 It's my pleasure to introduce my colleague, the Vice President of Sports Medicine for the U.S. Olympic and Paralympic Committee, Mr. Dustin Nabhan. Dustin is a proud University of Arizona Wildcat and a proud Kofa High School King. He has been an incredible peer in creating innovative partnerships with regional medical facilities all over the United States and importantly identifying opportunities to support Team USA athletes in preventative medicine. The Associate Director of Integrated Health and Athlete Performance for the U.S. Olympic and Paralympic Committee and we cannot, we had a question about Dave online, we cannot divulge any personal information, I'm joking. Up next, Dustin and Dave from the U.S. Olympic and Paralympic Committee. Now Dave and I are going to talk about preservation of athlete health at the USOPC and tips for high performance teams who are trying to implement similar models. The USOPC developed a research arm for sports medicine, the U.S. Coalition for the Prevention of Illness and Injury in Sport in 2017. Dave or I have any relevant disclosures? It is a collaboration between USOPC Sports Medicine and the Seven Philippines Research Institute and University of Utah Health. Within this collaboration, here's just a snapshot of all those involved within our coalition projects. It's been a phenomenal initiative to be a part of integrating orthopedic surgeons, sports medicine clinicians, biomedical engineers and academic researchers at each of these sites with their own broad skills and experiences. We aim to protect the health of athletes through the facilitation and knowledge translation of illness and injury prevention research in the United States. By facilitation, we are aiming to collaborate with other experts around the country and bring our findings here in the US back to the IOC to enhance sports medicine within the global movement. The extent and backing of this conference by the number of enrolled attendees shows the powerhouse that we can be when we all unite and work together. We are fortunate enough today to display some of our current and recent collaborators. You have all been fantastic to work with and learn alongside and we encourage all of you with additional initiatives focused towards the prevention of illness and injury to come forward. We want to develop a respect for epidemiology amongst high performance teams. We're going to show that you don't really know who your enemy is unless you understand the true illness and injury incidents amongst a group you're working with. And we're going to talk about the role of screening and monitoring as athlete health protection tools and some of the limitations of screening and monitoring programs. We follow the Van Mechelen Sport Injury Prevention Research Model and for each step of Van Mechelen's model, we've developed some specific tools or products that help teams accomplish this step. For example, we built the EMR system for injury surveillance. We use PCR for exact diagnosis of illness. We've built something called the Athlete 360 Program and Elite Athlete Health Profiles. These are tools that help us identify risk factors over time. We measure our interventions and compliance with interventions through the Athlete 360 Program and use comparative effectiveness models to see what works. And we're constantly measuring illness and injury incidents in our teams. So repeating our epidemiology in a live fashion as the season goes on. So there are some experts that have an opinion that there is a, there's some polarity between health and human performance where what's best for health is not always what's best for human performance. We think there's some good data showing that human performance and health are actually coupled where if someone is healthy but they're more likely to perform better. There's some good studies from professional sport showing injury rates and then final places in standing showing that healthier teams perform better. And there's a pretty good study out of Australia a couple years ago by Ray Smith and Drew that showed that athletes with a training availability of more than 80% were seven times more likely to hit their performance goal during that period that was studied. Now we're going to go into detail on how we apply Van Mechelen's model for clinical and research purposes. The first step is determining the extent of the problem which is really accurate injury surveillance to understand the illness and injury burden in the sport you're working with. It's pretty well-agreed upon in sports medicine the best predictor of a future injury is a prior injury and we spend a lot of time looking at the measurement tools we use in sports medicine. For example the validity of a new dynamometer but we haven't spent a lot of time looking at how we record previous injuries. Are health histories coded accurately? Do people when they talk to an athlete do they give them to reveal their complete injury history? The little forms that we hand out at the beginning of the year or at the beginning of enrollment into a new program where an athlete writes out their health history or fills out a health history survey those actually collect important information. And so we took a little bit of time looking into this. There are a lot of methods available to us so there's a written health history form that we talked about. You can look through an athlete's medical record and you're working in a sport environment a lot of times athlete transition from somewhere else getting a complete medical record is difficult so you'd have to put together multiple pieces of the puzzle in order to get an accurate definition of what happened in the past. A patient interview where you sit down and talk to the patient about their health history could be effective but you're depending on the patient actually understanding what happened in the past. And then some sports have really great injury reports you might be able to pull data from that but obviously those are pretty competition specific and you're going to miss a lot of what happened through competition only reports. We believe the true health history lies somewhere in the middle of all these and I shouldn't say in the middle it's at the sum of these when all of these are aggregated into one place that's athletes true health history. We think most sport organizations probably don't do this well. Us included we're striving very hard to get there. And almost regardless of the method we choose when we talk to an athlete or give an athlete a form and we ask them to tell us what happened in the past we run into recall bias and there's some major problems here. So some good research has been done on this athletic population show the athletes can remember about 80% of the injuries that occurred and they can remember details on them with about 61% accuracy. In the general medicine psychology literature there are a couple of things on this so patients remember 17 to 60% of information they're told by a physician after a month that decreases to 11 to 13% and half of what they think they recall they've actually imagined. So when you hand that athlete a health history form or you interview him there are a lot of problems that we have to think about before we consider that to be the truth. This has serious implications not just in clinical medicine but in the way we design research projects if we really think the previous injury is the best predictor of future injury. So we actually took the time to assess this in our patient population because we're looking at our written health history forms. We had a hunch that athletes weren't actually telling the truth on those until we went back in time we looked at all the health history forms that we'd collected on patient intake and we compared it to the health history information that a clinician got during an interview during a pre-participation exam. We just tallied out the number of serious injuries that athletes reported. We defined serious as being the athlete was out for two weeks or more. We found that the interview tells you about four times more injuries than the questionnaire and four times that's a lot of injuries. And so we really think that we need to either spend more time making our written tools better so athletes are more likely to comply or we need to actually take the time to talk to our patients and sit down have a specific time set up to understand their health history and talk through all the nuances of what happened in the past code that information so it's in our medical record in an accurate manner. And now we have a baseline that we can work off that we can call the true health history for that patient. As a result of this we developed an application utilizing clinician-based decision tree logic to aggregate patient information quickly leverage patient-guided interviews to obtain additional information within the health record and athlete's self-reported information for time periods and with athletes are separated from their medical teams. These reports push notifications to clinicians allowing them to follow up with patients in a timely manner coordinate care for them when they are away and most importantly allow for accurate documentation into a centralized database. No longer are reports of cervical ridicule pain documented as elbow tendonitis because we have empowered clinicians to contact patients and obtain a correct diagnosis. When we eventually combine all these medical records and the athlete monitoring information under common vernacular we are actually able to understand the true burden of injury and illness on our teams. An example highlighting this methodology was within our elite track and field group. They're the best in the world and rarely do the demographics in public-sized literature represent their athletic capabilities. Although hamstrings are well documented we quantified hamstring injury as an area of priority as it accounted for 17% of our total injuries and 55% of injuries within the sprinters and hurdlers. And so our take home for this section is that injury surveillance is incredibly important especially if you're dealing with a rare population where the true burden of injury isn't really understood which happens quite a bit in Olympic sport and you've got to combine your different systems make sure they're all valid and get the data from them in one place so you can understand the burden of injury on your teams. We're going to transition to the topic of screening which is a little ironic because our keynote speaker who's also my PhD advisor wrote this pretty famous review why screening tests to predict injury don't work and probably never will. And despite that I can tell you Dr. Barr runs a great screening program for the Norwegian Olympic Committee and here at Team USA we also have a pretty good program that we've put together that we want to show you a little bit more about. So what a lot of people didn't understand when they read Dr. Barr's commentary is he wasn't speaking out against screening programs. Dr. Barr was speaking out against using a screening test to predict a future injury which has become kind of a popular topic. There are a lot of models, techniques, clinical systems that are branded as prediction tools and Dr. Barr is saying when you look at the data you can't define a specific threshold at which an injury is going to happen in the future and therefore prediction is not possible. However Dr. Barr's literature, he's studied screening programs extensively and he has shown that if you screen you're going to find things. A lot of those things are injuries or illnesses that are present and just haven't been worked up yet or they are things that are present and possibly subclinical and are affecting the athlete. And our program here which we call the Lead Athlete Health Program for Team USA is really based on these principles. But we also understand that what happens in a screening program is just a measurement taken at one point in time. So we built the athlete 360 program which monitors athletes over time to identify other factors that could put athletes at risk for injury. For example, we look at training load, wellness and sport specific external loads. We've worked with our partner Conduct to develop a data aggregation platform that helps us make meaningful use of all the data collected on our athletes. The Olympic and Paralympic sport are complex. There are a lot of tools that different sports could use. They have different cultures. Coaches want to see things different way or measure things different way. So we've had to be really flexible with what we've created. We call our screening program the Lead Athlete Health Profile. The EHP includes a history and physical exam. When we take the history, we code it so we understand the athletes true health histories we discussed before. We do a sports specific orthopedic exam so we measure range of motion, do some special tests and classify them. We do blood testing, things like iron and vitamin D. Take urine analysis to check hydration. We do some baseline concussion screening, ECG. We look for sickle cell trait in new athletes we've never tested before. We do a movement screen with 3D video and 3D motion capture. We do a sleep study on some athletes, optometry for vision dependent athletes. And at the end of that, we do specialty referrals for anything that's indicated based off the exam. We've built tools to visualize the data from the screening and show where an athlete sits on specific biomarkers as compared to similar athletes. Some of the tools allow us to prescribe specific interventions for findings in the profile. For example, when we test shoulder strength, we provide each athlete with a report showing the specific loads they must use in shoulder strengthening exercises in order to have an adaptation effect. We strongly believe one of the common faults in injury prevention programs is failure to match prescribed loads with athlete capacity. And these tools help us combat this. And we track the performance of entire teams on the profile. So if there's a trend towards a common deficiency, we can build an intervention designed for the entire team. Whilst health profiling provides one piece to the risk factor ID puzzle, our athlete 360 platform acts as a data aggregator. It primarily was built around athlete self-report measures capturing wellness variables and training load measures. As this platform has evolved, we've learned the imperative nature of feedback and the usability of the platform drives engagement. Our kiosk mode facilitates athletes entry and the feedback that they receive at the end of the survey as well as the reporting structure provided to our performance teams has helped us receive approximately 150,000 athlete responses and approximately 1,400 changes in health be it injury or illness reports. We can break these reports down from team level to individual level or even bespoke case reports answering questions from health performance teams. And from aggregating such a volume of responses, we've recognized that we were doing ourselves a disservice by aggregating the wellness variables together. In fact, the graphic on the right highlights that we probably miss just over 17 and a half percent of wellness flags if we do so. Instead, we've found that the wellness fits a two factor model. We've internally proposed that these be described as physical and emotional wellness. This model provides a better fit than a one factor within or one factor between model. In splitting wellness, our team has been able to run logistic regression models to estimate athlete risk of changing health status at any given time. Some of our stronger modeling has surrounded looking at an individually sequentially reducing wellness variable. Knowing that athletes have a one to 3% chance of changing from healthy to not, added increases almost seven to 14 fold if a wellness variable decreases continuously for four days straight. Through aggregation, we have been able to cluster athletes into pre-travel sleep patterns and additionally cluster sports with similar sleep behaviors to ultimately enhance our analytical pool. We've analyzed how each cluster responds to travel demands and resultantly their risk of changing health status. In this multi-level analysis, the bottom exponential curves display an individual's probability of getting sick based on distance covered in a seven day window. The orange line are athletes who obtain the average amount of sleep in that travel block. Each block is a level of 15 minutes sleep different from an athlete's usual with the blue line showing that when an athlete obtained more sleep, their risk of illness decreased and the red line showing the amplified risk of illness in those who struggled to sleep through that travel period. This presentation has highlighted the power of aggregating data for your sports specific population from health history information, medical literature, monitoring surveys and screening athletes, it's all relevant. Even so, we still don't believe that we can predict new injury or events. However, compiling all of this information provides a greater contextual picture to better understand the athlete and what they go through. Understand your population, intervene and reassess and ultimately make adjustments to ensure you're enhancing athlete care. We really remain excited about the future.