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Digital Innovations for Global Mental Health: Opportunities for Data Science, Task Sharing, and Early Intervention

  • Technology and its Impact on Mental Health Care (J Torous and T Becker, Section Editors)
  • Published:
Current Treatment Options in Psychiatry Aims and scope Submit manuscript

Abstract

Purpose

Globally, individuals living with mental disorders are more likely to have access to a mobile phone than mental health care. In this commentary, we highlight opportunities for expanding access to and use of digital technologies to advance research and intervention in mental health, with emphasis on the potential impact in lower resource settings.

Recent findings

Drawing from empirical evidence, largely from higher income settings, we considered three emerging areas where digital technology will potentially play a prominent role: supporting methods in data science to further our understanding of mental health and inform interventions, task sharing for building workforce capacity by training and supervising non-specialist health workers, and facilitating new opportunities for early intervention for young people in lower resource settings. Challenges were identified related to inequities in access, threats of bias in big data analyses, risks to users, and need for user involvement to support engagement and sustained use of digital interventions.

Summary

For digital technology to achieve its potential to transform the ways we detect, treat, and prevent mental disorders, there is a clear need for continued research involving multiple stakeholders, and rigorous studies showing that these technologies can successfully drive measurable improvements in mental health outcomes.

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References and Recommended Reading

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

  1. • Bidargaddi N, et al. Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies. Mol Psychiatry. 2017;22(2):164 This paper introduces the concept of digital footprints, which refers to the large quantities of data that are continuously and automatically generated throughout our daily use and interaction with different forms of technology, and discusses the potential for these forms of digital data to advance psychiatric research, including opportunities for longitudinal measurement and real-time monitoring of mood, sleep, and activity.

    CAS  PubMed  Google Scholar 

  2. Twenge JM, Joiner TE, Rogers ML, Martin GN. Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clin Psychol Sci. 2018;6(1):3–17.

    Google Scholar 

  3. Primack BA, Shensa A, Escobar-Viera CG, Barrett EL, Sidani JE, Colditz JB, et al. Use of multiple social media platforms and symptoms of depression and anxiety: a nationally-representative study among US young adults. Comput Hum Behav. 2017;69:1–9.

    Google Scholar 

  4. Orben A, Przybylski AK. The association between adolescent well-being and digital technology use. Nat Hum Behav. 2019;3(2):173–82.

    PubMed  Google Scholar 

  5. Orben A, Dienlin T, Przybylski AK. Social media’s enduring effect on adolescent life satisfaction. Proc Natl Acad Sci. 2019;116(21):10226–8.

    CAS  PubMed  Google Scholar 

  6. •• Firth J, et al. The “online brain”: how the Internet may be changing our cognition. World Psychiatry. 2019;18(2):119–29 This review provides a comprehensive summary of the impact and effects of Internet use on cognitive development and brain function over the lifecourse.

    PubMed  PubMed Central  Google Scholar 

  7. Bucci S, Schwannauer M, Berry N. The digital revolution and its impact on mental health care. Psychol Psychother Theory Res Pract. 2019;92(2):277–97 1–21.

    Google Scholar 

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

    CAS  Google Scholar 

  9. We Are Social. Digital in 2019. 2019 [cited 2019 June 5]; Available from: https://wearesocial.com/global-digital-report-2019.

  10. •• Patel V, et al., The Lancet Commission on global mental health and sustainable development. The Lancet, 2018. This landmark report provides an essential overview of the state of global mental health and the necessary efforts to close the global treatment gap for mental disorders.

  11. • Russ TC, et al. How data science can advance mental health research. Nat Hum Behav. 2018;3:24–32. This article discusses the opportunities for data science to advance mental health research related to mental health aetiology, detection and diagnosis, treatment development, illness management, and improving health services.

    PubMed  Google Scholar 

  12. Ilieva RT, McPhearson T. Social-media data for urban sustainability. Nature Sustainability. 2018;1(10):553–65.

    Google Scholar 

  13. Insel TR. Digital phenotyping: a global tool for psychiatry. World Psychiatry. 2018;17(3):276–7.

    PubMed  PubMed Central  Google Scholar 

  14. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection-harnessing the web for public health surveillance. N Engl J Med. 2009;360(21):2153–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Shatte AB, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med. 2019;49(9):1426–48.

    PubMed  Google Scholar 

  16. Naslund JA, Bartels SM, and Marsch LA, Digital technology, including telemedicine, in the management of mental illness. Revolutionizing tropical medicine: point-of-care tests, new imaging technologies and digital health, 2019: p. 505–530.

    Google Scholar 

  17. 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.

    PubMed  PubMed Central  Google Scholar 

  18. De Choudhury M, Counts S, and Horvitz E. Social media as a measurement tool of depression in populations. in Proceedings of the 5th Annual ACM Web Science Conference. 2013. ACM.

  19. Gkotsis G, Oellrich A, Velupillai S, Liakata M, Hubbard TJP, Dobson RJB, et al. Characterisation of mental health conditions in social media using informed deep learning. Sci Rep. 2017;7:45141.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Naslund JA, et al., Exploring opportunities to support mental health care using social media: a survey of social media users with mental illness. Early intervention in psychiatry, 2017.

  21. De Choudhury M, et al. Discovering shifts to suicidal ideation from mental health content in social media. in Proceedings of the 2016 CHI conference on human factors in computing systems. 2016. ACM.

  22. •• Gruebner O, et al. Big data opportunities for social behavioral and mental health research. Soc Sci Med. 2017;189:167–9 This commentary describes opportunities for leveraging the vast quantities of social media data to investigate social behaviors that have an impact on individuals health and well-being.

    PubMed  Google Scholar 

  23. Thelwall M. TensiStrength: stress and relaxation magnitude detection for social media texts. Inf Process Manag. 2017;53(1):106–21.

    Google Scholar 

  24. Sykora MD, et al. Stresscapes: validating linkages between place and stress expression on social media. 2015. © the authors. Published by CEUR Workshop Proceedings.

  25. Shaughnessy K, Reyes R, Shankardass K, Sykora M, Feick R, Lawrence H, et al. Using geolocated social media for ecological momentary assessments of emotion: innovative opportunities in psychology science and practice. Canada Psychol/Psychologie canadienne. 2018;59(1):47–53.

    Google Scholar 

  26. Coppersmith G, Dredze M, and 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.

  27. Coppersmith G, Harman C, and Dredze M. Measuring post traumatic stress disorder in Twitter. in Eighth international AAAI conference on weblogs and social media. 2014.

  28. Seresinhe CI, Preis T, MacKerron G, Moat HS. Happiness is greater in more scenic locations. Sci Rep. 2019;9(1):4498.

    PubMed  PubMed Central  Google Scholar 

  29. • Gruebner O, et al. Spatio-temporal distribution of negative emotions in New York City after a natural disaster as seen in social media. Int J Environ Res Public Health. 2018;15(10):2275 This study showed the data captured from Twitter can reflect the distress experienced during a natural disaster as reflected by negative emotional responses posted on social media.

    PubMed Central  Google Scholar 

  30. Gruebner O, Lowe SR, Sykora M, Shankardass K, Subramanian SV, Galea S. A novel surveillance approach for disaster mental health. PLoS One. 2017;12(7):e0181233.

    PubMed  PubMed Central  Google Scholar 

  31. Gruebner O, Sykora M, Lowe SR, Shankardass K, Trinquart L, Jackson T, et al. Mental health surveillance after the terrorist attacks in Paris. Lancet. 2016;387(10034):2195–6.

    PubMed  Google Scholar 

  32. Robertson C, et al. Personal activity centres and geosocial data analysis: combining big data with small data. in The Annual International Conference on Geographic Information Science. 2017. Springer.

  33. Helbich M. Toward dynamic urban environmental exposure assessments in mental health research. Environ Res. 2018;161:129–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Gruebner O and McCay L, Urban Design. Urban Health, 2019: p. 256, Urban Design.

  35. • Shankardass K, et al. A unified ecological framework for studying effects of digital places on well-being. Soc Sci Med. 2019;227:119–27 This paper presents an ecological framework to expand our understanding of the mechanisms that influence social media expressions about well-being and the influence of place.

    PubMed  Google Scholar 

  36. Relia K, et al. Socio-spatial self-organizing maps: using social media to assess relevant geographies for exposure to social processes. Proc ACM Human-Comp Interact. 2018;2(CSCW):145.

    Google Scholar 

  37. •• Pendse S, et al. Mental health in the global south: challenges and opportunities in HCI for development. In: ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS 2019). Accra: ACM; 2019. This manuscript reviewed work on mental health in lower resource settings and describes research in human-computer interaction (HCI) and new opportunities in HCI for development to advance our understanding and treatment of mental illness.

    Google Scholar 

  38. Andalibi, N., P. Ozturk, and A. Forte. Sensitive self-disclosures, responses, and social support on Instagram: the case of# depression. in Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing. 2017. ACM.

  39. Chancellor, S., et al., Discovering alternative treatments for opioid use recovery using social media. 2019.

  40. De Choudhury M, Counts S, and Horvitz E. Predicting postpartum changes in emotion and behavior via social media. in Proceedings of the SIGCHI conference on human factors in computing systems. 2013. ACM.

  41. Pruksachatkun Y, Pendse SR, and Sharma A, Moments of change: analyzing peer-based cognitive support in online mental health forums. 2019.

  42. Saha K, et al. A social media study on the effects of psychiatric medication use. in Proceedings of the International AAAI Conference on Web and Social Media. 2019.

  43. Murnane EL, et al. Personal informatics in interpersonal contexts: towards the design of technology that supports the social ecologies of long-term mental health management. Proc ACM on Human-Comp Interact. 2018;2(CSCW):127.

    Google Scholar 

  44. O’Leary K, et al. “Suddenly, we got to become therapists for each other”: designing peer support chats for mental health. in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 2018. ACM.

  45. Slovák P, et al. I just let him cry...: designing socio-technical interventions in families to prevent mental health disorders. Proc ACM on Human-Comp Interact. 2018;2(CSCW):160.

    Google Scholar 

  46. Katikalapudi R, Chellappan S, Montgomery F, Wunsch D, Lutzen K. Associating internet usage with depressive behavior among college students. IEEE Technol Soc Mag. 2012;31(4):73–80.

    Google Scholar 

  47. De Choudhury M, et al. Predicting depression via social media. in Seventh international AAAI conference on weblogs and social media. 2013.

  48. Benton A, Mitchell M, Hovy D. Multi-task learning for mental health using social media text. arXiv preprint. 2017;arXiv:1712.03538.

    Google Scholar 

  49. Mitchell M, Hollingshead K, and Coppersmith G. Quantifying the language of schizophrenia in social media. in Proceedings of the 2nd workshop on Computational linguistics and clinical psychology: From linguistic signal to clinical reality. 2015.

  50. Feuston JL and Piper AM. Everyday experiences: small stories and mental illness on Instagram. in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. ACM.

  51. Kumar N, et al. Aspirations-based design. in Proceedings of the Tenth International Conference on Information and Communication Technologies and Development. 2019. ACM.

  52. MacLeod AK, Byrne A. Anxiety, depression, and the anticipation of future positive and negative experiences. J Abnorm Psychol. 1996;105(2):286–9.

    CAS  PubMed  Google Scholar 

  53. Morina N, Deeprose C, Pusowski C, Schmid M, Holmes EA. Prospective mental imagery in patients with major depressive disorder or anxiety disorders. J Anxiety Disord. 2011;25(8):1032–7.

    PubMed  PubMed Central  Google Scholar 

  54. Naditch MP, Gargan MA, Michael LB. Denial, anxiety, locus of control, and discrepancy between aspirations and achievements as components of depression. J Abnorm Psychol. 1975;84(1):1–9.

    CAS  PubMed  Google Scholar 

  55. • Panch T, Szolovits P, Atun R. Artificial intelligence, machine learning and health systems. J Glob Health. 2018;8(2):020303 This commentary describes the opportunities for use of artificial intelligence and machine learning in health systems in lower income countries with emphasis on potential benefits and risks.

    PubMed  PubMed Central  Google Scholar 

  56. Jaroszewski AC, Morris RR, Nock MK. Randomized controlled trial of an online machine learning-driven risk assessment and intervention platform for increasing the use of crisis services. J Consult Clin Psychol. 2019;87(4):370–9.

    PubMed  Google Scholar 

  57. Kakuma R, Minas H, van Ginneken N, Dal Poz MR, Desiraju K, Morris JE, et al. Human resources for mental health care: current situation and strategies for action. Lancet. 2011;378(9803):1654–63.

    PubMed  Google Scholar 

  58. Patel V, Chisholm D, Parikh R, Charlson FJ, Degenhardt L, Dua T, et al. Addressing the burden of mental, neurological, and substance use disorders: key messages from disease control priorities. Lancet. 2016;387(10028):1672–85.

    PubMed  Google Scholar 

  59. •• Raviola G, et al. Innovative models in mental health delivery systems: task sharing care with non-specialist providers to close the mental health treatment gap. Curr Psychiatr Rep. 2019;21(6):44 A comprehensive review of approaches to implementing task-sharing involving non-specialist health workers in the delivery of evidence-based interventions for mental disorders.

    Google Scholar 

  60. Patel V, Weobong B, Weiss HA, Anand A, Bhat B, Katti B, et al. The Healthy Activity Program (HAP), a lay counsellor-delivered brief psychological treatment for severe depression, in primary care in India: a randomised controlled trial. Lancet. 2017;389(10065):176–85.

    PubMed  PubMed Central  Google Scholar 

  61. Rahman A, Hamdani SU, Awan NR, Bryant RA, Dawson KS, Khan MF, et al. Effect of a multicomponent behavioral intervention in adults impaired by psychological distress in a conflict-affected area of Pakistan: a randomized clinical trial. Jama. 2016;316(24):2609–17.

    PubMed  Google Scholar 

  62. Chibanda D, Weiss HA, Verhey R, Simms V, Munjoma R, Rusakaniko S, et al. Effect of a primary care–based psychological intervention on symptoms of common mental disorders in Zimbabwe: a randomized clinical trial. Jama. 2016;316(24):2618–26.

    PubMed  Google Scholar 

  63. Agarwal S, et al., Mobile technology in support of frontline health workers. A comprehensive overview of the landscape knowledge gaps and future directions. 2016, Johns Hopkins University Global mHealth Initiative.

  64. Agarwal S, Perry HB, Long LA, Labrique AB. Evidence on feasibility and effective use of mHealth strategies by frontline health workers in developing countries: systematic review. Tropical Med Int Health. 2015;20(8):1003–14.

    Google Scholar 

  65. • Naslund JA, Shidhaye R, Patel V. Digital technology for building capacity of nonspecialist health workers for task sharing and scaling up mental health care globally. Harvard Rev Psychiatry. 2019;27(3):181–92 p. 1–12. This review summarizes recent studies using various forms of digital technology for offering support, training, and supervision for non-specialist health workers in task-sharing mental health care in lower resource settings.

    Google Scholar 

  66. Chibanda D, Bowers T, Verhey R, Rusakaniko S, Abas M, Weiss HA, et al. The Friendship Bench programme: a cluster randomised controlled trial of a brief psychological intervention for common mental disorders delivered by lay health workers in Zimbabwe. Int J Ment Heal Syst. 2015;9(1):21.

    Google Scholar 

  67. Diez-Canseco F, Toyama M, Ipince A, Perez-Leon S, Cavero V, Araya R, et al. Integration of a technology-based mental health screening program into routine practices of primary health care services in Peru (the Allillanchu Project): development and implementation. J Med Internet Res. 2018;20(3):e100.

    PubMed  PubMed Central  Google Scholar 

  68. Maulik PK, Kallakuri S, Devarapalli S, Vadlamani VK, Jha V, Patel A. Increasing use of mental health services in remote areas using mobile technology: a pre–post evaluation of the SMART Mental Health project in rural India. J Glob Health. 2017;7(1):010408.

    PubMed  PubMed Central  Google Scholar 

  69. Shields-Zeeman L, Pathare S, Walters BH, Kapadia-Kundu N, Joag K. Promoting wellbeing and improving access to mental health care through community champions in rural India: the Atmiyata intervention approach. Int J Ment Heal Syst. 2017;11(1):6.

    Google Scholar 

  70. Gureje O, et al. Expanding care for perinatal women with depression (EXPONATE): study protocol for a randomized controlled trial of an intervention package for perinatal depression in primary care. BMC psychiatry. 2015;15(1):136.

    PubMed  PubMed Central  Google Scholar 

  71. Zafar S, Sikander S, Hamdani SU, Atif N, Akhtar P, Nazir H, et al. The effectiveness of Technology-assisted Cascade Training and Supervision of community health workers in delivering the Thinking Healthy Program for perinatal depression in a post-conflict area of Pakistan–study protocol for a randomized controlled trial. Trials. 2016;17(1):188.

    PubMed  PubMed Central  Google Scholar 

  72. Xu DR, et al. Lay health supporters aided by a mobile phone messaging system to improve care of villagers with schizophrenia in Liuyang, China: protocol for a randomised control trial. BMJ Open. 2016;6(1):e010120.

    PubMed  Google Scholar 

  73. • Xu DR, et al. Lay health supporters aided by mobile text messaging to improve adherence, symptoms, and functioning among people with schizophrenia in a resource-poor community in rural China (LEAN): a randomized controlled trial. PLoS Med. 2019;16(4):e1002785 This study describes the first randomized controlled trial showing that digital technology can support non-specialist health workers in providing care for people living with schizophrenia in community-based settings in China.

    PubMed  Google Scholar 

  74. •• Rahman A, et al. Using technology to scale-up training and supervision of community health workers in the psychosocial management of perinatal depression: a non-inferiority, randomized controlled trial. Global Mental Health. 2019;6:e8 This study describes the first randomized controlled trial demonstrating that digital technology can enhance traditional methods of instruction for supporting the training and supervision of community health workers in the delivery of brief psychological treatment for perinatal depression in a low-resource setting.

    PubMed  PubMed Central  Google Scholar 

  75. Chaiyachati KH, Loveday M, Lorenz S, Lesh N, Larkan LM, Cinti S, et al. A pilot study of an mHealth application for healthcare workers: poor uptake despite high reported acceptability at a rural South African community-based MDR-TB treatment program. PLoS One. 2013;8(5):e64662.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Haberer JE, Kiwanuka J, Nansera D, Wilson IB, Bangsberg DR. Challenges in using mobile phones for collection of antiretroviral therapy adherence data in a resource-limited setting. AIDS Behav. 2010;14(6):1294–301.

    PubMed  PubMed Central  Google Scholar 

  77. • Long L-A, Pariyo G, Kallander K. Digital technologies for health workforce development in low-and middle-income countries: a scoping review. Global Health: Science and Practice. 2018;6(Supplement 1):S41–8 This article considers the various roles of digital technology for supporting capacity building of health workforce in low-income and middle-income countries.

    Google Scholar 

  78. •• Mishra SR, et al. Combating non-communicable diseases: potentials and challenges for community health workers in a digital age, a narrative review of the literature. Health Policy Plan. 2019;34(1):55–66 This review provides a conceptual framework for understanding how digital technology affects community health worker delivered programs for non-communicable diseases in lower resource settings, with emphasis on the benefits of digital technology as well as the challenges with using technology in these settings.

    PubMed  Google Scholar 

  79. Torous J, Nicholas J, 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 

  80. Hollis C, Falconer CJ, Martin JL, Whittington C, Stockton S, Glazebrook C, 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(4):474–503.

    PubMed  Google Scholar 

  81. Organization, W.H., Depression and other common mental disorders: global health estimates. 2017, World Health Organization.

  82. Firth J, Torous J, Nicholas J, Carney R, Rosenbaum S, Sarris J. Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. J Affect Disord. 2017;218:15–22.

    PubMed  Google Scholar 

  83. Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S, et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World Psychiatry. 2017;16(3):287–98.

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  85. Gulliver A, Griffiths KM, Christensen H. Perceived barriers and facilitators to mental health help-seeking in young people: a systematic review. BMC psychiatry. 2010;10(1):113.

    PubMed  PubMed Central  Google Scholar 

  86. United Nations Population Fund, The power of 1.8 billion: adolescents, youth and the transformation of the future. The State of the World Population 2014, 2014.

  87. Saraceno B, van Ommeren M, Batniji R, Cohen A, Gureje O, Mahoney J, et al. Barriers to improvement of mental health services in low-income and middle-income countries. Lancet. 2007;370(9593):1164–74.

    PubMed  Google Scholar 

  88. Patel V, et al. Mental health of young people: a global public-health challenge. Lancet. 2007;369(9569):1302–13.

    PubMed  Google Scholar 

  89. Keeley, B. and C. Little, The State of the worlds children 2017: children in a digital world. 2017: ERIC.

  90. Eysenbach G. What is e-health? J Med Internet Res. 2001;3(2):e20.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Mandryk RL, Birk MV. Toward game-based digital mental health interventions: player habits and preferences. J Med Internet Res. 2017;19(4):e128.

    PubMed  PubMed Central  Google Scholar 

  92. Torous J, Luo J, Chan S. Mental health apps: what to tell patients. Curr Psychiatr Ther. 2018;17(3):21.

    Google Scholar 

  93. Torous J, Roberts LW. Needed innovation in digital health and smartphone applications for mental health: transparency and trust. JAMA Psychiatry. 2017;74(5):437–8.

    PubMed  Google Scholar 

  94. Michael DR and Chen SL, Serious games: games that educate, train, and inform. Muska & Lipman/Premier-Trade. 2005

  95. • Lau HM, et al. Serious games for mental health: are they accessible, feasible, and effective? A systematic review and meta-analysis. Front Psychiatry. 2017;7:209 This review synthesizes empirical literature on the impacts of serious games on clinical mental health symptoms.

    PubMed  PubMed Central  Google Scholar 

  96. Price M, Ruggiero KJ, Ferguson PL, Patel SK, Treiber F, Couillard D, et al. A feasibility pilot study on the use of text messages to track PTSD symptoms after a traumatic injury. Gen Hosp Psychiatry. 2014;36(3):249–54.

    PubMed  PubMed Central  Google Scholar 

  97. Grist R, Porter J, Stallard P. Mental health mobile apps for preadolescents and adolescents: a systematic review. J Med Internet Res. 2017;19(5):e176.

    PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

  99. National Advisory Mental Health Council (NAMHC) Workgroup Opportunities and challenges of developing information technologies on behavioral and social science clinical research. 2018 [cited 2019 June 24]; Available from: https://www.nimh.nih.gov/about/advisory-boards-and-groups/namhc/reports/opportunities-and-challenges-of-developing-information-technologies-on-behavioral-and-social-science-clinical-research.shtml.

  100. •• Naslund JA, et al. Digital technology for treating and preventing mental disorders in low-income and middle-income countries: a narrative review of the literature. Lancet Psychiatry. 2017;4(6):486–500 This review offers a comprehensive summary of studies using digital technologies for mental health intervention in lower income countries.

    PubMed  PubMed Central  Google Scholar 

  101. Karbasi A, Haratian A. The efficacy of internet-based cognitive behavioral therapy on the anxiety disorders among adolescent girls. Adv Biomed Res. 2018;7:13.

    PubMed  PubMed Central  Google Scholar 

  102. Anttila M, Sittichai R, Katajisto J, Välimäki M. Impact of a web program to support the mental wellbeing of high school students: a quasi experimental feasibility study. Int J Environ Res Public Health. 2019;16(14):2473.

    PubMed Central  Google Scholar 

  103. Chandra PS, Sowmya HR, Mehrotra S, Duggal M. ‘SMS’for mental health–feasibility and acceptability of using text messages for mental health promotion among young women from urban low income settings in India. Asian J Psychiatr. 2014;11:59–64.

    PubMed  Google Scholar 

  104. Yardley L, et al. The person-based approach to intervention development: application to digital health-related behavior change interventions. J Med Internet Res. 2015;17(1):e30.

    PubMed  PubMed Central  Google Scholar 

  105. Gonsalves PP, Hodson ES, Kumar A, Aurora T, Chandak Y, Sharma R, Michelson D, Patel V. Design and Development of the "POD Adventures" Smartphone Game: A Blended Problem-Solving Intervention for Adolescent Mental Health in India. 2019;7:238.

  106. Bertrand P, et al. Learning empathy through virtual reality: multiple strategies for training empathy-related abilities using body ownership illusions in embodied virtual reality. Front Robot AI. 2018;5:26.

    Google Scholar 

  107. Carrasco AE. Acceptability of an adventure video game in the treatment of female adolescents with symptoms of depression. Res Psychother: Psychopathology, Process and Outcome. 2016;19:10–8.

    Google Scholar 

  108. Michelson D, Malik K, and Krishna M, Development of a transdiagnostic, low-intensity, psychological intervention for common adolescent mental health problems in Indian secondary schools. Behav Res Ther, In Review.

  109. • Gonsalves PP, et al. What are young Indians saying about mental health? A content analysis of blogs on the It’s Ok To Talk website. BMJ Open. 2019, 9(6):e028244 This study offers a thematic analysis of personal narratives of young people in India who participated in a novel digital media campaign.

    PubMed  PubMed Central  Google Scholar 

  110. Patel V. Talking sensibly about depression. PLoS Med. 2017;14(4):e1002257.

    PubMed  PubMed Central  Google Scholar 

  111. World Health Organization. World Health Day 2017: Depression Let’s Talk. 2017 [cited 2019 June 24]; Available from: https://www.who.int/campaigns/world-health-day/2017/toolkit.pdf?ua=1.

  112. Brown JS, et al. School-based early intervention for anxiety and depression in older adolescents: a feasibility randomised controlled trial of a self-referral stress management workshop programme (“DISCOVER”). J Adolesc. 2019;71:150–61.

    PubMed  Google Scholar 

  113. • McGorry PD, Mei C. Early intervention in youth mental health: progress and future directions. Evid-Based Mental Health. 2018;21(4):182–4 This clinical review summarizes recent advances and evidence supporting an innovative integrated model of youth mental health care, and considers future opportunities.

    Google Scholar 

  114. • Lutz C. Digital inequalities in the age of artificial intelligence and big data. Human Behav Emerg Technol. 2019;1(2):141–8 This paper offers a valuable discussion regarding the implications of inequitable access to digital technologies.

    Google Scholar 

  115. Rowntree O, Connected women: the mobile gender gap report 2018. 2018: London, UK.

  116. Välimäki M, Athanasopoulou C, Lahti M, Adams CE. Effectiveness of social media interventions for people with schizophrenia: a systematic review and meta-analysis. J Med Internet Res. 2016;18(4):e92.

    PubMed  PubMed Central  Google Scholar 

  117. Chandra PS, Satyanarayana VA. Gender disadvantage and common mental disorders in women. Int Rev Psychiatry. 2010;22(5):513–24.

    PubMed  Google Scholar 

  118. Stewart R, Davis K. ‘Big data’in mental health research: current status and emerging possibilities. Soc Psychiatry Psychiatr Epidemiol. 2016;51(8):1055–72.

    PubMed  PubMed Central  Google Scholar 

  119. Stiglic N, Viner RM. Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews. BMJ Open. 2019;9(1):e023191.

    PubMed  PubMed Central  Google Scholar 

  120. Rideout V, Fox S. Digital health practices, social media use, and mental well-being among teens and young adults in the U.S. San Francisco: Hopelab & Well Being Trust; 2018.

    Google Scholar 

  121. Naslund JA, Aschbrenner KA. Risks to privacy with use of social media: understanding the views of social media users with serious mental illness. Psychiatr Serv. 2019;70(7):561–8 p. appi. ps. 201800520.

    PubMed  Google Scholar 

  122. Ernala SK, et al., Methodological gaps in predicting mental health states from social media: triangulating diagnostic signals. 2019.

  123. Chancellor, S., et al. A taxonomy of ethical tensions in inferring mental health states from social media. in Proceedings of the Conference on Fairness, Accountability, and Transparency. 2019. ACM.

  124. 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):427–9.

    PubMed  PubMed Central  Google Scholar 

  125. Naslund JA, Aschbrenner KA. Digital technology for health promotion: opportunities to address excess mortality in persons living with severe mental disorders. Evid-Based Mental Health. 2019;22(1):17–22.

    Google Scholar 

  126. Naslund JA, Aschbrenner KA, Kim SJ, McHugo GJ, Unützer J, Bartels SJ, et al. Health behavior models for informing digital technology interventions for individuals with mental illness. Psychiatr Rehabil J. 2017;40(3):325–35.

    PubMed  PubMed Central  Google Scholar 

  127. Hollis C, et al. Technological innovations in mental healthcare: harnessing the digital revolution. Br J Psychiatry. 2015;206(4):263–5.

    PubMed  Google Scholar 

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Funding

Dr. Naslund reports receiving support from the National Institute of Mental Health (NIMH), grant number: U19MH113211.

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Authors and Affiliations

  1. Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA

    John A. Naslund PhD & Giuseppe Raviola MD

  2. Sangath, New Delhi, India

    Pattie P. Gonsalves MSc

  3. Sangath, Porvorim, Goa, India

    Pattie P. Gonsalves MSc

  4. Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland

    Oliver Gruebner PhD

  5. Department of Geography, University of Zurich, Zurich, Switzerland

    Oliver Gruebner PhD

  6. Microsoft Research India, Bangalore, India

    Sachin R. Pendse MS & Amit Sharma PhD

  7. Georgia Institute of Technology, School of Interactive Computing, Atlanta, GA, USA

    Sachin R. Pendse MS

  8. Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA, USA

    Stephanie L. Smith MD

  9. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Giuseppe Raviola MD

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  1. John A. Naslund PhD
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Correspondence to John A. Naslund PhD.

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John A. Naslund declares that he has no conflict of interest.

Pattie P. Gonsalves declares that she has no conflict of interest.

Oliver Gruebner declares that he has no conflict of interest.

Sachin R. Pendse declares that he has no conflict of interest.

Stephanie L. Smith declares that she has no conflict of interest.

Amit Sharma declares that he has no conflict of interest.

Giuseppe Raviola declares that he has no conflict of interest.

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Naslund, J.A., Gonsalves, P.P., Gruebner, O. et al. Digital Innovations for Global Mental Health: Opportunities for Data Science, Task Sharing, and Early Intervention. Curr Treat Options Psych 6, 337–351 (2019). https://doi.org/10.1007/s40501-019-00186-8

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