A Call to Action: Using and Extending Human-Centered Design Methodologies to Improve Mental and Behavioral Health Equity

Abstract

Mental health disparities directly tie to structural racism. Digital mental health (DMH), the use of technologies to deliver services, have been touted as a way to expand access to care and reduce disparities. However, many DMH fail to mitigate the persistent disparities associated with structural racism that impact delivery (e.g., costs, dependable internet access)–and may even exacerbate them. Human-centered design (HCD) may be uniquely poised to design and test interventions alongside, rather than “for,” marginalized individuals. In employing HCD methodologies, developers may proceed with a vested interest in understanding and establishing empathy with users and their needs, behaviors, environments, and constraints. As such, HCD used to mindfully address structural racism in behavioral health care may address shortcomings of prior interventions that have neglected to elevate the voices of marginalized individuals. We argue that a paradigm shift in behavioral health services research is critically needed–one that embraces HCD as a key methodological framework for developing and evaluating interventions with marginalized communities, to ultimately promote more accessible, useful, and equitable care. The current commentary illustrates practical examples of the use of HCD methodologies to develop and evaluate DMH designed with marginalized populations, while also highlighting its limitations and need for even greater inclusivity. Following this, calls to action to learn from and improve upon HCD methodologies will be detailed. Acknowledging potential limitations of current design practices, methodologies must ultimately engage representative voices beyond research participation and invest in their active role as compensated and true collaborators to intervention design.

mHealth Uses and Opportunities for Teens from Communities with High Health Disparities: A Mixed-Methods Study

“…Consistent with a growing literature for the benefits of concurrent human support in the use of digital tools (Lattie et al, 2022 ), all participants expressed an openness to adults checking-in about teen mental health and making recommendations for resources (e.g., teachers suggesting an app). Approaching and engaging teens with SCN—and their caregivers—is likely supported through community-engaged approaches that include representative stakeholders as collaborators from design inception through open deployment with community teens (Noel, 2020 ; Stiles-Shields et al, 2022 ).…”

“…To become a more equitable delivery mechanism, mHealth developed with HCD must be re-examined from a lens of equity, inclusion, and anti-racism (Kendi, 2019 ). A first step in this process is collaborating with minoritized populations to better understand how mHealth may fit into their lives and serve their needs (Baum et al, 2006 ; Stiles-Shields et al, 2022 ).…”

Social media to promote treatment adherence among adolescents and young adults with chronic health conditions: A topical review and TikTok application.

“…In our case, simply inquiring about interest in a TikTok project led us to AYAs who were already very active on this platform. User-centered design approaches can be applied to design content with, rather than for, AYAs, which offers methodological advantages for addressing structural racism and promoting equitable design (Stiles-Shields et al, 2022).…”

Patient and clinician perspectives of a remote monitoring program for COVID-19 and lessons for future programs

“…Success of these programs hinges on usercentered design to enhance experiences for both patients and clinicians 22 as well as intentional design for traditionally marginalized groups who have not historically been considered as early adopters of new technology-based care programs. [23][24][25] Understanding how a diverse group of patients engage with and experience remote patient monitoring programs, and how providers integrate them into daily work ow and clinical decision-making will be informative for future remote patient monitoring programs. The datasets generated and/or analyzed during the current study are not publicly available due to the researchers not compiling or analyzing any datasets as part of this qualitative research, but additional information from the corresponding author is on reasonable request.…”