The effectiveness of digital mental health interventions for marginalized populations

Schueller, S. M., Hunter, J. F., Figueroa, C., & Aguilera, A. (2019). Use of Digital Mental Health for Marginalized and Underserved Populations. Current Treatment Options in Psychiatry, 6(3), 243–255.

Summarized by Ariel Ervin 

Notes of Interest: 

  • Although digital mental health (DMH) can help make mental health services more accessible for marginalized people, there’s still a lack of research on the effectiveness of these interventions.
  • This article reviews several culturally-sensitive DMH interventions that are geared for marginalized populations (i.e. Latinx and African-Americans, rural populations, homeless individuals, and sexual and gender minorities).
  • This paper demonstrates the potential benefits of digital mental health interventions designed or tailored for marginalized populations, as well as the need for more large-scale efficacy, effectiveness, and implementation studies.

Introduction (Reprinted from the Abstract)

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 interventions 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.

Implications (Reprinted from Future Directions)

The state of the evidence and development of DMH interventions for underserved and marginalized populations mirrors themes that are present in DMH interventions more generally. Work has largely consisted of small pilot studies, many which have demonstrated feasibility and acceptability, but few which have demonstrated large-scale efficacy or scaling. For instance, a 2018 systematic review identified a number of pilot studies for digital interventions in underserved groups, but noted that these interventions usually did not followup with an implementation component [44•]. On one hand, this is not surprising, given that focus on underserved and marginalized populations often follows development of more general interventions as demonstrated by our examples of tailoring or augmenting interventions for a specific population. On the other hand, this is extremely disappointing given the discrepancy between the need and supply of available mental health services for these populations and the fact that following initial attempts provides the exciting potential to build and expand. Thus, we offer a few suggestions that might help accelerate and improve work that can increase the impact of DMH among underserved and marginalized populations.

One possibility for the future of DMH is to develop tools that do not require adaptation on the part of the developer to be relevant and appropriate for different populations. This could include language agnostic tools that leverage technology to create interactions based on clinical science that do not have to be conveyed verbally. For example, work has translated therapeutic evaluative condition [45] and attention bias modification [46] into mobile apps for widespread deployment. It is worth considering how much can be developed that does not require language especially when using digital media. Another possibility, however, would be to allow users of the DMH interventions to contribute to the intervention themselves. Some DMH interventions have leveraged peer involvement [i.e., 47, 48], and peers from different populations could assist in the delivery of content tailored to language or other differences on these general use platforms.

Another important step is increasing the transparency within DMH intervention as to whether individuals from different subpopulations have used that intervention and whether it has worked for them. The question most relevant to any potential user is not whether that intervention has an impact on average but whether that intervention will likely help him or her. Issues of the generalizability of benefits of DMH across diverse groups is key. Some of the work described in this paper, such as determining if DMH interventions contain content relevant for specific groups or having people from groups use an intervention to provide user feedback, is an important first step. But ultimately those responsible for reporting outcomes on the impact of DMH interventions should highlight this information.

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