wrong times and/or automatically rewards teens for positive behaviors, such as arriving to school or work on time. This application, still under development, is a good example of just-in-time ecological momentary interventions.

  48. Foa EB, Zandberg LJ, McLean CP, Rosenfield D, Fitzgerald H, Tuerk PW, et al. The efficacy of 90-minute versus 60-minute sessions of prolonged exposure for posttraumatic stress disorder: design of a randomized controlled trial in active duty military personnel. Theory, Res, Practice, Policy: Psychol Trauma; 2018.

    Google Scholar 

  49. Rothbaum BO, Price M, Jovanovic T, Norrholm SD, Gerardi M, Dunlop B, et al. A randomized, double-blind evaluation of D-cycloserine or alprazolam combined with virtual reality exposure therapy for posttraumatic stress disorder in Iraq and Afghanistan war veterans. Am J Psychiatr. 2014;171(6):640–8.

    PubMed  Google Scholar 

  50. Tuerk PW, Wangelin BC, Powers MB, Smits JA, Acierno R, Myers US, et al. Augmenting treatment efficiency in exposure therapy for PTSD: a randomized double-blind placebo-controlled trial of yohimbine HCl. Cogn Behav Therapy. 2018;47(5):351–71.

    Google Scholar 

  51. Australian Government Department of Defense, Australian Government Department of Veterans’ Affairs, National Health and Medical Research Council Partnership Grant. Rapid Exposure Supporting Trauma Recovery (RESTORE) (ACTRN12616001302448). https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370644. Accessed 19 May 2018.

  52. Wisco BE, Baker AS, Sloan DM. Mechanisms of change in written exposure treatment of posttraumatic stress disorder. Behav Therapy. 2016;47(1):66–74.

    Google Scholar 

  53. Colvonen PJ, Glassman LH, Crocker LD, Buttner MM, Orff H, Schiehser DM, et al. Pretreatment biomarkers predicting PTSD psychotherapy outcomes: a systematic review. Neurosci Biobehav Rev. 2017;75:140–56.

    PubMed  Google Scholar 

  54. • Wangelin BC, Tuerk PW. Taking the pulse of prolonged exposure therapy: physiological reactivity to trauma imagery as an objective measure of treatment response. Depression Anxiety. 2015;32(12):927–34. Documents psychophysiological responses to treatment-related stimuli as high-quality objective markers of EBT treatment response and pretreatment prognosis for response to exposure-oriented protocols. Sets the stage for mobile objective measurement by implementing digital measurements out of the laboratory and integrated with treatment.

    PubMed  Google Scholar 

  55. Norrholm SD, Jovanovic T, Gerardi M, Breazeale KG, Price M, Davis M, et al. Baseline psychophysiological and cortisol reactivity as a predictor of PTSD treatment outcome in virtual reality exposure therapy. Behav Res Therapy. 2016;82:28–37.

    Google Scholar 

  56. Geller, DA, McGuire JF, Orr SP, Small BJ, Murphy TK, Trainor K et al. Fear extinction learning as a predictor of response to cognitive behavioral therapy for pediatric obsessive compulsive disorder. J Anxiety Disorders. 2019.

  57. Choi KW, Jang EH, Kim AY, Fava M, Mischoulon D, Papakostas GI, et al. Heart rate variability for treatment response between patients with major depressive disorder versus panic disorder: a 12-week follow-up study. J Affect Disord. 2019;246:157–65.

    PubMed  Google Scholar 

  58. Calvo RA, Milne DN, Hussain SM, Christensen H. Natural language processing in mental health applications using nonclinical texts. Nat Lang Eng. 2017;23:1–37. https://doi.org/10.1017/S1351324916000383.

    Article  Google Scholar 

  59. Rabbi M, Aung MS, Gay G, Reid MC, Choudhury T. Feasibility and acceptability of mobile phone–based auto-personalized physical activity recommendations for chronic pain self-management: pilot study on adults. J Med Internet Res. 2018;20(10):e10147.

    PubMed  PubMed Central  Google Scholar 

  60. Vaidyam AN, Wisniewski H, Halamka JD, Kashavan MS, Torous JB. Chatbots and conversational agents in mental health: a review of the psychiatric landscape. Can J Psychiatr. 2019

  61. Lucas GM, Gratch J, King A, Morency LP. It’s only a computer: virtual humans increase willingness to disclose. Comput Hum Behav. 2014;37:94–100.

    Google Scholar 

  62. Hartanto D, Brinkman WP, Kampmann IL, Morina N, Emmelkamp PG, Neerincx MA. Home-based virtual reality exposure therapy with virtual health agent support. Cham: In International Symposium on Pervasive Computing Paradigms for Mental Health. Springer; 2015. p. 85–98.

    Google Scholar 

  63. Tielman ML, Neerincx MA, Bidarra R, Kybartas B, Brinkman WP. A therapy system for post-traumatic stress disorder using a virtual agent and virtual storytelling to reconstruct traumatic memories. J Med Syst. 2017;41(8):125.

    PubMed  PubMed Central  Google Scholar 

  64. ClinicalTrials.gov [Internet]. Bethesda (MD): National Library of Medicine (US). 2000 Feb 29 - . Identifier NCT02816684, Pegasys VR: Integrating Virtual Humans in the Treatment of Child Social Anxiety; 2016, June 28 [cited 2019 Feb 1]; [4 screens]. Available from:https://clinicaltrials.gov/ct2/show/NCT02816684.

  65. • Wong Sarver N, Beidel DC, Spitalnick JS. The feasibility and acceptability of virtual environments in the treatment of childhood social anxiety disorder. J Clinical Child Adolesc Psychol. 2014;43(1):63–73. Documents ongoing development of a serious game to facilitate practice of social skills for children with social anxiety, to be used in conjunction with exposure-oriented treatment. This project is significant because the development team collected data from over 650 children using tens of thousands of utterances to configure realistic and effective goal-directed conversational AI streams.

    Google Scholar 

  66. Beidel DC, Turner SM, Morris TL. Behavioral treatment of childhood social phobia. J Consult Clin Psychol. 2000;68(6):1072–80.

    CAS  PubMed  Google Scholar 

  67. Carl E, Stein AT, Levihn-Coon A, Pogue JR, Rothbaum B, Emmelkamp P, et al. Virtual reality exposure therapy for anxiety and related disorders: a meta-analysis of randomized controlled trials. J Anxiety Disord. 2019;61:27–36.

    PubMed  Google Scholar 

  68. Maples-Keller JL, Yasinski C, Manjin N, Rothbaum BO. Virtual reality-enhanced extinction of phobias and post-traumatic stress. Neurother. 2017;14(3):554–63.

    Google Scholar 

  69. Rothbaum BO, Rizzo A, Difede J. Virtual reality exposure therapy for combat-related posttraumatic stress disorder. Annals NY Academ Sci. 2010;1208(1):126–32.

    Google Scholar 

  70. Beidel DC, Frueh BC, Neer SM, Bowers CA, Trachik B, Uhde TW et al. Trauma management therapy with virtual-reality augmented exposure therapy for combat-related PTSD: a randomized controlled trial. J Anxiety Disord 2017.

  71. Reger GM, Koenen-Woods P, Zetocha K, Smolenski DJ, Holloway KM, Rothbaum BO, et al. Randomized controlled trial of prolonged exposure using imaginal exposure vs. virtual reality exposure in active duty soldiers with deployment-related posttraumatic stress disorder (PTSD). J Consult Clinical Pscyhol. 2016;84(11):946.

    Google Scholar 

  72. Lindner P, Miloff A, Fagernäs S, Andersen J, Sigeman M, Andersson G, et al. Therapist-led and self-led one-session virtual reality exposure therapy for public speaking anxiety with consumer hardware and software: a randomized controlled trial. J Anxiety disord. 2019;61:45–54.

    PubMed  Google Scholar 

  73. Freeman D, Haselton P, Freeman J, Spanlang B, Kishore S, Albery E, et al. Automated psychological therapy using immersive virtual reality for treatment of fear of heights: a single-blind, parallel-group, randomised controlled trial. Lancet Psychiatr. 2018;5(8):625–32.

    Google Scholar 

  74. Miloff A, Lindner P, Hamilton W, Reuterskiöld L, Andersson G, Carlbring P. Single-session gamified virtual reality exposure therapy for spider phobia vs. traditional exposure therapy: study protocol for a randomized controlled non-inferiority trial. Trials. 2016;17(1):60.

    PubMed  PubMed Central  Google Scholar 

  75. Bouchard S, Dumoulin S, Robillard G, Guitard T, Klinger E, Forget H, et al. Virtual reality compared with in vivo exposure in the treatment of social anxiety disorder: a three-arm randomized controlled trial. Br J Psychiatry. 2017;210(4):276–83.

    PubMed  Google Scholar 

  76. Hone-Blanchet A, Wensing T, Fecteau S. The use of virtual reality in craving assessment and cue-exposure therapy in substance use disorders. Frontiers in human neuroscience. 2014;17(8):844

  77. Pallavicini F, Serino S, Cipresso P, Pedroli E, Chicchi Giglioli IA, Chirico A, et al. Testing augmented reality for cue exposure in obese patients: an exploratory study. Cyberpsychol Behav Soc Netw. 2016;19:107–14.

    PubMed  Google Scholar 

  78. Smith MJ, Fleming MF, Wright MA, Roberts AG, Humm LB, Olsen D, et al. Virtual reality job interview training and 6-month employment outcomes for individuals with schizophrenia seeking employment. Schizophr Res. 2015;166(1–3):86–91.

    PubMed  PubMed Central  Google Scholar 

  79. Naslund JA, Aschbrenner KA, Marsch LA, Bartels SJ. The future of mental health care: peer-to-peer support and social media. Epidemiology Psychiatr Sci. 2016;25(2):113–22.

    CAS  Google Scholar 

  80. • Morris RR, Schueller SM, Picard RW. Efficacy of a web-based, crowdsourced peer-to-peer cognitive reappraisal platform for depression: randomized controlled trial. J Med Internet Res [Electronic Resource]. 2015;17(3):e72. https://doi.org/10.2196/jmir.4167. This work is significant because it uses crowdsourcing to provide users with real-time feedback on cognitive distortions as a way to promote reappraisals.

    PubMed  PubMed Central  Google Scholar 

  81. Tong HL, Laranjo L. The use of social features in mobile health interventions to promote physical activity: a systematic review. NPJ Digit Med. 2018;1:43.

    PubMed  PubMed Central  Google Scholar 

  82. Lauder S, Chester A, Castle D, Dodd S, Gliddon E, Berk L, Chamberlain J et al. A randomized head to head trial of MoodSwings.net.au: an internet based self-help program for bipolar disorder. J Affect Disord. 2015;171:13–21

    PubMed  Google Scholar 

  83. Ichikawa D, Kashiyama M, Ueno T. Tamper-resistant mobile health using blockchain technology. JMIR mHealth uHealth. 2017;5(7):e111.

    PubMed  PubMed Central  Google Scholar 

  84. Créquit P, Mansouri G, Benchoufi M, Vivot A, Ravaud P. Mapping of crowdsourcing in health: systematic review. J Med Internet Res. 2018;20(5):e187.

    PubMed  PubMed Central  Google Scholar 

  85. ClinicalTrials.gov [Internet]. Bethesda (MD): National Library of Medicine (US). 2000. Identifier NCT03601312, Randomized Controlled Trial of Standard ERP and OC-Go (OC-GoPhaseII); 2018 July 26 [cited 2019 Feb 1]; [5 screens]. Available from: https://clinicaltrials.gov/ct2/show/NCT03601312. Still under development and testing, the product associated with this RCT combines asynchronous telehealth and crowdsourcing to define a new paradigm of EBT dissemination, supervision, fidelity, and implementation.

  86. Piacentini J, Langley A, Roblek T. Cognitive behavioral treatment of childhood OCD: it’s only a false alarm therapist guide. Oxf University Press; 2007.

  87. Jackson CB, Macphee FL, Hunter LJ, Herschell AD, Carter MJ. Enrolling family participants in a statewide implementation trial of an evidence-based treatment. Prog Community Health Partnerships: Res Educ Action. 2017;11(3):233.

    Google Scholar 

  88. Masse JJ, Quetsch LB, McNeil CB. Taking PRIDE in your home: implementing home-based Parent–Child Interaction Therapy (PCIT) with fidelity. In: Handbook of Parent-Child Interaction Therapy. Cham: Springer; 2018. p. 161–81.

    Google Scholar 

  89. Myers K, Cummings JR, Zima B, Oberleitner R, Roth D, Merry SM, et al. Advances in asynchronous telehealth technologies to improve access and quality of mental health care for children and adolescents. J Tech BehavSci. 2018;3(2):87–106.

    Google Scholar 

  90. Greene CJ, Morland LA, Durkalski VL, Frueh BC. Noninferiority and equivalence designs: issues and implications for mental health research. J Trauma Stress. 2008;21(5):433–9.

    PubMed  PubMed Central  Google Scholar 

  91. D'Alfonso S, Santesteban-Echarri O, Rice S, Wadley G, Lederman R, Miles C, et al. Artificial intelligence-assisted online social therapy for youth mental health. Front Psychol. 2017;8:796.

    PubMed  PubMed Central  Google Scholar 

  92. • Mohr D, Cuijpers P, Lehman K. Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions. J Med Internet Res. 2011;13(1):e30. This work is significant because the authors combine several goal-directed technology and protocol functions with theory-guided implementation.

    PubMed  PubMed Central  Google Scholar 

  93. Pramana G, Parmanto B, Lomas J, Lindhiem O, Kendall PC, Silk J. Using mobile health gamification to facilitate cognitive behavioral therapy skills practice in child anxiety treatment: open clinical trial. JMIR Serious Games. 2018;6(2):e9.

    PubMed  PubMed Central  Google Scholar 

  94. Lau HM, Smit JH, Fleming TM, Riper H. Serious games for mental health: are they accessible, feasible, and effective? A systematic review and meta-analysis. Front Psychiatr. 2017;7:209.

    Google Scholar 

  95. • Merry SN, Stasiak K, Shepherd M, Frampton C, Fleming T, Lucassen MF. The effectiveness of SPARX, a computerized self-help intervention for adolescents seeking help for depression: randomized controlled non-inferiority trial. Bmj. 2012;344:e2598. This work is significant because results from a multicenter non-inferiority trial of a CBT-oriented game for adolescent depression offer evidence that participants randomized to the game demonstrated close to a standard deviation improvement. While significant effects are quite often found for self-help interventions, evidence is somewhat sparser related to large clinical effect sizes close to or over a standard deviation for self-help digitally delivered interventions.

    PubMed  PubMed Central  Google Scholar 

  96. Grist R, Cavanagh K. Computerised cognitive behavioural therapy for common mental health disorders, what works, for whom under what circumstances? A systematic review and meta-analysis. J Contemp Psychother. 2013;43(4):243–51.

    Google Scholar 

  97. Pogue D. Out with the real: why digital design doesn’t have to imitate the physical world. Sci Am. 2013;308:29.

    PubMed  Google Scholar 

  98. Ben-Zeev D, Schueller SM, Begale M, Duffecy J, Kane JM, Mohr DC. Strategies for mHealth research: lessons from 3 mobile intervention studies. Adm Policy Ment Health Ment Health Serv Res. 2015;42(2):157–67.

    Google Scholar 

  99. Bakker D, Kazantzis N, Rickwood D, Rickard N. Mental health smartphone apps: review and evidence-based recommendations for future developments. JMIR Ment Health. 2016;3(1):e7.

    PubMed  PubMed Central  Google Scholar 

  100. Schueller SM, Muñoz R, Mohr DC. Realizing the potential of behavioral intervention technologies. Curr Dir Psychol Sci. 2013;22:478–83.

    Google Scholar 

  101. Whiteside SP, Biggs BK, Tiede MS, Dammann JE, Hathaway JC, Blasi ME et al. An online-and mobile-based application to facilitate exposure for childhood anxiety disorders. Cogn Behav Practice. 2019.

  102. •• Bunnell BE, Mesa F, Beidel DC. A two-session hierarchy for shaping successive approximations of speech in selective mutism: pilot study of mobile apps and mechanisms of behavior change. Behav Therapy. 2018;49(6):966–80. This work is significant because it represents a wholly different paradigm of technology integration into EBTs; rather than creating a new technology or application to fit an existing EBT, the authors created a treatment protocol that capitalizes on existing technologies and applications for broad and immediate usability and adoption.

    PubMed  Google Scholar 

  103. Bunnell BE, Beidel DC. Incorporating technology into the treatment of a 17-year-old female with selective mutism. Clin Case Stud. 2013;12(4):291–306.

    Google Scholar 

  104. van den Berk-Clark C, Hughes R, Haywood S, Andrews B, Gordin P. Texting as a means of reducing pediatric adolescent psychiatric emergency encounters wait times. Ped Emergency Care. 2018;34(7):524–9.

    Google Scholar 

  105. Tolou-Shams M, Yonek J, Galbraith K, Bath E. Text messaging to enhance behavioral health treatment engagement among justice-involved youth: qualitative and user testing study. JMIR mHealth uHealth. 2019;7(4):e10904.

    PubMed  PubMed Central  Google Scholar 

  106. Mohr DC, Riper H, Schueller SM. A solution-focused research approach to achieve an implementable revolution in digital mental health. JAMA Psychiatr. 2018;75(2):113–4.

    Google Scholar 

  107. Brooke J. SUS—a quick and dirty usability scale. Usability Evaluation in Industry. 1996;189(194):4–7.

    Google Scholar 

  108. • Sauro J. A practical guide to the system usability scale: background, benchmarks & best practices. Denver, CO: measuring usability LLC; 2011. This work is significant because it offers empirical benchmarking for usability outcomes as they relate to technology adoption.

  109. Leigh S, Flatt S. App-based psychological interventions: friend or foe? Evidence-based Mental Health. 2015;18(4):97–9.

    PubMed  Google Scholar 

  110. Price M, Yuen EK, Goetter EM, Herbert JD, Forman EM, Acierno R, et al. mHealth: a mechanism to deliver more accessible, more effective mental health care. Clinical Psychol Psychotherapy. 2014;21(5):427–36.

    Google Scholar 

  111. Watts S, Mackenzie A, Thomas C, Griskaitis A, Mewton L, Williams A, et al. CBT for depression: a pilot RCT comparing mobile phone vs computer. BMC Psychiatr. 2013;13:49.

    Google Scholar 

  112. Brown W III, Yen PY, Rojas M, Schnall R. Assessment of the health IT usability evaluation model (health-ITUEM) for evaluating mobile health (mHealth) technology. J Biomed Inform. 2013;46(6):1080–7.

    PubMed  Google Scholar 

  113. Luxton DD, McCann RA, Bush NE, Mishkind MC, Reger GM. mHealth for mental health: integrating smartphone technology in behavioral healthcare. Prof Psychol Res Pract. 2011;42(6):505.

    Google Scholar 

  114. Yeager CM, Benight CC. If we build it, will they come? Issues of engagement with digital health interventions for trauma recovery. Mhealth. 2018;4:37.

    PubMed  PubMed Central  Google Scholar 

  115. Schuster R, Fichtenbauer I, Sparr VM, Berger T, Laireiter AR. Feasibility of a blended group treatment (bGT) for major depression: uncontrolled interventional study in a university setting. BMJ Open. 2018;8(3):e018412.

    PubMed  PubMed Central  Google Scholar 

  116. Birney AJ, Gunn R, Russell JK, Ary DV. MoodHacker mobile web app with email for adults to self-manage mild-to-moderate depression: randomized controlled trial. JMIR mHealth uHealth. 2016;4(1):e8.

    PubMed  PubMed Central  Google Scholar 

  117. Schlosser D, Campellone T, Kim D, Truong B, Vergani S, Ward C, et al. Feasibility of PRIME: a cognitive neuroscience-informed mobile app intervention to enhance motivated behavior and improve quality of life in recent onset schizophrenia. JMIR Res Protocols. 2016;5(2):e77. https://doi.org/10.2196/resprot.5450.

    Article  Google Scholar 

  118. Torous JJ, Larsen ME, Firth J, Christensen H. Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements. Evidence-Based Mental Health. 2018;21(3):116–9.

    PubMed  Google Scholar 

  119. Kwasny MJ, Schueller SM, Lattie E, Gray EL, Mohr DC. Exploring the use of multiple mental health apps within a platform: secondary analysis of the IntelliCare field trial. JMIR Mental Health. 2019;6(3):e11572.

    PubMed  PubMed Central  Google Scholar 

  120. Insel TR. The NIMH research domain criteria (RDoC) project: precision medicine for psychiatry. Am J Psychiatr. 2014;171(4):395–7.

    PubMed  Google Scholar 

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Funding

This work was partially supported by NIMH 5 R42 MH111277-03 (Tuerk, Piacentini), the Pettit Foundation (Piacentini, Tuerk), and NIMH R42 MH094019-05 9 (Tuerk).

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Correspondence to Peter W. Tuerk.

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Conflict of Interest

Peter W. Tuerk was partially supported by NIMH 5 R42 MH111277-03, the Pettit Foundation, and NIMH R42 MH094019-05 9. Dr. Turek is a consultant for Virtually Better Inc. and Cohen Veterans Network. These organizations did not support any aspect of the submitted work, but related research is referenced in the work so I am disclosing for transparency.

Cindy M. Schaeffer is an MPI on an NIMH-funded SBIR award with Dr. Linda Dimeff at the Evidence-Based Practice Institute (EBPI). EBPI is the grant awardee and my institution is the subcontractor. This award is funding the development and evaluation of a digital technology, iKinnect, mentioned in this manuscript (National Institute of Mental Health, R44MH097349). Dr. Schaeffer will be entering into a profit-sharing agreement with Evidence-Based Practice Institute if the iKinnect mobile phone app mentioned in this manuscript is ever commercially available.

Joseph F. McGuire receives research support from the Tourette Association of America and the American Academy of Neurology. He receives consulting fees from Brackett, Syneos Health, and Luminopia, and also receives book royalties from Elsevier.

Margo Adams Larsen reports grants from NIMH 5R42MH111277-03, 5R42MH094019-05, and NIMH 2R44MH104102-03, which did not fund the published work, but funded projects related to the content of the published work.

Nicole Capobianco declares no potential conflicts of interest.

John Piacentini was partially supported by NIMH 5 R42 MH111277-03 and the Pettit Foundation.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on Psychiatry in the Digital Age

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Tuerk, P.W., Schaeffer, C.M., McGuire, J.F. et al. Adapting Evidence-Based Treatments for Digital Technologies: a Critical Review of Functions, Tools, and the Use of Branded Solutions. Curr Psychiatry Rep 21, 106 (2019). https://doi.org/10.1007/s11920-019-1092-2

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