389: 320,000 Dogwalks

Welcome to Bad Dog Agility, a podcast helping you reach all of your dog agility goals. Whether it's competing under the bright lights of the televised finals at West Minster, or successfully navigating a homemade course in your own backyard, we'll bring you training tips, interviews, and news about the great sports of dog agility. Are you ready? I'm ready. I'm ready. I'm Sarah. And I'm Sarah. And this is episode 389. Today's podcast is powered by Canine Handler Fitness with Liz Joyce, built for agility handlers who want to keep up with their dogs. With Liz's all-access pass, agility handlers at every fitness level can hop in right where they are and work towards their specific goals. From building strength and balance to boosting speed and stamina, the short effect of workouts target exactly what you need on course. Your dogs ready? Are you? Head to caninecandlerfitness.com to get started today. Today, we are very excited to be joined on the podcast by Ariel Peshett-Marcly and Abby Shobin. There are two of the authors on two different pieces of writing that have come out in the last week. One is an official paper that was posted about dog agility speeds and the other was a post, kind of an update post on the incident reporting data on contact obstacles that is kind of a multi-organization project to kind of get some real data about safety and injuries in our sport. So, you know, these things kind of came out, you know, one, two. And you know, both of them caused quite a bit of back and forth conversation on Facebook and things like that. And so we wanted to have them on today to talk about those two studies and kind of what they tell us, what they don't, what they're leading towards. And I know you all have been fielding a lot of questions on Facebook and we just felt like, well, this would be a great time to get you on to kind of those questions that keep coming up over and over again. We can answer them one time right here on the podcast and then you can refer people back here. So before I kind of kick it back over to you guys, I just wanted to let the audience know that Ariel is a board certified sports medicine and rehabilitation veterinarian. And Abby is a PhD in biostatistics. And I've actually worked with both of these ladies on some of this study information as kind of the data provider because we have this all of the AKC data that we have gathered for bad dog agility. So I'm also a co-author on the paper. But mainly I just hand over to Abby by data because she does, you know, how to work with this data at a level will beyond what I can do. So welcome to the podcast, ladies. Thanks for having us. All right. Well, let's start with, I guess for those who maybe haven't seen everything on Facebook, can you give us kind of a broad strokes overview of these two different things that have come out. And like, I guess at a high level, what the studies are their purpose and maybe kind of some of the big takeaways. Yeah. So I guess since you mentioned the speed data first, we can, you know, I think that one's a little bit more simple to talk about. So we can start there. The data that Sarah provided us was basically like a decades worth of data of speeds across like AKC competitions. And so what we did is we looked at trends in the speed by jump class, by preferred class through kind of multiple different analyses. And Abby can talk a little bit more about the actual statistics piece of it. But really the biggest take home from that study is that speeds have been consistently increasing over the last decade. But really, I think that that's the biggest take home. There we didn't look one of the things that was kind of floating around Facebook was how this was related to injury. And this paper didn't look at relationship to injury at all. We're just looking at speed by itself. And I think speed could be one of the many factors in injury. But this paper didn't do any sort of correlation or causation or anything with anything else. Right. So I seem to remember and please correct me if I'm wrong. So I'm going to kind of play the part of the non academic on this on this podcast. Like I've never been in that kind of academic setting, like writing papers and things like that. And so I think one thing that is interesting about this work is that it is like a true academic paper. And so I think that people when they're looking at this information, they have to take a little bit of that lens to it. Because when I write something for bad dog agility, I might find something that is interesting. I might have a theory about it. Here are my theories. And that's my prerogative as kind of a podcast host or as a blog writer or something like that. But there's much more of kind of a scientific underpinning to everything that you're doing. And one of the things that I believe I heard Y'all mention kind of over dinner sometimes when we were working on some of this stuff is that this speed study is really almost like a step one study. Like it is, it gives us a baseline on which future papers can then refer back to. So have I got that right? So that like, you know, people may be like, okay, fine. Yes, we've been getting faster. Like, you know, maybe some people are like, so what? Or they're hungry for what's next. But this is kind of that academic process. Like here's a baseline. And now another paper may refer back to this paper, etc. Yes, I would say absolutely. That's completely correct. That's sort of like one of the things like because it's an academic paper, it's sort of like everybody in a agility world. I think you could say dogs are getting faster. Nobody would bat an eye. They'd be like, yes, of course dogs are getting faster. This is done. So what? Like sort of from an academic perspective, nobody had actually looked at that and proven that. And so this was really to like prove that sort of see, I would argue to see what was happening over the past decade. And this like, in fact, showed that the dogs were getting faster, both sort of in an average trajectory and that sort of there was actually an even greater increase in speed among like the top 5% of dogs. So sort of the fastest dogs were getting even faster. And no one had really looked at that. So now when we want to write papers to say dogs are getting faster, we can actually cite that and not just sort of assume that it's like true just because everybody thinks it's true. Right. And one of the questions that I had about that specific paper or that came to me by email was somebody mentioned that it doesn't address breed. So like one of the thoughts is, are dogs getting faster or are we choosing faster dogs? Like, you know, all the people that started out with one breed of dog and then somehow found themselves with a border call, you know, like two or three or four dogs into their agility career. So I, so this is now a little dangerous because I'm trying to remember the data off the top of my head. But I, we did look at sort of the most common breed, sort of border callies being most common and then things like shelters. And those percentages, so again, this is a little bit of a weird data set because it's like only the qualifying runs. But those percentages seem relatively stable. It's not like we went from 10% of the qualifying runs in 2012 for border callies to like 30% of the qualifying runs were in 20, 22 border callies. It's really like more like 14% of the runs for border callies kind of the whole time. So like it's, there wasn't a huge demographic shift in breed that was driving this. Yeah. Yeah. And that's a really good point that you made about the data just to let people know that one kind of big hole in the data is that we do not have access to non qualifying runs. So there's already kind of a self selection happening in the data. Now hopefully, you know, we don't have a ton of dogs out there with like 0% Q rates. So we're getting, you know, some of it, but yeah, it is a big missing piece of the data. All right. So that was the speed study. And so the other one that kind of came hot on the heels is something that y'all are both a part of, but working with various organizations on this dogwalk safety kind of mega a collection project that's been happening. So for people who may not know kind of what went into that and then what is this first look at the data that we're getting? Yeah, so this started early last year and it was prompted once again kind of by the, you know, dog walk discussion on social media. And what we, the conclusion that we came to was that we really don't have any good information on what the incident rates are across different obstacles. And this is across all obstacles. It's not just the dog walk. Really the only study that we have was looking at tunnel incidents. And so we don't have anything on jumps. We don't have anything on contact obstacles. So to give us a better idea of what the incident rate actually is, we really, we needed to do a study. And the best way to do that was to try to get all of the organizations involved. And so we had a lot of back and forth discussions with organization, all the organizations in the United States, as well as some of the overseas organizations and developed a set of surveys to have the judges and trial secretaries fill out. And so this was started in what was it March or April April April of last year. And we collected data for six months and all US organizations participated. So AKC UKI USDA, ASCA, NADAC, CPU, I was like, oh, I'm missing the six one. There we go. And so we collected data for six months and the data that we put out last week was the very kind of tip of the iceberg of the data that we got from that. And really, you know, what we're looking for is the incident rate across the different contact obstacles. And I don't know Abby, how much more you want to talk about the study itself or whether, you know, we want to actually jump into some of the details of it. So I think I would just sort of say a big picture that one of the reasons we had to work with the organizations and sort of get the judges and the trial seekers to participate is that in order to get a real sense of the true incident rates, we needed reports on every incident. So this is like every we'll talk about how we define this, but sort of broadly speaking, this is when something weird happens on the contact obstacle. And then we also needed the denominators because we needed to know how many total runs that were. So that's why we needed both pieces of that. And that the data we put out was sort of overall average across all organizations. This was the full six months worth of like agility in the US. And we found these numbers that was like 2.1 per 1000 runs for the dog walk and then about 1 per 1000 runs for the Titor and 0.4 per 1000 runs for the 8 frame. So these are relatively rare events on sort of a per run basis, but the dog walk was had the most reported incidents followed by the Titor and then the 8 frame. Right. And so some of the comments that I saw, so you know, there's there's a lot of I think there's a lot of big feelings around around safety and around the contacts and around the dog walk specifically, but is like, how do we define rare? So I would agree on a purely percent basis, right? That 2% is like, you know, in a very dictionary definition way, that is rare. But some people didn't like the use of that word rare. So like, how would you, I don't know, how would you respond to that? And that actually would be 0.2% not 2%. That was going to be my first comment. That's you out of 1000, not 2.1. Oh, yes. Thank you. Okay. Yeah. Right. So it is this 0.2% number and sort of like the, which like per sort of dictionary definition or sort of like, if you Google like Gemini, I will tell you that like usually it's 0 to 5% is classified as rare, that sort of things that only happen. Like once every 20 times people are sort of like, yeah, that's kind of unusual. Like you'd pay attention when it happened. And we're like, well, well, less common than that. That's sort of this is really once every like almost 500 times is when this is happening. But sort of this like what I say all the time is sort of like rare events really do happen. It's sort of like if you get enough sort of observations, you will see it. So it's sort of like the 1000 runs, right? If you're going to a big trial and it's three days, you probably see 300, 350 standard runs each day. And so like on average, you would see sort of one incident on the dog walk in that or two incidents on the dog walk in that time for sort of like this 1000 runs is not that hard to get to when you're sort of watching a lot, a lot of dogs. And so sort of it's the, it is rare in the sort of if you just watch one one, you're very unlikely to see it. But if you watch like tens of thousands of runs, you're going to see some. I think people are like having, I think people are kind of maybe jumping ahead to what they think this is going to mean. And so I think that there's people that are scared that you came out with this data and it is just going to empower the organizations to say, this isn't really a problem. We don't need to do anything about it, right? And I mean, maybe that maybe they do say that, right? And maybe they don't. But I think that like I personally think that it is good to come at this from a scientific perspective and to have data and to have information and to treat this in kind of a serious academic way. And I don't necessarily think that like you said, it's rare, but it happens. And when we're talking cumulatively, we do see this. And then when you add in how many dog walks people do not in trials, like it does happen, I don't think that we can just jump from like, oh, two and a thousand means nothing's ever going to happen. Yeah. And I mean, I think that one of the things that people, like, I'm not sure that they realize is this was not meant to be an end-all-be-all study. This was not meant to be like, oh, here's the data. And we're just going to be done. Just like you were talking about with the speed paper, the, you know, academics in us, we have to break down research into very small bits in order to perform a high quality study. You can't study all the things all at once. You really have to narrow down variables, you know, study one thing, then use that as a good reason to study something else. So really what we were looking at is what was the incident ready for the three different obstacles. And then what do we need to do about that? So what do we need to study next to give us more information? Oh, this is just it and, you know, make all of your decisions based on one study and one piece of data. So then what, when we're talking about incidents, right, how did you define an incident? So, you know, where did that come from? So we define incident, and I know, I mean, we got a lot of pushback on Facebook about this was we defined it as any unexpected exit from the obstacle. So that didn't necessarily mean a fall, but it included all of the falls. It could be a teeter fly off. It could be a dog that made it part of the way up the up ramp on the dog walk and then decided, maybe my balance is off, I'm going to bail and not actually fall. And it was a little bit hard for us to figure out how to define this because we didn't want to include just falls because then that gives the judges more gray zone to be able to be like, well, was this a fall or was this dog purposely jumping off? And then they could maybe say, oh, well, that wasn't a real fall. That was just them jumping. And so we wanted to include the other kind of incidents plus if we're thinking about causes of injury down the road, any sort of exit from the obstacle, even if it's a bail and not a fall, could potentially result in injury. Now the question that I saw a lot on Facebook was, well, why didn't you include minor incidents like missteps? And I think that it's already a lot for us to ask the judges to give us all, you know, give us all of this data about incidents in general, but to ask them to try to watch for any little minor misstep while they're also trying to monitor the contact is kind of a bait ask. And it's not necessarily realistic either. A lot of times you know, these minor missteps aren't even seen in real time. They're seen on video review, or even, you know, more so like slow-motion video review. When we've done some of our other studies where we've done a lot of like slow-motion video analysis, there's some dogs that look completely normal in real time. And then when you're slowing it down and watching it frame by frame, you see a slight misstep. And so for us to ask the judges to try to make calls on that in real time, isn't really fair to the judges and doesn't really make our data super accurate. I think in order for us to really say, okay, well, we're going to evaluate all missteps across, you know, contact obstacles. We would have to have video analysis of all runs for that. That's what I was literally just thinking while you were saying that, I was wondering like, how close are we to being able to tell every judge, every trial, just like we had them do this, like you have to get somebody to volunteer their equipment to video the dog walk for every run and you just upload it to the cloud and magically some, you know, machine learning thing reads it and then tells you how many incidents there were and, you know, and all of that like, like, is that a possibility? I mean, someday for sure. Well, I mean, we're doing a lot of machine learning video analysis as per research group and it is not anywhere close to being there yet. Now, it could for sure detect if the dog jumped or fell off of an obstacle. But it is not anywhere close enough to being able to define minor like missteps or performance. So right. All right. Sounds good. Maybe in five years, we'll have that. But I mean, I wouldn't doubt it, right? Things are happening fast. Yeah. Yeah. Yeah. So really like when, you know, you're talking about, you know, asking for video analysis, then that's just me sitting at a computer watching everything, brain by brain by brain, brain by brain. And I have, we have enough of that in our regular research that trying to think about 300,000 runs, which is what we had in this data set. I'd be doing that for the rest of my life. Right. Right. Yeah. That's why we got to wait for the technology to catch up. Right. So that explains, you know, why? Because I know several people mentioned things like, you know, the foot coming off of the edge and things like that as being things that weren't captured. But it's, it was just kind of like, by design, like, let's capture what we can reasonably with good clean data capture. Right. And see what that tells us and know that, you know, there are other types of incidents that, you know, that we are purposely excluding from the study. Yes. I mean, I would say the other part of this is to say that we prioritize getting more of sort of the bigger incidents, the where the dog actually comes all the way off the contact by sort of doing this over all organizations and every trial sort of thing so that you could have up, I mean, we only had like 1200 total incidents across all three contacts. And that was with the like 300,000 plus runs. And if we wanted to sort of look, given the constraints of like, we can't just watch video forever with our little robot friends, like if we were going to look at sort of the slips, we would have been doing it on a much smaller set where like we might have had one or two where the dog actually came all the way off the contact. Right. And so then tell me about the injury data because then that's the other piece. So there's the incident data and then there's injury data. So, you know, where or what were the, I guess, constraints and why were those constraints chosen on the injury data side? Yes. So I would say that that is a huge constraint. And that was never the goal of this study. So I just I want to make that pretty clear because I think there was a little bit of confusion about that as post kind of got shared around is that the goal of this was always just to capture incidents. We asked the judges to provide a lot more information than just the incidents. And so this is a piece that we'll be coming out later. So we asked them to provide things like what they thought were involved with the incident. You know, was it, you know, you know, entry line error? Was it surface? Was it lighting? Was it, you know, we asked them all sorts of, you know, things around the incident. We also asked the trial secretaries to provide, you know, surface equipment manufacturer and a lot of that information. And so one of the pieces that we asked the judges to provide was whether they thought there was any apparent injury at the time of the incident. That was it. Like that was the constraint right there. And so most people, you know, on Facebook were like, well, you don't know about injury unless you have follow up and absolutely that's true. Like this is just apparent, which really is kind of catastrophic injury, right? Like you're really only capturing the small percentage of dogs that have really severe injuries that they're obviously lame at the time of incident. You're not capturing what the majority of injuries, which are the kind of mild to moderate soft tissue injuries because those are usually apparent later. You know, whether that's after the dogs been created at the end of the day, the next day, the next week, these are kind of these chronic, like low grade, you know, tendon and ligament injuries that we're not going to capture in this data. And that was never, never our goal. And so I think that when people are talking about this data and then trying to argue that there's a very low injury rate, we absolutely cannot say that. We can say that there's kind of a very low kind of catastrophic injury rate, but that doesn't tell us about the actual injury risk overall. Right. And kind of going back to what I said before about how I think people are like jumping two steps forward and anticipating how people are going to, for lack of a better word, weaponize your studies, right? So you understand the constraints. Do you feel like in talking with the organizations that they have a good understanding of what it is that you all are finding and what it means and what it doesn't mean. Like have they been very receptive to, you know, to basically all of your data, but also all of the constraints on what it can really tell you. I mean, I mean, Abby can chime in if she wants to, too, but I mean, I think that we've had a lot of really good discussions with the organizations. So we met with the organizational heads kind of all throughout this process. So as we were formulating the initial surveys, as we were designing the questions and deciding what questions to even ask the judges, all the way through kind of the initial data analysis. And so we had a meeting with them a couple of weeks ago before we released any of the data on on social media. And we'd kind of given them an executive summary, then we met with them kind of went over all of the data. So they've been kind of very intimately involved with the whole process and understand the limitations. And then they will also be involved as we finish writing the paper for publication. They'll be able to review all of that now that just to clarify that before anybody gets all, all, you know, upset about that. They won't have any ability to say don't you can't include this data because that's, you know, a bias that obviously we're not, you know, going to have in our data. But we do want them involved in understanding kind of the data process. And the conclusions that we're making in the paper that we're going to publish. And so do you know and you may not know or you may not be able to say, but do you know what they're thinking in terms of when they're going to make any sort of determination about any kind of change or publicly saying that there will be no change based on this data. Like to do any of them have timeframes and then how involved. I mean, I assume that you wouldn't be involved in the decision. But would they have the two of you in as part of the discussion to continually be there kind of clarifying, you know, what the data is telling us? Okay. I shouldn't. We haven't talked about that at all. That has not been a discussion. I think, you know, we have a lot of data. So what we released on Facebook was just a very small snippet of the data. And I think that that also makes it really hard for people to gather conclusions or easy for people to kind of misinterpret. And that will wasn't necessarily by design, but going back to your initial comments here about the academic piece of it is that when we submit a paper for peer-reviewed publication in a scientific journal, they specifically ask if the data has been published anywhere else. We have to be very careful about how much data we write up and release in advance of us actually submitting the paper for publication, which is why we kind of just took the top highlights from the data and didn't give people a lot of additional context because that will come in the paper. And then, so there will be a lot more information in that initial paper that talks about the incident rates because that will break it down by organization. We also asked more details about the incident where the dog came off on the obstacle and then kind of the next data set that we're already working on is, you know, for maybe a better word like risk factors, which maybe isn't the best terminology for that, but that's all of the information that we got from the judge and the trial secretaries about surface, equipment manufacturer, what the judge thought, you know, might have been involved in the incident. And so that will be another set of data that will hopefully have kind of a public facing announcement, maybe like middle to end of May-ish. And then that will be another paper that gets written up and submitted for publication. And so I would assume that the organizations aren't going to make any decisions until after all of the data has been like fully analyzed and released. One of the other projects that our research group is working on is looking at analyzing videos of dog walkfalls. So, you know, this data that we've been working on is all three contact obstacles. They're working on looking at videos that that handlers have sent them of falls off the dog walk to see if there are any patterns that can be found regarding that information. And so I'm not sure what the timeframe on that particular study being done, but I think that would be another important piece to take into account when making a decision. And so I guess one thought that I have on the research side is, I guess I'll play a little bit of devil's advocate. So I, like I said, I personally am a big fan of us doing research. I think it's really important. But I could definitely see the perspective of somebody who felt like, well, we can always do research, right? There's always another question, right? We could look, we could, you know, and paralysis through analysis kind of thing, right? We could analyze and never actually act or make a change or anything like that. So I guess how do you balance the academic piece with the, let's draw some conclusions and let's, you know, maybe do something piece? But I would say a couple of things. And then I'm sure area will have some more smarter things to say. But the, I think a couple of things. One is to say that like one of the reasons that that I was really excited to do this particular study was that sort of there was this perception that sort of like that this was a big problem and that like lots and lots of dogs were, were falling off the dog walk and having these like catastrophic injuries. And like if that happens to you, like it sucks, like, right, like really bad things do happen. And we wanted to sort of quantify like how often is that actually happening? Because I think if we had found that this rate was much higher sort of on the order of like 10,000, so sort of a full 1% of runs were having these sort of big dramatic falls. And I think that would be really big impetus to be like, okay, like we got to do something like this is a lot. And sort of, but the flip side is sort of if we had found that it was actually much less that if we had found sort of a rate of like one and every 10,000 runs, so it's like it was really, really rare. The dogs are falling off the dog walk, but like the rate for the a frame was much higher than that would have sort of more clearly to me said like we actually should be looking at the a frame over the dog walk. And I think we're kind of in the in between stage, we're sort of like we, the rate is relatively low, caveat with all the discussion about sort of rare. But like the dog walk is where we were staying the most incidents relative to the other two contacts, so sort of it makes sense to sort of think about the dog walk if we're considering it compared to the other two contacts. And the only other sort of incident data that we have is the tunnel. And so there was a study that looked at rates of sort of delayed exits or slips out of tunnels and found that that was like 15-ish per thousand tunnel. So not even runs, but tunnels. So if you figure that the dog does the tunnel sort of twice in a course, it's actually 30 per thousand runs. So it's actually much, much, much more common than a dog walk fall that the dog sort of has a slip in the tunnel. And to me, that says like if we're actually sort of thinking about dog safety, we're not going to sort of abandon the dog walk, but maybe we should spend a little more time sort of thinking about tunnels and what is leading to sort of the slips in the tunnels that could be causing also could be causing lots of injuries. And sort of that's, that's I think where I am is sort of like we should be spending our time like thinking about where we're going to get the biggest sort of improvement in overall safety, not necessarily just sort of like trying to reduce the one in, you know, a lot chance of something really, really, really dramatic happening and sort of to go from one in a huge number of chance to like a 0.5 in huge number of chance like for a ton of like cost, both sort of financial and opportunity cost because that's time not spent looking at other potential safety improvements to me seems a little short-sighted. That's super interesting. And I think the first time I'm popping in and saying something, you know, I'll start anecdotally just my experience with dogs. I've had, I think all of our dogs at one point or another in practice or trials have fallen off the dog walk in various points of the dog walk. And I don't recall anyone sustaining a serious injury or even a non serious injury. I have had dogs slip in the tunnel like we know they slipped in the tunnel because you can see it on the video and you can time the tunnel. And I've had them have these like nagging, bad soft tissue injuries. Of course, that's a very small sample size just my own personal experience. When I read about the tunnels, I was shocked, right? So I think it's pretty interesting that you have the dog walk more so than the teeter, more so than the a frame, I think that's intuitive to a lot of people. It's something that makes sense. And then the tunnel kind of comes out of nowhere. One of the things I like to say about the tunnel though is I, and again, this is just my own knowledge, I am only aware of one dog die on course due to equipment issue. And that was obviously in the tunnel, the cinch to tight and issue with the neck. A larger dog. I think I'm one of those very rare people that think the tunnel is a little bit dangerous, right? Because of the falling and the slipping, but also like the aperture of the diameter of the tunnel itself. I wonder if tunnels were clear if we would have a lot more people, a lot more upset about tunnels because you would see what's happening. We all have known those times when the dog doesn't come out when you expect. And sometimes we see them slip on the way out and we're like, oh, but I bet if we could see everything that goes on in the tunnel, we would be appalled. Well, I think this is where it gets interesting because there's the science of it. Science takes time and there's, you know, there's only so many. It's very important not to generalize your findings and have these amazing conclusions that aren't really being supported by the data. But as I think everyone's kind of pointed out and Facebook has shown us, a social media has shown us, you know, people are definitely jumping ahead, jumping ahead several steps. And you know, there's there are people who are like, coming into this, we need to get rid of the dog walk. There are people who are like, no, we don't need to get rid of it, but it needs to be wider or lower and so on and so forth. And yeah, so the tunnel thing jumps out to me and now I'm all like, well, let's do something about the tunnels. Right. Well, you're really about to say. You know, is like, what about when we combine the tunnel before the dog walk? Right. So we're in theory trying to control the dog's line out of the tunnel to give them a straight approach to the dog walk. But if you watch enough videos, that is definitely not always what happens. Right. Especially if, I mean, in rare cases, if the tunnel is like shifting or moving or things like that. But even if the approach looks straight, it's not always a straight approach for every dog. And if the dog comes out of the tunnel on the wrong lead and then has to change leads on the approach to the dog walk or even on the up ramp of the dog walk, that can significantly affect their balance and their ability to. to complete the dog walks safely. So, you know, maybe it's even a combination of high incidence with the tunnel and that how that affects the approach to other obstacles. And I know that one of the researchers that was involved with that tunnel study was commenting about like how the dogs exit from the tunnel was not always in the line that was expected. And that was particularly apparent when there was a dog walk afterwards. And so maybe, you know, and I'm just kind of throwing throwing this out here as like my non-research paper writing person because I can't support it with any data. But that would be a much easier, safety thing to implement, right? Rather than change the whole dimensions of an obstacle, if we can say like, okay, this, you know, tunnels, this shape or this line to the dog walk, have a higher incidence rate, then we can change the course design. That's a much easier fix for safety than overhauling like obstacles completely. - Sure, sure. Do you have that data? Do you have the course maps? - So, if we fed it to, I don't know, the AI, we got some machine learning going here that you could say, oh, look, it correlates highly with these approaches and these obstacles before the dog walk. And now you eliminate them. And suddenly, your fall rate is less than that of the A frame. - Yeah, I wish. That would be so amazing. We did talk about when we were originally designing this study about whether we should have the judge provide the course map for the course where the incident happened. But Abby from her statistician side of things was like, well, that doesn't do us any good if we don't have that denominator. So we would literally have to have the course map. - For everything. - And all of the runs, you know, that were part of the study to be able to say truly this particular course design increased the incident rate. Because yeah, maybe a lot of the incidents happened with a tunnel before the dog walk, but there could be way more tunnels before the dog walk where incidents don't happen. So unless we have all of the course maps, we can't make any definitive conclusions. And once again, the AI's not quite there yet, we've also been working on trying to do that, have AI be able to read course maps so that we can do that and feed it, you know, thousands of course maps to be able to get that kind of information, but it hasn't been quite as straightforward as you would think. - All right, 2030. We'll try it in 2030. - We'll come back. - I don't know if it'll even take that long. - I don't know if it'll even be that long. - But it's a nice round number, so. - Sure, I'm sure. - I would hope that it's before then, but. - All right, right. - So I have another question. So I'm not sure if you had like an order of topics, questions. - No, don't worry, jump in. One question I think that a lot of people have is how our organization is going to react to this study, right? And so I think everyone has publicized the data, right? The snippets that you have put out. But I do wonder, and right now they're like, okay, it's not over yet, guys. So I think organizations are kind of sitting on it, right? They're gonna say, okay, and the interest of science is there's more to come. But I think rationally, when I think about it, organizations behave in ways that are good for the organizations. And so, you know, not to be cynical about this at all, but like, if we were truly interested in dog safety, I am asking a lot of questions about the tunnels, and I'm gonna put that out here in the podcast. It's the obvious thing that jumps out to me as a pseudo scientist, 'cause certainly I don't have a PhD in statistics, and I struggled in the statistics courses required in my master's programs and med school and all that kind of stuff. And that's why Dr. Schoen is here. But that's really, it really jumps out at me, right? And now it bothers me, and like, I'm gonna think about it at night, and when I wake up in the morning, like if we are really concerned about safety for our dogs in the sport, what are we talking about here? We have uncovered, right? As classically happens in science, right? We were studying one thing, and then we discovered something else over here, right? So I think that's something that I would look at, and I don't want organizations to suddenly be like, look guys, the incidents on the contacts, all of them are really, really low, and the dog walks, sure, the highest of the three, but you know what, it's not that bad, 'cause we get the tunnel over here, but actually don't only get the tunnel over here, 'cause everybody loves the tunnel, and we know emotionally and psychologically, the dogs love tunnels, therefore, you know, that's not an issue here, and science is less dangerous. There are fewer incidents per run than the tunnel, then it's perfectly okay to have, we don't need to make any changes around it, right? So that's my hot take there. What would you say if they came to you and they were like, okay, well, what are we gonna do about this tunnel? Like don't you need to like try and duplicate what the study that was done previously? Like what's the very next step that you would take as scientists? If the organizations now quietly, they came to you and said, hey, this tunnel thing is making the rounds, and now this guy is talking about it on the podcast, and now people are writing it about tunnels, and wanting to know about tunnel safety, and is it grippy, and how many sandbags we have on it, and are they straight and curved, and how curved are they, and are we resetting them after every class? There are a lot of issues with tunnels, frankly, right? What is the first step that y'all would recommend? - Oh, I'm gonna come out of left field and say the bigger gap is jumps. Like we at least have a paper looking at the tunnel incidents, and it was a well done paper. I mean, they, the benefit of that particular paper is they did video review. So they were able to capture all like the minor kind of incidents, the minor slips, the delayed exits. They looked at tunnel shape, they looked at surface, they looked at like approaches, and so it was a pretty comprehensive study. And so do we still need to study tunnels more absolutely? But we still have no data on jumps, and jumps are the most common obstacle on every single course. - Oh no, now we are headed for anarchy, because as soon as you say that jumps are the thing, and let's say jumps, what if the incident is like, I don't know, what if it's insane? Like what if it's like 55X or something, right? And then how are people gonna take that? 'Cause they're gonna be people that are like, "Well, once you start looking at everything, "hey, everything in life is dangerous, "you walk out of your house in the morning, "and you can get hit by a car." You run into those very fatalistic people, and mostly they just wanna keep doing what they're doing. - But I think that that's true to some extent, right? Like there is risk with everything, and there's in here at risk in every sport, regardless of the species. And so the question is like, what's kind of an acceptable level of risk? And how can we modify that? And so I mean, this is something that's been studied for many decades on the human sports side, right? I mean, with concussions and head trauma and football, you have improvements in equipment and helmets to reduce that. - That's right. - You have injuries in soccer players, so you have improvements in turf and surface to reduce the risk of lower limb injuries. And so on the human side, there's a ton of research that goes into sport injuries, and then how to mitigate that. And so I think that that's what we're kind of on the very verge of is like we're trying to develop kind of this foundational data to be able to then evaluate injury and risk because incidents do not equal injury. So even if the jumps had way higher incidents than other obstacles is the injury rate because of that higher. Also what's the injury from just chronic repetitive stress of just training? - Right. - For all of the different pieces of obstacles and approaches and exit lines and all of that. And so I think that part of what we're trying to do is identify what plays into the risk of injury, not just incident, and then how can we then create modifications to improve safety? But that's like you said, that's a big jump, right? Like everybody wants to get there, but we're not very, yet. Because I think that there's a lot of things potentially that we can do to improve safety without getting rid of the sport completely, right? People are like, oh, well, if you get rid of this and you get rid of that, they're just going to be running around an empty field. Right. Well, I mean, an empty field isn't completely risk free, either. They can step in a hole. Right. Right. Right. But I mean, we don't want to change the sport of agility as it is necessarily, but if we can make minor adjustments here and there that can improve safety or, you know, one of the things in this is completely unrelated to either of the studies that we've talked about, though, I guess, sort of related to the speed, is in humans, they've done so much research on workload. And so that's like the training and competition load. So there's, like, you know, speed and repetitions and duration, but also things like your physiologic response to exercise and how kind of workload and injury are very like, I mean, high, like acute workload is a significant risk factor for injury. And we don't have anything like that in dogs. And so we're blaming a lot of this on the obstacles or the course design or this, you know, he's or that piece, but we haven't really looked at all of the other things that can cause injury when, you know, doing agility. Right. And that leads me to a question that I'm pretty sure you can't answer, but that, you know, has come in from multiple people and they basically want to know, like, how can I take this data and do something to make my dog safer, right? Is there anything that I the handler can do? What are the actionable steps? And so I'm pretty sure that's a question that you cannot answer, but that's kind of where people's heads are at, right? Oh gosh, Abby, do you want to? I, yeah, I like that question. That's great. That question. No, I mean, I think that this is very just like basic low level incident rate doesn't tell us anything about any modifiable factors, right? Like if once we get into some of the, you know, analysis of the other factors, could there be something that comes out of that or the video analysis of the dog walk falls that could say, okay, well, maybe this, you know, should be done, but right now, like, we're just not, we're not there yet. Right. And, and, you know, Abby, it reminded me when you were talking about the tunnel or you and Estevan, you were talking about the tunnel having such a higher incident than the dog walk, but again, we always have to like compare apples to apples. And, you know, when Ariel, you were saying that like that study, like was video analysis, they captured everything. So you on the, on the T, sorry, on the tunnel, the incident rate includes all the things, but we've already said that on the dog walk, it only, you know, is those certain types of falls, and it doesn't include all the things that we could get with video review, like a misfoot or like one paw off or something like that. So it could be that when we did, you know, a study like that on maybe a smaller data set or whatever, maybe it does then jump up ahead of the tunnel again, right? Like we don't really know. Well, I mean, sure, there's lots of things like that for either of those studies, right? Right. The, I took some notes and one thought I had was obviously this doesn't include all the dog walks done in practice, right? So if you think about any one dog, right? For every one dog walk they're doing at a trial, how many dog walks have you done over your career, you know, at home, right? And you mentioning the, the exercise and fatigue being a huge factor, that's a big thing, because as you said, they, human sports, present billions of dollars, right? And so they are very invested in these athletes who generate owners are, who generate these large amounts of money for them, and so they want them to be healthy. Because when your star athletes sit out, less people show up in the stands, less people watch on TV, et cetera, et cetera, right? So they want them to be healthy. And so one professional organization that looked at this was the National Basketball Association, the NBA for men. And what they were looking at was the demands of an 82 game schedule every season, right? And the players were like, "Hey, this is a lot, right? This is really tough on us, especially playing three games in four nights." So you have one night off, but of those four nights and you're traveling. So usually you're traveling, you're playing three games in four nights. And a couple of interesting things that even high quality teams, if you control for a lot, many of the variables, you tend to lose more on nights three and four, right? So you're at a disadvantage to these other teams. And it turned out that players dunk the ball a lot less, right? So they use that as like a marker of effort. It's not consciously, they're like, "Oh, I'm not going to dunk this ball." And yet for some strange reason, they're dunking the ball less on nights three and four, right? It's like a straight decline. And so very smart coaching staffs got wise to this. And they essentially started throwing the fourth game. They would sit their starters. Their entire starting lineup rolled out the youngsters and say, "We're basically conceding this game and we're going to get some playing time for our people." Lee didn't like that. And so the league started making rules because people were paying so much money to come and watch these visiting superstars coming from out of town. But they're like, "Hey, we're the visiting team. There's our third game and four nights. We're probably going to lose this game. Andrew risked for our veterans in particular, very, very high." And they would sit their best players, right? This is a response to real data that was coming out. So I think the science is so important. But as you said, I'm throwing this out as more of an example of like you were saying, we shouldn't just be blindly blaming the equipment because like you could be a little bit fresher. Maybe the dogwalk is actually way more dangerous in practice because you're doing 10 of them in a row or during running contact training or something like that. Maybe even performance of the dogwalk itself, the incidence is variable based on whether it's running or whether it's stopped, right? That would be pretty interesting to look at as well. And you know, all your other points as well. Like we need data on the weave pulse. We need data on jumps. We do jumps more than we do any other obstacles. So I think all of that is super interesting. So super interesting. But I just wanted to throw all that out there. Yeah, that is super interesting. And I think people would find it interesting to know how this is being funded because like Y'all are doing this as a job, right? And it is published research. So Y'all are having to go out and generate grants to be able to do this stuff, right? So that's a fun subject. So this study, the incident reporting was completely unfunded. Abby and I donated all of our time for the study. So we were doing it in evenings and weekends. Like we both have like regular full-time jobs outside of the agility research. And so part of the reason for that is one, agility research funding is very hard to come by. But also for this particular study, we didn't want any funding to potentially have perceived bias. So even if like one of the, say one of the organizations had been like, Hey, we want to help support the study. But none of the other organizations did that on paper could look like, Oh, well, that organization's paying off the researchers to give us the data that we want. And we did not want that to have any sort of perception. And so this study was all done by us volunteer basis. Some of our other research is funded by small grants that American Kennel Club canine health foundation has funded a number of our research projects, particularly our sensor-based projects. So yes, grants to some degree, but really there's very little funding for agility research, which is part of the reason why the research happens even more slowly than research normally happens. I mean, research is a slow process anyway is, but when you don't have funding for us being able to hire a whole slew of students to delegate pieces of the analysis or watching thousands of videos or doing this and that then that means that it all falls on us kind of core group of of researchers and all of us have regular jobs. So we're all just kind of fit in this research kind of on the side. And so we can't dedicate as much time as we would like to it. Yeah, I think people are not not everyone is aware of this. I think some people obviously know I think the vast majority of people do not. So I think it's perfectly fair to look at studies and be like, What about this or what about that? I don't think it's fair to come after the researchers for whatever you perceive the study. to be saying are if people are drawing their own conclusions about the study or applying the findings of the study in whatever correct or incorrect way that they will, it is not appropriate to come after the researchers on social media and things like that because we would not have any data if these scientists here, these doctors, were not volunteering their time, and their equipment, their knowledge to get us this data and so that we can improve the sport essentially, and we see this happen a lot. It makes me think about women in sports, once we started studying sports, it was all men, men's sports, and then finally we started studying women. We're like, oh, well, maybe there are differences, maybe you can't generalize the women. And there were, right? It turns out women have higher rates of ACL tears when they're teenagers, like basketball players and other sports as well. And they're like, hmm, why? What is going on? Why are they different from boys? So I think it's great that we are doing this. And so I guess I want to express my appreciation and encourage others to do the same for what you all are doing. We really do appreciate everything that you are doing here. And I guess this is kind of a little bit off topic, but it has to, you know, to do with like the injuries, you know, stuff keeps coming up again and again. And one of the things that I saw a lot and we kind of glossed over it a little bit earlier was, you know, how the apparent injury and why we didn't do follow-up. And so it kind of popped back into my head when thinking about just the logistics and like the time that we as researchers have. And I think part of it was one that would be a big ask for the judge, right? Every single incident, we had what a 1500 or more incidents across the six-month time frame that the judge would have had to, you know, find that competitor, follow-up with them, or at the very least, collect their contact information. And then somebody else would have had to follow up with them, which would have been me because it was just me and Abby doing all of the data. So that would have been me following up with 1500 handlers over the course of six months. And then the question is, when do you follow up with them? Do you follow up with them three days later, a week later, a week later, and a month later, which then is 3000 contacts. And if they don't respond, you know, if they don't answer their phone, which I don't answer my phone when I get unknown calls. Right. And how many times you follow up before you say, okay, this person's not actually going to, you know, answer me back. And we're just going to not have that injury data. And so I think that people being like, well, why didn't you get follow-up? There's a lot more to follow up than just like asking the judge to track down the person after a trial and follow up with them. So for us to get real good follow-up data would have been logistical nightmare. And maybe we had had, you know, a whole slew of students who could have been following up them. Maybe that would have been possible. But I didn't have time for that. Yeah, it kind of brings me back to where we started the podcast, which is basically like this is research and like part of the research process is you define the problem that you are analyzing, right? And you put your constraints around it. And then you go forth. And so, you know, those decisions were made based on a variety of factors. But the, you know, the main thing is, you've always got to make choices on what it is that you are tracking. And the focus of this particular one was just the incidents themselves. And then maybe some tertiary data that you happen to be able to easily collect, you know, at the same time. But, but yeah, it's not, it's not everything all at once. It's just like the first thing. Yeah. I think it's also great that the organizations that did participate, right, went along with it and the judges did that because that, you know, that was them volunteering their time as well. I think in trying to advance the sport. When I gave such a complete picture, something that we had never had in the history of the sport to have that kind of coordination among so many different organizations. No, we got rid of the shoe. I guess I wasn't coordinated though. And it wasn't database. That was like emotional reaction that happened by the entire fancy all at once. But never have we had this level of across the board every single run that happened in a six month period in North America. I mean, that or in the United States, sorry. I mean, that is a pretty incredible feat. And so, you know, I think we can't gloss over what an accomplishment it is to even get that level of data and that level of commitment and that level of coordination. Now, before we wrap up, I wanted to address something that Sarah was saying about how people are responding to this. And I think Dr. Markley had mentioned CTE, right? And so, when you talk to former football players, some of whom have severe psychological issues and health issues that they know are due to concussions. And the lead at first was very adamant that of course had nothing to do with it, right? But once these people died and they started looking at their brains under microscopes and so on and so forth, it was pretty undeniable. And then CTE became a thing. But if you talk to players who are alive now, some of them playing, some of them recently retired, some long retired, they're split. If they know the risks now, okay, we didn't know for sure. Like you kind of thought and then people were like, no, it could be all these other things, but you know the risks now, right? And they're split. And there were some that for their own children, they pulled them right out of football. They were like, I don't even care. Like you are playing something else or you're not playing any sports at all, right? And they are very good and happy with that decision and others where you know, it is up to my kid, right? And so that's like a that's one decision tree. And the other decision tree, I think it's hypothetical. It's the question they get asked a lot. If you could go back in time, would you play it again, right? And there are people who are adamant and they're like, you know, I can't even walk from here to there and check my mail. And I am so severely depressed. I think about, you know, hurting myself, right? They're like, no, I wouldn't do it again. There are other people that are like, oh, it's terrible. Like I can't make it to the mailbox, but absolutely I would do it again. You know, I owe my life to the sport or it was the thing that I loved or whatever. And what I want people to notice there is that there is not necessarily a right answer. And I think a lot of the discussion and debate that you have on social media are people taking one side or another or a third or a fourth. And they were like, this is a side it should be. Let me convince you that everyone should take this tag. Whereas a different approach is this is the data. This is the information. Now, what are you going to do about it? Right? What are the values that you hold? What do you believe about your dog? Because we also have this other, a second layer that maybe the football player doesn't have. And it's that you are making decisions for someone else, the someone else being your dog, right? The canine partner and what risks that they are assuming, right? So I think there's another moral layer on there, right? That you need to think about that maybe they think about in terms of like their own children. At the age of 12, not being able to fully comprehend and understand what would happen to them. They start playing tackle football in seventh grade, eighth grade in preparation for playing in high school. Right. Yeah. So I just wanted to put that out there. And I just had to jump in because I love everything about that and sort of the analogy with football is really, really insightful. Because like really, I mean, we were talking before about sort of the fatal istic, like you could hit by a car, whatever. And like, yes, but sort of everything has this sort of benefit to risk calculation that you're thinking about that sort of like there are a lot of benefits to be able to do agility and doing agility with their dog. And there are also risks and sort of like, when as a statistician, I think about it as a biostatistician, I think about it more in the context of human medicine. You're looking at clinical trials, you're looking at like, how is this drug working and what are some of the potential side effects and sort of what are these risks and sort of how do you weigh those things? But like, this is really the same thing, sort of thinking about like how much enjoyment am I getting out of it? How much enjoyment is my dog getting out of it? Could we get similar levels of enjoyment doing something else? Is this something else a lot? That's risky. Is it just as risky? Like sort of, and that is sort of something that each individual really needs to think about for themselves. And that our job, sort of as scientists, is sort of to quantify those actual risks so that you can make them based on like real data and real numbers and not just sort of like this vague like, oh, it's super scary because I saw it happen to one person that thing. Right, right. Right. I think that's very well said. All right. So the way I want to end this is, think ideally, if we're all sitting around in a circle, every agility competitor, you know, and we're all holding hands, what is the information that everyone should really be taking? that is safe to take from the data so far. Or are we really in a situation where here's a couple tidbits, but honestly, just wait guys, there's more information coming. And really, there's nothing. Hey, this is interesting, but you guys are often running, but the race hasn't started yet. There's still more data coming. What is your take? Is it, should people be running? Or should people be, oh, good, this is interesting, but not time to get on social media yet, even though they are? So the mighty way would be sort of where we started and sort of the big picture, like, result, that sort of, like, I have a lot of confidence in as sort of those numbers that we were quoting in terms of, like, the incidents as we describe them, so sort of the unexpected exits from the contact obstacles are rare in the sense that, like, we're talking about two every thousand runs, but not so rare that you're not going to see them. And then sort of that sort of should give you some level of sort of scale, sort of how likely is this event to happen? And like, if it happens sort of how likely is it that it's going to be truly catastrophic? This was the sort of, like, the apparent injury, like, the dog is actively limping or something like that, is really low. And sort of, I think that sort of gives people a numeric, hopefully, numeric sense of sort of how likely is this event. But like, when it happens, like, everybody talks about it and everybody, like, posts a video that seems super scary and sort of, like, being able to bring that back into, like, real context, I think is really useful and a good outcome of this, of these data. Yeah, I agree with Abby on that, that I think that, you know, our primary goal was evaluating incident rates. And honestly, like, they were about what we were expecting, which is good. And I guess we didn't really talk at all about that, but there have been some other-- one other formal study out of Finland that looked at dog walk falls. And they found about 3 out of 1,000. And then Greg Derritt did a kind of a preliminary study with some of the UKI judges before our study started and found a very similar rate. So it was not out of the realm of magnitude from kind of what we were expecting. So I think if we had found that, you know, it was seven instead of the previous reported three, or found a significantly lower, you know, number than it had been previously reported, then that would have made us kind of pause. But I think that the number of runs that we had across the six months, and kind of how similar that total number was at the end to some of the previous data also kind of helps give us confidence that, you know, somewhere in that two to three range is probably a real number. And so I think that's the biggest takeaway is that we finally do have a fairly good confidence on what the incident rate is in competition. Now, once again, like we don't have that data in training and that's a whole, you know, we talked briefly about it but that's a whole another thing and a whole another study. But at least in competition, we now have some data. I think the other biggest takeaway is we don't know about actual injury. Like all we did was ask about like that apparent kind of catastrophic injury. So everybody who's like then trying to take that information and saying there's no risk to your dog from falling off the dog walk, that's not true. And so I think that that's kind of my other kind of concluding thing is that it gives us a little tiny piece of information about injury but not a full picture of the actual risk associated with incidents. Right. And, you know, I guess my takeaway here is or one of the thoughts that I had that is that I think it would be interesting for people to take this incident data and kind of put it in a kind of a lay person's viewpoint and because it would require a lot of conjecture, this is probably not the job for a couple of researchers but it might be a very good job for a couple of podcasters and bloggers and that is to put together some estimates of how many shows does your average dog go to? How many dog walks does your average dog do in competition? And then what does this incident rate look like for the average dog? Right. How often, you know, is it once a month? Is it once every two months, every six months? Would you expect to see your own dog fall in competition? So, you know, maybe here over the next month or so we'll take some time and just kind of put together a story about what this data could mean for, you know, you and your dog that kind of lets people put it in perspective. Yeah, and I think that that's one of the things that makes the training data more complicated, right? Is people can give you a pretty good estimate of how many, you know, numbers of dog walks their dog does in competition if you ask them. They can say, okay, well, you did this number of trials, this number of days, you know, this number of standard runs. And so you can kind of get that denominator, but in training, some people keep very good records of every single repetition they do. And other people are just kind of, you know, doing their thing and not keeping track. And so, you know, that's a much bigger like span, I think of potential, you know, dog walk performances 'cause I think that the study in Finland, some of the dogs in a six month period were only doing, I maybe I shouldn't even make up numbers 'cause I, the some, like the, some of the dogs were doing so few dog walks over the period of six months, I was kind of surprised. And then some dogs were doing a lot of dog walks over a six month period. And so, you know, when you do your little story, you can also kind of take that to what it might look like in training, assuming we have a similar incident rate, which we don't actually know. - Right. - But assuming that there's the same, you know, incident rate, you could talk about what that looks like for, you know, different, like training scenarios. And obviously that, you know, very based on where in their career they are, you know, are they doing a ton of repetitions because they're, you know, doing their running contact training versus maintaining their dog walk, you know, as they're established in their career. - Right. - So that might be something interesting to kind of play around with too, is those kind of numbers. - Yep. Awesome. Well, thank you, ladies, so much for joining us. I thank you for being on the podcast. - Thanks for having us. - And that's it for this week's podcast. We'd like to thank our sponsor, Kenan Handler Fitness with Liz Joyce. Happy training. (upbeat music) - Thank you for listening to Bad Dog Agility. We hope you enjoyed today's episode. For more information, updates, and links to all our socials, just check out our website, www.baddogagility.com. If you haven't already signed up for our email subscription, we would love to have you join the BDA community. Until next time, take care. 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