All right. Good morning, everybody. Thanks for coming out. How do we get the slide to show up here? Excellent. There, it's working. Okay. So it's my pleasure to be here and to deliver this lecture. It was originally scheduled for tomorrow, but I want to thank the Academy for being flexible and rescheduling to today. The title of my talk is Physiatry is the OG in the Future of Precision MSK Medicine. So I'm going to start off with some big picture items, and then I'm going to focus down a little bit on why you believe I'm here lecturing today, and why I was bestowed with the honor of the Pastor Legacy Award, and I'm here talking to you today. I have some disclosures, but none of them are relevant to the items I'll be discussing today. I do want to say that I'm going to dedicate this talk to my sister who passed away at this time last year. It's why I didn't give the talk at the Academy meeting last year, because I had the fortunate ability to be with her last year during this time. So this is for you, Christy. All right. So PM&R and PASR timeline. PM&R was established, the AB PM&R was established in 1947. PASR was established in 1993. I was a first-year medical student in 1993 when PASR was established. Then PASR was disbanded, and the PASR Legacy Award created in 2008. Now it's 2023, but that's not a very big timeline, right? I mean, we're talking that this idea of PASR and the growth of musculoskeletal physiatry is a very recent phenomenon, but yet think how far it's come and how much we've accomplished during that time, and just project out into the future or where that's going. It's pretty, it's almost a little bit mind-blowing if you think about it. Innovation in musculoskeletal physiatry really goes back to the beginning, right? Like the physical medicine and the rehabilitation people that came together and created our field, they were already doing musculoskeletal physiatry from day one. I mean, it was really inherent into the development of the field. But things picked up with electrodiagnostics and PM&R's involvement in that space. There was a lot of growth in that area. When I first got involved in the society, electrodiagnostics was kind of what spine care and pain became in the 1990s to 2000, is what sports and MSK is now. There's this cycle of innovation that you can see happening in the society over time. It's almost like a peristalsis of innovation that happens within musculoskeletal physiatry, where we enter into a new area, develop new approaches and technologies, and it's expanding. But at the same time, people are saying, hey, maybe we need to be careful about how we're doing this, and then things contract down into a much more focused, but effective way of approaching things. But then the next thing comes along and the same process happens, and I've been involved in this long enough that I've now seen it happen in real time a couple of different times. It's in line with what the greater society understands about innovation. Gartner, which is kind of a leading group that thinks about innovation and a consulting firm, has a lot of their fingers in a lot of different things that have to do with innovation, and they've developed what's called the Gartner Hype Cycle, which is an explanation of what happens with innovation. That you have a trigger of innovation and all sorts of promise is perceived around that innovation because of all the potential that it provides. But soon after that, as the technology or the trigger is shown not to live up to all of these inflated expectations, you drop into a trough of disillusionment, from which you emerge into a plateau of productivity where the true value of this new technology is finally understood and put into practice. This is sort of this peristalsis that we see in musculoskeletal physiatry and the innovation that's been happening, and isn't stopping now. I mean, I would say right now we're probably definitely within the EMG world, far into the plateau of productivity, in the spine and pain world, heading into that direction, but new technologies are coming into that space that are following in this cycle, and in the sports and regenerative medicine area, probably still somewhere up in the peak, and haven't quite fallen into the trough yet, but that's coming. It doesn't mean that it's bad, it just means that then it'll focus down, and we'll find very good uses for some of these things. It'll be much better defined. So, what does this have to do with precision medicine? Well, it is precision medicine. The physiatry from the start is all about that. In fact, I found this online, a description about physiatry, and this person said that most medical specialties focus on acute management and stabilization of a pathology, like pneumonia or fractured femur, but PM&R also focuses on a holistic patient-centered care that addresses individual goals, societal circumstances, the living space, activities of daily living. Physiatrists customize treatment plans for patients based on these parameters, and what does that describe? Well, it describes precision medicine. Precision medicine is the right treatment for the right person at the right time. So, that is what physiatry has been about since the beginning. So, that's why I say we're the OG of precision medicine, because we really are. It's what we do, and it's what we've always done. So, with that, and that perspective on innovation at large, and innovation of musculoskeletal care, I want to then transition to talking about why I think I'm up on this stage, and the contributions that I've tried to make with advancing precision musculoskeletal care. So, these are just an overview of my attempt to contribute into this space. So, we'll start off by talking about my motivation. You know, what we know about the things that we treat, and how we discuss them with one another, and how we communicate with our patients is defined by the things that we measure. And in the musculoskeletal realm, there's really two things that we measure for our research, for our clinical outcomes, to monitor how our patients are doing, and that is their pain and their function. And there's a lot of interesting things happening in the space of improving how we measure pain, but we're going to ignore that for now, because I'm not going to get into that part of it. And instead, I'm going to focus on the part that's maybe more centered to our core in physiatry, and that's how we measure function. Thinking big picture about that, you know, I like to think about the way the ICF defines human function. And the ICF is the International Classification for Function. It's, you know, a large volume that goes really deep on this topic. It's a very useful piece of information for physiatrists to be knowledgeable in. And they describe different aspects of human function, but say there's two basic parts of human function. There's capacity and performance. And I have performance circled here for a reason. I'll get into that. Capacity is what happens under controlled circumstances. So when you do a physical exam on somebody and test their motor strength or test their tandem balance, that's a capacity measurement. When you're at the NFL Combine and you're showing how fast you are and how strong you are, those are capacity measurements. When you're at your job and loading boxes on the UPS truck, that's performance. This is what happens in your real life. This is your physical existence in the world. Or when you're the NFL player that's out on the field playing in a game, that's your performance. OK, so two aspects of human function, capacity and performance. What do we do when we try to measure function on most of our patients with musculoskeletal disease? We do a couple of capacity measurements when we do our physical exam. But we primarily focus on the performance piece. And patients generally come to us with performance concerns. When they tell us how they can't function or what functional impact their disorder has on them, they're talking about their performance. It's not unheard of. A weightlifter may come and tell you that they can't bench press in the way they used to. But generally speaking, people are talking about their performance. And then the things that we measure that we use in research aren't the things we do on our physical exam, typically. They're performance measurements where we ask people about their performance. Like, how much is your back pain impacting your ability to do this, that and the other? So, we measure the perceived performance of the individual through questionnaires. And in back pain, it looks like this. This is an assessment in the year 2010 of the different questionnaires, different functional questionnaires that have been used in back pain research. So, just ask people about their performance in relationship to their low back pain. That list should bother you because it tells you we don't really know what we're measuring, right? I mean, why are there so many things out there for this? So, all of that is to point out that there's a problem that we now have as a result of this approach that we've been undertaking for the past many decades. And that is these three things. Number one, that there's a sampling problem. If you really want to measure somebody's function and you're going to do it by questionnaires, that's just not feasible at scale. For the individual, it's problematic. And then for large numbers of people, it becomes problematic from a system standpoint. Number two is something I touched on already, and that's the precision of it. You know, how precise is it to ask somebody about how they're performing in life? Well, it gives you an idea of how they feel about it, but it might not reflect reality. Not only that, these questionnaires have significant floor and ceiling effects. You give the same questionnaire to two different people, depending on the questions that are contained in the questionnaire, it may measure very precisely what's happening with somebody, or it may have very imprecise measurement distinction between two different people. And then the third thing is the communication piece, which I mentioned to start this off, is that the way we talk about things is determined by what we're measuring. And that creates a problem for us when we're trying to take care of people, and it makes a problem for the people we're trying to take care of by not being able to understand how their decisions about their health are going to impact them in ways that are meaningful to them. So problem one's pretty simple. Let's talk a little bit more about problems two and problem three. So problem two, the measurement and precision, is illustrated by these two examples. So a patient comes into the clinic with chest pain. Let's say this is an urgent care clinic. Patient comes into that clinic with chest pain. They are approached in a certain way. Next patient comes into the clinic with low back pain, and let's say the level of pain is the same in these two patients. Well, the back pain patient comes to the urgent care clinic, and what happens? They say, what's been your experience with back pain in the past? How bad does it bother you? What medicines have you tried? Have you tried this or that? What things have you done previously to help with this back pain? How do you feel about those treatments? And what's your attitude about doing this or that? That's kind of the encounter. The chest pain patient sees that same provider. Do they have that same conversation? What's been your experience with this chest pain in the past? How do you feel about it? What medicines have you tried, and did you like them or not? What do you think you should do? No, that's not what happens for the chest pain patient. The chest pain patient gets an EKG, which gives you a quantifiable, objective measure of whether or not you need to be worried. If you need to be worried, then you go to the next step, order another quantifiable test in a lab that tells you how worried you need to be. If that's worrisome and they're going to give treatment, they go to the cath lab, where quantifiable measurements that happen in the cath lab determine what happens next. So there's objective measurements that are used to determine the next steps of care and predict what will happen to that person down the line if you give them that treatment or not. But that doesn't exist for the patient with the low back pain. What about the communication problem? Well, a patient with a herniated disc, I want you to think about this with me on a level of a really curious patient. And at Stanford, I happen to see patients that ask some pretty hard questions. They go deep sometimes. And any of you that see engineers in your clinic know what I'm talking about. And sometimes you'll have a patient that just asks really good questions. A lot of the time, we get away with using vague terminology with people. And for most people, we get away with that. But I want to give it, and I'll tell you what I mean by that. And then I'm going to give you the example of the opposite, where somebody is asking really good questions and explain what I mean by this system that we have in place causing a communication problem. So patient has a herniated disc. I've given them an epidural or two that's helped transiently. And it's time for them to have surgery. And I tell them, look, I think you need to go talk to my surgeon colleague to have this taken care of. The easy discussion goes like this. The patient says, oh, really? You don't want to do another epidural? No, odds are a third one's not going to help any better than the first two. You're really suffering at this point. The quickest way to get better is to have surgery. And surgery is very successful under circumstances like yours with your symptoms and your MRI findings. And the patient's like, great. Dr. Smook told me surgery is successful. I'm going to go talk to the surgeon. They have surgery. And in the end, they're probably better, right? Now, some patients don't have that conversation. Some patients will ask me. They'll say, OK, Dr. Smook, fine, I'll go see surgery. But you said surgery is successful. What do you mean by success? And that's where the conversation falls apart. Because what I mean by success is that odds are about 90% that they'll achieve the MCID in pain and ODI if you measured it before and after surgery, right? But what does that mean to the patient? It means nothing to them. That has no bearing at all on what they want to know. Because they want to know, am I going to be able to get on the floor and play with my kids? And if they ask me that, I can only provide anecdotal information. Like, well, yeah, I remember five years ago, somebody said that that was their problem, and they had surgery, and they were able to get on the floor and play with their kids. So does that mean you're going to be able to? I have no idea, right? Like, there's no way I can answer their questions about what they mean when they say, is this surgery going to be successful? And the only answers I can give are things that are meaningful to me in a very kind of scientific but very abstract way for what the patient wants to know. Now, there's an exception that proves the rule here, and that's if an NFL player comes in with that same scenario. When I tell that NFL player to have surgery, instead of saying that there's a 90% success rate from surgery, I tell the NFL player there's a 74% chance of success. And when the NFL player says, what do you mean by success? I say, success means you return to play once you've recovered from surgery, and your on-field performance is the exact same as it was before you got injured. Now, that answers the question, right? And why is that possible? It's possible because this NFL player's performance is objectively measured every week on the field, and it's measured before they got injured, and it gets measured after they got injured. And we can look back at NFL players that have had microdiscectomies and say, what's the likelihood of a return to play at the same level? And it's 74%. So either we need to find a way to measure the performance of regular people in and out in the real world doing their thing, or we need to get everybody signed up in the NFL so that we can measure them on the field every week. So one solution or the other, I'm going to go with the first one. And so that brings me to this part. The solution to this problem is what we measure. And the final piece of my motivation was in 2008 when I got my first iPhone, a lot of these thoughts were kind of happening in the back of my head. And when I realized that this iPhone had sensors and things in it that we could use to measure what people were doing, I thought, OK, finally, there's going to be a way that we can measure the performance of people in the real world in an ongoing manner, and let me see if I can figure out ways of doing that. So I looked into the literature to see, had people been using accelerometers to measure physical activity in people with musculoskeletal problems? In 2008, physical activity research was very robust. Accelerometry had been around for a long time. Prior to that, pedometers were used. And so there's a huge amount of research on physical activity at that point in time. I knew the iPhone had an accelerometer in it, and I thought they weren't using it to measure physical activity at that point in time. It was just used for games mostly. But it was obvious that that was the direction it would go. And so I thought, well, let's see how accelerometers are being used in musculoskeletal research. And at that point in time, there were five, six, seven, or eight published studies in the musculoskeletal realm that had used accelerometers. Every other study had only just asked people about their physical activity and didn't objectively measure it. And the results of those published studies were counterintuitive. And what I mean by that was that you know as well as I do, the patients that we see, they say, I can't do this. I can't do that. I have all these activity restrictions as a result of my knee arthritis or my lumbar spinal stenosis. But the studies using accelerometers showed really no differences in the measured physical activity of these disease groups and the controls that they were being compared to. And so looking at that and having this idea, I thought, well, there's either a problem with what people are telling us, either that people are lying to us, or the sensor is not measuring what it needs to measure. And so thinking about it, I thought, it's probably an issue with the sensor and decided to proceed forward with investigating things. Now let's pause for a second and just think about physical activity monitoring. Everybody here that has a cell phone, a smartphone, it passively measures your physical activity where you want it to or not. If you happen to wear a smartwatch, it does the same thing. But if you remember the early days of physical activity trackers, like when Fitbit first came out and all that was happening, like 2010, 2011, there's a lot of hype about it. It was on that hype cycle with the peak of expectations. And then rapidly went into a trough of disillusionment because it was reported very accurately that people that bought activity monitors would use them for one, maybe two months, maybe three months, and then stop using them. And why is that? It's because physical activity monitoring, just as a look at your physical activity, is not very sophisticated on the evolution of analytic technology. It's at the bottom end of the scale. It's just providing information. It's descriptive analytics. So you look at your activity monitor and you say, oh, well, I already knew this about myself. So what use is that to me? It's not really very much use to you. What we're talking about here is taking a measurement of somebody's physical existence in the world and moving it up the scale from just descriptive information about what happened to diagnostic capabilities and even further up the scale into predictive analytics. So to understand how we can use physical activity to do that, I need to describe just a little bit about how physical activity is measured. And what the state of the art is, even still today. So in physical activity research, typically a research grade physical activity monitor is used. Most commonly used one is called the actigraph, which is pictured in the top left here. And the actigraph is a triaxial accelerometer that provides data on accelerations in three axes, the x, y, and z-axis. And you can see a couple seconds of measurement next to the accelerometer there showing in green, red, and blue, the acceleration in the three axes in real time. And it's provided at about a 60 to 100 Hertz, so 60 to 100 measurements per second with accelerations in all three axes. Now, definitely in the early days of physical activity monitoring, and even really today in a lot of ways, collecting that amount of data is just too burdensome. You can't, you can't maintain a hundred, you know, 300 pieces of information per second on somebody 24 hours a day. So that data has to be compressed, and they compress it into time epochs, or what's called a count per something. And the count per minute is the most commonly used one, where all of those accelerations are are clustered into a one-minute measurement and given a score for that minute called the count per minute. And this next scale shows the count per minute measurements of an individual over a seven-day time frame. And the coloration on the on that scale shows the different zones of their activity, where the red is their sedentary time, the yellow is their light activity, and the green is the moderate and vigorous physical activity that occurred over those seven days. And so that's the state of the art, and you measure minutes of moderate and vigorous physical activity, you measure what's called a total daily counts, which summarizes the count per minute over a 24-hour cycle, you measure minutes of sedentary time, and these are the primary features of physical activity assessment, which turns out are pretty good for measuring things like metabolic demand, and was the reason they were developed, actually, to look at look at people's physical activity and how it influenced their fitness, okay? And they were validated in the lab, and these measurements in the different areas of activity, the different metabolic areas of activity, are all validated. But that's the type of research that was used already and not showing a lot of insight. Now if you take people with really severe spinal stenosis at the point at which they say, you know, I give up on my therapy, I give up on everything else, I'm ready to have surgery, this is just too burdensome for me, and you measure their physical activity, it's actually really poor. Only four percent of people with that degree of spinal stenosis at the point they're giving up and say, you know, I need to go have surgery, only four percent of them meet the 2008 physical activity guidelines in their normal lives. And that's very low compared to age-matched controls. Americans, about 20% of Americans in this age range will meet that 2008 guidelines, so four percent is way below that. In knee osteoarthritis, severe knee osteoarthritis, the rate's about ten percent. So you can see that lumbar spinal stenosis, severe lumbar spinal stenosis, has a huge impact on people's routine physical activity. But that's about the level of the insight you can get from that. So thinking that maybe there's an issue with a monitor and the way the monitor is measured, we said, well, what if we don't care about the metabolic equivalence of physical activity? What if that's not the dependent variable that we want to look at here, and what if we replace that with pain? And so how does pain influence physical activity, and does that give us a different way of segmenting these count-per-minute scales? And so we researched that question with a large database that was available and came up with different intervals that the pain measurements told us we should look for. And I won't go into the details of how this was developed, but the interesting part about this research was essentially this part here. So this is the normal segmentation of physical activity to sedentary, light, and moderate and vigorous physical activity. This is the segmentation when you ask, how does pain influence the way people behave? And you can see there's a greater segmentation in this light range of activity. And that made sense to me because patients coming to me with complaints about their abilities related to their musculoskeletal disease primarily describe issues that happen in the light range of physical activity. I can't play with my kids, you know, I have trouble doing, you know, unloading the dishwasher. Like things, normal things that normal people do are what they complain about. They don't say, you know, I can't go for my 10-mile jog. Now at Stanford I do get those patients too, but by and large that's not what most people are complaining about. So there was some face validity to that finding, and this gave us a tool where we could now start at measuring actual physical performance instead of just asking people about it. So then how do we use that tool? Well, we used it to look to see does it provide insight into disease mechanisms, and we're able to describe for the first time the role that physical activity plays in the relationship between back pain and obesity, which had been looked at by researchers several times before this study, but no, there was no observable role of physical activity in modulating that relationship and using, instead of asking people about physical activity, but actually measuring their physical activity objectively with this new performance measurement, we're able to describe that relationship in detail. We're also able to identify new targets for treatment, and this is where moving forward we have opportunity to develop new precision medicine tools because we can define thresholds of activity that have meaningful differences, and that creates a chicken-or-the-egg question. You can say, is this a consequence of the disease or is this contributing to the disease? And if it's a contributor, it's something we can rehab and make better. This study also showed that in patients that had surgery for spinal stenosis, the same individuals that had really poor physical activity before surgery, when you put them in the lab and did capacity measurements of them, you know, what's their balance like, how fast do they walk, and did capacity measurements before surgery, they were really affected. Their capacity was terrible, and their physical activity at home was terrible. After they had surgery, they go into the lab, their capacity gets way better, their balance is better, they're walking faster, they walk longer, much longer distances, but at home their physical activity didn't change. And that's our rehab story, people, right? Like why didn't it change? It didn't change because for years they've been beset with this lumbar stenosis problem that created terrible patterns for them that were just normal to them at the point they had surgery. After surgery, they could do things and feel better about it, but it didn't change their behavior at large. And so we need to do a better job of following these people and rehabbing them and helping them capture that capacity in a meaningful way in their lives. Maybe we also need to think about intervening earlier, excuse me, intervening earlier in spinal stenosis, because maybe it's bad for somebody to wait until they get to the point where they say, I give up, I just can't take it anymore. And maybe that maybe there's a way of doing a better job of helping these people live longer and better lives by intervening in meaningful ways earlier, and sometimes maybe that means earlier surgery. But now we have tools where we can answer these questions. We're also able to uncover biomarkers of disease, so can you take somebody's physical activity recordings just out in their normal life and then ask your machine learning algorithms to tell you, does this person have knee arthritis or do they have spinal stenosis or do they have neither? Well, it's really easy to tell the disease people from the controls. That's a pretty easy question, but telling knee arthritis from spinal stenosis is a challenging problem, but with greater than 70% accuracy, you can do that just by looking at the monitors and using data from somebody's activity during their normal life. And then also discover new targets for treatment, like I mentioned earlier. Thresholds where meaningful things happen and where we can intervene on people. You can also predict future function. So these were two studies. One looked at the patients with osteoarthritis. Well, they're both osteoarthritis studies. One looked at their baseline WOMAC scores and their physical activity, and then based on their physical activity, predicting their future functional WOMAC score. The other one looked at whether the activity monitor at baseline could predict their gait speed changes over time. And so again, measurements you can take from somebody in their real life starting to provide the ability to predict where they're going and what's happening to this person in the future. So now you can see, you know, we're talking about using physical activity monitoring in a way I call physical performance monitoring, because we're looking at a person's performance in their real life, and we're not using it as just a descriptive tool, but in ways that provide diagnostic capability with the biomarkers, as well as predictive analytics, and even eventually some prescriptive analytics, which is where you get into the real benefits from precision medicine. Now, just for a moment, I want to shift gears back to this ICF model, and we were talking about actual performance on the bottom left, but now we're going to talk about capacity a little bit and how capacity then influences actual performance. So by placing sensors in multiple locations, you can mimic what happens in a gait lab. And so you can make, start converting the real world into a gait lab and have more continuous measurements of what somebody's doing out in the real world. So a kinematic assessment of someone, not just an assessment of their volume of activity, but actual body kinematics. This busy slide, you don't have to read anything that's going on here, because I just want to point out a couple of things about this slide. This slide used sensors on people's shoes, so just what's happening at their feet, and we measured different spatial and temporal features of their gait from the sensors. And we looked at controls, people with NeoA, and people with lumbar stenosis. And on the graph on the left side, the spider web graph, you have controls in black, and they're the dotted circle in the center. And that control group is compared to the lumbar stenosis subjects in red, and the knee arthritis subjects in blue. And you can see that the disease subjects deviate from the controls in different parameters of gait in a very similar ways, which was kind of surprising to me. I expected to see bigger differences between how knee arthritis influenced gait versus spinal stenosis, but the one place where those two groups differed was in their gait variability. And the lumbar stenosis subjects had a much larger deviation in gait variability than the NeoA people. And the variability is not, in this setting, the variability is not a measure of how the right side versus the left side functions, because in knee arthritis, you can imagine, you know, somebody with unilateral knee arthritis is going to have that type of variability. This is talking about how their gait varied over time, and so it's like a motor control issue. And when you think about it, it makes total sense, because in knee arthritis, it's a mechanical problem. Their gait's affected by the pain, but in lumbar stenosis, it's a neurologic issue. It's dysfunction of the cataquina that's causing their pain and their gait instability, and so you would expect more variability in that situation. And so what we're seeing from this research is a signature of the mechanism of the disease, which again starts to provide opportunities for us to measure things more accurately. So all of these things, you know, very promising, but not very useful to us right now. Like, I don't use any of the stuff in my clinic currently. When I started this research more than 10 years ago, if you asked me would I be using it in my clinic now, I would have said yes, and I was wrong. In fact, I did say yes back then. I was like, we're gonna discover these things and do all this great stuff, and it's gonna be great, but science is harder than that, and that's been a good lesson for me over time. But there are exciting things happening now where it is merging together. There's apps being developed that provide ways of monitoring people and providing therapeutic input to them at proper points in time. You know, there's just recently I've talked to two different companies that are providing CBT for chronic pain in different formats, but through apps that make it available to people and affordable in ways that just currently isn't available to folks in the current health care setting. But that's an exciting part of management of a chronic disease that now is becoming available to people in certain ways, and blending these things together becomes very important because the stuff I'm talking about isn't by itself going to solve precision medicine problems. It's going to be one piece of the puzzle that allows us to develop an electronic signature of people that is similar to what we as physiatrists do, and that is consider a person in their real circumstances with their goals in mind and their baseline issues all taken into account to get them to where they want to go. And once we start to piece together objective measurements of people from multiple different arenas, we'll have much better answers about how to help people. And I'll pause for a moment here too to just talk about an experience I had, which I think is very informative. I remember in medical school deciding to go into PM&R, and one of the things that drove me into PM&R was the fact that it seemed like there was a lot of opportunity for research. Like when I looked at cardiology, which also appealed to me in some ways, I thought well that's kind of boring. Like, you know, the questions that they're answering, the research that they're doing, it's pretty easy. It really is. Like when you think about it, like cancer research, cardiac research, the hard part of that research is developing the new drug, right? But the actual science of it's pretty simple. Do you have a heart attack or not? Do you die or not? You know, those are really easy research questions. And what we are talking about here, the stuff that we've wrangled with since the beginning of our specialty, are a bunch bigger picture and involve multiple variables and multiple issues. And that's precision medicine. This whole move towards precision medicine is all about understanding bigger picture and doing the right thing for the right person at the right time. And so as physiatrists, we understand that as part of our DNA. And so we are leaders in this space and this is something for us to carry forward. And it's exciting that we've been working on it all along and continue to work on it now. I was asked to give a talk at a cardiology conference. The American whatever of cardiology was having their annual meeting in San Francisco at the Moscone Center. This is pre-COVID. And they asked me to come and give a talk. They said, Matt, why don't you come and talk to the interventional cardiologists about, you know, how to avoid low back pain when you wear lead all day. And I was like, okay, that's, yeah, that sounds interesting. So I went to the conference and put together this talk to them about back pain prevention. And I showed up to the conference early because I was like, you know, I've never been to a cardiology conference. I want to kind of hear some speakers and see what it's like so I can adjust kind of the tone of my talk to meet the audience where they're at. And it was amazing. I was sitting in the audience in this cardiology conference and these interventional cardiologists, almost the entire group of like four presentations that I listened to before mine, were talking about the need to measure how people feel about their care. They were like, we got the science down. We know how to save people's lives. We do the best care for all of our people, but our patients complain that we don't hear them and we don't understand their journey and that we don't know what things are like. I'm like, oh my god, like that's, like it blew my mind. I'm like, you guys are doing the opposite, exact opposite of what I'm doing. I'm complaining that I don't know how to measure what's really going on with my people or how to do precise things for them, but I know everything about how my patients feel. Like we got that part down. So it made me realize that, you know, this bigger picture thing, right? Like it's really exciting that we're focused on this precise measurement and finding better ways to measure what we do because the harder part of it we've already kind of figured out and we got working for us. So it puts us in a really good place to carry things forward. You know, none of this isn't all airy-fairy stuff either. This is really happening now in places where there are easier measurements and easier ways of approaching it. So a couple years ago we convened a meeting around this at Stanford and got together of multiple stakeholders in the healthcare space. Insurance people from insurance companies, patient advocacy organizations, hospital administrators, physicians, nurses, and we had a panel where we talked about the use of wearables in the healthcare space. And we reviewed two successful systems that were using wearables to better the healthcare of the patients in their systems. One was a diabetes management app that was developed through Kaiser and the other was a hypertension app and management tool developed by Ochsner, which is here in Louisiana. And we reviewed both of those apps and the way they used the wearables and how it was set up in order to distill out the commonalities between those two things and the things that we thought accounted for their success. Because both of these were being used at the time in the real world and both of them compared to routine care in the primary care clinic were doing better. We're getting better health outcomes by using these things than the routine process of care. And so if you're interested in this space, this paper is a really good read about, you know, how do you think about using technology in solving a healthcare problem and how would you want to, and if you want to build something, what kind of principles do you need to keep in mind in order to achieve success that we've seen in certain applications so far. And then because of my role at Sanford and because I've become known as a person interested in wearables, I have all sorts of startups and not just startups but the big companies in the Bay Area, the ones you've all heard of, that will come and talk to me and ask me to look at things that they're working on and say, how do you think this can be used in the healthcare environment? And it includes sensors that are measuring everything you can imagine, you know, the diapers that, you know, smart diapers used for elderly people to measure what's in their urine. There's all sorts of sensors that can be placed here and there. There's ingestibles. At a patient in clinic the other day that has chronic constipation and tried every medicine under the book and the one thing that finally worked for her is the little pill that she swallows that vibrates at certain times as it gets into her colon and stimulates it to allow her to have bowel movements. And it was the first thing that ever worked for her to treat her chronic constipation. So the ability to innovate in the space is almost endless and so if you have an interest in something or an arena, I'll tell you the engineers that are out there developing these tools are really, really, really smart people. They have no clue about what it's like to take care of real people and they need us to be involved in the process in order for these things to succeed. So don't undercut the value that you bring into a situation even if you have no idea what the technology really does. Your clinical insight to how it might be used is extremely valuable. So hopefully I've been able to convey that the little piece of this puzzle that I've been working on is hoping to solve some important problems and you know that we can solve a responder burden by measuring things passively from people where they don't even have to give their input about their physical performance. They just use their phone or wear their watch and we can collect that data in the cloud and use it in meaningful ways. Hopefully we can measure things that are important to people so we can answer their questions and talk to them you know about what the real-life implications are of the choices that they make. And then lastly I have a lot of people to thank. People that work in my lab. Robin Sun's the lead scientist. He's the one that does all the hard work of analyzing and processing the data from the monitors that we look at. So many excellent residents and students. I got started in this space because I came to Stanford to work on this and I was trying to figure out how to do it. I was meeting with different people at Stanford and one of our residents at Stanford walked in the door and said Matt what are you working on? And I'm like I'm working on this. He's like oh that's interesting. You know I had a PhD in biostatistics from Harvard before I went to medical school. You mind if I help you out with it? And I said yes and and Ming is the one that did the analysis of this large data set that produced the physical performance measures that I talked about in the beginning. And you know it's just a resident that happened to be at the program at the time at the right time. Other collaborators at Stanford that I've worked with in bioengineering and and the data science groups have been you know hugely impactful on the work that we do. And then colleagues that participate in the work that have encouraged me along the way and then and students that you know have contributed and have had a mutually beneficial relationship with. I just I can't say enough about how exciting and how much fun this whole thing has been. And then I also want to thank the Academy and the Awards Committee for noticing my work. DJ for your promotion of what I've been doing. I really appreciate it and thanks to everybody for being here.

physiatry

precision musculoskeletal medicine

physical performance

wearable devices

objective data

biomarkers of disease

tailored treatment plans

collaboration