Current Treatment Options in Psychiatry

, Volume 6, Issue 3, pp 243–255 | Cite as

Use of Digital Mental Health for Marginalized and Underserved Populations

  • Stephen M. SchuellerEmail author
  • John F. Hunter
  • Caroline Figueroa
  • Adrian Aguilera
Technology and its Impact on Mental Health Care (J Torous and T Becker, Section Editors)
Part of the following topical collections:
  1. Topical \Collection on Technology and its Impact on Mental Health Care


Purpose of review

Digital mental health (DMH) interventions provide opportunities to alleviate mental health disparities among marginalized populations by overcoming traditional barriers to care and putting quality mental health services in the palm of one’s hand. While progress has been made towards realizing this goal, the potential for impactful change has yet to be realized. This paper reviews current examples of DMH interventions for certain marginalized and underserved groups, namely, ethnic and racial minorities including Latinx and African-Americans, rural populations, individuals experiencing homelessness, and sexual and gender minorities.

Recent findings

Strengths and opportunities, along with the needs and considerations, of each group are discussed as they pertain to the development and dissemination of DMH interventions. Our review focuses on several DMH interventions that have been specifically designed for marginalized populations with a culturally sensitive approach along with other existing interventions that have been tailored to fit the needs of the target population. Overall, evidence is beginning to show promise for the feasibility and acceptability of DMH inter ventions for these groups, but large-scale efficacy testing and scaling potential are still lacking.


These examples of how DMH can potentially positively impact marginalized populations should motivate developers, researchers, and practitioners to work collaboratively with stakeholders to deliver DMH interventions to these underserved populations in need.


Technology Mental health Disparities mHealth Treatment Health information technology 


Compliance with ethical standards

Conflict of interest

Stephen M. Schueller declares that he has no conflict of interest. John F. Hunter declares that he has no conflict of interest. Caroline Figueroa declares that she has no conflict of interest.

Adrian Aguilera reports personal fees from Care Message.

Human and animal rights and informed consent

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

References and Recommended Reading

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

  1. 1.
    Cook BL, Trinh N, Li Z, Hou SS, Progovac AM. Trends in racial-ethnic disparities in access to mental health care, 2004-2012. Psychiatr Serv. 2016;68:1–16.Google Scholar
  2. 2.
    Orozco R, Borges G, Medina-Mora ME, Aguilar-Gaxiola S, Breslau J. A cross-national study on prevalence of mental disorders, service use, and adequacy of treatment among Mexican and Mexican American populations. Am J Public Health. 2013;103:1610–8.CrossRefGoogle Scholar
  3. 3.
    Sarkar U, Gourley GI, Lyles CR, Tieu L, Clarity C, Newmark L, et al. Usability of commercially available mobile applications for diverse patients. J Gen Intern Med. 2016;31:1417–26.CrossRefGoogle Scholar
  4. 4.
    National Institute of Mental Health and Health Disparities. The 2019–2022 NIH minority health and health disparities strategic plan. Accessed 1 April 2019.
  5. 5.
    Duran DG, Pérez-Stable EJ. Novel approaches to advance minority health and health disparities research. Am J Public Health. 2019;109:S8–S10.CrossRefGoogle Scholar
  6. 6.
    Williams DR, Yu Y, Jackson JS, Anderson NB. Racial differences in physical and mental health: socio-economic status, stress and discrimination. J Health Psychol. 1997;2:335–51.CrossRefGoogle Scholar
  7. 7.
    Pew Research Center. Mobile fact sheet. February, 5, 2018: Accessed 15 April 2019.
  8. 8.
    Alegría M, Alvarez K, Ishikawa RZ, DiMarzio K, McPeck S. Removing obstacles to eliminating racial and ethnic disparities in behavioral health care. Health Aff. 2016;35:991–9.CrossRefGoogle Scholar
  9. 9.
    Ramirez V, Johnson E, Gonzalez C, Ramirez V, Rubino B, Rossetti G. Assessing the use of mobile health technology by patients: an observational study in primary care clinics. JMIR mHealth and uHealth. 2016;4:e41.CrossRefGoogle Scholar
  10. 10.
    Krebs P, Duncan DT. Health app use among US mobile phone owners: a national survey. JMIR mHealth and uHealth. 2015;3:e101.CrossRefGoogle Scholar
  11. 11.
    Aguilera A, Bruehlman-Senecal E, Demasi O, Avila P. Automated text messaging as an adjunct to cognitive behavioral therapy for depression: a clinical trial. J Med Internet Res. 2017;19:e148.CrossRefGoogle Scholar
  12. 12.
    Dahne J, Collado A, Lejuez CW, Risco C, Diaz VA, Coles L, et al. Pilot randomized controlled trial of a spanish-language behavioral activation mobile app (¡Aptívate!) for the treatment of depressive symptoms among United States latinx adults with limited english proficiency. J Affect Disord. 2019;250:210–7.CrossRefGoogle Scholar
  13. 13.
    •• Pratap A, Renn BN, Volponi J, Mooney SD, Gazzaley A, Arean PA, et al. Using mobile apps to assess and treat depression in hispanic and latino populations: fully remote randomized clinical trial. J Med Internet Res. 2018;20:e10130 A large scale study looking at engaging Latinos through a fully remote clinical trial. Found significant reductions in depressive symptoms but low rates of adoption and sustained use.CrossRefGoogle Scholar
  14. 14.
    Muroff J, Robinson W, Chassler D, López LM, Gaitan E, Lundgren L, et al. Use of a smartphone recovery tool for latinos with co-occurring alcohol and other drug disorders and mental disorders. J Dual Diagn. 2017;13:280–90.CrossRefGoogle Scholar
  15. 15.
    Connelly K, Stein KF, Chaudhry B, Trabold N. Development of an ecological momentary assessment mobile app for a low-literacy, mexican american population to collect disordered eating behaviors. JMIR Public Health Surveill. 2016;2:e31.CrossRefGoogle Scholar
  16. 16.
    Aguilera A, Berridge C. Qualitative feedback from a text messaging intervention for depression: benefits, drawbacks, and cultural differences. JMIR mHealth and uHealth. 2014;(2):e46.Google Scholar
  17. 17.
    James DCS, Harville C. Barriers and motivators to participating in mHealth research among african american men. Am J Mens Health. 2017;11:1605–13.CrossRefGoogle Scholar
  18. 18.
    Ziemba SJ, Bradley NS, Landry LAP, Roth CH, Porter LS, Cuyler RN. Posttraumatic stress disorder treatment for operation enduring freedom/operation Iraqi freedom combat veterans through a civilian community-based telemedicine network. Telemed J E Health. 2014;20:446–50. Scholar
  19. 19.
    Tan G, Teo I, Srivastava D, Smith D, Smith SL, Williams W, et al. Improving access to care for women veterans suffering from chronic pain and depression associated with trauma. Pain Med. 2013;14:1010–20.CrossRefGoogle Scholar
  20. 20.
    Ben-Zeev D, Buck B, Chu PV, Razzano L, Pashka N, Hallgren KA. Transdiagnostic mobile health: smartphone intervention reduces depressive symptoms in people with mood and psychotic disorders. JMIR mental health. 2019(6):e13202.Google Scholar
  21. 21.
    Ratcliffe M, Burd C, Holder K, Fields A. Defining rural at the U.S. Census Bureau: American community survey and geographic brief 2016: Accessed 15 April 2019.
  22. 22.
    Benavides-Vaello S, Strode A, Sheeran BC. Using technology in the delivery of mental health and substance abuse treatment in rural communities: a review. J Behav Health Serv Res. 2013;40:111–20.CrossRefGoogle Scholar
  23. 23.
    Federal Communications Commission. Mapping broadband health in America. Accessed 22 April 2019.
  24. 24.
    Pisani AR, Wyman PA, Gurditta K, Schmeelk-Cone K, Anderson CL, Judd E. Mobile phone intervention to reduce youth suicide in rural communities: field test. JMIR Ment Health. 2018;5:e10425.CrossRefGoogle Scholar
  25. 25.
    Bauer AM, Hodsdon S, Hunter S, Choi Y, Bechtel J, Fortney JC. Lessons from the deployment of the SPIRIT app to support collaborative care for rural patients with complex psychiatric conditions. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers; 2017. p. 772–80.
  26. 26.
    •• Bauer AM, Hodsdon S, Bechtel JM, Fortney JC. Applying the principles for digital development: case study of a smartphone app to support collaborative care for rural patients with posttraumatic stress disorder or bipolar disorder. J Med Internet Res. 2018;20:e10048 Illustrative study of the development of a mHealth system to support collaborative care in rural settings. Identified five additional design principles that should be considered for rural settings.CrossRefGoogle Scholar
  27. 27.
    Unertl KM, Schaefbauer CL, Campbell TR, Senteio C, Siek KA, Bakken S, et al. Integrating community-based participatory research and informatics approaches to improve the engagement and health of underserved populations. J Am Med Inform Assoc. 2015;23:60–73.CrossRefGoogle Scholar
  28. 28.
    Rhoades H, Wenzel SL, Rice E, Winetrobe H, Henwood B. No digital divide? Technology use among homeless adults. J Soc Distress Homel. 2017;26:73–7. Scholar
  29. 29.
    Rice E, Lee A, Taitt S. Cell phone use among homeless youth: potential for new health interventions and research. J Urban Health. 2011;88:1175–82. Scholar
  30. 30.
    •• Schueller SM, Glover AC, Rufa AK, Dowdle CL, Gross GD, Karnik NS, et al. A mobile phone-based intervention to improve mental health among homeless young adults: a field trial. JMIR MHealth UHealth. 2019. Field trial demonstrating the potential to engage homeless young adults through a mobile phone-based intervention but did not find large impact on clinical symptoms. Demonstrates potential and challenges of mHealth for mental health within homeless populations.
  31. 31.
    Sheoran B, Silva CL, Lykens JE, Gamedze L, Williams S, Ford JV, et al. YTH StreetConnect: development and usability of a mobile app for homeless and unstably housed youth. JMIR Mhealth Uhealth. 2016;4:e82.CrossRefGoogle Scholar
  32. 32.
    Calvo F, Carbonell X. Using facebook for improving the psychological well-being of individuals experiencing homelessness: experimental and longitudinal study. JMIR Ment health. 2018;(5):e59.Google Scholar
  33. 33.
    Rozbroj T, Lyons A, Pitts M, Mitchell A, Christensen H. Assessing the applicability of e-therapies for depression, anxiety, and other mood disorders among lesbians and gay men: analysis of 24 web-and mobile phone-based self-help interventions. J Med Internet Res. 2014;16:e166.CrossRefGoogle Scholar
  34. 34.
    Lucassen MF, Hatcher S, Fleming TM, Stasiak K, Shepherd MJ, Merry SN. A qualitative study of sexual minority young people’s experiences of computerised therapy for depression. Australas Psychiatry. 2015;(3):268–73.Google Scholar
  35. 35.
    Seidenberg AB, Jo CL, Ribisi KM, Lee JGL, Buchting FO, Kim Y, et al. A national study of social media, television, radio, and internet usage of adults by sexual orientation and smoking status: implications for campaign design. Int J Environ Res Public Health. 2017;14:E450.CrossRefGoogle Scholar
  36. 36.
    O’Connell M. Pride in mental health: an interview with the Trevor project and crisis text line. 2017. Accessed 1 June 2019.
  37. 37.
    Crisis Trends. Crisis text line. Accessed 1 June 2019.
  38. 38.
    The Trevor Project. Accessed 1 June 2019.
  39. 39.
    Safren SA, Hollander G, Hart TA, Heimberg RG. Cognitive-behavioral therapy with lesbian, gay, and bisexual youth. Cogn Behav Pract. 2001;8:215–23.CrossRefGoogle Scholar
  40. 40.
    Lucassen MF, Hatcher S, Stasiak K, Fleming T, Shepherd M, Merry SN. The views of lesbian, gay and bisexual youth regarding computerised self-help for depression: an exploratory study. Adv Ment Health. 2013;12:22–33.CrossRefGoogle Scholar
  41. 41.
    Lucassen MF, Merry SN, Hatcher S, Frampton C. Rainbow SPARX: a novel approach to addressing depression in sexual minority youth. Cogn Behav Pract. 2015;22:203–16.CrossRefGoogle Scholar
  42. 42.
    Burns MN, Montague E, Mohr DC. Initial design of culturally informed behavioral intervention technologies: developing an mHealth intervention for young sexual minority men with generalized anxiety disorder and major depression. J Med Internet Res. 2013;15:e271.CrossRefGoogle Scholar
  43. 43.
    • Fleming JB, Hill YN, Burns MN. Usability of a culturally informed mHealth intervention for symptoms of anxiety and depression: feedback from young sexual minority men. JMIR Hum Factors. 2017, 4:e22 Demonstrates the use of standard technology evaluation methods, like usability testing, to engage sexual minority individuals in early formative testing of mHealth tools.Google Scholar
  44. 44.
    • Anderson-Lewis C, Darville G, Mercado RE, Howell S, Di Maggio S. mHealth technology use and implications in historically underserved and minority populations in the United States: systematic literature review. JMIR mHealth and uHealth. 2018;6:e128 A systematic review of mHealth technology among underserved and minority populations. Demonstrated established work in text messaging but less work in mobile phones and tablet applications.CrossRefGoogle Scholar
  45. 45.
    Franklin JC, Fox KR, Franklin CR, Kleiman EM, Ribeiro JD, Jaroszewski AC, et al. A brief mobile app reduces nonsuicidal and suicidal self-injury: evidence from three randomized controlled trials. J Consult Clin Psychol. 2016;84:544–57.CrossRefGoogle Scholar
  46. 46.
    Dennis TA, O’Toole LJ. Mental health on the go: effects of a gamified attention-bias modification mobile application in trait-anxious adults. Clin Psychol Sci. 2014;2:576–90.CrossRefGoogle Scholar
  47. 47.
    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. 2015;17:e72.CrossRefGoogle Scholar
  48. 48.
    O’Leary K, Schueller SM, Wobbrock JO, Pratt W. 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 2018:331.
  49. 49.
    Zou J, Schiebinger L. AI can be sexist and racist—it’s time to make it fair. Nature. 2018;559:324–6.CrossRefGoogle Scholar
  50. 50.
    • Aguilera A, Bruehlman-Senecal E, Liu N, Bravin J. Implementing group CBT for depression among latinos in a primary care clinic. Cogn Behav Pract. 2018;25:135–44 Discusses barriers and opportunities in providing mental health services to Latinxs, especially in integrated behavioral health contexts.CrossRefGoogle Scholar
  51. 51.
    Smith A, Anderson M. Social media use in 2018. Pew Research Center March, 2018.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Stephen M. Schueller
    • 1
    Email author
  • John F. Hunter
    • 1
  • Caroline Figueroa
    • 2
  • Adrian Aguilera
    • 2
    • 3
  1. 1.Department of Psychological ScienceSchool of Social EcologyIrvineUSA
  2. 2.School of Social WelfareUniversity of CaliforniaBerkeleyUSA
  3. 3.UCSF, Department of PsychiatryZuckerberg San Francisco General HospitalSan FranciscoUSA

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