Video The Profound Positive Impact of CGM on Glycemic Control and Patient Engagement Play Pause Volume Quality 1080P 720P 576P Fullscreen Captions Transcript Chapters Slides The Profound Positive Impact of CGM on Glycemic Control and Patient Engagement Overview CONTINUE TO TEST Back to Symposium Hello, everybody. Uh, my name is Michael Wallis. I'm a health psychologist from Canada, and it's my pleasure to welcome you to the ADDT uh conference. And, um, I'd like to deliver a presentation that I hope you'll, uh, see connects directly to the presentation we just heard from Doctor Callier around um CGM use in those with GLP one, agonist Soport. And what I'm gonna talk about is slightly different, but it's a behavioral presentation. And I want to talk about how CGM is actually a behavior change intervention in and of itself. I'd like to convince you that it may in fact be a powerful behavior change intervention. And so, um, with that, I'd like to begin. These are my disclosures. And um I'd like to just anchor in the standard approach to the management of type 2 diabetes, and here you're seeing the ADA and EASD standards that really focus in on patient-centered care. The patient is an active participant in their care. This is a wonderful ambition. for us all, but we all recognize with type 2 diabetes that not all people are able to adhere to the recommendations made. As a matter of fact, the evidence suggests that it's very, very common, as you see from this slide, that many people struggle to adopt and maintain the lifestyle recommendations. I think that there's a reason for that, and I think technology can be the solution to that. We also know very consistent with the low level of self-management behavior, many people with type 2 diabetes struggle to achieve their targets, both targets when we look at it in terms of individualized targets and also targets when we look at our standard for less than 7% A1C. OK, so where am I going with this? And I'd like to just sort of stimulate your thinking a little bit at the beginning of my presentation by helping you to understand psychology. And one of the things about psychology that's important to understand is that the more work a person with diabetes unwillingly does to manage their disease, the lower their quality of life. Because you see from this perspective, you can see how the this work would be perceived as a burden. It adds to the stress. I have to do this. On the other hand, the more work a person with diabetes willingly does to manage the disease, the higher their quality of life, because the self-management behaviors are perceived as a solution to their problem. They reduce stress, they want to do this. So isn't this interesting? The same behavior can be positive or negative depending on the Engagement of the individual. So how can we get people living with type 2 diabetes to be more engaged in their care? And I'm gonna try to demonstrate this to you in a fairly brief way by addressing these three issues. First, The recognition, I think, long overdue that the demands for self-management behaviors may in fact exceed the average person's capabilities. Is it possible that those people living with type 2 diabetes who are doing super well are actually super capable? This isn't sufficient. We have to provide care for individuals that addresses all levels of capacity. I believe that continuous glucose monitoring devices actually function as a biofeedback device, and biofeedback is a very important behavior change strategy. And I believe that CGM can mediate behavior change by impacting on theoretical constructs that have been shown to be incredibly beneficial for health behaviors, and those are capability, opportunity, and motivation. So, let's begin. And I want to start by really helping us understand how the brain works. You're seeing a, a, a, a Nobel Prize winning psychologist by the name of Daniel Kahneman, who really helps us to understand that we have actually two brain systems that operate. We've got a fast thinking system that's on automatic and that operates from more of an emotional perspective. And then we've got a slow thinking system which requires a lot of energy, is not easy to operate, and fatigues very quickly. And so, there you can see this on the slide here. This is the challenge that health behavior really encourages us to reflect on, which is we have a hedonic system. What do you feel like doing? This is interesting because let's talk about what normal human behavior would be like. But then we also have a logical system, what we know we should be doing. And this language, I know what I should do and I struggle to do it, is extremely common in type 2 diabetes management, as I'm sure you're aware. As a psychologist, I can give you a very brief overview of, of the normal psychology of, of behavior, which is healthy behavior is abnormal. We recognize that the environments in which we live are deviant from the past. We used to be hunter gatherers. We used to have to work to get our food, so physical activity was part of lifestyle. Uh, uh, a, a, a sweet was a fruit, etc. etc. We live in a world now that makes health behavior actually very difficult to go to engage in and avoidance is the most common voping strategy. We also know that people don't follow recommendations, they follow their own beliefs. So somehow we have to really engage the person such that they come to view the value of the recommendations that we're making. And as we've just talked about, the emotional system dominates the logic system. And so this is a way of helping us to understand the experience of managing type 2 diabetes. I love to use these word cloud activities with people living with diabetes. And here's an example of the typical description of what it's like to live with diabetes. And when you look at these words, what you'll encounter very, very quickly is that these are not positive experiences. People don't want to be sick. So the easiest thing with type 2 diabetes, if you don't want to be sick, is actually to just pretend you're not. And so this allows us to appreciate that the challenges in diabetes management not only reflect hypoglycemia and hyperglycemia, as all of us in diabetes would be uh quite aware of, but also the lived experience of managing diabetes in the context of Life, what we call the mental health signal. And as we understand the importance of using technologies to help people with the mental health aspects of their disease, that will then translate into hypo and hyperglycemia management. But you and I know time is limited, and as your attention at diabetes goes up, your ability to focus on other life domains go down. And so this is a very important component. We can't just be recommending a set of behaviors for people. We have to take into consideration what are the other choices that they make. And when you think about diabetes tasks, this is an overwhelming study just looking at its results, how much work is involved in type 1 diabetes? And you can see, you know, when you look at self-management, 615 daily tasks. Here's a diabetes educator survey, basically where we looked at type 2, type 1 diabetes and asked how much time a day is required to do all that's being asked of. And just a quick reflection here can show you that you get what I'm saying, the amount of time required. It is very significant for each and every individual. And yet, how much time are we with our patients where we can encourage them, where we can support them, where we can help them navigate these challenges? Obviously, there's a huge disconnect here. And so how does continuous glucose monitoring become a behavior change intervention? And I think this one is actually incredibly beneficial for us to appreciate. Again, you're seeing the fact that many of our people who we work with who live with type 2 diabetes don't achieve the goals that are set for them. Is it any surprise? How can you hit a target that you can't see? And so this is where it becomes really, really important and from a method point of view. We often think about how technology will impact outcomes. So you think about, OK, CGM is the input. How does that affect hemoglobin A1C is the output. And we don't pay much attention to what goes on between. And in psychology, we call this the black box. You've got a, an activating event, and you've got a consequential outcome. Well, what goes on in between? And it turns out that what goes on in between inside this black box is actually quite important. My research has been to try to look at this black box and to ask ourselves, what's really going on here? And I'd like to introduce you to this strategy called biofeedback as a behavioral intervention. CGM can be seen as a biofeedback device for individuals living with type 2 diabetes that is the conduit to their excursions with regard to glucose, which is incredibly beneficial. I want to illustrate to you this is what's called the behavior change taxonomy that lists the evidence-based behavior change strategies and just to demonstrate to you that biofeedback is part of a whole collection of feedback and monitoring interventions highly associated with effective behavioral interventions, and there is literature here to support us of showing you a review of, of biofeedback as a behavior change technique in Um, in, in clinical medicine, and you can see that glucose, blood pressure, anthropomorphic measures are very common. The top three biomarkers used in biofeedback research is glucose, and diet and physical activity are extremely common behaviors that are incorporated. So you can see that the biofeedback evidence is very consistent with what we know and what we want to achieve in diabetes management. However, A1C and finger pricking doesn't provide enough feedback. Three months is too long. It's unrealistic to expect a person to test frequently enough to get a really good sense of where things are going with regard to the impact of physical activity or eating or any other form of intervention on their glucose outcomes. And so this black box allows us to link biofeedback to link CGM technologies to behavior change. How through this model called the Kombi model, which is the uh contemporary best evidence approach around how to support behavior change. Behavior change and behaviors result from capability, motivation, and opportunity. I was interested in understanding whether CGM technology can directly impact capability, motivation, and opportunity. And I'd like to share with you the results of some of this work that I hope will convince you that we're on to something here and the use of type of, of CGM technologies rather in type 2 diabetes, I think is something that's really burgeoning. Some of these technologies, we start with them and we think about what are the sort of most risky scenarios. So obviously, hypoglycemia and type 1 diabetes. But I believe that these CGM technologies aren't just there to keep people safe. I think they're actually motivational. I think they can actually be used to help people actually bring diabetes management much closer to their day to day behaviors. And the reason for that is because key to effective self-management. And remember, with type 2 diabetes, it's largely asymptomatic, and there's a lot of things to do on a day to day basis. This is not easy for people to do. So what we're really hoping to do is improve engagement, the active involvement in self-management behaviors, and also urgency, motivation to priorize self-management choices over other behaviors which are basically the activities of daily living. And so we did a series of studies, and the first study we did was a qualitative study. Um, and this was a study in which we, uh, wanted to understand the experience of people who we call game changers. In other words, we, in Canada, we, um, appealed through Diabetes Canada to ask for people living with type 2 diabetes who experienced CGM technologies as a game changer in their diabetes management. And then we wanted to understand what their experience was and we used that qualitative work. To identify items that would help us to describe the way in which sensing technology can actually improve a person's life. What did we discover? Importantly, we discovered a number of themes that underlie the experience of the benefits of CGM, and they include improved personalized knowledge, the perception of the ability to improve health, improve relationships both with healthcare professionals and in social relationships, and Monitoring for sensing devices as having tech challenges associated with them and directly improving motivation. Now, what's really interesting about these results is I hope you can see how they line up almost perfectly with the capability. Opportunity and motivation perspectives. And so we look at this and we see that the access to personalized glucose data, the trends, understanding how food and exercise impacts glucose levels, this technology provides the degree of feedback where the individuals were able to. Um, course correct based on the results, not because they had to, not because they were told to, but simply because the information that was showing them that their sugars were going high or going low was motivational for them. And this becomes extremely beneficial for them. And some of the quotes that we heard from our participants from a capability perspective, it made it much more real now that I can see the sugar levels move. I can really see how the ebbs and flows. You can really see how your diet, that your decisions affect everything, motivation. So it's come more to the front. I'm much more engaged, and now with this new technology, I feel like I've taken a turn that I never thought was coming. Opportunity. I really, truly believe that I'm in the center of my healthcare and everyone else is around me. Person-centered care described explicitly by our participants. So then what we did is we wanted to create a scale. Could we develop a scale that could measure those constructs? And this scale we call the impact of co monitoring on self-management. And what I'd like to show you is the results of this study and then where we can go with this. So the methodology involves um first taking uh uh an item pool of 42 items and then we reduced those items and validated those items. We did that um in Canada by enrolling just over 500 people living with type 2 diabetes and using CGM technologies. And then we compared, um, we used a number of scales and developed items that were valid and reliable. And let me just show you quickly, um, the initial 42 items were reduced to a set of 22. And so we have 7 items that tap into capability, both personalized knowledge and improved health. And you can see that the internal consistency estimates here at Cronbach alphas are really quite acceptable. Opportunity, we had 20 items, reduced those to 9 that talked about positive relationships with healthcare providers, social relationships, and their experience of the device, and motivation. So, We have a number of items that we believe can be of benefit. The reliability of the items was generally good, with one exception. You're seeing here the intraclass correlation coefficient where we were able to reassess the scale 4 months after the initial scale completion in a small sample of 130 participants. And you see that the test, retest reliability is strong for all scales except the device characteristic. This is really interesting to us because what it suggested is that um with retesting, that it was actually associated with increased positive experiences of CGM over time. So this is actually really quite, quite interesting for us. We feel that we need to understand these device characteristics and how CGM may be able to address these issues. With regard to construct and predictive validity, the constructive validity was a we, we, we compared this to the glucose, glucose monitoring satisfaction scale, and you can see strong relationships there. Although we're not tapping into exactly the same constructs there. You see that they're in the moderate range. Also, predict the validity and the, the scales of this new scale, um, we're able to predict diabetes self-management. And, uh, quality of life with some relationships, particularly around device, uh, characteristics with regard to diabetes, distress, and depression. So the scale seems to be able to, to, to pick up a construct that's different from what we already know and has predictive abilities associated with it. Interestingly, we kind of looked at who scores high and who scores low on these scales. And what you're seeing here are those who can endorse the capability items, they endorse the personalized knowledge items. So you get those that are scoring high on the scale. to those scoring low on the scale. Agree is high, disagree is low. And what you see here is in this sample of 500 people, the vast majority of people are reporting positive experiences of the use of CGM from a motivation point of view. This is the scale and we're in the process of translating and um working to develop next steps. And so the next steps we want to address are what aspects of self-management can this system impact, how can it impact behavior in the long term, and what about different patient groups and importantly, the relationship between improved experiences using technology and Improve biomedical outcomes. So this is where we, our work is going, and we hope that it will be of interest to you. So that's kind of the overview, but just before I finish, there's something I'd like to, to make a comment on, and I just want to ask us all as clinicians in diabetes to reflect on our own attitudes regarding technologies in the use of type 2 diabetes. And I think it's important because what we understand is that there's a disconnect between providers and persons with diabetes regarding a number of concerns that would be relevant to technologies, and you see here this discrepancy don't like devices on the body, nervous to rely on. Technology too busy to learn new devices, don't understand what to do with the information. These are much more common concerns for clinicians than they are for persons with diabetes. So let's be careful here because our beliefs, we don't want to necessarily filter who gets access to this technology. And the evidence, this is from JDRF 2020 survey, the evidence suggests that we are currently becoming a barrier to people considering technology. The main reason why people aren't using technology is because clinicians aren't recommending the technology. And so we should remove any of these hurdles where we, you know, put these barriers in place. You have to demonstrate that you're capable of using these devices in order to get access to these devices. I worry that that may be, um, a, a challenge for us. So we really need to consider who are the ideal users. And I really think that if you think about this flip, this could be super helpful, primarily because we know type 2 diabetes is moving from A1C to time and range as as type 1 diabetes. And there's evidence to show that technology use is not only increasing, but technology use is associated with improved control. All right, so that's my last caution to you is let's just beware, be careful, let's use technology for its best advantage to help patients access strategies for increased engagement and willingness and ability to act. Thank you very much. I hope this presentation has been useful. Published Created by Related Presenters Michael Vallis, PhD, R.Psych Health Behaviour Change ConsultantAssociate Professor in Family MedicineDalhousie UniversityHalifax, Nova Scotia, Canada
