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1 Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; 2 Massachusetts Mental Health Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; 3 Digital Research Unit, Greater Manchester Mental Health NHS Foun- dation Trust, Manchester, UK; 4 Centre for Health Informatics, University of Manchester, Manchester, UK; 5 Orygen, Melbourne, VIC, Australia; 6 Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia; 7 Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark; 8 Copenhagen Affective Disorder Research Center, Copenhagen, Denmark; 9 Department of Psychiatry, University of Toronto, Toronto, ON, Canada; 10Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada; 11IMPACT (Innovation in Mental and Physical Health and Clinical Treat- ment) Strategic Research Centre, Deakin University, Geelong, VIC, Australia; 12Deakin University, Centre for Social and Early Emotional Development and School of Psychology, Burwood, VIC, Australia; 13Division of Psychology and Mental Health, University of Manchester, Manchester, UK; 14NICM Health Research Institute, Western Sydney University, Westmead, NSW, Australia.
Список исп. литературыСкрыть список 1. Rehm J, Shield KD. Global burden of disease and the impact of mental and addictive disorders. Curr Psychiatry Rep 2019;21:10. 2. World Health Organization. Depression. Geneva: World Health Organization, 2020. 3. Keynejad RC, Dua T, Barbui C et al. WHO Mental Health Gap Action Programme (mhGAP) Intervention Guide: a systematic review of evidence from low and middle-income countries. Evid Based Ment Health 2018;21:30-4. 4. US Substance Abuse and Mental Health Services Administration. Behavioral health workforce report. Rockville: US Substance Abuse and Mental Health Services Administration, 2020. 5. Kinoshita S, Cortright K, Crawford A et al. Changes in telepsychiatry regulations during the COVID-19 pandemic: 17 countries and regions’ approaches to an evolving healthcare landscape. Psychol Med (in press). 6. Insel TR. Digital phenotyping: a global tool for psychiatry. World Psychiatry 2018;17:276-7. 7. Torous J, Andersson G, Bertagnoli A et al. Towards a consensus around standards for smartphone apps and digital mental health. World Psychiatry 2019;18:97-8. 8. Carey B. The therapist may see you anytime, anywhere. New York Times, February 13, 2012. 9. Silver L. Smartphone ownership is growing rapidly around the world, but not always equally. Pew Research Center, February 5, 2019. 10. Young AS, Cohen AN, Niv N et al. Mobile phone and smartphone use by people with serious mental illness. Psychiatr Serv 2020;71:280-3. 11. Luther L, Buck BE, Fischer MA et al. Examining potential barriers to mHealth implementation and engagement in schizophrenia: phone ownership and symptom severity. J Technol Behav Sci (in press). 12. Torous J, Wisniewski H, Liu G et al. Mental health mobile phone app usage, concerns, and benefits among psychiatric outpatients: comparative survey study. JMIR Mental Health 2018;5:e11715. 13. Lecomte T, Potvin S, Corbière M et al. Mobile apps for mental health issues: meta-review of meta-analyses. JMIR mHealth and uHealth 2020;8:e17458. 14. Targum SD, Sauder C, Evans M et al. Ecological momentary assessment as a measurement tool in depression trials. J Psychiatr Res 2021;136:256-64. 15. Chevance A, Ravaud P, Tomlinson A et al. Identifying outcomes for depression that matter to patients, informal caregivers, and health-care professionals: qualitative content analysis of a large international online survey. Lancet Psychiatry 2020;7:692-702. 16. Weizenbaum E, Torous J, Fulford D. Cognition in context: understanding the everyday predictors of cognitive performance in a new era of measurement. JMIR mHealth and uHealth 2020;8:e14328. 17. Lewis S, Ainsworth J, Sanders C et al. Smartphone-enhanced symptom management in psychosis: open, randomized controlled trial. J Med Int Res 2020;22:e17019. 18. Liu G, Henson P, Keshavan M et al. Assessing the potential of longitudinal smartphone based cognitive assessment in schizophrenia: a naturalistic pilot study. Schizophr Res Cogn 2019;17:100144. 19. Parrish EM, Kamarsu S, Harvey PD et al. Remote ecological momentary testing of learning and memory in adults with serious mental illness. Schizophr Bull 2021;47:740-50. 20. Glenn CR, Kleiman EM, Kearns JC et al. Feasibility and acceptability of ecological momentary assessment with high-risk suicidal adolescents following acute psychiatric care. J Clin Child Adolesc Psychol 2020;3:1-7. 21. Torous J, Kiang MV, Lorme J et al. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment Health 2016;3:e16. 22. Cohen AS, Cox CR, Masucci MD et al. Digital phenotyping using multimodal data. Curr Behav Neurosci Rep 2020;7:212-20. 23. Huckins JF, DaSilva AW, Wang W et al. Mental health and behavior of college students during the early phases of the COVID-19 pandemic: longitudinal smartphone and ecological momentary assessment study. J Med Int Res 2020;22:e20185. 24. Wang W, Mirjafari S, Harari G et al. Social sensing: assessing social functioning of patients living with schizophrenia using mobile phone sensing. Presented at the CHI Conference on Human Factors in Computing Systems, Honolulu, April 2020. 25. Fulford D, Mote J, Gonzalez R et al. Smartphone sensing of social interactions in people with and without schizophrenia. J Psychiatr Res 2021;137:613-20. 26. Miralles I, Granell C, Díaz-Sanahuja L et al. Smartphone apps for the treatment of mental disorders: systematic review. JMIR mHealth and uHealth 2020;8:e14897. 27. Baxter C, Carroll JA, Keogh B et al. Assessment of mobile health apps using built-in smartphone sensors for diagnosis and treatment: systematic survey of apps listed in international curated health app libraries. JMIR mHealth and uHealth 2020;8:e16741. 28. Faurholt-Jepsen M, Busk J, Vinberg M et al. Daily mobility patterns in patients with bipolar disorder and healthy individuals. J Affect Disord 2020;278:413-22. 29. Jongs N, Jagesar R, van Haren NE et al. A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data. Transl Psychiatry 2020;10:211. 30. Henson P, D’Mello R, Vaidyam A et al. Anomaly detection to predict relapse risk in schizophrenia. Transl Psychiatry 2021;11:28. 31. Gillan CM, Rutledge RB. Smartphones and the neuroscience of mental health. Annu Rev Neurosci (in press). 32. Torous J, Staples P, Shanahan M et al. Utilizing a personal smartphone custom app to assess the Patient Health Questionnaire-9 (PHQ-9) depressive symptoms in patients with major depressive disorder. JMIR Mental Health 2015;2:e8. 33. Barnett I, Torous J, Staples P et al. Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data. J Am Med Inform Assoc 2018;25:1669-74. 34. Koppe G, Meyer-Lindenberg A, Durstewitz D. Deep learning for small and big data in psychiatry. Neuropsychopharmacology 2021;46:176-90. 35. Shatte AB, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med 2019;49:1426-48. 36. Thieme A, Belgrave D, Doherty G. Machine learning in mental health: a systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Trans Comput Hum Interact 2020;27:34. 37. Belsher BE, Smolenski DJ, Pruitt LD et al. Prediction models for suicide attempts and deaths: a systematic review and simulation. JAMA Psychiatry 2019;76:642-51. 38. Goodday SM, Friend S. Unlocking stress and forecasting its consequences with digital technology. NPJ Digit Med 2019;2:75. 39. Andersson G, Titov N, Dear BF et al. Internet-delivered psychological treatments: from innovation to implementation. World Psychiatry 2019;18:20-8. 40. Mohr DC, Schueller SM, Tomasino KN et al. Comparison of the effects of coaching and receipt of app recommendations on depression, anxiety, and engagement in the IntelliCare platform: factorial randomized controlled trial. J Med Int Res 2019;21:e13609. 41. Graham AK, Greene CJ, Kwasny MJ et al. Coached mobile app platform for the treatment of depression and anxiety among primary care patients: a randomized clinical trial. JAMA Psychiatry 2020;77:906-14. 42. Arean PA, Hallgren KA, Jordan JT et al. The use and effectiveness of mobile apps for depression: results from a fully remote clinical trial. J Med Int Res 2016;18:e330. 43. de Girolamo G, Barattieri di San Pietro C, Bulgari V et al. The acceptability of real-time health monitoring among community participants with depression: a systematic review and meta-analysis of the literature. Depress Anxiety 2020;37:885-97. 44. Kidd SA, Feldcamp L, Adler A et al. Feasibility and outcomes of a multifunction mobile health approach for the schizophrenia spectrum: App4Independence (A4i). PLoS One 2019;14:e0219491. 45. Ben-Zeev D, Brian RM, Jonathan G et al. Mobile health (mHealth) versus clinic-based group intervention for people with serious mental illness: a randomized controlled trial. Psychiatr Serv 2018;69:978-85. 46. Porras-Segovia A, Díaz-Oliván I, Gutiérrez-Rojas L et al. Apps for depression: are they ready to work? Curr Psychiatry Rep 2020;22:11. 47. Marshall JM, Dunstan DA, Bartik W. Clinical or gimmickal: the use and effectiveness of mobile mental health apps for treating anxiety and depression. Aust N Z J Psychiatry 2020;54:20-8. 48. Nunes A, Castro SL, Limpo T. A review of mindfulness-based apps for children. Mindfulness 2020;11:2089-101. 49. Temkin AB, Schild J, Falk A et al. Mobile apps for youth anxiety disorders: a review of the evidence and forecast of future innovations. Prof Psychol Res Pract 2020;51:400-13. 50. Goreis A, Felnhofer A, Kafka JX et al. Efficacy of self-management smartphone-based apps for post-traumatic stress disorder symptoms: a systematic review and meta-analysis. Front Neurosci 2020;14:3. 51. Weisel KK, Fuhrmann LM, Berking M et al. Standalone smartphone apps for mental health – a systematic review and meta-analysis. NPJ Digit Med 2019;2:118. 52. Nahum-Shani I, Smith SN, Spring BJ et al. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med 2018;52:446-62. 53. Wang L, Miller LC. Just-in-the-moment adaptive interventions (JITAI): a meta-analytical review. Health Commun 2020;35:1531-44. 54. Baumel A, Torous J, Edan S et al. There is a non-evidence-based app for that: a systematic review and mixed methods analysis of depression-and anxiety-related apps that incorporate unrecognized techniques. J Affect Disord 2020;273:410-21. 55. Lau N, O’Daffer A, Colt S et al. Android and iPhone mobile apps for psychosocial wellness and stress management: systematic search in app stores and literature review. JMIR mHealth and uHealth 2020;8:e17798. 56. Singer N. In screening for suicide risk, Facebook takes on tricky public health role. New York Times, December 31, 2018. 57. Aschbrenner KA, Naslund JA, Tomlinson EF et al. Adolescents’ use of digital technologies and preferences for mobile health coaching in public mental health settings. Front Publ Health 2019;7:178. 58. Davidson BI, Shaw H, Ellis DA. Fuzzy constructs: the overlap between mental health and technology “use”. PsyArXiv 2020;10.31234. 59. Jensen M, George MJ, Russell MR et al. Young adolescents’ digital technology use and mental health symptoms: little evidence of longitudinal or daily linkages. Clin Psychol Sci 2019;7:1416-33. 60. Vogel L. Quality of kids’ screen time matters as much as quantity. CMAJ 2019; 191:E721. 61. Firth J, Torous J, Stubbs B et al. The “online brain”: how the Internet may be changing our cognition. World Psychiatry 2019;18:119-29. 62. Birnbaum ML, Ernala SK, Rizvi AF et al. Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook. NPJ Schizophr 2019;5:17. 63. Saha K, Torous J, Caine ED et al. Psychosocial effects of the COVID-19 pandemic: large-scale quasi-experimental study on social media. J Med Int Res 2020;22:e22600. 64. Sarker A, Lakamana S, Hogg-Bremer W et al. Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource. J Am Med Inform Assoc 2020;27:1310-5. 65. Low DM, Rumker L, Talkar T et al. Natural language processing reveals vulnerable mental health support groups and heightened health anxiety on reddit during COVID-19: observational study. J Med Int Res 2020;22: e22635. 66. Saha K, Sugar B, Torous J et al. A social media study on the effects of psychiatric medication use. Presented at the International AAAI Conference on Web and Social Media, Munich, July 2019. 67. Baker JT, Pennant L, Baltrušaitis T et al. Toward expert systems in mental health assessment: a computational approach to the face and voice in dyadic patient-doctor interactions. Iproceedings 2016;2:e6136. 68. Reece AG, Danforth CM. Instagram photos reveal predictive markers of depression. EPJ Data Sci 2017;6:1-2. 69. Odgers CL, Jensen MR. Annual research review: Adolescent mental health in the digital age: facts, fears, and future directions. J Child Psychol Psychiatry 2020;61:336-48. 70. Abi-Jaoude E, Naylor KT, Pignatiello A. Smartphones, social media use and youth mental health. CMAJ 2020;192:E136-41. 71. Alvarez-Jimenez M, Rice S, D’Alfonso S et al. A novel multimodal digital service (Moderated Online Social Therapy+) for help-seeking young people experiencing mental ill-health: pilot evaluation within a national youth emental health service. J Med Int Res 2020;22:e17155. 72. Schlosser DA, Campellone TR, Truong B et al. Efficacy of PRIME, a mobile app intervention designed to improve motivation in young people with schizophrenia. Schizophr Bull 2018;44:1010-20. 73. D’Alfonso S, Phillips J, Valentine L et al. Moderated online social therapy: viewpoint on the ethics and design principles of a web-based therapy system. JMIR Ment Health 2019;6:e14866. 74. Hao K. Nearly half of Twitter accounts pushing to reopen America may be bots. MIT Technol Rev 2020:6-25. 75. Robinson P, Turk D, Jilka S et al. Measuring attitudes towards mental health using social media: investigating stigma and trivialisation. Soc Psychiatry Psychiatr Epidemiol 2019;54:51-8. 76. Parrott S, Billings AC, Hakim SD et al. From #endthestigma to #realman: stigma-challenging social media responses to NBA players’ mental health disclosures. Commun Rep 2020;33:148-60. 77. Chancellor S, Birnbaum ML, Caine ED et al. A taxonomy of ethical tensions in inferring mental health states from social media. Presented at the Conference on Fairness, Accountability, and Transparency, Atlanta, January 2019. 78. Fiske A, Henningsen P, Buyx A. Your robot therapist will see you now: ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. J Med Int Res 2019;21:e13216. 79. Abd-Alrazaq AA, Alajlani M, Ali N et al. Perceptions and opinions of patients about mental health chatbots: scoping review. J Med Int Res 2021;23:e17828. 80. Henson P, Wisniewski H, Hollis C et al. Digital mental health apps and the therapeutic alliance: initial review. BJPsych Open 2019;5:e15. 81. Tremain H, McEnery C, Fletcher K et al. The therapeutic alliance in digital mental health interventions for serious mental illnesses: narrative review. JMIR Ment Health 2020;7:e17204. 82. Frank AF, Gunderson JG. The role of the therapeutic alliance in the treatment of schizophrenia. Relationship to course and outcome. Arch Gen Psychiatry 1990;47:228-36. 83. Lucas GM, Gratch J, King A et al. It’s only a computer: virtual humans increase willingness to disclose. Comp Hum Behav 2014;37:94-100. 84. Martínez-Miranda J, Martínez A, Ramos R et al. Assessment of users’ acceptability of a mobile-based embodied conversational agent for the prevention and detection of suicidal behaviour. J Med Syst 2019;43:1-8. 85. Laranjo L, Dunn AG, Tong HL et al. Conversational agents in healthcare: a systematic review. J Am Med Inform Assoc 2018;25:1248-58. 86. Vaidyam AN, Linggonegoro D, Torous J. Changes to the psychiatric chatbot landscape: a systematic review of conversational agents in serious mental illness. Can J Psychiatry 2021;66:339-48. 87. Miner AS, Milstein A, Schueller S et al. Smartphone-based conversational agents and responses to questions about mental health, interpersonal violence, and physical health. JAMA Intern Med 2016;176:619-25. 88. Vaidyam AN, Wisniewski H, Halamka JD et al. Chatbots and conversational agents in mental health: a review of the psychiatric landscape. Can J Psychiatry 2019;64:456-64. 89. Bell IH, Nicholas J, Alvarez-Jimenez M et al. Virtual reality as a clinical tool in mental health research and practice. Dialog Clin Neurosci 2020;22:169-77. 90. Freeman D, Reeve S, Robinson A et al. Virtual reality in the assessment, understanding, and treatment of mental health disorders. Psychol Med 2017; 47:2393-400. 91. Valmaggia LR, Latif L, Kempton MJ et al. Virtual reality in the psychological treatment for mental health problems: a systematic review of recent evidence. Psychiatry Res 2016;236:189-95. 92. Kessler R, Wittchen H, Abelson J et al. Methodological issues in assessing psychiatric disorders with self-reports. In: Stone AA, Turkkan JS, Bachrach CA et al (eds). The science of self-report: implications for research and practice. Mahwah: Erlbaum, 2020:229-55. 93. Parsons TD. Virtual reality for enhanced ecological validity and experimental control in the clinical, affective and social neurosciences. Front Hum Neurosci 2015;9:660. 94. Maples-Keller JL, Bunnell BE, Kim SJ et al. The use of virtual reality technology in the treatment of anxiety and other psychiatric disorders. Harv Rev Psychiatry 2017;25:103-13. 95. Freeman D, Bradley J, Antley A et al. Virtual reality in the treatment of persecutory delusions: randomised controlled experimental study testing how to reduce delusional conviction. Br J Psychiatry 2016;209:62-7. 96. Pot-Kolder RM, Geraets CN, Veling W et al. 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;5:217-26. 97. Dellazizzo L, Potvin S, Luigi M et al. A. Evidence on virtual reality-based therapies for psychiatric disorders: meta-review of meta-analyses. J Med Int Res 2020;22:e20889. 98. De Carvalho MR, Dias TR, Duchesne M et al. Virtual reality as a promising strategy in the assessment and treatment of bulimia nervosa and binge eating disorder: a systematic review. Behav Sci 2017;7:43. 99. Howard MC, Gutworth MB. A meta-analysis of virtual reality training programs for social skill development. Comp Educ 2020;144:103707. 100. Chandrasiri A, Collett J, Fassbender E et al. A virtual reality approach to mindfulness skills training. Virtual Real 2020;24:143-9. 101. Navarro-Haro MV, Modrego-Alarcón M, Hoffman HG et al. Evaluation of a mindfulness-based intervention with and without virtual reality dialectical behavior therapy® mindfulness skills training for the treatment of generalized anxiety disorder in primary care: a pilot study. Front Psychol 2019;10:55. 102. Seabrook E, Kelly R, Foley F et al. Understanding how virtual reality can support mindfulness practice: mixed methods study. J Med Int Res 2020;22: e16106. 103. Veling W, Lestestuiver B, Jongma M et al. Virtual reality relaxation for patients with a psychiatric disorder: crossover randomized controlled trial. J Med Int Res 2021;23:e17233. 104. Brown P, Waite F, Rovira A et al. Virtual reality clinical-experimental tests of compassion treatment techniques to reduce paranoia. Sci Rep 2020;10:8547. 105. Falconer CJ, Rovira A, King JA et al. Embodying self-compassion within virtual reality and its effects on patients with depression. BJPsych Open 2016; 2:74-80. 106. Realpe A, Elahi F, Bucci S et al. Co-designing a virtual world with young people to deliver social cognition therapy in early psychosis. Early Interv Psychiatry 2020;14:37-43. 107. Thompson A, Elahi F, Realpe A et al. A feasibility and acceptability trial of social cognitive therapy in early psychosis delivered through a virtual world: the VEEP study. Front Psychiatry 2020; 25;11:219. 108. Fortune Business Insights. Virtual reality market size, share & industry analysis. https://www.fortunebusinessinsights.com. 109. Vigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry 2016;3:171-8. 110. Drissi N, Ouhbi S, Janati Idrissi MA et al. An analysis on self-management and treatment-related functionality and characteristics of highly rated anxiety apps. Int J Med Inform 2020;141:104243. 111. Marshall J, Dunstan D, Bartik W. Apps with maps – anxiety and depression mobile apps with evidence-based frameworks: systematic search of major app stores. JMIR Mental Health 2020;7:e16525. 112. Linardon J, Cuijpers P, Carlbring P et al. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry 2019;18:325-36. 113. Bakker D, Kazantzis N, Rickwood D et al. Mental health smartphone apps: review and evidence-based recommendations for future developments. JMIR Mental Health 2016;3:e4984. 114. Wasil AR, Gillespie S, Shingleton R et al. Examining the reach of smartphone apps for depression and anxiety. Am J Psychiatry 2020;177:464-5. 115. Jaworski BK, Taylor K, Ramsey KM et al. Exploring usage of COVID Coach, a public mental health app designed for the COVID-19 pandemic: evaluation of analytics data. J Med Internet Res 2021;23:e26559. 116. Firth J, Torous J, Nicholas J et al. Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. J Affect Disord 2017;218:15-22. 117. Wasil AR, Venturo-Conerly KE, Shingleton RM et al. A review of popular smartphone apps for depression and anxiety: assessing the inclusion of evidence-based content. Behav Res Ther 2019;123:103498. 118. Liu JY, Xu KK, Zhu GL et al. Effects of smartphone-based interventions and monitoring on bipolar disorder: a systematic review and meta-analysis. World J Psychiatry 2020;10:272-285. 119. Depp CA, Ceglowski J, Wang VC et al. Augmenting psychoeducation with a mobile intervention for bipolar disorder: a randomized controlled trial. J Affect Disord 2015;174:23-30. 120. Faurholt-Jepsen M, Frost M, Ritz C et al. Daily electronic self-monitoring in bipolar disorder using smartphones – the MONARCA I trial: a randomized, placebo-controlled, single-blind, parallel group trial. Psychol Med 2015;45:2691-704. 121. Faurholt-Jepsen M, Frost M, Christensen EM et al. The effect of smartphonebased monitoring on illness activity in bipolar disorder: the MONARCA II randomized controlled single-blinded trial. Psychol Med 2020;50:838-48. 122. Ly KH, Trüschel A, Jarl L et al. Behavioural activation versus mindfulnessbased guided self-help treatment administered through a smartphone application: a randomised controlled trial. BMJ Open 2014;4:e003440. 123. Ly KH, Topooco N, Cederlund H et al. Smartphone-supported versus full behavioural activation for depression: a randomised controlled trial. PLoS One 2015;10:e0126559. 124. Watts S, Mackenzie A, Thomas C et al. CBT for depression: a pilot RCT comparing mobile phone vs. computer. BMC Psychiatry 2013;13:1-9. 125. Roberts AE, Davenport TA, Wong T et al. Evaluating the quality and safety of health-related apps and e-tools: adapting the Mobile App Rating Scale and developing a quality assurance protocol. Internet Interv 2021;24:100379. 126. Wisniewski H, Gorrindo T, Rauseo-Ricupero N et al. The role of digital navigators in promoting clinical care and technology integration into practice. Digit Biomark 2020;4(Suppl. 1):119-35. 127. Wisniewski H, Torous J. Digital navigators to implement smartphone and digital tools in care. Acta Psychiatr Scand 2020;141:350-5. 128. Rauseo-Ricupero N, Henson P, Agate-Mays M et al. Case studies from the digital clinic: integrating digital phenotyping and clinical practice into today’s world. Int Rev Psychiatry (in press). 129. Colombo D, Palacios AG, Alvarez JF et al. Current state and future directions of technology-based ecological momentary assessments and interventions for major depressive disorder: protocol for a systematic review. Syst Rev 2018;7:233. 130. Dogan E, Sander C, Wagner X et al. Smartphone-based monitoring of objective and subjective data in affective disorders: where are we and where are we going? Systematic review. J Med Internet Res 2017;19:e262. 131. Beiwinkel T, Kindermann S, Maier A et al. Using smartphones to monitor bipolar disorder symptoms: a pilot study. JMIR Mental Health 2016;3:e2. 132. Faurholt-Jepsen M, Vinberg M, Frost M et al. Behavioral activities collected through smartphones and the association with illness activity in bipolar disorder. Int J Methods Psychiatr Res 2016;25:309-23. 133. Faurholt-Jepsen M, Vinberg M, Frost M et al. Smartphone data as an electronic biomarker of illness activity in bipolar disorder. Bipolar Disord 2015; 17:715-28. 134. Faurholt-Jepsen M, Busk J, Frost M et al. Voice analysis as an objective state marker in bipolar disorder. Transl Psychiatry 2016;6:e856. 135. Antosik-Wójcińska AZ, Dominiak M, Chojnacka M et al. Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling. Int J Med Inform 2020;138:104131. 136. Stanislaus S, Faurholt-Jepsen M, Vinberg M et al. Mood instability in patients with newly diagnosed bipolar disorder, unaffected relatives, and healthy control individuals measured daily using smartphones. J Affect Disord 2020;271:336-44. 137. Yim SJ, Lui LM, Lee Y et al. The utility of smartphone-based, ecological momentary assessment for depressive symptoms. J Affect Disord 2020;274:602-9. 138. Burns MN, Begale M, Duffecy J et al. Harnessing context sensing to develop a mobile intervention for depression. J Med Internet Res 2011;13:e55. 139. Faurholt-Jepsen M, Tønning ML, Frost M et al. Reducing the rate of psychiatric Re-ADMISsions in bipolar disorder using smartphones – The RADMIS trial. Acta Psychiatr Scand 2021;143:453-65. 140. Tønning ML, Faurholt-Jepsen M, Frost M et al. The effect of smartphonebased monitoring and treatment on the rate and duration of psychiatric readmission in patients with unipolar depressive disorder: the RADMIS randomized controlled trial. J Affect Disord 2021;282:354-63. 141. Hidalgo-Mazzei D, Mateu A, Reinares M et al. Self-monitoring and psychoeducation in bipolar patients with a smart-phone application (SIMPLe) project: design, development and studies protocols. BMC Psychiatry 2015;15:1-9. 142. Ritter PS, Bermpohl F, Gruber O et al. Aims and structure of the German Research Consortium BipoLife for the study of bipolar disorder. Int J Bipolar Disord 2016;4:26. 143. Saunders KE, Cipriani A, Rendell J et al. Oxford Lithium Trial (OxLith) of the early affective, cognitive, neural and biochemical effects of lithium carbonate in bipolar disorder: study protocol for a randomised controlled trial. Trials 2016;17:1-5. 144. Kessing LV, Munkholm K, Faurholt-Jepsen J et al. The Bipolar Illness Onset study: research protocol for the BIO cohort study. BMJ Open 2017;7:e015462. 145. Bucci S, Schwannauer M, Berry N. The digital revolution and its impact on mental health care. Psychol Psychother 2019;92:277-97. 146. Chivilgina O, Wangmo T, Elger BS et al. mHealth for schizophrenia spectrum disorders management: a systematic review. Int J Soc Psychiatry 2020; 66:642-65. 147. Barnett I, Torous J, Staples P et al. Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology 2018; 43:1660-6. 148. Adler DA, Ben-Zeev D, Tseng VW et al. Predicting early warning signs of psychotic relapse from passive sensing data: an approach using encoderdecoder neural networks. JMIR Mhealth and Uhealth 2020;8:e19962. 149. Ben-Zeev D, Brian R, Wang R et al. CrossCheck: integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse. Psychiatr Rehabil J 2017;40:266-75. 150. Wisniewski H, Henson P, Torous J. Using a smartphone app to identify clinically relevant behavior trends via symptom report, cognition scores, and exercise levels: a case series. Front Psychiatry 2019;10:652. 151. Cella M, He Z, Killikelly C et al. Blending active and passive digital technology methods to improve symptom monitoring in early psychosis. Early Interv Psychiatry 2019;13:1271-5. 152. Clarke S, Hanna D, Mulholland C et al. A systematic review and meta-analysis of digital health technologies effects on psychotic symptoms in adults with psychosis. Psychosis 2019;11:362-73. 153. Bucci S, Barrowclough C, Ainsworth J et al. Actissist: proof-of-concept trial of a theory-driven digital intervention for psychosis. Schizophr Bull 2018; 44:1070-80. 154. Garety P, Ward T, Emsley R et al. Effects of SlowMo, a blended digital therapy targeting reasoning, on paranoia among people with psychosis: a randomized clinical trial. JAMA Psychiatry (in press). 155. Berry N, Machin M, Ainsworth J et al. Developing a theory-informed smartphone app for early psychosis: learning points from a multidisciplinary collaboration. Front Psychiatry 2020;11:602861. 156. Gumley A, Bradstreet S, Ainsworth J et al. Early signs monitoring to prevent relapse in psychosis and promote well-being, engagement, and recovery: protocol for a feasibility cluster randomized controlled trial harnessing mobile phone technology blended with peer support. JMIR Res Protoc 2020;9: e15058. 157. Mohr DC, Lyon AR, Lattie EG et al. Accelerating digital mental health research from early design and creation to successful implementation and sustainment. J Med Internet Res 2017;19:e153. 158. Greenhalgh T, Wherton J, Papoutsi C et al. Analysing the role of complexity in explaining the fortunes of technology programmes: empirical application of the NASSS framework. BMC Med 2018;16:66. 159. Camacho E, Levin L, Torous J. Smartphone apps to support coordinated specialty care for prodromal and early course schizophrenia disorders: systematic review. J Med Internet Res 2019;21:e16393. 160. Bardone-Cone AM, Thompson KA, Miller AJ. The self and eating disorders. J Pers 2020;88:59-75. 161. Halmi KA. Perplexities of treatment resistence in eating disorders. BMC Psychiatry 2013;13:292. 162. Weissman RS, Rosselli F. Reducing the burden of suffering from eating disorders: unmet treatment needs, cost of illness, and the quest for cost-effectiveness. Behav Res Ther 2017;88:49-64. 163. Linardon J, Shatte A, Tepper H et al. A survey study of attitudes toward, and preferences for, e-therapy interventions for eating disorder psychopathology. Int J Eat Disord 2020;53:907-16. 164. Linardon J, Messer M, Lee S et al. Perspectives of e-health interventions for treating and preventing eating disorders: descriptive study of perceived advantages and barriers, help-seeking intentions, and preferred functionality. Eat Weight Disord 2021;26:1097-109. 165. Juarascio AS, Manasse SM, Goldstein SP et al. Review of smartphone applications for the treatment of eating disorders. Eur Eat Disord Rev 2015;23:111. 166. Fairburn CG, Rothwell ER. Apps and eating disorders: a systematic clinical appraisal. Int J Eat Disord 2015;48:1038-46. 167. Wasil AR, Patel R, Cho JY et al. Smartphone apps for eating disorders: a systematic review of evidence-based content and application of user-adjusted analyses. Int J Eat Disord 2021;54:690-700. 168. Linardon J, Shatte A, Messer M et al. E-mental health interventions for the treatment and prevention of eating disorders: an updated systematic review and meta-analysis. J Consult Clin Psychol 2020;88:994-1007. 169. Linardon J, Shatte A, Rosato J et al. Efficacy of a transdiagnostic cognitive-behavioral intervention for eating disorder psychopathology delivered through a smartphone app: a randomized controlled trial. Psychol Med (in press). 170. Wilson GT, Fairburn CC, Agras WS et al. Cognitive-behavioral therapy for bulimia nervosa: time course and mechanisms of change. J Consult Clin Psychol 2002;70:267-74. 171. Sivyer K, Allen E, Cooper Z et al. Mediators of change in cognitive behavior therapy and interpersonal psychotherapy for eating disorders: a secondary analysis of a transdiagnostic randomized controlled trial. Int J Eat Disord 2020;53:1928-40. 172. Juarascio AS, Parker MN, Lagacey MA et al. 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;51:826-30. 173. Hildebrandt T, Michaeledes A, Mayhew M et al. Randomized controlled trial comparing health coach-delivered smartphone-guided self-help with standard care for adults with binge eating. Am J Psychiatry 2020;177:134-42. 174. Hildebrandt T, Michaelides A, Mackinnon D et al. Randomized controlled trial comparing smartphone assisted versus traditional guided self-help for adults with binge eating. Int J Eat Disord 2017;50:1313-22. 175. Hendershot CS, Witkiewitz K, George WH et al. Relapse prevention for addictive behaviors. Subst Abuse Treat Prev Policy 2011;6:17. 176. Tofighi B, Chemi C, Ruiz-Valcarcel J et al. Smartphone apps targeting alcohol and illicit substance use: systematic search in in commercial app stores and critical content analysis. JMIR mHealth and uHealth 2019;7:e11831. 177. Institute for Clinical and Economic Review. Digital therapeutics as an adjunct to medication assisted therapy for opioid use disorder. https://icer.org. 178. Whittaker R, McRobbie H, Bullen C et al. Mobile phone text messaging and app-based interventions for smoking cessation. Cochrane Database Syst Rev 2019;10:CD006611. 179. Staiger PK, O’Donnell R, Liknaitzky P et al. Mobile apps to reduce tobacco, alcohol, and illicit drug use: systematic review of the first decade. J Med Internet Res 2020;22:e17156. 180. Nuamah J, Mehta R, Sasangohar F. Technologies for opioid use disorder management: mobile app search and scoping review. JMIR mHealth and uHealth 2020;8:e15752. 181. Carreiro S, Newcomb M, Leach R et al. Current reporting of usability and impact of mHealth interventions for substance use disorder: a systematic review. Drug Alcohol Depend 2020;215:108201. 182. Liao Y, Tang J. Protocol: Efficacy of cognitive behavioural therapy-based smartphone app for smoking cessation in China: a study protocol of a randomised controlled trial. BMJ Open 2021;11:e041985. 183. Coughlin LN, Nahum-Shani I, Philyaw-Kotov ML et al. Developing an adaptive mobile intervention to address risky substance use among adolescents and emerging adults: usability study. JMIR mHealth and uHealth 2021;9:e24424. 184. Bricker JB, Watson NL, Mull KE et al. Efficacy of smartphone applications for smoking cessation: a randomized clinical trial. JAMA Intern Med 2020;180:1472-80. 185. Manning V, Piercy H, Garfield JB et al. Personalized approach bias modification smartphone app (“SWIPE”) to reduce alcohol use among people drinking at hazardous or harmful levels: protocol for an open-label feasibility study. JMIR Res Protoc 2020;9:e21278. 186. World Health Organization. Caring for children and adolescents with mental disorders: setting WHO directions. Geneva: World Health Organization, 2003. 187. Punukollu M, Marques M. Use of mobile apps and technologies in child and adolescent mental health: a systematic review. Evid Based Ment Health 2019;22:161-6. 188. Hollis C, Falconer CJ, Martin JL et al. Annual research review: Digital health interventions for children and young people with mental health problems – a systematic and meta-review. J Child Psychol Psychiatry 2017;58:474-503. 189. Grist R, Croker A, Denne M et al. Technology delivered interventions for depression and anxiety in children and adolescents: a systematic review and meta-analysis. Clin Child Fam Psychol Rev 2019;22:147-71. 190. Hill C, Creswell C, Vigerland S et al. Navigating the development and dissemination of internet cognitive behavioral therapy (iCBT) for anxiety disorders in children and young people: a consensus statement with recommendations from the #iCBTLorentz Workshop Group. Internet Interv 2018;12:1-10. 191. Peiris D, Miranda JJ, Mohr DC. Going beyond killer apps: building a better mHealth evidence base. BMJ Glob Health 2018;3:e000676. 192. Mohr DC, Riper H, Schueller SM. A solution-focused research approach to achieve an implementable revolution in digital mental health. JAMA Psychiatry 2018;75:113-4. 193. Kitson AL, Rycroft-Malone J, Harvey G et al. Evaluating the successful implementation of evidence into practice using the PARiHS framework: theoretical and practical challenges. Implement Sci 2008;3:1-2. 194. Roberts ET, Mehrotra A. Assessment of disparities in digital access among Medicare beneficiaries and implications for telemedicine. JAMA Intern Med 2020;180:1386-9. 195. Fischer SH, Ray KN, Mehrotra A et al. Prevalence and characteristics of telehealth utilization in the United States. JAMA Netw Open 2020;3:e2022302 196. Nouri S, Khoong EC, Lyles CR et al. Addressing equity in telemedicine for chronic disease management during the COVID-19 pandemic. NEJM Catalyst Innovations in Care Delivery, May 2020. 197. Szinay D, Jones A, Chadborn T et al. Influences on the uptake of and engagement with health and well-being smartphone apps: systematic review. J Med Internet Res 2020;22:e17572. 198. Hoffman L, Wisniewski H, Hays R et al. Digital Opportunities for Outcomes in Recovery Services (DOORS): pragmatic hands-on group approach toward increasing digital health and smartphone competencies, autonomy, relatedness, and alliance for those with serious mental illness. J Psychiatr Pract 2020;26:80-8. 199. Torous J, Lipschitz J, Ng M et al. Dropout rates in clinical trials of smartphone apps for depressive symptoms: systematic review and meta-analysis. J Affect Disord 2020;263:413-9. 200. Baumel A, Muench F, Edan S et al. Objective user engagement with mental health apps: systematic search and panel-based usage analysis. J Med Internet Res 2020;22:e17572. 201. Bradway M, Gabarron E, Johansen M et al. Methods and measures used to evaluate patient-operated mobile health interventions: scoping literature review. JMIR mHealth and uHealth 2020;8:e16814. 202. Ng MM, Firth J, Minen M et al. User engagement in mental health apps: a review of measurement, reporting, and validity. Psychiatr Serv 2019;70:538-44. 203. Pratap A, Neto EC, Snyder P et al. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digit Med 2020;3:21. 204. Morton E, Barnes SJ, Michalak EE. Participatory digital health research: a new paradigm for mHealth tool development. Gen Hosp Psychiatry 2020;66: 67-9. 205. Hetrick SE, Robinson J, Burge E et al. Youth codesign of a mobile phone app to facilitate self-monitoring and management of mood symptoms in young people with major depression, suicidal ideation, and self-harm. JMIR Ment Health 2018;5:e9. 206. Rudd BN, Beidas RS. Digital mental health: the answer to the global mental health crisis? JMIR Ment Health 2020;7:e18472. 207. Carter H, Araya R, Anjur K et al. The emergence of digital mental health in low-income and middle-income countries: a review of recent advances and implications for the treatment and prevention of mental disorders. J Psychiatr Res 2021;133:223-46. 208. Merchant R, Torous J, Rodriguez-Villa E et al. Digital technology for management of severe mental disorders in low-income and middle-income countries. Curr Opin Psychiatry 2020;33:501-7. 209. Naslund JA, Gonsalves PP, Gruebner O et al. Digital innovations for global mental health: opportunities for data science, task sharing, and early intervention. Curr Treat Options Psychiatry 2019;6:337-51. 210. Connolly SL, Hogan TP, Shimada SL et al. Leveraging implementation science to understand factors influencing sustained use of mental health apps: a narrative review. J Technol Behav Sci 2020;10.1007. 211. Bird KA, Castleman BL, Denning JT et al. Nudging at scale: experimental evidence from FAFSA completion campaigns. J Econ Behav Organ 2019;183: 105-28. 212. L attie EG, Nicholas J, Knapp AA et al. Opportunities for and tensions surrounding the use of technology-enabled mental health services in community mental health care. Adm Policy Ment Health 2020;47:138-49. 213. Lederman R, D’Alfonso S. The digital therapeutic alliance: insights on the effectiveness of online therapy. Presented at the First Annual Symposium on the Digital Therapeutic Alliance, Melbourne, August 2019. 214. Chikersal P, Belgrave D, Doherty G et al. Understanding client support strategies to improve clinical outcomes in an online mental health intervention. Presented at the CHI Conference on Human Factors in Computing Systems, Honolulu, April 2020. 215. Graham AK, Lattie EG, Powell BJ et al. Implementation strategies for digital mental health interventions in health care settings. Am Psychol 2020;75: 1080-92. 216. Schueller SM, Torous J. Scaling evidence-based treatments through digital mental health. Am Psychol 2020;75:1093-104. 217. Rodriguez-Villa E, Rauseo-Ricupero N, Camacho E et al. The digital clinic: implementing technology and augmenting care for mental health. Gen Hosp Psychiatry 2020;66:59-66. 218. Mordecai D, Histon T, Neuwirth E et al. How Kaiser Permanente created a mental health and wellness digital ecosystem. NEJM Catalyst Innovations in Care Delivery, January 2021. 219. Owen JE, Kuhn E, Jaworski BK et al. VA mobile apps for PTSD and related problems: public health resources for veterans and those who care for them. Mhealth 2018;4:28. 220. Nielsen JC, Kautzner J, Casado-Arroyo R et al. Remote monitoring of cardiac implanted electronic devices: legal requirements and ethical principles – ESC Regulatory Affairs Committee/EHRA joint task force report. Europace 2020;22:1742-58. 221. Ghafur S, Van Dael J, Leis M et al. Public perceptions on data sharing: key insights from the UK and the USA. Lancet Digit Health 2020;2:e444-6. 222. Parker L, Halter V, Karliychuk T et al. How private is your mental health app data? An empirical study of mental health app privacy policies and practices. Int J Law Psychiatry 2019;64:198-204. 223. Stern AD, Gordon WJ, Landman AB et al. Cybersecurity features of digital medical devices: an analysis of FDA product summaries. BMJ Open 2019;9: e025374. 224. Germain T. Mental health apps aren’t all as private as you may think. Consumer Reports, March 2, 2021. 225. Larsen ME, Huckvale K, Nicholas J et al. Using science to sell apps: evaluation of mental health app store quality claims. NPJ Digit Med 2019;2:18. 226. Nebeker C, López-Arenas A. Building Research Integrity and Capacity (BRIC): an educational initiative to increase research literacy among community health workers and promotores. J Microbiol Biol Educ 2016;17:41-5. 227. Alon N, Stern AD, Torous J. Assessing Food and Drug Administration’s riskbased framework for software precertification with top US health apps: quality improvement study. JMIR mHealth and uHealth 2020;8:e20482. 228. Carl JR, Jones DJ, Lindhiem OJ et al. Regulating digital therapeutics for mental health: opportunities, challenges, and the essential role of psychologists. Br J Clin Psychol (in press). 229. Rodriguez-Villa E, Torous J. Regulating digital health technologies with transparency: the case for dynamic and multi-stakeholder evaluation. BMC Med 2019;17:226. 230. Eyre HA, Singh AB, Reynolds III C. Tech giants enter mental health. World Psychiatry 2016;15:21-2. 231. Powell A, Torous J. A patient-centered framework for measuring the economic value of the clinical benefits of digital health apps: theoretical modeling. JMIR Ment Health 2020;7:e18812. 232. Mitchell LM, Joshi U, Patel V et al. Economic evaluations of Internet-based psychological interventions for anxiety disorders and depression: a systematic review. J Affect Disord 2021;284:157-82. 233. Gordon WJ, Patel V, Thornhill W et al. Characteristics of patients using patient-facing application programming interface technology at a US health care system. JAMA Netw Open 2020;3:e2022408. 234. Lagan S, Emerson MR, King D et al. Mental health app evaluation: updating the American Psychiatric Association’s framework through a stakeholderengaged workshop. Psychiatr Serv (in press). 235. Torous J, Choudhury T, Barnett I et al. Smartphone relapse prediction in serious mental illness: a pathway towards personalized preventive care. World Psychiatry 2020;19:308-9.