Tools, Strategies, and Approaches to Improve Inclusions in Research

Join moderator Dr. Heather Whitson from Duke University School of Medicine to talk about Tools, Strategies, and Approaches to Improve Inclusion Across the Lifespan in Multiple Chronic Conditions Research with panelists, Maya Clark-Cutaia, PhD, ACNP-BC, from NYU, Hanzhang Xu, PhD, RN from Duke University School of Medicine and Barrett Bowling, MD from, Duke University School of Medicine. They also discuss the development of the 5Ts framework as well as other research frameworks that support inclusion in populations with multiple chronic conditions.   

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Transcript

Heather Whitson, MD: All right. Hello, I'm Heather Whitson. I'm a professor of medicine and the Director of the Duke Aging Center. And I'm here today with an esteemed panel to talk about Tools, Strategies, and Approaches to Improve Inclusion Across the Lifespan in Multiple Chronic Conditions Research. 

So one of my panelists is Dr. Barrett Bowling, an associate professor and colleague of mine at Duke. And Barrett, I was hoping that we could talk with you a little bit about some of the practical tips. And I know that you're a developer of the 5Ts Framework, and I wonder if you could give some of the practical tips that we can get from the 5Ts Framework when investigators are setting out to collect their own data for their research.

Barrett Bowling, MD: Yeah. Thank you for having me. That's a, that's a great question. When we think about primary data collection or collecting research data and including older adults or other populations, I think it's helpful to step back and think about it from multiple [00:01:00] perspectives. 

And when we develop the 5Ts Framework, we really kind of talked to research investigators and tried to think through what were their perspectives and challenges and barriers that they faced. We talked to research staff. These are oftentimes the frontline people who are actually interacting with participants. And really heard from them that they oftentimes learn by trial and error, that they don't have any kind of standard approach to addressing inclusion of, of older adults in particular, and really don't have a way to kind of share that: what they learn from one study to another study. And, and so it feels like they're reinventing the wheel from study to study. 

And then we also also heard from older adults who talked about participation really coming down to this issue of being motivated to participate versus the inconvenience or the burden and if they were really motivated to be part of, something, they were willing to kind of overcome lots of burdens and inconveniences. But if the motivation was low that they didn't want to kind of [00:02:00] deal with these inconveniences. And so those kind of perspectives really are what guided the development of the 5Ts.

And the 5Ts are the target population. Thinking about who these research data should be generalizable to when your final data collection is complete. Thinking about the team. Thinking about who is on that team. Not just the research investigators, which oftentimes I think that's where we kind of stop, but thinking about the research staff, all the people who will interact with a participant throughout the course of the study.

And then also thinking about the participant as a team member and their caregivers as team members. So a very broad conceptualization of team. 

We think about the time, which really gets into this whole inconvenience and burden, and what are the strategies to reduce that time burden, especially if in studies where the motivation may not be very high. We talk about specific tips to accommodate. And these are practical strategies like supporting transportation, thinking about hearing impairment and how that might, or hearing loss and how that [00:03:00] might impact someone's ability to meaningfully participate in planning for it.

What are the tasks that participants are being asked to do? And how would mobility limitations impact each of those? And can we tweak them or change them to address that? 

And then the last T is tools, which is around using tools to collect data on outcomes that are important to older adults or what matters to older adults.

And what we really heard is that one way to increase motivation for participation in the study is measure, have the study outcomes be things that are important to the population, to the- of interest and. So that might be function or other outcomes that are important. 

So that's kind of a, kinda a big picture way of thinking about a framework for walking through these different Ts to help your research team come up with then some of those specific strategies that you're gonna use to address each of those.

Heather Whitson, MD: Yeah, thanks. I think those are so helpful to use a framework like that. And since you mentioned frameworks, I'm gonna go ahead and introduce our other two [00:04:00] panelists. So Dr. Hanzhang Xu is an assistant professor of family medicine and nursing at Duke University. And also Maya Clark-Cutaia is an assistant professor of nursing and medicine at NYU.

And they both have a lot of experience with frameworks and in particular, I know in their module they give some examples of a couple of frameworks that are particularly useful to supporting inclusion in populations with multiple chronic conditions. So I was wondering if maybe we could, I could ask one of you to talk about the two frameworks and, and why those are particularly relevant to supporting inclusion in our population of people with MCCs.

Hanzhang, you wanna go first? 

Hanzhang Xu, PhD, RN: Sure. Absolutely. That's a great question, Heather, and thanks for having me here today. I think Maya and I put two different frameworks in our module one in the sociological model. The other is the [00:05:00] NIA/NIMHD health disparities framework. And we know there's a lot of frameworks that can help researchers to address the issues around health disparity and inclusion and inclusion in their research but we were particularly interested in these two. Is really not only these two frameworks highlight that the disparity we observed in ordered us with MCC are multifactoral, but also at multiple different levels. 

And another thing I really want to point out, especially because I talked more about the the NIA Health disparity framework, because I'm trying, as a social dermatologist, often to think about the trajectory of health status over time. So basically in this framework, not only highlight what factors are contributing to the disparity, we observe, but at what time point. So basically think about the cumulative advantage and disadvantage of across life course to a given house outcome that we're interested in studying and to think about how that affects the inclusion, like who [00:06:00] will be participating in your study.

Heather Whitson, MD: And Maya, I wonder if you wanna say more about frameworks or, or if you may even have examples from your own research where frameworks either came in handy or sometimes those cautionary tales, where in retrospect it was clear that a framework would've come in handy. 

Maya Clark-Cutaia, PhD, ACNP-BC: Sure. Thank you for having me today as well and to be a part of this opportunity.

I do think that the other framework that we had talked about a lot was the Minority Health Disparities research framework. It's very similar to that of NIA, allowing you to sort of look at different domains and across different levels of influence. 

In terms of a cautionary tale, we were attempting to recruit a population of minoritized women with multiple chronic conditions, and we chose probably one of the worst settings possible. Because it was very face-to-face, there wasn't a lot of privacy and it was in a clinic. And so not only did the patients feel as though they were being targeted because [00:07:00] we were walking up to them and just assuming that, you know, we were taking for face value what they appeared to be. So we were, for example, they appeared to be black women. And so we were talking to them and approaching them to participate in the study. But also, because of what their body habitus was. You know, we were assuming they had a certain chronic condition rather than the doing it a more rigorous way to determine a sample to recruit from.

And so it led to a lot of people feeling more marginalized and more isolated. And also really got some not so nice comments back about being targeted in an inappropriate way. And we, they probably will never participate in a research study because of the way they were approached in this particular instance.

And so having had an opportunity to really think about what environment would've been more conducive to recruiting these patients. How to have a more interpersonal conversation, but also protecting their privacy. How they may interact with the healthcare system in particular that. And so I think it's really reframed [00:08:00] how I discuss recruitment with my team, the trainings that we go through and how we use these frameworks to ensure that we're hitting all of those levels that we might be interacting with them on.

Heather Whitson, MD: Yeah. Thank you. Thank you for that example. And I think such a powerful example of also, you know, reminds us that some chronic conditions are stigmatized and, and sometimes in some cultures or some age groups more than others. And so, you know, that importance of that intersection, I think between culture and multiple chronic conditions, is something good for all of us to keep in mind. And these frameworks can really help with that. 

I wanna kind of pivot a little bit and ask another question, which has to do with, you know, sometimes we've been talking about how we can support inclusion when we are the ones collecting the data, or we are the ones approaching the participants.

But what about when we're dealing with data sets that are extant data, existing data and we're coming at it. What are some of the practical [00:09:00] approaches we can use to think about inclusion even when we're doing secondary data? 

Barret, you wanna take that one? 

Barrett Bowling, MD: Yeah, that's a, that's a really important question too. And you know, one of the advantages of primary data collection is you have control over your inclusion and exclusion criteria. You have some control to plan out what your recruitment strategies are gonna be. 

But a lot of times junior faculty researchers, and especially those that are working with multiple chronic conditions, use existing data sets. And those might be things like electronic health record data or large population based surveys, or Medicare or health insurance claims. And you know, the advantages of those are, the sample sizes are usually much, much larger. You have a wider range of people with multiple chronic conditions, especially if you're using claims or electronic health record data because they're collected for the purpose of knowing about diseases. And so that's really important for multiple chronic conditions research. 

Then the downside [00:10:00] is that you don't have control over the selection of those criteria, and I think the probably key understanding for using secondary data sets is really understanding what was the purpose of the original collection of these data. And if you're using secondary data from another research study, what was the purpose of that research study and what were the recruitment strategies used and how might have those approaches impacted participation and inclusion?

If it's claims data or health insurance data or medical records data, who are the people that are patients are that are kind of, and what were the reasons that those data were collected and what might be reasons that certain populations would've been excluded? And so I think that kind of first step is most important, understanding the reasons and the purposes of the data.

A strategy that we've seen that can be really effective then knowing the limitations of those existing data sets are, is there another [00:11:00] data set that you could link to, to then kind of fill in some of those gaps? Or that missing data? So I think first, once you understand what's missing in that first data set, is there some other source that you could use to kind of link or fill in or provide some of that missing information.

But that approach really does take a very thoughtful kind of step-by-step approach that's gonna take much longer than just someone handing you over a data set. But I, but I think the, the end result is really an opportunity to have a more inclusive data set and answer these types of questions that are really important.

Heather Whitson, MD:  Great. Thank you. Well, I wanna thank all of our panelists and again, I was here today with Dr. Xu, Dr. Barrett Bowling, and Dr. Maya Clark-Cutaia.

Thank you all and thanks for your wonderful work on the module as well.