Integrating and Optimizing Technology for Behavioral Medicine Interventions for Diabetes: An Interview with Chandra Osborn

Diabetes SIG Student Co-Chairs: Naomi Kane, MA; Maggie Crawford, BS; and Amanda Phillips, MS

The Society of Behavioral Medicine (SBM) Diabetes Special Interest Group (SIG) interviewed the 2017 Diabetes SIG Annual Meeting Coordinator Chandra Osborn, PhD, MPH, about using technology to deliver behavioral interventions.

Dr. Osborn is an assistant professor of medicine and biomedical informatics and the co-director for the Center for Health Education and Behavior at the Vanderbilt University School of Medicine. She is a social psychologist with expertise in health communication and behavioral informatics. Dr. Osborn has published on the Information-Motivation-Behavioral (IMB) model’s application to diabetes self-care, the relationship between limited health literacy and numeracy skills, nonadherence, and suboptimal glycemic control, and leveraging technology to promote self-care. Her current NIH funding includes an mHealth intervention for vulnerable patients with diabetes, and web-delivered interventions to promote medication management and adherence.

Diabetes SIG: How did you become interested in using digital health technologies in research?​

Osborn: My first iPhone sparked my interest in technology. Unlike any other device, the iPhone changed how we send and receive information, including health information. My career had to change too. As a health psychologist, I’m knowledgeable about what works in behavioral interventions, but because people’s expectations of how they receive health information changed based on this device, my intervention delivery methods had to change, too. Doing this work isn’t a choice; it’s now a requirement. We have to stay relevant, and flexible – meeting patients where they are and where they want to be.

Diabetes SIG: What behavioral theory is your research based upon?

Osborn: I use the IMB model, a hybrid theoretical model that integrates the most useful parts of a handful of other behavior change theories. For the past 30 years, the pathways of the IMB have been empirically supported across a wide range of behaviors and populations. In 2003, I was the first to apply the model to diabetes self-care. Now, all my technology-based interventions are grounded in the IMB model. However, having a theory-based intervention isn’t enough, so my interventions present information in literacy- and numeracy-appropriate ways.

​Diabetes SIG: Which diabetes self-​management behavior​(s) ​are you most passionate about affecting through technology​?

Osborn: I think all diabetes self-care behaviors are important, but I see medication adherence as the most influential behavior for glycemic control. There has been less focus on promoting medication adherence relative to the other behaviors, so my research aims to promote this, and, in turn, improve glycemic control, prevent complications and premature mortality.

Diabetes SIG: What sort of research and/or advocacy work is needed to reach disadvantaged groups who might lack access to these newer technologies?

Osborn: People who are sicker and more disadvantaged adopt technology faster, even though they have limited resources to obtain the devices. These people are often the hardest to engage in the healthcare setting, so leveraging technology to reach them can be very powerful. In our work, we have found no racial disparities in technology use, and that responding to text messages is as likely among Whites and non-Whites irrespective of age, gender, or socioeconomic status.

​Diabetes SIG: How can SBM members who are not Diabetes SIG members get more involved in contributing to this important work?

Osborn: Performing research at the intersection of technology, disparities, and self-care support has application across all behavioral research contexts. It isn’t restricted to people doing diabetes research. Behavioral researchers conducting self-care promotion research in other disease contexts should use technology to reach, engage, and optimize intervention utility among populations with the greatest need. It’s important work that needs to be done, and more of us need to be doing it.