Reflections on the Values of Community-Based Participatory Research in Supporting Mobile Health Technology Use

See Article by Liu et al

Providers and researchers searching for innovative ways to improve patients’ health behaviors and outcomes may find mobile health (mHealth) technologies and wearable devices to offer novel solutions to persistent problems. mHealth can provide situation- and location-specific nudges that can offer personalized and timely reminders about behavior modification. It can deliver health promotion messages in a language of patients’ choosing. And it can generate a store of data that can give providers a more detailed understanding of how patients’ health concerns may be shaped by their environment and social ties. Yet although there is great potential to use technology to innovate how we deliver care and facilitate better health outcomes, we must also consider the broader social and political contexts in which these interventions are deployed—the social determinants of health (SDOH)—to appreciate more fully the kind of support mHealth needs to make sustainable improvements to community health.

In this editorial, written in response to Liu et al,¹ we offer reflections on community-based participatory research (CBPR) in the development and deployment of mHealth. The Liu et al¹ study examines how community health workers (CHWs) can help resolve barriers to the adoption of mHealth—in this case, an app that allows users to connect to their electronic health data, sync wearables, and participate in research studies—within low-income communities by providing participants with regular access to CHWs, who help to set up and manage the use of these technologies. Addressing what the authors identify as a gap in scholarship on digital health uptake, the study produces valuable data about how working with community members to document facilitators and barriers to use can promote wider adoption of these tools.

We commend the authors for their focus on facilitators and barriers as being central to building sustainable digital health platforms, for being attuned to patient comfort concerning language preference and data privacy, and for sharing their findings with the study’s participants post-participation. Where we part with the authors is in our belief that patients should be involved in the design of mHealth, that mHealth should engage more fully with SDOH, and that the values of CBPR should inform how we think about the sustainability of our interventions.

Engaging in CBPR calls on us to promote the following: equitable partnerships with community members in defining problems and seeking solutions; a focus on public health concerns within a local context; the dissemination of knowledge and sustainable support for our interventions; and a focus on how race, ethnicity, racism, and class shape health outcomes.² Research teams can prioritize communities with high need and work with them to develop interventions tailored to specific concerns. This can be especially useful in communities where significant barriers to health equity exist. The community-academic partners in the Liu et al¹ study included a community health organization, an academic primary care center, a digital health company, and perhaps participants, who designed participant enrollment procedures and surveys. In addition to community involvement into the research portions of the study, we think the study would have been strengthened by including community end-users in the development and adaptation of the app. Designing the app with the community rather than for the community may increase participant engagement, decrease the need for technological support, and ultimately improve the outcomes.

Likewise, we cannot ignore the environments and social contexts in which these technologies will be used. In our understanding, this means that CBPR interventions should engage substantively with SDOH when defining problems and designing interventions, in deploying these interventions, and in reporting findings. Documenting facilitators and barriers to digital health uptake is vital, but we think that the research could be strengthened by centering these values beyond the problem definition stage. We offer two suggestions for what this might look like.

The Liu et al¹ study accounts for how various concerns—including a lack of “fluency with mobile application use, limited health literacy, lack of empowerment, and historical mistrust of healthcare systems—can function as barriers in the adoption of mHealth.” We agree that it is important to account for these barriers, but we think that a CBPR approach should engage more squarely with broad structural issues, rather than mentioning them alongside concerns like a lack of mobile application fluency and moving on. By gesturing toward these limitations, we miss an opportunity to truly examine and work to intervene in the “economic and social contexts,” to borrow a phrase from the authors, that shape patients’ lives and health status. Using mHealth in these contexts without engaging substantively with SDOH risks creating an individualized solution to a structural problem.

Our first suggestion is to engage more explicitly with how SDOH may reflect policy decisions that impact patients’ lives and communities. In addition to identifying a lack of empowerment or historical mistrust of healthcare systems as threats to patient health, we could also directly address how these issues shape patient health in specific, local contexts. For example, this could involve working with scholars who study mistrust of healthcare systems and health policy decision-making to detail more effectively how these factors interact and to then develop novel intervention strategies. For studies that are committed to doing CBPR, this should also involve centering the role of race, ethnicity, racism, and class in defining these problems and in pursuing solutions. We are encouraged by the authors’ acknowledgment that patients’ economic and social conditions may limit the effectiveness of mHealth; however, we think we can all do more.

We might look to this report from the Kaiser Family Foundation that explicitly ties the use of mHealth to policy decisions as one example of how to refocus on the values of CBPR,³ but this is not the only viable approach. Looking to future studies, providers and researchers should think about new questions they could ask, new kinds of partnerships they could develop, and new priorities they could adopt. The hope of this engagement with policy decision-making and structural, rather than interpersonal, barriers is that, as providers and researchers who may have access to data and a measure of power that patients and underserved communities may not, we can do more than lament the persistence of these issues.

In addition to considering the role of structural determinants like policy decision-making as a public health concern, we think it is also important to consider the value of building a workforce infrastructure around mHealth. The Liu et al¹ study highlights the critical role that CHWs play in supporting communities vulnerable to persistent health disparities as well as the interventions we design to ameliorate these disparities. But we must be mindful of the kind of work we ask CHWs to do. In this study, for example, CHWs are tasked with recruiting and consenting participants, initiating the app and teaching participants how to use it, maintaining the mHealth technologies deployed as part of the study, and providing extra support services to Spanish-preferred language communities participating in the study. We wonder if their time and labor are best suited in managing research study recruitment efforts and, in particular, in the maintenance of mHealth devices. In addition, if we make this kind of community health care contingent on access to CHWs, and CHWs rely on our research funding to remain in operation, we may be creating difficult disruptions in care when our studies end.

Given that there is state-to-state variation in CHW regulation and funding is commonly through short-term categorical grants and contracts, remuneration for their work may exist only insofar as funding for mHealth studies does.⁴ Furthermore, when we ask CHWs to serve in these different support capacities, the pay they receive may not be commensurate with the labor they perform. We should be explicit about this. After all, if we are committed to understanding the economic and social contexts that shape the health of our patients, we should include the economic and social contexts of supportive workforces like the CHWs who keep these efforts afloat day-to-day.

Future studies could acknowledge these financial conditions and be clear about how they limit our efforts to provide sustainable interventions. Engaging more substantively with SDOH, they could generate actionable plans to support CHWs at the conclusion of these studies. And they could reflect on whether or not the work of mHealth technology maintenance should fall to CHWs or if is better suited for a new, yet to be defined, position.

Identifying and seeking solutions for structural problems like persistent health disparities is critical work. However, we must not be satisfied with acknowledgment alone. We should grapple with the fact that mHealth technology cannot be developed or deployed in a vacuum and that the economic and social conditions that shape patients’ lives cannot be managed with an easy fix. Overall, we applaud the work of Liu et al¹ as a positive first step and are excited to see their future work.

Footnotes